Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs

Thomson, Dana R., Linard, Catherine, Vanhuysse, Sabine, Steele, Jessica E., Shimoni, Michal, Siri, José G., Caiaffa, Waleska T., Rosenberg, Megumi, Wolff, Eléonore, Grippa, Taïs, Georganos, Stefanos and Elsey, Helen, (2019). Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs. Journal of Urban Health, 96(-), 514-536

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  • Sub-type Journal article
    Author Thomson, Dana R.
    Linard, Catherine
    Vanhuysse, Sabine
    Steele, Jessica E.
    Shimoni, Michal
    Siri, José G.
    Caiaffa, Waleska T.
    Rosenberg, Megumi
    Wolff, Eléonore
    Grippa, Taïs
    Georganos, Stefanos
    Elsey, Helen
    Title Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs
    Appearing in Journal of Urban Health   Check publisher's open access policy
    Volume 96
    Issue No. -
    Publication Date 2019-06-18
    Place of Publication New York
    Publisher Springer US
    Start page 514
    End page 536
    Language eng
    Abstract Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products.
    Keyword Spatial data
    GIS
    Satellite imagery
    Mobile phone data
    Copyright Holder The Authors
    Copyright Year 2019
    Copyright type Creative commons
    ISSN 1099-3460
    DOI 10.1007/s11524-019-00363-3
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    Created: Mon, 05 Aug 2019, 18:54:11 JST by Nur Madihah Mat Latip on behalf of UNU IIGH