Abstract
While population size data at the national and subnational levels are critical for planning and resource allocation, most PSE studies are conducted in a few selected cities and towns due to resource and time constraints. Extrapolation methods to estimate the population size for locations not included in the PSE exercise are critical to providing data for all subnational jurisdictions and the entire country as a whole. The decision on how to select cities or towns that reflect best the national picture is a critical first step. You may need to include cities or towns from areas with low, medium, or high prevalence of your target population(s). Having some data from all these (three) areas increase the power of your study and helps to better extrapolate the results to unobserved areas, and to the national level. Through this process, data smoothing of subnational data is often needed due to relatively small samples size problem. In this chapter, we describe methods that are required for smoothing subnational data, model subnational data to extrapolate estimates to regional and national levels, and triangulate findings from different size estimation methods by a Bayesian framework to produce credible estimates.
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Mirzazadeh, A., Baneshi, M.R. (2021). Data Smoothing, Extrapolation, and Triangulation. In: Rutherford, G. (eds) Methods in Epidemiology. Advances in Experimental Medicine and Biology, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-75464-8_4
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