Study Design and Measures of Disease Frequency

Intermediate Epidemiology

Objectives

• To review age, cohort, and period effect

• To discuss study designs

• To discuss measures of disease occurrence

• To practice interpretation of measures of disease occurrence

Study Designs

Descriptive Analytic Experimental

correlational

case report/case series

cross-sectional

case control

cohort

clinical trial

community trial

Study Design

• Descriptive– To describe the population at risk– To develop hypotheses

• Analytic– To test hypotheses

• Experimental– To test hypotheses

Hypothesis Testing

Epidemiologic Assumptions

• Human disease does not occur at random.

• Human disease has causal and preventive factors that can be identified through systematic investigation of populations.

Ecological Studies

• Units of analysis are usually geographically defined populations

• example: the correlation between alcohol outlets and violent crimes)

• Example: the association between “broken windows” (a measure of social decay) and sexually transmitted disease rates)

Examples• Aggregate measure – summarizes characteristics

of individuals within a group. (eg. mean income, percent male, education level from census track data).

• Environmental measure – physical characteristics of the geographic (e.g. location percentage of homes that are blighted, percentage of of schools without after-school programs).

• Global measure – characteristics that are not reducible to the individual level Aggregate –Environmental – (e.g. drinking age laws, social security benefits)

Difficulties with interpretation of ecological studies

• No temporality• Can’t really adjust for confounders because

the analysis is on the aggregate level• Susceptible to ecological fallacy or aggregate

bias – when the association observed on the aggregate level does not necessarily represent the association that exists at the individual level.

Cohort Study

• A defined population is identified, persons are categorized by exposure then followed to an outcome of interest.

• There is a comparison of the rate of the outcome among those who are exposed compared to those who are not exposed. (measure of association – relative risk)

• Persons who are lost to follow-up are called censored or withdrawls

Cohort study (con’t)

• Can be concurrent (prospective), non-concurrent (retrospective or historical) or mixed

Table 1. Factors associated with an incident STD among HIV-infected women in Cox Regression Analysis (N=741)

n

Incident STD (n=109)

Hazard Ratio (95% C.I.)

Race Non-white

White

624 117

16.0% 7.7%

1.41 ( .70, 2.85)

Age < 22 years 22 years

141 600

27.0% 11.8%

1.92 (1.25, 2.96)*

CD4

200/mm3 < 200/mm3

506 161

18.6% 8.7%

1.03 ( .58, 1.83)

Substance use Yes No

284 457

18.7% 12.3%

1.71 (1.13, 2.59)*

History of STD Yes No

145 596

49.7% 6.2%

9.80 (6.44, 14.9 )*