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