Assignement introduction Obesity and Workplace Injury in Hazardous Occupations .pdf

Problem Statement

Considering the fact that more than one third of the adult population of the United

States is obese, in this study, I assessed how obesity affects workplace performance and

injuries (CDC, 2012). If not considered, this could increase the cost for healthcare by

negating the actual events for workplace absenteeism due to complications such as

diseases related to obesity or even increase workmen’s compensation cases due to injury.

According to the Occupational Safety and Health Administration (OSHA) Environmental

and Safety News, (2010), most high-risk occupations such as agriculture, construction,

and fishing were performed by younger workers 18-19 years of age, with fatality rates of

5.6 times greater for Hispanic workers compared to non- Hispanics, African Americans,

or European Americans (OSHA, 2010). In this study, I explored through, federal and

independent national databases, what factors cause loss of work and how it may be

related to obesity.

Although there is little published research about the relationship between obesity

and workplace injuries, the phenomenon was reviewed by the National Health Interview

Survey (1986-2002). The researchers suggested that the increase of obesity rates over

time among employed workers not inclusive of race or gender and the relationship

between obesity and occupational injury was not fully investigated with attention to

injury that can involve loss or restricted work time, loss of consciousness or even death

that require reporting to the Occupational Safety and Health Administration (Caban, et

al., 2005). Ethical concerns, or limited mechanisms for determining obesity related

injury, may be influenced by other listed factors such as chronic diseases such as

diabetes, hypertension, fatigue or sleepiness due to sleep apnea, ergonomics, or other

physical limitations that may confound reporting this information (Pollack & Cheskin,

2007; Pollack et al., 2007).

Obesity is a precursor to multiple health problems such as hypertension, diabetes,

cardiovascular diseases, and arthritis and can be confounders for injuries. Therefore,

these were eliminated in the final analysis utilizing data only for obesity as a major

contributing factor for workplace injuries. This information is especially relevant for the

Hispanic/Latino population who tend to work in occupations that are more hazardous and

have the higher rate of obesity as compared to non-Hispanic Whites working in the same

occupations (Bureau of Labor Statistics [BLS], 2013). The data available may not be

inclusive for this population. There are several reasons why there is under reporting of

workplace injuries in this population. One such reason may be due to the fact that many

workers are undocumented and fear for their jobs or if not illegal or undocumented have

less understanding of their workplace rights (Vega, et al, 2009). Many companies list

injuries as first aid to avoid workers’ compensation cases and probable increase in their

insurance rates (BLS, 2013).

Nature of the Study

In this secondary comparative cross-sectional study, I analyzed existing databases

for comparison of workplace injuries and a possible relationship to obesity. I used

resources that included the Bureau of Labor Statistics (BLS), and the Workplace Data

and Statistics Gateway database provided by CDC INFO (CDC, 2012). The operational

methods for determining significance included obesity as the independent variable,

workplace injury as the dependent variable, hazardous workplace as the moderating

variable, and age, gender, race/ethnicity, socioeconomic level, level of education, and

cultural context as the mediating variables.

The purpose of this analysis was to determine the association between BMI and

injuries in hazardous work places that require special training to avoid injury. These

databases included histories of pre-employment physical examinations that included

diagnostic procedures such as pulmonary function and strength analysis, laboratory tests

for gathering information for health status-eliminated confounders such as diabetes,

hypertension or other chronic health conditions that may cause workplace injury.

The survey databases that I used for analysis had a focus on overweight and

obesity, health risk factors, and exposure to hazardous workplace conditions. I conducted

an evaluation of obesity related to workplace injuries, and hazardous occupations to

determine whether there were correlations with the Behavioral Risk Factor Surveillance

System (BRFSS), Occupational Safety and Health Administration databases, the Bureau

of Labor Statistics (BLS), NIOSH, the CDC, and other databases associated with the

Department of Health and Human Services (HHS, 2012). I did a review for the

investigation of the health and safety rules as applied to hazardous work conditions was

considered from results of the preemployment examinations established by NHANES,

2012. These included BMI measured by licensed medical providers in accordance with

the standards of the NIH, blood analysis was done to rule out metabolic diseases such as

diabetes, drug and alcohol screening, as well as strength testing, and pulmonary functions

in accordance with OSHA regulations for the most hazardous occupations. The surveys

that I included in my study consisted of questions about health and safety factors

combined into four demands of work: time, physical, mental, or interpersonal aspects

related to socioeconomic level, level of education, and cultural context as related to

expected output (Gates, Succop, Brehm, Gillespie, & Sommers, 2008). I will discuss

these in greater detail in Chapter 3.

Research Questions and Hypotheses

Research Question 1: Does obesity have a direct influence on injury?

: Obesity has no direct influence on injuries in a hazardous workplace.

: Obesity does have a direct influence on injuries in a hazardous workplace.

Research Question 2: Is cultural context a major factor as related to

race/ethnicity’s interpretation of

obesity that may contribute to workplace injury?

Cultural interpretation related to obesity determined by race/ethnicity has no

effect on workplace injury.

: Cultural interpretation related to obesity determined by race/ethnicity has an

effect on workplace injury.

Purpose of the Study

The purpose of this secondary, comparative cross-sectional research study was to

assess whether high BMI (greater than 30) contributes to workplace injuries in hazardous

occupations for the Hispanic/Latino population.

Theoretical Framework

Schulte et al. (2008) theorized that there is potential relationship between the

prevalence of obesity in workers and occupation-related health problems by assessing the

concurrent association of occupational hazards with obesity. Schulte’s research team

developed a framework based on workplace injuries relating to its impact due to obesity.

Schulte and his research team tested their hypotheses by using the following theories: (a)

obesity affects occupational exposure-disease association, (b) workplace factors can be

precursors to obesity, and (c) obesity and occupational exposures still considered for

independent risk factors for disease and injury. Schulte, et al., (2008) utilized the

definition for obesity established by the criteria from the NIH where obesity is defined as

having a BMI of greater than 30 (as determined by the formula kg/m

) with kg indicating

weight and m measuring for height. Additionally, I did include cultural context as a

mediating variable, as different race/ethnic groups especially the Hispanic/Latino

population that work in hazardous occupations may have different perceptions of what is

considered overweight or obese that may be precursors that lead to injuries.

Operational Definitions

Body mass index (BMI): Is a formula used to calculate a person’s body mass ratio

measure based on his or her weight in kg and height in meters squared defined by the

World Health Organization in the 1980’s. This method is determined by mathematically

calculated by using weight in kilograms using the formula kg for weight and m for height

or kg/m

(NIH, 2010).

Hazard: potential to cause harm as described in Occupational and Environmental

Epidemiology (Oxford Dictionary, 2008).

Overweight: BMI between 25 and 29.9 measured by kg/m

(CDC Healthy People,

2010)

Obesity: BMI 30 or greater measured by kg/m

(CDC Healthy People, 2010).

Workplace injuries: includes acute sprains and strains, falls, burns, contusions,

abrasions, lacerations, eye injuries, fractures, amputations, blisters, foreign bodies,

punctures, bites or stings, inhalation of toxic chemicals, and other exposures not yet

recognized ( Pollack et al., 2007).

Assumptions

Over the past two decades, obesity has become an epidemic in the United States.

This epidemic is a significant precursor to many chronic diseases, although research has

yet to link this to increased workplace injury (CDC, 2007; McKenzie, Page, & Kotecki,

2008). There is little current scientific data that support the assumption that increased

BMI can contribute to increased injury in hazardous occupations; however, Pollack et al.

(2007) found in the medical records of eight aluminum-manufacturing plants that 85% of

injured workers were overweight or obese, yet the MMWR (2010) gives limited

information regarding these events. For most medical practitioners it is a known fact that

significant injuries are under-reported and listed as first aid. Medical providers could

realize the practice of underreporting although not documented.

Limitations

The limitations of this study depended upon available data regarding obesity as

either documented in databases or self-reported in NHANESIII and BRFSS, as well as

databases that provide recent statistics of workplace injuries in hazardous occupations

reported by the Bureau of Labor Statistics and CDC INFO. Sample size for this study

was dependent upon the availability of the most recent databases and what was available

for determining if indeed there is a relationship between obesity and increased workplace

injury.

Delimitations

This study was delimited to workers 18 to 65 years of age, with BMI’s greater

than 30. Only age, gender, race/ethnicity/culture, socioeconomics level, and level of

education of employees that has incurred an injury that requires medical care and not first

aid treatment are included. Injuries that incur workers’ compensation depending upon

disability, morbidity or mortality while currently working in hazardous occupations are

included (BLS, 2013).

Scope

The scope of this study included secondary analyses of all available and

appropriate pre-existing databases. Population-based survey data from the CDC, BLS, the

Behavioral Risk Factor Surveillance System, and NIOSH will be analyzed. The sampling

method included a stratified, age, gender, and race/ethnicity group ranging in age from

18-65. Inclusion criteria for the proposed analysis included participants with a BMI > 30

kg/m2 , as measured by a calibrated scale designated by the NIH (2010). The NHANES

and BRFSS databases included a complete physical examination with blood analysis for

exclusion criteria that include diabetes, thyroid malfunction, and screening for drug,

alcohol, and nicotine that may confound results.

Evaluation of variables was done to rule out that only obesity will be the

independent variable when compared to the self-reporting BRFSS telephone survey

results. Using databases that include statistics for the mediating variables such as age,

race/ethnicity/ gender, socioeconomic level, education level, comparison of data was

done for a possible relationship of obesity and workplace injuries especially for

Hispanic/Latino workers eliminating possible confounding variables such as pre-existing

chronic diseases such as diabetes, hypertension, or sleep apnea. These diseases are related

to obesity which would eliminate participants who have these medical problems from this

study. The final analysis was derived from federal and other pertinent databases by using

SPSS Version 21 for frequency, ANOVA, MANOVA, and linear regression statistical

analyses. I will also analyze the rate of injury as described by NIOSH incurred for these

comparative groups who are not obese. Non-obese workers will be compared by

frequency of injuries incurred during the same specified period for this study.

Significance of the Study

Obesity and overweight are epidemic in the United States (CDC, Healthy People,

2010). Establishing a statistical relationship between obesity and overweight and the

predilection for workplace injuries among this population, newer injury prevention

measures might be established, including health and nutrition education and promotion

for both employers and employees (Schulte et al., 2012). The social reform that might

accrue could reduce worker injuries, loss of productive workdays, and reduce the high

cost of workers’ compensation insurance and claims (Pollack et al., 2007).

If it is found through the databases surveys that Hispanic/Latino workers are

employed in disproportionate numbers in hazardous occupations, and that they are also

more likely to be obese or overweight, this information would have significance.

Understanding that Hispanic/Latino workers, most of whom are undocumented,

employed in hazardous occupations due to lack of higher education, socioeconomic

status, and lack of regular access for healthcare which may provide preventative

medicine, needing to provide a reasonable living for their families are willing to take this

risk. If social justice is to be included into this equation then there should be no doubt a

study such as this is current.

Transition and Summary

With increasing awareness of how obesity can predispose chronic health problems

such as diabetes, hypertension, and other entities that can interfere with workplace

performance little research has been specifically pertaining to obesity itself as a precursor

for workplace injuries in hazardous occupations. This study was based on available

databases, established federal, state and local labor statistics as well as other databases

that may provide insight by comparing these differences further discussed in chapter two.

Chapter 2: Literature Review

In 2000, the CDC published its Healthy People preliminary report, indicating an

increase in the number of overweight and obese adults in the United States. In the report,

the CDC noted an increase in workplace injuries and rising claims for workers’

compensation, especially in hazardous occupations. These claims had risen to $143

billion per year between 2007 and 2010 and continue to increase (CDC 2010). What is

also recognized by the scientific community, and studies that were undertaken to see if

Researchers have examined whether there was a correlation between increasing adult

obesity in the United States and workplace injuries in high-risk occupations (Shulte et al.,

2012).

Literature Search Strategies

I searched the databases Pub Med and MEDLINE, as well as the government

databases NIOSH, OSHA, CDC, EPA, and NIH. I also sought peer-reviewed, clinical

journal articles about obesity and its relationship to workplace injury in hazardous

occupations. Until the CDC recognized that obesity was becoming epidemic in the

United States, with at least 60% of the adult population overweight (CDC, 2010), there

had been little study of whether obesity might lead to increasing workplace injuries. This

increase was noticed first by workers’ compensation insurance agencies tracking trends

over 1999-2009 (BLS, 2013). The Johns Hopkins School of Public Health Center for

Injury Research and Policy was the impetus for further investigation by Pollack and

Cheskin, (2007).To find peer-reviewed articles on the topic, I used the search terms

obesity, overweight, Hispanic overweight, workplace injury and overweight hazardous

work and overweight, effects of overweight on workplace injuries, ethnic minorities and

obesity, increased body mass index, and similar terms that would lead to appropriate

sources of information. From the site that these terms led to, I pursued sub-elements such

as age, gender, race/ethnicity, socioeconomic level, and education.

Background

The purpose of this study was to analyze a relationship between obesity in the

Hispanic/Latino population regarding increased workplace injuries in hazardous

occupations through analysis of existing databases. Until the time the CDC recognized

that recognized that obesity was becoming an epidemic in the United States, with at least

60% of the adult population overweight (CDC, 2012), little study had been done

determining whether obesity might lead to increasing workplace injuries. Pollack &

Cheskin, 2002 first reviewed this hypothesis and later in 2007 in collaboration with the

Johns Hopkins School of public Health Center for Injury Research and policy (Pollack &

Cheskin, 2007). There has been little research about the relationship between obesity and

injury, increased adult obesity in the United States has been documented to be a precursor

for chronic diseases and problems related to increased body mass index (BMI; CDC,

2012). Until about 2007, there was scant research on the correlation of obesity with

workplace injuries. Obesity has become an epidemic in the United States, with

approximately 37.9% of the adult population, a condition that can lead to chronic health

problems that may interfere with the work place (Schulte, et al, 2012).

The incidents of obesity are increasing (Pollack & Cheskin, 2007). The NIH

reports the Hispanic/Latino population as 39.1% obese and 78.8% overweight (NIH,

2012). By reviewing recent data on occupational safety (NIOSH, 2012) the question is if

obesity has a direct influence on injury, or do diseases such as diabetes, hypertension,

cardiovascular disease, or arthritis associated with obesity influence and increase the

probability of accidents leading to injury especially in the Hispanic/Latino population or

is it not related to only this particular population of workers in hazardous occupations?

Other considerations and variables such as age, socioeconomic level, and level of

education or pre-existing medical conditions due to other than obesity may negate this

prediction since they can be considered as confounders.

Obesity

For the purposes of this study, obesity is defined as a BMI of 30 or greater (CDC

Healthy People, 2010). BMI is measured by weight in kilograms divided by height in

meters squared, the formula defined as kg/ m2 (NIH, 2010; Romero-Corral et al., 2008).

The relationship of obesity delineated by the CDC included the leading health indicators

associated with obesity as (a) amount of physical activity; (b) mental health; (c)

environmental quality; and (d) access to healthcare which considers overweight and

obesity as precursors to chronic diseases such as diabetes, cardiovascular disease,

hypertension, arthritis, and certain cancers (CDC, 2010).

According to Kline and Huff” study (2007), the diffusion of innovation and social

cognitive theory were compared to help support and possibly develop a theoretical

framework that includes health behaviors influenced by culture, ecology, race/ethnicity,

age and gender. Recognizing there are differences in perception of healthy lifestyles

based in cultural context this may help explain inequalities in healthcare intervention,

accessibility, socioeconomic level, level of education, and the importance of support

needed for community-based intervention and prevention programs (Kline & Huff,

2007). Each theory has significant factors and can be employed individually or as an

amalgam for mapping a healthier lifestyle and decrease in BMI (Bartholomew et al.,

2006; Kline & Huff, 2007). These are explained further under the section titled Theories

and Conceptual Framework in this proposal. Research for developing a framework that

can help explain how obesity can contribute to workplace injury was first explored by

Pollack and Cheskin (2007) and later redefined by Schulte, Wagner, Downes, and Miller

(2008), which is explained in detail later in this chapter.

There are significant economic consequences of obesity as defined by the CDC,

2012) which has estimated the total medical costs for obesity in the United States in 2008

dollars at $147 billion. Overweight and obesity, with their associated health problems,

significantly affect United States healthcare organizations through both direct and

indirect costs. Direct medical costs may include diagnostic, preventative, and treatments

incurred related to obesity. Indirect costs relate to morbidity and mortality. The morbidity

costs, according to the CDC, are defined as value of income lost from decreased

productivity, activities that are restricted, absenteeism from work, and bed days.

Premature death due to mortality costs are defined as value lost for future income (CDC,

2009). If workplace injuries related to overweight and obesity were validated this

information could be incorporated into developing a new paradigm for healthcare costs .

Injury

OSHA was created, during the administration of President Richard M. Nixon, to

ensure healthful conditions for the workforce by regulating standards for healthy

workplaces defined as:

injury or illness as any abnormal condition or disorder ranging from injuries that

include minor cuts to amputations. Illness would include both acute immediately

needing medical care and chronic conditions developed from long exposure to

toxins in the work environment [such as] asbestos. Further differentiation would

include traumatic injury such as concussion, fracture, burn, or laceration caused

by an external force which affects a specific part or function of the body with an

identifiable time and place where the injury incurred such as a single work shift.

Occupational illness or disease must meet reported conditions that do not include

traumatic injury but relate to a systemic infection such as respiratory illnesses

[and] gastrointestinal illnesses that are related to occupational exposure to

hazardous materials, repeated stress and strain or other exposures that result in

harm or loss of function. (OSHA, 2010)

Defining hazardous occupations can be based on the number of related job

fatalities in a given occupation, which are recorded as fatality frequencies. Another

method is based on fatality rates that take into account differing total numbers among

occupations. This includes occupations of nonfatal injuries and illnesses that are

measured by days away from work. These processes can result in complex statistical

methods not always relevant or accurate and are estimated as chances of occupational

injury or illness expressed as the cumulative number of workers injured in a specific

group when compared with the number of workers not injured in that group.

An example is that truck drivers have a ratio of 1:15 chance of serious injury in

any year. Also calculated is the number of median days to recuperate from an injury

(Toscano, 1997). The CDC (2010) estimated 3.9 million workers in private, state, and

local government have a nonfatal occupational injury or illness each year. Also noted was

that of those workers, 2 million were transferred, placed on work restrictions, or took

time away from work due to injury or illness. According to NIOSH (2012), 2.6 million

workers were treated in emergency departments for occupational injuries and illnesses,

with 110,000 of these workers hospitalized. Some of the most dangerous industries

include (a) agriculture, (b) commercial fishing, (c) aviation in Alaska, (d) construction,

(e) firefighting, (f) logging, and (g) commercial motor vehicle operation such as long

haul trucking (CDC, 2011).

Mechanism of injury is defined as how the injury occurred for occupational injury

and can be a wound or damage caused to the body from an event in the work

environment (OSHA, 2012). Conventional injuries are classified in several categories,

depending upon how the injury incurred. The most typical injuries include (a)

penetrating, (b) nonpenetrating or blunt, (c) blast overpressure, (d) thermal, and (e)

chemical. Other injuries included are crush and barotraumas.

Descriptions of Types of Injury Related to Hazardous Occupations

According to NIOSH (2012), workplace illnesses can also include:

1. Skin diseases or disorders caused by exposure to chemicals, plants or other

substances that produce an inflammation that includes contact dermatitis,

eczema, or rash friction blisters, and ulcers.

2. Poisonings that are identified by abnormal concentrations of toxic substances

in the blood, tissues, body fluids and breath caused by either adsorption or

ingestion into the body. Examples include lead, mercury, arsenic, and other

heavy metals as well as noxious gases such as hydrogen sulfide, organic

solvents such as benzene, carbon tetrachloride, insecticide sprays such as

parathion or lead arsenate, as well as exposure to formaldehyde.

3. Respiratory exposures, including breathing hazardous biological agents,

chemicals, gases, dust, fumes, vapors that are associated with diseases such as

pharyngitis, rhinitis, and acute congestion as well as chronic diseases such as

asbestosis, silicosis, pneumonitis, farmers’ lung, and tuberculosis,

4. Hearing loss induced by noise with change in the hearing threshold as

compared to the baseline audiogram considered to be an average of 10 db or

more at 2,000, 3,000, and 4,000 hertz in either ear where the employee’s total

hearing level is above the audiometric zero to be 25 or above which is also

averaged by 2,000, 3,000, and 4,000 hertz in the same ear(s).

5. Heat stress that includes exhaustion/stroke, freezing, frostbite, effects of non-

ionizing radiation that include welding flash burns, ultra-violet rays, and

lasers, ionizing radiation that includes exposure to isotopes, x-rays, and

radium. Other illnesses considered occupational include bloodborne

pathogenic diseases that include HIV, hepatitis B/C, brucellosis, tumors that

are malignant or non-malignant, histoplasmosis, and coccidioidomycosis.

(NIOSH, 2012)

Work safety programs were developed by the BLS in accordance with statistical

data compiled by NIOSH that relate to the industries reporting the most morbidity and

mortality. These include agricultural safety, anthropometry, commercial aviation,

commercial fishing, confined spaces, construction, electrical, highway work zones,

logging, motor vehicle, and machine safety. There are several case study research

programs directed by NIOSH to continue investigation for changes in environmental

hazards that delineate where further investigation is needed for change in exposure to

certain chemicals such as lead in companies that dispose of lead from car batteries and if

their employees are adequately prevented from exposure as well as other toxic chemicals

used in processing plants. Yearly physical exams are provided to determine blood lead

levels for these workers as well as pulmonary function tests to determine lung damage

(NIOSH, 2012). This is not always the case in states with lower EPA standards (OSHA,

2012). In this research I analyzed the databases from the Bureau of Labor, as well as

NIOSH, citing results for type of industry and hazardous exposures as well as the results

from types of yearly tests on the workers to determine if upper limits of exposure may

cause disease and help clarify what and why the statistical data can support the need to

change safety regulations. For example, in 2009, the EPA reported excessive levels of

lead exposure for the Exide Technologies Lead Smelters in Vernon California (EPA,

2009).

Workers’ Compensation based on Type of Injury

Workers’ compensation Insurance reporting is based on case characteristics that

are delineated for determining work-related injury or illness that include the following:

(a) nature of injury/illness determining principal physical characteristics of disability, and

(b) part of body that has been affected that is directly linked to the nature of injury or

illness that has been cited. These, for example, include sprains of back, finger cuts, or

carpal tunnel syndrome associated with the wrist. Others include the source of the injury

or illness such as object inflicting injury, substance, exposure or bodily motion that can

be responsible for directly inducing or inflicted a disabling condition. This is further

differentiated into bodily motion of an injured or ill worker such as pushing, pulling,

lifting heavy objects, exposure to a toxic substance, and exposure to flame/fire.

Event or exposure includes the manner in which the injury or illness occurs, was

produced, or inflicted by overexertion, falls, or lifting. Examples include median days

away from work. Measures used for summary for length of absences from work as

compared to days away from work of other cases to discern half cases that involved more

or less days as reported. For example, musculoskeletal disorders that include injury

related to soreness, pain, strain, sprain, tears, carpal tunnel syndrome, hernia, and when

reported this event leads to injury by bodily reaction that with demands for bending,

crawling, climbing, twisting, repetition, and over exertion will help to determine if the

injury is significant requiring medical care or a needing first aid intervention. This also

includes exposure to toxic substances that require medical care (NIOSH, 2012). The

importance of this information help determine cost indexes for worker’s compensation

cases. By determining from the data the most type of injury and in what industry helps

also to establish newer safety guidelines Leigh (2011) documented the economic burden

of occupational injury and illness in the United States and published the findings in the

Milbank Quarterly. Leigh stated that the costs of indirect and direct medical costs of

occupational injuries and illnesses to be at least as much as the cost for cancer. The

findings included the fact that workers’ compensation covers less than 25% of costs,

which illustrates that society shares the burden, and total costs are more than originally

assumed.

The last reported number of injuries in 2007 was estimated to be more than 5,600

fatal and 8,559,000 nonfatal at a cost of $6 billion. Illnesses were estimated at more than

53,000 fatal ($67 billion) and nearly 427,000 nonfatal ($12 billion). The total estimated

costs were approximately $250 billion that have been adjusted for inflation. This

information was compiled to show relative costs associated with occupational injuries

and illnesses for the purpose of allocating funds for future preventative measures,

including injuries and diseases (Leigh, 2011). The CDC (2010) reported that more than

4,500 United States workers have died from occupational injuries from 2007-2010 at the

time this report was published, and although it is difficult to enumerate, about 49,000

deaths annually are attributed to workplace illnesses.

The Patient Safety and Quality Improvement Act of 2005 was established as a

voluntary reporting system for improving and enhancing data to assess and resolve

patient safety and health care issues, including analysis of medical errors. This is

especially important for information collected and created during analysis and reporting

of patient safety events. The act became effective in 2009 (HHS, 2005).

Theoretical Foundation for Cultural Context and Obesity

In 1988, the Institute of Medicine recognized that the social environments that

people live in affect their lifestyles and behaviors that could influence the incidence of

illnesses in the respective populations. Conversely, engaging actively in community

health decision-making by self-intervention supported by the community would improve

health promotion, protection, and possible disease prevention (CDC, 1998). Furthermore,

the CDC reported that community norms may be held responsible for phenomena that

molds behaviors through modeling that contribute to weight gain and may be due to

differences in individual attitudes of acceptance for overweight and obesity and are

included as culture norms based in ethnicity/race, (CDC, 2007-2010; Hang, 2009).

Interventions that encompass all populations have not proven successful in the

past. In fact, the results suggest evidence of increasing obesity in the United States, with

the Get America Fit Foundation reporting almost 60% of the adult population now in this

category. The evidence has shown that no one strategy will reduce obesity (CDC INFO,

2012). Therefore, when selecting a theoretical model, consideration should include not

only cultural context, includes socioecological model that considers the demographics,

politics, and subcultures in a selected community (Center for Excellence, 2007).

The causative inequities in healthcare, intervention, education, and promotion are

associated with variables that cannot be modified or changed, such as age or gender,

race/ethnicity, as well as geographic location (Center for Excellence, 2007). What might

be altered is socioeconomic status, level of education, and decreased risk factors by

change in lifestyle, including health education and promotion.

Factors that contribute to success of programs advocating behavioral change

include the following:

1. Environmental: collaboration of groups that include favorable a political and

social climate, groups, and agencies seen as leaders.

2. Membership: cross-section of members that have mutual respect,

understanding, trust, and ability to compromise.

3. Process/structure: ownership for sharing process and outcome, collaboration of

decision-making groups, clarity of roles, guidelines, and ability to sustain

change.

4. Communication: frequent, open dialogue, with sharing of information through

informal or formal interaction

5. Purpose: clear goals are realistic to all partners with a shared vision.

6. Sustainable resources; in kind contributions, proficiency in grant writing,

mentoring capabilities. (Bartholomew, Parcel, Kok, & Gottlieb, 2006; CDC,

1998)

In an attempt to better understand where obesity and workplace injuries have a

relationship this study will include reference to two theoretical models chosen for

representation and discussion. They are the social cognitive theory and diffusion of

innovation and will be discussed separately to review how they may be utilized for

developing safety for workers through education as well for prevention of obesity. The

reason for these choices is based on objective descriptions of the health-related problems

caused by obesity especially related to Hispanic/Latino populations with noted high

incidence of overweight and obesity that work in hazardous occupations and that

workplace injuries may be exacerbated because of increased BMI. The social cognitive

theory of Bandura (1962) is based on the premise that three factors interact and

constantly influence each other: people, environment, and behavior (Miller & Dollard,

1941). Miller and Dollard (1941) suggested that modeling, developing self-control, and

self-efficacy through observation, self-monitoring, reinforcements, and possible rewards

for positive results could result in behavioral change. Learning emotional coping

responses through training in problem solving and stress management and reciprocal

determinism, a person could change through multiple choices influenced by environment

and behavioral changes (University of Twente, 2002).

Since most human behaviors are learned through observation or vicariously, Kline

and Huff (2007) suggested four constituents that govern this modeling: (a) attention and

perception, (b) retention, representation, and remembering, (c) using appropriate actions

production, and (d) motivation. Keeping in mind that ecology differs according to

cultural context is most likely the strongest influence of certain behavioral life-styles that

be counterproductive to health. That race/ethnicity, age, gender, socioeconomic level, and

level of education the community may not recognize the resulting consequences of long

term participation in unhealthy behaviors. Additionally it may be noted that the

Hispanic/Latino populations may have higher prevalence of occupational injuries due to

the fact that they work in more hazardous occupations coupled with the increased

incidence of overweight and obesity. Increasing modeling of beneficial behaviors may

help establish a new paradigm thereby decreasing workplace injuries. By influence

through verbal persuasion as utilized in the diffusion of innovation theory an intervention

by role modeling supported by leaders of a community that have social impact and may

help in mastering experiences that enhance positive behaviors while eliminating negative

ones which then allows for the development of higher self-esteem and self-efficacy

leading to the goal of achievement--in this case decrease of BMI (Kline & Huff, 2007).

Behaviors that are socially determined are complex and include health issues through

social support, participation and organized communication looking for impact, risk, and

compatibility to lifestyle. The major ecology is determining if these methods are

applicable in change and may alter behavioral change that may be sustained and help

prevent future injury in the workplace. The textbook by Kline and Huff (2007), Health

Promotion in Multicultural Populations: a Handbook for Practitioners and Students, was

designed to help practitioners working outside their own culture/ethnicity/race address

issues related to health problems and workplace-related injuries when their patient’s BMI

greater than 30 was supporting greater chance for injury in their designated workplace.

This may suggest a newer paradigm for evaluation significance for performance in a

designated hazardous workplace environment. Through the application of social

cognitive theory and behavioral modification that is culturally sensitive, although beyond

the scope of this study, may increase awareness of health providers of how cultural

context explains how, where and when obesity is acceptable to certain populations

without regard to consequences. This information may eliminate potential harm by denial

of employment in hazardous occupations to those physically unfit for overweight and

obese applicants.

The Diffusion of Innovation Theory (Rogers, 1995) may support change over a

period of time through certain channels in a social system. The rate of acceptance and

adoption of the intervention program methods are determined through characteristics

such as (a) relative advantage for change, (b) compatibility with cultural context, (c)

complexity of innovations for behavioral change, (d) trial ability for ease of change, and

(e) observation of the social system. If indeed certain populations that work in hazardous

occupations are less concerned about the impact of overweight and obesity and how it

may add to increased chance of injury it has a major impact that has not been addressed.

These concepts can be supported by further diffusion through modeling,

conversations, social media and community support throughout the target community

with active communication. The acceptance or rejection for the changing attitudes related

to healthy lifestyle and in this case decreasing obesity are not evaluated in an empirical

manner, but are subjective and determined by the fact that they have been accepted by

closely related peers, family, or community leaders. The rate of adoption for a new idea

and acceptance for these newer concepts is the key factor for gaining the knowledge base

for innovative changes. The innovator that has accepted these newer concepts and

recognized by the given community then becomes the leader for influencing the rest of

the community acting as a role model (Bartholomew et al., 2006; Rogers, 2003). The

portents of theories in this study that include the social cognitive theory (Bandura, 1962)

and the diffusion of innovation theory (Rogers, 1995) when applied to the increasing

healthcare problems associated with obesity have been found to have neither positive or

negative results and still are questioned for success or failure as noted in these examples.

It is noteworthy to address the need for future studies that incorporate cultural context to

interpretation of obesity and how it may increase probability for workplace injury in

hazardous occupations.

Social Cognitive Theory Relating to Obesity

Although this secondary study will explore the relationship between obesity and

workplace injury it is important to keep in mind how certain behavioral theories may

apply although not fully appreciated. Therefore I have included social cognitive theory

for possible future study for this variable and if in sub-context does indeed contribute to

the overall statistics. This study is related to adults but it is in childhood exposure to what

the environment one is raised there is a mindset developed for what is considered the

acceptable norm such as lifestyle and how it is interpreted. The major concepts of the

social cognitive theory include these perceptions that the environment also containing

outside physical influences, cultural, and economic factors may also have influence by

providing opportunities for social support through which self-efficacy, coping, self-

control and expectations to name a few can be possibly achieved through psychosocial

and behavioral change (Glantz, Rimer, & Lewis, 2002). The impact of developing a

cultural recognition of differences in the Hispanic/Latino population and their

interpretation of healthy weight and who work more than most other populations in

hazardous occupations has reason for further consideration. Additionally the value of

family for social support to form good eating habits and encouraging behaviors that

include exercise regularly as discussed by Gruber and Haideman (2009) supports the

influence family has on development of weight control and physical activities promoting

weight loss through the reciprocal nature of the adult-child relationship in influencing

healthy behaviors. Conversely not all families hold high standards for healthy eating. It

should be noted that at present there is no theory for involving family behavior. Gruber

and Haideman stressed that the parents play a significant role in behavioral change, and

as adults are considered in this study, indeed the direction that adults may not understand

is how families influence has been directed. The barriers that exist for change through

family health changes may be influenced by conflict with cultural context to dietary

changes. Programs that are community or school based that have a coordinated approach

to child health may be directed to the future workers that may have no choice but to work

in hazardous occupations. It is important to recognize that the major gap that is

formulated by loss of advantage for higher education and to compete for better

occupations have found more success when parents or other authority figures in the

family were involved. The major challenge is in influencing adoption of healthy

behaviors that include diet change and increased physical activity although not well

defined by the beliefs of all families who have a different perception of health based in

cultural context. The significance for this ultimately may be in actuality becoming

precursors for future choices for workplaces and may be accounted for how obesity could

actually becomes a variable that increases the chances for future health problems or

workplace injuries. If not addressed at the primary level of family and community

interpretation of obesity and its consequences indeed this has socioeconomic

consequences on healthcare. In analyzing databases on workplace injury and possible

relationship to obesity should include knowledge of background cultural context of the

Hispanic/Latino population analyzed for this study.

Marghani Reever (2008) suggested that a significant barrier to weight loss and

weight maintenance is the social environment especially in families. This includes what

value the particular society places on food and how easy access to food can sabotage a

weight reduction/control program. In addition, food may also be used to fulfill emotional

needs. Influence of the social environment, coupled with a lack of physical activity, has

been shown to be a significant barrier to behavioral change. Cognitive behavioral

treatment components integrated within a behavior change intervention should be

considered for the definition of success in attaining these goals. The treatment strategies

Reever outlined in the cognitive-behavioral interventions for obesity included (a)

motivational interviews, (b) self-monitoring, (c) stress management, (d) hypnosis, (e)

cognitive restructuring, and (f) social support. To help prevent relapse, the plan included

multiple acting-out situations that may sabotage the goals by implementing the newer

plan. The controversy of to what degree of cognition and behaviors contribute as

compared to genetics or metabolic factors is still up for debate (Reever,2009).

Annesi (2012) suggested that a change in exercise behaviors, psychosocial

factors, and behaviors of eating can be influenced by defining how these variables are

compared During a 26-week study with either a standard nutrition education paradigm (n

= 183) that included a cognitive-behavioral method emphasizing self-regulation and

calorie tracking and cognitive restructuring with eating cue awareness (n = 24). The

results showed that both methods of treatment included (a) self-efficacy, (b) self-

regulation, (c) mood, (d) self-regulation in eating, (e) increased consumption of fruits and

vegetables, (f) exercise, and (g) weight. Waist circumference was significantly greater in

the cognitive-behavioral nutrition state statistically and showing that changes in exercise,

eating related self-efficacy (R

= 0.40, 0.17) respectively along with self-regulation

changed with exercise and mood change showed results as (R

= 0.43, and 0.20)

respectively. The final analysis showed that self-efficacy for behavioral change and

exercise were found to be associated with an improvement in BMI (β = 0.53 and 0.68

respectively). Annesi proposed development of longitudinal testing of different and

newer approaches for behavioral treatments and to observe participants after the study for

significance in developing a healthier lifestyle through diet and exercise. Few studies

include minority populations sometimes due to lack of statistical data which supports the

need for a paradigm shift. Populations that are more vulnerable to obesity based on

race/ethnicity as well as socioeconomics that do not have the tools for needed changes is

the reason this study includes this variable.

Diffusion of Innovation Theory Relating to Obesity for Cultural Context

The behavioral theories are not integrated in most databases but if recognized may

indeed be an integral premise that contributes to how obesity is viewed by the

populations most likely to be injured in hazardous occupations and help determine how

most vulnerable populations are indeed in danger. The premise for Everett Rogers theory

of diffusion of innovation (1995) can be related to social change through changing

behaviors not only for individuals but also for communities. When introducing newer

concepts related to the complex issue of obesity it should be noted that consideration be

made for the inclusion of cultural context. Demographics can be a strong determining

factor of how food is valued. Geographic location may determine the availability for

accessing healthier dietary choices. Dissemination of information of healthier lifestyles

may need to be framed for the target population. By understanding how information is

disseminated in the community based on age, gender, race/ethnicity, and cultural values

could be the key actor for successful intervention programs.

The use of mobile e-health interventions for obesity supported by Tulfano and

Karras (2005) supported using the Internet and cellular phones for dissemination of

messages for modifying behavior. Conventional methods of information dissemination by

health providers was questioned when it was recognized that 57.2% of American

clinicians were not conveying the information (Tulfano & Karras, 2005). The prospectus

used in social ecological models whose efforts are to change health behavior are more

likely to be successful when they work within the sphere of community, family, schools,

workplaces, and religious organizations, especially in low income groups where there is a

need for increasing knowledge, skills, and self-efficacy by diffusing innovations from the

top down. Using accepted community leaders that present information that is spread by

word of mouth or social media has the potential for promoting behavioral changes and

decreasing the morbidity and mortality associated with obesity (Washington State

Department of Health, n.d.).

Dearing (2009) showed that dissemination for science needed to be designed on

evidence-based practices as a priority not only for internal validity but to increase

external validity for diffusion of innovation theory. Dearing also noted that it was

important to communicate why innovation works. Taking the basic concepts of the theory

of diffusion through innovation and by adapting those aspects to the environment for use

most likely the cultural context will shape the infrastructure since in essence the theory is

really a natural phenomenon that involves new ideas and concepts as well as patterns of

behavior in community settings.

Association of Injuries and Obesity

In Healthy People (2010), the CDC acknowledged the impact of obesity on

morbidity and mortality for Americans and noted that obesity ultimately increases the

cost of healthcare because of chronic diseases associated with the condition. However,

the CDC did not discuss whether obesity resulted in an increase for injury (CDC, 2010).

In a cross-sectional analysis Finkelstein, Chen, Prabhu, Trogdon, and Corso (2007)

quantified a relationship between BMI and rates of injuries needing medical treatment

caused by falls, motor vehicles, sports, and injuries that included strains/sprains, lower

extremity fractures, and dislocations with associated treatment costs. Combined datasets

from 1999-2000, 2000-2002, and 2001-2002 from the Medical Expenditure Panel Survey

resulted in a final analysis of 42,304 adults in the sample (Finkelstein et al., 2007).

Measures included injury rates by mechanism, nature of injury, and related treatment

costs. Estimating the odds of sustaining any of the injury types by mechanism or nature

for obesity or non-obesity with populations compared three different groups of obese

adults and a normal weight control group. The second set tested for a specific injury that

obese individuals sustained and incurred greater injury treatment costs showed that

slightly more than one in five adults did have an injury each year requiring medical

treatment with odds of injury: 15% (over weight) to 48% (Class 3 obesity). Without

significant explanation of how the authors summarized their results medical as well as

NIOSH cannot verify correlation between obesity and workplace injury from this study;

however, the authors of this study concluded that there was a clear association between

BMI and the probability of sustaining an injury that increased as obesity increased

(Finkelstein, Chen, Prabhu, Trogdon, & Corso, 2007). This information if indeed

significant may help to determine possible injury related to obesity as compared to other

variables such as lack of safety information or proper training in the workplace becomes

significant. More studies are needed to confirm these results. Without the variable of

cultural context related to race/ethnicity as to which populations are more vulnerable is

the reason for including this concept in this study (IOM, 2009).

Matter, Sinclair, Hostetler, and Xiang, (2007), compared characteristics of injuries

of obese and non-obese inpatients. Using discharge records from the 2002 Nationwide

Inpatient Sample of the Healthcare Cost and Use Project, Matter et al. analyzed records

compared with an International Classification of Diseases, Ninth Revision, Clinical

Modification injury diagnosis code (ICD-9-CM). Using records exclusively considering

obesity as a co-morbidity, proportionate injury ratios with 95% confidence intervals were

compared to characteristics of obese to non-obese persons hospitalized for injury. They

analyzed 160,707 discharge records for type and cause of injury requiring hospitalization

and found obesity status was significant, with p < 0.001 and more frequently caused by

falls, overexertion, and poisoning compared to non-obese persons (Matter et al., 2007).

Bouchard, Pickett, and Janssen (2010) considered older age as another moderating

variable besides obesity. The sample included 52,857 men and women 65 years or older,

from the Canadian Health Survey and was focused on weight, height, and type of injury

incurred. Although the participant studied were no longer actively employed, this study

provided a comparison of anatomical sites most likely involved in injuries in obese

individuals such as lower limbs. Bouchard et al. found that obese individuals had a higher

risk for sprains/strains at any anatomical site (odds ratio, 95% confidence interval: (men

1.48, 1.48-1.62. and women 1.14, 1.10-1.27) due to increasing age. An interesting

observation from this study was that obese individuals were less likely to sustain a

fracture for any anatomical location (men 0.56, 0.50—0.63), (women 0.66, 0.51—0.92).

The study concluded that obesity may provide some protection against fractures, but

higher odds for sprains and strains (Bouchard, Pickett, & Janssen, 2010).

Examining the effect of obesity on falls, injury and disability, Himes & Reynolds

(2012) developed a longitudinal population-based survey for the Five Waves of the

Health and Retirement Study (HRS), 1998 through 2006. ten thousand five hundred fifty

five respondents aged 65 and older were used in thirty one six hundred two time frames

recorded. In any two-year interval there were nine thousand six hundred twenty one

recorded falls. Of those, three thousand one hundred thirty one required medical

attention. Assistance in daily living (ADL) due to falls was two thousand one hundred

sixty two events for the 2-yeartime span studied.. Obesity was divided into three classes

based on BMI: class 1 (35.0-39.9 kg/m

), class 2 (greater or equal 40.0 kg/m

)

and

class

3, which were calculated from self-reports for weight and height along with doctor’s

diagnosis of diabetes mellitus, stroke, or arthritis. The greater risk of obesity appeared to

be associated with falling in older adults as well as the need for assistance in daily living.

Being underweight was not related to risk of falling. The odds ratios (OR) for falling risk

were 1.12 (95%) confidence interval (CI) = 1.01-1.24) for obesity Class 1, 1.26 (95% CI

= 1.05-1.51) for obesity Class 2, and 1.50 (95% CI = 1.21-1.86 for obesity Class 3).

There was a suggestion that Class 3 obesity may prevent fracture injuries. Himes and

Reynolds (2012) related that further investigations were needed to learn how obesity and

falls were correlated.

Obesity is a complex medical problem related to genetic precursors. Additionally

it is now recognized also a behavioral problem with the need for use of social cognitive

theory and diffusion of innovation by learning behavioral lifestyle changes that affect

healthy lifestyles. Although not until recently has these theories become prevalent due to

the recognition that cultural context affects of how lifestyle is a determining factor for

interpretation of or acceptance for obesity as the norm. Populations that participate in

hazardous workplaces need to be able to accomplish this goal through health

maintenance recognized through community based programs sensitive to their needs and

presented by a trusted gate keeper. Recognizing that both the social cognitive theory and

the diffusion of innovation theory include methods of behavioral change either through

self-evolving or by modeling the innovator/motivator by coupling these theories may be

incorporated as the framework for lifestyle changes that decrease the potential for

workplace injury due to increased BMI. Changes may be achieved through planning

health promotion programs by industry to develop environmental conditions affecting

both the at-risk population as well as the agents that control the workplace. Incorporating

community stakeholders as well as upper and middle management along with

reinforcement for positive behavioral changes can help determine attitude and outcome

expectations again repeated for clarity of need (Bartholomew, Parcel, Kok, & Gottlieb,

2006). An in-depth discussion of the social, psychological, genetic, and cultural aspect of

the complex nature of obesity is beyond the scope of this study but to the changing

demographics of the United States work force in hazardous occupations, mainly

Hispanic/Latinos and recognizing that lack of access, availability, and health education

for not only the populations discussed, but also for the management level there will be no

change in interpretation of what safety requires and decrease in injuries.

Obesity and Workplace Injuries in Hazardous Occupations

Recognizing differences in cultural context, biological, and behavioral aspects as

precursors for obesity, a researcher would involve the community studied as well as

individual responses (Baranowski et al., 2009). Pollack and Cheskin (2007) used peer

reviewed literature for meta-analysis beginning January 1980 through 2005 from which

12 studies for risk of injury of obese vs. non-obese employees were examined after the

authors followed up with their own cohort study that included eight industrial plants

located in the East, Midwest and South. Pollack and Cheskin’s results showed a slight

increase in risk of injury for obese workers compared to non-obese workers, but results

were not statistically significant. The results from this study had limited exploration of

mechanisms of injury such as lifting heavy equipment, pushing or pulling heavy

equipment, and falls for obesity-related injuries as related to non-obese injuries which

then suggested that further exploration was needed relationship of increased injuries due

to obesity. Increased fatigue or sleepiness, ergonomics, chronic diseases, and physical

limitations were the most hypothesized for increased workplace injuries. The conclusion

was that further studies were needed for evaluating how obesity influences workplace

injury. Included were the needed to explore environmental as well as socio-cultural risk

factors about weight.

According to Schulte, Wagner, Downes, and Miller (2008), suggest the

framework for determination of obesity and workplace hazards be developed by

including several concepts, while seemingly independent, may together be related to

injuries. These include:

• Relationship between occupational hazards and obesity

• The impact of occupational morbidity, mortality, and obesity on workplace

absence, which incurs decreased productivity, disability and increases

healthcare costs

• Workplace preventative programs

• Comprehensive healthcare promotion for workers

• Recognizing and instituting ethical, legal and social issues associated with the

framework (Schulte, et al., 2008).

Huang, Drewnowski, Kumanyika, and Glass (2009) used a systems-oriented approach to

learn the multiple factors and levels associated with the affects of obesity. Included were

“the biological mechanisms of differing metabolisms, cultural context of interpretation of

obesity, economics as it functioned in socio-environmental groups, and behavioral

aspects as variables for interpreting the concept of obesity and how it interacts with

lifestyle” (Huang, et al., 2009, p.1). There are significant gaps in the training of medical

providers as well as management of workplaces that take into consideration the cultural

context of how obesity and injury are related.

Literature Review Related to Key Variables, Relationships, and Contrasts

A study conducted at the Johns Hopkins School of Public Health Center for Injury

Research and Policy was published in the American Journal of Epidemiology. It included

statistics for 7,690 workers at eight manufacturing companies using a cohort study to

examine the distribution of odds of occupational injury between January 1, 2002, and

December 31, 2004. The results showed that more than 28% of injuries occurred among

employees classified as overweight, with 30% in the obese categories I and II (BMI 30 -

34.9 Class I; BMI 35-39.9 Class II) and 34% in category III (BMI > / = 40)as defined by

NIH (NIH, 2012). The severely obese had BMIs greater than 40 that accounted for the

most hand and wrist injuries, 22%, compared to other injuries in other weight categories.

Leg and knee injuries accounted for 10% in the obese category III as compared to 7% of

other workers classified as overweight (Pollack et al., 2007). This information was

extracted from annual physical medical examinations where the BMI was categorized for

29% of employees (n = 2,221). The odds ratio of injury relating to obesity continues to be

debated.

Schulte et al. (2007) used a retrospective, cohort study that obesity may be a co-

risk factor for occupational asthma and cardiovascular diseases, and modified response to

stress and immune response to chemical exposures and disease from occupational

neurotoxins. Schulte et al., developed five conceptual models of interrelationships of

work and obesity that highlighted the ethical, legal, and social issues related to the role of

obesity and occupational health and safety. Schulte et al., recommended (a) an

investigation into the relationship between occupational hazards and obesity; (b) an

exploration of the impact of occupational morbidity and mortality in workplace absence

due to obesity; (c) an assessment of the workplace for preventative programs; (d)

promotion of a comprehensive approach to workers health; and (e) studying legal, ethical

and social issues associated within the framework. They concluded there was growing

evidence of a relationship of obesity to workplace injury and reiterated the need for

further study of associated ethical and social problems.

In another study, Ostbye, Dement, and Krause (2007) attempted to correlate BMI

to numbers and types of workers’ compensation claims associated with costs and

workdays lost using a retrospective cohort study. Participants were 11,728 health care

and university employees (34,858 full-time equivalents with at least one health risk

appraisal between January 1, 1997, and December 31, 2004). Stratified main measures

were based on workers’ compensation claims, associated costs, employment duration,

lost workdays calculated by BMI, sex, age, race/ethnicity, and smoking status. Body parts

affected, as well as the nature of injury, and cause of illness or injury were also

investigated. Using a Poisson multivariate regression model, Ostbye et al. examined BMI

effects, controlling for demographics and work-related variables and found a clear linear

relationship between BMI and rate of claims compared to appropriate weight employees

and the effect on lost workdays. Employees in obesity class III (BMI greater than40) had

11.65 claims per 100 full time employees (FTE). Recommended-weight employees had

5.80. The effects of lost workdays were 183.63 versus 14.19 workdays per 100 FTE.

Medical claims costs were $51,0191 vs. $7,503 per 100 FTE, while indemnity claims

costs of 59%, 196 versus %5396 per 100 FTE. The claims most strongly related to BMI

were lower extremity, wrist or hand, and back with the nature of illness related to

inflammation, sprain/strain, contusion or bruise, falls, slips, lifting or exertion. They also

noted types of injuries and injured body parts and conclude that the combination of

obesity and high-risk occupations was particularly detrimental.

A marginally significant association between extreme obesity and elevated risk

for injuries was observed by Xiang, Smith, Wilkins, Chen, Hostetier, and Stallones

(2005) comparing obese to non-obese workers through the Center for Injury Research

and Policy at Columbus Children’s Institute and Children’s Hospital at Ohio State

University. The study of adults 18 years or older took place from January 1999 to

October 2000 and was based on BMI and injuries incurred compared to obese and non-

obese respondents. Xiang et al. (2005) used a multivariate logistic regression study that

controlled for confounding demographics. An estimated 7% of underweight individuals

reported injuries (BMI < 18.5). Injuries by gender was reported as 26% of men and

21.7% of women with BMI > or = 35.0 reported injuries. The odds ratio of individuals

with a BMI > or + 35.0 was 2.00 (95% confidence interval = 1.07-3.74, p < 0.05)

controlling for gender, age, education, marital status, family income status, and residence

area.

Ford, Hegmann, White, and Holmes (2005) reported the tendency towards

meniscal tears and obesity. Using surgical case data from 1996-2000 from electronic

databases of two large Utah hospitals, they found significant associations between BMI

and meniscal surgeries in both genders of obese and overweight adults.

An article in Insurance Networking News reported tracking trends over a 10-year

period from 1999-2009 by analyzing the differences between obesity and nonobese

claims for workplace injuries, types of injuries, and treatment patterns for more than

7,000 claimants with obesity as a secondary diagnosis compared to 20,000 claims of

identical characteristics that included gender, industry identity, state, age and no obesity.

Dramatically higher medical costs associated with these claims to be types and nature of

injuries sustained by obese or morbidly obese U.S. workers that resulted in permanent

disabilities.

Caban, Lee, Fleming, Gormez-Martin, Le Blanc, and Pitman (2002) analyzed the

relationship of obesity and occupation using the National Health Survey through self-

reported weight and height reported annually for U.S. workers 18 years or older from

1986-1995 and 1997-2002 (Caban et al., 2002). Occupation, race, and gender-specific

rates of obesity based on BMI of > 30.0 kg/m2 were calculated by pooling the data from

both surveys (n > 600,000), along with annual occupation-specific prevalence rates and

their time trends. The results showed obesity increased over time regardless of race or

gender, with a yearly increase of 0.61% (+ /- 0.4) during 1998-1995 to 0.95% (+ /-11)

during 1997-2002 with pooled obesity prevalence rates highest in motor vehicle operators

(31.7%) in men and (31.0%) in women. The conclusion was there was a need for weight

loss prevention programs.

Gates, Succop, Brehm, Gillespie, and Sommers (2008) published a detailed

procedure in which they correlated obesity and lost work days as well as how BMI

affected workplace productivity using randomly selected manufacturing employees (n =

341) that were assed through height/weight measurements, demographic surveys, wage

data, and the Work Limitations Questionnaire based on four dimensions of productivity.

Using ANOVA and ANCOVA, they computed identity of productivity based on BMI.

Gates et al. (2008) showed moderately or extremely obese workers with BMI > or = 35

experienced the greatest health-related work limitations, especially those associated with

physical demands. The results showed workers with these limitations experienced a 4.2%

health-related loss in productivity, greater than 1.18% than other employees which

equated to an additional $506 annual productivity loss per worker. Gates et al. concluded

there was a relationship between BMI and absenteeism as a threshold effect that showed

extremely or moderately obese workers were significantly less productive than those who

were mildly obese.

To date, studies are still limited, but the increasing rate of obesity in the United

States indicates the problem exists. According to the CDC, WHO and Healthy People

2010, the average work days lost to injuries can be at least 6 weeks to a year and may

even end the ability to work (CDC, Healthy People 2010). When multiplied by the cost to

replace an injured worker, which slows down production through the training of a new

employee, and the medical expense incurred for treatments of the injured worker, this is a

significant issue for a company (NIH, 2007; CDC, Healthy People 2010).

Most high risk occupations, as defined by the Labor Commissioner of

Occupations, are those that present a clear and significant life-threatening danger, require

exercise of discriminating judgment or care (Department of Labor, Labor Commissioner

of Occupations, n.d.). These occupations are usually staffed by younger workers, as

reported by the OSHA Environmental and Safety News (2010), which also stated that the

fatality rate for younger Hispanic workers is significantly higher at 5.6 times more than

non-Hispanic, European American, or African American workers. If this population is

overweight or obese, that fact may contribute to workplace injury or mortality (CDC,

Healthy People, 2010; OSHA: EHS Today, 2010). According to the CDC, three reasons

may for these phenomena may be differences in behavior that contribute to weight gain,

differences in individual attitudes towards overweight or obesity, and cultural norms

(CDC, 2010; Huang, 2009; NIH 2007). Studies of obesity and workplace injuries have

been limited because it may have larger implications for discrimination (Ostbye et al.,

2007; Pollack et al., 2007).

The American epidemiological approach to work place injury control was begun

by William Haddon Jr. MD, MPH, then president of the Insurance Institute for Highway

Safety (Haddon, 1990). Newer studies are postulating increased disability, morbidity, and

mortality related to injuries incurred by obese workers (Caban et al., 2005; Ostbye et al.,

2009; Pollack et al., 2007; Schulte et al., 2007, 2008).What has been recognized is

improper ergonomic job design, a factor that accounted for about one third of costs for

compensation of state workers according to OSHA (policy almanac.org, 2002).

Additionally, underreporting is significant not only for treatment and rehabilitations of

workers, but also for accurate gathering of epidemiological data and planning

interventions (U.S. House of Representatives, 2008).

Sorteriades, Hauser, Kawachi, Critintiani, and Kales (2008) examined the

association of obesity and risk of 358 firefighters in Massachusetts for a statewide

medical surveillance program in a prospective cohort study to determine utilizing a

multivariable-adjusted Cox proportional hazard model for associated BMI with disability.

The results showed that those with BMIs < 27.2 had a 5% less events of injury than those

with BMIs equal or greater than 30.2. There was a significant dose-response relationship

of risk within the highest categories of BMI of 60%-90% risk of job disability compared

to the lowest or normal weight categories (Soteriades et al., 2008).

Janssen, Bacon, and Pickett (2011), a Canadian research team developed a

premise about the relationship of obesity and occupational injury for the Canadian

workforce that incorporated a biophysical framework for addressing this hypothesis.

Janssen et al. determined that there had been a shortcoming in theory development in

previous studies that linked obesity to workplace injury due to “limitation of cross-

sectional studies, small sample sizes, a narrow breadth of the workplaces studied, and

poor recognition of different injury types and locations” (Janssen, et al., 2011, p.1).

Theories about the relationship of obesity and increased risk for occupational injuries is

based on existing evidence and risk factors for unintentional injuries and related to

physical attributes that could contribute to the development of a biophysical framework.

The study population reported through the National Population Health Survey from the

1998-2000 cycles consisted of a longitudinal analysis examining how BMI status in 1998

predicted reports of occupational injury in the 2000 work population.

In addition, Janssen et al. (2011) used the number of risk factors for injuries,

increased use of psychotropic medications, and altered gait and balance which could

contribute to falls, sleep apnea, increased extremity friction, and fatigue. What they noted

also was that increased BMI may prevent bone fractures because excess fat may cushion

falls. However, risk factors outweigh the benefits of increased BMI. The confounding

variables included in the analysis were gender, age, occupation category, race/ethnicity,

smoker or not, alcohol use, income, education, and pre-existing medical conditions.

Occupations were stratified as sedentary or moderately active based on a generalized list

of 25 groups described by the 1991 Standard Occupational Classification. Race/ethnicity

was divided into white and other; alcohol intake was calculated from the number of

drinks for the week prior to the interview. Nondrinkers had zero consumption, moderate

drinkers had one to seven drinks per week, and heavy drinkers had eight or more drinks

per week. Participants were either current smokers at the time of the survey or former

smokers if they smoked more than 100 cigarettes in their lives. Co-morbidities of pre-

existing medical conditions were found to be too low for consideration of modeling

process. Association between BMI and occupational injuries were categorized as minor,

moderate or serious, and location, using analysis adjusted for age, sex, smoking, alcohol,

race/ethnicity, income, education, and type of occupation. Analyses were included

according to gender and age groups (< 40 versus > / = 40).

The population attributable risk factor (PAR) was to detect effect size (OR) of 1.5

or greater for obese or normal weight statistics that were estimated for obesity prevalence

of the Canadian workforce and risk of occupation (Janssen et al., 2011). More than half

of the participants were male, less than 40 years of age, employed in sedentary

occupations, and overweight or obese. Strains/sprains were the most common injury

reported, usually of the lower limb related to obesity (1.5 fold increase in adjusted

relative odds of serious occupational injury) due to some accidental incident reported

within the first 12 months of completion of the survey.

The PAR estimates in the Janssen et al. (2011) study, calculated based on adjusted

risk estimates and observed prevalence of obesity, showed 8% for any occupational

injury and 10% for serious injury that included the entire cohort. Further evaluation for

PAR estimates were female worker for each type of injury listed = 16%, and when

compared to sedentary workers equal to or greater than 40 years of age = 19% and 15%

respectively. What Janssen et al. reported as primary findings from this national Canadian

study was that obese workers experienced risks for injury that were approximately 40-

49% higher than cohorts of normal weight. This was more evident in women more than

40 years of age as well as sedentary workers.

Summary of Most Recent Data of Obesity’s Relationship to Workplace Injuries

The Janssen et al. (2011) findings are the most recent and significant to date.

Most high-risk occupations in the United States are performed by younger workers as

reported by the OSHA Environmental Health and Safety News (2010), which also stated

that the fatality rate for younger Hispanic male workers was significantly higher at 5.6

times than that of non-Hispanic, white, or African American workers. It has also been

documented by CDC Healthy People 2010 that Hispanic/Latino populations suffer from a

greater proportion of overweight and obesity, which could be a contributing variable for

my proposed study. obesity in the United States and the possible increased injuries in

high risk occupations.

Other factors may contribute to lack of sufficient data, including the possibility of

under-reporting of workplace injuries by undocumented workers for risk of losing their

jobs or deportation (Occupational Health and Safety Administration, 2012). Another

factor announced by the CDC is a newer method for measuring overweight and obesity

instead of the BMI. This newer ratio is a waist to height ratio since the waist

circumference is more accurate for determining overweight/obesity if the ratio is greater

than one half the height (CDC, 2012).

The question to ask is whether reports of workplace injuries are reliable and whether

there has been sufficient screening based on physical ability required for the occupation.

The major impedance appears to be influenced more by economics than public health

regarding whether research will be done. In addition, thorough research will reveal other

factors, which will opens new avenues for multiple studies, not only for quantitative

values, but also for qualitative aspects such as how different cultures perceive what

obesity is. In Chapter 3 I will describe the methodology I used to conduct this study.

Chapter 3: Research Method

Introduction

Although the CDC Healthy People (2010) declared obesity at epidemic

proportions in the United States for over 33.9% of the adult population, there have been

few peer-reviewed studies linking this factor to increased workplace injuries in hazardous

occupations, particularly for ethnic minority populations (Huang, Drewnowski,

Kumanyika, & Glass, 2009; Ostbye, Derment, & Krause, 2007; Schulte, Wagner,

Downees, & Miller, 2008; Schulte, Wagner, Ostry, Blanciforti, et al., 2007; Soteriades.

Hauser, Kawqchi, Christiani, & Klaes, 2008). In my research I assessed whether obesity

increases workplace injury in hazardous occupations. I also explored whether obesity in

lower socioeconomic levels, lower levels of education including the Hispanic/Latino

population that tend to work in hazardous occupations are more prone to these events.

Databases Explored

Federal and state databases that I used included CDC INFO, which included the

database links for NHANES and BRFSS to clarify obesity status and including NIOSH

for determination of injury. The Bureau of Labor Statistics was also analyzed for the

most recent data available for workplace injuries as well. These resources were used to

help determine if there is a correlation between obesity and workplace injuries and

included obesity as the independent variable, and the dependent/moderating variable for

injury in hazardous workplaces such as manufacturing, fishing, transportation as a few

examples using inclusive records of injury based on obesity only eliminating mediating

variables such as obesity with pre-existing medical problems that may be confounders.

Proposed Methodology

This was a secondary analysis of existing databases to determine if there is a

relationship between obesity and increased injury in high risk occupations. In this way,

direct measurement of BMI as well as waist ratio to height, number and types of injuries

was used to determine if indeed they correlated to increased overweight and obesity and

injury. Overweight/obesity was the independent variable, and workplace injury was the

dependent variable. Moderating variables such as gender, age, occupation, education,

economic status, and race/ethnicity, culture may influence injury instead of obesity. The

moderating variables were considered in the cultural context of the participants the least

explored venue regarding what is considered to be obesity. The importance of including

cultural context is based in social justice. That is to say that most hazardous workplace

occupations employ persons that have lower levels of education and are from

socioeconomically deprived communities willing to chance injury for the better pay

afforded to them with accepting the risk If injured, they receive minimal care or

dismissed from their work. The Hispanic/Latino population may also come from

neighborhoods that lack access to nutritious foods at reasonable prices or not at all. The

target population was participants 18 to 65 years of age with BMI 30 or greater (defined

as overweight or obese).

The sample size was based on recent available data for each of the variables listed

above for participants employed that have BMI 30 or greater and considered obese,

working in high risk occupations. The BRFSS database and the NHANES database was

used to compared overweight and obese participants with normal weight participants and

type of injury incurred during the same time based on available data from the

aforementioned databases. Frequency of relationship between obesity and workplace

injury could only be defined by descriptive statistical methods for determining

percentages of incurred injury. There are no other statistical methods utilized in this study

since there were no available databases for level of education, or cultural-context related

to either the independent or dependent variables from the Bureau of Labor Statistics or

other databases -available from the CDC for the possible relationship of obesity to injury

(BRFSS, 2012; BLS, 2013, CDC, 2012).

Research Questions and Hypotheses

Research Question 1: Does obesity have a direct influence on injury?

: Obesity has no direct influence on injuries in a hazardous workplace.

: Obesity does have a direct influence on injuries in a hazardous workplace

Research Question 2: Is culture as related to race/ethnicity interpretation of

obesity contribute to workplace injury?

Cultural interpretation related to race/ethnicity has no effect on workplace

injury

Cultural interpretation related to race/ethnicity has an effect on workplace

injury

Research Design

This study I designed and based as a secondary quantitative analysis of databases

government and private database sources for relationship of obesity to workplace injury.

Databases were further analyzed for variables, reliability, and external and internal

validity based on proposed hypotheses and applying the statistical methods mentioned

above.

Obesity Databases

The National Health and Nutrition Examination Survey (NHANES) is a survey

that combines both a physical medical examination and an intensive interview covering a

variety of health issues and nutrition, and risk factors for developing certain diseases or

conditions. Lifestyle aspects included smoking, alcohol consumption, drug use, physical

fitness and activity, weight, and dietary intake. Other aspects were heredity and

environment that may affect health.

The NHANES began in the late 1960s and is now a continuous series of surveys

for different populations and different health topics. It is the major program for the

National Center for Health Statistics (NCHS). The NHANES III study for obesity were

used in this research using databases from surveys from 2010-2012. To assure validity,

the NHANES survey oversamples persons 60 years and older as well as African

Americans and Hispanics. The physical examination consists of medical, dental, and

physiological measurements, as well as laboratory tests conducted by trained medical

personnel. The database files described are considered public domain and do not require

permission for use. There are restricted databases in the National Center for Health

Statistics and the Department of Health and Human Services available for professional

researchers. Direct identifiers such as name, social security number, and address cannot

be accessed, but by using data linkage products through the Restricted Data Center, I

located indirect identifiers such as dates, geography, and facility ID when these variables

are combined with other data leading to identification. I were used no identifiers, and

thereby did not encroach on any ethical areas. Bias were controlled by using only the

datasets available without interjection of other data (e.g., [CDC], 2012).

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based

telephone system for health surveys originating from the CDC with more than 400,000

adults interviewed per year. For most states, this is the only means for accessing a

population-based source for evaluating health behavior data concerned with chronic

disease prevalence and behavioral risk factors. Two major changes have been made to

BRFSS: cell phone numbers will be used for the survey after recognizing that many

American adults use cell phones only and replacement of post-stratification weighting

method with iterative proportional fitting ranking which are a more advanced method for

measuring data (Bazzare, 2009).

In addition, the BFRSS data are consistently reviewed for quality, validity, and

reliability through multiple peer-reviewed studies that either look at particular topic areas

or the three that provide original and secondary analyses across topic areas and an

overview of the BRFSS as the fourth evaluation, which includes data quality, challenges

for the system, and future actions through recommendations. BRFSS includes interactive

databases and survey data and includes downloads, general information, and

questionnaires. The interactive database systems that were used in this study include

injury, chronic disease, health risk behavior (obesity), and occupational health (hazardous

workplaces). Prevalence and trends data for overweight and obesity measured by BMI,

will be analyzed across states for variable that include underweight BMI 12.0-18.4,

normal weight BMI 18.5-24.9, overweight BMI 25.0-29.9 and obesity BMI 30.0-99.8.

The variables studied will consist of age, gender, race/ethnicity, socioeconomic level,

education level, and cultural context if available. The BRFSS 2011 used three types of

variables: (a) variables used to stratify and weight the data; (b) intermediate variables or

variables derived from a response question response to calculate another variable or risk

factor; and (c) variables used to classify or categorize respondents with the common

focus on health behaviors associated with a risk for illness or injury.

NIOSH Hazardous Workplaces Databases

NIOSH provides national as well as worldwide leadership to prevent work-related

illness, injury, disability, and death by gathering information and research of products

and services for improving safety and health in the workplace based on the Occupational

Safety and Health Act of 1970. The burden of work-related injuries and illnesses are

costly. NIOSH research through its many affiliates helps to identify risk for potentially

severe work-related injury and diseases. NIOSH operates in every state for improving

health and safety of workers by evaluating workplace hazards with solutions requested by

employers, workers, or state and federal agencies. Its funding is through grants and

cooperative agreements with universities and other organizations and are subsets of

databases affiliated with NIOSH are available for delineating diseases and illnesses

related to workplaces as well as the most hazardous workplaces (BLS, 2013). These

databases include data sharing and privacy through the CDC privacy rules for the

protection of “certain individually identifiable health data, a board of scientific

counselors, as well as providing appropriate public health purposes as soon as possible

without compromising privacy concerns, federal and state confidentiality, propriety

interests, national security interests, or law enforcement activities” (CDC, 2012, pp.1-2).

In addition NIOSH activities also conform to scientific excellence in occupational

safety and health for current needs, either alone or with outside collaboration for learning

answers to research questions for application and dissemination. The National

Occupational Injury Research Symposium (NOIRS) provides a forum in partnership with

the Liberty Mutual Research Institute for Safety and the National Safety Council (2011)

presenting current data regarding hazardous workplaces. I will concentrate on the

variable factor of anthropometry and its potential relationship to increased workplace

injury in hazardous occupations. Additional databases used for this research study include

CDC Resources which provides the database Work-Related Injury Statistics Query

System, and Workplace Data, and Statistics Gateway.

Injury/Illness Databases

The Bureau of Labor Statistics is the principle fact-finding agency of the federal

government designated as an independent national statistical agency within the

Department of Labor for collection, processing, analyzing, and disseminating statistical

data essential for Congress, federal agencies, state and local governments, the American

public, and business and labor. Two major surveys have been used for accumulating data

yearly since the inception of the Bureau of Statistics in the early 1990s (CDC Resources,

2012). One is the Census of Fatal Occupational Injuries (CFOI). This program collects

data on all fatal work injuries each year in the United States. By using diverse data

sources from state, federal, as well as independent data sources to identify, verify, and

describe workplace fatal injuries helps assure that the counts are accurate and complete

within acceptable limits. Over 20,000 unique source documents were reviewed in 2011 as

part of the data collection (BLS, 2012). The other is the Survey of Occupational Injuries

and Illnesses (SOH) which reports frequency and incidence rates identified by industry

along with circumstances for all nonfatal work resulting in absenteeism by calculating

days away from work. Information posted on the BLS Internet site includes case

circumstances and worker characteristics with technical information about how to

procure the statistical data.

Other Injury, Illnesses, and Hazards Databases

Data and statistics can be assessed by industry sector, including the Occupational

Research Agenda (NORA) in 21 specific areas. NORA is focused on research problems

of greatest relevance for workers and employers, as well as occupational safety and

health practitioners including major industrial sectors of agriculture, healthcare,

construction, mining, manufacturing, service transportation, and trade. This tool provides

key statistical health surveillance information that is produced by NIOSH, state and

federal agencies. Examples that were used in this study include: Coal Workers Health

Surveillance Program, Mining Statistics, Fire Fighters Fatality Investigation and

Prevention Program, National Occupational Exposure Survey (NOES), National

Occupational Mortality Surveillance (NOMS), Occupational Respiratory Disease

Surveillance (ORDS), Work-Related Lung Disease (WORLD) Surveillance System, and

Worker Health Chart book (BLS, 2013).One particular area of interest for this study is

the area of health disparities, a program focused on socioeconomic status, discrimination,

and work organization for studying how health and safety are related to race/ethnicity.

Descriptions of Methods of Statistical Analysis

Using the relevant databases listed above will determine the methods of analysis

used in this secondary study. The Pearson correlation matrix will be used to determine if

there is a relationship between obesity and increased workplace injury with the inclusion

of age, gender, socioeconomic level, education, race/ethnicity and cultural context which

can be used for relating the dependent variable injury with the independent variable

obesity and then relating mediating variables of age, gender, race/ethnicity to

socioeconomic level, education and cultural context. ANOVA and/or MANOVA can be

used to see what probability of injury is related to obesity. Finally, to confirm if the

databases have provided enough information, I were usedlinear regression. If the

databases do not provide a statistical tool such as SPSS Version 21 then Med Calc

version 12.75 last modified 10/9/13 for Cox-proportion hazards regression will be used

for analysis (MedCalc software bvba, 2013).

Study Variables

The independent variable helps define probable cause or the ability to influence or

affect outcomes(Creswell, 2009). Often the independent variables are described as

treatments or variables that are manipulated or act as predictors or antecedents. The

independent variable in this study is obesity defined according to the CDC Healthy

People 2010 as having a BMI of 30 or greater. BMI is measured by weight in kilograms

divided by height measured in meters squared with the formula defined mathematically

as kg/m

(NIH, 2010). Although BMI is the most-used measurement, Romero-Corral et

al. (2008) define obesity as a diagnosis using BMI as well as percentage of body fat and

lean body mass. Romero-Corral et al. asserted that the diagnosis of obesity excludes the

detection of excess body adiposity adequately. I will attempt to reproduce the method

these researchers developed from the Third National Health and Nutrition Examination

Survey that measured bioelectrical impedance analysis for estimating the percentage of

body fat (BF%). Additionally, the World Health Organization (WHO) reference standard

will be used for obesity: BF% > 25% in men and > 35% in women for testing correlation

between BMI, BF%, and lean body mass defined by the mediating variables age groups

and gender. The results were BMI defined for obesity greater than 30 kg/m

which was

present in 21% of men and 31% of women compared to BF% which defined obesity as

50% in men and 62% in women. These values will be used as standards where possible

for measurements.

Statistical Analysis of Existing Databases for Obesity Based on BMI of > 30

Independent Variable

The NHANES III Survey and BRFSS databases will provide the information to

statistical analyze for determining overweight and obesity for the variables age, gender,

race/ethnicity, socioeconomic level, level of education using SPSS Version 21.

Comparison of workplace injury to BMI using ANOVA and MANOVA will use to

determine probability or relationship. To evaluate correlation of BMI to injury the Cox-

proportional-hazards regression using MedCalc software will provide additional

statistical analysis.

Determining Hazardous Workplaces for Injury and Illness: Dependent

Variable

Injury as defined in this paper described by NIOSH utilizes databases by the CDC

Resources for Work-Related Injury Statistics Query, and Workplace Data and Statistics

Gateway. Again, age, gender, race/ethnicity, socioeconomic level, education, and cultural

context will be the areas of interest. Hazardous workplaces and occupations are

determined by several methods. One method is based on the number of job fatalities in a

given occupation recorded as fatality frequencies. Another method is based on fatality

rates that take into account differing total numbers among occupations. Nonfatal injuries

and illnesses were determined by days away from work to recuperate. The resulting

complex statistical methods are not always relevant or accurate. What has been estimated

is chance of occupational injury or illness expressed as the cumulative number of workers

injured in a specific group compared with the number of workers in that group. An

example is that truck drivers have a 1 in 15 chance of serious injury every year. Another

method used is based on calculated number of median days to recuperate from an injury,

a definition originally presented by Toscano (1997). The CDC (2010) estimated 3.9

million workers in private, state, and local government had a nonfatal occupational injury

or illness (CDC, Healthy People 2010). Also noted was that of those workers, 2 million

were transferred, placed on work restrictions, or took time away from work due to injury

or illness. According to NIOSH (2012), 2.6 million workers were treated that year in

emergency departments for occupational injuries and illnesses, and 110,000 of these

workers were hospitalized. Examining these data sources relate to how, where, what,

how, and why accidents happen and what demographics are more prone to these

incidents. Using the above mentioned statistical methods will

Mediating and Confounding Variables

Data for confounding variables that may skew data could include chronic diseases

associated with obesity that may contribute to workplace injury. This information will be

used to exclude pre-existing medical problems related to obesity that will not considered

in this study. These include diabetes, hypertension, cardiovascular disease and arthritis

for example. Another factor to consider is use of alcohol, smoking, and substance abuse

again these variable will not be considered in this study. Mediating variables, according

to Creswell (2009) are constructed by the researcher and have an additive effect when

multiplying one variable by another. In this study this could include the additive effect of

cultural context for determining attitudes toward lifestyle that may be contributing to

obesity and its impact on workplace injury. Recognizing “population- level variations” in

diet, ecological correlations of lack of support for risky lifestyle choices, and whereby

obesity rates differ in certain socioeconomic environments may be more important than

once thought as contributing to workplace injuries (Huang et al., p.7 2009). This can be

determined through an analysis from existing databases such as OSHA (2012), NIOSH

(2012), BLS (2012), and other CDC resource databases described above.

Target Population

The target population were Hispanic/Latino obese workers identified in hazardous

workplaces of both genders, by race and ethnicity, and who are ages 18-65. Statistics for

socioeconomic level, education, gender and cultural context were included if available.

Sources will include CDC Healthy People (2010), OSHA (2010), BRFSS (2012),

NHANES (2012), NIOSH (2012), and BLS (2012).

Constraints with Research Choice and Resources

The validity of the results of this study depended upon database reliability and

accuracy. Since all databases that was analyzed in this study are government agencies, it

is assumed that they are compiled by competent professionals, that all data are scrutinized

before publication, and that there is no way to contaminate and slant the data. The

NIOSH Board of Scientific Counselors provides the guidance and advice to the NIOSH

for the development and evaluation of research hypotheses through systematically

documenting findings, disseminating results, and conforming to scientific excellence.

Delimitations for this study currently are determined by the small amount of peer

reviewed material and databases for review. Although obesity has been an increasing

problem in the United States research on the possible correlation of increased workplace

injuries has only been presented in 2007(Schulte, Wagner, Ostry, Blanciforti, Cutlip,

Krajnak, et al., 2007).

Ethical Considerations

Data privacy and sharing is protected by the CDC/ATSDR policy on releasing

and sharing data, which ensure that NIOSH will routinely disperse data to its partners for

public purposes. This data released and shared is without compromise regarding privacy,

federal and state confidentiality, national security, proprietary interests, or activities

concerned with law enforcement (CDC, 2012). I will be using public domain databases

and will not know participants. Bias control strengthened by using published databases.

Calculating Tools for Obesity and Workplace Injury Used in Databases

The calculating tools will be determined by the databases. For, obesity a medical

model was used in the NHANES III Survey which included a complete medical

examination, in-depth adiposity testing using physiological means, as well as a health

questionnaire that was also supported by additional laboratory blood test analyses (2004).

The self-reported BRFSS Survey participants gave estimated height and weight

measurements for determining BMI (2010) The methods for determining injury will be

described by NIOSH (2012) and related databases described above with explanations of

mechanisms of injury and anatomical areas that were injured, filed workers’

compensation insurance claims, work days lost, morbidity, mortality, and disability

statistics where available (CDC INFO, 2012).

Calculating Tool for Injury

Determining whether workplace injuries in hazardous occupations are related to

obesity has been prominent in insurance claims and is not easily determined. What may

be determined in this study using the newer definition of obesity is correlating this

relationship of increased probability for injury. Recognizing that most pre-employment

examinations for hazardous occupations will test requirements for fulfillment of tasks,

there is still the probability that not all issues will be investigated due to cost and need for

qualified workers as well as out of concern for appearing discriminatory. What I hoped

to discover is the impending chance for injury.

Software Used for this Analysis

Database statistical analysis as well as SPSS Version 21.0 will be used for this

study. The statistical methods for individually comparing obesity, injuries, and hazardous

workplaces described in NHANES III (2004), BRFSS (2010), NIOSH (2012), OSHA

(2012), and BLS (2012). Other CDC resources using the variables described above

include frequency for testing the hypotheses and measure for what variables are strongly

related. After accumulating data for each of the variables from each database for obesity,

injury, and hazardous workplaces, I will compare them using MANOVA and then see if

linear regression and correlation data are related and if indeed there is significant

relationship between obesity and workplace injury.

Summary

The purpose of this study is to learn whether there is a relationship between

obesity and increased workplace injuries in hazardous occupations that may also relate to

race/ethnicity and cultural context. Available databases that include age, gender,

race/ethnicity, socioeconomic level, level of education and cultural context are limited.

Although the CDC has declared that obesity has become an epidemic in the adult

population of the United States (2012) there has been little research on how this may

affect workers in hazardous occupations (CDC. 2012). Statistical analysis for how, what,

where, why, and when injuries occur will be based on evidence-based medicine. I

analyzed databases that include variables such as age, gender, race/ethnicity,

socioeconomic level, level of education and cultural context and then include the variable

of obesity. Extrapolation of this information was used for determining whether obesity

contributes to workplace injury in hazardous occupations.

Chapter 4: Results

Introduction

The purpose of this study was to determine whether there is a relationship

between obesity and workplace injury in hazardous occupations for the Hispanic/Latino

population. According to the CDC’s National Health and Nutrition Examination Survey

2009-2010 more than 35% of United States men and women were obese. Adult obesity

for Hispanics is 42.5% in the United States (CDC, 2012). In 2010, The Occupational

Safety and Health Administration (OSHA) reported that most high-risk occupations were

held by younger workers (ages 18 to 19) with fatality rates of 5.6 times greater for

Hispanic workers compared to non-Hispanics, African Americans, or European workers

(OSHA, 2010).

Research Questions and Hypotheses

Research Question 1: Does obesity have a direct influence on injury?

: Obesity has no direct influence on injuries in a hazardous workplace

: Obesity does have a direct influence on injuries in a hazardous workplace

Research Question 2: Is culture as related to race/ethnicity interpretation of

obesity contribute to workplace injury?

Cultural interpretation related to race/ethnicity has no effect on workplace

injury

Cultural interpretation related to race/ethnicity has an effect on workplace

injury

Data Collections

The databases that I used for this study included: NHANES, BRFSS, NIOSH,

OSHA, and BLS. The National Health and Nutrition Examination Study (NHANES)

1999-2009, a cross-sectional survey whose main goal and design consisting of interviews

conducted in participant’s homes, standardized physical examinations and laboratory

tests consisting of blood and urine samples conducted in mobile medical examination

centers (NCHS, CDC, 2009). The sample populations for NHANES study is determined

by complex, multistage probability designs. In 2009-2010, Hispanic persons, age 60 or

greater, and persons with low incomes were oversampled to obtain reliable estimates for

these subgroups health and nutritional status (NHANES, 2010). The NHANES public-

used data files released in 2-year cycles, an on ongoing process covering all civilian age

groups. The Current Population Survey of totals for 2009-2010 were calculated as an

estimate of the number of obese individuals.

The Behavioral Risk Factor Surveillance Survey (BRFSS) measures height and

weight through self-reported state-based surveys with the data released on a yearly basis.

NHANES only provides national estimates, not state-specific. The CDC used information

from this study in 2009 for estimating state-specific prevalence for obesity among adults

eighteen years of age and older (BRFSS, 2009). The method used by BRFSS researchers

consist of an annual telephone survey of United States civilian, non-institutionalized

adults monitoring behaviors, health conditions, for evaluating and development of risk

preventative measures. Utilizing body mass index (BMI) measured in weight [kg]/ height

measured in[m]

. The person’s self- reporting of having a BMI greater than30 defines

them as obese. Since 1996, all states have contributed to this survey.

The National Institute for Occupational Safety and Health (NIOSH) databases

consists of surveillance data and statistics relating to occupational injuries covers

multiple issues regarding health and safety in the workplace. In 2012 an estimated 3.8

million private industry workers, federal, state and local government workers suffered

from either a non-fatal injury or illness. The emergency departments recorded treating

approximately 2.9 million workers for occupational injuries and illnesses of which

150,000 were hospitalized (NIOSH, unpublished data, 2013). The CDC Health

Disparities and Inequalities Report-United States, 2013, published in the Morbidity and

Mortality Weekly Report (MMWR), underscored the need for consistent data on

population characteristic lacking in health surveys. Factors leading to health disparities

listed among race/ethnicity, geographic, socioeconomic is to develop and provide

accurate and useful data such as the potential impact of obesity on work-related disease

and injury (CDC, 2013).

The Occupational Safety and Health Administration (OSHA) is a small agency

servicing with only 2,200 inspectors for the health and safety of at least 130 million

workers meaning there are about one compliance officer for every 90,000 workers

(OSHA, 2012). In 2013, 4,405 were workers killed on the job. From the statistics

available this included 797 Hispanic/Latino workers reported, that is two deaths every

day all year. The major cause reported for fatalities included falls, struck by an object,

electrocutions. Most of these jobs are construction related (OSHA, 2014).

The Bureau of Labor Statistics (BLS) reported, in their Survey of Occupational

Injuries and Illnesses (SOII), that nearly 3.0 million nonfatal workplace injuries and

illnesses reported by private industry in the year 2011, indicating the results as an

incidence rate of 3.5 cases per 100 equivalent full-time workers (BLS, 2013). This was

unchanged during the last ten years in which the total recordable cases (TRC) for injury

and illness incidence rate among private industry. Agriculture, forestry, fishing and

hunting in two private industry sectors due to increases in crop production and animal

production (dairy cattle and milk production) accounting for an increase rate of injuries

and illnesses. More than 2.8 million of nearly 3.0 million nonfatal occupational injuries

and illnesses were injuries (94.8%). Goods-producing industries accounted for the 17.5

percent of private industry covered by this survey in 2011(BLS, 2013).The Bureau of

Labor Statistics has generated estimates for injuries and illnesses for many industries

defined in the 2007 North American Industry Classification System (NAICS) Manual,

available electronically for current and prior years. The inherent underreporting of

illnesses can be located in Chapter nine of the BLS Handbook of Methods (BLS, 2012).

I created the independent variable, obesity, from the NHANES and BRFSS databases for

age, gender, race/ethnicity, socioeconomic level, and level of education for percentage

for ages 18-65 years of age and the incidence of obesity in each of the mediating

variables. None of the databases gave reference to culture and the possible influence it

has on obesity. The databases from NIOSH, OSHA and BLS were referenced for the

dependent variable injury. Additionally the mediating variables: age, gender,

race/ethnicity, socioeconomic level, and level of education for ages 18-65 years of age

were explored to support incidence rates. There was no reference to cultural context, or

level of education in the databases.

Completing my evaluation of databases that I used in this study did not provide

sufficient information for statistical studies’ analysis. The variables provided insufficient

information regarding obesity and relationship to workplace injuries in hazardous

occupations for the Hispanic/Latinos population. To date there is scant available studies

on the possible correlation of obesity to increasing the probability of workplace injury in

hazardous occupations. Although there is no way to provide statistical analysis for this

study due to the lack of data relating individual’s obesity measurements to workplace

injury, results from the databases that I studied supported the potential correlation

between obesity and workplace injury in hazardous occupations for the Hispanic/Latino

population.

Tables 1-4 results refer to obesity demographics for the mediating variables, age,

gender, race/ethnicity, socioeconomic level, level of education and cultural context when

variables were available and if the data could be used to be calculated using the Web

Enabled Analysis Tool (WEAT) provided by the Behavioral Risk Factor Surveillance

System for frequencies, linear regression and cross-tabulation. Tables 5-7 for the NIOSH

and BLS databases’ software, Occupational Injury and Illnesses Classification System,

was used for calculating injuries in hazardous occupations for the same mediating

variables if available (Caution suggested in reviewing table results since all information

may be void of represented variables).

Results

Table 1.

Age-adjusted Prevalence of Overweight (OW), Obese (O), and Extreme Obesity (EXO)

Among Adults Aged 20-74, by Sex: United States. Selected Years 1960-1962 through

2011-2012

Variable Total population Men Women

Overweight 33.3 (1.4) 37.3 (1.5) 29.5(2.0)

Obese 36.3 (1.4) 33.9 (1.5) 36.6 (1.8)

Extremely Obese 6.6 (0.6) 4.5 (1.0) 8.8 (0.7)

Note. Survey Period: 2011-2012; n =4,574

*Estimate has a relative standard error greater than 30% and less than or equal to 40%

and should be used with caution because it does not meet standards of reliability or

precision.

Overweight is body mass index (BMI) greater than or equal to 25.0 kg/m

and less than 30.0 kg/m

Obese is BMI greater than or equal to 30.0 kg/m

Extremely obese is BMI greater than or equal to 40.0kg/m2 Age-adjusted by the direct

method to the year 2000 U.S. Census Bureau estimates using the age groups 20-39, 40-

59, and ages 20-74. NHES 1960-1962 included adults aged 18-79 and NHANES 1971-

1974 and 1976-1980 did not include over age 74 so trend estimates is the base for ages

20-74. (Analysis for pregnant women excluded)

Adapted from CDC/NCHS: National Health Survey and National Health and Nutrition

Examination Survey Retrieved from http://www.cdc.gov/nchs

Table 2 references comparison of prevalence for obesity in the Hispanic/Latinos

population to non-Hispanic White populations only. The lack of comparison to other

underrepresented groups leaves a gap in understanding how interpretation for obesity

prevalence differs for different race/ethnicity populations. The statistical data information

can be used in future research. Adapted from (CDC, 2012).Summary Health Statistics for

U.S. Adults Retrieved from http://www.cdc.gov/data/series/sr_10_25

Table 2.

Age- adjusted Percentages of Person 18 years of Age and Over Who are Obese, 2011

Persons were considered obese if they have a Body Mass Index (BMI) of 30 or Greater.

National Health Interview Survey (NHIS)

Gender Hispanic/Latinos Non-Hispanic White

Hispanic/Non-

Hispanic White Ratio

Men 30.9 27.7 1.1

Women 32.6 24.8 1.3

Total 31.8 26.2 1.2

Note: Cross Tabulation Information from the Behavioral Risk Factor Surveillance System

the world’s largest telephone survey in the United States tracks risky health for producing

frequencies and percentages generated for the independent variable obesity with cross

tabulation of the mediating variables age, gender, race/ethnicity, education and income.

Table 3 provides demographic information for overweight or obesity risk factor

divided into range of age (18-24, 25-44, 45- 64, 65 +) the table also displays demographic

information for 8 race/ethnicity categories.

Table 3.

Cross- tabulation of demographic overweight or obese-risk factor

18-24 25-44 45-64 65 or older Row Total

Not at Risk

Sample Size 385 780 1,002 627 2,794

Row % 28.8 33.8 25.5 11.9 100.0

Column % 56.1 32.3 29.0 32.7 36.5

Total %

(Weighted)

10.2 12.0 9.1 4.2 36.5

Sample Size

(Weighted)

343,118 402,583 304,129 141,882 1,191,712

At Risk

Sample Size 1,520 8,745 9,161 4,623 24,049

Row % 12.8 47.0 30.5 9.7 100.0

Column % 49.4 70.5 77.5 70.9 68.7

Total %

(Weighted)

8.8 32.2 20.9 6.7 68.7

Sample Size

(Weighted)

2,884,372 10,561,070

6,849,632

2,186,318 22,471,393

Column Total

Sample Size 3,210 12,882 12,158 6,875 36,125

Row % 17.8 45.7 27 9.4 100.0

Column % 100.0 100.0 100.0 100.0 100.0

Total %

(Weighted)

17.8 45.7 27 9.4 100.0

Sample Size

(Weighted)

5,840,295

14,965,019

8,839,256

3,082,190 32,726,761

Note: Race/ethnicity included White only, non-Hispanic, Black only, non-Hispanic,

Asian only, non-Hispanic, Native Hawaiian or Pacific Islander only, non-Hispanic,

American Indian or Alaskan Native only, non-Hispanic, Other race only, non-Hispanic,

Multiracial only, non-Hispanic. This table describes descriptive statistics on the

demographic examined for obesity excluded the Hispanic/Latino population. There is no

reason for exclusion given. This helps explain difficulty in developing social change for

underreported populations due to lack of sufficient statistical data.

Adapted from Behavioral Risk Factor Surveillance System Retrieved from http:

http://nccd.cdc.gov/s/broker/broker/weatsql.exe/wear/data_anlysis_fr.hsql

Table 4 Demographic information income level (Hispanic) =$10,000 <=income

<$15,000 and demographic information education level (Hispanic) = Grades 1-8

(Elementary)

Table 1.

Cross- Tabulation on Demographic Information: Hispanic Risk for Overweight and Obesity

Based on Level of Education and Income Level

_____________________________________________________________

Not at Risk

Hispanic Row Total

Sample size 184 610

Row % 57.0 100.0

Column % 24.5 27.7

At Risk

Sample size 547 1,411

Row % 66.7 100.0

Column % 75.4 72.3

Column Total

Sample size 731 2,021

Row % 64.0 100.0

Column % 100.0 100.0

Note. This table represents the lower income level based on lower education level for the

Hispanic/Latino population and how it may contributes to the percentage of obesity.

Adapted from Behavioral Risk Factor Surveillance System Retrieved from:

http://nccd.cdc.gov/s_broker/broker.exe

Obesity tends to be most prevalent in populations that lack higher levels of education a

precursor for lower income levels. The Hispanic/Latino population is subject to both

variables. Statistical data derived from Healthy People 2010 has indicated the high-risk

for obesity (CDC, 2012).

Table 5 represents occupational injuries and illnesses derived from the Bureau of

Labor Statistics which developed the Occupational Injury and Illness Classification

System (OIICS, 1992) which was redesigned in 2010 with subsequent revisions in 2012

providing statistical information regarding fatal, non-fatal occupational injuries and

illnesses reported incidence (BLS, 2013). In collaboration with the National Institute for

Occupational Safety and Health (NIOSH), the BLS developed a web site and software

application as a resource utility for occupational safety and health researchers.

Table 2.

Numbers of Nonfatal Occupational Injuries and Illnesses by Case Type and Ownership for

Selected Industries, 2011 (thousands) no relationship to race/ethnicity

Industry Total-Cases Recorded

Natural Resources

Agriculture, forestry,

Fishing, and hunting 48.3

crop production 19.7

animal production 12.4

forestry, logging 2.5

Fishing and hunting, trapping 0.2

Mining 17.2

Oil and gas extraction 1.4

Mining (except oil and gas) 7.3

Construction

Construction of buildings 39.1

Heavy and civil engineering

construction 27.6

Support trade contractors 123.5

Manufacturing

Food manufacturing 502.7

Beverage and tobacco 80.5

Product manufacturing 12.5

Textile mills 4.2

Textile product mills 3.8

Apparel manufacturing 3.5

Leather and allied product 1.7

Wood product manufacturing 20.9

Paper manufacturing 13.6

Printing and related support activities 13.2

Petroleum and coal products 2.3

Chemical manufacturing 19.3

Plastics and rubber products 31.0

Nonmetalic mineral products 19.5

Industry Total-Cases Recorded

Primary metal manufacturing 24.3

Fabricated metal products 74.4

Machinery manufacturing 46.3

Computer and electronic products 15.2

Electrical equipment, appliance, and

component manufacturing 11.2

Transportation equipment 70.6

Furniture and related products 17.7

Miscellaneous manufacturing 18.1

Trade, Transportation, and Utilities

Truck transportation 68.0

Warehouse and storage 32.2

Leisure and hospitality

Accommodations and food services 278.6

Accommodation 68.8

Food services and drinking places 209.8

Note: Case results of injury and illnesses for the annual average employment 2011 (no

race/ethnicity noted).

This additional statistical data that is not included in this table for case results of injury

and illnesses listed by occupation and number of cases of injury and illness: Agriculture,

forestry, fishing, and hunting 974.9, Crop production 413.8, Animal production 163.5,

Forestry and logging: 57.1, Fishing, hunting and trapping 8.6, mining 669.7, Oil and

gas extraction: 149.4, Mining (except oil and gas) 208.1, Construction 5,576.7,

Construction of buildings 1,259.5, Heavy and civil engineering construction 826.4,

Specialty trade contractors 3,490.6, Manufacturing 11,627.7, Food manufacturing

1,458.8, Textile mills 186.2, Textile product mills 120.8 , Apparel manufacturing 119.6,

Leather and allied products 163.3, wood products 338.1, Paper manufacturing: 386.1,

Printing and related support: 478.1, Petroleum and coal products: 109.5, Chemical

manufacturing 808.2, Plastics and rubber productions: 632.2, non-metalic mineral

products 367.4, Primary metal 37.9, Fabricated products: 1,312.3, Machinery

manufacturing 1,015.6, Computer and electronic products 1,091.4, Electrical equipment,

appliance and component manufacturing 366.9, Transportation equipment manufacturing

1,337.6, Furniture and related products 363.9, Miscellaneous manufacturing 575.8,

Trade, transportation, and utilities 24,924.5, Leisure and hospitality 13,514.8.

Totals include data for industries not shown separately. North American Industry

Classification System- United States, 2007 used. Agriculture- excludes farms with fewer

than 11 employees. Data for mining (Sector 21 in the North American Industry

Classification System- United States, 2007 include establishments not governed by the

Mine Safety and Health Administration rules and reporting such as those in Oil and Gas

Extraction and related support activities. The data is for mining operators in coal, metal,

and nonmetal mining, as provided to BLS by the Mine Safety and Health Administration,

U.S. Department of Labor and independent mining contractors excluded from the coal,

metal, and nonmetal mining industries. The data do not reflect the changes the

Occupational Safety and Health Administration made to its recordkeeping requirements

effective January 1, 2002. Estimates for these industries are not comparable to estimates

in other industries because of rounding components that may not add to totals (BLS,

2011). Adapted from the U.S. Bureau of Labor Statistics, U.S. Department of Labor

Employment expressed as an annual average derived primarily from the BLS Quarterly

Census of employment and Wages (QCEW) program. Retrieved from:

http://wwwn.cdc.gov/wizards/oiics/

Table 6 provides mediating variable for age, gender, and race/ethnicity for number of

non-fatal injuries and illnesses in several hazardous occupations such as manufacturing, building

supplies, and warehouse and storage industries.

Table 3.

Worker Characteristics (Gender, Age, Race/Ethnicity: Occupation for Number of Nonfatal

Injuries and Illnesses Manufacturing, Building Material and Garden Equipment Supplies

Dealers, Warehouse and Storage, Private Industry, 2013(most recent data)

Food manufacturing Building supplies

and garden

equipment supplies

dealers

Warehousing and

storage

Gender

Male 33,280 25,940 17,500

Female 16,560 8,950 6,390

Not reported - 80

Race or ethnic origin

White only 15,030 6,830 7,460

Hispanic or Latino only 14,540 1,250 6,390

Black only 5,190 620 21,030

Asian only 1,540 90 2,000

American Indian or

Asian Native only

150 20 -

Native Hawaiian or

Pacific Islander only

310 - 30

Hispanic or Latino and

other race

60 - -

Age

16-19 870 1,270 440

20-24 5,340 5,090 3,230

25-34 11,740 7,810 6,460

35-44 11,480 6,810 5,310

45-54 12,440 7,650 5,070

55-64 6,450 5,040 2,830

65 and over 920 1,190 360

Not reported 670 40 400

Note. The Dash indicates data do not meet the publication guidelines.

Adapted from U.S. Bureau of Labor Statistics, U.S. Department of Labor This table

represents hazardous occupations and the demographics of workers.

Demographic statistical data is presented here to determine if there is a relationship for

non-fatal workplace injury and illness based on gender, age, race/ethnicity.

Table 7 provides information regarding the incidence rates for injuries and illnesses

selected for hazardous occupations as defined by the Bureau of Labor Statistics (BLS,

2013).

Table 4.

Incidence rates of Nonfatal Occupational Injuries and Illnesses by Selected Industries, 2011

Industry 2011 Annual

Average

Incidence Rate Number of Cases

(thousands)

Agriculture,

Forestry, fishing

And hunting

974.9 5.2 45.8

Mining 669.7 2.2 16.6

Construction 5576.7 3.8 184.7

Manufacturing 11827.7 3.9 455.6

Trade,

Transportation,

Utilities

24924.6 3.8 782.1

Warehousing

Storage 636.8 5.3 31.2

Accommodation

Food services 11499.2 3.7 269.2

Note. The incidence rates represent the number of injuries per 100 full-time workers and

calculated as: (NEH) x 200,000 where N= number of injuries, EH = total hours worked

by all employees during the calendar year, 200,000 = base for 100 equivalent full-time

workers (working 40 hours per week, 50 weeks per year). Totals include data for

industries not shown separately. The North American Industry Classification System

United States, 2007 used for data clarification. Employment expressed as an annual

average and derived primarily from the BLS Quarterly Census of Employment and

Wages (QCEW) program. Farms industry with less than 11workers is not included in this

table. The statistical data is for mining operators in coal, metal, and nonmetal mining

provided to the BLS by the Mine Safety and Health Administration, U.S. Department of

Labor. Independent mining contractors is not included from the coal, metal, and nonmetal

mining industries. The data does not reflect changes the Occupational Safety and Health

Administration made to its recordkeeping requirements effective January 1, 2002;

therefore, estimates for these industries are not comparable to estimates in other

industries. Because of rounding components may not add to totals (BLS, 2011). Industry

scope changed in 2009. Because of rounding, components may not add to totals.

Adapted from U.S. Bureau of Labor Statistics, 2013: Retrieved from:

http://www.cdc.gov/wizards/oiics

Summary

No database supported my hypothesis for obesity as a predictor for workplace

injury. Examination of multiple databases showed each database to be independent and

not correlated. There was significant information provided by federal, state, and local

databases for both obesity and workplace injury, but this information was not collected in

a representative table.

After examination of the available databases I was unable to find a predictor of

obesity for injury in the Hispanic/Latino population. The reason may be due because:

• Lack of databases

• Too many variables that make up obesity

• Under-reporting of workplace injury

If social change is to occur for predicting obesity and a possible relationship to workplace

injury for the Hispanic/Latino population there is need for inclusion in research studies.

Social choice may be determining factor as well as social awareness for social change to

occur.

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