विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्रैग्मैटिक इकोलॉजिकल स्टडी× | अनुप्रस्थ काट (Cross-sectional) महामारी विज्ञान अध्ययन× | |
|---|---|---|
| क्षेत्र | महामारी विज्ञान | महामारी विज्ञान |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1967–1982 (pragmatic concept 1967; ecological study formalized ~1982) | 1960s (formal codification); widely practiced since mid-20th century |
| प्रवर्तक≠ | Morgenstern (ecological study framework); Schwartz & Lellouch (pragmatic design concept) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| प्रकार≠ | Observational ecological study with pragmatic framing | Observational, descriptive/analytic epidemiological design |
| मौलिक स्रोत≠ | Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344. DOI ↗ | 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 | real-world ecological study, effectiveness ecological study, population-level pragmatic study, pragmatic ecologic design | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | A pragmatic ecological study is an observational epidemiological design that examines associations between exposures and outcomes at the population or group level — using routinely collected, real-world data — with the explicit goal of informing practical public health decisions under everyday conditions. Rather than controlling every variable in a laboratory-like manner, it embraces the complexity and heterogeneity of natural settings to answer effectiveness questions relevant to policy. | 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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