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

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          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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          The online version of this article (10.1007/s11524-019-00363-3) contains supplementary material, which is available to authorized users.

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          Civil registration systems and vital statistics: successes and missed opportunities.

          Vital statistics generated through civil registration systems are the major source of continuous monitoring of births and deaths over time. The usefulness of vital statistics depends on their quality. In the second paper in this Series we propose a comprehensive and practical framework for assessment of the quality of vital statistics. With use of routine reports to the UN and cause-of-death data reported to WHO, we review the present situation and past trends of vital statistics in the world and note little improvement in worldwide availability of general vital statistics or cause-of-death statistics. Only a few developing countries have been able to improve their civil registration and vital statistics systems in the past 50 years. International efforts to improve comparability of vital statistics seem to be effective, and there is reasonable progress in collection and publication of data. However, worldwide efforts to improve data have been limited to sporadic and short-term measures. We conclude that countries and developmental partners have not recognised that civil registration systems are a priority.
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            China: Open access to Earth land-cover map.

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              Review of the Current State of UAV Regulations

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                Author and article information

                Contributors
                dana.r.thomson@gmail.com
                Journal
                J Urban Health
                J Urban Health
                Journal of Urban Health : Bulletin of the New York Academy of Medicine
                Springer US (New York )
                1099-3460
                1468-2869
                18 June 2019
                18 June 2019
                August 2019
                : 96
                : 4
                : 514-536
                Affiliations
                [1 ]GRID grid.475139.d, Flowminder Foundation, ; Stockholm, Sweden
                [2 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, Department of Geography and Environment, , University of Southampton, ; Southampton, UK
                [3 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, Department of Social Statistics, , University of Southampton, ; Southampton, UK
                [4 ]ISNI 0000 0001 2348 0746, GRID grid.4989.c, Spatial Epidemiology Lab, , Université libre de Bruxelles (ULB), ; Brussels, Belgium
                [5 ]ISNI 0000 0001 2242 8479, GRID grid.6520.1, Department of Geography, , Université de Namur, ; Namur, Belgium
                [6 ]ISNI 0000 0001 2348 0746, GRID grid.4989.c, Department of Geosciences, Environment and Society (DGES-IGEAT), , Université libre de Bruxelles (ULB), ; Brussels, Belgium
                [7 ]ISNI 0000 0004 0645 1099, GRID grid.16499.33, Signal and Image Centre, Faculty of Electrical engineering, , Royal Military Academy, ; Brussels, Belgium
                [8 ]International Institute for Global Health, United Nations University, Kuala Lumpur, Malaysia
                [9 ]ISNI 0000 0001 2181 4888, GRID grid.8430.f, Observatory for Urban Health in Belo Horizonte, School of Medicine, , Federal University of Minas Gerais, ; Belo Horizonte, Brazil
                [10 ]Center for Health Development, World Health Organization, Kobe, Japan
                [11 ]ISNI 0000 0004 1936 8403, GRID grid.9909.9, Nuffield Centre for International Health and Development, , University of Leeds, ; Leeds, UK
                Author information
                http://orcid.org/0000-0002-9507-9123
                Article
                363
                10.1007/s11524-019-00363-3
                6677870
                31214975
                9176c319-dec0-43ca-9a1e-ef73906c2e67
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: University of Southampton
                Categories
                Article
                Custom metadata
                © The New York Academy of Medicine 2019

                Public health
                spatial data,gis,satellite imagery,mobile phone data
                Public health
                spatial data, gis, satellite imagery, mobile phone data

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