So sánh phương pháp

	Nghiên cứu sinh thái hồi cứu	Nghiên cứu dịch tễ học cắt ngang hồi cứu
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	20th century (formalized ~1980s–1990s)	Mid–late 20th century
Người khởi xướng	Epidemiological tradition; formalized by Morgenstern and others	Epidemiology tradition (formalized in mid-20th century; Rothman, Greenland and others)
Loại	Observational epidemiological design	Observational study design
Công trình gốc	Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Tên gọi khác	retrospective aggregate study, historical ecological study, retrospective correlational ecological design, population-level retrospective study	retrospective cross-sectional survey, record-based cross-sectional study, retrospective prevalence study, secondary-data cross-sectional study
Liên quan	5	5
Tóm tắt	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 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.