Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Andrew J. Tatem, Susana Adamo, Nita Bharti, Clara R. Burgert, Marcia Castro, Audrey Dorelien, Gunter Fink, Catherine Linard, Mendelsohn John, Livia Montana, Mark R. Montgomery, Andrew Nelson, Abdisalan M. Noor, Deepa Pindolia, Greg Yetman, Deborah Balk

    Research output: Contribution to journalReview articlepeer-review

    70 Scopus citations

    Abstract

    The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

    Original languageEnglish (US)
    Article number8
    JournalPopulation Health Metrics
    Volume10
    DOIs
    StatePublished - May 16 2012

    Bibliographical note

    Funding Information:
    AJT is supported by a grant from the Bill & Melinda Gates Foundation (#49446), which also supports DP. AJT acknowledges funding support from the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. CL is supported by a grant from the Fondation Philippe Wiener - Maurice Anspach. This paper is the result of a working group meeting held in May 2011 in New York, funded by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. AD received financial support from the National Institute for Child Health and Human Development, Grant No. R24HD047879. AMN is supported by a Wellcome Trust Intermediate Research Fellowship (##095127). SA is supported in part by NASA under contract NNG08HZ11C for the continued operation of the Socioeconomic Data and Applications Center (SEDAC). CRB is supported by the US Agency for International Development-funded MEASURE Demographic and Health Survey.

    Keywords

    • Demography
    • Disease mapping
    • Epidemiology
    • Population

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