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Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale

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  • Kevin Watjou

    (Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, 3500 Hasselt, Belgium)

  • Christel Faes

    (Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, 3500 Hasselt, Belgium)

  • Yannick Vandendijck

    (Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, 3500 Hasselt, Belgium)

Abstract

Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance.

Suggested Citation

  • Kevin Watjou & Christel Faes & Yannick Vandendijck, 2020. "Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale," IJERPH, MDPI, vol. 17(3), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:786-:d:313599
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    References listed on IDEAS

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    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Raghunathan, Trivellore E. & Xie, Dawei & Schenker, Nathaniel & Parsons, Van L. & Davis, William W. & Dodd, Kevin W. & Feuer, Eric J., 2007. "Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 474-486, June.
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