Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ekoloģiskais pētījums vairākos centros× | Epidemioloģisks šķērsgriezuma pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1980s–1990s (formal methodological description) | 1960s (formal codification); widely practiced since mid-20th century |
| Autors≠ | Epidemiological tradition; methodologically articulated by Morgenstern (1982) and Susser (1994) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Tips≠ | Observational epidemiological study design | Observational, descriptive/analytic epidemiological design |
| Pirmavots≠ | Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344. DOI ↗ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195080407 |
| Citi nosaukumi | multi-site ecological study, multinational ecological study, pooled ecological analysis, multicenter aggregate study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | A multicenter ecological study is an observational epidemiological design in which the units of analysis are groups — such as cities, regions, or countries — rather than individuals, and data are pooled from two or more distinct centers or geographic areas. The approach links aggregate exposure measures (e.g., average pollution levels, vaccination coverage rates) to aggregate outcome rates (e.g., disease incidence per 100,000) across multiple populations, enabling comparisons that would be infeasible within any single site. | A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes. |
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