HM404 Ab120916 ch05

© 2017 American Health Information Management Association
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:
Number of U. S. ambulatory healthcare facilities that use digital radiology systems
Number of ambulatory care facilities in the U. S.× 10 𝑛
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)
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.

(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.

(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.

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.

(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.

(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.

Header
45

HM404 Ab120916 ch05

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to HM404 Ab120916 ch05

Similar to HM404 Ab120916 ch05 (20)

More from BealCollegeOnline

More from BealCollegeOnline (20)

Recently uploaded

Recently uploaded (20)

HM404 Ab120916 ch05