1. 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

2. 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

3. 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

4. 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

5. 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

6. 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)

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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)

13. 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)

14. 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

15. 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

16. 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)

17. 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

18. 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

19. 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

20. 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.)

21. 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.)

22. 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

23. 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

24. 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?

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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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)