Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pragmatisks šķērsgriezuma epidemioloģiskais pētījums× | Ekoloģiskais pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | Mid-20th century onwards; pragmatic framing prominent from 1967 | 19th century (Snow 1854); formalised mid-20th century |
| Autors≠ | Classical epidemiology tradition; pragmatic framing refined by Schwartz & Lellouch (1967) and subsequent real-world evidence literature | Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues |
| Tips≠ | Observational epidemiological design | Observational epidemiological study |
| Pirmavots≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗ |
| Citi nosaukumi | pragmatic cross-sectional survey, real-world cross-sectional study, observational cross-sectional study, prevalence survey | aggregate study, correlational study, ecological correlation study, population-level study |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | A pragmatic cross-sectional epidemiological study measures the prevalence of exposures, outcomes, and risk factors in a defined population at a single point in time, conducted under real-world conditions rather than tightly controlled experimental settings. It provides a snapshot of the health status of a community or patient group, making it one of the most widely used designs for surveillance, needs assessment, and hypothesis generation in clinical and public-health epidemiology. | An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease. |
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