Salīdzināt metodes

	Poisson Rate Regression	Small-Area Health Estimation
Nozare	Social Epidemiology	Social Epidemiology
Saime	Regression model	Regression model
Izcelsmes gads	1983	1979
Autors	E. L. Frome (rate formulation); A. C. Cameron & P. K. Trivedi (modern count-data treatment)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Tips	Generalized linear model for event rates and counts with log link and person-time offset	Model-based estimator for reliable indicators in data-sparse areas
Pirmavots	Frome, E. L. (1983). The Analysis of Rates Using Poisson Regression Models. Biometrics, 39(3), 665-674.	Fay, R. E., & Herriot, R. A. (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association, 74(366), 269-277.
Citi nosaukumi	Poisson Regression for Rates, Log-Linear Rate Model, Incidence-Rate-Ratio Regression, Poisson Regression with Offset	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Saistītās	3	3
Kopsavilkums	Poisson rate regression is the standard generalized linear model for analyzing event rates and counts, such as the number of deaths, hospitalizations, or new cases observed over a span of person-time. It models the logarithm of the expected event rate as a linear function of covariates, using a Poisson likelihood and a log link, and accommodates differing amounts of exposure by including the log of person-time as an offset. Because coefficients enter on the log scale, their exponentials are incidence-rate ratios that quantify multiplicative effects on the rate. The rate formulation was crystallized in Frome's 1983 Biometrics paper, and the model sits within the broader count-data framework developed comprehensively by Cameron and Trivedi, who also detail its central practical concern: overdispersion, where the variance exceeds the Poisson assumption and standard errors must be corrected.	Small-area estimation produces reliable health indicators for places where the survey sample is too thin to support a trustworthy direct estimate. A national health survey may interview only a handful of people in a given county or census tract, so a county-level prevalence computed straight from the data swings wildly from area to area. The model-based solution, pioneered by Robert Fay and Roger Herriot in 1979 for estimating income in small places, is to borrow strength: combine each area's noisy direct estimate with a regression prediction built from auxiliary variables that are known for every area, weighting the two by their relative reliability. Rao and Molina's comprehensive treatment codified this area-level mixed model and its variants as the foundation of small area estimation. Applied to public health, the approach underpins local prevalence maps for chronic disease and health behaviors, such as the CDC PLACES project, that decision-makers use to target resources at neighborhood and county scale.