Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Ретроспективное экологическое исследование× | Поперечное эпидемиологическое исследование× | |
|---|---|---|
| Область | Эпидемиология | Эпидемиология |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 20th century (formalized ~1980s–1990s) | 1960s (formal codification); widely practiced since mid-20th century |
| Автор метода≠ | Epidemiological tradition; formalized by Morgenstern and others | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Тип≠ | Observational epidemiological design | Observational, descriptive/analytic epidemiological design |
| Основополагающий источник≠ | Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven. link ↗ | 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 |
| Другие названия | retrospective aggregate study, historical ecological study, retrospective correlational ecological design, population-level retrospective study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Связанные≠ | 5 | 6 |
| Сводка≠ | A retrospective ecological study examines associations between exposures and outcomes using pre-existing aggregate data from defined populations or geographic units. Rather than following individual subjects, the unit of analysis is a group — a country, region, or time period — and all measurements come from historical records already collected before the study began. It is a rapid, low-cost way to generate hypotheses about environmental, social, or policy determinants of disease at the population level. | 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. |
| ScholarGateНабор данных ↗ |
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