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Comparison of Healthcare Delivery Systems in Low- and High-Income Communities
Christina A. Nguyen, AB; Michael E. Chernew, PhD; Isabel Ostrer, AB; and Nancy D. Beaulieu, PhD

Comparison of Healthcare Delivery Systems in Low- and High-Income Communities

Christina A. Nguyen, AB; Michael E. Chernew, PhD; Isabel Ostrer, AB; and Nancy D. Beaulieu, PhD
This large-scale, national study shows geographic variation in provider supply and hospital access for low- and high-income communities following the Affordable Care Act.
DISCUSSION

In this national study, we documented that physician supply is lower in low- compared with high-income communities and that residents in low-income communities need to travel greater distances to specialty but not necessarily general acute care hospitals. Disparities in physician supply for low- versus high-income communities are greater in suburban areas and for specialty physicians. Disparities were lessened but not eliminated when we examined within-state variation in income and provider supply. Our findings likely underestimate the extent of income disparities in access to healthcare because some physicians do not deliver services to uninsured or Medicaid patients.

Prior work has shown geographic variation in the delivery of healthcare and the importance of the relationship between provider availability and rates of utilization of services such as physician visits.32-34 Provider availability is also related to quality of care.35-37 However, much of this work is outdated, and more recent debates on disparities have limited evidence-based discussion of delivery system infrastructure. This study quantifies the current local delivery systems on a national scale by describing the supply of primary care and specialty physicians, as well as access to different types of hospitals. We show that disparities exist between low- and high-income communities, particularly in physician supply in suburban areas and in distance to specialty hospitals for residents in rural areas.

Limitations

Our study focuses on one piece of the pathway from access to outcomes (ie, delivery system). Although there is support for this relationship in the literature,38-40 our lack of claims data prevents us from making connections to outcomes in this paper. We cannot be certain that areas with fewer providers are underserved or that places with more abundant providers are overserved. In addition, it is possible that some of the uninsured, particularly those in low-income, urban areas, utilize emergency department or safety net clinics as a default primary care access point. We did not have these data. Nevertheless, given concerns about primary care shortages generally, we think it is unlikely that residents in low-income communities with access to fewer primary care physicians are optimally served and believe that policies to address health disparities would benefit from greater attention to the local delivery system. Similarly, given the increasing interest among policy makers in transportation issues for low-income patients, understanding travel distances is important.

There are also measurement issues worth noting. First, defining markets is challenging because patients can cross boundaries. The measurement of disparities in provider supply related to income may be sensitive to how markets are defined and academic medical centers are treated. Large urban medical centers have many academic physicians who see patients on a part-time basis. These centers are mostly located in urban, often low-income, communities and may artificially inflate provider supply estimates. Second, without recent national Medicaid claims data, we could not examine the extent to which being restricted to providers who accept Medicaid contributed to disparities in access. Third, we did not have cost and quality data for comparing within and across urban, suburban, and rural areas. Fourth, because of data limitations, we could not identify all of the sites in which nonphysician providers such as nurse practitioners delivered care, and therefore did not include them in our analyses. Finally, our results are based on averages across all markets, masking much heterogeneity at the local level. Although the heterogeneity within and across local areas may make it difficult to generalize across the United States, this high-level view is important for informing both federal and local policy.

CONCLUSIONS

This study complements other related work. Specifically, recent studies of access focus attention on health insurance expansions (ie, patients’ ability to pay for care).3,8,10,28,41-45 The 2016 National Healthcare Quality and Disparities report documented income- and race-related disparities in healthcare utilization and quality.15 A more recent study by Chetty and colleagues went a step further in documenting substantial local area variation in income-related disparities in health outcomes that were uncorrelated with health insurance coverage.46 This study on local delivery systems broadens the policy discussion to include nonfinancial barriers to accessing care such as the resources and services available in local communities.

The stratification of our analyses by urban, suburban, and rural areas generated findings consistent with and complementary to those of Schnake-Mahl and Sommers.29 Based on data from a nationally representative survey defining access as having health insurance, a usual source of care, unmet need due to cost, and receipt of a routine checkup, they find that suburban low-income residents face higher barriers to accessing care. Combining their findings with ours suggests that suburban areas may be high-potential places for delivery system interventions to close the divide between the low- and high-income residents. Improving access to care for low-income populations may require greater attention to provider availability and access to specialty hospitals in local delivery systems.

Author Affiliations: Massachusetts Institute of Technology, Sloan School of Management (CAN), Cambridge, MA; Department of Health Care Policy, Harvard Medical School (CAN, MEC, NDB), Boston, MA; University of California, San Francisco, School of Medicine (IO), San Francisco, CA.

Source of Funding: Research reported in this study was supported by The Commonwealth Fund under award number 20160683. The content is solely the responsibility of the authors and does not necessarily represent the official views of The Commonwealth Fund.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (CAN, MEC, IO, NDB); acquisition of data (CAN, IO, NDB); analysis and interpretation of data (CAN, MEC, IO, NDB); drafting of the manuscript (CAN, NDB); critical revision of the manuscript for important intellectual content (CAN, MEC, NDB); statistical analysis (CAN, IO, NDB); obtaining funding (MEC); administrative, technical, or logistic support (CAN, IO); and supervision (CAN, NDB).

Send Correspondence to: Christina A. Nguyen, AB, Massachusetts Institute of Technology, MIT Sloan School of Management, 100 Main St, E62-489, Cambridge, MA 02142. Email: canguyen@mit.edu.
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