Applications of GIS in Assessing Migrant Access to Health Care: A Systematic Review

Background/Purpose: Social infrastructure includes the mechanisms and facilities through which social capital is built and impacts the ways in which populations access resources. This includes the healthcare system. For migrant populations, social infrastructure is particularly influential as they tend to depend more heavily on these mechanisms and systems as they build social capital in new spaces. The current study aimed to examine how geographic information systems (GIS) and geospatial analysis have been used to measure and address inequities in social infrastructure, specifically access to healthcare for migrant populations. This inventory highlights trends and provides examples of practices that utilize geospatial analysis to address disparities in access to healthcare.

Methods: A systematic review was conducted following the PRIMA guidelines. Five databases were used to identify literature including Academic Search Premier, ProQuest, ABI/Inform, Scopus, and CINAHL for articles published in English. To be included in analysis, articles must have referred to refugees, migrants, or immigrants as a population being examined, either from a source of primary or secondary data. The article must also utilize some type of GIS, include a health-related outcome, and include an operationalization of access.

Results: Most of the studies included in the review operationalize access as distance to healthcare services and ability to obtain health insurance. Studies often incorporated multiple sources of data, both qualitative and quantitative. Study data was collected through surveys and interviews as well as drawn from secondary sources, such as censuses and health departments. The two main types of articles we identified included those which utilized GIS as a tool to demonstrate the distribution of study variables and results, and those studies that utilized GIS to conduct geospatial analysis. Studies that conducted geospatial analysis included those with descriptive analysis and those with inferential analysis. Descriptive types often utilized GIS to determine distances to resource centers and demonstrate population and sample characteristics. Inferential types of articles included both spatial autocorrelation and regression analysis, demonstrating systematic patterns of access and how variables measuring access predict health outcomes.

Conclusions and Implications: We found that access to health care for migrant populations around the world is often substandard. Fortunately, much of the literature included in this analysis calls attention to present and future efforts to improve access for these populations. Social work practitioners and scholars can utilize the findings from these studies employing geospatial analysis as tools to advocate for improvements in access to health care for migrant populations.