Lecture 15: Cross-sectional studies and ecologic studies Jeffrey E. Korte, PhD BMTRY 747: Foundations of Epidemiology II Department of Public Health Sciences Medical University of South Carolina Spring 2015

Cross-sectional studies Also known as prevalence studies Exposure status and disease status are determined at a single timepoint (or over a short period of time) Prevalence rates can be compared among those with and without the exposure, or for varying levels of exposure Always good for public health planning; can also be good for understanding disease etiology Often cross-sectional analyses are published based on the baseline visit for a cohort study

Cross-sectional studies: understanding disease etiology Terrific study design for understanding exposures that are immutable (or are relatively invariant over the long term): Genetically determined factors Blood type, genetic polymorphisms, sex, hair color, tanning ability Ethnicity Birth order Education This is because the problem of temporal ambiguity (cause versus effect) is potentially obviated

Cross-sectional studies: understanding disease etiology Reasonable study design for understanding any exposure in relation to diseases with: Slow onset Long duration Low likelihood of seeking medical care e.g. arthritis, bronchitis, mental disorders This is because these diseases are difficult to study using other study designs

Cross-sectional studies: understanding disease etiology If disease has a slow onset: Cohort study is impractical Difficult to define disease incidence Need long follow-up, probably large sample size Case-control study may be impractical (if it is difficult to define or identify cases) Disease of long duration: Makes sense for a cross-sectional study: prevalence will be higher than for a disease of short duration

Cross-sectional studies: understanding disease etiology If medical care is not sought at outset: Case-control study is impractical, or at least difficult to interpret (case group would be mostly people with advanced disease)

Cross-sectional studies: advantages Relatively quick Relatively inexpensive Cross-sectional study can be clearly representative of a specific population Population random sample Sample of people seeking medical care

Cross-sectional studies: limitations Difficult to separate cause and effect “Exposure” and “disease” are measured at the same time (may be impossible to determine which came first) Example 1: people in low social classes have higher prevalence rates of: many mental illnesses chronic bronchitis other factors making job retention less likely

Cross-sectional studies: limitations Difficult to separate cause and effect Example 2: rates of chronic bronchitis are higher in low-pollution areas (people moved away from high-pollution areas) Conclusion: current exposure level may not be related to (or, may be inversely related to) the exposure level experienced when disease began

Cross-sectional studies: limitations Prevalent cases are over-represented by cases of long duration People who recover or die quickly are less likely to be designated as diseased in a cross-sectional study Relationship between exposure and disease may differ between short-duration and long-duration cases Characteristics of cases (exposure or disease) may differ: vulnerability, cofactors, alternative etiologic pathways, disease subtypes, etc. Less problematic if disease is normally of long duration

Cross-sectional studies: limitations If disease may have remissions: Cases in remission may be (incorrectly) classified as being disease-free e.g. cancer, viral infections If disease may be treated: Cases being successfully treated may be (incorrectly) classified as being disease-free e.g. hypertension, hypercholesterolemia

Cross-sectional studies: choice of study population May be selected based on: Exposure status (e.g. age range, unusual ethnicity, unusual occupation) Geographic area (e.g. neighborhood, proximity to point source of exposure, etc.) Sampling procedures have major impact on study efficiency (depending on question)

Cross-sectional studies: measurement of exposure Similar to cohort and case-control studies Questionnaires, records, lab tests, physical measurements, special procedures Important to determine when exposure occurred, and how long exposure persisted Limitations of self-report (memory lapse, bias)

Cross-sectional studies: measurement of disease Similar to cohort and case-control studies Questionnaire: symptomatology e.g. evidence of chronic respiratory disease Physical examination: signs e.g. evidence of arthritis in joints Special procedures e.g. tests of respiratory function for chronic respiratory disease; X-rays for arthritis

Cross-sectional studies: measurement of disease Time of disease onset should be determined First symptoms and/or diagnosis Disease may be detected during the study Onset may be gradual For diseases with periods of remission: Ask about past symptoms and/or diagnosis Diagnostic criteria must be established in advance, applied in a standard way If standard criteria are used, surveys from different areas can be compared

Cross-sectional studies: analysis: basic measures Prevalences: compare exposure groups “prevalence rate” (redundant jargon) prevalence rate ratio prevalence rate difference Prevalence odds Odds ratio (odds of having disease, not developing disease)

Cross-sectional studies: analysis: controlling for confounding Matching (control during design phase): not usually done General population samples: information on matching factors is not usually available before study is initiated

Cross-sectional studies: analysis: controlling for confounding Controlling for confounding in the analysis phase: similar to other types of studies Outcome may be dichotomous Presence or absence of prevalent disease Stratified analysis of cross-sectional data (Mantel-Haenszel procedures) Logistic regression, negative binomial regression, etc. Outcome may be continuous e.g. blood pressure, bone mineral density Linear regression

Cross-sectional studies: analysis: controlling for confounding Vulnerable to residual confounding Interpretation is dependent on cross-sectional design Timing of exposure relative to disease Characteristics of prevalent cases Information available about disease onset

Cross-sectional studies: serologic studies Blood samples from: General population Target sub-population (e.g. military recruits, college students) Antigen type, antibodies, immune complexes, various biochemical components, genetic characteristics (blood group, HLA antigens, etc.)

Cross-sectional studies: serologic studies Seroepidemiology: Different from diagnostic testing (this is performed in individuals with some disease) Applied to population groups Usually healthy Determine current and past patterns of infection and disease Can analyze serological results in relation to other data (questionnaire items, etc.)

Cross-sectional studies: seroepidemiology Antibody prevalence reflects exposures during certain time period IgG antibodies normally last a lifetime Cumulative experience of the population since birth IgM antibodies: shorter duration Proportion of population infected during last few months

Cross-sectional studies: seroepidemiology: possible uses Determine prevalence: Presence of antibody, antigen, chemical, hormone, etc. (Need at least 2 timepoints to determine incidence) Diagnostic serology: Identification of various causes of a clinical syndrome Identification of the spectrum of disease associated with a single causal agent or risk factor Identify an association between 2 or more markers

Ecologic studies Unit of analysis is a group of individuals Defined by geography, time period, etc. Usually depends on available data Disease information from health agencies for geographic units (country, state, county, etc.) Exposure information from another agency that tracks industries, farming techniques, food sales, etc. Question: do areas with high exposure have the highest rates of disease?

Ecologic studies Area 1 Area 2 10% exposed 1% diseased 40% exposed

Ecologic studies Area 1 Area 2 Dis Not dis Exp ? 100 No exp 900 10 990 Problem: we only have the marginal values for the 2 x 2 table Area 1 Area 2 Dis Not dis Exp ? 100 No exp 900 10 990 1000 Dis Not dis Exp ? 400 No exp 600 30 970 1000

Ecologic study (?) Area 1 Area 2 Dis Not dis Exp 1 99 100 No exp 9 891 OR=1 Area 2 OR=1 Dis Not dis Exp 1 99 100 No exp 9 891 900 10 990 1000 Dis Not dis Exp 12 388 400 No exp 18 582 600 30 970 1000

Ecologic study (?) Area 1 Area 2 Dis Not dis Exp 1 99 100 No exp 9 891 OR=1 Area 2 OR=0.22 Dis Not dis Exp 1 99 100 No exp 9 891 900 10 990 1000 Dis Not dis Exp 4 396 400 No exp 26 574 600 30 970 1000

Ecologic study (?) Area 1 Area 2 Dis Not dis Exp 8 92 100 No exp 2 898 OR=39 Area 2 OR=6.3 Dis Not dis Exp 8 92 100 No exp 2 898 900 10 990 1000 Dis Not dis Exp 24 376 400 No exp 6 594 600 30 970 1000

Ecologic studies Ecologic fallacy: the attempt to generalize associations seen at the ecologic level to individuals The lack of cross-tab information prevents us from being able to prove whether the association holds for individuals, or whether the ecologic association was due to something else

Ecologic studies Control for confounding Can use aggregate-level data on confounders to fit a multivariable model in an ecologic study But: controlling for confounding does not make it any more likely that any observed association holds for individuals Warning: ecologic-level association may be overestimated, underestimated, or inverse from individual-level association

Ecologic studies: why do them? They may be very quick and inexpensive They can be very valuable for hypothesis generation They may be the best approach when studying broad social or cultural factors But: epidemiologic studies are not usually limited to these types of factors (usually need individual-level data too)