A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability

Abstract

BackgroundCommercial geospatial data resources are frequently used to understand healthcare utilisation. Although there is widespread evidence of a digital divide for other digital resources and infra-structure, it is unclear how commercial geospatial data resources are distributed relative to health need.MethodsTo examine the distribution of commercial geospatial data resources relative to health needs, we assembled coverage and quality metrics for commercial geocoding, neighbourhood characterisation, and travel time calculation resources for 183 countries. We developed a country-level, composite index of commercial geospatial data quality/availability and examined its distribution relative to age-standardised all-cause and cause specific (for three main causes of death) mortality using two inequality metrics, the slope index of inequality and relative concentration index. In two sub-national case studies, we also examined geocoding success rates versus area deprivation by district in Eastern Region, Ghana and Lagos State, Nigeria.ResultsInternationally, commercial geospatial data resources were inversely related to all-cause mortality. This relationship was more pronounced when examining mortality due to communicable diseases. Commercial geospatial data resources for calculating patient travel times were more equitably distributed relative to health need than resources for characterising neighbourhoods or geocoding patient addresses. Countries such as South Africa have comparatively high commercial geospatial data availability despite high mortality, whilst countries such as South Korea have comparatively low data availability and low mortality. Sub-nationally, evidence was mixed as to whether geocoding success was lowest in more deprived districts.ConclusionsTo our knowledge, this is the first global analysis of commercial geospatial data resources in relation to health outcomes. In countries such as South Africa where there is high mortality but also comparatively rich commercial geospatial data, these data resources are a potential resource for examining healthcare utilisation that requires further evaluation. In countries such as Sierra Leone where there is high mortality but minimal commercial geospatial data, alternative approaches such as open data use are needed in quantifying patient travel times, geocoding patient addresses, and characterising patients’ neighbourhoods.Electronic supplementary materialThe online version of this article (10.1186/s12942-018-0134-z) contains supplementary material, which is available to authorized users.

Creating small-area deprivation indices: a guide for stages and options

“…Both administrative and census data may be affected by statistical disclosure control or data sharing policies, meaning that deprivation cannot be measured for some areas 12. For these reasons, geospatial data are appealing, especially in high-income countries where these data are more readily available 19. However, these can be expensive to purchase or multiple data providers may need to be approached 10.…”

“…However, these can be expensive to purchase or multiple data providers may need to be approached 10. Sometimes data coverage is inconsistent, for example, available only for private but not public services,20 and there is mixed evidence on whether the accuracy of geospatial data is related to deprivation itself 19…”

District division administrative disaggregation data framework for monitoring leaving no one behind in the National Health Insurance Fund of Sudan: achieving sustainable development goals in 2030

“…The existing measurements of accessibility and health care services utilization include raster and network data models [25], Digiroad [26], the combination of land cover (GlobCover), elevation, road and river layers in AccessMod [27], Euclidian distance [28], the combination of the two-step floating catchment area method, the distance factor, the Huff-based competitive model [29], model-based estimates of average trip length [30], geographic information system-shortened path analysis and two-step floating catchment area methods [31], the index of commercial geospatial data quality/availability [32], the potential geographic accessibility in different scenarios [33], geographic information system maps [34], demographic mapping methods [35], straight-line distance, network distance, network travel time, raster travel time, mechanized, and nonmechanized [36], the two-step floating catchment area [37], and proximity [38]. These methods measure the accessibility or utilization of health care services by travel time, travel distance, travel behavior information, care resources, commercial geocoding, and neighborhood characterization.…”

Slums, Space, and State of Health—A Link between Settlement Morphology and Health Data

“…The ideal situation would be that uniform information on health data of different health classes as well as data on the physically existing building structure would be available for all relevant places in the world at a high resolution. Previous studies that have investigated the link between poor populations and health data have done so at the national, or in two cases, sub-national level [119]. However, this resolution is much too low to detect local effects, for example, when a specific spatial environment exert an effect on the slum dwellers.…”