Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Kikundi cha Nyuma (Retrospective Cohort Study)× | Utafiti wa Ki-epidemiolojia wa Msalaba× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | Mid-20th century (widely formalized 1950s–1970s) | 1960s (formal codification); widely practiced since mid-20th century |
| Mwanzilishi≠ | Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Aina≠ | Observational analytic study | Observational, descriptive/analytic epidemiological design |
| Chanzo asilia≠ | 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 |
| Majina mbadala | historical cohort study, non-concurrent cohort study, retrospective follow-up study, historical prospective study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | A retrospective cohort study assembles a group of individuals who share a common starting point and reconstructs their exposure history and subsequent outcomes entirely from pre-existing records. Because the data have already been collected before the study begins, the design is far faster and cheaper than a prospective cohort; however, the researcher must work with whatever information was recorded at the time rather than collecting purpose-built measurements. | 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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