So sánh phương pháp

	Nghiên cứu sinh thái tiềm cứu	Nghiên cứu dịch tễ học cắt ngang
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1950s–1970s (ecological epidemiology); prospective variant widely applied from 1980s onward	1960s (formal codification); widely practiced since mid-20th century
Người khởi xướng	Ecological study design formalised in epidemiology mid-20th century; prospective variant established through environmental and chronic disease research	Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s)
Loại	Observational epidemiological study design	Observational, descriptive/analytic epidemiological design
Công trình gốc	Morgenstern, H. (1998). Ecological studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.	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
Tên gọi khác	prospective ecologic study, prospective aggregate-level study, prospective group-level study, ecological cohort study	prevalence study, cross-sectional survey, transversal study, cross-sectional design
Liên quan	4	6
Tóm tắt	A prospective ecological study is an observational epidemiological design in which groups — not individuals — serve as the unit of analysis, and exposure data are collected going forward in time before outcomes are measured. Investigators define geographically, politically, or socially bounded populations, characterise their aggregate exposures at baseline, then ascertain group-level outcomes (disease rates, mortality rates) at one or more later time points. Because exposure precedes outcome measurement, this design provides stronger temporal evidence than retrospective ecological studies.	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.