Abstract
Demographic estimates of population at risk often underpin epidemiologic research and public health surveillance efforts. In spite of their central importance to epidemiology and public-health practice, little previous attention has been paid to evaluating the magnitude of errors associated with such estimates or the sensitivity of epidemiologic statistics to these effects. In spite of the well-known observation that accuracy in demographic estimates declines as the size of the population to be estimated decreases, demographers continue to face pressure to produce estimates for increasingly fine-grained population characteristics at ever-smaller geographic scales. Unfortunately, little guidance on the magnitude of errors that can be expected in such estimates is currently available in the literature and available for consideration in small-area epidemiology. This paper attempts to fill this current gap by producing a Vintage 2010 set of single-year-of-age estimates for census tracts, then evaluating their accuracy and precision in light of the results of the 2010 Census. These estimates are produced and evaluated for 499 census tracts in New Mexico for single-years of age from 0 to 21 and for each sex individually. The error distributions associated with these estimates are characterized statistically using non-parametric statistics including the median and 2.5th and 97.5th percentiles. The impact of these errors are considered through simulations in which observed and estimated 2010 population counts are used as alternative denominators and simulated event counts are used to compute a realistic range fo prevalence values. The implications of the results of this study for small-area epidemiologic research in cancer and environmental health are considered.
Highlights
In recent years, a growing demand for small-area demographic estimates has been observed. Much of this demand comes from epidemiologists, who utilize these estimates for small-area surveillance efforts in the areas of cancer and environmental epidemiology in particular [1,2,3,4,5]
It is known that single-year-of-age estimates can be relatively more volatile than those constructed in five-year age intervals [11,20]
The perspective taken in this paper is to evaluate how much better one might do by employing a polynomial interpolation method than they would do by using a naive model based on simple rectangular pro-rating
Summary
A growing demand for small-area demographic estimates has been observed. It is well known that as the size of the population to be estimated decreases, errors in demographic estimates increase [6,7,8,9,10,11] These errors can be surprisingly large [6,7,8,9,10,11], but at present their impact on small-area epidemiologic measures has been incompletely described, and the. This example is extreme in both its spatial scale (census tracts represent very small areas, often a single neighborhood) [18] as well as in the fine-grained age intervals to be estimated. The first two procedures have been the most widely applied within applied demography; a rather long historical discussion of spline-fitting has not resulted in its general implementation by demographers working in non-academic settings (such as state government) where functionally utilized population estimates are typically made
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