Nearby provider supply did not affect identification of usual sources of primary or dental care among new Medicaid enrollees. Strategies to improve access are needed.
ABSTRACTObjectives: Adequate access to primary and dental care is essential for population health, and some state Medicaid programs have expanded insurance coverage for both. However, there are few data on new Medicaid enrollees’ ability to access services. We examined the relationship between provider supply and enrollees’ identification of usual sources of care.
Study Design: Between November 2015 and February 2016, we surveyed low-income adults newly insured through Medicaid in Philadelphia, Pennsylvania, to determine if they had a usual source of care. Additionally, we used geospatial methods to calculate adult population per provider ratios by Census tract for primary and dental care providers who accepted Medicaid patients, then identified low-supply clusters.
Methods: We used multivariable logistic regression models to describe the odds of identifying usual sources of care based on being in low- or high-supply clusters, adjusting for patient demographics.
Results: Of 1000 contacted individuals, 312 completed the survey. Among respondents, 168 were previously uninsured and newly enrolled in Medicaid; 66.7% of this group identified a usual primary care provider and 42.3% identified a usual dental care provider. In adjusted analyses, individuals living in low- and high-supply areas had similar likelihoods of identifying a usual source of primary or dental care.
Conclusions: Many new Medicaid enrollees did not have usual sources of primary or dental care, regardless of nearby provider supply. Efforts to understand what improves access or engagement in healthcare among Medicaid enrollees are critical after low-income adults gain insurance.
Am J Manag Care. 2019;25(3):135-139Takeaway Points
Recent expansions in Medicaid have renewed policy debates on how to improve access to primary and dental care among low-income populations. Access to primary care is essential because it provides an entry point to other forms of healthcare, delivers preventive services, and treats chronic diseases. Better primary care access is also associated with improved health outcomes at a lower cost, including reductions in heart disease and cancer mortality.1,2 Better dental care access is associated with lower rates of dental caries,3,4 diabetes,5 heart disease,6 and low-value emergency department utilization.7 Low-income populations in particular experience more access barriers to these 2 types of care than other populations.8-10
The Affordable Care Act (ACA) expanded coverage for primary and dental care services for low-income populations. As of 2016, when this study was conducted, 31 states and the District of Columbia had expanded Medicaid through the ACA, and 23 states included preventive dental service coverage for adults.11,12 However, in states that have expanded coverage, newly insured Medicaid enrollees still face access barriers. The ACA Medicaid expansion focused on addressing affordability13-16; however, health reforms have not equally prioritized provider availability17,18 and accessibility.18,19
The relationship between provider supply and the identification of a usual source of care among low-income populations for both primary and dental care is poorly understood. Therefore, the objective of this study was to examine the association between the supply of nearby Medicaid-accepting primary and dental care providers and the likelihood of identifying a usual source of care among newly enrolled Medicaid beneficiaries in an urban environment. Prior to our study, Pennsylvania began administering Medicaid to all of its expansion population through managed care organizations. In addition to covering primary care visits, these plans covered a limited set of dental visits for preventive, diagnostic, and minor restorative needs up to $1000.20,21 The population sampled resides in Philadelphia, a racially diverse city with a high density of providers and the fifth-largest population, but also the highest poverty rate among the 10 largest cities in the United States.22
STUDY DATA AND METHODS
The University of Pennsylvania Institutional Review Board approved this study.
We surveyed new Medicaid enrollees between November 2015 and February 2016. We identified Medicaid applicants through a partnership with Benefits Data Trust (BDT), a Philadelphia-based nonprofit organization that facilitates access to comprehensive public benefits and provides Medicaid application assistance. We randomly selected 1000 individuals to participate in a mailed survey if they received assistance from BDT within the year prior to November 2015, were aged 18 to 64 years, were able to read English, and had a Philadelphia mailing address. We excluded individuals who had not yet enrolled in Medicaid or had insurance before enrolling to focus on responses from newly insured adults. Individuals first received a letter informing them of the upcoming survey. One week later, we mailed the survey, consent information, a prepaid return envelope, and a $2 cash participation incentive. Nonrespondents received up to 2 additional reminders, then were called by phone to complete the survey. Respondents received an additional $10 gift card.
We developed the survey assessing usual sources of care based on questions used by the Agency for Healthcare and Research and Quality23 and our own pilot testing (see eAppendix [available at ajmc.com]). Specifically, the primary care question asked, “Is there a particular doctor’s office, clinic, health center, or other place that you usually go if you are sick or need advice about your health?” The dental care question asked, “A regular dentist is the one you would go to for check-ups and cleanings or when you have a cavity or tooth pain. Do you have a regular dentist or dental clinic?” The survey confirmed whether respondents had ultimately enrolled in Medicaid and captured demographic and self-reported health measures. We geocoded survey respondent addresses using ArcGIS 10.3 (Environmental Systems Research Institute; Redlands, California) to identify their Census tract (a close approximation to neighborhoods in Philadelphia), which is more relevant than larger geographic areas, such as zip code, for policy makers and planners in dense urban environments.17
Primary Care and Dental Practice Database
We constructed a database of Medicaid-participating primary and dental care providers in and around Philadelphia. We aggregated providers within office locations for the purposes of geospatial analyses. Primary care providers included physicians, nurse practitioners, and physician assistants. As described in a prior study,17 we used SK&A (2014), a proprietary database, to identify all primary care providers in and near Philadelphia participating in Medicaid. We supplemented this database with provider directories from the Philadelphia-based Medicaid plans and public lists of federally qualified health centers. Practices were contacted by phone to verify their address, the number of practicing clinicians, and each clinician’s full-time equivalent (FTE) workload to calculate an aggregate FTE for each office. Dental care providers consisted of general practice dentists who treated adults. We utilized the 2014 American Dental Association masterfile as the initial file and supplemented it with the National Provider Identifier dentist registry and the Medicaid provider file from Pennsylvania’s Department of Human Services. We defined the number of dental FTEs in a dental office based on the number of offices for which a dentist works.24
Census Tract Primary Care and Dental Care Provider Supply
Using the practice file described previously and population density data from the American Community Survey, we calculated a Medicaid adult population per provider ratio for each Census tract using the 2-step floating catchment area (2SFCA) method to estimate the ratio of Medicaid-enrolled adults per Medicaid-participating provider based on a 5-minute travel time. The 2SFCA method accounts for the providers in and around Philadelphia-based Census tracts and the population around a provider office.24 In addition, the 2SFCA method helps account for the modifiable areal unit problem, or the error introduced into spatial analyses by drawing unit borders and by aggregating units.25 We used ArcGIS 10.3 to account for traffic history and street restrictions in order to accurately measure travel times.
We defined low-provider areas as those with 5 or more contiguous Census tracts in the lowest quintile of supply—the population per provider ratio within a 5-minute travel time of a Census tract.17 Our goal was to identify geographic clusters with lower supply (as opposed to isolated Census tracts that may be adjacent to higher supply areas) to more accurately reflect experiences of patients seeking usual sources of care within and outside of their neighborhood. We utilized a relative instead of absolute measure of supply because there is no agreed-upon definition of the ideal adult population per provider ratio in urban areas for either primary or dental care.
For all analyses, we evaluated primary and dental care access separately. We assessed whether living in a low-provider supply area for primary or dental care was associated with having a usual source of primary or dental care, respectively. We used multivariable logistic regression models clustered at the level of Census tracts adjusting for age, gender, race/ethnicity, self-rated health, educational attainment, and employment status. In an additional analysis, we used the aforementioned models but changed the exposure of primary care and dental care supply to continuous measures of adult population per provider ratios. As a sensitivity analysis, we used 10-minute travel times to define supply. A 2-sided P <.05 was considered statistically significant. All analyses were carried out using Stata version 14.0 (StataCorp, LLP; College Station, Texas).
Of 1000 contacted individuals, 312 completed the survey. Among respondents, 168 reported being enrolled in Medicaid and were previously uninsured. These individuals (Table 1) were predominantly female (60.1%), 45 years or older (67.9%), African American (65.5%), and non-Hispanic (86.3%). A total of 59.5% had a high school education or less, and few (35.1%) were employed full time or part time. The majority (78.0%) reported having good to excellent health.
Within the study cohort, 112 of 168 (66.7%) had a usual source of primary care and 71 of 168 (42.3%) had a usual source of dental care. Those with a usual source of primary and dental care did not differ significantly from those without a usual source based on their sociodemographic characteristics. However, those with a usual source of dental care were more likely to have better self-reported health (excellent, very good, good) compared with respondents without a usual source of dental care (63/71 [88.7%] vs 56/84 [66.7%], respectively; P = .001). For primary care, there were no significant differences in self-reported health (P = .56).
Table 2 shows the unadjusted and adjusted associations between the supply of providers for respondents’ Census tracts and respondents’ identification of a usual source of primary and dental care. Individuals living in lower-supply areas were as likely to identify a usual source of primary or dental care as those living in higher-supply areas. These findings were no different when supply was modeled as a continuous variable (adult population per provider ratio) or when using 10-minute travel times.
In this study of new Medicaid beneficiaries from Philadelphia, we report 3 key findings. First, only 66.7% of our respondents identified a usual source of primary care, whereas nationally 78% did in 2016.26 Second, only 42.3% identified a usual source of dental care, whereas nationally 64% did during a comparable time period. Third, the supply of nearby providers was not associated with a higher or lower likelihood of identifying a usual source of primary or dental care.
The high percentage of individuals without a usual source of primary or dental care could be due to lack of perceived need, difficulty finding a provider who accepts new Medicaid patients,27 costs not covered by insurance (eg, missed work to attend appointments), or lack of knowledge about the healthcare system.28 Although we hypothesized that individuals living in lower-supply areas would be less likely to find a source of care, Philadelphia is a uniquely high-supply market, potentially affecting our findings and limiting generalizability. For example, the Philadelphia hospital referral region has 87.5 primary care physicians per 100,000 residents compared with the national median of 73.5, and it is near the 90th percentile for physicians overall.29 Second, beneficiaries may seek sources of care that are not necessarily near their home,30 basing their decisions on convenience in relation to shopping centers, workplaces, or public transportation networks. Finally, the point estimates from our model suggest that a relationship between supply and having a usual source of care may exist and is worth exploring in larger data sets across multiple cities.
Our study should be interpreted in the context of several limitations. First, given the observational nature of our study, our results are measures of association, not causation. Second, our supply measure does not utilize detailed accounts of provider effort, productivity, services offered, or acceptance of new Medicaid patients. Similarly, our demand measure (population counts) does not consider factors such as health status and healthcare-seeking preferences. Third, our approach assumes that most people prefer to obtain primary care or dental care near their home. The optimal distance to primary or dental care in urban areas is not known and may vary based on individual preference or travel patterns. Fourth, our study population was identified through a benefits outreach organization in a single urban area and may not be representative of all Medicaid enrollees in Philadelphia or other urban environments. Fifth, we were unable to determine which of 4 Medicaid managed care plans patients were enrolled in. Variations in plan types could lead to plan-level differences in provider availability, Census tract differences in plan-specific network adequacy standards, and different likelihoods of identifying a usual source of care. As a result, we may have overestimated access to care because not all nearby providers will participate in all Medicaid managed care plans. In addition, some individuals may have been assigned a provider by their insurance plan but may not have actually established a provider—patient relationship. Sixth, our generalizability is limited because we do not know the addresses or demographics of nonrespondents and cannot compare them with respondents. Finally, the modest response rate could lead to nonresponse bias. However, our response rate (31%) is an improvement compared with recent surveys of Medicaid patients, who are historically difficult to contact.31
Although we did not observe a relationship between nearby provider supply and the identification of a usual source of primary or dental care, there are several key issues moving forward. This relationship should be evaluated in other geographic areas (urban and rural) and in those with low provider supply. These evaluations may help determine whether characterizing access in terms of a population to provider ratio—a measure commonly used by policy makers and researchers—is useful.32 Our results ultimately suggest that policy makers and insurance plan managers should better understand what improves access or engagement in healthcare among Medicaid enrollees so that gains in insurance coverage can be translated into gains in health.
The authors thank Carolyn Cannuscio, ScD, for her critical input on survey development and Brittany Harrison, MA, for reviewing earlier versions of this manuscript. They also thank the staff at Benefits Data Trust for survey administration and for identifying individuals in Philadelphia whom they had helped apply for Medicaid.Author Affiliations: Division of General Internal Medicine, Perelman School of Medicine (KHC, XC, AB, DG), and Leonard Davis Institute of Health Economics (KHC, DG), University of Pennsylvania, Philadelphia, PA; Department of Public Health (JKH), Philadelphia, PA; Department of Pediatrics (CW) and Duke-Margolis Center for Health Policy (CW), Duke University, Durham, NC; Health Policy Institute, American Dental Association (KN, MV), Chicago, IL; Benefits Data Trust (EZ), Philadelphia, PA.
Source of Funding: This work was supported by the American Dental Association (ADA). Two of the authors (Drs Chaiyachati and Hom) received training support from the Veterans Health Administration (VHA) and Robert Wood Johnson Foundation (RWJF) during data collection and initial drafts of the manuscript. The ADA, VHA, and RWJF had no role in the study design; collection, analysis, or interpretation of the data; writing the report; or the decision to submit the report for publication.
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 (KHC, JKH, CW, KN, EZ, MV, DG); acquisition of data (KHC, JKH, CW, EZ, MV, DG); analysis and interpretation of data (KHC, JKH, CW, KN, XC, MV, DG); drafting of the manuscript (KHC, CW, XC, AB, MV, DG); critical revision of the manuscript for important intellectual content (KHC, JKH, CW, KN, AB, MV, DG); statistical analysis (KHC, XC); obtaining funding (CW); administrative, technical, or logistic support (KHC, KN, AB, EZ); and supervision (MV).
Address Correspondence to: Krisda H. Chaiyachati, MD, MPH, MSHP, University of Pennsylvania, 423 Guardian Dr, 1313 Blockley Hall, Philadelphia, PA 19104. Email: firstname.lastname@example.org.REFERENCES
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