Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ekoloģiskais pētījums vairākos centros× | Daudzkodolu kohortas pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1980s–1990s (formal methodological description) | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Autors≠ | Epidemiological tradition; methodologically articulated by Morgenstern (1982) and Susser (1994) | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| Tips≠ | Observational epidemiological study design | Observational longitudinal study |
| Pirmavots≠ | Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Citi nosaukumi | multi-site ecological study, multinational ecological study, pooled ecological analysis, multicenter aggregate study | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| 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 multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research. |
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