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Health Informatics Research Methods: Principles and Practice, Second Edition

Chapter 5: Epidemiological Research

Learning Objectives

Explain the purpose of epidemiology and its importance in health informatics.

Apply epidemiological principles and models to examine health informatics topics.

Describe the different types of epidemiological study designs and how they can be used in health informatics research.

Assess the impact of confounding, recall bias, and other types of bias in epidemiological studies.

Determine which statistical tests should be used for each study design, such as the odds ratio (OR) for the retrospective study and the relative risk (RR) for the prospective study.

List and explain the rules of evidence when considering whether an association is causal.

Introduction

Epidemiology examines patterns of disease occurrence in human populations, and the factors that influence these patterns in relation to time, place, and persons.

Essential tool when developing specific research methodologies in health informatics.

This chapter provides examples of epidemiological principles to study disease and health informatics.

John Snow Map of the Outbreak of Cholera

How might this map look today using modern HIT?

Types of Epidemiology

Epidemics—What caused them and how they could be controlled and prevented

Expanded rapidly beyond the study of infectious diseases into the study of all types of illnesses

Cancer epidemiology, pharmaco-epidemiology, environmental epidemiology, nutritional epidemiology, chronic disease epidemiology, health services epidemiology

Epidemiology and Health Informatics

Epidemiological principles can be used to study any type of behavior, outcome, occurrence, community, or healthcare system. The key is to know which epidemiological study design to use to inspect a particular problem.

Epidemiological principles and study designs are used to examine many of the health informatics systems and structures that sustain the healthcare system today.

Example

Bell and colleagues (2003) used a cross-sectional study to determine whether physician offices located in high-minority and low-income neighborhoods in southern California have different levels of access to information technology than offices located in lower-minority and higher-income areas.

Example (cont.)

Used epidemiological principles similar to those that Snow developed

Researched physician offices in targeted geographic areas and neighborhoods to determine the use of different types of health information technology

Did not establish the cause of any particular disease, but determined whether or not socioeconomic demographics play a part in the use of information technology

Infectious Disease Model

Host

Agent

Environment

Age, gender, race religion,

marital status, ethnicity, genomics,

social behaviors, anatomy and physiology,

prior illness or disease

Nutritional, chemical,

physical, infectious

Physical environment

Tornado, flood, hurricane, war

Occupational environment

Infectious Disease

Source: Gordis 2004, 16; Lilienfeld 1994, 37–38.

Chronic Disease Model

Chronic Disease Model: Example of Lung Disease

Using the Epidemiological Models of Causation in Health Informatics

Host

Agent

Environment

Experience

Training

Understanding of CAC system

Computer problems

Coding errors in system

User friendly

Encoder issues

Documentation in EHR incomplete

Structured text

Free text

Artificial Intelligence

Epidemiological model: Health informatics example—

Computer-assisted coding (CAC)

Chronic Disease Model: Example of Reluctance to Use PHR

Epidemiological Study Designs

Descriptive study

Cross-sectional or prevalence study

Analytic Studies

Retrospective (case-control) study

Prospective study

Experimental study

Clinical and community trial

Progression of Epidemiological Study Designs

Analytic Study Design:

Retrospective case-control

Prospective study

Historical-prospective study

Experimental Study:

Clinical trial

Community trial

Descriptive Study Design:

Cross-sectional

Prevalence

Components of the Cross-sectional or Prevalence Study

Describes health characteristics at one point or period in time

Generates hypotheses

Determines whether the disease or health characteristic exists now

Generates new ideas

Performed when very little is known about a topic

Excellent design when studying new concepts in health informatics

Leads to analytic studies

Prevalence Rate

Example of how a prevalence rate is determined:

Where 10 n is usually 1 or 100 for common attributes. The value of 10 n might be 1000 if expressing the rate per 1,000 facilities, 10,000 if expressing the rate per 10,000 facilities, and so forth (Source: CDC 2012)

Sensitivity and Specificity

Sensitivity and specificity rates can be used in prevalence studies when assessing correct measurement or correct labeling.

True positives (TP): Correctly categorize true cases as cases (cases are individuals with the disease or outcome) = valid labeling

False negatives (FN): Incorrectly label true cases as non-cases (non-cases are those individuals without the disease or outcome = invalid labeling

Sensitivity and Specificity (cont.)

True negatives (TN): Correctly label non-cases as non-cases = valid labeling

False positives (FP): Incorrectly label non-cases as cases = invalid labeling

Sensitivity = Percentage of all true cases correctly identified where TP/(TP+FN)

Specificity = Percentage of all true non-cases correctly identified where TN/(TN+FP)

(Source: Lilienfeld and Stolley 1994)

Example of Prevalence Study

The American Hospital Association (AHA) conducted a prevalence study by surveying AHA member hospitals to determine their use of health information technology. Survey instruments were sent to hospital CEOs from all types of hospitals and from different geographic areas across the country.

Analytic Study Designs: Case-Control (Retrospective)

Steps to follow when conducting a case-control (retrospective) study

Step 7	Design the instrument used to collect the exposure or risk factor data. Collect it through phone or in-person interviews, self-report questionnaires, abstracts from existing sources such as the EHR, cancer registry, birth certificates, death certificates, financial records and so forth.
Step 8	Analyze the data to include the appropriate statistics.
Step 9	Summarize the results and determine if they support or refute the hypothesis
Step 10	Publish the results

Step 1	Determine the hypothesis and decide whether to use prevalence cases (existing cases of disease) or incidence cases (new cases of disease)
Step 2	If using prevalence cases, seek out cases from the state or hospital-based cancer registry. If using incidence cases, have healthcare facilities provide new cases as they are treated
Step 3	Decide who will be part of the study by using inclusion criteria to validate the disease under study, such as ICD-10-CM codes, laboratory reports, radiology reports, and health records.
Step 4	Randomly select the cases by obtaining a list of possible cases (either from the state or hospital-based cancer registry or from a list of ICD-10-CM codes, and so forth) and using a systematic sample, such as choosing every fifth case.

Analytic Study Designs: Case-Control (Retrospective) (cont.)

Step 5

Choose controls from siblings or friends who are the same gender and of similar age and socioeconomic status, or from similar patients at the same hospital. Controls and cases should share all characteristics except the disease under study. For example, if studying melanoma, controls could be chosen from the same hospital-affiliated cancer registry as the cases, but the controls would have another type of cancer, such as colon cancer or lung cancer. Select these controls from a list of cancer cases identified by their ICD-10-CM code and validate the diagnosis through pathology reports and health records. Also, choose control participants from this list who are similar in age by at least five years.

Step 6

Decide whether to match the cases and controls for certain variables. Matching on variables such as age, gender, race and so forth should only be used when the researcher is certain that there is a relationship between a given variable and the dependent variable. For example, age is always related to cancer because the likelihood of developing cancer increases as people age. Therefore, age is a confounding variable in case-control studies of cancer because it may be the underlying factor that leads to the development of the cancer. Matching patients by age will reduce the chance that age confounds the study of the specific cancer risk factor being studied.

Analytic Study Designs: Case-Control (Retrospective) cont’d

Step 7	Design the instrument used to collect the exposure or risk factor data. Collect data through phone or in-person interviews, self-report questionnaires, or abstracts from existing sources such as the EHR, cancer registry, birth certificates, death certificates, and financial records. Some researchers, such as Watzlaf, design a research instrument to collect both interview-related data and health record data (Watzlaf 1989). The full research instrument is provided in online appendix 5A. It is extremely important that the researcher choose appropriate data sources so that information related to both the cases and controls can be found.
Step 8	Analyze the data to include the appropriate statistics.
Step 9	Summarize the results and determine if they support or refute the hypothesis.
Step 10	Publish the results.

Example—Odds Ratio (OR)

The OR for this example is:

The value of 4 means that individuals who use tanning lamps are 4 times more likely to develop melanoma than individuals who do not use tanning lamps.

If the OR for this example equaled 1, the risk for melanoma would be equal for the cases and controls and use of tanning lamps is not a risk factor for melanoma.

If an OR is less than 1, the factor (the use of tanning lamps) decreases the risk of disease and provides a protective effect.

Example: Case-Control Study in Health Informatics

Vinogradova, Coupland, and Hippisley-Cox (2014; 2015) used the case-control design to examine the relationship between oral contraceptives and risk of venous thromboembolism (VTE).

The authors of this study used two research databases that have been tested and validated in the United Kingdom called QRESEARCH and Clinical Practice Research Data link (CPRD) to examine this relationship.

Data within this database were tested and analyzed, and found to be valid in more than 90 percent of the cases when compared with paper-based records.

Cases included those individuals with VTE and they were individually matched with up to five female controls with the same age and physician practice.

Odds ratios were computed. Combined oral contraceptives was associated with an increased risk of VTE (OR = 2.97).

Confounding variables, or other variables that can also play a part in the development of the disease, VTE were also collected and controlled for, and included smoking status, alcohol consumption, ethnic group, body mass index, comorbidities, and other contraceptive medications.

Cohort (Prospective) Study Design

This study design has two groups of study participants:

One with the exposure (independent variable)

One without the exposure (dependent variable)

Both groups are then followed forward in time to determine if and when they develop the disease or outcome variable under study.

Calculating the Relative Risk and Incidence Rate

The calculation for relative risk (RR) is:

The calculation for incidence rate is:

Calculating the Relative Risk (cont.)

To calculate relative risk, perform the following:

Incidence rate of exposed =

Incidence rate of unexposed =

Relative risk:

RR Example for Risk of Migraines in Children Who Play Video Games

The RR for this example is:

Incidence rate of exposed =

Incidence rate of unexposed =

Relative risk:

In this example, the RR of 9.4 is very high for the association between use of video games and migraine headaches, and those children who play video games are almost nine times more likely to develop migraine headaches than those who do not play video games.

JBlock (JB) - Reviewer: The result should be explanted as children who play video games would be 9 times as likely as those who do not play video games to develop migraine headaches.

Prospective Study Example in Health Informatics

A prospective study (Tierney et al. 2015) in which researchers restricted access to providers from an urban health system’s EHR information, based on patient preferences, was conducted in one clinic setting.

Patients could choose to allow or restrict providers’ access to medications, diagnoses, results and reports or only sensitive data such as STDs, HIV, drugs, alcohol use, behavioral health information, and so on.

Providers were followed over time to study what occurred when these restrictions to patient information were made.

It was found that providers did have to “break the glass” over 100 times to gain access to EHR information in order to provide patient care.

Experimental Study Designs in Epidemiology

Experimental research studies expose participants to different interventions (independent variables) to compare the result of these interventions with the outcome (dependent variables).

Two examples of experimental research studies in epidemiology include the clinical and community trial.

Clinical Trials

Clinical trials are designed to help healthcare professionals test new approaches to the diagnosis, treatment, or prevention of different diseases.

Patients who are at high risk for developing these diseases are often the ones who participate in the clinical trial.

The clinical trial is designed to test new medications (most common) and surgical procedures, as well as new treatments or combinations of treatments to prevent disease.

Community Trials

Very similar to clinical trials but take place in a particular community and have less control over the intervention than one would have with the clinical trial.

The community trial’s goal is to produce changes in a specific population within a community, organization, or association.

Participation includes all members of the community and the intervention tends to be provided throughout the population.

(Friis and Sellers 2014, 322–323; UPMC 2015).

Clinical and Community Trial Protocol

Rationale and background

Specific aims

Randomization

Blinding or masking

Types and duration of treatment

Number of subjects

Criteria for including and excluding participants

Outline of treatment procedures

Procedures for observing and recording side effects

Informed consent

Analysis of data

Dissemination of results

Types of Clinical Trials

Treatment trials test experimental treatments, new combinations of medicines, different types of surgery, radiation or chemotherapy.

Prevention trials aim to prevent disease in a person who has never had the disease or to prevent it from advancing or reoccurring.

Diagnostic trials are conducted to find better tests, procedures, or screenings to detect a disease or condition.

Types of Clinical Trials (cont.)

Screening trials examine the best method to detect diseases or health conditions.

Quality of life trials explore methods used to improve comfort and the quality of life for individuals with a chronic disease

Phases of Clinical Trials

Phase I, II, III, or IV based on the size of the population and the intervention being tested. The FDA provides guidelines for the different types of clinical trials.

Phase I: Usually test a new drug or treatment in a small group of people (20–100)

Phase II: Study the intervention in a larger group of people (100–300)

Phase III: The study drug or treatment is given to even larger groups of people (300–3,000)

Phase IV: Include studies that collect additional information after the drug has been marketed, such as the drug’s risks, benefits, and optimal use

Rules of Evidence for Causality

Strength of association

The strength of the association is measured by the RR.

A strong RR is important, and those >2 are effective to show causality.

Repeated findings of weak RRs may be of equal importance if it is found in studies with reliable methodology.

Consistency of the observed association

Confirmation of results in many different types of epidemiological studies in different populations and different settings.

Rules of Evidence for Causality (cont.)

Specificity

A one-to-one relationship between an independent variable and a dependent variable, or between the exposure and the disease is necessary to add weight to causality.

Because some exposures may lead to many different adverse outcomes, if specificity is not found this does not mean an association is not causal.

Temporality

The independent variable must precede the dependent variable, not follow it.

For example, in order to state that decision support systems decrease medical errors, the use of the decision support system must precede the development of the medical error. Sometimes this is not easy to determine.

A prospective study design can help support this rule.

Rules of Evidence for Causality (cont.)

Dose-response relationship

As the dose of the independent variable is increased, it strengthens the relationship with the dependent variable.

In epidemiology, this can be demonstrated for smoking, in which dose and duration increase risk of disease.

In health informatics, if clinical reminder systems for colonoscopy reduce the likelihood of developing colon cancer, increasing the use of the clinical reminder systems for other types of cancer screening can be assumed to also reduce the development of cancer.

Rules of Evidence for Causality (cont.)

Biological plausibility

The relationship must make sense in relation to what is known about it in the sciences, animal experiments, and so forth.

Experimental evidence

A well-conducted RCT may confirm the causal relationship between an independent variable and a dependent variable.

Rules of Evidence for Causality

Coherence: Association should be in accordance with other factors known about the disease.

Analogy: If similar associations have demonstrated causality, then the more likely this association is probably causal.

Summary

Epidemiology and its principles can be used effectively when studying health informatics.

Researchers can use infectious disease or chronic disease models of causation to do this.

Many different types of epidemiological study designs can also be used to examine health informatics.

Summary cont’d

These include the descriptive (prevalence or cross-sectional), case-control (retrospective), prospective, and the experimental (clinical and community trial) study designs.

Most epidemiologists conduct research by beginning with the cross-sectional or prevalence study, and then move forward to the case-control, prospective, and experimental study designs.

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