1. Descriptive Studies: Person, Place and Time October 26 2004 Epidemiology 511 W.A. Kukull

2. Today’s quote “…Disease does not occur randomly but in patterns which reflect the operation of underlying causes…Knowledge of these patterns…constitutes the major key to understanding causation, and hence, devising methods of control and prevention. In essence, the pattern of disease in populations is described by the composite answers to three basic questions: Who is attacked? Where does disease occur? When does it occur?” John P. Fox, MD, MPH

3. Descriptive Studies Observations of disease occurrence in –Person –Place –Time Description Hypothesis Hypothesis Analytic Study Results Description –Hypothesis generation leads to analytic studies

4. Case Reports/Series Focus on single patients or series of patients Describe unusual occurrence of disease Recognition of new diseases Formulation of risk factor hypotheses –3 vinyl chloride workers with angiosarcoma –5 cases of Pneumocystis pneumonia in young men Cannot test; no comparison group

5. Ecologic studies (a.k.a. correlational studies) Population characteristics correlated with disease occurrence –cigarette consumption and CHD deaths –Fluoride concentrations and fractures Strength: quick, inexpensive, use available data Weakness: cannot link exposures to individuals; cannot control for confounding

6. Ecologic Studies Correlation (or “association”) is not necessarily causation –Lack of a correlation does not imply no causation Ecologic study data represent average exposure levels –per capita alcohol consumption and CHD: opposite findings ecologic vs cohort

7. Levels Levels of Measurement –Who/what is measured Levels of Analysis –What is the unit of analysis; group or individual Levels of Inference –Is the inference level consistent with measurement and analysis levels

8. Ecologic bias Ecologic bias: “failure of ecologic effect estimates to reflect biologic effect at the individual level…” (Morgenstern, 1998) Cross-level inference Other

9. Cross-sectional Surveys Exposure and disease are assessed simultaneously-- “snapshot” Useful for prevalence, health planning Temporal relation not observable –different conclusions at different times? –reflect survival as well as etiology –Example: inactive farmers and high CHD

10. Time Short term changes; point source epidemics Time specification: –36 cases of polio within 12 month of tonsillectomy; 16 within 7 - 18 days Clustering in time –Triggering exposures –new statistical methods

11. Time (2) Secular Changes Changes over long periods –Completness of the data source In 1917, 35% of Death Certificates listed >1 cause; in 1979, 73% listed >1 cause Reasons? –Changes in diagnostic ability and classification –Demographic changes; treatment and survival –Concomitant environmental circumstances –Change in natural history of disease

12. Time (3) Secular change Generational trends or “Cohort” effects –Are persons born at different times at different risk? –Is yearly cross-sectional mortality by age misleading –Not to be confused with “cohort studies”

13. Time Cyclic Changes Repetitive cycling e.g., by season Flu and mortality Suicide and season, day of week Insect vectors; recreational activities Most MI’s/strokes occur in morning hours Usually evaluated by plotting occurrence of cases (assuming stable population)

19. Place International comparisons: Infectious and non-infectious diseases –High stomach Ca in Japan and Iceland –High CHD and colon Ca in US –Provides a basis of broad classification for countries as “probably” high or low Cross-National surveys: specific studies standardized protocols

20. Place (2) Disease Mapping: intra- or inter-national –John Snow and the Broad Street pump –Proximity to shipyards and cancer –Environmental contamination : Love Canal Boundary problem –No prior hypothesis –Prior hypothesis: set boundaries before determining disease –Observed cluster: don’t allow case occurrence to set boundaries

21. Person: Age effects Influences on Age Curves –diagnostic difficulties and trends –Stage of disease, incident or prevalent cases –Early life mortality and diseases of aging Bimodality –Hodgkins disease: two etiologies?

22. Person Gender differences –ratio –mortality: causes of death –morbidity women higher for thyrotoxicosis, diabetes, cholecystitis men: IHD(?), respiratory disease, PUD, accidents –more ill health in women; earlier deaths in men –Has it helped our understanding of disease?

23. Person Race, Ethnicity, Religion Inconsistent definition in place and time –Birth and Death certificate –Hispanics: race, ethnicity or culture?? Religion –cultural background –dietary practices Major gene diseases: Sickle cell; Tay- Sachs, Thalassemia, alcohol dehydrogenase

24. Person Migration: genes or environment Does a change in disease rates accompany a change in environment? –Cardiovascular disease; colon cancer Multi-generational studies Biases in Migrant studies –Diagnostic, reporting and coding practices –Migrants not representative of home country –Migration stresses may increase risk –Denominator inaccuracies

25. Person: other factors Socioeconomic Status –restricted activity and disability days decline with increasing income –infant mortality impact –No uniform definition; often based on occupation; years of education Occupation: toxic exposures, accidents Marital Status: mortality lowest among married people

26. Conclusion descriptive studies Generate hypotheses Describe confounders Reveal biases Ease of execution vs interpretation