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Extending data for urban health decision-making: a menu of new and potential neighborhood-level health determinants datasets in LMICs

Extending data for urban health decision-making: a menu of new and potential neighborhood-level health determinants datasets in LMICs
Extending data for urban health decision-making: a menu of new and potential neighborhood-level health determinants datasets in LMICs

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.

GIS, Mobile phone data, Satellite imagery, Spatial data
1099-3460
Thomson, Dana R.
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Linard, Catherine
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Vanhuysse, Sabine
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Steele, Jessica E.
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Shimoni, Michal
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Siri, José
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Caiaffa, Waleska Teixeira
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Rosenberg, Megumi
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Wolff, Eléonore
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Grippa, Taïs
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Georganos, Stefanos
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Elsey, Helen
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Thomson, Dana R.
1ad13f81-f22e-4d89-a288-b05fb08b6c39
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Vanhuysse, Sabine
b14a29d0-693a-43e6-a5d9-a492eefd74d4
Steele, Jessica E.
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Shimoni, Michal
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Siri, José
1da147a1-4907-43da-bce2-c70de6029d21
Caiaffa, Waleska Teixeira
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Rosenberg, Megumi
59c7fd3d-a07b-40b0-9767-be46b50720ad
Wolff, Eléonore
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Grippa, Taïs
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Georganos, Stefanos
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Elsey, Helen
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Thomson, Dana R., Linard, Catherine, Vanhuysse, Sabine, Steele, Jessica E., Shimoni, Michal, Siri, José, Caiaffa, Waleska Teixeira, 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. (doi:10.1007/s11524-019-00363-3).

Record type: Article

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.

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e-pub ahead of print date: 18 June 2019
Keywords: GIS, Mobile phone data, Satellite imagery, Spatial data

Identifiers

Local EPrints ID: 432233
URI: http://eprints.soton.ac.uk/id/eprint/432233
ISSN: 1099-3460
PURE UUID: 4f2758a1-5744-415b-9258-d35fd3be4b45

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Date deposited: 05 Jul 2019 16:30
Last modified: 16 Dec 2019 17:35

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Contributors

Author: Dana R. Thomson
Author: Catherine Linard
Author: Sabine Vanhuysse
Author: Michal Shimoni
Author: José Siri
Author: Waleska Teixeira Caiaffa
Author: Megumi Rosenberg
Author: Eléonore Wolff
Author: Taïs Grippa
Author: Stefanos Georganos
Author: Helen Elsey

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