A Spatially Based INDEX of Primary Care Accessibility in New Jersey

Background and Purpose: Primary health care has been acknowledged as an agent to promote population health and reduce health disparities. Understanding the cumulative effects of these factors on primary care access is essential for developing effective policies and interventions to address health disparities and promote health equity. Although various indices exist, primary care access is shaped by multiple complex, interrelated factors that may not be fully captured by previous measures or indices.

Methods: This study used Geographic Information Systems (GIS) and a multicriteria technique to identify census tracts vulnerable to limited primary care access. It expanded on the factors currently included in existing indices to develop a comprehensive, composite measure of sociodemographic and individual influences on primary care access across New Jersey. The study then compared these findings to the state's Medically Underserved Area (MUA) designations. It is hypothesized that the findings from the newly created index will differ substantially from the federally designated MUA maps and underscore discrepancies and shortcomings in existing access measures. A shapefile of New Jersey and its census tracts was used as the spatial references for this study. Sociodemographic census data comprised the 17 factors in the final index and were grouped into four categories: material instability, at-risk populations, health behaviors, and neighborhood/household disadvantage. ArcGIS was used to create a composite index of social determinants of primary care access.

Results: Out of 2,181 census tracts throughout New Jersey, 207 tracts (9.4%) were identified to be high risk, representing a population of 842,140 individuals. 932 tracts (42%) were identified as medium risk representing a population of 3,744,558, and 676 tracts (30%) were identified to be low risk, representing a population of 2,778,408. Then, these results were compared to the medically underserved area designation areas, and 1,139 census tracts were identified to be at medium or high risk using the PCA index, while only 248 areas were classified as medically underserved areas/populations.

Conclusions and Implications: The results of this analysis offer critical insights into the geographic distribution of factors that influence primary care access in New Jersey. This GIS-based index can be used to contribute to the literature of healthcare access by identifying the most vulnerable communities throughout the state. By identifying areas of high risk to primary care access, policymakers, public health officials, and healthcare providers can develop targeted interventions to improve healthcare access for all New Jersey residents.

Based on these findings, this research argues that a comprehensive primary-care-access map should include a multitude of sociodemographic individual factors that can be used to evaluate geographic locations to provide a more nuanced understanding of access disparities across different communities in the state. While high and medium risk areas are highlighted in the results, it is important to note that the “low” risk communities still demonstrate a level of cumulative risk of experiencing limited primary care access. Few areas indicated no risk or inadequate data. Therefore, future considerations of primary care access should consider the state as whole, in addition to the higher risk areas.