विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मिलान पारिस्थितिक अध्ययन× | अनुप्रस्थ काट (Cross-sectional) महामारी विज्ञान अध्ययन× | |
|---|---|---|
| क्षेत्र | महामारी विज्ञान | महामारी विज्ञान |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1970s–1990s (methodological consolidation) | 1960s (formal codification); widely practiced since mid-20th century |
| प्रवर्तक≠ | Extension of classical ecological study design; matching principles formalized in 20th-century epidemiology | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| प्रकार≠ | Observational study design | Observational, descriptive/analytic epidemiological design |
| मौलिक स्रोत≠ | Morgenstern, H. (1998). Ecologic studies in epidemiology: Concepts, principles, and methods. Annual Review of Public Health, 16, 61–81. 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 |
| उपनाम | matched ecologic study, geographically matched ecological study, area-matched ecological design, matched aggregate study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| संबंधित | 6 | 6 |
| सारांश≠ | A matched ecological study is an observational epidemiological design in which aggregate units — such as geographic areas, communities, or time periods — are systematically paired or matched on key characteristics before comparing exposure and outcome rates. Matching at the group level controls for area-level confounders and improves comparability between exposed and unexposed units, producing more credible estimates of ecological associations than an unmatched counterpart. | 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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