Sammenlign metoder

	Lee-Carter Mortality Model	Small-Area Health Estimation
Fagområde	Demografi	Social Epidemiology
Familie	Regression model	Regression model
Oprindelsesår	1992	1979
Ophavsperson	Ronald D. Lee & Lawrence R. Carter	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Type	Log-bilinear model for forecasting age-specific mortality	Model-based estimator for reliable indicators in data-sparse areas
Oprindelig kilde	Lee, R. D., & Carter, L. R. (1992). Modeling and Forecasting U.S. Mortality. Journal of the American Statistical Association, 87(419), 659-671.	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.
Aliasser	Lee-Carter Method, Log-Bilinear Mortality Model, LC Mortality Forecast, Poisson Lee-Carter Model	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Relaterede	3	3
Resumé	The Lee-Carter model is the benchmark method for forecasting human mortality. Introduced by Ronald Lee and Lawrence Carter in 1992 for U.S. data, it captures the entire schedule of age-specific death rates with a remarkably parsimonious structure: the logarithm of the death rate at each age is a fixed average age profile, plus an age-specific sensitivity multiplied by a single time index that summarizes the overall level of mortality in each year. Because mortality has fallen steadily across the twentieth century, this single index trends downward over time, and forecasting it as a simple time-series process, typically a random walk with drift, propagates the historical pace of improvement into the future for every age at once. Brouhns, Denuit, and Vermunt later recast the fitting step as a Poisson regression on observed death counts, giving the model a proper statistical likelihood and more reliable uncertainty, and the approach now anchors official population and pension projections worldwide.	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.