INTRODUCTION This dissertation examines
This dissertation examines the contributions of anthropological reports in the
resolution of missing persons’ cases from the medical examiners offices in New York City,
New York; Pima County, Arizona; and Harris County, Texas. To date, there have been no
comprehensive examinations of forensic anthropological methods in casework; rather
emphasis has been placed on validation studies administered by the academic
community. Academic validation studies have increased in recent years in response to
the 2009 National Academy of Science’s (NAS) report titled, “Strengthening Forensic
Sciences in the United States: A Path Forward.” The NAS report recommends that
scientific studies evaluate the accuracy and reliability of forensic techniques and reflect
actual practice on realistic scenarios. Unfortunately, validation studies conducted in the
forensic anthropological community do not reflect forensic casework where multiple
methods are typically employed; rather these studies tend to examine forensic
anthropological methods in isolation and tested on limited sample populations.
The medicolegal community relies on the expertise of forensic scientists to resolve
casework and provide answers to families of the missing. Often, skeletal analysis assists
the investigation of unknown remains. In these cases, the forensic anthropologist may
be asked to distinguish population-level biological characteristics, recognize unique
identifying features, interpret the etiology of bone trauma, and estimate the postmortem
interval, if possible. Forensic anthropologists use a variety of methods to make
identifications, including radiographic analysis. A basic investigative tool is the biological
profile, comprising estimates of sex, age, ancestry, stature, pathology and individualizing
characteristics. The biological profile functions to reduce the number of missing persons
who could be associated with the remains.
While validation studies of the methods used to estimate the biological profile
saturate the forensic anthropology literature (see: Hartnett, 2010; Saunders et al., 1992;
Murray and Murray, 1991; Martrille et al., 2007; Berg, 2008; Kimmerle et al., 2008;
Fleischman, 2013; Hueze and Cardoso, 2008; Hens et al., 2008; Williams and Rogers,
2006; Rogers and Saunders, 1994; Rogers, 2005; Ramsthaler et al, 2007; Pritchard,
2007; Kemkes-Grottenhaler, 2005; Ubelaker and Volk, 2002; Ubelaker, 2009; Kosiba,
2000; Hughes et al., 2011; Urbanova et al., 2014; Guyomarc’h and Bruzer, 2011;
Williams et al., 2005; Trotter and Gleser, 1958; Bedford et al., 1993; Klales and
Kenyhercz, 2015; Kales et al., 2012), there are no studies that evaluate the application of
the biological profile in casework. In essence, there is no external validation for the
methods forensic anthropologists rely upon to conduct analyses. Further, validation
studies conducted in academic research settings show disparities in the applicability of
forensic methods.
Difficulties include, but are not limited to, the inability to effectively use the
technique, the inability to apply the technique to various population groups, variability in
the experience levels of the observers, ability to replicate results, and the level of bias
entering into the analysis. Despite these variables, and the unknown error rates in
practice, anthropological methods are perceived to work sufficiently to justify their
continued use (Sauer, 1992). A goal of this research is to discover if elements of the
biological profile are prone to misclassification, and if they are, to identify which are most
problematic, and what factors inherent to forensic casework contribute to these
misclassifications.
To address the inherent disconnect between academic validation studies and the
accuracy of the methods in forensic casework, this dissertation analyzes resolved skeletal
cases from medical examiners’ offices. Additionally, this study provides vital statistics of
unresolved casework to document the demography of unidentified individuals
represented in the medical examiners’ offices, the physical condition of these remains,
and any observable differences between resolved and unresolved casework. This
research will provide the anthropological community with information regarding the
accuracy of the biological profile of individuals in forensic casework and understand
challenges associated with solving the large number of unresolved cases in U.S. medical
examiners’ offices. The ultimate purpose of this research is to allow forensic professionals
to examine the efficacy of current techniques and guide the development of future forensic
anthropological methods.
Following this introduction, chapters two and three provide a background to the
main dissertation themes. The second chapter (1) discusses the medicolegal system
providing missing persons statistics and chronicling difficulties associated with
administering a search for the disappeared, (2) explores the rise of forensic anthropology
and its inclusion into the medicolegal system, building an argument for the importance of
anthropological analyses to resolve unidentified human remains, (3) provides information
about historic court cases that dictate the admissibility of forensic evidence and expert
witness testimony. The constraints on forensic practice placed by the courts have
profound implications for forensic anthropologists, as the scientific methods used rarely
meet rigorous standards.
The final component of this chapter introduces forensic anthropology as a
discipline, outlines the history of the field, examines the reference collections used to
develop forensic anthropological methods, and describes the components of the
biological profile and the commonly used methods to estimate the biological profile.
Chapter three discusses the challenges associated with providing law enforcement
and death investigators a social race category from the estimation of biological ancestry.
This chapter includes sections that (1) detail the history of racial classification, (2) describe
the differences between race and ancestry including how the terms are used, (3) discuss
the use of race and ancestry in forensic anthropology, and (4) address racial self-
identification among the U.S. population.
Chapter four provides a description of the study sample locations. These offices
were chosen for this research because each employs teams of anthropologists with large
caseloads in populous and diverse cities.
Chapter five details the three research expectations posed to address the study
aims.
Chapter six outlines the materials and methods used to test these research
expectations and chapter seven presents the results.
Finally, chapter eight provides a thorough discussion of the findings and concluding
thoughts.
In summary, this dissertation synthesizes several methodological processes that
have garnered recent attention in anthropology and in the forensic sciences. These topics
have an effect on the implementation and accuracy of forensic anthropology, but are not
well understood outside the confines of experimental studies. Thus, the analysis of
forensic cases within the context of this dissertation will document the accuracy of forensic
anthropological casework within the framework provided by the academic and scientific
communities as well as the framework provided by the medicolegal system specifically,
the process of obtaining antemortem descriptions of missing persons.
CHAPTER TWO
BACKGROUND
Missing in the United States
Although this dissertation primarily focuses on the role of the anthropological report in the
identification of unknown human skeletal remains, the purpose for discovering such
information is to serve the population of the missing and their family members. Not all
missing individuals become deceased or unidentified subjects of anthropological analysis
and, in this review, we must not be reductionist about the resolution of missing persons
cases, as countless factors influence such events. Rather, this dissertation focuses on
the strategies used by forensic anthropologists to provide evidence for the identity of
unidentified remains. Before addressing these strategies, this section identifies who goes
missing, who remains missing, and a collection of factors that delay or hinder the
investigation of missing persons.
Missing Persons Statistics
As of January 1, 2015 the United States FBI National Crime Information Center
(NCIC) contained 84,924 active missing persons records. According to the NCIC, more
than 2,000 individuals are reported missing in the United States each day. During 2014,
there were 635,155 total entries in NCIC with most of those entries removed or cancelled
due to the successful resolution of cases. However, these numbers do not tell the entire
story. Many more people are actually missing, as this total does not account for those
Americans who have gone missing in other countries, POW/MIAs, or those never reported
missing here at home. People go missing for a variety of reasons and, unfortunately,
some die due to natural causes, accidents, suicide, or because they become victims of
crime. In an ideal world, any deceased individual is identified and the remains returned
to the family. In reality, not all remains are located and/or identified and not all remains
are identifiable with current forensic techniques. The Bureau of
Justice Statistics reports the extent of this situation finding that there are approximately
4,400 sets of unidentified human remains discovered each year with over 1,000 remaining
unidentified after one year (Hickman et al., 2007). Currently, it is estimated that there are
40,000 sets of unidentified human remains housed in medical examiners offices around
the country (Matthews, 2013; Ritter, 2007).
Resources for Families
The responsibility of locating missing persons primarily relies on family and friends
as well as a conglomeration of local, county, and state law enforcement. In addition to
local agencies, there are a number of national organizations that provide resources to
identify missing persons and, when appropriate, identify the remains of deceased
individuals. The National Center for Missing and Exploited Children (NCMEC) is an
organization that provides services and resources for victims, families, and law
enforcement in cases of missing children, child abduction, and sexual exploitation. The
International Committee of the Red Cross (ICRC) focuses on international efforts to locate
missing persons and identify the deceased. Although they are primarily involved in
locating missing persons outside of the US, they assist with locating missing Americans
abroad. In addition, large-scale databases have been developed to facilitate information
sharing to connect the dots of missing person’s cases. Currently, there is no singular
national integrated database to search for the missing or to identify unknowns in the
United States; rather there exists a number of databases with varying levels of
accessibility.
The National Missing and Unidentified Person System (NamUs) is a useful
database that medicolegal professionals and the general public can access and search
for missing persons. The database is comprised of information about missing persons
cases and also unidentified deceased individuals. When an entry is made, the program
automatically cross-references new information with records already contained within the
database.
Similarly, the National Crime Information Center (NCIC), a function of the FBI, is a
database that includes information about missing and wanted persons that can be readily
searched. Unlike NamUs, which allows the free and open search of its records, only law
enforcement officials can access the information contained within the NCIC.
The National Institute of Justice (NIJ) has funded several DNA identification
projects to assist death investigations including a collaborative project with the University
of North Texas Health Science Center. This program assists medical examiners and
coroners with costs associated with DNA sampling, including exhumations of unknown
buried individuals to facilitate sampling, and the collection of family reference samples.
The listed organizations typically work in conjunction with death investigators,
medical examiners and coroners, forensic anthropologists, forensic odontologists,
nonprofit and volunteer organizations, military personnel, and the Federal Bureau of
Investigation (FBI) to resolve missing persons and unidentified cases. However, no
comprehensive system exists to coordinate efforts across jurisdictions (Moore, 2011).
Demographics of the Missing
There are innumerable reasons why an individual may become a missing person. Some
people disappear voluntarily, fed up with their lives wishing to start anew. Some people
are victims of crime and disappear at the hands of another. Some people are victims of
their own circumstances; where becoming one of the missing is a byproduct of their
actions or inability to make decisions in their own best interest (Moore, 2011).
Despite the circumstances for disappearance, many people, when discovered
missing, are reported to police by a concerned party, such as a family member, friend, co-
worker, or other acquaintance. However, actions taken by law enforcement when a report
is filed vary among departments.
Carole Moore is a former police officer and trained investigator with an interest in
missing person’s cases. Her book, The Last Place You’d Look (2011), chronicles the
barriers faced by family members and police officers in the resolution of missing person’s
cases. Real stories of missing persons provide narratives that illustrate the obstacles
encountered from the beginning to the end of an investigation. She notes that there is no
particular protocol in place to deal with missing person’s cases when they arise and not
all cases are given the same level of attention. In fact, very few police departments have
officers trained in missing person’s cases, let alone entire missing persons units. Those
that do are concentrated in large cities, but their reach is limited by their jurisdiction.
Deficiencies in training, jurisdictional constraints, and a lack of communication between
police departments, are among the main obstacles facing investigations of missing
persons cases. How cases are handled varies dramatically from state to state and even
from precinct to precinct; resources, (monetary and personnel) are disparate (Moore,
2011). For example, police do not hesitate to take a report when a young child is reported
missing, however, adults, and even very young adults barely at the age of majority, are
treated very differently (Moore, 2011).
Children
Missing children are often given the full attention of law enforcement because they are
considered vulnerable and unable to access employment or resources (Kiepal et al.,
2012). As such, police reports are typically taken in a timely manner, AMBER Alerts are
issued in the case of alleged abductions, media outlets are notified, and large-scale
search operations are deployed. However, not all children are given this kind of attention.
In fact, many states consider teenagers to be adults at the age of sixteen and law
enforcement will not look for them unless foul play is suspected in their disappearance
(Moore, 2011). Furthermore, if a child deemed old enough to be on their own is found,
officers do not have the legal authority to inform the parents of their whereabouts (Moore,
2011).
Moore spends a considerable amount of time in her book discussing runaways,
who are of particular concern because they are more likely to be exposed to harm and
more likely to experience personal victimization, sexual exploitation, and drug and alcohol
abuse (Moore, 2011; James et al., 2008; Gaetz, 2004, Payne, 1995). She cites that many
police departments lack the resources necessary to find runaway children. Children
making the decision to leave home do so for a variety of reasons, but are usually
subjected to unfortunate circumstances in the home such as violence, parental substance
abuse, or other conflicts, that compel them to leave (James et al., 2008; Dalley, 2007;
Gaetz, 2004; Dedel, 2006). Some children have mental conditions including, but not
limited to, attention deficit disorder and bipolar disorder. These young people are typically
impulsive and can make the decision to leave home when angry with their parents or face
some situation they cannot control (Moore, 2011). More often than not, runaways occur
as a result of troubled and broken homes, sexual abuse, parental substance use, child
substance use, problems in school, and violence (James et al., 2008; Gaetz, 2004; Moore,
2011). In some cases, being found is not a desired outcome for the missing child (Payne,
1995).
Adults
Unlike children, adults are considered to have the right to disappear if they want
to. Unless a disappearance occurs under suspicious circumstances, police often do not
take a report of a missing adult or young adult, citing shoestring budgets and lack of
personnel to conduct investigations (Moore, 2011). Some police departments fall back
on the old forty-eight hour rule, which dictates that a report is taken only after a person
has been missing for at least 48 hours, denying the family a formal report prior to that
time. Although law enforcement generally acknowledges that waiting to file a report is
detrimental to an investigation, many agencies still resist taking a report on a missing
adult (Moore, 2001). The widespread misperception that adults go missing voluntarily not
only hinders the investigation, but also continues to dampen efforts to understand what
factors underlie adult disappearances (Biehal et al, 2003).
Marginalized Populations
Not everyone is afforded the financial and social resources required to live a
socially acceptable standard of living (Room, 1995; Percy-Smith, 2000; Burchardt et al.,
1999; Byrne, 2005; Peace, 2001 Sen, 2000). Marginalized members of society are
individuals who, for whatever reason, live on the fringe of society. These individuals
include the homeless, prostitutes, the elderly, minority groups, the mentally ill, children
with a history of running away, illegal immigrants, and others living without strong ties to
society (Moore, 2011; Keipal et al., 2012; Patterson, 2005).
In general, socially excluded individuals are more likely to go missing and are less
likely to be found (Kiepal et a., 2012). For example, underprivileged women, especially
those who experience domestic abuse, are more likely to go missing than women who
have access to resources (Patterson, 2005; James et al., 2008). This situation is
exasperated when these underprivileged women are members of a minority group (Kiepal
et al., 2012).
Another example is the homeless. Homeless individuals go missing in high
numbers because they lack relationships with family members, are typically unemployed,
and experience other avenues of social exclusion (Gaetz, 2004; Keipal et al., 2012).
According to the National Student Campaign for Hunger and Homelessness, there are
approximately 3.5 million homeless people in the United States; 35% are families with
children, 23% are U.S. military veterans, 5% are children under 18 years of age, 30%
have experienced domestic violence, and 20-25% suffer from mental illness (retrieved
March 6, 2015).
To locate a missing person, law enforcement requires notification, a physical
description of the missing, last known whereabouts, and any other information useful to
an investigation. Law enforcement depends on a willing third party for this information
(Payne, 1995). Therefore, persons lacking connections with society or those without
concerned family members or friends will not be reported (Payne, 1995; Kiepal et al.,
2012). Also, social or familial networks associated with missing marginalized individuals
are similarly marginalized and thus, may be reluctant to approach law enforcement with
information (Kiepal et al., 2012). Illegal immigrants and individuals with criminal records
wanted by law enforcement are less likely to report a missing person for fear of legal
action being taken against them. Similarly, if a missing person is in the country illegally,
a concerned third party may not report their disappearance for fear that legal action may
be taken against the surviving family members (Anderson, 2008).
Missing marginalized individuals often do not have recent medical or dental
records on file and may not have family members to provide antemortem information or
DNA samples for comparison (Anderson, 2008). As a result, marginalized individuals are
more challenging to identify and may represent a significant portion of the nation’s
unidentified human remains (Moore, 2001; Keipal et al., 2012). In addition, missing
marginalized individuals often are not afforded the full attention and resources of law
enforcement (Moore, 2011).
In contrast, prominent individuals such as young white women and children are
typically reported missing in a timely manner (Barton, 2011; Moore, 2011). This is
because the general population takes an interest in these cases. Barton (2011) explains
that the media perpetuates the “missing white woman syndrome,” where missing young
white women (e.g. Lacey Peterson and Natalie Holloway) are given the full attention of
the media, bolstering considerable interest among viewers, increasing viewership and
ratings. Moore (2011) highlights the 2007 case of Adam Kellner, a middle-aged
schizophrenic man who went missing from the home he shares with his family. His
mother, Sherrill Britton, states that mentally ill individuals are less valued and there is
prejudice against the mentally ill. “If he were a beautiful blond or a five-year old, his case
would get tons of publicity. A thirty-five-year-old mentally ill man is not a sexy case; it just
isn’t” (p.103).
It is a sad and desperate situation when someone goes missing in the United States.
Families, friends, and concerned parties expect law enforcement to provide answers and
deploy all available resources to resolve cases. However, police departments face
financial constraints, manpower issues, and communication failures that hinder efforts to
locate the missing. Unfortunately, socially excluded individuals who lack meaningful
relationships and access to resources are the ones at risk of remaining unidentified.
Forensic anthropology has become a part of the medicolegal system in an effort to identify
unknown deceased individuals. The next section outlines the responsibilities of the
anthropologist in the medicolegal system.
Forensic Anthropology in the Medicolegal System
Forensic anthropologists occupy a relatively new and unique division within biological
anthropology. This section provides a brief history of forensic anthropology detailing the
events that led it to be a prominent discipline that is touted through worldrenowned
university departments, and popular crime TV shows and documentaries alike. But, it is
first necessary to describe the function of the medicolegal system in order to understand
why anthropologists are necessary and valuable contributors.
The Medicolegal System
According to the American Heritage Dictionary (2011) the medicolegal system is
defined, “of, related to, or concerned with both medicine and law, as when medical testing
or examination is undertaken for a legal purpose.” Simply stated, this definition describes
the application of medical sciences for the purpose of legal investigations. In the United
States, the medicolegal system consists of a network of law enforcement, physicians,
medical examiners/pathologists, coroners, forensic scientists, death investigators,
lawyers, and courts. Forensic anthropology’s rapid and recent development has
encouraged broad applications of the science directly into the medicolegal system.
Before we explore the history and evolution of forensic anthropology, the following section
defines the contributions of forensic anthropology within the medicolegal system.
Forensic anthropology is the “application of anthropological and skeletal biological
principals to medicolegal issues” (Steadman, 2009; p.1). Due to their unique osteological
training, forensic anthropologists are essential to the medicolegal system as they
frequently assist law enforcement with the many aspects involved with the identification
process of deceased individuals. However, duties of a forensic anthropologist are not
limited to skeletal analysis. Responsibilities are diverse and include the search and
recovery of human remains, maceration of soft tissue from bones, analysis of the
biological profile, the evaluation of skeletal health, pathology, and trauma events, and the
assessment of the postmortem interval and other taphonomic processes (Byers, 2005).
To assist with identifications, anthropologists document ante- and peri-mortem skeletal
trauma, skeletal pathologies and anomalies, surgical implants and other individualizing
characteristics.
To develop a biological profile, forensic anthropologists rely on population level
morphological characteristics of skeletal remains to compare with missing person
descriptions. The biological profile typically consists of an individual’s sex, ancestry, age,
and stature, and is used to help narrow the field of potential matches. Additionally,
forensic anthropologists compile a detailed report of their findings and, if necessary,
present those findings in court.
A Brief History of Forensic Anthropology
One of the earliest accounts of anthropological methods utilized comes in the late
1800s by way of a scientist, forensic investigator, photographer, and forerunner of
fingerprint analysis named Alphonse Bertillon (Rhodes, 1956). Bertillon spent a good
portion of his career developing standards to analyze fingerprints at crime scenes and
collect anthropomorphic data. Using this expertise, Bertillon was asked to assist with
victim identification following the 1896 Drummond Castle tragedy. The Drummond Castle
was a large ship carrying 252 passengers and crewmembers that sank in the
Fromveur Sound off Ushant, a French island at the southwestern end of the English
Channel. Over the course of several days, a total of 53 bodies were recovered with 27
receiving “official descriptions” that included photographs and physical descriptions of the
deceased and their personal effects. Physical descriptions were detailed and crafted with
an attempt to reconstruct what the person looked like in life. The descriptions were
provided by Bertillon in his official reports and resulted in 10 positive identifications.
Although victim identification did not directly lead to the development of forensic
anthropology, it is noted that the process of identifying the dead has undergone gradual
changes as scientific discoveries are made. The process of putting a face, and ultimately
a name to the deceased is rich in history while its transition to identifying a face and name
from skeletal materials is fairly recent.
Forensic anthropology, as it is known today, underwent a rapid evolution within the
discipline of physical anthropology. This new sub-discipline was born from the
combination of physical anthropology and specialized training and analyses of human
anatomical structures. Knowledge of human anatomy was seen as a valuable tool for
early police investigators as they discovered it could be used to help identify and interpret
evidence to solve crimes.
It is said that Thomas Dwight (1843-1911) was the first to make a career from the
analysis of skeletal remains and that his writings were the first of their kind to apply the
analysis of the human skeleton to forensic cases. His work and life-long commitment to
researching forensic applications of the skeletal form and participation in forensic cases
gained him the title, Father of Forensic Anthropology in the United States (Stewart, 1979).
Although the roots of employing anatomical expertise to help identify victims of crime in
the United States has extended back to the mid-1800s, the so-called modern era of
forensic anthropology can be marked with W.M. Krogman’s landmark publication, Guide
to the Identification of Human Skeletal Material, in 1939. The guide served as the
authoritative work in forensic anthropology and was utilized by the larger anthropological
community and the FBI (Stewart, 1979). During this time, many notable anthropologists,
H.L Shapiro, Charles E. Snow, Mildred Trotter, and T. Dale Stewart were involved in the
identification of war dead and had used Krogman’s research to assist with their operations
(Ubelaker, 2009). The extensive use of Krogman’s work led to advancements in methods
and techniques such as Trotter and Gleser’s stature estimation methods (1952) and
McKern and Stewart’s age estimations (1957). When
Ales Hrdlicka retired from the Smithsonian Institution in 1942, his successor, T. Dale
Stewart, immediately began working on FBI cases. By 1969, he had worked 254 cases
with 169 being at the request of the FBI (Ubelaker, 2009). His casework experience
allowed him to recognize the need to improve forensic methods and techniques, and as
such, published, Skeletal Changes in Young American Males in 1957, with his colleague,
Thomas McKern. Several more publications followed and a culmination of forensic
research was presented in his 1979 textbook, Essentials of Forensic Anthropology.
Advancements in forensic science during this historic period were not just limited
to the achievements of individuals. In 1950, the American Academy of Forensic Sciences
(AAFS) was founded (Eckert, 1980) as a multi-disciplinary professional organization to
provide leadership and advance science with its application to the legal system (Reichs,
1988). The objectives of the academy are to promote professionalism, integrity,
competency, and education, foster research, improve practice, and encourage
collaboration in the forensic sciences. It wasn’t until 1972 that the Physical Anthropology
section was created, a development that marked a new beginning for forensic
anthropology (Kerley, 1978). The field’s certifying board, the American Board of Forensic
Anthropology (ABFA), was founded in 1977 (Reichs, 1988) and was
essential to the development of scientific credibility with the initial certification of 22
diplomats. Today, the Physical Anthropology section has been renamed “Anthropology”
to include the foundation of the discipline.
Forensic Anthropology Today
Today, forensic anthropology is a highly competitive field encompassing high
standards for education, research, and professional applications. Notably, the past 15
years have seen an increased interest in forensic anthropology (Hart, 2009); likely a result
of popular media, books, and TV shows (Ubelaker, 2009) highlighting the glamorous work
of anthropologists solving difficult cases (e.g., “Bones” and “CSI”). The effects of the
excitement around this growing field are evidenced by an increased demand for education
and specialized instruction in anthropology departments within universities all over the
world (Black, 2013). Many colleges and universities now offer undergraduate and
graduate level coursework in forensic disciplines as well as a variety of training
certification programs. Workshops, short courses, and internship opportunities are
offered up as additional training and specialization for individuals interested in pursuing a
career in forensic anthropology (see: University of Tennessee Forensic Anthropology
Center, Mercyhurst University, Southern Institute of Forensic Sciences). In addition to
academic positions, aspiring forensic anthropologists can seek out employment positions
in the private sector including, but not limited to, death investigation, autopsy technicians,
photography, and compliance officers (Hart, 2009).
Research and technology have also greatly benefited from the recent rise of
forensic anthropology. There is an ever-increasing need to discover the next new
breakthrough method that will help identify the unknown. It is true that significant research
advancements have been made, not only through the efforts of an increased number of
researchers, but also through the expansion of study databases, skeletal collections,
sophisticated statistics, and computer software (Ubelaker, 2009). Research topics have
expanded well beyond efforts to study the biological profile and include issues related to
trauma analysis, individualization, the postmortem interval, child abuse, observational
bias, validity of techniques, evidentiary standards, comingling, disaster victim
identification (both here and abroad), and more.
Employment in Medical Examiners’ Offices
By 1980, physical anthropologists were slowly being considered for employment
in medical examiner’s offices. However, the demand for anthropological casework was
irregular and therefore, full-time employment was difficult to justify (Berryman, 2009). The
first full-time forensic anthropologist in the United States was Dr. Hugh E. Berryman at the
Shelby County Medical Examiner’s Morgue in Memphis, Tennessee. The position as
Morgue Director provided administrative duties that supplemented his anthropological
responsibilities and thus justification for the full-time position (Berryman, 2009). In 1981
the New Jersey State Police hired Donna Fontana as a forensic anthropologist and
forensic microscopist. Amy Zelson Mundorff was the first forensic anthropologist at the
Office of Chief Medical Examiner (OCME) in New York City. During her tenure (1999-
2004), she assisted with a number of local cases and demonstrated the value of having
a forensic anthropologist readily available to help recover and analyze skeletal materials.
In her employment, she conducted the largest triage operation in the history of the United
States demonstrating the unique and specialized skills anthropologists utilize to handle
large-scale disasters.
The attacks on the World Trade Center in 2001 left in its wake 2,996 deceased
individuals and Dr. Mundorff helped lead the operation to identify thousands of sets of
human remains. The effort to identify every victim from that tragic event continues today
with the expertise of forensic professionals in many fields, including forensic
anthropologists (Checker et al., 2011).
Today, the number of anthropologists employed in medical examiner’s offices has
grown markedly (Mundorff, 2011) and many more anthropologists provide consultations
for medical examiner’s offices, coroners, local law enforcement, and FBI. The offices
researched for this study include Office of Chief Medical Examiner in New York City, New
York; the Harris County Institute of Forensic Science in Houston, Texas; and the Pima
County Office of the Medical Examiner in Tucson, Arizona. These offices utilize full-time
anthropologists, postdoctoral students, interns, and volunteers.
While it is true that the dramatization of forensic anthropology in the media and popular
television shows has drawn substantial attention to the profession, it is the tireless work
of many physical anthropologists over the years that define the discipline today. Direct
participation in missing persons cases and the expertise utilized in disaster victim
recovery and identification has proven that anthropologists are key players in the
medicolegal community. The continued partnership and cooperation between forensic
professionals and law enforcement officers is invaluable for the successful resolution of
missing persons cases. To do so, forensic anthropologists are required to meet the
standards of evidence prescribed by the courts. The following section describes the
evolution of the standards of evidence and expert witness testimony in the United States
and how forensic anthropology works to meet those requirements.
Evidentiary Standards for Forensic Anthropology and Historic Court
Decisions
This section addresses the standards of forensic evidence and expert witness
testimony in the United States court system over the past 22 years. Several landmark
federal court decisions have determined the criterion and have deemed judges the
gatekeepers for the admissibility of credible scientific evidence. This allows judges to
“evaluate expert witness testimony on a case-by-case basis” (Grivas and Komar,
2008:771). Although forensic anthropology was not specifically cited in the NAS report,
these significant court decisions have impacted the rigor of scientific applications and
methods for all disciplines under the realm of forensic science.
Frye v. United States (1923) and the Federal Rules of Evidence (1975)
In 1923, the District of Columbia Court of Appeals made the first decision by a
federal court to standardize the admissibility of forensic evidence for testimony. This
ruling was considered to be one of many “common law rules,” (Grivas and Komar,
2008:771) the United States judicial system followed regarding the admissibility of expert
witness testimony in all cases, but predominantly in criminal cases (Bernstein, 2001). The
rule stated that scientific evidence, “must have gained general acceptance in the
particular field in which it belongs” (Frye, 1923) and as such, testimony was considered
reliable if the “methods and procedures of science” (Frye, 1923) were applied. The
purpose of this ruling was to determine the relevance and reliability of scientific evidence
and the methodologies used to glean the results presented in court. However, the Frye
test was fundamentally ambiguous, allowing variation in its interpretation between
jurisdictions and judges (Lyons, 1997) and was formally cited in only a few dozen
published cases in the 1960’s (Bernstein, 2001). The ambiguity of the Frye test as well
as the unknown influence the Frye ruling had on court cases went largely ignored until
1975 when the Federal Rules of Evidence (FRE) were established for federal courts
(Bernstein, 2001).
The Federal Rules of Evidence were created and subsequently adopted by
Congress to address the ambiguity and variability in the applications that the Frye test
presented in federal courts. The purpose of the Federal Rules of Evidence was to
“formally standardize and clarify the trial process in the federal judicial system” (Grivas
and Komar, 2008:772). Specifically, Rule 702 determined who qualified as an expert
witness and when witness testimony was appropriate. It did not, however, address the
application of the Frye test, which, as previously described, dealt with the admissibility of
scientific evidence.
In 1993 the Supreme Court made a landmark decision in the case of Daubert v.
Merrill Dow Pharmaceuticals, Inc. (1993). This ruling ultimately clarified the confusion of
the Frye test and the Federal Rules of Evidence in federal courts. Many states still use
the Frye rule today.
Daubert v. Merrill Dow Pharmaceuticals, Inc. (1993)
The Daubert v. Merrill Dow Pharmaceuticals, Inc. (1993) decision required
scientific evidence to attain reliability and relevance standards and was intended to clarify
the unclear principles of the Frye ruling and FRE 702 (Holobinko, 2012). Ultimately,
Daubert determined that the Federal Rules of Evidence superseded the Frye rule in
federal cases (Holobinko, 2012). In addition, the court concluded that individual judges,
not the scientific community, have the authority to determine the reliability and
admissibility of scientific evidence and expert witness testimony. Since judges are not
scientific experts, Daubert established five guidelines for which judges could follow to
evaluate scientific witness testimony. These guidelines mandate that the content of
witness testimony must: (1) be testable and have been tested through the scientific
method, (2) have been subject to peer review, (3) have established standards, (4) have a
known or potential error rate, and (5) have widespread acceptance by the relevant
scientific community (Daubert, 1993). In essence, the Daubert ruling allows judges to be
the gatekeepers of expert witness testimony while keeping junk science out of the
courtroom (Faigman et al., 1997). Although well intentioned, these guidelines, as Grivas
and Komar (2008) state, “put clear and significant constraints on the admissibility of
scientific expert witness testimony” (p.772). However, it is important to note that the
Daubert ruling applies to federal courts observing the Federal Rules of Evidence. Some
states adhere to Frye while other states have adopted standards similar to Daubert.
Since the Daubert ruling, two Supreme Court cases have refined the Daubert
standards for expert witness testimony, General Electric v. Joiner (1997) and Kumho Tire
Co. v. Carmichael (1999). Collectively, these cases are known as the Daubert
trilogy.
General Electric v. Joiner (1997)
In a Supreme Court ruling, General Electric v. Joiner (1997) established that the
expert witness needed to justify his or her opinions by explaining how the scientific
methodology relates to the evidence in the case, and thus, how the conclusions were
made. In this case, the expert witness testimony failed to demonstrate a link between
polychlorinated biphenyl (PCB) exposure and small-cell lung cancer that the plaintiff
claimed to have developed as a result of working around PCBs on the job. This ruling
focused attention back to the final conclusions determined, rather than the strict rules for
scientific evidence (Christensen and Crowder, 2009). According to the court, there is
nothing in the Federal Rules of Evidence or in Daubert that requires the court to accept
unproven assertions of the expert witness (Joiner, 1997). The court’s decision found that
an expert’s conclusion should not be admissible if valid reasoning does not support the
methodology and conclusions. However, it was still unclear if the Daubert standards and
the judge’s gatekeeping responsibilities applied only to scientific testimony or also to
technical testimony. Scientific testimony refers to testimony that is given by scientists and
supported by the five guidelines set forth in the Daubert criteria, whereas technical
testimony is testimony given by experts in a given field that are not scientists but the
testimony is based on scientific principles. These questions would be addressed in the
1999 Supreme Court ruling in Kumho Tire Company, Ltd v.
Carmichael.
Kumho Tire Company, Ltd v. Carmichael (1999)
In Kumho Tire Company, Ltd v. Carmichael, the trial judge ruled that the expert witness
testimony in the case of an accident potentially caused by a failed tire did not satisfy the
requirements set by the Daubert ruling and thus, was not admissible. The appellate court,
however, ruled that because the expert witness was not a scientist, the non-scientific
testimony given was not subjected to the Daubert criteria. Ultimately, the Supreme Court
ruled that expert witnesses, even if they are not scientists, could develop and apply
theories for the court based on their observations and experiences so long as these
theories are reliable and relevant to the case at hand (Kumho, 1999). The judge, being
the gatekeeper of expert witness testimony, now does not have to differentiate between
scientific testimony and technical testimony, as all expert witnesses can now be evaluated
at the same level. This ruling also suggested that the strict Daubert standards do not
always apply; instead, they were actually designed to be a “set of flexible guidelines rather
than a strict set of rules” (Grivas and Komar 2008:772) for evaluating the validity of expert
witness testimony. Furthermore, the flexibility of the guidelines recognizes that scientific
advancements often outpace publication and general acceptance within any given field
and that testimony utilizing emerging scientific techniques can still be considered valid.
Thus, Kumho does not supersede Daubert; rather it provides flexibility to evaluate the
merit, and therefore the admissibility, of forensic evidence and expert witness testimony.
Implications for Forensic Anthropology
The preceding court rulings have profound implications for the admissibility of
scientific evidence and the standards for expert witness testimony. More specifically,
these rules affect the development of scientifically admissible methods within
anthropology.
In their 2009 article titled, “Evidentiary Standards for Forensic Anthropology”,
Christensen and Crowder explicitly acknowledge that forensic anthropology is a
problematic discipline in regards to evidentiary standards. The authors outline concerns
regarding the credibility of forensic anthropological methods and how they fail to meet
rigorous standards for evidence outlined by the Daubert ruling. Christensen and Crowder
(2009) explain that the techniques used today are a combination of results gleaned from
rigorous scientific methods and also less rigorous observational methodologies, examples
from casework, and personal experience. According to Daubert, anecdotal examples
from casework, personal experience, and non-scientific observational patterns are not
deemed appropriate for evidence in court. In addition to the problems associated with the
standards of evidence, the forensic anthropology community has no single standard for
which to conduct the recovery and analysis of human skeletal materials. This implies that
each anthropologist or institution is permitted to employ his or her own guidelines and
standards for forensic practice (Christensen and Crowder, 2009). Regardless, Grivas and
Komar (2008) state that many anthropological methods meet the Kumho criteria.
Christensen and Crowder (2009) point out that Kumho is not a stand-alone criterion
because the ruling allows for flexibility in the application of the Daubert guidelines.
Nevertheless, most forensic anthropological methods do not meet Daubert guidelines for
admissibility and may be deemed too subjective (Christensen and Crowder, 2009).
In 1977, the American Board of Forensic Anthropology (ABFA) was formed to
provide certification of the expertise for individuals who practice forensic anthropology.
Despite the success and growth of the organization, there are still no protocols in place
to “ensure the consistency and reliability in the application of forensic anthropological
methods” among its members (Christensen and Crowder, 2009:1211). Moreover, the
American Society of Crime Laboratory Directors/Laboratory Accreditation Board
(ASCLD/LAB) can and has accredited forensic anthropology laboratories, but they do not
recognize anthropology as an independently certifiable field.
In 2009, the National Academy of Science (NAS) published a document titled,
“Strengthening Forensic Science in the United States: A Path Forward.” This report was
the result of the formation of a special forensic science task force ordered by the United
States Congress to identify the needs and shortcomings of various forensic disciplines.
The comprehensive analysis of “the state of forensic science” in the United States was
followed by a set of recommendations for future forensic practice. One such
recommendation asks that the forensic community, “disseminate the best practices and
guidelines concerning the collection and analysis of forensic evidence to help ensure
quality and consistency in the use of forensic technologies to solve crimes, investigate
deaths, and protect the public” (NAS Report, 2009:2).
In response to the suggestions put forth by the NAS report, the American
Academy of Forensic Science (AAFS) encouraged the development of Scientific Working
Groups (SWG), to establish best practices for each sub discipline. The physical
anthropology section of AAFS along with the Federal Bureau of Investigation, and the
Department of Defense Central Identification Laboratory formed the Scientific Working
Group of Anthropologists (SWGANTH) to develop guidelines for best practice in forensic
anthropology. These guidelines recommend that forensic anthropologists use methods
accepted by the anthropological community and to be cognizant of reference samples,
statistics, and population demographics when employing each method. Further,
SWGANTH recommends that anthropologists be conservative in their assessments so
not to unintentionally exclude individuals from a presumptive
identification.
Although the guidelines outlined by SWGANTH are appropriate, they do nothing
to ensure the consistency or precision of scientific practice in forensic anthropology. And
while the NAS report doesn’t specifically target the biological profile as being problematic,
it makes the point that forensic methods need to provide error statistics (e.g. confidence
intervals). These error statistics help to evaluate the accuracy or precision of methods.
The overall lack of regulation and scarcity of measurement error in forensic
anthropological practice leads to inconstant applications of forensic methods, a reduction
in evidentiary value, and may result in the possibility that deceased missing persons
remain unidentified.
Although many recent anthropological publications emphasize the importance of
meeting Daubert criteria (Christensen, 2004; Christensen 2005; Rogers and Allard, 2004;
Rogers, 2005), anthropological methods reliant on subjective analysis, largely do not
meet the rigorous standards required by the rulings. Judges, being gatekeepers of
evidentiary testimony, decide before hearings whether the expert will testify and if the
evidence is admissible. This is not a problem when we remember that the Kumho
decision allows for more lenient interpretation for the standards of evidence and
testimony. However, savvy lawyers will argue the credibility of evidence according to the
Daubert standards. Further, the CSI effect has had profound affects on how juries view
evidence (Dauria and Quintyn, 2007). The CSI effect is a phenomenon where popularized
television shows such as CSI and Bones generate unrealistic expectations for the
accuracy and expediency of forensic techniques (Dauria and Quintyn, 2007). Yet despite
the Kumho ruling, Steadman et al. (2006) state, “A judge may disallow expert testimony
if the report is poorly written, the scientific method is unsound, error rates are not included,
or the report otherwise does not demonstrate a minimum threshold of reliability and
relevance” (p.16). Reliability and relevance here means that the methods employed and
the conclusions drawn by the expert witness from the evidence pertains to the case at
hand. Steadman et al. (2006) add that when the identity of an individual is at issue, the
forensic anthropologist must (1) define technical terms, (2) document any problems when
applying methods, (3) include error rates, and (4) include a summary statistic, such as a
likelihood ratio, that reveals the strength of the presumed identification.
Presumptive vs. Positive Identification
The identity of deceased individuals may not be known at death or at the time of
discovery, but is of utmost importance to the investigation of death (Wiersema et al.,
2009). Some individuals are discovered with identifying information such as a driver’s
license that can indicate the possible identity of the decedent; additionally friends and
family members can verify the identity of fresh or intact bodies (Holobinko, 2012).
However, individuals that are skeletonized, decomposed, dismembered, or lack
identifying media pose a challenge for death investigators when establishing
identification. There are two types of identifications, positive and presumptive and
depending on the circumstances of death, a positive identification may be required.
A positive identification is reached when evidence is found to connect the identity
of human remains to the identity of a missing person through the use of scientific
processes (Holobinko, 2012; Komar and Buikstra, 2008; Rogers and Allard). According
to the 2010 SWGANTH document titled “Personal Identification,” positive identifications
can be made when “antemortem and postmortem information match in sufficient detail to
conclude that they are from the same individual to the exclusion of all other reasonable
possibilities” (p.2). In other words, a positive identification can be made based on a
unique or individualizing characteristic. Accepted methods for positive identification of
deceased individuals include fingerprints, nuclear DNA, and medical and dental records
(Holobinko, 2012). For unidentified skeletal remains, positive identification is usually
achieved through a DNA match, anthropological comparison of antemortem and
postmortem medical or dental radiographs (Mundorff et al., 2006, Christensen 2005), or
the identification of a surgical device (Wilson et al., 2011;
SWGANTH, 2010).
A presumptive identification is established when scientific modalities are not
obtainable and instead the identification relies on contextual and circumstantial evidence
(Wiersema et al., 2009). Anthropologists can typically provide information that can lead
investigators to a presumptive identification through the construction of the biological
profile, but cannot provide positive identification in the absence of antemortem medical
and/or dental radiographs. Oftentimes, the anthropologist is helpful in excluding a
potential identification based on the mismatch of antemortem and postmortem
radiographs, bones, and comparisons to the antemortem reports.
In cases going to trial, however, a presumptive identification may be insufficient
and methods for positive identification are subject to the Daubert criteria (Wiersema et al.,
2009). In the absence of fingerprints or DNA, comparisons of antemortem and
postmortem radiographs can be used to establish a positive identification. Christensen
(2004) and Wiersema (2006) have published methods for positive identification utilizing
the frontal sinuses and petrous portion of the temporal, respectively. These methods
satisfy the Daubert criteria not only providing the probability of an individual match, but
also the likelihood estimate - the probability of the correct identification among the general
population (Christensen, 2004; Wiersema, 2006; Steadman, 2006).
Without strict standards for evidence and expert witness testimony, many forensic
practitioners could willingly or unwillingly falsify evidence. Forensic anthropologists acting
as expert witnesses and the methods used are not immune from scrutiny based on the
standards set forth by the courts. The heightened evaluations of anthropological methods
serve to improve their accuracy and precision, and make forensic anthropologists better
scientists, statisticians, and expert witnesses. This is a change forensic anthropologists
should welcome, as poor science has no place in the courtroom when the identity of a
person and circumstances surrounding their death, as well the fate of the accused, is on
the line.
The Biological Profile
As noted, the anthropologist is responsible to construct a biological profile from
skeletal remains to assist with identification. This section reviews four elements of the
biological profile: sex, age, ancestry, and stature and the reference collections commonly
used to develop forensic anthropological methods. The review will also focus on the
complications associated with assessing these components biological profile including
the benefits and drawbacks of using different anthropological methods. Emphasis will be
placed on adult estimation techniques, as they represent the majority of cases examined
for this study.
Reference Collections
Anthropological methods used to establish the biological profile were developed from
skeletal reference collections. Due to the court decisions previously discussed, many
forensic anthropological procedures are now under scrutiny, stemming from a myriad of
issues including that many methods lack statistical robusticity and rely on subjective
observations dependent on the skill level of the observer. The improvement of evidentiary
value in forensic anthropology lies in the proper execution of methods as well as the
proper materials from which to extrapolate information. Data-based methods with the
ability to separate individuals with an acceptable degree of accuracy (as deemed by the
courts) is a desired outcome of the NAS report. However, skeletal reference collections
where much of the data is gathered are limited in scope and have reduced power when
they do not reflect the population upon which the method or technique is used (Komar
and Grivas, 2008).
In the 1930s during the early developmental years of forensic anthropology,
researchers led by Wilton Marion Krogman focused on studies of skeletal growth,
nutrition, descriptive techniques, and paleodemography (Iscan, 1988). Examinations
were dedicated to age estimation in subadults, and sex and race assessment in adults.
Many of the investigations were conducted on two skeletal collections available at the
time: the Terry Collection at the Smithsonian Institution and the Hammon-Todd Collection
at the Cleveland Museum of Natural History (Iscan, 1988). These valuable skeletal
collections were compiled during the first half of the 20th century and include 19th century
individuals of known sex, age (approximated in Hammon-Todd), race, height, and cause
of death. Up until this point, these collections represented the most comprehensive
information available for skeletal analysis.
Over time, a number of skeletal reference collections have amassed including
historic cemetery collections, anatomical collections from medical examiners’ offices, and
donated collections of modern-day Americans. The expansion of available skeletal
materials has allowed anthropologists to create new methods and perform validation
studies of existing methods from other collections. Although collections and access to
them have increased over the years, they are not reflective of the general population
(Komar and Grivas, 2008). Further, new methods developed from these collections are
strongly influenced by the original methods and are limited in their application to the
overall population.
In order to evaluate the validity of forensic analyses, it is important to understand
the impact these collections have on forensic practice, even today. The way in which
early research was conducted with these collections permeates the development of
research questions and creation of modern techniques. In other words, preliminary
studies utilizing these collections have set the precedence for, and have influenced how
anthropologists conduct studies and frame contemporary research questions.
Influential Skeletal Collections
Table 1 is an abridged table taken from White et al. (2012) that outlines the most utilized
skeletal collections in forensic anthropological analysis. This table is updated to reflect
current populations of these collections. A more detailed discussion of the six most
influential collections (Terry Anatomical Collection, Hammon-Todd Collection,
McKern and Stewart Collection, LA County Coroner’s Office Collection, Maxwell
Museum Documented Skeletal Collection, and the William M. Bass Donated Skeletal
Collection) follows. Note the Maxwell Museum Documented Skeletal Collection and the
William M. Bass Donated skeletal collections continue to receive donations and their
demographics reflected in the table constantly change.
Table 1 shows the most utilized skeletal collections for the development of forensic
anthropological methods. The information clearly illustrates that the collections are
primarily comprised of black and white individuals and the majority of the individuals are
male. The significance of such limited demographic representation in these collections
cannot be ignored (Komar and Grivas, 2008). The methods developed from the above
collections are limited in their applicability to underrepresented populations; populations
that, as indicated in previous sections, are marginalized and may be represented in larger
numbers in the medicolegal system.
Terry Anatomical Collection
The Robert J. Terry Anatomical Collection, also known simply as the Terry Collection, is
currently housed at the Smithsonian Institution Museum of Natural History in Washington
D.C. Dr. Terry was an anatomy professor and head of the Anatomy Department at
Washington University Medical School in St. Louis, Missouri from 18991941. He was
interested in pathological conditions of the human skeleton, but efforts to improve his
research were hindered by a lack of reference and research materials. In the 1920s, he
began to collect skeletal materials from cadavers donated to the university’s medical
school. The bodies were primarily sourced from a local hospital, but other institutions in
Missouri also made contributions. In all, there are 1,728 individuals of known sex, age,
ancestry, cause of death, and pathological conditions
Table 1. Documented Skeletal Collections – Modified from White et al.
(2012)
Collection
Individuals
Dates of Death
Sex Bias
Age Bias
Ancestry
Hamann-Todd
3,713
1912-1938
80% male
Most 20-80
61% white
38% black
Korean War
Dead
450
1950-1953
Primarily
male
Most 17-25
Primarily
white
Terry
1,728
1920-1965
59% male
Most > 45
45% white
54% black
Huntington
4,054
1892-1920
75% male
70% white
LA County
Coroner
1,225
1977-1979
60% male
W. Montague
Cobb
634
1932-1969
70//5 Male
Most > 25
84% black
19% white
NMNH Fetal
320
1904-1917
54% male
Fetal
neonate
43% white
54% black
Maxwell
Museum
Approx. 300
and growing
1975-present
William M.
Bass
1,700 and
growing
1981-present
65% male
80% adult
89% white
5% black
University of
Iowa/Stanford
1,100
1910-1920
St. Thomas
Cemetery
579
1821-1874
European
American
J.C.B. Grant
202
1928-early 1950’s
87% male
75% over
40 years
European
American
Christ Church
968
1729-1859
81% adult
European
St. Bride’s
Church
244
1761-1851
94% over
18 years
European
Universiteit
Leiden
European
Museu Bocage
1,692 and
growing
1880-1975
European
Coimbra
Cemetery
570
1904-1938
63% male
European
Dart Collection
2,605
1920’s-present
71% male
94% > 20
years
71% SA
African 18%
white
Cape Town
University
200
1980-1999
Most > 50
years
African
Pretoria Gone
Collection
290 skeletons
704 skulls
541 postcrania
1943-present
African
represented in the collection. Personal information from the cadavers was collected
through morgue records. Individuals in this collection are 59% male and 41% female,
45% white, 54% black, and 1% other. The collection contains 461 white males, 546 black
males, 323 white females, 323 black females, 5 Asiatic males, and one individual of
unknown origin. Ages at death range from 16-102 years with the date of birth from 1822
to 1943. Most of the individuals in the collection are 45 years of age or older. A breakdown
of age ranges by racial group is provided by the Smithsonian’s National Museum of
Natural History and illustrated in Table 2.
The Terry collection contains mostly older black and white individuals from the late
19th century to early 20th century. A variety of forensic anthropology methods have been
developed from this collection including sex estimation (Phenice, 1969), age estimation
(Yoder et al., 2001), ancestry (Giles and Elliot, 1962), and stature estimation (Trotter and
Glesser, 1952). The methods developed on antiquated collections may not be accurate
for modern blank and white Americans due to physical changes that have
Table 2. Age Ranges by Racial Category, Terry Anatomical Collection
Age
0-20
21-
30
31-
40
41-
50
51-
60
61-
70
71-
80
81-
90
91-
100
101-
110
Black Males
20
83
114
104
110
70
30
8
2
0
White Males
7
10
30
77
107
129
80
15
0
0
Black
Females
21
53
61
66
58
52
45
17
6
2
White
Females
13
7
11
29
56
80
6
42
4
0
occurred in these populations over time. Meadows and Jantz (1995) and Jantz and Jantz
(2000) describe these physical changes to the cranial and facial forms among black and
white individuals in the United States over the past 125 years. Specifically, they note a
gradual lengthening of the cranial vault height and the overall narrowing of the cranial
vault and facial skeleton in these populations. Additionally, individuals represented in this
collection have birth dates primarily between 1850 and 1900 when
Americans are recorded as having the shortest stature (Meadows and Jantz, 1995). The
phenomenon of morphological change is referred to as secular change and is a result of
changing environmental conditions, increased nutritional status, and overall better health.
Hamann-Todd Collection
The Hamann-Todd Collection is housed at the Cleveland Museum of Natural
History in Cleveland, Ohio. In 1912, Dr. T.W. Todd, a professor of anatomy at Western
Reserve University (now Case Western Reserve University), assisted by the dean of the
School of Medicine, Dr. Carl Hamann, began to collect skeletons of donated cadavers
used by medical students. The collection consists of 3,000 skeletons and 3,600 records
of skeletons from the 19th century that include sex, approximate age, ancestry, and cause
of death. The individuals in this collection consist primarily of black and white Americans
of unknown ages, however, the majority of males are estimated to be older than 40 years.
The ages for the specimens were largely estimated and therefore, are not ideal for age-
related studies. There is a significant sex bias in this collection as 80% of the individuals
are male. White individuals make up 61% of the sample while black individuals make up
38%.
McKern and Stewart Collection
McKern and Stewart developed methods based on a skeletal sample comprised of
soldiers who died during the Korean War. The collection contains predominantly white
Americans males in their early twenties (Katz and Suchey, 1986). The ages recorded for
these men are considered highly accurate as birth certificates and dates of reported death
estimate them. Although a number of studies authored by McKern and Stewart and others
were conducted on this sample, the methods developed reflect characteristics of young
white male individuals.
L.A. County Coroner’s Office – Pubic Symphysis Collection
Judy Meyers Suchey collected pubic bones from 1,225 autopsied individuals at the L.A.
County Department of Chief Medical Examiner-Coroner from 1977-1979. The sample
was collected to develop standards to reflect ages-at-death for modern population groups.
Previous pubic symphyseal age studies were developed from the Hammond-Todd and
the McKern and Stewart Korean War collections and have been criticized in their
application in forensic casework. The Hammond-Todd collection included individuals of
unknown ages, thus studies evaluating the morphology of the pubic symphysis for age
related studies using that collection are based on estimated rather than known ages (Katz
and Suchey, 1986; 1989). The McKern and Stewart collection is made up of young,
predominately white males, so methods developed to age the pubic symphysis from this
collection are specific only to that narrow population of individuals (Katz and Suchey,
1986).
The LA County Coroner’s Office sample consists of 739 males and 486 females of
known age and race. The males range in age from 14-92 and females range in age from
13-99. Individuals in their twenties are heavily represented in this sample, followed by
those in their thirties, forties, and fifties (Suchey and Katz, 1998). Fewer individuals in
their sixties, seventies, eighties, and nineties are represented in this sample (Suchey and
Katz, 1998). This collection was used to develop the Suchey-Brooks method for aging
the pubic symphysis and is widely used in forensic practice today. The collection is
currently not accessible for study.
The Maxwell Museum Documented Skeletal Collection
As of 2013, the Maxwell Museum Documented Skeletal Collection consists of 278
contemporary male and female individuals of varying racial backgrounds. Although only
207 of the donors have been positively identified, demographic information such as sex,
age, ancestry, and cause of death for the majority of the identified individuals is known.
All of the samples have been voluntarily donated to the collection or given to the museum
by next of kin or the Office of the Medical Investigator when next of kin could not be
located (Komar and Grivas, 2008). In 1990, a very small sample of this collection was
used to develop Stanley Rhine’s quintessential study on non-metric skull racing. Rhine
selected a sample of 87 complete skulls of known identity to conduct the study. Table 3
details each group in his study.
The Rhine (1990) skeletal sample is heavily biased toward Caucasian male
individuals with Hispanic skulls classified in Caucasian group. Both modern and
prehistoric Amerindians were included for the study. It has not been shown that prehistoric
Amerindians have a “typical” phenotype or that they are similar to the modern Native
American population in the United States. Only seven “Negroid” skulls were assessed
for the study and two of those skulls were casts of the originals. The sample population
selected to document non-metric ancestry traits reflects the characteristics of a small
number of select individuals underscoring the scope of
Table 3. Skull Sample From the Maxwell Museum Collection, Rhine
(1990)
Race
Male
Female
Total
Anglo
40
13
53
Hispanic
13
2
15 = 68 Caucasoid
Modern
Amerindian
3
0
3
Prehistoric
Amerindian
9
0
9 = 12 Mongoloid
Black
5
0
5
Black Casts
2
0
2 = 7 Negroid
Totals
72
15
87
morphological variation within and between population groups. The Rhine method does
not represent the diversity of the American population and, as a result, cannot be
expected to perform accurately. Despite all of the conundrums with this sample group,
the traits established by this study are methodically used today, not only to distinguish
racial groups, but also to serve as a baseline or set of standards for other studies to use
when developing or revising a method (Birkby et al., 2008; also see: Hefner, 2009).
Komar and Grivas (2008) analyzed the Maxwell Museum donated collection to
compare the demographics of the collection to relevant populations in New Mexico (living
NM population, deceased NM population, and decedents with medicolegal death
investigations). The results show that Maxwell Museum Donated Collection differs
significantly from the three populations compared in that (1) males are overrepresented,
(2) age cohorts are disproportionate with those over 45 are over represented with more
than 20% of the collection being over the age of 80, and (3) white individuals are
overrepresented while Native American and Hispanics being significantly
underrepresented. In addition, 60% of the individuals represented in the Maxwell
Museum collection are labeled with population affinities determined by a pathologist at
autopsy or the collections curator, not self-reported or reported by family (Komar and
Grivas, 2008).
William M. Bass Donated Skeletal Collection
In 1981, Dr. William M. Bass began curating a collection of skeletons from individuals
donated to the Forensic Anthropology Center at the University of Tennessee in Knoxville,
Tennessee. The modern-day collection consists of over 1,700 individuals and each year,
approximately one hundred of new donations are added (Steadman, personal
communication). Individuals in this collection are predominantly from Tennessee and as
such, reflect the demographics of this area. Wilson et al. (2007) found that self-donors
had higher education levels and represented a higher socioeconomic class than those
donated by family or by a medicolegal authority. The sample is biased toward older white
males (Wison et al., 2007) although black, Asian, and Hispanic individuals of both sexes
are represented in smaller numbers. In addition, 47 cremated individuals are available
for study. This collection represents the largest sample of modern-day Americans and is
a common location for students and academics to conduct osteological research.
Special Considerations for Reference Collections
This review of skeletal reference collections is limited to only a few of the more
recognizable and widely used American collections in forensic anthropology. Many more
collections have amassed over the years to help researchers expand research goals and
interests and also perform validation studies. The descriptions of these key collections
should be kept in mind when evaluating the efficacy of the methods used to estimate the
biological profile because The SWGANTH recommendations and best practices for
estimating the biological profile emphasize the use of appropriate population-specific
standards. Therefore the forensic anthropologist must understand the strengths and
limitations of methods used and the population demographics from which the methods
are derived.
Nevertheless, several researchers have suggested that the Terry and
HamannTodd collections are not representative of modern populations due to secular
change
(Ousley, 1995; Meadows and Jantz, 1995; Erikson, 1982; Ousley and Jantz, 1998, 1992;
Dirkmaat et al, 2008). Although some methods, notably sex estimation methods
developed from reference collections are widely applicable (Klales et al., 2012; Klales,
2016), Hoppa (2000) noted that there is a difference in aging processes among samples
and points out that one reference sample does not work for estimations of age in all
populations. Kemkes-Grottenthaler (2002) similarly notes that there may be biological
differences among population groups that affect aging processes, particularly among
females. Further, Kimmerlee et al. (2008) found that there are differences in aging
processes in females among American and Eastern European populations.
LangleyShirley and Jantz (2010) found that the clavicles of modern Americans fuse 4
years earlier than 20th century Americans and 3.5 years earlier than Korean War era
Americans. Finally, anthropological methods to estimate ancestry developed from limited
samples does not encompass the wide range of variation seen in modern population
groups (Komar and Grivas, 2008; Albanese and Saunders, 2006; Spradley
et al., 2008).
In addition, it is not enough to discuss the reference collections themselves without
considering the donation bias that exists with the collections. Komar and Grivas
(2008) outline sources of donation bias including the cultural and religious acceptance of
body donation and the socioeconomic status of donors. These factors, they argue,
selectively bias the collection and should not be used as proxies for modern populations
(Komar and Grivas, 2008). Further, when a subsample of individuals are selected from a
skeletal reference collection, or from many skeletal reference collections (in the case of
the Forensic Data Bank), it cannot be considered representative of modern populations
because the samples selected are drawn from collections that suffer from this formation
bias (Komar and Grivas, 2008).
Sex Estimation
Anthropologists have long recognized that there is a difference between sex and
gender and that sex is not a binary characteristic. Sex refers to the biological sex of an
individual: male and female based on physical characteristics controlled by genetic
expression. These biological traits vary in their degree of expression. Gender, on the
other hand, is a cultural construction of identity independent of primary and secondary
biological sex characteristics. Forensic anthropologists rely on biological sex
characteristics to determine the sex of human remains because only biological sex can
be extrapolated from skeletal materials. The sexing of adult skeletal elements is one of
the most important aspects of the biological profile because it narrows the pool of potential
victims by half and influences all other components of the profile (ancestry, age, and
stature). Because males and females display varying degrees expression in traits used
to estimate other components of the biological profile, sex is often the first feature
anthropologists estimate.
Sex estimation can be conducted with both metric and non-metric analyses of the
pelvis and skull as well as through metric analysis of the postcranial elements (France
1998, Spradley and Jantz, 2011). Sexing the adult skeleton relies on the discrimination
of the physical size and shape differences between males and females. As such, sex can
be reliably determined from the morphological features of the pelvis because the
differences observed between males and females in the pelvic region are due to the
function of childbirth in females (Bass, 1995; Phenice 1969; White et al., 2012). In
general, females have wider and broader os coxae to allow for the passage of an infant
though the birth canal, while males have narrower and longer os coxae (Buikstra and
Ubelaker, 1994; Phenice; 1969). Phenice (1969) developed a visual method for sexing
the os pubis utilizing three landmarks. When tested, the accuracy rate for this method is
reported to be between 70% and 96% in controlled experiments (Sutherland and Suchey,
1991; Ubelaker and Volk, 2002). Recently, a revised Phenice method developed by
Klales et al. (2012) demonstrates 94.5% accuracy in the sample population and 86.2% in
and independent validation sample. Additional validation studies of this method on South
African samples found the method correctly classified sex in 99.2% (Kenyhercz, 2012)
and in 97.59% (Stull et al., 2013) of individuals sampled. Additionally, validation of this
method on a sample of modern forensic cases demonstrated 90.9% classification
accuracy (Kenyhercz et al., 2012).
Other morphological characteristics of the pelvic bone that are indicative of an
individual’s biological sex include the shapes and size of the pubic bone, greater sciatic
notch, obturator foramen, the presence or absence of the preauricular sulcus (Bass, 1995;
Steadman and Anderson, 2009), and to a lesser degree the presence or absence of
parturition scars (Kelley, 1979). The ventral arc, subpubic concavity, ridge of the
ischiopubic ramus, greater sciatic notch, and preauricular sulcus are characteristics that
can be scored on an scale of 1 to 3, with 1 representing the morphology of females and
3 representing males (Buikstra and Ubelaker, 1994). It is important to note that, sex
estimation techniques developed from the pelvis and skull have largely been limited to
samples of black and white Americans (Rogers and Saunders 1994; Rogers 2005;
Walker, 2005; Williams and Rogers, 2006; Walker, 2008). Although most population
groups exhibit predicable sexually dimorphic traits, some Asian and Hispanic groups are
generally gracile and differences between males and females can be harder to detect
(Birkby et al., 2009; Rogers and Saunders, 1994). This poses a challenge for forensic
anthropologists attempting to estimate the sex of individuals from populations where
sexual dimorphism is less pronounced. Additionally, in relatively heterogeneous areas,
where the population demographics consist of a variety of diverse groups, the
estimation of sex is less straightforward, particularly if ancestry is not known.
The skull is another sexually dimorphic portion of a skeleton. The skull
demonstrates differences between males and females and the examination of the size
and rugosity of muscle markings and other characteristics can be useful for sex estimation
(Buikstra and Ubelaker 1994; White et al., 2012). Sex estimation of the cranium also
depends on the degree of sexual dimorphism existing within population groups.
Regardless, the common morphological traits used for sex estimation of the skull include
the size and rugosity of the muscle attachments associated with the nuchal crest, external
occipital protuberance and mastoid processes (Bass 1995; Walker, 2008). The shape
and size of the supraorbital ridges, supraorbital margins, forehead, chin, and the gonial
angle can also be used to infer sex (Bass 1995; Walker, 2008).
Additionally, measurements of the skull are often utilized for statistical analysis
(Bennett, 1993; France, 1998) and can be entered into FORDISC 2 for discriminant
function analysis (Ubelaker, 1998). FORDISC is a statistical computer program that
utilizes discriminant function analysis to compare the unknown individual against the
existing database of known males and females. The program will utilize measurements
of the skull to classify the unknown individual into a male or female category. Although
FORDISC 2.0 will classify the sex of an individual independent of ancestry, FORDISC 3.0
will not classify sex independent of ancestry (Guyomarc’h and Bruzek, 2011).
Estimation of sex from postcranial elements can be useful for cases where the
pelvis, skull, or both are missing or in cases where sex estimation from the skull or pelvis
is inconclusive. Although the skull is often considered to be the second best option for
sex estimation (Bass, 2005; Byers, 2002; Pickering and Bachman, 1997), numerous
studies (Berrizbeitia, 1989; France 1998; Robling and Ubelaker, 1997, Tise et al., 2013)
suggest otherwise. Spradley and Jantz, (2011) conducted univariate and multivariate
discriminant models on postcranial elements from the Forensic Data Bank and reported
that most postcranial elements outperform the skull in estimating sex.
Measurements of postcranial elements can be entered into FORDISC for metric analysis
and, like the skull, will be compared against the existing database of known males and
females. Methods utilizing metrics can be useful if the appropriate data is available for
the population group considered for analysis. As with non-metric approaches, many
metric postcranial sex estimation methods rely on limited sample sizes primarily
consisting of black and white Americans.
While attempts have been made to develop techniques to reliably estimate the
sex of subadult skeletal remains, these techniques are not generally accepted. The
features and characteristics detailed above for adult sex estimation do not typically appear
in juveniles until the onset of puberty or the onset of secondary sex characteristics (both
of which vary on an individual basis). Genetic analyses of amelogenin are especially
useful in juvenile cases (Gibbon et al., 2009).
The SWGANTH sex assessment (2010) document recommends anthropologists
employ the following best practices to estimate sex from skeletal remains:
• Sex assessment should be made independently of suspected or presumptive
identification to avoid bias.
• When appropriate, use population and period-specific standards.
• Assess and measure the maximum number of age-appropriate cranial and
postcranial variables, emphasizing the most dimorphic elements present,
especially in the case of fragmentary remains.
• Document and describe the location of any inconsistent indicators.
• If an observation cannot be made or a measurement cannot be taken, explain its
absence: missing, broken, fractured, congenital, pathological, or anomalous.
• Sex assessment, as well as assessments of other skeletal parameters, should be
performed, even if samples for DNA analyses will be taken.
• Express degree of certainty when reporting sex assessments, especially when a
sex assessment is less than certain, e.g. “male?”
• When an assessment of skeletal sex is not possible (e.g. partial remains or those
of subadults), sex assessment by DNA analysis may be helpful.
Age Estimation
The estimation of age at the time of death is an important component of the
biological profile. Along with sex, an accurate age range serves to narrow the pool of
potential victims among missing individuals (Krogman, 1962; Iscan et al., 1984a; Iscan et
al., 1984b; Meena et al., 2012, 2013; Wolff et al., 2012). There exist different methods for
age estimation of juvenile versus adult individuals. Juveniles are aged by the
interpretation of growth and development processes, while adults are aged through the
interpretation of degenerative processes of the skeleton.
Juvenile Age Estimation
Juvenile age estimation from skeletal remains generally relies on changes related
to growth and development such as dental mineralization and eruption and long bone
growth and epiphyseal fusion. Dental development is a highly conserved trait, strongly
controlled by genetics, and dental development patterns have been well documented (see
Moorreess et al., 1963; Schour and Massler, 1941, 1944; Ubelaker,
1999; Scheuer and Black, 2000; Saunders, 2000; Fitzgerald and Rose, 2000).
Assessments of developmental patterns provide accurate and reliable subadult age
estimations. While dental mineralization is considered the most accurate method for
aging sub adults, tooth eruption patterns are also informative. However, eruption patterns
are more variable than developmental patterns (Ubelaker, 1989), for example, the third
molars (wisdom teeth) are considered unstable and highly variable, even absent
(agenesis) in some individuals.
Forensic anthropologists can reliably estimate subadult age from dentition up to
approximately 15 years of age, when the roots of the second molars finish developing.
Although dental age indicators are highly conserved genetically, they are not resistant to
environmental influences that may affect development. Cardoso (2008) and Hueze and
Cardoso (2008) attribute socioeconomic status with the disturbance of dental
development.
Growth and development (epiphyseal fusion) of long bones can also a reliable age
indicator for juvenile remains (Stewart, 1979; Scheuer and Black, 2000). Bones grow and
develop at predictable rates and epiphyseal fusion occurs on different bones at different,
but generally known, timeframes (Scheuer and Black, 2000). For example, Webb and
Suchey (1985) found that the medial clavicle and anterior iliac crest are the last elements
to fuse. The medial clavicle will fuse between 20-34 years for females and 21-31 years
for males while the anterior iliac crest will fuse in females between 1824 years of age and
17-24 years for males. Most recently, Langley-Shirley and Jantz (2010) developed
confidence intervals using a Bayesian approach to document the age of transition
between phases of clavicle fusion in modern American males and females. These reliable
age markers allow anthropologists to accurately calculate the age interval of individuals,
particularly those over 15 years of age, when dental indicators become less reliable.
However, Saunders (2000) noted that subadult females age faster than males and, akin
to dentition, Cardoso (2008) reported that socioeconomic status would affect subadult
skeletal growth rates. Lower socioeconomic status is an key variable in the differential
growth patterns observed in subadult skeletal materials that may impact rates of skeletal
maturation (Kim et al., 2008; Malina, 1979; Bagga and
Kulkarni, 2000; Low et al, 1982; Laska Mierzejewska et al, 1982; Rimpela and Rimpela,
1993; Prado, 1984; Cardoso, 2008; Abioye-Kuteyi et al, 1997; Alberman et al, 1991;
Bodzsar, 2000; Todd, 1937; Meijerman et al, 2007).
Adult Age Estimation
Adult age estimation relies on the interpretation of degenerative changes that
manifest in the skeleton. Physiological age related changes are documented in skeletal
reference collections and correlated with chronological age ranges. Age related changes
are chronological but their degree of manifestation can also be affected by life
experiences. Age estimation, therefore, is presented as a range of years (e.g. 30-45
years of age) because aging techniques rely on the anthropologist’s ability to interpret
these highly variable degenerative skeletal changes. While most people progress through
the “phases” associated with different age ranges, the rate of progression, or the rate of
aging, is influenced by genetic and environmental factors (Berg, 2008; Meena et al.,
2013).
Following the development and complete fusion of bones, forensic anthropologists
analyze the morphological changes of joint surfaces over time to provide a statistically
derived age range. Currently, adult age estimation techniques depend on the analysis of
joint surfaces not affected by stresses associated with body weight, locomotion, or muscle
attachments. Baring any trauma to the joint, the pubic symphysis is ideal for age
estimation because it is resistant to activity related stress. The sternal rib ends (Iscan,
1984a, 1986a, 1986b) and the auricular surface of the ilium (Lovejoy et al, 1985;
Buckberry and Chamberlain, 2002) are also resistant to activity related stress and can
also be used to estimate age. In addition to the analysis of joint surfaces, dental attrition
(Lamendin et al., 1992) and cranial suture closure (Meindl and Lovejoy, 1985) are also
used for adult age estimation. Besides the best practices outlined by SWGANTH, there
is no anthropological standard for aging the adult skeleton. Garvin and Passalacqua
(2012) found that 78% of 145 forensic anthropologists responding to a survey prefer the
pubic symphysis for age estimation followed by the sternal rib ends and the auricular
surface of the ilium, respectively. Cranial sutures and dental attrition were least preferred
(Garvin and Passalacqua,
2012).
The most commonly cited and used pubic symphyseal aging methods in
anthropology are the Suchey-Brooks method (Katz and Suchey, 1986, 1989), the McKern
and Stewart method (1957), and the Todd methods (1921a, 1921b). In forensic
anthropological casework, the Suchey-Brooks method is preferred (Hartnett, 2010;
Fleischman, 2013), as it is sex specific and derived from 1,200 modern American
individuals of various ages collected from the Los Angeles County Office of the Coroner.
The McKern and Stewart (1957) and Todd methods (1921a, 1921b) are not commonly
cited in forensic casework as they were developed from the McKern and Stewart
Korean War sample and the Hammond-Todd collection. The Defense POW/MIA
Accounting Command (DPAA) and other non-profit organizations that analyze the
remains of deceased US servicemen of past wars more commonly employ these methods
because they reflect age related changes of persons living around the beginning of the
20th century. As discussed earlier, these collections are not appropriate for age analysis
of forensic cases because the sample populations used to derive the methods, being
developed on antiquated populations, do not reflect the population of the unidentified
remains. Although the Suchey-Brooks method is widely used in forensic contexts, studies
indicate that the method’s accuracy is limited due to the use of wide age ranges. The
wide age ranges help to ensure that the unknown’s chronological age is included so to
not unintentionally exclude potential matches. Moreover, this method has proved to
perform better on skeletal materials of younger individuals than older individuals due to
variation in the degeneration process of individuals (Martrille et al., 2007; Berg, 2008;
Hens et al., 2008; Kimmerle et al., 2008; Hartnett, 2010). Overall the Suchey-Brooks
method has low evidentiary value (Konigsberg et al., 2009) primarily due to wide age
intervals and the large amount of variation within stages. Konigsberg et al. (2009) further
note that the method is applied better than expected at random, “however with only six
stages, it cannot be expected to provide much information for identification purposes”
(Konigsberg et al., 2009:554).
The auricular surface of the ilium is another area of the pelvis used for forensic age
estimation, (Lovejoy et al., 1985; Buckberry and Chamberlain, 2002) although is not
commonly used in casework. The auricular surface is the non-weight bearing joint where
the sacrum articulates with the broad surface of the ilium on the anterior-superior surface
of the bone. Similar to the methods developed for the pubic symphysis, the auricular
surface technique is used to evaluate joint surface degeneration over time to provide an
age range. The Lovejoy et al. (1985) method was developed for archaeological purposes
from the 19th century Todd and Libben collections and describes 8 phases with 5-10 year
age ranges, but provides no error statistics. Murray and Murray (1991) tested the method
on the Terry Collection to see if the methods could be used in modern forensic cases,
Bedford et al. (1993) tested the method on the Grant Collection at the University of
Toronto, Schmitt (2004) tested the method on a collection of 66 Asian individuals and
Saunders et al. (1992) tested the method on an archaeological sample with known ages
at death. All four studies found that the method consistently underestimated the age of
older individuals and overestimated the age of younger individuals. Murray and Murray
(1991) attributed these findings to differences in age structures between the collections.
Bedford et al. (1993), Schmitt (2004), and Saunders et al. (1992) found that the method
performed better for younger individuals., underestimated the age at death for older
individuals and had a high interobserver error. Saunders et al. (1992) attributed these
findings to the inability of the method to encompass variation in skeletal aging. Buckberry
and Chamberlain (2002) sought to improve the accuracy of the auricular surface method
and modified Lovejoy et al. (1985) to include a quantitative scoring system. Their
approach, although easier to apply, lacks error rates and includes large age ranges that
encompass all of adulthood (e.g., 16-65 years and 29-88 years). Overall, methods
utilizing the auricular surface of the ilium are difficult to apply and result in wide age
intervals that, although may include the age of the unknown individual, are not narrow
enough to be useful to law enforcement; with the exception of Lovejoy et al. (1985) where
the age ranges are too narrow to encompass individual variation.
Garvin and Passalacqua (2012) note that sternal rib end analysis for age
estimation is the second most preferred technique by forensic anthropologists. The
sternal rib ends offer sex-specific age phases with narrower age ranges than the
Suchey-Brooks method and the technique is easy to apply (Meena et al., 2013).
Additionally, unlike the os coxae, rib ends are not subjected to the stresses of pregnancy
in females. Iscan et al. (1984a) and Iscan and Loth (1986a, 1986b) conducted a series
of studies on the morphological changes of the sternal end of the right fourth rib for males
and females. This method, developed with a modern U.S. sample from Medical
Examiner’s Offices, provides full statistical analyses. The age related changes that can
be observed from the fourth rib have provided reliable age estimations (Saunders et al.,
1992; Iscan and Loth, 1997) and can be applied to ribs 3-6 on both sides of the body
(Yoder et al., 2001). However, Cerazo-Roman et al. (2014) found that the method
consistently underestimated the age of a Mexican population. Unfortunately, the sternal
ends of the ribs are fragile and not always preserved in forensic contexts. Damaged ribs
resulting in the differential preservation or destruction of the sternal surface are caused
by a variety of factors including perimortem trauma and taphonomic conditions such as
postmortem fracturing, animal activity, and weathering. Thus, poor preservation of the
fourth rib may preclude age estimation using this method.
When only the cranium is available, cranial suture closures and dental attrition can
be analyzed. Analysis of cranial suture closures can be conducted on ectocranial (Meindl
and Lovejoy, 1985) and endocranial (Todd and Lyon, 1924) sutures. Cranial suture
analysis is controversial because cranial suture closure is highly variable among
individuals. The method is also generally considered unreliable and difficult to apply, and
is used when other options have been exhausted. However, dental aging methods have
shown some promise. Cameriere et al. (2004) have developed a protocol for dental aging
that presents high levels of accuracy for adult individuals, particularly those over 30 years
of age. This method assesses chronological age based on the correlation between age
and the pulp/tooth area ratio in single-rooted teeth. Forensic odontologists utilize
radiographic images to analyze tooth morphology, which places estimated ages of adult
individuals within months of their actual ages.
Adult aging methods and techniques were primarily developed on skeletal
collections comprised of black and white Americans. Degenerative changes are variable
within and between adult individuals due to a variety of factors including lifestyle, health,
nutrition, and genetics (Crews and Garruto, 1994). Marginalized groups lacking
consistent access to healthcare and proper nutrition may deviate from the age standards
developed from these skeletal collections. Moreover, the error rates of many aging
techniques are unknown making age estimation problematic for forensic application and
expert witness testimony. SWGANTH (2013) recommends
anthropologists employ the following best practices when estimating age from skeletal
remains:
• Whenever possible, standards for age assessment should be used that are most
appropriate for the ancestry/population origin of the remains examined. Both sex-
based and ancestry-based variation exists in dental and osteological development.
When population-specific standards are not available, standards that are more
inclusive (and with greater variance) should be used, and those standards should
be documented in the report.
• Practitioners should have extensive knowledge of skeletal anatomy and
experience with the various methods of age estimation. This includes both
experience in the technique used during age estimation, as well as experience with
osteological material. The analyst should be knowledgeable about the ranges of
normal human variation in the teeth and skeleton. They should be attentive to
pathological conditions that might have an effect on traits used for aging purposes,
especially degenerative changes.
• The final age estimate is a matter of expert judgment by synthesizing all available
information. Factors to be considered are: appropriateness of the reference data,
skill in using one method over another, condition of the remains,
applicability of statistical models, etc.
Ancestry Estimation
Several methods have been developed over the past 60+ years, to estimate
ancestry from skeletal remains (For a small sampling, see: Rhine, 1990; Gill et al., 1988;
Gill, 1998; Birkby et al., 2008; Burris and Harris, 1998; Edgar, 2005; Fransciscus and
Long, 1991; Hanihara, 1996; Hefner, 2009; Hughes et al., 2011; Wescott and Moore-
Jansen, 2001). Although methods have been developed for numerous skeletal elements,
the skull has been identified as the most useful area of the skeleton for ancestry
estimation (Howells, 1973; Rhine, 1993).
Morphological trait analysis is a common technique with a long history in the
anthropological literature (see Rhine, 1990), and its supposed ease of implementation
(Albanese and Saunders, 2007). Ancestry estimation methods using the interpretation of
morphological traits are based on the idea that these characteristics are heritable
(Relethford, 2002; Havarti and Weaver, 2006; Carson, 2006; Devor, 1987), although they
vary in degrees of expression (Hughes et al., 2011). The numerous methods developed
to interpret skeletal clues of ancestry require the observer to have extensive experience
using the methods on a large and varied sample of skeletal remains
(Hefner, 2007, 2009; Hefner et al., 2001; Wheat, 2009; Hooton, 1926, 1946; Hinkes, 1993;
Rhine, 1990; Stewart, 1979).
There are two main types of morphological trait assessment that rely on visual
evaluation of skeletal characteristics: anthroposcopic traits and non-metric traits
(Albanese and Saunders, 2006). Anthroposcopic traits are those that are present in
varying degrees on all skeletons utilizing an ordinal-scale to record their degree of
expression, while non-metric traits are those that can be determined by their presence or
absence in skeletal materials (Albanese and Saunders, 2006).
Examples of anthroposcopic traits include the shape of the dental arcade, the
degree of prognathism, the shape of the nasal aperture, the protrusion of the nasal spine,
and the projection of the zygomatic bones. Non-metric trait examples include the
presence or absence of the malar tubercle, shovel-shaped incisors, and Carabelli’s cusp.
Note that the terms anthroposcopic traits and non-metric traits are often conflated
in the forensic anthropological literature and used interchangeably (Albanese and
Saunders, 2006). For example, Byers (2005) in an introductory forensic anthropology
textbook refers to all traits as anthroposcopic, Gill (1995) terms all traits non-metric,
Hughes et al. (2011) refers to anthroposcopic traits as morphoscopic traits and nonmetric
traits as binary traits and Hefner (2009) refers to traits as non-metric or morphoscopic.
Despite differences in terminology, all of these skeletal traits are believed to tell us
something about the ancestral origin of the individual because skeletal traits are heritable
and found to be common among regional population groups
(Hooton, 1926; Boyd, 1950; Brues, 1977; Bamshad et al., 2003; Cheverud, 1982, 1988;
Cheverud and Buikstra, 1981, 2005; Ishida and Dodo 1993; 1997; McGrath et al., 1984).
Descriptions of skeletal traits can be attributed to the work of Earnest Hooton, a physical
anthropologist at Harvard University. In the 1920s he developed the “Harvard
List,” a list of cranial non-metric traits for skeletal analysis (Brues, 1990; Hefner, 2009).
Many of the traits identified by Hooton are used today in forensic practice and provide a
theoretical and practical foundation for modern ancestry estimation methods (Birkby et
al., 2008; Hefner, 2009).
Rhine (1990) in his article “Non-Metric Skull Racing” summarized morphological
traits from the Harvard List commonly used by anthropologists to estimate the ancestry
of unidentified individuals (Rhine, 1990; Smay and Armelagos, 2000). Additionally, he
addressed the varied and unstandardized criteria used for ancestry estimation among
practicing anthropologists in an attempt to standardize morphological trait assessment
(Rhine, 1990). He tested 45 traits of the skull and developed a trait list with illustrations
that can be used to separate individuals into three groups: American Caucasoid,
Southwest Mongoloid, and American Blacks. Despite being a resource that is considered
to have had a “major influence on American forensic anthropology” (Birkby et al, 2008:30)
and is “useful today” (Birkby et al., 2008:30), the study has serious methodological flaws
as it is based on a limited skeletal sample, uneven subsamples, and heavily biased toward
white individuals (Albanese and Saunders, 2007; Smay and Armelagos, 2000). Moreover,
suspected Hispanic skulls were grouped into the white category (Birkby et al., 2008), casts
of black skulls in place of real skulls were used in the analysis, and traits were assigned
based on anecdotal experiences of the few study participants (Rhine, 1990). Since
Hispanic individuals were grouped into the white sample in Rhine’s (1990) study, Birkby
et al. (2008) sought to provide a series of nonmetric traits frequently observed among the
undocumented border crossers along the southern border of the United States. The
authors note that this population of Hispanic individuals displays a suite of characteristics
observed in both European and Native American populations (Birkby et al., 2008).
Further studies of non-metric trait analysis expanded on the methods developed
by Rhine (1990) but typically compared skeletal traits of just two groups such as black
and white or white and Native American (See: Gill et al., 1998; Burris and Harris, 1998;
Wescott and Moore-Jansen, 2001; Edgar, 2005; Yokley, 2009; Klales and Kenyhercz,
2015). However, these studies oversimplify the variation that exists among the American
population by comparing few population groups. Furthermore, the assignment of
morphological traits is largely based on assumptions about the apportionment of human
diversity. Articles such as Rhine’s (1990) and Gill’s (1998) demonstrate a typological
approach to the assessment of ancestry. However, typological studies like Rhine (1990)
do not explicitly capture group differences and he noted that classification of the samples
could be problematic. Further, Rhine (1990) found large amounts of variability in the
sample groups with 37 out of 45 traits occurring with frequencies up to 30% in more than
one group.
To address concerns that typological methods oversimplify the frequency of
variation between population groups, Wescott (2005) and Hefner (2009) describe and
quantify the frequency of morphological traits that have been found to occur in more than
two groups. Studies like these attempt to understand the varying frequencies of traits
within populations and are a step in the right direction, but are affected by the sample
groups selected from reference populations. Wescott (2005) evaluated within and
between group variation of proximal femur shape in five groups: American Whites,
American Blacks, Native Americans, Hispanics, and Polynesians. However, the samples
selected may not represent contemporary forensic casework. The Native American
sample is comprised of historic and prehistoric individuals, the Hispanic population is from
the Forensic Data Base (FDB) where no specifics regarding their ancestry is known
(Wescott, 2005), the American black and white samples are from the
Terry collection and the FDB, and the Polynesian group is an archaeological sample of
Hawaiians. Likewise, Hefner (2009) used population groups for his analysis of cranial
traits that may not represent the variation of individuals in forensic casework. For
example, the African sample was collected from Africa in 1909, the American black and
American white samples were taken from the Terry collection, the Asian sample
represents individuals from the 1800s, and the Native American sample represents
prehistoric and protohistoric individuals (Hefner, 2009). These studies document trait
characteristics and frequencies within several population groups, but the sample
collections used are not contemporary.
Metric analysis, on the other hand, utilizes instrumentation to measure skeletal
elements. Measurements of unknown skeletal remains can be compared to the
dimensions of known individuals recorded in a database. Ideally, measurements are
taken from complete or mostly complete sets of skeletal remains for analysis. The
analysis of skeletal measurements is considered to be more objective than
morphological evaluation of skeletal traits, as the results are quantifiable pieces of data
devoid of observer subjectivity. As such, metric programs like FORDISC have improved
in accessibility and usability over the years and have become a viable tool for the forensic
examiner.
FORDISC
FORDISC 3.0 is the latest generation of computer statistical software that uses
Linear Discriminant Function Analysis (LDFA) to estimate sex and ancestry (Jantz and
Ousley, 2005), and linear regression to estimate stature (Ousley, 2012). In 1985, the
National Institute of Justice (NIJ) awarded Dr. Richard Jantz a grant to establish the
Forensic Data Bank (FDB) (Jantz and Ousley, 2013). The FDB consists of metric data
from forensic cases and skeletal collections that mostly have documented demographic
information such as date of birth, medical history, occupation, measurements, and
nonmetric traits (Jantz and Ousley 2013). In addition to containing information from the
most well-known and extensive collection of modern humans, the William M. Bass
donated collection curated at the University of Tennessee; the Smithsonian Institution and
the University of Mexico have also contributed skeletal data. Metric data from forensic
cases by practicing anthropologists are also regularly submitted for inclusion in the
database. The creation of the FDB was encouraged by research documenting secular
change in the American population (see: Meadows and Jantz, 1995; Jantz and Jantz,
1999; Jantz and Jantz, 2000).
FORDISC is a common tool to analyze metric data by forensic anthropologists and
researchers worldwide (Ousley and Jantz, 2005) and forensic anthropologists at the
NYC-OCME, the Pima County Office of the Medical Examiner, and the Harris County
Institute of Forensic Sciences regularly use the program for their casework. Data from
an unknown individual is entered into the program and will be classified into one of the
groups represented in the database. The program requires the user to be proficient in
taking correct measurements to input into the program and to interpret the results
accurately.
Historically, Giles and Elliot (1962) were the first to apply a 3-way LDFA to calculate
ancestry among a small population of white and black Americans and American Indians
(75 each). Research on secular change suggested that LDFA would not be accurate in
the forensic analysis of contemporary human remains because (1) they require analysis
of non-fragmented human remains and (2) the samples themselves are limited to black
and white Americans born in the 19th century and American Indians from the Indian Knoll
site (3000-2000 BC) in Kentucky (Jantz and Ousley, 2013). Because forensic cases vary
in the number of recovered skeletal elements and the condition of recovered elements,
LDFA is limited in its utility. Furthermore, since the American population is undergoing
secular change and the Indian Knoll site is representative of a small group of Native
Americans, the formulae developed by Giles and Elliot may not accurately reflect a
forensic population.
FORDISC 3.0, however, was designed to perform a number of functions that can
be useful not only to forensic anthropologists, but to anyone conducting research on
modern humans. According to Jantz and Ousley (2013), the program’s use is widespread
and Dirkmaat et al. (2008) state that nearly every practicing forensic anthropologist
utilizes the program. Further, various researchers across the world have utilized the
program (see: Marquez-Grant, 2005; Lambert, 2006; Verhoff et al., 2008; Leach et al.,
2009; Seidemann et al., 2009; Guyomarc’h and Bruzer, 2011; Urbanova et al., 2014).
Additionally, anthropologists in private and governmental agencies use FORDISC to
analyze historical remains and the Defense POW/MIA Accounting Agency uses it to help
identify American war dead (DPAA SOP 3.4, 2015). As an additional tool, the program
also contains Howell’s published worldwide data set of past populations, the Terry
collection, and Hamann-Todd collection so that users studying historic samples can also
benefit from the program.
FORDISC is a user-friendly program that provides forensic anthropologists with
computationally derived results based on statistical models (Urbanova et al., 2014).
Linear discriminate function analysis allows the researcher to use combinations of skeletal
measurements to classify individuals into biological categories such as sex and ancestry
based on the sample contained within the database. The user can also choose to perform
a number of statistical analyses, which identifies the most suitable measurements for a
particular examination.
As noted earlier, quantifiable scientific analysis is preferred in the courtroom and
metric analysis provides forensic anthropology with tools, such as FORDISC that are
required to meet the ever-increasing demands of evidentiary standards (Dirkmaat et al.,
2008). The success of the program can be seen today, 20 years after its creation, as
there are currently over 4,081 individuals recorded in the FDB and over 2,400 with
recorded sex and ancestry. The FDB is a living document that continues to grow with the
continuous additions of donated remains from the University of Tennessee’s
Anthropological Research Facility (ARF) and other outside agencies, including
undocumented border crosser cases from the Pima County Office of the Medical
Examiner. In all, 100 laboratories have contributed cases to the database (Jantz and
Ousley, 2013).
Although FORDISC is a powerful computer program, it has significant limitations and has
been criticized (see: Elliott and Collard, 2009; Guymarc’h and Bruzek, 2011;
Kosiba, 2000; Ubelaker et al., 2002; Williams et al., 2005; Armelagos and Van Gerven,
2003), particularly when used to estimate ancestry (Kosiba, 2000; Williams et al, 2005;
Elliott and Collard, 2009; Belcher et al, 2002; Hubbe and Neves, 2007; Keita, 2007;
Pritchard, 2007). The program operates under the assumption that measurements of the
skull are unique to different population groups and that these measurements undergo few
changes over time (Williams et al., 2005; Hubbe and Neves, 2007). FORDISC, therefore,
attempts to classify individuals into racial groupings based on measurements belonging
to the groups represented in the database. Despite the limitations of its applications, the
program is widely regarded as a powerful tool for forensic analysis (Ubelaker et al., 2002).
The following examples feature some of the concerns researchers encounter when using
FORDISC to assess sex and ancestry.
A study published in 2007 by Ramsthaler et al. (2007) utilized FORDISC 3.0 and
morphological assessment to estimate the sex of a sample of 98 modern German
Caucasian crania of known sex and age from forensic cases. Discriminant function
analysis of biological sex using FORDISC resulted in an average accuracy of 86% for
both sexes, while morphological analysis yielded an average accuracy of 94% for both
sexes. The authors recommend that morphological analysis, not FORDISC be used for
individuals of European descent until a proper reference population is entered into the
database. The authors caution that even with a good classification rate (86% in this
sample), if a forensic case differs from the reference population in the database, it should
be also evaluated morphologically. The reason is simply because morphological
assessment of the skull was more accurate than FORDISC to estimate sex in this sample
(Ramsthaler et al., 2007). Since FORDISC 3.0 does not allow the user to evaluate sex
without ancestry, they emphasize that it may be difficult or impossible to even know if the
ancestral group of the unknown forensic case is represented in the database, thus,
providing more reason to assess the morphological sex characteristics of the skull in
addition to using FORDISC (Ramsthaler et al., 2009).
In 2011, Guyomarc’h and Bruzek conducted a study on a known sample of French
and Thai individuals to test FORDISC 3.0’s accuracy for sex estimation. Again, since
FORDISC 3.0, unlike its predecessor 2.0, does not allow for the independent assessment
of sex or ancestry, both must be considered together. This created an unnecessary
problem for the European researchers because they do not rely on ancestry estimations
in forensic analysis to the degree that Americans do (Guyomarc’h and Bruzek, 2011).
Regardless, the need for discriminant function analysis to be population specific is
generally understood in the forensic community (see: Bidmos and Dayal, 2004; Kemkes-
Grottenhaler, 2005; Gualdi-Russo, 2007). However, in forensic casework, it is not always
known which ancestral group an unknown individual belongs to, making sex analysis
dependent on ancestry impractical. In addition, if the unknown’s ancestral group is not
represented in the database, or is represented in low numbers, the results for sex and
ancestry may not be accurate. In reference to this problem, Elliot and Collard (2009)
state, “the nature of discriminant function analysis is such that the program can only be
expected to perform adequately if a specimen’s source population is represented in the
reference sample.”
Studies conducted by Ubelaker et al. (2002) and Williams et al. (2005) underscore
the difficulty of assigning ancestry to ancient skeletal samples (see also
Belcher et al., 2002; Fukuzawa and Maish, 1997 and Leathers et al., 2002). Although
FORDISC is not intended to classify archaeological specimens, the results of these
studies are evaluated here to evaluate the ongoing debate of the program’s
effectiveness. The Ubelaker et al. (2005) study attempted to classify 50 individuals from
a sample derived from a 16th and 17th century Spanish cemetery. FORDISC, using the
Howell’s dataset, categorized these individuals into 21 distinct groups ranging from
Austria, Egypt, Hungary, Norway and half of the sample from Asia. This is not the
outcome that was expected with a homogeneous Spanish sample and likely due to the
lack of a Spanish sample in FORDISC. However, almost half of the sample classified into
groups with no geographic or ancestral relationship to the Spanish sample (Ubelaker et
al., 2002). In addition, size factors within the Spanish sample resulted in the incorrect sex
estimation of males, with 57% classifying as females further emphasizing the importance
of morphological sex estimation in addition to computational methods. Unsatisfied with
the accuracy of the results, Ubelaker and colleagues agreed with Ousley and Jantz (1996)
that FORDISC should be used with caution, particularly when assessing individuals with
ancestries not represented in the database. Again, this condition is tricky for forensic
anthropologists who do not know the ancestry of an unknown individual and do not know
if the group the unknown belongs is represented in the database.
In 2014, Urbanova and colleagues echoed the concerns of Ubelaker et al. (2005)
by testing FORDISC’s ability to correctly classify 147 crania of Brazilian origin composed
of different ancestral groups, including European Brazilians, Afro-Brazilians, and
Japanese Brazilians and Brazilians of admixed ancestry. Since there is not a Brazilian
sample in FORDISC, the point of this study was “to stress the limitations when an
individual that falls outside the targeted reference sample is to be entered” (Urbanova et
al., 2014:269). However, since ancestral origins of the Brazilians were known (e.g.
European Brazilians, Afro-Brazilians and Japanese Brazilians) it was expected that the
crania would classify into the European, African, and Asian parental groups, respectively.
The results showed that FORDISC 3.0 analysis was only able to correctly classify 44% of
the sample using Howell’s dataset and 50% of the sample using the FDB. In particular,
Afro-Brazilian and admixed samples were especially prone to misclassification.
Japanese Brazilians were easily recognizable as being from Asian descent but many were
classified as Hispanic, most likely because the Hispanic group represented in the forensic
data bank is of Mexican origin with a strong indigenous component (Urbanova et al.,
2014). In addition, the correct classification of sex was lower than expected garnering a
success rate of 60-71%. The authors cite Ross et al. (2004) noting that Latin American
population groups have complex ancestral backgrounds and contain various parental
populations across the globe. Further Dudzik and Jantz (2016) observed that FORDISC
routinely misclassified Hispanic individuals into Asian categories within FORDISC. The
authors suggest that shared ancestry among Japanese and Native Americans could
contribute to the misclassification of
Hispanic individuals as Japanese. Further they postulate that the Japanese, like the
Hispanic groups, are a hybrid population themselves and the overlap of traits between
the two groups may be a relic of admixture. Forensic anthropologists observe biological
cues of unknown skeletal remains in an attempt to interpret the evolutionary histories of
modern people. Studies like these demonstrate the difficulties associated with the
application of tools and methods, such as FORDISC and visual morphological
inspection, to correctly classify individuals.
Finally, a review of the application of FORDISC wouldn’t be complete without the
discussion of what is perhaps considered one of the most controversial studies conducted
on the use of FORDISC by Williams et al. in 2005 (see Hubbe and
Neves/Williams and Armelagos, 2007 and Freid et al., 2005). Williams and colleagues
(2005) attempted to classify ancient Nubian crania using the Howell’s dataset in
FORDISC 2.0. Results produced poor results with ten crania utilized for the study were
not classifiable and eight were incorrectly classified. When the FDB was used as a
reference population, 12 crania were identified as white, 11 as black, three as Japanese,
one as Hispanic, and one as Native American. These results led the authors to conclude
that FORDISC is “fundamentally flawed,” and that statistics cannot adequately address
the vast amount of variation within population groups. Moreover, they conclude that the
idea of distinct lines existing between groups is based on social rather than biological
realities. Although this critique of the ability to classify population affinities is reasonable,
the Williams et al. (2005) study, not FORDISC, is considered by many to be unsound
(Fried et al., 2005; Ousley et al., 2009; Hubbe and Neves, 2007). Critics of the study
claim the poor results obtained by Williams et al. (2005) are because they employed only
12 variables to classify the crania (Hubbe and Neves, 2007). Ousley et al. (2009) critique
the study with the fact that ten of the Nubian crania showed typicality probability statistics
that are too low (P < 0.05) to be assigned with confidence. The typicality probability
statistic informs analysists about the relative similarity of the unknown individual as
compared to those within the reference group. This is important because of the program’s
use of discriminant function analysis. Discriminant function analysis will force a
classification for any skull entered into the program whether it be a different species or a
soccer ball (Fried et al., 2005; Ousley et al., 2007). Thus, low typicality probability
statistics indicate outliers or poor measurement data (Maindonald and Braun, 2003; Hair
et al., 2006; Tabachnick and Fidell, 2007). Despite disagreements on the interpretation
of the data in the above studies multivariate analysis of human variation can be useful
(Ousley et al., 2009) because using multiple measurements of crania provide better
overall assessment. Many studies (Ousley and
Jantz, 2002; Spradley, 2006; Spradley et al., 2008a, 2008b; Ross et al., 2004, 2005; Slice
and Ross, 2004; Ousley and Billeck, 2001; Ousley et al., 2005) have shown success in
using the program to discriminate American whites, Africans, Hispanics, and Native
Americans.
To address the controversy surrounding the utility of FORDISC when an unknown
individual’s source population is not represented in the database, Elliott and Collard
(2009) designed a study to understand the effect of including and excluding the
individual’s source population. This was to observe the impact of the analysis when the
individual’s source population was present or absent. To accomplish this, metric data
from six iterations of 10 non-overlapping variables from a large sample in Howell’s dataset
(which is one of the two reference datasets used in FORDISC) was analyzed in FORDISC
both with and without their source populations. The number of variables, 10 in this case,
was determined by a formula provided by Jantz and Ousley (2005) for determining the
number of variables that should be used in a FORDISC analysis. Nonoverlapping sets of
variables were used to control for the possibility that different areas of the cranium are
more useful in determining ancestry than others (Harvati and Weaver, 2006). Results
showed when the source population was excluded; the program’s performance was
based on the percentage of test specimens assigned to the most closely related
population in the reference sample. Additionally, Elliott and Collard (2009) used the
posterior probability (PP > 0.5) and typicality probability (TP > 0.01) statistics
recommended by the FORDISC manual to evaluate group membership.
Further, they state that in a February 2007 workshop, FORDISC’s designers
suggested that posterior probabilities less than 0.8 are more likely to be incorrect than
correct, so a separate analysis was conducted to reflect PP > 0.8 and TP > 0.01. When
the posterior probability threshold was 0.5 and greater, 80% of the specimens were
correctly classified when the source population was included in the analysis. Correct
classification dropped to 24.5% when the source populations were not included in the
analysis. When the posterior probability threshold was 0.8 and greater, 69.5% of the
specimens were correctly classified when the source population was included in the
analysis. Correct classification dropped to 12% when the source populations were not
included in the analysis. Results also showed lower classification accuracies when ten
measurements were used from various portions of the skull (see: Elliott and Collard, 2005
for specifics) to test whether the number of variables used in the discriminant function
analysis affected the classification accuracies (see: Hubbe and Neves, 2007 and Jantz
and Ousley, 2005 for debate on this). Taken together authors conclude that ancestry
estimation using FORDISC is problematic (Elliott and Collard, 2009). When 56 variables
were used and the source population was included in the analysis, the program classified
between 69.5% (PP > 0.8) and 80% (PP >0.5) of the specimens correctly. Given these
results, and that the other analyses performed fared much worse, suggests that
FORDISC can be useful when the unidentified individual is largely complete and belongs
to one of the source populations in the database.
All of this information taken together, the fundamental problem with FORDISC lies
in the inability for the program to correctly classify individuals that have phenotypes not
represented within the population samples in the database. A contributing factor that
underlies the aforementioned problems encountered with FORDISC is the sample
population itself. Even though the database contains the largest population of known
modern Americans and other groups (a marked improvement over the samples
provided by Giles and Elliot), the program is not likely to accurately categorize unknown
individuals whose ancestral groups are not represented in the database (Jantz and
Ousley 2013, Freid et al., 2005; Elliott and Collard, 2009). Currently, the database is
comprised of individuals that are classified as black and white Americans, Chinese,
Japanese, Guatemalan, Hispanic, Native American, and Vietnamese. These groups are
not equally represented, nor do they each contain equal representation of males and
females. Therefore, it us up to the user to assess whether the results are realistic or
spurious. Table 4 shows the current population of forensic cases in the FORDISC
database (FORDISC Help File Version 1.36, obtained July, 2016). These numbers do not
reflect the Hamann-Todd or Terry collections that are also available for use.
Additionally, some of the 19th century groups from the Hamann-Todd and Terry
collections are only represented by only a few samples. Taken together, the populations
represented in FORDISC are not robust enough to address the variation that exists within
each group. The analyses of unknown individuals that do not fit a typical phenotype are
unlikely to yield a correct classification from FORDISC.
Furthermore, the database is not equipped to analyze individuals of recently mixed
Table 4. FORDISC Forensic Population
Ancestry
Male
Female
Total
American White
518
340
858
American Black
156
96
252
American Indian
59
32
91
Hispanic
227
62
289
Chinese
79
0
79
Guatemalan
83
0
83
Japanese
84
58
142
Vietnamese
51
0
51
Total
1,257
588
1,845
ancestry and therefore may not represent the growing number of people in the United
States who identify with multiple races (Urbanova et al., 2014). Currently, there are no
individuals in the database that are listed as having mixed ancestry of any kind. This is
fundamentally related to how race and ancestry are perceived in American culture and
within the medicolegal system, including how the ancestries of forensic cases are
recorded in the database. Recall the United States census data where in 2010, nine
million people identify as being members of two or more races. However, missing persons
reports and law enforcement are interested in the perceived race of the individual, not
necessarily their ancestral background, which, consequently, is what FORDISC is
designed identify. Thus, individuals with admixed ancestral backgrounds may not self-
report that information upon body donation to the Forensic Anthropology Center at the
University of Tennessee or in the instance forensic cases, the reporting party may not
disclose that information. The consequence is that individuals with admixed ancestries
and/or atypical phenotypes may not classify correctly in FORDISC; rather the program
will force the individual into a group that represents the best fit based on ancestral features
that may not correlate with racial categories.
Further, the program allows the user to select which sample groups to compare
against the unknown and if those selections are inappropriate, the classification will yield
an assessment of ancestry that will provide low probability and typicality statistics. It is
currently unknown how accurately forensic anthropologists are at selecting population
groups for analysis. Also important is that the program is not designed to account for user
error or the misapplication of the probability and typicality statistics provided with the
assessment. The FORDISC program is available to anyone for purchase from the
University of Tennessee, but formal training is not required to use the program. Without
proper training in FORDISC, misinterpretation of the results is likely. Notwithstanding
these limitations, FORDISC also includes a number of unknown samples from a collection
of modern forensic cases housed at the University of Tennessee. The program uses
these unknown individuals in calculations when a researcher or forensic professional
enters information about an individual. Finally, anthropologists are encouraged to submit
measurements from research projects and forensic cases regardless without knowledge
of how the measurements were taken and if they are accurate.
Ancestry estimation methods are tools to help afford identifications. One of the
underlying assumptions is that some ancestral characteristics can be useful for population
assignment, yet anthropologists understand that this assumption is commonly violated,
making it imperative that the forensic anthropologist not only apply the techniques
correctly but also interpret the results carefully. In many cases the results are ambiguous
and ancestry should not be reported. Variation within population groups is extensive and
analytical methods are often unable to isolate or identify population level variation
(observed by Lewontin, 1972). Further, none of the methods currently available are
equipped to reliably estimate the ancestry of mixed population groups or recently admixed
individuals (Hughes et al., 2011).
First, there is so much variation within population groups that current methods for
placing individuals into discrete ancestral categories cannot manage an individual with an
atypical phenotype (Hefner, 2009). Second, recently admixed individuals display an
assortment and blending of skeletal traits that have traditionally been reported to occur
more frequently in some groups more than in others (see: Rhine, 1990; Gill, 1998; Gill et
al., 1988). The blending of skeletal traits in mixed populations and in recently admixed
individuals further confounds the process of ancestry estimation for the forensic
anthropologist and there is no way to predict which traits will be expressed or will be more
pronounced.
SWGANTH (2013) provides best practices for ancestry estimation of skeletal
remains. The document outlines the following recommends for when estimating ancestry
from skeletal remains:
• Ancestry assessment should be made independently of suspected or
presumptive identifications.
• Use methods based on large appropriate sex-and period-specific
standards/samples with sufficient sample sizes and with objective ways of
measuring and recording traits.
• Measurements and non-metric observations should always be recorded, even if
samples for DNA analyses will be taken.
• Use adequate traits with appropriate statistical methods of classification. Express
probability of certainty when reporting ancestry assessments, especially because
ancestry assessments should never be given with 100% certainty as expressed
in posterior probabilities.
• Use all appropriate and available groups for your case, but remember that the most
appropriate reference samples may be unavailable for analysis.
• Know metric or non-metric trait definitions and be sure to understand how to score
and record them.
• Understand the appropriate statistical methods employed in ancestry
assessment and understand the interpretation of the results.
• Use terminology that is appropriate and widely accepted within the local
vernacular, e.g. these remains likely represent a person who self-identified as
Black during life.
• When comparing against reference groups, if the skeleton is complete and sex is
unambiguous, perform a sex specific analysis, (i.e. if skeleton is clearly male,
compare against male reference groups only).
• Anonymized raw data should be submitted to open-access anthropological data
repositories to support future research and methodological improvement.
Stature Estimation
Stature is the portion of the biological profile that estimates the approximate height
of the deceased individual. There are many ways in which stature can be estimated, but
the method employed depends on the recovered skeletal material available to the forensic
anthropologist. Before the various methods are discussed, a brief description of the types
of stature (living stature, forensic stature, and cadaveric stature) is warranted. Living
stature is the “actual stature of a person standing in a standardized position as measured
using calibrated equipment such as an anthropometer or stadiometer” (Cardoso et al.,
2016; p55). Living stature varies throughout an individual’s lifetime and also at any given
time of the day. People tend to be tallest in the morning after vertebral discs have
decompressed after a period of rest. Over the course of the day, vertebral disks become
compressed and the individual becomes slightly shorter. Over a lifetime, people gradually
become shorter as a result of years of joint degeneration (Cardoso et al., 2016). Forensic
stature is the recorded height of an individual on government documents such as
identification cards, passports, driver’s licenses, etc. (Ousley, 1995). These records vary
depending on whether the stature was measured or if it was self-reported. Cadaveric
stature is the length of the cadaver taken prior to autopsy (Cardoso et al., 2016).
Cadaveric stature is measured while the individual is lying in the supine position and is
subjected to error because compression of joints normally present in a standing position
is not occurring (Cardoso et al., 2016). Cadaveric stature is used by medical examiners
with a fleshed body and is not used for skeletal remains.
Law enforcement investigators obtain information about a missing persons living
stature from family members, friends, or from driver’s licenses and medical records.
However, this information may not always be accurate as family members and friends
may not accurately report the height of the missing. Additionally, people (particularly
men), tend to overestimate their height on driver’s licenses (Willey and Falsetti, 1991) and
stature tends to decrease with increasing age (Giles and Hutchenson, 1991). There are
also instances where major discrepancies between reported stature and living stature
have been documented. In one case at the University of Tennessee, Ousley (1995) found
that one individual, a 24-year-old male, was eight inches taller than what was reported on
his driver’s license. Discrepancies, as in this case, could also be a result of typos or data
entry errors.
Forensic anthropologists estimate living stature by applying regression formulae to
the direct measurements of long bone lengths. This method is used because of the strong
correlation between living stature and lower limb bone measurements (Trotter and Gleser,
1952; 1958; Wilson et al., 2010). Using regression analysis, early stature formulae were
derived from the Terry Collection, as well as remains from WWII and the Korean War
(Trotter and Gleser, 1952; Trotter, 1970), but limited to males only. Recent regression
formulas were developed from modern skeletal materials in the Forensic Data Bank at
the University of Tennessee (Ousley, 1995). These formulae were developed for white
and black Americans only, although new formulae are currently being developed for Asian
individuals (Jeong, 2014). In addition to long bone measurements, the Fully method,
which sums skeletal element heights, can be used when all skeletal elements contributing
to stature are present and complete (Fully, 1956). However, the revised Fully technique
for anatomical stature estimation is advised (Raxter et al., 2006). The revised Fully
method measures each bone that contributes to stature and applies calculations for soft
tissue corrections to obtain stature estimations. This method, like the Fully (1956)
methods, requires that all skeletal elements contributing to stature are recovered and in
good condition, however, statistics have been developed to account for missing data
(Auerbach, 2011).
Although, typically accurate, but not precise (refer to the discussion on accuracy
versus precision), the usefulness of stature estimation for the identification of unidentified
individuals is currently unknown. Stature estimations include wide prediction intervals
that typically encompass several inches on each side of the estimate to ensure accuracy;
therefore the living stature of unidentified individual is likely to fall within this range.
Despite efforts to ensure accuracy, the height of an unidentified person is not a unique
identifying piece of information. Most Americans fall within an average range of height
thus, stature is not particularly useful unless the missing individual occupies an extreme
end of the height continuum, (e.g., the person is very short or very tall). To further prove
this, Steadman et al. (2006) calculated the likelihood ratio for stature using the length of
the femur. Since the majority of individuals in the population at large have little variance
in stature, the likelihood ratios produced for stature are not very informative (Steadman
et al., 2006). Nevertheless, stature estimation can be used to exclude a potential
identification if the estimate of the unknown individual is considerably different than the
height of the missing person.
SWGANTH (2012) provides best practices for stature estimation of skeletal
remains. The document outlines the following recommends for when estimating stature
from skeletal remains:
• If complete remains are present, either the anatomical or regression methods should
be used. The measurement guidelines for the chosen technique should be
followed. When complete skeletons or whole limb bones are not available or
external factors require altering best practices, other means of estimating stature
may be employed including the use of fragmentary limb bones and non-limb
bones. The precision and accuracy of these alternative methods may be less than
those approaches presented in the previous section. Additional adjustments to all
methods should also be considered where appropriate.
Utility of Forensic Methods
The previous sections outline problems associated with the standards of evidence
and difficulties encountered in the generation of the biological profile. Overall, the
biological profile provides information about the decedent that can be used to help in an
investigation, however, elements of the biological profile may not be sufficiently accurate
or precise to lead to investigators to a presumptive identification. The biological sex of an
unknown provides investigators with male or female label, but it does little to provide
specific information about the individual. The approximate age at death estimations for
adult individuals can be too wide-ranging to narrow the potential list of matches, because
methods for adult age estimation are based on degenerative processes that vary among
individuals. Estimated age ranges can vary as much as 20+ years, rendering the
estimation accurate, but not precise. Ancestry estimation can be informative if it is
biologically correct and matches the social/cultural label the individual ascribed to in life.
Even if correct on these two fronts, ancestry estimation is not informative in relatively
homogenous communities. Examples include rural areas of the
United States such as Gary, Indiana where the majority of residents identify as African
American or rural Iowa where the population primarily identifies as white (Konigsberg et
al., 2009). Additionally, ancestry estimation may not be informative when identifying
victims of genocide in mass graves because the deceased are typically targeted and killed
because of a shared ancestral background. Finally, stature estimation, although usually
accurate within the calculated range, is uninformative unless the individual occupies the
extreme edges of the normal range of human height.
To address the problem of the utility of the biological profile, Konigsberg et al.
(2009) make use of likelihood ratios and informed priors to determine the evidentiary value
of the sex and ancestry components biological profile. In their article, sex and ancestry
from an active forensic case are used to illustrate an example. The article details the
relative ease of making a correct estimation of sex in a forensic case, but that sex
estimation is relatively uninformative to make an identification since the maximum
likelihood for sex is two. Ancestry estimation in this case proved to be more difficult as
the FORDISC 2.0 analysis of the skull indicated that the unknown was an Easter Islander.
The skeletal remains were found in rural Iowa where the census data shows that 93.82%
of the population of Iowa identifies racially as white. Having prior knowledge that the
probability of an Easter Islander in this racially homogenous area is low, the anthropologist
reported the race of the unknown as white. Interestingly, if this individual were found in
Hawaii, where a high proportion of Pacific Islanders reside, the original estimation of
Easter Islander would be acceptable. Further, if this individual were found in Gary,
Indiana, where the population is primarily black, the informed prior coupled with the metric
analysis would lead the anthropologist to conclude that this individual is black. These
results show the usefulness of informed priors in forensic analysis (Konigsberg et al.,
2009). Further, the study found that if an anthropologist works in a relatively homogenous
population, the ancestry of the individual likely matches the demography of the region and
therefore, contributes minimal information to a potential identification. For anthropologists
in heterogeneous areas, such as large cities like New York, Houston, or Tucson, ancestry
estimation can be a valuable component of the biological profile assuming the
anthropologist is adept in assessing ancestry in heterogeneous populations (Konigsberg
et al., 2009) and if the phenotype of the unknown mimics the reference population from
which the methods were developed.
Chapter Summary
The information presented in this background chapter highlights the role of the forensic
anthropologist in the medicolegal system. As a part of the medicolegal system, forensic
anthropologists are required to perform duties to the rigorous standards and expectations
set by the courts. This chapter outlined the four components of the biological profile
evaluated by this research and discussed their applications and theory behind the
methods. The next chapter further details theory surrounding the estimation of biological
ancestry and how it relates to social and cultural understanding of race.
CHAPTER THREE
BACKGROUND
Race and Ancestry
This chapter discusses the ongoing debate within anthropology about human
biological variation. A large discussion within the field of forensic anthropology involves
the differences and the use of the terms ancestry and race and how scientists and the lay
public alike interpret their meanings.
In the United States, forensic anthropologists and law enforcement officials face a
fundamental barrier when it comes to racial identifications of unknown individuals. The
disconnect lies in perceptions and expectations: law enforcement asks for a racial
classification, more specifically someone’s skin color or what they looked like, whereas
forensic anthropologists attempt to provide an assessment of biological ancestry.
Ancestry estimation assumes that skeletal traits reflect a biological reality (ancestry) and
that these observed traits can be translated into a corresponding social category (race).
The topic of race permeates American culture, yet few people understand the biology
behind our differences, or similarities for that matter.
To unpack these matters, an abbreviated history of racial classification and its
evolution within physical anthropology will be discussed first. Second, ancestry and race
will be defined in light of the history discussed. Third, the role of race and ancestry in
forensic casework will be explored and finally, the complications associated with racial
self-identity and the assignment of racial categories to skeletal materials in forensic
casework will be examined.
History of Racial Classification in Forensic Anthropology
The initial framework of the concept of race can be attributed to the European worldview
in the 18th century (Brace, 2005). Racial classification in the 18th century was centered
on the ability to visually assess physical characteristics of our species championed by
Carlous Linnaeus (1707-1778), a botanist who published Systema Naturae in 1735
(Brace, 2005). Systema Naturae outlined a system to arrange and categorize plants and
animals based on physical features. He classified humans into four geographical
varieties: Homo sapiens europaeus, Homo sapiens asiaticus, Homo sapiens americanus,
and Homo sapiens afer (Linneaus, 1759).
The labels assigned to all living organisms in Systema Naturae were also designed
to fit with a hierarchical structure known as the Great Chain of Being that categorized all
living beings and inorganic materials from the top, God, down (Lovejoy, 1936). The
hierarchical nature of the Great Chain of Being was challenged by late 18th century
scholars but still influenced Western views of the world (Brace, 2005). Examples of this
influence can be seen in the scientists of the mid to late 18th century. Peiter Camper
(1722-1789) was one such scientist who employed a method to hierarchically categorize
humans. He developed the “facial angle,” a measurement of the angle defining the facial
profile by which he classified individuals with steep facial profiles into works of art and
those with angled facial profiles among the animals (Camper, 1791; Meijer, 1997).
Meanwhile, Johann Friedrich Blumenbach (1752-1840) believed that the human
form was influenced by a host of environmental factors that eventually allowed man to
digress from the ideal form. For Blumenbach, the ideal form was the people from Mount
Caucasus, whom he called Caucasians. Despite ascribing to the idea that man
degenerated from superior (white) to inferior forms, Blumenbach was one of the first to
acknowledge that people resembled their ancestors and that people in different
geographical areas had different visual characteristics (Brace, 1997).
In America, Samuel Morton (1799-1851), a physician and anatomist was also a pioneer
in anthropology amassing a large collection of human crania from which he studied the
races of man (Brace, 2005). Morton in his 1839 work titled, Crania Americana, identified
races that are unrelated from one another and thus categorically distinct. He used his
measurements to identify racial types and also filled skulls with lead shot to discover if
cranial volume differed between the races and, if so, how they differed. His results
showed Caucasians had the largest cranial capacity, followed by the Malays, the Negro
group, and lastly Native Americans. Gould, in his book, The Mismeasure of Man (1981)
criticized Morton’s work as inherently biased, as he believed that Morton skewed his data
to fit his views of human variation. Allegations by Gould ranged from the idea that Morton
selectively reported his data, manipulated sample compositions, made analytical errors,
and mismeasured skulls. However, Lewis et al. (2011) reanalyzed Morton’s data and
concluded that he did not manipulate his data and that Gould’s criticisms are unfounded.
In the 20th century Ales Hrdlička (1869-1943) and Franz Boas (1858-1942) emerged as
two important figures in American Anthropology (Brace, 2005; Caspari, 2009). At the time,
physical anthropology in the United States was regarded as racial studies, and Hrdlička,
a Czech immigrant, acknowledged racialized foundation of European anthropology
(Caspari, 2009). In contrast, the German-born Boas practiced the four-field approach to
anthropology and over the course of his career challenged the validity of human types
(Caspari, 2009). He was interested in metric studies of human variation and compiled
data to document the human form. His studies documenting the cranial morphology of
immigrant descendants (see: Boas, 1910; 1912; 1940) began to question the idea that
races are types, instead showing that the skull is subject to plasticity. Debate surrounding
the validity of his findings has surfaced (see: Fisher and
Gray, 1937; Morant and Samson, 1936; Sparks and Jantz, 2002; Gravlee et al., 2003).
Regardless, Boas’s work is generally accepted in the sciences (Sparks and Jantz, 2002).
Also during this time, Earnest Hooton (1887-1954) emerged as an influential figure
in anthropology. He has been credited as the father of physical anthropology in America
(Shapiro, 1981) and believed in the concept of pure races. For Hooton, the variations
observed among the races could be explained by the inbreeding of the pure races, which
he called secondary races (Caspari, 2009). In all, race in 20th century
America referred to geographic patterning of human variation and assumptions of
essentialism to explain those variations (Caspari, 2009). As such, races were considered
types, or unchanging natural categories. Despite the work of Boas demonstrating the
nonconcordance of features, racial typology continued. Caspari (2009) offers an
explanation of essentialism, the idea every being has a fixed immutable form, that allowed
typological thinking to persist despite scientific evidence showing otherwise.
More recently, the debate around race has changed due to the continuance of scientific
studies of human variation from blood serum analysis, morphological and metric studies
of the human skeleton, and DNA research. A number of researchers and scholars have
contended that races do not exist, rather race is a cultural construction devoid of biological
reality (Armelagos, 1995; Armelagos et al., 1982; Blakey, 1987;
Brace, 1982, 2005; Brace et al., 1993; Goodman, 1997a, b; Goodman and Armelagos,
1997; Hahn and Stroup, 1994; Livingstone, 1962; Lock 1993; Marks, 1994, 1995, 1998;
Montagu, 1942, 1978; and Mukhopadhyay and Moses, 1997). Further, studies of neutral
genetic variation have supported Lewonton’s results (Nei and Roychoudhury, 1982;
Barbujani et al., 1997; Jorde et al, 2000; Rosenberg et al, 2002). Meanwhile, other
scholars argue for its continued use (Sarich and Miele, 2004). The argument against race
is partly based on Lewonton’s publication of The Apportionment of Human Diversity in
1972 where he demonstrated that race accounted for 6% of all human variation (Smay
and Armelagos, 2000). From here, Livingstone in 1962 famously stated that races didn’t
exist; rather there are only clines.
Regardless of the position, assumptions about race persist in anthropological work
today (Caspari, 2009). These assumptions include but are not limited to, statistical
classification of individuals into a pre-set number of population groups, assumptions of
group membership based on genetic features, and the logical disconnect that exists
among researchers that argue for the non-existance of human races all the while
discussing the genetic evolution of races (Brown and Armelagos, 2001). In addition,
inconsistent terminology used to describe group membership confounds understandings
of complex relationships between biological variation and the social/cultural
understandings of race.
Ancestry and Race
The terms race and ancestry have been defined in various ways. The following
section outlines some of the many definitions of race and ancestry employed in the
literature.
Ancestry
Ancestry can be described geographically. Many scholars refer to ancestry as the
biological or genetic makeup of individuals that can be used to trace geographic or
continental origins (Royal and Dunston, 2004; Molnar, 1992). Similarly, Garn (1965)
describes what he calls geographic race by the subdivision of humans into continental
groupings such as Europeans, Asians, and Africans. He explains these groupings can be
further subdivided into subcontinental groupings such as Southeast Asians, and East
Asians. These definitions demonstrate that an individual’s ancestry is influenced by, but
not limited to, a combination of genetic drift, gene flow, migrations, microevolution and
environmental pressures (Konigsberg et al., 2009; Relethford, 2009), examples being
climate or mating practices (Cartmill, 1998). It is assumed that individuals cluster into
broad geographical or continental groups because of shared genetic history and
environment, and therefore possess similar physical traits and characteristics.
Studies conducted by Lewonton (1972) and Livingstone (1962, 1964) were among
the first to challenge the idea that population groups were genetically distinct, finding that
population groupings had vast amounts of genetic diversity within them.
Livingstone famously stated, “there are no races, there are only clines” (1964:47).
These words and his scathing review of the race concept in Ashley Montagu’s, The
Concept of Race, ignited a firestorm of debate surrounding the reality of race. In his
landmark publication, Livingstone recognized that morphological traits do not support the
distinction of races. In fact, Livingstone remarks:
If one genetic character is used, it is possible to divide a
species into subspecies according to the variation in this
character. If two characters are used, it may still be possible,
but there will be some “problem populations,” which, if you are
an anthropologist, will be labeled composite or mixed. As the
number of characters increases it becomes more nearly
impossible to determine what the “actual races are”
(Livingstone, 1964:47-48)
Thus the use of physical traits to distinguish racial groups is inherently flawed
because genetic and morphological variation is continuous and overlapping within and
between populations (Livingstone, 1964; Lewonton, 1972). In his innovative study,
Lewonton (1972) analyzed blood serum proteins and red blood cell enzyme variants. His
results support the assertions made by Livingstone and indicate that racial classification
accounts for only 6.3% of total genetic diversity, population differences account for only
8.3%, while individual differences account for the remaining 85.4%. This study was the
first of its kind to determine the extent that racial characteristics account for human genetic
variation and it proved that genetic traits are nonconcordant. His conclusions clearly show
that genetic differences between individuals have little to do with racial or ethnic
boundaries.
Today, population geneticists routinely utilize genetic data to identify ancestral
groupings worldwide because genetic data can help reconstruct population histories,
analyze within and between group differences, and calculate biological distance (Long et
al., 2009; Hunley et al., 2009; Serre and Paabo, 2004; Manica et al., 2005; Prugnolle et
al., 2005; Ramachandran et al., 2005; Handley et al., 2007). Through the exploration of
genetic information of various human groups, researchers have found that geographically
and socially distinct populations have more genetic variation within, rather than between
groups. Thus, genetic studies provide the most recent evidence supporting the
nonconcordance of biological traits (see: Serre and Paabo, 2004; Barbujani and Bell,
2006). These studies confirm findings by Lewonton indicating that the majority of genetic
variation exists within population groups rather than between.
Therefore, the majority of human genetic variation is shared among all humans with little
variation between supposed racially defined groups.
Relethford (1994; 2002) demonstrated that the apportionment of global human
genetic diversity is not discrete. He found that craniometric variation and genetic diversity
follow patterns of neutral traits (traits not affected by selection), where there is more
variation within than between populations. Interestingly, his 2002 study results show that
skin color (often used by law enforcement and the general public to infer race) follows the
opposite trend of craniometrics (what forensic anthropologists use to estimate ancestry)
and genetics, as skin color operates under natural selection and thus, is not neutral
(Relethford 2002). The results of his craniometric analysis show that approximately 13%
of the diversity among the samples is between regions, 6% of diversity is between local
populations within regions, and 81% of the variation exists within local populations. These
results mirror blood protein polymorphism studies
(Latter, 1980; Ryman et al., 1983) and DNA polymorphism studies (Barbujani et al., 1997;
Jorde et al., 2000) showing that approximately 85% of genetic diversity exists within
populations (Relethford, 2002). Further, Relethford’s (2002) results indicate that skin
color, shows more variation between populations than within. Although forensic
anthropologists cannot identify skin color, skeletal analysis of ancestry is used to infer
skin color to assist law enforcement with the identification of unknown individuals. The
inference of skin color based on skeletal traits commonly associated with racial groups is
contested in the literature. Thus, Relethford (2002) implies that various traits, such as
skin color, used by law enforcement and the general public to distinguish races, are not
supported by craniometric and genetic analysis. Therefore, morphological metric
analyses utilized by forensic anthropologists to estimate ancestry for the purpose of
providing social race categories for law enforcement is ambiguous, at best.
However, this is not to say that geographic patterning doesn’t exist. To the contrary,
Relethford (1997; 2009), shows that there is geographic patterning to skin color and to
craniometrics. In his 1997 article, Relethford demonstrated that human skin color is
darker in the southern hemisphere than in the northern hemisphere, corresponding to
higher UV radiation in the southern hemisphere as opposed to lower UV radiation levels
in the northern hemisphere. Relethford (2009) showed that there is an association with
craniometric variation and geography indicating that populations further apart from one
another will be less similar than to geographically closer populations. However, there are
no breaks or distinct cut off points that separate geographical groupings, and overlap
exists between the six groups used in the analysis (Relethford, 2009). However,
Relethford (2009) cautions that the population groupings analyzed for his study, and
groupings analyzed in other studies as well, are subjective and defined by the researcher.
In other words, these arbitrary groupings do not reflect biological realities or actual races.
To illustrate the point in a different way, most human populations today are the product of
admixed genes from many different groups (Shipman, 1994). Shipman (1994) further
explains that ethnic groupings of people, such as African Americans, Asian Americans,
and Native Americans, are genetically mixed and thus, are not biological races.
Race
Races have been synonymous with types and often considered to be natural
categories, reflecting typological thinking (Caspari, 2009). In human biology, race refers
to a culturally constructed classification system with roots in Western colonialism by which
individuals are typically categorized according to physical appearance (Montagu, 1942;
Brace, 2005; Smedley and Smedley, 2012). Unlike biological ancestry, race is considered
to be a “culturally structured, systematic way of looking at, perceiving, and interpreting”
reality (Smedley 2007, p. 18). Komar and Grivas (2008) note that “race and ethnicity are
flexible social constructs in which an individual’s membership within a specific group can
be self-proclaimed or perceived (p. 229). These definitions reflect the American worldview
that human populations can be divided based on physical characteristics believed to be
biologically significant (Edgar, 2009; Smedley, 2007; Smedley and Smedley, 2012; Keita
and Kittles, 1997). Relethford, (2009) discusses the underlying biological significance of
physical traits explaining that some physical characteristics are geographically structured
and that race can be defined as a
“culturally constructed label that crudely and imprecisely describes real variation”
(Relethford, 2009).
Elements of physical appearance, such as skin color, hair color, hair type, and
facial features are often understood by society to be objective biological attributes upon
which social constructions are built (Smedley 1998; Wade, 1993; Wade 2002;
Relethford, 2009; Albanese and Saunders, 2007). However, Smedley and Smedley
(2012) draw attention to the fact that being a member of a certain racial category does
not require having all or any of the physical attributes commonly associated with that
category. An example rests within the African American community. Smedley and
Smedley (2012) remind us that those who identify as African American are perhaps
among the most heterogeneous population in the world. African Americans range from
fair-skinned, blond-haired, and blue-eyed to dark brown skinned, dark haired and brown-
eyed, and combinations in between.
Tishkoff and Kidd (2004) state that one drawback with using race as an identifier
is the lack of a clear definition of race. Many people associate race with skin color or
other physical characteristics. However, morphology and skin color are poor indicators
of race because they are the result of adaptations subject to convergent evolution
(Tishkoff and Kidd, 2004; Relethford, 1997; 2000; 2009). Relethford (1997; 2009) further
explains that the distribution of human skin color is continuous and has an atypical pattern
in within human groups providing evidence that human skin color, although geographically
patterned, does not differentiate human races. In other words, skin color demonstrates
patterns of clinal variation. Further, culture, language, and religion are also poor indicators
of race. An example is the use of the term Hispanic to describe a diverse population of
individuals with varying nationalities, identities, cultural traditions, and biological origins
that include European, Native American and African ancestries in various amalgamations
(Birkby et al., 2008; Lisker et al, 1996). Overall, the vast degree of variation within and
between population groups problematizes the lay concepts of racial divisions. Forensic
anthropologists must deal with the variations of the phenotypic expression of genetic traits
as well as the translation of that information to the public whom may not understand the
nuances of racial identity and biological ancestry.
Taken together, ancestry is how human biological variation is understood in terms
of geographic dispersion of physical and genetic traits. Race, is a socially constructed
understanding of the physical features of human populations that are associated with
geographic and biological ancestry.
Ambiguous Terminology
A confounding factor in forensic ancestry estimation is that the terms race and
ancestry are used interchangeably among forensic anthropologists and law enforcement.
It has become standard practice for forensic anthropologists to use the term ancestry
instead of the term race (Albanese and Saunders, 2006; Ousley et al., 2009). Introductory
level forensic anthropology textbooks try to explain this transition to aspiring students by
explaining, “race engenders too many negative connotations” (Byers, 2005:11).
Presumably these negative connotations stem from the deep-rooted history of racism in
the United States, for which anthropological practice had also contributed (Caspari, 2003).
However, this explanation is simplistic and undermines the scientific progress made to
understand human biological variation resulting from complex population histories.
Further, anthropologists prefer the term ancestry because that is what is being assessed;
cultural race labels cannot be estimated from skeletal remains.
There are many examples of the interchangeable use of typological terminology
throughout scientific literature and through time. Rhine (1990), a reference for typological
ancestry estimation, refers to “racial groups” as “American Caucasoid,” “Southwestern
Mongoloid,” and “American Black.” Byers (2005), in an introductory forensic anthropology
textbook, describes populations as “ancestral groups” and identifies them as “Black,”
“White,” and “Asian.” In France and Horn’s (1988) introductory textbooks, A Lab Manual
and Workbook for Physical Anthropology, a change in the attitude between taxonomic
descriptions over many editions can be found. In the first edition, the terms “Negroid,”
“Caucasoid,” and “Mongoloid” are used to classify unknown individuals, but by the 4th
edition (France, 2001) uses the terms “African,” “European,” and “(East) Asian.” The
revised 4th edition also shows the adjustment of the term “race” to “ancestry” (compare p.
30 of the first edition to p. 123 of the 4th). Since the terms Caucasoid, Mongoloid, Negroid
are antiquated terms, it is most likely the reason for the change in France’s 2001 edition.
Yet, as of 2005, Dr. William Bass’ quintessential osteological field manual still retains the
outdated typological terminology and it was only in 2013 that the Joint POW MIA
Accounting Command (now the Defense POW/MIA Accounting Agency) removed the
terms from practice instead now using European, Asian, and African (DPAA SOP 3.4,
2015).
It is important for the anthropological community to recognize inconsistent use of
the terms race and ancestry in scientific literature and in practice because loose
terminology confounds further already complicated topics. The distinctions outlined in
this section are not just semantic, as they show little agreement in the scientific community
about the definitions of race and ancestry, and even less consensus about how to label
groups. The consequences of these misunderstandings vary in degree from simple
terminology to key breakdowns in communication between forensic professionals and the
communities they serve.
In sum, ancestry refers to the biological or genetic makeup of individuals that can
be used to trace geographic or continental origins. Race is a socially constructed
classification system primarily based on visible physical features. The American
Anthropological Association (AAA) provided a statement on race in 1998 that they believe
outlines the general consensus and scholarly positions among the majority of
anthropologists. In this statement, they contend, “human populations are not
unambiguous, clearly demarcated, biologically distinct groups” and that race is an
ideology of human differences that perpetuates the myth of human dissimilarities. The
forensic anthropological application of methods and use of tools such as FORDISC to
identify the ancestral background of unknown skeletal remains for the purpose of
assigning a social race category is not in opposition to the AAA statement. Rather, the
practice is an attempt to provide vital information to law enforcement though the use of
socially understood terms to help identify unknown individuals.
Ancestry and Race in Forensic Anthropology
Ancestry assessment is considered the most controversial component of the
biological profile (Albanese and Saunders, 2007). If studies show the non-existence of
human races, why do anthropologists continue to include ancestral information in reports?
The answer partially lies in expectations. Racial information is useful for law enforcement
since physical descriptions of perpetrators, victims, and missing persons help guide
investigations. Forensic anthropologists are unable to detect racial features such as skin
color, hair color, hair type, or other such characteristics from skeletal materials. It cannot
be emphasized enough that forensic anthropologists can only estimate ancestry to infer
race, and not race itself (Konigsberg et al., 2009). This is of little consequence to law
enforcement, as they are products of modern racialized societies and rely on racial
descriptions such as skin color. The lay public generally uses visual characteristics
associated with races to describe missing loved ones to law enforcement. Thus, forensic
anthropologists must operate under the assumption that morphology can infer geographic
or continental origin; and therefore race.
Although many forensic anthropologists acknowledge that race and ancestry are
not the same (Brace 1995; Kennedy, 1995), forensic anthropologists feel obligated to
estimate the ancestry of unknown individuals (Sauer 1992; Kennedy 1995) and even have
felt that failure to do so would be regarded as neglect of responsibilities or hindrance of
an investigation (Sauer, 1992; Gill, 1998; France, 2001; Brace, 1995; Kennedy, 1995). In
essence, forensic anthropologists “play their game” (Kennedy, 1995) and can be accused
of perpetuating the myth of races. As discussed earlier, forensic anthropologists use
ancestry to theoretically infer social race through the identification of visible physical
features assumed to have biological significance
(Albanese and Saunders, 2007). This practice may be successful but also may result in
misclassifications, as socially prescribed race does not always match the anthropologist’s
assessment of ancestry. As a result, victims may go unidentified and interested parties
consequently may be deprived of information. Although error rates associated with
ancestry estimation to infer social race in casework are unknown (Hughes et al., 2011),
the methods are perceived by forensic anthropologists to work well enough to justify their
continued use (Sauer, 1992). This argument is tautological because there is no way to
know if unidentified individuals remain unidentified because the assigned social race
category is incorrect.
Sauer (1992) argues that the reason forensic anthropologists are so “good at
estimating race” is because there is concordance between cranial morphology and
American black and white social race categories. Yet, it remains unclear how well cranial
morphology corresponds with individuals that have an atypical phenotype, nonblack and
non-white racial categories, or with admixed populations or recently mixed individuals.
This concern is important because the United States is far more diverse than just black
and white individuals. The 2010 US census reported an increase in the number of
individuals identifying as non-white and non-black. Specifically, 14.7 million individuals
identified as Asian and 50.5 million people identified as Hispanic. The
Hispanic population constituted half of all growth in the United States between 2000 and
2010 (US Census Report, 2011). Additionally, 9 million people reported more than one
race and in 2012, the Pew Foundation reported that 15% of all new marriages in the US
and over 7% of the 3.5 million births in the US are multiracial (Pew Research Foundation,
2012).
Despite demographic information, perceptions of race continue to drive research
that reinforces culturally constructed categories. For example, population studies of
genetics and craniometrics assume prior population groupings and the number of groups
can be subjectively changed (Relethford, 2009). In addition, it is unclear how forensic
anthropologists treat the question of admixed ancestry (e.g. someone who identifies as
admixed black and white) and how anthropologists interpret and report unidentified
individuals that do not conform to a standard phenotype with known
ancestral traits.
Racial Self-Identification
Confounding the subject of ancestry and race in biological anthropology is racial
self-identification. Racial self-identification can be described as how a person identifies
with their lineage. Medical studies in the United States have found self-identified
race/ethnicity to be a useful proxy for genetic differentiation between groups that vary in
continental ancestry (Tang et al., 2005). However, a study conducted by Hahn et al.
(1996) revealed that self-identification varied among participants over the study period of
four years. Forty-two percent of individuals reported different ancestry selfidentifications
at different times, and individuals who identified with multiple ancestries were up to 3.4
times more likely to change their ancestry classification over time.
Additionally, M’Charek (2013) argues that self-identification of race is not concrete and
often changes among individuals through time in response to their social environment.
The complexities of social race categories and their meanings among the U.S. population
are beyond the scope and expertise of the forensic anthropologist. However, it is these
social race categories that the general public and law enforcement recognize as tangible
pieces of information that can help discover the identity of skeletal remains. If identities
continually shift, as indicated by Hahn et al. (1996) and M’Charek (2013), then clear social
boundaries of race do not exist. Thus, forensic anthropological assessment of biological
ancestry to infer social race categories may not align in a society where identities and
perceptions of race are fluid.
The shift in the American populations perceptions of race and self-identity are not
just theoretical. The changing demographics of the United States have been continually
documented in the US census data. As social and cultural attitudes regarding race have
changed over time, the census questions have also evolved to better document the racial
composition of the United States. According to the Population Reference Bureau (PRB)
“Reports on America” (Mather et al., 2011), the census questions concerning race and
ancestry have changed over time to reflect social and cultural definitions and perceptions
of race. Prior to the 1950s, census enumerators were responsible for identifying census
participants using limited terms for racial categories. For example, in 1910, categories
used to identify people included “White,” “Black,”
“Mulatto,” “Chinese,” “Japanese,” “American Indian,” or “Other” (Mather et al., 2011). In
1980, the census included a separate question about Spanish/Hispanic origin but, to date,
many Hispanics do not distinguish between race and ethnicity, instead opting to choose
“Some Other Race” to designate their country of origin. According to the Mather et al.
(2011), 37 percent of Hispanics marked “Some Other Race,” as compared to 0.2 percent
of non-Hispanics marking that category in the 2010 census. Interestingly, between the
2000 census and 2010 census, the number of Hispanics who identified as “White”
increased from 48 percent to 53 percent. This change in racial identity could reflect a
change in cultural self-identification after having been born or having lived in the United
States for long periods of time and adopting American culture, or it could be a reflection
of changes in the census instructions that stated, “For this census, Hispanic origins are
not races” (Mather et al., 2011).
The 2000 census was the first to include options for people to check more than
one race, and at that time 5.8 million people indicated they were more than one race. In
the 2010 census, this number increased to 9 million people. Of those 9 million people,
three-fourths of them identified as white in combination with another race (black, Asian,
Native American, Some Other Race) with the number of people identifying as both black
and white more than doubling since the 2000 census (Mather et al., 2011). Given the
increase in biracial marriage in the United States, Byrd and Garwick (2006) note that
social categories of race are not clearly defined.
It is evident from the population census data that the racial composition of the
United States is varied and changing. The social and cultural attitudes and perceptions
of race in America, as well as technical changes to the US census forms, demonstrate
that racial self-identification is a product of perception fueled by culture, physical
characteristics, and changing, often confusing, terminology. There exists a fundamental
disconnect between how people identify themselves, the way they choose to identify
loved ones when reporting missing persons (missing persons reports, NCIS database,
NamUs database), how law enforcement utilizes that information, and the forensic tools
utilized to identify race based on ancestral traits. Reconciliation between self-identity and
the missing persons reporting process will go a long way to ensure forensic tools such as
morphological ancestry assessment and FORDISC cannot accurately reflect the current
self-identified nomenclature in the U.S.
Chapter Summary
The information presented in this chapter serves to frame the discussion of biological
ancestry and race around the circumstances associated with the resolution of missing
persons cases in the United States. The process of victim identification has evolved over
time beginning with simple physical descriptions, photographs, and fingerprints of the
deceased. Today, scientific methods guide the process of victim identification and the
discipline of forensic anthropology works within the medicolegal system to provide
accurate descriptions of the deceased. Although, the non-existence of human races has
been demonstrated through numerous studies ranging from craniometrics to genetics,
social race categories persist in society. Thus, forensic anthropologists are stuck between
a rock and a hard place when evaluating biological ancestry and catering to the needs of
law enforcement. As such, forensic anthropologists rely on a combination of scientific
analyses, personal experiences, and contextual informed-priors to make the best
ancestry estimate possible. Despite all efforts, forensic anthropologists may not correctly
identify social race categories through the application of scientific methods, rendering the
estimation useless. This does not mean the effort is unnecessary, rather solutions to
address the fundamental disconnect between law enforcement, the missing persons
reporting process, and forensic anthropological practice should be addressed. The next
chapter introduces the study sample locations, while chapter five outlines the
expectations that guide this research.
CHAPTER FOUR
STUDY SAMPLE LOCATIONS
This chapter describes the study sample locations selected for this research.
This sample is introduced first with a demographic description the areas of New York City,
Harris County, and Pima County as well as the functions and operations of the medical
examiners offices.
The Offices of the Medical Examiner, New York City New York; Harris
County, Texas; and Pima County, Arizona
Data is comprised of forensic anthropology reports from three urban medical
examiners offices in the United States, specifically, The Office of Chief Medical
Examiner, New York City (NYC OCME); The Harris County Institute of Forensic Science
(HCIFS) in Houston, Texas; and the Pima County Office of the Medical Examiner
(PCOME) in Tucson, Arizona. These offices were chosen because they have high
skeletal caseloads and employ in-house teams of forensic anthropologists to assist with
the identification of unknown human remains. No other medical examiner offices in the
United States employ multiple full-time anthropologists, rather opting to utilize private
consultants. Anthropologists in the offices chosen for this research routinely assist
medical examiners with all aspects of skeletal identification, trauma, and postmortem
interval. In addition, working within these offices allows the anthropologists access to a
variety of resources and build close working relationships with local law enforcement and
local and national agencies dedicated to the resolution of missing persons cases. The
structural similarities between the offices make them ideal for comparison even though
they serve different communities.
New York City, New York
Often dubbed the “big apple,” New York City is the nation’s largest city with its
8,405,837 million people occupying only 302.64 square miles (U.S. Census Bureau,
2010). As such, it is also the country’s most densely populated city with 27,012.5 people
occupying each square mile in the five boroughs: Manhattan, Brooklyn, The
Bronx, Queens, and Staten Island that, in 1898, were joined to make up New York City.
New York City’s impact on the world cannot be understated, as this large metropolitan city
has had a significant economic and cultural impact on commerce, fashion, media,
research, technology, education, and entertainment. It is no wonder that New York City
is also home to the headquarters of the United Nations, the center for international
diplomacy.
The diversity of New York’s population is a relic of the city’s prosperity and its
position at the center for world commerce. In addition, New York City is the premiere
location for legal immigration in the United States (U.S. Department of Homeland
Security, 2013). More than 12 million European immigrants were received at Ellis Island
between 1892 and 1924 (Jones, 2008). Today the city’s diversity is evidenced by its social
and cultural opportunities and also by the most recent U.S. Census report.
According to the 2010 US Census Bureau, 44% of New York City’s residents identified as
“white,” and 33% reported as “white alone, not Hispanic.” The difference here may
indicate some level of mixed ancestry or a difference in the meaning of the term “white”
(refer to the background chapter for a discussion). The census also reports 25.5% of the
population identified as “black,” 28.6% identified as “Hispanic or “Latino,” 12.7% identified
as “Asian,” 0.7% identified as “American Indian and Alaskan Native,” and 0.1% identified
as “Native Hawaiian and other Pacific Islander.” Interestingly, only 4% of New York City’s
population indicated that they were members of “two or more races.” Keep in mind those
calling themselves “white” versus “white only” could account for the low number of those
identifying as “two or more races.” Also according to the 2010 U.S. Census, 37% of New
York City’s population is foreign born with people representing every country in the world;
however, no single country or region of origin dominates. As such, New York is home to
many cultural communities including, but not limited to, six Chinatowns, Little Italy, and
four Koreatowns. Asian individuals number over one million in the city, the highest
population of Asians of any U.S. city. Chinatown has the highest concentration of Chinese
people in the western hemisphere and New York has the largest Israeli and Orthodox
Jewish communities outside of Israel; half reside in Brooklyn. Moreover, there are
approximately 800 languages spoken in the city attesting to the tremendous cultural
diversity that permeates the city.
The city of New York also has the highest rate of homelessness in the United States.
According to the Coalition for the Homeless, in November of 2014 there were a recorded
60,352 homeless people living in New York City shelters on any given night, an increase
of 64% from ten years ago. This figure includes 14,519 families with
25,640 children and represents the largest population of homeless people in New York
City since the Great Depression of the 1930s. Sadly, this figure represents only those
homeless individuals seeking shelter in the New York City municipal shelter system and
does not include the thousands of unsheltered homeless people sleeping in the streets,
subway system, and other public spaces.
In addition to living population demographics, reported deaths in New York City by gender
and race are provided in Table 5. This information was taken from the New York State
Department of Health Statistics website, 2014 statistics.
(https://www.health.ny.gov/statistics/vital_statistics/2014/#mort).
Table 5. 2014 Death Statistics (NYC)
White
Black
Hispanic
Other
Total
Male
14,808
(24.5%)
6,874
(11.4%)
4,845
(8.0%)
3,039 (5.1%)
29,566
(48.9%)
Female
15,962
(26.4%)
7,671
(12.7%)
4,713
(7.8%)
2,500 (4.1%)
30,846
(51.1%)
Unknown
0
(0.0%)
0
(0.0%)
0
(0.0%)
1 (0.0%)
1
(0.0%)
Total
30,770
(50.9%)
14,545
(24.1%)
9,558
(15.8%)
5,540
(9.2%)
60,413
(100%)
Office of Chief Medical Examiner – NYC
The Office of Chief Medical Examiner (OCME) in New York City is located in Manhattan
and, although it has jurisdiction over all five boroughs, it primarily serves the borough of
Manhattan. Established in 1918 as the first facility of its kind, the OCME has grown today
to be one of the country’s largest morgues. Additionally, the office assists law
enforcement with the investigation of crimes, deaths, and identities of missing persons.
The OCME employs a Chief and 620 scientists and staff members that manage day-to-
day operations including pathology, toxicology, serology, histology, DNA, and forensic
anthropology. Included among the staff are 30 forensic pathologists that handle 12,000
bodies and conduct 5,500 autopsies annually (Goodman, 2015). In addition to their large
caseload, the OCME is also responsible for the identification of mass fatality incidents
such as those who perished in the attacks on the World Trade
Center in 2001, the 2001 American Airlines flight 587 crash, and the 2003 Staten Island
Ferry Crash.
The anthropology unit, housed within the OCME, has a staff of eight forensic
anthropologists that serve all of New York City’s five boroughs. These staff members are
primarily responsible for skeletal cases, although they are frequently consulted in soft
tissue cases involving bone trauma or ambiguous biological characteristics, such as age
and ancestry. Forensic anthropologists are responsible for a wide-range of tasks
including scene recovery, photography, maceration of soft tissues, skeletal analysis of
biological characteristics and trauma events, estimation of postmortem interval, histology,
and report writing. Forensic anthropology case reports are in-house peerreviewed by
fellow anthropologists prior to final submission (Bradley Adams, Personal
Communication).
There are also important large-scale undertakings at the OCME that require the
skills of forensic anthropologists. For instance, a grant awarded from the National Institute
of Justice allows the OCME to investigate cold cases that have accumulated over the
years through the disinterment of human remains in Potters Field on Hart Island. The
goal is to identify unknown individuals through anthropological methods as well as DNA
sampling and analysis that were not available when these individuals were initially
interred. In addition to this project, the staff at the OCME is constantly at work identifying
the victims of 9/11. A special team of anthropologists within the unit specializes in the
management of the hundreds of thousands of human remains as well as the identification
processes associated with the victims of 9/11. These anthropologists work both at the
OCME and at the repository within the newly constructed 9/11 memorial.
Harris County, Texas
With a 2014 population estimate of 4,441,370, Harris County, Texas is the third largest
county in the Untied States and the largest county in Texas. Houston, the county’s seat,
is the county’s largest city with a population of 2,196,000. Houston is also Texas’ largest
city and the fourth largest city in the United States. Harris County occupies 1,703.48
square miles, making the population density of approximately 2,607 people per square
mile. The county is situated on the Galveston Bay and its primary industry is energy,
specifically, oil and natural gas as well as manufacturing and aeronautics. Its position
along the Galveston Bay at the Gulf of Mexico and the Port of
Houston allows for the mass export of goods. According to the U.S. Department of
Commerce’s International Trade Commission, in 2013, the Port of Houston surpassed
New York City for the top area of exportation of goods.
Houston is one of the country’s most diverse large cities. According to the 2010 US
Census Bureau, 70% of Harris County’s population identified as “white,” with 31.9%
reporting, “white alone, not Hispanic.” As with residents in New York City, this difference
suggests that the term “white” here may indicate a difference in the meaning of the term
“white” or some level of mixed ancestry. In contrast to New York’s data of a difference of
10% reporting “white” and “white only”, there is a 38.9% difference between these two
categories in Harris County. This indicates a demographic difference between the two
cities. The census also reported Harris County’s population as 41.6% “Hispanic/Latino,”
19.5% “black,” 6.8% “Asian,” 1.1% “American Indian and Alaskan Native,” and 0.1%
“Native Hawaiian and other Pacific Islander.” Only 1.7% of respondents identified as
being of “two or more races.” The census also found that
25% of Harris County’s residents are foreign born and the high number of
“Hispanic/Latino” individuals is likely due to Harris County’s proximity to the Mexican
border. According to Sharon Derrick, a forensic anthropologist at HCIFS, many Latin
American migrants settle in Harris County to work.
The census results may also reflect a population influx that occurred in Harris
County following the aftermath of Hurricane Katrina on New Orleans, Louisiana in 2005.
The city of Houston alone absorbed approximately 250,000 people, many of them taking
up permanent residence (Hamilton, 2010). The majority of Houston’s new residents were
poor black and Vietnamese individuals from the most heavily impacted areas of New
Orleans (Hamilton, 2010). Despite claims of increased crime in Houston from the arrival
of new immigrants, crime in Houston was minimally impacted (Hamilton, 2010).
Houston is also home to 6,876 homeless persons (Coalition for the Homeless, 2014) a
figure that fortunately, has been declining over the years. When Houston’s homeless
population spiked 25% between 2010 and 2011, the U.S. Department of Housing and
Urban Development prioritized assistance to the region to reduce the numbers, an effort
that seems to be working.
In addition to living population demographics, reported deaths in Harris County by
gender and race are provided in Table 6. This information was taken from the Texas
Department of State Health Services website, 2014 statistics
(https://www.dshs.texas.gov/chs/vstat/vs14/data.aspx#death).
Table 6. 2014 Death Statistics (Harris County)
White
Black
Hispanic
Other
Total
Male
6,477
(27.0%)
2,916
(12.1%)
2,421
(10.1%)
714
(3.0%)
12,528
(52.2%)
Female
6,275
(26.2%)
2,782
(11.6%)
1,844
(7.7%)
563
(2.3%)
11,464
(47.8%)
Total
12,752
(53.2%)
5,698
(23.7%)
4,265
(17.8%)
1,277
(5.3%)
23,992
(100%)
Harris County Institute of Forensic Science (HCIFS)
The Harris County Institute of Forensic Science is located in Houston, Texas and has
jurisdiction over all of Harris County assisting local and federal law enforcement agencies
to resolve crimes and unidentified forensic cases. The office employs a Chief Medical
Examiner as well as a team of pathologists, anthropologists, and forensic scientists
specializing in histology, toxicology, drug chemistry, and trace evidence analysis
(including fire debris, gunshot residue and paint analysis). As of 2016, the
HCIFS has obtained full lab accreditation through the American Society of Crime
Laboratory Directors – Lab Accreditation Board (ASCLD-LAB).
The HCIFS employs four full-time forensic anthropologists and a group of interns and
researchers that assist with casework when necessary. They have similar responsibilities
as those at the New York City OCME, as they manage skeletal casework and assist the
medical examiners with requests for biological and trauma analysis of skeletal materials
in fleshed individuals. Anthropologists in this office perform a wide variety of tasks such
as scene recovery, photography, soft tissue maceration, skeletal analysis of biological
characteristics and trauma events, estimation of postmortem interval, postmortem dental
x-rays and dental comparisons, and report writing. Reports written at the HCIFS are also
peer-reviewed.
The anthropology unit at HCIFS also runs the “Identification Unit” also called the
“ID Unit.” The ID Unit is made up of HCIFS forensic anthropologists and an Identification
Specialist who participates in and tracks the identification progress of each case. This
division aggressively attempts to identify both skeletal and soft tissue cases that come
into the morgue by immediately distributing information about the decedent to the National
Information Crime Center, National Missing and Unidentified Persons System, and to the
National Association of Medical Examiners Unidentified Decedent website. In addition,
the ID Unit issues a comprehensive “Unidentified Decedent Flier” to local law enforcement
and missing person’s units. The Unidentified Decedent Flier is also posted on the HCIFS
website and can be accessed by anyone wishing to locate a missing loved one. The ID
Unit is also responsible for tracking down family members of deceased individuals to
inform of death. When efforts to locate next of kin are not successful, the Identification
Specialist releases the name of the decedent to local television and newspaper media for
help. If the individual continues to be unidentified, the body is released for burial in the
Harris County Potters Field, newly located in Crosby, Texas. Although some cases remain
unidentified, the ID Unit is extremely successful and most cases are resolved quickly
(Sharon Derrick, personal communication).
Pima County, Arizona
With a 2014 population estimate of 1,004,516, Pima County, Arizona is the second largest
county in Arizona and the 53rd most populous metropolitan area in the United States.
Tucson, the county’s seat, is the county’s largest city with a population of 527,972 and
the second largest city in the state. Pima County occupies 9,187.04 square miles, making
the population density approximately 109 people per square mile. According to the 2010
US Census Bureau 85.5 % of Pima County’s population
identified as “white,” with 53.3% of those reporting, “white alone, not Hispanic.” As with
residents in New York City and Harris County, this difference suggests that the term
“white” here may indicate a particular interpretation of the term “white” or some level of
mixed ancestry. Similar to Harris County’s data of a 38.9% difference between these two
categories, Pima County shows a difference of 32.2% between these categories. These
figures stand in contrast to New York’s demographic data of a difference of only
10% reporting “white” or “white alone, not Hispanic.” The census also reported Pima
County’s population as 36.1% “Hispanic/Latino,” 4.1% “black,” 3.1% “Asian,” 4.3%
“American Indian and Alaskan Native,” and 0.2% “Native Hawaiian and other Pacific
Islander.” Only 2.8% of respondents identified as being of “two or more races.” The
census also found that 12.8% of Pima County’s residents are foreign born and the high
number of “Hispanic/Latino” individuals in the area is likely due to Pima County’s shared
border with Mexico.
Pima County is home to the largest homeless population in Arizona. As of 2013,
one out of every 131 (approximately 7,303) people in Pima County experienced
homelessness in 2013 (Homelessness in Arizona Annual Report, 2013). According to the
report, 44% of the adult homeless population is over the age of 45 and 12% are families
with children. Among homeless adults, 55% reported substance abuse and
46% suffer from mental or physical disabilities. A large majority of the homeless (5,
501) are listed as “white” and 29% of all homeless (2,195) reported themselves to be of
“Hispanic” ethnicity.
In addition to living population demographics, reported deaths in Harris County by
gender and race are provided in Table 7. This information was taken from the Arizona
Department of Health Services website, 2014 statistics
(http://www.azdhs.gov/plan/report/ahs/2014/index.php?pg=counties).
Finally, Pima County has experienced an influx of undocumented border
crossers as its location is situated along the southern border of the United States. This
Table 7. 2014 Death Statistics (Pima County)
White
Black
Hispanic
American
Indian or
Alaska Native
Asian or
Pacific
Islander
Total
Male
3,480
(39.9%)
130
(1.5%)
770
(8.8%)
110
(1.3%)
30
(0.3%)
4,520
(51.8%)
Female
3,250
(37.2%)
120
(1.4%)
680
(7.8%)
110
(1.3%)
50
(0.5%)
4210
(48.2%)
Total
6730
(77.1%)
250
(2.9%)
1,450
(16.6%)
220
(2.6%)
80
(0.8%)
8,730
(100%)
area of the U.S. was part of a larger land deal acquired from Mexico in 1854, known as
the Gadsden Purchase and spans 261 miles from the border of New Mexico to the Yuma
county line. This area is known as the Tucson sector. Pima County contains portions of
the Tohono O’odham Nation, the San Xavier Indian Reservation, and the
Pascua Yaqui Indian Reservation. The Tohono O’odham Nation is the second largest
Indian reservation in the United States encompassing over 2.7 million acres of land in
Arizona as well as land over the border into Mexico. Pima County is within in the
Sonoran Desert, a main route for border crossers entering the United States from Mexico.
The Tohono O’odham reservation straddles 75 miles of the U.S./Mexican border where
approximately 1,500 undocumented migrants pass each day (Pyclik and
Leibig, 2005). The U.S. government granted the nation its sovereignty in 1937 but Border
Patrol agents regularly traverse the border along the reservation. Members of the tribe
could freely cross the border until the 1990s when the U.S. began to see a rise in illegal
immigration, but now residents of the reservation need to provide proof of tribal
membership to cross the border. (Briefing Before the Arizona Advisory Committee to The
U.S. Commission on Civil Rights, 2002). Despite increased Border Patrol presence, the
Tohono O’odham reservation is the state’s top entry point for undocumented migrants
Briefing Before the Arizona Advisory Committee to
(The U.S. Commission on Civil Rights, 2002).
With its close proximity to Mexico and being along a route used for border
crossings, the Pima County Office of the Medical Examiner experiences a host of
challenges in identifying the individuals that perish along the southern border.
Pima County Office of the Medical Examiner (PCOME)
The Pima County Office of the Medical Examiner is located in Tucson, Arizona and has
jurisdiction over all of Pima County as well as 10 neighboring counties
(Apache, Cochise, Gila, Graham, Greenlee, La Paz, Navajo, Pinal, Santa Cruz, and
Yuma). The Pima County Office of the Medical Examiner assists local and federal law
enforcement agencies, including Border Patrol, to resolve crimes and unidentified cases.
The office employs a Chief Medical Examiner as well as a team of pathologists and
medicolegal death investigators, one forensic anthropologist, and one rotating
postdoctoral forensic anthropologist.
The forensic anthropologists are responsible for the examination of all skeletal and
decomposed or mummified remains, including the remains of suspected undocumented
border crossers (UBCs). Unlike the medical examiners’ offices in New York City and
Harris County, the PCOME handles casework of Arizonans as well as a large number of
UBCs. There has been a marked increase in the number of migrants crossing the border
into Arizona due to an increased Border Patrol presence along migrant corridors in Texas
and California, forcing UBCs through the dangerous Sonoran
Desert who then succumb primarily to heat-related illnesses (Anderson, 2008). Today,
UBCs represent the majority of anthropology cases in Pima County. For example, UBC
cases totaled 129 out of the entire 149 anthropology cases in 2014 alone. Since 2001,
there have been 2,330 suspected border crosser cases with 65% (1,504) of those cases
ending in successful identifications, while 35% (826) are unidentified. According to
Anderson (2008) the vast majority (92%) of unsuccessful migrants were identified as
Mexican Nationals.
The anthropologists at the PCOME perform a wide variety of tasks such as scene
recovery, photography, soft tissue maceration, skeletal analysis of biological
characteristics and trauma events, estimation of the postmortem interval, postmortem
dental x-rays and dental comparisons, and report writing. Due to the large caseload, the
two anthropologists are typically not available to conduct scene recoveries on most, if not
all, UBC cases along the border. Rather, they rely upon Border Patrol, local law
enforcement, and the general public to recover and transport remains to the PCOME.
Unlike the offices in New York City and Harris County, anthropology reports at the PCOME
are not peer-reviewed, but post-doc reports are available for peer-review by the full-time
anthropologist upon request.
The undocumented migrant situation along the border requires constant
communication with border patrol and the Mexican Consulate. The staff at the PCOME
meets once a week with representatives of the Mexican Consulate to report new UBC
cases, review pending UBC cases, and exchange information helpful in the successful
resolution of unidentified cases. To assist the identification process, the Mexican
Consulate attempts to make contact with potential family members of victims to obtain
DNA samples and view personal effects. DNA analyses are funded by the Mexican
government and conducted on all UBC remains and possible family members for
comparison purposes. The UBC situation in Pima County is unique and no other office
in the country deals with this amount of unidentified individuals along the border.
CHAPTER FIVE
EXPECTATIONS AND RESEARCH AIMS
The aim of this study is to evaluate the accuracy of resolved forensic anthropology
case reports from three urban medical examiners’ offices with large skeletal caseloads.
This evaluation is the first of its kind and will provide the anthropological community with
an understanding of the power and limitations of scientific methods in practice. Ancillary
to this goal is to understand how the use of biological ancestry to estimate race translates
to social race categories in forensic casework. A second aim of this study is to analyze
the skeletal analysis of unresolved casework to identify potential reasons why those cases
persist as unresolved.
Forensic anthropologists typically examine the accuracy of individual methods
used to establish a biological profile by testing the various methods on skeletal
populations other than the ones used to develop the method. These studies are controlled
validation tests for the purpose of measuring a method’s applicability to a particular
population group (refer to the previous chapter for examples). However, until now, these
studies have been limited to evaluating specific components of the biological profile.
There has yet to be a study evaluating the accuracy of the biological profile in its entirety
from forensic anthropology casework.
The following expectations and research goals address the accuracy of the
anthropological report and identify specific challenges associated with the methods used
to develop a biological profile.
Expectations and Research Aims
Expectation One
This project is designed to discover if elements of the biological profile are difficult
to assign in casework. The expectation, based on the literature, is that ancestry
estimation will have a lower rate of accuracy than sex, age, and stature. The difficulties
associated with estimating biological ancestry based on trait expression and using the
results to assign social race categories, as outlined in the background chapters, will
manifest in casework.
If this expectation is supported, then biological ancestry estimations are prone to
misclassify social race descriptions provided by the reporting party. This information can
be used to justify changes in missing persons reporting procedures as well as justify
changes to the reporting of racial categories from the anthropological assessment of
biological ancestry.
Expectation Two
Stature estimation, regardless of its accuracy will be uninformative. First, stature
estimation methods have been largely developed for black and white individuals, while
methods developed for Asian and Hispanic groups is limited. Second, self-reported
stature is prone to inaccuracies making antemortem and postmortem comparisons
imprecise (Wiley and Falsetti, 1991).
If this expectation is supported, then stature is either an unnecessary component
of the biological profile unless the height of the individual falls outside the average
height of the general population or forensic stature estimation methods should be
improved.
Expectation Three
Unresolved casework will exhibit logistical challenges that hinder the
anthropological evaluation of the biological profile. Examples include fewer skeletal
elements available for analysis, damage to diagnostic elements, and/or ambiguous trait
expression that confounds the evaluation of the biological profile.
Chapter Summary
This dissertation evaluates four elements of the biological profile in forensic practice with
the goal of identifying areas that need improvement. The research expectations outlined
in this chapter are based upon the background information presented in chapters 2 and
3. A goal of this project is to help identify areas of anthropological assessment in
casework that need improvement.
CHAPTER SIX
MATERIALS AND METHODS
This chapter outlines the materials and methods utilized to analyze the data
collected for this research. The conditions required for cases to be analyzed are
discussed first. This will be followed by descriptive and analytical statistics for the
casework examined including the number of resolved and unresolved cases, the number
of cases with a presumptive identification, and the sex, age, and ancestry distributions of
the dataset. Finally, methods for obtaining the data and an explanation of the analysis
are described.
Forensic Anthropology Casework Sample
Selection Criteria and Final Sample
To be included in this study, forensic cases had to meet certain criteria. The data for this
study were derived from written case reports summarizing the anthropological analyses
of skeletal remains, where more than one component of the biological profile was
examined. Typical autopsy or soft tissue cases where the anthropologist was consulted
to establish the age of an individual (for example) were not considered. All data is
anonymized, as no names are associated with the records and no case numbers were
used.
Two separate data sets were maintained and all data was recorded in excel
spreadsheets for analysis. The first data set is comprised of forensic anthropology case
reports of resolved cases. This data set includes postmortem information about the
recovered skeletal materials and antemortem information about the missing person.
Postmortem information was obtained through the report and includes the estimated sex,
ancestry, age, and stature of the individual as well as information regarding the state of
preservation and the approximate percent of recovered skeletal elements. Antemortem
information includes the individual’s sex, ancestry, age, and stature. This information was
collected from the files and databases in the medical examiners’ offices. In the rare
instance this information was not provided, the missing person’s report, other media
(driver’s license, ID card, military ID, etc.) positively associated with the remains, or the
death certificate was used. The death certificate was used to obtain antemortem
information as a last resort, due to potential inaccuracies (Jennifer Love and Christian
Crowder, personal communication). If available, data also includes the scientific method
used to establish a positive identification. Examples of positive ID methods include, dental
comparisons, radiograph comparisons, fingerprints, or DNA matching.
The second data set is comprised of forensic anthropology case reports of
unresolved cases. This data set is limited to postmortem information about the individual.
Postmortem information obtained from the forensic anthropology report includes the
estimated sex, ancestry, age, and stature of the unidentified individual as well as
information regarding the state of preservation, and the approximate percent of recovered
skeletal elements.
Keeping with these criteria, data from 204 resolved cases (47 from New York
City, 47 from Harris County, and 110 from Pima County) is presented as well as 285
unresolved cases (42 from New York City, 4 from Harris county, and 239 from Pima
County).
Data Analysis
Analysis of the first data set comprised of resolved cases consists of the direct
comparison of the antemortem and postmortem information of each individual. Correct
and incorrect estimations were tallied, counted, and converted to percentages for data
dissemination. This examination documented similarities and discrepancies between the
anthropological report and the actual physical characteristics reported to law
enforcement. Biological sex was considered accurate if the assessment from the
anthropology report matched the reported sex of the individual. Age was considered
accurate if the age range provided in the anthropology report encompassed the age
provided of the individual in the antemortem information. Ancestry was considered
accurate when the estimation provided by the anthropology report corresponded with the
reported antemortem race of the individual (ex: European ancestry for an individual
identified as white). Stature was considered accurate if the range provided in the report
encompassed with reported height of the individual. The accuracy of the biological profile
was evaluated by these standards because these are the criterions by which the methods
are expected to perform.
However, the accuracy of the biological assessment of ancestry is nuanced and
requires explanation. For the purposes of this study, correct ancestry estimations are
defined as the congruence between antemortem biological ancestry estimations and
reported postmortem social race categories. For example, if the estimated biological
ancestry of an unknown is listed as white, Caucasian, or European and the identified race
of the individual is listed as either white, Caucasian, or European, with the understanding
that these terms are interchangeable, then the estimation is considered correct. If the
ancestry estimation did not match the antemortem race category (e.g. ancestry estimated
as black and individual was Asian) then the estimation is considered incorrect.
There were a few other scenarios where ancestry was considered correct even
though the anthropological assessment did not exactly match the antemortem description.
First, individuals estimated as two or more races (e.g. white or Hispanic) but the
antemortem information for those individuals reported one but not both of those races,
were scored as correct. The rationale behind this decision is because by providing two
or more ancestry estimates, the anthropologist observed skeletal traits consistent with
both groups. The anthropologist, in an effort to include, rather than exclude a potential
match, included both ancestral groups in the final assessment of ancestry. In a second
scenario, the ancestry assessment may indicate a degree of admixture (ex: black and
white admixture). The skeletal ancestry assessment may be correct biologically
(anthropologists observed traits consistent with both black and white individuals), even if
the decedent was identified only as one. The rationale here is that anthropologists use
the term admixture or will often classify the unknown into multiple ancestries if the skeletal
materials exhibit traits found in multiple ancestral groups or have ambiguous phenotypes
not typical of one ancestral group. The reason anthropologists claim admixture when they
observe the blending of traits is to acknowledge the variation they observe and also to
help ensure these individuals are not unintentionally excluded from possible
identifications.
Chapter three discussed the differences between race and ancestry and several
reasons for the variation of ancestral trait expression among population groups. First,
morphological traits are non-concordant; second morphological traits are expressed in all
populations in varying frequencies; and third, craniometric variation and skin color do not
follow the same patterns of expression. Additionally, there is no standard application of
ancestry estimation methods in forensic practice. There is no threshold of ancestral trait
expression that needs to be met to classify an individual into a social race category. How
many characteristics does it take to classify someone as white or black, or a mixture of
the two? How many traits of one ancestry are enough for an anthropologist to declare
that there is some admixture in a person? How does the anthropologist know when traits
are expressed because there is admixture versus that person having a non-typical
phenotype?
The use of the term admixture to explain traits that are supposed to exist in all
population groups at varying frequencies is a way to reconcile these questions. This
practice also has utility in Pima County. Here, the term admixed Native American is used
to describe the Hispanic population of undocumented migrants. This term is preferred
because Hispanic does not adequately describe the variation that exists within the
Hispanic group and also because families of the deceased often do not identify with the
Hispanic label for cultural and political reasons (Anderson, personal communication). As
such, the families are more willing to accept the identification of their loved one if the
deceased is described as admixed Native American (Anderson, personal
communication). Lastly, the missing persons report differs across jurisdictions but allows
the reporting party to either check the race category from a pre-prepared list of race
categories or by filling in a blank space on the form that describes the race or ethnicity
missing person. Some forms contain space for a description of the missing persons
complexion. Examples can be found at:
http://www.eri-online.com/uploads/MLSO_Missing_Person_Rpt_Jan.pdf.,
http://lib.post.ca.gov/Publications/Missing_Persons_Forms/mp_report.pdf,
http://www.texasequusearch.org/report-a-person-missing/. Although missing persons
reports do not restrict the number of check boxes that can be marked and the reporting
party can indicate multiple races, the forms do not ask about ancestry. However, on some
forms, immediately following the race categories exists a section for the reporting party to
describe the skin tone of the decedent. This provides the authorities with additional
information about what the person looks like.
To determine if a sex bias exists between correct and incorrect ancestry
assessments among FORDISC classification results, a contingency tables and chisquare
analysis was conducted for reports in each of the three offices examined for this study.
Finally, for informational purposes, records related to how the positive identification
was established were tallied, counted, and converted to percentages to discern trends
(e.g. DNA, radiographs, fingerprint, etc.).
Ultimately, this data will provide forensic anthropologists with vital information
regarding the ability to apply forensic methods and the frequency of their application to
casework.
Information from the second data set includes only postmortem information and
was tallied, counted, and converted to percentages for data dissemination. The results
were analyzed to help explain why the individual remains unidentified. These indicators
may include missing skeletal elements, poor preservation, inability to analyze, or
ambiguous findings (evaluated in the same manner as with resolved cases). The findings
gleaned from the unresolved cases serve to identify any factors that may hinder
identifications or consistently present themselves. For example, does the number of
skeletal elements or the presence/absence of diagnostic skeletal elements impact the
completeness of reports and the potential chance of a positive identification? To address
this, a chi-square analysis was conducted to evaluate the difference in the percentage of
remains recovered among resolved and unresolved cases. The methodological collection
of this data from both resolved and unresolved cases will greatly increase our
understanding of forensic anthropological methods in practice and the challenges
associated with identifying individuals.
Chapter Summary
The materials and methods selected for this dissertation are intentional and strategic.
The offices of New York, Harris County, and Pima County encompass large urban areas
that provide an opportunity to examine anthropological issues associated with the
analysis of diverse populations. Concurrently, the medical examiners’ offices in these
cities are ideal for research because they handle large skeletal caseloads and employ
teams of forensic anthropologists that work collaboratively. The Pima County office
provides a contrast to the offices in Harris County and New York City, as their caseload
differs in demographics and there is no peer-review process for written anthropology
reports. As a result, the direct comparison of casework within and between these offices
will provide the forensic anthropology community with fundamental statistics that,
regardless of the outcome, will better our understanding of forensic casework.
CHAPTER SEVEN
RESULTS
This chapter presents the results for the analyses described in chapter six. Vital
statistics for each medical examiner’s office are presented, including the demographics
of individuals in both resolved and unresolved cases. These demographics are followed
by the results describing the accuracy of the anthropological report in forensic casework.
A discussion of the results presented here is reserved for chapter eight.
Vital Statistics
This section presents the results of the analysis of fundamental statistics at the medical
examiners’ offices in New York City, Harris County, and Pima County. The statistics are
presented separately for each element of the biological profile. The demographics of
resolved cases are presented first followed by results from unresolved cases. The
demographics analyzed include the sex, ancestry, and age distributions of casework
followed by a summary of the anthropologist’s estimations of the remains. Information
regarding the completeness of the remains and how individuals were
positively identified is also given.
Second, the vital statistics for unresolved cases are described. The information
gleaned from the anthropological analysis of these unidentified individuals, although not
verified through a positive identification, will serve as a proxy for the demographics of
these missing individuals.
Descriptive Statistics of Resolved Cases
There were a total of 204 cases that met the research criteria from the three study
locations. There were 47 resolved cases from New York City, 47 resolved cases from
Harris County, and 110 resolved cases from Pima County. Of the 110 individuals analyzed
from Pima County, 22 (20%) were U.S. citizens and 88 (80%) were identified as
undocumented border crossers. The following results make up the vital statistics for these
locations that will be discussed in Chapter eight.
Biological Sex Estimation
New York City Office of Chief Medical Examiner
Of the total 47 individuals suitable for this analysis, antemortem data indicated 31 (66%)
were male and 16 (34%) were female. As described in Chapter two, the pelvis is the
primary region of the skeleton used for sex estimation. In New York City, sex estimations
are primarily conducted using morphological characteristics of the pelvis as well as
morphological and metric characteristics of the skull. Here, sex could be estimated
through forensic anthropological methods in 44 out of 47 cases. The three cases in which
sex was indeterminate included a 14-month-old baby, a 14-year-old child, and a case of
cremated bone fragments. In the remaining 44 cases, the pelvis, the skull, or both were
available for sex estimation. There were no cases when metric analysis of postcranial
elements was needed to estimate sex. In the 44 cases available for sex analysis, all were
estimated correctly (31 males, 16 females) providing an accuracy rate of 100%.
Harris County Institute of Forensic Sciences
Of the total 47 individuals suitable for this analysis, antemortem data indicated 34
(72%) were male, 13 (28%) were female. As in New York City, sex estimations at the
Harris County Institute of Forensic Science (HCIFS) were conducted using morphological
characteristics of the pelvis as well as morphological and metric characteristics of the
skull. Here, sex could be estimated in all 47 cases. In these cases, the pelvis, the skull,
or both were available for sex estimation and there were no cases when metric analysis
of other postcranial elements was needed to estimate sex. All cases at the HCIFS were
estimated correctly (43 males, 13 females) providing an accuracy rate of 100%.
Pima County Office of the Medical Examiner
Of the total 110 individuals suitable for this analysis, antemortem data indicated 100
(91%) were male and 10 (9%) were female. The vast difference between the number of
males and females in this population is due to the large number of males that perish after
crossing the southern border of the United States into Arizona. Sex estimations are
primarily conducted using morphological characteristics of the pelvis as well as
morphological and metric characteristics of the skull. Here, sex could be forensically
estimated in 108 out of 110 cases. The two cases where sex was not estimated included
one individual where sex was determined from soft tissue at autopsy and one individual
that was a 14-year old juvenile. In the 108 cases where sex was estimated, the pelvis,
the skull, or both were available for sex estimation. There were no cases when metric
analysis of postcranial elements was needed to estimate sex. In all 108 cases available
for sex analysis, all were estimated correctly (98 males, 10 females) providing an
accuracy rate of 100%. Additionally, among the total resolved cases, 22 were U.S.
citizens. Eighteen were male, four were female, and sex was estimated correctly in 22
out of 22 cases (100%).
Ancestry Estimation
New York City Office of Chief Medical Examiner
Of the 47 individuals analyzed, antemortem data indicated 27 (57%) were white,
nine (19%) were black, four (9%) were Hispanic, three (6%) were Asian, and four (9%)
lacked antemortem information about race. Of those four, all were estimated as white.
Table 8 shows the breakdown of antemortem reported race combined with antemortem
sex in this sample.
Table 8. Antemortem Race and Sex Demographics (NYC)
Race
Sex
Number of Cases
(out of 47)
Percentage of Total
Cases
White
Male
17
36%
Female
10
21%
Black
Male
6
13%
Female
3
6%
Hispanic
Male
3
6%
Female
1
2%
Asian
Male
1
2%
Female
2
5%
Unknown
Male
4
9%
Female
0
0%
To estimate ancestry, New York City anthropologists routinely utilize non-metric
traits of the skull summarized by Rhine (1990) in conjunction with metric analyses of the
cranium and the use of FORDISC. Ancestry was estimated in 41 of 47 cases (87%).
There are many reasons why ancestry may not be estimated. Cases not meeting criteria
for ancestry estimation include cases without crania, children, and cases with extremely
fragmented remains. In this sample, four of the six indeterminate ancestry estimations
were missing crania and two were juveniles. Of the 41 cases where ancestry was
estimated, 37 of those estimations could be compared with antemortem data for missing
individuals and 36 (97%) yielded correct racial approximations. In this analysis, one case
(3%) as incorrectly estimated for ancestry. Among the correct estimations, two indicated
that the individual was either one race or another (e.g. “black or Hispanic”) and four
indicated that the individuals were admixed (e.g. “white/Hispanic” or “white/black
admixture”). These categories suggest that the anthropologists interpreted biological
traits of these skulls as consistent with multiple ancestries. Although no level of racial
admixture was reported in the antemortem data of these cases, they are considered
accurate estimations of ancestry because the results would not exclude the missing
individual from being a possible match.
One incorrect estimation of ancestry occurred for a completely skeletonized
individual. This individual was estimated as black, but antemortem information indicated
he was Hispanic. Records suggest that his ancestry was difficult to estimate due to the
cranium appearing to exhibit a mixture of black, white, and Asian morphological
characteristics. However, FORDISC produced results that suggested this individual was
white or Hispanic, correctly identifying population level
characteristics.
FORDISC was used as a tool to help estimate ancestry in 33 out of 47 cases (70%)
eligible for metric analysis. Twenty-nine (88%) of these 33 cases yielded interpretable
FORDISC results while four cases (12%), were listed as inconclusive. Inconclusive
results by FORDISC may indicate that the program produced results the anthropologist
believed would not add informative value to the overall ancestry estimation. In the 29
cases where FORDISC was used as a tool to help estimate ancestry and could be
compared to antemortem data for accuracy, 26 (90%) of the FORDISC results alone
correctly estimated the antemortem information, while three cases (10%) contained
FORDISC results that did not match antemortem descriptions of the decedent. In these
cases, the anthropologist was able to interpret the FORDISC results appropriately and
made a final estimation of ancestry that matched the antemortem description of the
decedent. In this analysis, each of the three inaccurate FORDISC results dealt with the
Hispanic category. Table 9 illustrates the comparison between the results produced by
FORDISC, the final anthropological estimation of ancestry, and the antemortem
information of the decedent provided to the medicolegal system. Table 9 shows the final
ancestry estimation provided by anthropologists as correct in these cases despite
inconclusive or potentially inaccurate FORDISC results. In making the final assessment
of ancestry, these findings suggest that the anthropologists preferred the morphological
analysis of ancestry to the FORDISC
results.
Harris County Institute of Forensic Sciences
Of the 47 individuals analyzed, antemortem data indicated 24 (51%) were white,
11 (23%) were black, nine (19%) were Hispanic, two (5%) were Asian, and one (2%)
lacked antemortem information about race. That individual was estimated as white.
Table 10 shows the breakdown of ancestry and sex in this sample.
Table 9. Incorrect FORDISC Results (NYC)
Morphological
Analysis
FORDISC Results
Final Ancestry
Estimation
Antemortem
Information
White
Hispanic
White
White
Hispanic
White
Hispanic
Hispanic
White/Hispanic
Black
White/Hispanic
Hispanic
Table 10. Race and Sex Demographics (Harris County)
Ancestry
Sex
Number of Cases
(out of 47)
Percentage of Total
Cases
White
Male
15
32%
Female
9
19%
Black
Male
7
15%%
Female
4
9%
Hispanic
Male
9
19%
Female
0
0%
Asian
Male
2
4%
Female
0
0%
Unknown
Male
1
2%
Female
0
0%
Similar to New York City, Harris County anthropologists routinely utilize nonmetric
traits of the skull summarized by Rhine (1990) in conjunction with metric analyses of the
cranium and the use of FORDISC. Ancestry was estimated in 44 of 47 cases (94%). In
this sample, all three of the indeterminate ancestry estimations had fragmentary crania
and two of the three were also burned or charred. There was one individual where
ancestry was estimated, but was unable to be compared with antemortem information;
therefore, in the 44 cases where ancestry was estimated, 43 of those estimations could
be compared with antemortem data for missing individuals. Of the 43 cases where
ancestry could be evaluated for accuracy, all 43 (100%) yielded correct racial
approximations.
FORDISC was used as a tool to help estimate ancestry in 44 out of 47 cases
(94%). In the 44 cases where FORDISC was used, 43 could be compared with
antemortem information. FORDISC correctly estimated ancestry in 36 (84%) of these
cases while seven (16%) yielded incorrect estimations. Similar to the data from New York
City, five of the seven inaccurate results produced by FORDISC concerned the Hispanic
category. Table 11 illustrates the comparison between the morphological ancestry
estimation, the results produced by FORDISC, the final estimation anthropological
estimation of ancestry, and the antemortem information of the decedent provided to the
medicolegal system.
Table 11 shows the final ancestry estimation provided by anthropologists as
correct in these cases despite potentially inaccurate FORDISC results. With the
exception of one case, the table shows the anthropologists preferred to include the
FORDISC results in their final assessments of ancestry even when it differed from the
morphological assessment. This strategy is often employed to ensure that the decedent
is not unintentionally excluded as a possible match to a missing person.
Table 11. Incorrect FORDISC Results (Harris County)
Morphological
Analysis
FORDISC Results
Final Ancestry
Estimation
Antemortem
Information
White/Hispanic
Hispanic
White, possible
Hispanic admixture
White
Asian
White
Asian, possible white
admixture
Asian
White
Hispanic
White
White
Hispanic
American Indian
Hispanic (Amerindian)
Hispanic
Black
American Indian
Black, possible
American Indian
admixture
Black
Hispanic
Black
Hispanic, possible
admixture
Hispanic
White
Hispanic
White, Hispanic, or
admixed
white/Hispanic
White
Pima County Office of the Medical Examiner
Before presenting the results of this category, discussion of the unique
demographics of the population at the Pima County Medical Examiner’s office is
warranted. Recall from Chapter four that the anthropologists in Pima County are
challenged with identifying unknowns in the local population as well as deceased migrants
that cross America’s southern border. Due to the hot and dry desert climate where many
of their cases are found, unknowns often arrive at the medical examiner’s office in various
stages of decomposition, typically skeletal, mummified, or both.
For migrants, there are many social, political, and biological factors that confound
the identification process. Pima county anthropologists have identified biological traits
that exist among the migrant population (see: Birkby et al., 2008) and have adjusted
communication of biological findings to family members and foreign governments.
Therefore, terminology used to describe ancestry in reports from Pima County is different
than in other cities, as it reflects an underrepresented population. The information
reported here reflects their terminology.
Among the 110 identified individuals, antemortem data provided the following vital
statistics: 11 (10%) white, zero (0%) black, zero (0%) Asian, seven (6%) Hispanic, four
(4%) Native American, 70 (63%) Mexican Nationals, seven (6%) Guatemalan
Nationals, four (4%) Honduran Nationals, one (1%) El Salvadoran, one (1%) Costa Rican,
one (1%) Latin American, one (1%) Mexican National/Tohono O’odham Nation, and three
(3%) lacked antemortem information about race. Of those three, one was forensically
estimated as white, one indicated that there was “some degree of Native American,” and
one was estimated as Admixed Native American (ANA). Table 12 shows the breakdown
of antemortem reported race combined with antemortem sex in this sample.
Similar to New York City and Harris County, Pima County anthropologists routinely
utilize non-metric traits of the skull summarized by Rhine (1990) in conjunction with metric
analyses of the cranium and the use of FORDISC. However, many current methods to
estimate ancestry do not adequately reflect the variation existing in the populations
represented in the Pima County Medical Examiner’s Office (Birkby et al.,
2008). To address this variation, Pima County anthropologists began to use the term
“Admixed Native American” (ANA) in 2011 to describe individuals that may otherwise be
considered Hispanic. For forensic anthropologists using term Hispanic typically implies
that an individual displays a blend of characteristics consistent with individuals of Native
American, European, and African ancestries (Anderson, 2008). The transition away from
the term Hispanic in Pima County is philosophical and also biologically appropriate.
Socially, Hispanic describes a cultural tradition or an ethnicity associated with Spanish
speaking populations outside of Spain, not an ancestral category. The families of many
individuals discovered along the southern border often do not identify with Hispanic
culture or recognize admixture in their lineage (Bruce Anderson, personal
communication). The anthropologists at Pima County found that families were more
willing to accept an identification of a loved one when the term Admixed Native American,
rather than Hispanic, was used to describe the deceased (Bruce Anderson, personal
communication).
Table 12. Antemortem Race and Sex Demographics (Pima County)
Race or Country of
Origin
Sex
Number of Cases
(out of 110)
Percentage of Total
Cases
White
Male
10
9%
Female
1
1%
Black
Male
0
0%
Female
0
0%
Hispanic
Male
6
5%
Female
1
1%
Asian
Male
0
0%
Female
0
0%
Native American
Male
3
3%
Female
1
1%
Mexican National
Male
67
60%
Female
3
3%
Guatemalan National
Male
5
5%
Female
2
2%
Honduran National
Male
4
3%
Female
0
0%
El Salvadoran
Male
0
0%
Female
1
1%
Costa Rican
Male
1
1%
Female
0
0%
Latin American
Male
1
1%
Female
0
0%
Mexican
National/Tohono
O’odham
Male
1
1%
Female
0
0%
Unknown
Male
2
2%
Female
1
1%
Ancestry was estimated in 100 of 110 cases (91%). Of the 100 cases where
ancestry was estimated, 97 of those estimations could be compared with antemortem
data for missing individuals. Of the 97 cases where ancestry could be evaluated for
accuracy, all 97 (100%) yielded correct racial approximations. Again, for the purposes of
this study, correct ancestry estimations are defined as the congruence between
postmortem biological ancestry estimations and reported antemortem social race
categories. For example, if the estimated biological ancestry of an unknown is listed as
Admixed Native American (ANA), some degree of Native American, Hispanic, or
Southwest Hispanic, with the understanding that these terms in this office are
interchangeable, then the estimation is considered correct if the individual identified as a
Mexican National, Honduran National, Hispanic, etc. in life.
FORDISC was used as a tool to help estimate ancestry in many cases, however, not all
the FORDISC analyses were available in all files for analysis. Out of the 97 cases eligible
for metric analysis comparable to antemortem data, FORDISC information was available
for 54 (55%) cases. In these 54 cases, 27 (50%) of FORDISC results yielded correct
estimates, while 27 (50%) of FORDISC results yielded inaccurate estimates.
Further analysis of the FORDISC results in Pima County provides interesting conclusions
that correlate with the challenges associated with identifying Hispanic individuals.
FORDISC classified 20 Admixed Native American individuals into Asian categories such
as Japanese, Chinese, and Vietnamese. Table 13 illustrates the comparison between the
morphological ancestry estimation, the results produced by FORDISC, the final
estimation anthropological estimation of ancestry, and the antemortem information of the
decedent provided to the medicolegal system. Table 13 shows that the morphological
assessment of ancestry detected traits consistent with two or more ancestries and the
term Admixed Native American was used to describe these observations. In the majority
of these cases, FORDISC classified the unknowns into Asian groups. Given the
demographics of the region and experience identifying undocumented border crossers,
the final anthropological assessment relied on the morphological interpretation of
features. Interestingly, of the five Guatemalan individuals that were assessed using
FORDISC, none of them were classified as Guatemalan, a population group included in
the FORDISC database. Two of the individuals were classified as Japanese, two were
classified as Hispanic, and one was classified as Chinese. Further, two non-Guatemalan
individuals were classified as Guatemalan by FORDISC. Table 14 highlights these
results.
The Guatemalan sample in FORDISC consists of males and represents a small
subset of the general Guatemalan population. FORDISC’s inability to correctly classify
the Guatemalans in this sample does not mean the program is ineffective, rather it
suggests that the variation within the Guatemalan population is greater than the variation
represented among the current FORDISC population of Guatemalans.
Table 13. Incorrect FORDISC Results (Pima County)
Morphological
Assessment
FORDISC Results
Final Ancestry
Estimation
Antemortem
Information
Southwest Hispanic
White
Southwest Hispanic
El Salvadoran
Native American or
Southwest Hispanic
Black
Native American or
Southwest Hispanic
Mexican National
Southwest Hispanic
Vietnamese
Asian, probable
Southwest Hispanic
Mexican National
Admixed Native
American
Vietnamese
Admixed Native
American
Mexican National
Southwest Hispanic
Vietnamese
Probable Southwest
Hispanic
Mexican National
European
Japanese
Admixed European
Caucasian
Admixed Native
American
Japanese
Admixed Native
American
Mexican National
Admixed Native
American
White
European and
Admixed Native
American likely
Latin American
Admixed Native
American
Japanese
Admixed Native
American
Mexican National
Native American
Chinese
Native
American/Asian
Native American
Admixed Native
American
Japanese
Admixed Native
American
Hispanic
Admixed Native
American
Japanese
Admixed Native
American
Mexican National
Admixed Native
American
Japanese
Some degree of
Native American
Mexican National
Admixed Native
American
White
Admixed Native
American
Mexican National
Admixed Native
American
Black
Admixed Native
American
Mexican National
Admixed Native
American
Native American
and Black
Admixed Native
American, European,
African
Mexican National
Admixed Native
American
Vietnamese
Admixed Native
American
Hispanic
Admixed Native
American
Chinese
Admixed Native
American
Mexican National
Admixed Native
American
Japanese
Admixed Native
American
Guatemalan
Table 13. Continued
Morphological
Assessment
FORDISC Results
Final Ancestry
Estimation
Antemortem
Information
Admixed Native
American
Japanese
Admixed Native
American
Honduran National
Admixed Native
American
Chinese
Admixed Native
American
Mexican National
Admixed Native
American
Japanese
Some degree of
Native American
Hispanic
Admixed Native
American
Japanese
Admixed Native
American
Mexican National
Admixed Native
American
Black
Admixed Native
American
Mexican National
Admixed Native
American
Chinese
Some degree of
Native American
Hispanic
Southwest Hispanic
Chinese
Southwest Hispanic
Guatemalan
Admixed Native
American
Japanese
Admixed Native
American
Guatemalan
Table 14. FORDISC Results Guatemalan (Pima County)
Morphological
Assessment
FORDISC Results
Final Ancestry
Estimation
Antemortem
Information
Hispanic
Guatemalan
Southwest Hispanic
Mexican National
Hispanic
Chinese
Primarily Asian,
probable Southwest
Hispanic
Guatemalan
Southwest Hispanic
Southwest Hispanic
Southwest Hispanic
Guatemalan
Admixed Native
American
Japanese
Admixed Native
American
Guatemalan
Admixed Native
American
Guatemalan
Admixed Native
American
Mexican National
Admixed Native
American
Hispanic
Admixed Native
American
Guatemalan
Admixed Native
American
Hispanic
Admixed Native
American
Guatemalan
Sex Bias Among FORDISC Ancestry Estimations – All Locations
To determine if a sex bias exists in the FORDISC results of ancestry, contingency
tables and chi-squares were calculated for the calculations provided by the
FORDISC analyses. The chi-squares were calculated using the following equation:
𝜒! = !" ! !" (!!!!!!!)
(!!!)(!!!)(!!!)(!!!)
Table 15 displays the contingency table that provided the numerical values used for the
chi-square analysis for sex bias of the FORDISC ancestry estimations for all sample
locations.
The chi-square analysis revealed that the sex differences in the FORDISC
calculations of these reports are non-significant, (p > 0.05), indicating that no sex bias
exists.
Table 15. Contingency Table – Sex Bias of FORDISC Calculations
(All Locations)
Male
Female
Total
Correct
55
24
79
Incorrect
41
8
49
Total
96
32
128
Age
New York City Office of Chief Medical Examiner
Of the 47 individuals analyzed in New York City, age was estimated in all cases and
reported as a minimum age or as an age range. Antemortem data shows that 12 (26%)
individuals were 25 years of age and under, 24 (51%) individuals were 26-64 years of
age, and five (11%) individuals were reported as age 65 and over. Six (12%) individuals
lacked antemortem age data. The youngest individual represented in the dataset was 14
months of age, and the oldest was 87 years of age.
Age estimations were typically reported as age ranges where the unknown presumably
fell within. A variety of aging methods were used to estimate the diverse population of
deceased individuals, as no one aging method alone should be used to estimate age.
However, frequently used methods for age estimation of the adult skeleton include the
Suchey-Brooks (Katz and Suchey, 1986; 1989) method of aging the pubic symphysis, as
well the methods associated with aging the sternal end of the 4th rib (Iscan and Loth,
1984; 1986) when available. For this analysis, comparisons of antemortem and
postmortem data were examined to determine if the reported age of the identified
individual fell within the age range estimated by the anthropologist. Of the 41 estimations
that could be compared with antemortem data, 40 (98%) were correct and one (2%) was
incorrect. The incorrectly aged individual was a 56-year-old white male whose age was
under-estimated by one year (age range reported was 40-55).
Among all of the 47 age estimations provided by anthropologists, 32 estimations
were presented as true age range (e.g. 30-45 years) and 15 estimations were presented
more generally as a minimum age (e.g. 55+) and were considered correct if the individual
was at least that age. In reference to age ranges, the overall average length of the age
range for this sample is 14.5 years. The average age range for individuals known to be
25 years of age and under is 6.8 years. The average age range for individuals known to
be 26 and over is 15.5 years. Additional calculations were performed to determine the
average age ranges for more specific age categories to determine if the age ranges differ
for younger, middle, or older individuals. The average age range for individuals 21 years
of age and under is 4.8 years. This average increases to 9.9 years for individuals 22-35
years of age, and again the average increases to 18.8 years for individuals 36 years of
age and over. Table 16 illustrates these results.
Table 16. Age Estimation Results – Average Ages by Age Range
(NYC)
Estimated Age Range
Average Number of Years in Each Range
25 years and under
6.8 years
26 years and over
15.5 years
21 years and under
4.8 years
22-35 years
9.9 years
36 years and over
18.8 years
Overall for Sample
14.5 years
A total of 10 age estimations were recorded as a minimum age (e.g. 55+). This is
a strategy used to estimate the age of older individuals, as they are typically more difficult
to age, have damaged remains, or are represented only by a cranium or few other skeletal
elements whereby aging techniques are limited (see Chapter two for a complete
discussion). For example, one 39-year-old individual found fragmented and burned was
estimated to be an adult. Since the age estimations for these individuals were recorded
as a minimum age, they are considered separately, and not included in the average age
range calculations shown in table 16.
Harris County Institute of Forensic Sciences
Of the 47 individuals analyzed at Harris County, age could be estimated in all
cases. There were two (4%) individuals 25 years of age and under, 35 (75%) individuals
26-64 years of age, and 10 (21%) individuals aged 65 and over. The youngest individual
represented in the dataset was 23 years of age, and the oldest was 86 years of age.
As in New York City, anthropologists in Harris County frequently use methods for age
estimation of the adult skeleton that include the Suchey-Brooks (Katz and Suchey, 1986;
1989) method of aging the pubic symphysis as well the methods associated with aging
the sternal end of the 4th rib (Iscan and Loth, 1984; 1986) when available. In addition to
these methods, cranial suture closure (Meindl and Lovejoy, 1985; Nawrocki,
1998) was also routinely used in this office. Of the 47 total estimations that could be
compared with antemortem data, 40 (85%) were correct and seven (15%) were incorrect.
Table 17 shows the anthropologically estimated age range compared to the age at death
among the incorrect estimations.
Among all of the 47 age estimations provided by anthropologists, 37 estimations
were presented as true age range (e.g. 30-45 years) and 10 estimations were presented
more generally as a minimum age (e.g. 55+) and were considered correct if the individual
was at least that age. In reference to age ranges, the overall average length of the age
range for this sample is 16 years. The average age range for individuals known to be 25
years of age and under is 8.5 years. The average age range for individuals known to be
26 and over is 16.4 years. Additional calculations were performed to determine the
average age ranges for more specific age categories to evaluate if the age ranges differ
for younger, middle, or older individuals. The average age range for individuals 21-35
years of age is 11.3 years. This average increases to
17.5 years for individuals 36 years of age and over. Table 18 illustrates these results.
Table 17. Incorrect Age Estimation Results (Harris County)
Estimated Age
Age at Death
Sex
Reported Race
25-40 years
41
F
White
40-60 years
62
F
White
Middle-aged adult
83
M
Asian
35-45 years
23
M
Hispanic
30-45 years
26
M
Hispanic
45-65 years
69
M
Black
50-65 years
67
M
White
Table 18. Age Estimation Results – Average Ages by Age Range
(Harris County)
Estimated Age Range
Average Number of Years in Each Range
25 years and under
8.5 years
26 years and over
16.4 years
21 -35 years
11.3 years
36 years and over
17.5 years
Overall for Sample
16 years
A total of eight age estimations for older individuals were recorded as a minimum
age (e.g. 55+). Two individuals were estimated to be adults, as the nature of the
recovered remains precluded more specific analysis. Since the age estimations for the
above individuals were recorded as a minimum age, they are considered separately, and
not included in the average age range calculations in table 18.
Pima County Office of the Medical Examiner
Of the 110 individuals analyzed in Pima County, age could be estimated in 108
cases with varying degrees of ranges. Antemortem data shows there were 14 (13%)
individuals 21 years of age and under, 50 (46%) individuals 22-35 years of age, 39 (36%)
individuals aged 35 and over, and five (5%) did not have antemortem data. The youngest
individual represented in the dataset was 14 years of age, and the oldest was 74 years of
age.
Age estimations were reported as age ranges where the unknown presumably fell within.
As with the previous offices, a variety of aging methods were used to estimate the diverse
population of deceased individuals including the Suchey-Brooks (Katz and Suchey, 1986;
1989) method of aging the pubic symphysis as well the methods associated with aging
the sternal end of the 4th rib (Iscan and Loth, 1984; 1986) when available. Of the 108
estimations that could be compared with antemortem data, 95 (88%) were correct and 13
(12%) were incorrect. For the 22 U.S. citizens, age was estimated correctly in 19 out of
22 cases (86%) and incorrectly in 3 (14%) cases. Table 19 shows the anthropologically
estimated age rage compared to the age at death among the incorrect estimations.
Among all of the 108 age estimations provided by anthropologists, 106 estimations
were presented as true age range (e.g. 30-45 years) and two estimations were presented
as a minimum age (e.g. 55+). In reference to age ranges, the overall
Table 19. Incorrect Age Estimation Results (Pima County)
Estimated Age
Age at Death
Sex
Reported Race or
Nationality
30-50 years
57
M
Mexican National
17-22 years
16
M
Mexican National
28-40 years
41
M
Mexican National
30-50 years
51
M
White/USA
28-40 years
25
M
Honduran National
35-50 years
34
M
Mexican National
25-40 years
24
M
Mexican National
30-50 years
27
F
Hispanic/USA
28-44 years
50
M
Mexican National
24-32 years
23
M
Costa Rican
National
30-50 years
29
M
Mexican National
40-66 years
72
M
Native
American/USA
30-50 years
51
M
Mexican National
average length of the age range for this sample is 15.9 years. The average age range for
individuals known to be 25 years of age and under is 7.5 years. The average age range
for individuals known to be 26 and over is 19.5 years. Additional calculations were
performed to determine the average age ranges for more specific age categories to
determine if the age ranges differ for younger, middle, or older individuals. The average
age range for individuals 21 years of age and under is 5.9 years. This average increases
to 14.9 years for individuals 22-35 years of age, and again the average increases to 22.1
years for individuals 36 years of age and over. Table 20 illustrates these results.
A total of two age estimations for older individuals were recorded as a minimum
age (e.g. 55+). Since the age estimations for these individuals were recorded as a
minimum age, they are considered separately, and not included in the average age range
calculations in Table 20.
Table 20. Age Estimation Results – Average Ages by Age Range
(Pima County)
Estimated Age Range
Average Number of Years in Each Range
25 years and under
7.5 years
26 years and over
19.5 years
21 years and under
5.9 years
22-35 years
14.9 years
36 years and over
22.1 years
Overall for Sample
15.9 years
Stature
New York City Office of Chief Medical Examiner
Stature was calculated for 39 out of the 47 individuals in the dataset. Similar to age,
stature was recorded as a range (in inches) rather than as a single number. All
calculations for stature were performed in FORDISC using long bone measurements.
The smallest stature range was reported as five inches and the largest stature range was
reported as eight inches. The average stature range was 6.2 inches.
Unfortunately, antemortem stature data is largely lacking in the dataset, as it was often
absent in the case files. This indicates that stature does not seem to be noteworthy or
useful to include with the other antemortem data when identifications are made. In all,
there were only 27 antemortem stature records. Due to a lack of appropriate skeletal
materials to conduct the analysis, three individuals with known antemortem statures were
not evaluated for stature. In all, stature comparisons were made for 24 out of the 47
individuals in the dataset. Comparisons were determined by whether or not the reported
stature fell within the stature range estimated by the anthropologist. Of the 24 individuals
available for this kind of analysis, 22 (92%) were correct and two (8%) were incorrect.
The incorrect estimations include an individual that was reported to be 77 inches and was
underestimated by 2.6 inches while the other was reported to be 72 inches and was
underestimated by 0.4 inches.
Harris County Institute of Forensic Sciences
Stature was calculated for 41 out of the 47 individuals in the dataset. Due to the
lack of appropriate skeletal materials stature was not conducted for six individuals. All
calculations for stature were performed in FORDISC using long bone measurements. The
smallest stature range was reported as 2.5 inches and the largest stature range was
reported as 16.9 inches. The average stature range was 5.2 inches. Similar to New York
City, antemortem stature data is severely lacking in the dataset, again, as it does not seem
to be noteworthy or useful to include as a part of the identification. In all, there were only
13 antemortem stature records; therefore stature comparisons were made for 13 out of
the 47 individuals in the dataset. Comparisons were determined by whether or not the
reported stature fell within the range estimated by the anthropologist. Of the 13 individuals
available for stature analysis, 11 (85%) were correct and two (15%) were incorrect. The
incorrect estimations include an individual that was reported as 71 inches that was
underestimated by two inches and individual that was reported as 68 inches tall and was
underestimated by four inches. However, this individual was measured at autopsy and
cadaveric stature was recorded as 64 inches which corresponds with the anthropological
stature range of 59.5 – 64.8 inches
Since stature analysis is population specific, further analysis of the relationship between
ancestry and stature was conducted to investigate whether incorrect estimations of
ancestry resulted in incorrect stature estimations. There were a total of
10 cases where ancestry was inaccurate, but only three cases had stature estimations
that could be compared with living statures. In two out of three cases, living stature fell
within the estimated age ranges.
Pima County Office of the Medical Examiner
Stature was calculated for 46 out of the 110 individuals in the dataset. Calculations
for stature among non-Admixed Native American individuals were performed in FORDISC
using long bone measurements. Stature calculations of individuals suspected to be UBCs
classified as Admixed Native American were primarily conducted using a method
developed by Sjovold, (1990), but FORDISC 3.0 was sometimes used. The smallest
stature range was reported as two inches and the largest stature range was reported as
8.4 inches. The average stature range was 3.7 inches, smaller than the ranges reported
by the offices in NYC and Harris County. The Sjovold (1990) method for stature estimation
yielded small stature ranges that directly contributed to the number of incorrect stature
estimations as antemortem stature fell mere inches outside the estimated ranges. Similar
to New York City and Harris County, antemortem stature data is largely lacking in the
dataset. In all, there were only 33 stature estimations that could be compared to reported
antemortem stature information. Comparisons were determined by whether or not the
reported stature fell within the stature range estimated by the anthropologist. Of the 33
individuals available for this analysis, 18 (55%) were correct and 15 (45%) were incorrect.
The problems with forensic stature outlined in chapter two that apply to the results from
New York City and Harris County are confounded by the additional problems associated
with identifying undocumented migrants. Of the 22 U.S. citizens, postmortem stature
estimations could be compared to antemortem living stature in 13 out of 22 cases (59%).
Of those, nine (69%) yielded correct stature estimations while four (31%) were incorrect.
Table 21 presents the data associated with the inaccurate stature estimations in Pima
County and shows that stature estimations largely underestimate the known statures of
the decedents in these cases. There were only two cases where the stature was
overestimated.
Table 21. Inaccurate Stature Estimation Results (Pima County)
Stature Estimation
Antemortem Stature
Difference
60.9 – 64.0 inches*
66 inches
- 2 inches
69.6 – 72.8 inches
66 inches
+ 3 inches
61.5 – 65.5 inches*
66 inches
- 0.5 inches
64.2 – 67.8 inches *
70 inches
- 2.2 inches
64.0 – 67.6 inches
68 inches
-0.4 inches
63.0 – 67.4 inches
70 inches
- 2.6 inches
60.0 – 63.6 inches
65 inches
- 1.4 inches
63.7 – 67.3 inches
68.5 inches
- 1.2 inches
62.5 – 66.1 inches
67 inches
-0.9 inches
65.2 – 68.8 inches
63 inches
+ 2.2 inches
60.9 – 62.9 inches
64 inches
- 1.1 inches
66.1 – 69.7 inches
70 inches
- 0.3 inches
59.5 – 63.1 inches
68 inches
- 4.9 inches
67.9 – 70.9 inches*
71 inches
- 0.4 inches
63.7 – 67.3 inches
68 inches
- 0.7 inches
*indicates U.S. citizens.
Considerations for Identifications
There are several variables that influence the generation of the biological profile and
subsequent identifications including, but not limited to, the percentage of skeletal
elements recovered for analysis, the presence of the cranium, the ability to extract and
successfully amplify DNA, and having access to antemortem radiographs for
comparisons. The cranium is essential to the estimation of ancestry and is also often
used in sex estimation. Additionally, without a cranium, comparison of dental radiographs
or frontal sinuses cannot be made. This section outlines the amount and type of skeletal
remains available for analysis as well as the identification methods used to resolve cases.
New York City Office of Chief Medical Examiner
Of the 47 cases available for analysis, the cranium was present in 40 (85%) of the
cases, while the remaining seven cases (15%) did not have a cranium. In addition to the
cranium, the percent of total remains recovered was recorded in four ranges, 125%, 26-
50%, 51-75%, and 76-100% and was determined by visual inspection of photographs and
descriptions within the reports. Table 22 shows the number of cases falling within these
categories.
A more complete anthropological assessment of the biological profile as well as
opportunities to compare antemortem radiographs increases with the number of skeletal
Table 22. Percent of Skeletal Remains Available for Analysis
(NYC)
Percent Available
Number of Cases
(out of 47)
Percentage of Total Cases
1 – 25%
6
13%
26 – 50%
3
6%
51 – 75%
2
4%
76 – 100%
36
77%
elements recovered. As noted in Table 22, 77% of the resolved cases in New York City
represent mostly complete sets of skeletal remains.
Since the biological profile generated from the anthropological analysis of remains is not
sufficient for positive identification, the methods used to make positive identifications were
recorded to discern any trends. Table 23 shows the number of cases in where different
identification methods were employed.
DNA was used to confirm identifications in 52% of cases. DNA comparison is
possible through the successful extraction and amplification of the decedents DNA. The
sample is compared to DNA known to have belonged to the decedent in life (such as from
a toothbrush or hairbrush) or from DNA provided by a biologically close family member.
Successful DNA comparisons can also be made if the decedent’s DNA is stored in the
Combined DNA Index System (CODIS).
The unknown category represents 27% of the cases at New York City. The
available records did not indicate how the decedents were eventually identified but of
those 13 unknown cases, DNA sampling was confirmed in five cases; three cases had
Table 23. Identification Method (NYC)
Identification
Method
Type
Number of Cases
(out of 47)
Percentage of Total
Cases
DNA
CODIS
4
9%
Comparison
20
43%
Radiographs
Medical
Records
2
4%
Dental
6
13%
Fingerprints
1
2%
Surgical Implants
1
2%
Circumstantial
Evidence
0
0%
Unknown
13
27%
circumstantial evidence associated with the remains, and one was confirmed to have had
dental charting.
Harris County Institute of Forensic Sciences
Of the 47 cases available for analysis, the cranium was present in 46 (98%) of the
cases, while the remaining case representing 2% of the dataset did not have a cranium.
In addition to the cranium, the percent of total remains recovered was recorded in four
ranges, 1-25%, 26-50%, 51-75%, and 76-100% and was determined by visual inspection
of photographs and descriptions within the reports. Table 24 shows the number of cases
falling within these categories and notes that 81% of the resolved cases in Harris County
represent mostly complete sets of skeletal remains.
Table 24. Percent of Skeletal Remains Available for Analysis
(Harris County)
Percent Available
Number of Cases
(Out of 47)
Percentage of Total Cases
1 – 25%
3
6%
26 – 50%
3
6%
51 – 75%
3
6%
76 – 100%
38
81%
As with New York City, the method for positive identification was recorded to discern any
technological trends. Table 25 shows the number of cases where different identification
methods were employed.
The unknown category represents only 6% of the cases in Harris County. The
available records did not indicate how the decedents were eventually identified, but of
those 15 unknown cases, a DNA sample was taken in one case and strong
circumstantial evidence associated with two cases was noted.
Pima County Office of the Medical Examiner
Of the 110 cases available for analysis, the cranium was present in 107 (97%) of
the cases, while the remaining three cases (3%) did not have a cranium. In addition to
the cranium, the percent of total remains recovered was recorded in four ranges, 125%,
26-50%, 51-75%, and 76-100% and was determined by descriptions within the reports.
Table 26 shows the number of cases falling within these categories and notes that 71%
of the resolved cases in Pima County represent mostly complete sets of skeletal remains.
Table 25. Identification Method (Harris County)
Identification
Method
Type
Number of Cases
(out of 47)
Percentage of Total
Cases
DNA
CODIS
0
0%
Comparison
18
17%
Radiographs
Medical
6
13%
Dental
8
17%
Fingerprints
8
17%
Surgical Implants
0
0%
Circumstantial
Evidence
4
9%
Unknown
3
6%
Similar to New York City and Harris County, data was recorded to discern trends
regarding how the decedent was positively identified. Table 27 shows the number of
cases where different identification methods were employed and notes that DNA
comparison was used to positively identify the majority of individuals in these cases.
Familial DNA comparisons are primarily used to identify the remains of undocumented
border crossers. The Pima County Medical Examiner’s Office and the Mexican Consulate
work cooperatively to obtain DNA samples from family members.
Summary of Resolved Cases for NYC, Harris County, and Pima
County Combined
Taken together, the accuracy of the biological profile can be summarized as
follows:
1) Sex was accurate in all of the resolved cases analyzed in this research.
Table 26. Percentage of Skeletal Remains Available for Analysis
(Pima County)
Percent Available
Number of Cases
(out of 110)
Percentage of Total Cases
1 – 25%
8
7%
26 – 50%
8
7%
51 – 75%
16
15%
76 – 100%
78
71%
Table 27. Identification Method (Pima County)
Identification
Method
Type
Number of Cases (out
of 110)
Percentage of Total
Cases
DNA
CODIS
5
4%
Comparison
70
64%
Radiographs
Medical
5
4%
Dental
8
7%
Fingerprints
12
11%
Surgical Implants
0
0%
Circumstantial
Evidence
(Includes personal
effects and
tattoos)
7
7%
Unknown
3
3%
1) Ancestry estimation was accurate in 176 out of 177 resolved cases (99%) where
ancestry estimations could be compared to antemortem descriptions.
a. Morphological assessment of ancestry was accurate in 176 out of 177 resolved
cases (99%)
FORDISC was accurate in 89 out of 130 resolved cases (68%) where FORDISC
results could be compared to antemortem descriptions. Thirty-
b. four out of the total 37 incorrect FORDISC estimations (92%) involved the
Hispanic category.
2) Age range estimations were accurate in 175 out of 190 resolved cases (92%)
where ranges used to estimate age could be compared to antemortem
descriptions.
3) Stature calculations were accurate in 51 out of 70 resolved cases (73%) where
stature estimations could be compared to antemortem descriptions.
4) The biological profiles of the U.S. citizens in Pima County show similar rates of
accuracy as compared to the biological reports of the U.S. citizens in New York
City and Harris County.
Vital Statistics for Unidentified Individuals
Statistics for unresolved cases for both New York City and Harris County are presented
together because there were 42 unresolved cases from New York City but only three
unresolved cases from Harris County. Statistics for Pima County are presented
separately, as there are 239 unresolved cases represented in the dataset that also
characterize a demographic consisting primarily of unidentified border crossers. In total,
there are 284 unresolved cases available for this examination.
The background chapter details several factors that may prohibit the successful
identification of human remains in the medicolegal system. The information provided here
serves to identify potential anthropological explanations only as to why these individuals
have yet to be identified. Before exploring these potential reasons, the demographics, as
estimated by the anthropologists, are presented first; a complete examination of these
results will be presented in the discussion chapter.
New York City Office of Chief Medical Examiner and Harris County Institute of
Forensic Sciences Combined
Biological Profile
The demographic information for 45 unresolved cases in NYC and Harris County
from 2005 to 2014 is presented here. Of these cases, there are a total of 24 (53%) males,
17 (38%) females; three (7%) of unknown sex, and one (2%) individual estimated as being
a “probable male.” Amelogenin results were available for 23 (51%) of the 45 original
cases. Amelogenin results showed that there were no incorrect estimations of sex by
forensic anthropologists; however, one individual whose sex could not be estimated was
identified as female by the amelogenin results. Since amelogenin information was not
reported in all cases, the estimations by
anthropologists in these cases serve as the biological sex statistics.
Statistics of ancestry are derived from anthropological estimation. Results indicate
that 11 (25%) of the individuals are estimated as white, three (6%) are estimated as black,
two (4%) are estimated as Hispanic, one (2%) is estimated as possible Hispanic, one
(2%) is estimated as Asian, six (14%) are estimated as admixed, six (14%) are estimated
as belonging to one race or possibly another, and two (4%) are estimated to be not white.
There were 13 (29%) individuals where ancestry was indeterminate or not estimated (12
had no skull). Table 28 shows the breakdown of ancestry and sex for the unidentified sets
of remains.
The sex and race demographics show a relatively even distribution of ancestries
among the unknown except that white males and females represent a slight majority over
other groups. Among the population of unresolved cases, the admixed and two or more
ancestries categories are also represented in large numbers.
FORDISC was used as a tool to help estimate ancestry in 28 (62%) cases.
FORDISC classified 11 individuals as white, five as black, one as Asian, two as
Hispanic, one as white and black, one as not white, one as American Indian and
Japanese, one as “Amerindian, Chinese, Hispanic and Japanese groups,” and five cases
yielded inconclusive results. Of the 11 individuals classified as white, the forensic
anthropologist estimated nine as white, while two were ultimately estimated as admixed.
Among the five cases classified as black, the forensic anthropologist ultimately estimated
two as black and three having varying degrees of admixture. The individuals classified in
the Asian or Amerindian groups were estimated by FORDISC as having a variety of
ancestry combinations.
The age ranges provided by the anthropologists are used to evaluate the age
distributions among the unresolved cases. There were 31 out of 45 cases that included
age ranges. The average age range of these individuals is 13.6 years.
There were two cases assigned age ranges where the skeletal elements were
represented by only the cranium or just the cranium and a few non-diagnostic skeletal
elements. The average estimated age of these cases, using cranial suture and dental
analyses, is 18.5 years. Among the unresolved cases, five individuals were estimated as
“adult,” one was estimated as “middle to old adult,” three were estimated as “young to
middle aged adult,” one was estimated as a “young adult” and five individuals were
estimated as a minimum age and over (e.g. 30+).
Stature was estimated in 36 (80%) of 45 cases. Of the remaining 9 (20%) cases,
two were soft tissue cases where stature were measured by the medical examiner, one
was a juvenile, and six did not have skeletal elements suitable to conduct stature
estimations.
Completeness of Remains
The total percentage of the body recovered in forensic contexts affects the evaluation of
the biological profile and affects the identification rate (Komar and Potter, 2007). When
fewer skeletal elements are available for study, there is an increased risk
Table 28. Ancestry and Sex Demographics – Unresolved Cases
(NYC and Harris County Combined)
Ancestry
Sex
Number of Cases
(Out of 45)
Percentage of Total
Cases
White
Male
5
11%
Female
6
13%
Black
Male
1
2%
Female
2
5%
Hispanic
Male
1
2%
Female
1
2%
Possibly Hispanic
Male
1
2%
Female
0
0%
Asian
Male
1
2%
Female
0
0%
Admixed
Male
5
11%
Female
1
2%
Two or More
Male
4
9%
Female
2
5%
Not White
Male
2
5%
Female
0
0%
Indeterminate
Male
5
11%
Female
5
11%
Indeterminate
3
7%
that important information about the decedent will be undiscovered. Of the 45 unresolved
cases in NYC and Harris County, 32 (71%) of the remains included a cranium or portions
of a cranium, while 13 (29%) did not. As described in the background chapter, the
absence of a cranium has deleterious effects on the anthropological estimation of sex and
ancestry. Additionally, the absence of dentition and/or incomplete or fragmentary cranial
remains makes positive identification with antemortem dental and frontal sinus
radiographs impossible. Table 29 shows the percentage of the body recovered describing
the same criteria used for the resolved cases. The data includes four ranges, 1-25%, 26-
50%, 51-75%, and 76-100% and was determined by skeletal inventories, visual
inspection of photographs, and descriptions of the remains provided within the reports.
The percentage of remains recovered for the unresolved cases is variable with the
majority, 22 (49%), having more than 75% of the total remains recovered. The second
largest category, at 17 (37%) cases, is the one that represents cases where the fewest
number of skeletal elements were available for analysis. Of these cases, four are
represented by only a cranium, portions of a cranium, or by a cranium and a small number
of non-diagnostic skeletal elements.
Table 29. Percentage of Skeletal Remains Available for Analysis –
Unresolved Cases (NYC and Harris County Combined)
Percent Recovered
Number of Cases
(Out of 45)
Percentage of Total Cases
1 – 25%
17
37%
26 – 50%
3
7%
51 – 75%
3
7%
76 – 100%
22
49%
Pima County Office of the Medical Examiner
Biological Profile
The demographic information for 239 unresolved cases in Pima County from
2010 to 2013 is presented here. Of these cases, there are a total of 194 (81%) males, 39
(15%) females, and six (3%) of unknown sex. Among those of unknown sex, one was
estimated as probable female, two were estimated as probable male, and three were
indeterminate. Amelogenin results were available for 173 (72%) of the 239 original cases.
Amelogenin results showed that there were six (3%) incorrect estimations of sex by
forensic anthropologists; four males were estimated as females, and two females were
estimated as males. All of these individuals are believed to be undocumented border
crossers. Each of the six cases were represented by less than
25% of the skeleton and only two cases were metrically conducive to sex analysis using
FORDISC. FORDISC accurately predicted the sex of these two individuals (male), but
the final sex estimation by the anthropologist was female. Since amelogenin information
was not reported in all cases, the estimations by anthropologists in these cases serve as
the basis for the biological sex statistics.
Statistics of ancestry in unresolved cases are derived from anthropological
estimation only. The demographic composition of the unknown cases in Pima County is
different than those of NYC and Harris County. Results indicate that only one (0.005%)
of the unresolved cases is estimated as white, zero (0%) are estimated as black, nine
(4%) are estimated as Hispanic, one (0.005%) is estimated as Asian, 78 (33%) are
estimated as Southwest Hispanic, 12 (5%) are estimated as probable Southwest
Hispanic, one (0.005%) is estimated as American Indian, and 30 (13%) were considered
indeterminate. There are a number of cases where the individual was considered
admixed. This is not to mean that the terms Hispanic and Southwest Hispanic used to
describe the groups above do no imply a level of admixture, just that the anthropologists
were more explicit in indicating that the individual displayed characteristics commonly
found in more than one population group.
The following terms reflect the language used by the anthropologists. Of the 239
unresolved cases 22 (9%) are estimated as Admixed Native American (ANA), 34 (14%)
are estimated as admixed American Indian and European, three (1%) are estimated as
admixed American Indian and African, one (0.005%) is estimated as admixed
Indigenous Mesoamerican, five (2%) are estimated as Southwest Hispanic and American
Indian, one (0.005%) is estimated as Hispanic and Amerindian mix, 14 (6%) are estimated
as Southwest Indian/Hispanic, and one (0.005%) is estimated as
Admixed Native American, possible African Admixture (Hispanic). Finally, there are some
cases where anthropologists indicated the possibility that an individual was either one
race or another. Of the 235 cases nine (4%) are estimated as Caucasian or
Southwest Hispanic, nine (4%) are estimated as American Indian or Southwest
Hispanic, two (1%) are estimated as American Indian or admixed American Indian and
European, one (0.005%) is estimated as Southwest Hispanic or African American, and
one (0.005%) is estimated as probable Southwest Hispanic or European. Table 30 shows
the distribution of ancestry and sex for these sets of remains.
The sex and ancestry demographics are vastly different than those of NYC and
Harris County. Results from Pima County show an uneven distribution of males and
female individuals, where males outnumber females considerably (194 males, 39
females). Ancestry estimation of these unidentified individuals reveals that a large
number are recorded as Hispanic and Admixed Native American individuals.
One unknown is estimated as white and none estimated as black. These
disparities likely reflect the demographics of the population among undocumented
migrants who cross the US and Mexico border.
FORDISC was used as a tool to help estimate ancestry in 137 (57%) of cases.
FORDISC classified 75 individuals as Hispanic, 15 as Guatemalan, 14 as Native
American (Amerindian) 12 as white, eight as Vietnamese, five as Chinese, five as black,
and three as Japanese. Of the 75 individuals classified as Hispanic by FORDISC, the
forensic anthropologist ultimately estimated 45 as Hispanic or Southwest Hispanic. The
remaining 30 cases were variations of admixture among Hispanic, Native American, and
European groups. None of the individuals classified by FORDISC as being
