Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Ретроспективне поперечне епідеміологічне дослідження× | Поперечне епідеміологічне дослідження× | |
|---|---|---|
| Галузь | Епідеміологія | Епідеміологія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | Mid–late 20th century | 1960s (formal codification); widely practiced since mid-20th century |
| Автор методу≠ | Epidemiology tradition (formalized in mid-20th century; Rothman, Greenland and others) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Тип≠ | Observational study design | Observational, descriptive/analytic epidemiological design |
| Основоположне джерело≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | 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 |
| Інші назви | retrospective cross-sectional survey, record-based cross-sectional study, retrospective prevalence study, secondary-data cross-sectional study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | A retrospective cross-sectional epidemiological study measures the prevalence of exposures and outcomes at a single analytical time point using data that were originally recorded in the past — such as medical records, administrative databases, or disease registries. It combines the snapshot logic of a cross-sectional design with the efficiency of retrospective data access, making it a practical choice when prospective data collection is unfeasible or when large existing datasets are available. | 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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