Primary Care for New vs Established Medicaid Enrollees

February 11, 2021
Derek DeLia, PhD

The American Journal of Managed Care, February 2021, Volume 27, Issue 2

Under Affordable Care Act Medicaid expansion in New Jersey, new and distinct patterns of primary care utilization emerged for new vs established enrollees.

ABSTRACT

Objectives: To understand changes in primary care (PC) utilization in Medicaid and the Children’s Health Insurance Program (CHIP) 3 years after the Affordable Care Act (ACA).

Study Design: Secondary data analysis using Medicaid/CHIP paid claims and managed care encounters.

Methods: Pre-/post-ACA trends in total enrollment and PC visits among newly enrolled and established patients were analyzed in half-year increments from the first half of 2012 to the second half of 2016.

Results: After ACA expansion, there was a temporary surge in new Medicaid/CHIP enrollment (which included surges in pre-ACA eligibility categories) and slow, steady growth in total enrollment. The percentage of new enrollees completing a PC visit within 90, 180, and 365 days of enrollment fell markedly in the first half of 2014 and then rebounded to pre-ACA levels thereafter. Conversely, the percentage of new enrollees remaining enrolled at 90, 180, and 365 days spiked upward in the first half of 2014 and gradually fell thereafter. Among established enrollees, PC visits per person exhibited a downward trend throughout the post-ACA period, driven mostly by a decline in the percentage of individuals with any PC visits.

Conclusions: The first 6 months of ACA implementation in New Jersey were marked by a surge in Medicaid/CHIP enrollment that extended beyond the ACA target population, greater enrollment retention, and apparent bottlenecks in PC delivery. After the initial surge, new enrollees used PC at rates at least as high as in the pre-ACA period, whereas established enrollees used PC at a declining rate throughout the post-ACA period. PC delivery for new enrollees may have limited the availability of services for some established enrollees.

Am J Manag Care. 2021;27(2):72-78. https://doi.org/10.37765/ajmc.2021.88585

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Takeaway Points

  • Medicaid expansion under the Affordable Care Act raised concern that new enrollees would overburden primary care (PC) providers serving Medicaid and Children’s Health Insurance Program enrollees.
  • The New Jersey experience suggests that Medicaid expansion did create bottlenecks in the delivery of PC visits to new enrollees, but these bottlenecks were temporary.
  • After Medicaid expansion, established enrollees used PC at a modestly declining rate, driven primarily by a reduction in the proportion of established enrollees with any PC visits.
  • PC delivery for new enrollees may have limited the availability of services for some established enrollees.

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Since the implementation of the Affordable Care Act (ACA), the number of individuals ever enrolled in a given year in Medicaid or the Children’s Health Insurance Program (CHIP) has grown by 18%: from 80.1 million in 2013 to 94.8 million in 2017.1,2 This growth, along with increases in private coverage in the ACA health insurance exchanges, has heightened concerns about the adequacy of primary care (PC) capacity to meet emerging demands.3,4 These concerns are especially acute in Medicaid/CHIP, which have a history of limited provider participation, partly due to payment rates much lower than those in Medicare and private insurance.5-10 However, the ACA also authorized the Medicaid Primary Care Payment Increase, known as the “payment bump,” which provided federal funds to states to increase Medicaid payments for PC services to the level of the Medicare fee schedule in 2013 and 2014. This increase affected payments in Medicaid managed care as well as fee-for-service (FFS). After 2014, some states maintained the payment bump with their own funds (with the potential for federal matching funds under the usual matching formulas) whereas others did not.

Many studies have been published on the effects of the ACA Medicaid expansion (and pre-ACA expansion in Massachusetts), with nearly all of them, thus far, focusing on the first 1 to 2 years. Although the evidence is mixed, the balance shows mostly positive impacts on access to care and health outcomes.11-13 The evidence on utilization of PC is somewhat more mixed and is mostly based on patient surveys, administrative data from community health centers, or simulated patients who are actors requesting appointments that are immediately canceled. Results from research on the payment bump and prior studies on interstate variation in Medicaid physician fees have found that higher fees are associated with a variety of positive effects related to health care access and health outcomes, including access to PC appointments.14-22 One study, however, found that the ACA fee increase did not increase physician participation in Medicaid.23

This paper uses Medicaid/CHIP claims and encounter data from New Jersey as a single-state case study to help fill multiple gaps in the existing literature on PC delivery under the ACA. It allows for the separation of immediate and longer-term responses to enrollment growth. In contrast with simulated patient studies, it captures appointments that are completed rather than just scheduled. This distinction is important because Medicaid patients are significantly more likely to miss scheduled appointments for many reasons, which include forgetting, miscommunication, transportation problems, and long waits at the site of care.24-27 Although most ACA Medicaid expansion research examines Medicaid patients as a single group, this study recognizes the programmatic overlap between the Medicaid and CHIP populations and the diversity of Medicaid/CHIP eligibility groups, which reflect substantial differences in demographics, health, and disability status.

This study also distinguishes between newly enrolled and established patients. Although established patients may have greater access to PC through established relationships, they may also face new scheduling constraints if their providers make appointments available for newly enrolled populations. Such negative spillover effects on previously insured populations were demonstrated with the introduction of the National Health Service in the United Kingdom and the establishment of universal health insurance coverage in the Canadian province of Quebec.28,29 In contrast, the introduction of Medicare in the United States led to positive spillover effects on utilization by the nonelderly population.30 This difference is likely driven by Medicare’s initial open-ended cost-based FFS payment system that encouraged providers to expand their capacity. More recently, a study on the initial CHIP implementation found negative spillover effects that were offset in states that also increased their Medicaid/CHIP reimbursement rates to providers.7

METHODS

Study Setting

In 2017, New Jersey had approximately 1.5 million Medicaid/CHIP enrollees, accounting for 17% of the state’s total population.31 New Jersey ranks second nationally in Medicaid managed care enrollment at 92.9%.31 Historically, the state’s Medicaid FFS program has had lower-than-average fees for PC services. For example, in 2016, the state’s mean FFS payment was 42% of the Medicare fee schedule compared with 66% nationally.31 New Jersey did not continue the PC payment bump after 2014. However, through its Medicaid managed care contracting, the state allocated additional funds to health plan capitation payments and set price floors to promote greater payment for PC services and PC provider participation in health plan networks. As a result, a broad index of fees paid for PC services in New Jersey Medicaid/CHIP fell slightly in 2015 followed by successive increases in 2016 and 2017.32

Data and Measures

Study data come from the New Jersey Medicaid Management Information System, which includes all adjudicated Medicaid FFS claims and managed care encounter records. The study includes records with service dates from January 1, 2012, through January 1, 2016, grouped into half-year intervals. This grouping enables identification of gradual transition patterns. All Medicaid/CHIP eligibility categories are included in the study. (Stratified analysis by eligibility category is presented in the eAppendix [available at ajmc.com].) Individuals receiving temporary emergency Medicaid benefits are excluded, because they do not have coverage for PC services. Individuals living in facilities (eg, nursing homes) are also excluded, because their use of PC is often tightly connected to their facility-based care.

The key outcome measure is PC visits defined as ambulatory visits for evaluation and management (E/M). These are identified in Healthcare Common Procedure Coding System/Current Procedural Terminology codes as office/other outpatient services (99201-99215), office/other outpatient consultations (99241-99245), or preventive medicine services (99381-99397). Although specialists sometimes provide PC services, the focus of this analysis is on care delivered by PC providers specifically. Thus, E/M visits are included in the analysis only if they were delivered by a provider specializing in PC (eg, family medicine, pediatrics, internal medicine) and in an ambulatory care setting that is not a hospital emergency department.

Analysis

We examine statewide trends in measures of enrollment, PC visits, and disenrollment in half-year increments, with special attention given to breaks in trend before and after the beginning of the ACA in the first half of 2014. We begin with a description of statewide trends in new and total enrollment in New Jersey Medicaid/CHIP and the number of PC providers participating in the program. Total enrollment includes individuals with at least 1 day of enrollment in each 6-month interval, whereas total participation includes all providers with at least 1 visit. (Using alternative visit thresholds [eg, 10, 20] changes the level of participation but not the trends described below.)

Next, we examine PC visit and enrollment patterns among new enrollees over time. Specifically, we calculate the Kaplan-Meier failure function, with first PC visit as the failure event, for each half-year to identify changes in the percentage of new enrollees who have a PC visit with 90, 180, and 365 days of enrollment, conditional on being enrolled up to each time point.33 Similarly, we use the Kaplan-Meier survivor function, with disenrollment as the failure event, to identify changes in the percentage of new enrollees who remain enrolled in Medicaid/CHIP at the same 3 time points.

Finally, we describe trends in PC visits among established Medicaid/CHIP enrollees. Visit trends are partitioned by percentage of enrollees with any PC visits and number of PC visits among enrollees with at least 1 visit. To understand whether visit trends are affected by changes in enrollee demographics, risk scores, and duration of program enrollment, we also examine visit trends after controlling for these factors. Risk scores are calculated using the Chronic Illness and Disability Payment System.34 (Because risk scores depend on prior utilization, they are available for analysis of established patients only.) As visits are in count form, we model them using negative binomial regression and use the results to generate predicted averages for each half-year. All analyses were done in Stata 15 (StataCorp).

Because patients in different Medicaid/CHIP eligibility categories can vary greatly in their health risk and the types of PC providers they see (eg, pediatricians), all analyses are stratified by the age and eligibility categories below (which account for nearly all program enrollment):

  • Aged, Blind, Disabled (ABD): includes individuals receiving Supplemental Security Income and “spend down” populations. Within this program, the analysis separates children (≤ 19 years), nonelderly adults (20-64 years), elderly adults (≥ 65 years) dually eligible for Medicare, and elderly adults not dually eligible.
  • ACA expansion: childless nonelderly adults with income up to 138% of the federal poverty level.
  • Non-ABD children: includes Medicaid and CHIP children with income up to 350% of the federal poverty level.
  • Non-ABD (nonelderly) parents: Before the ACA, this group included parents with income up to 200% of the federal poverty level. After the ACA, it covered parents with income up to 138% of the federal poverty level. (Those whose incomes were between 138% and 200% were no longer eligible for Medicaid but were eligible for subsidized coverage through the ACA health insurance exchanges.)
  • Children’s services: This program includes foster children, children under the Division of Child Protection and Permanency, and children with serious behavioral issues, but excludes those in the non-ABD group above.

RESULTS

New Medicaid/CHIP enrollment spiked substantially in the first half of 2014 and then fell thereafter to just above pre-ACA levels at the end of the study period (Figure 1). A little more than half (56.5%) of the enrollment spike came from enrollment of the ACA expansion population and more than one-third (38.5%) came from new non-ABD children and parents. From the second half of 2013 to the first half of 2014, new non-ABD enrollment increased by 23% among children and 182% among nonelderly adults. Total Medicaid/CHIP enrollment grew steadily in the early ACA period (the first half of 2014 to the second half of 2015) then leveled off thereafter. Still, total enrollment at the end of the study period was substantially higher than in all the pre-ACA periods (first half of 2012 through second half of 2013).

The number of PC providers participating in Medicaid/CHIP fluctuated in the pre-ACA period and then trended slightly upward through the end of the study period (Figure 2). But due to the rapid increase in total enrollment, the number of PC providers per 1000 enrollees dropped substantially in the early part of the post-ACA period and then leveled off after the second half of 2015.

Figure 3 shows the percentage of new enrollees who had a PC visit within 90, 180, and 365 days of enrollment among those who maintained their Medicaid enrollment at least up to these time points. After a time of relative stability in the pre-ACA period, these percentages fell markedly in the first half of 2014 but then recovered in the second half of 2014. For example, the percentage with a PC visit within 90 days of enrollment fell from 7.5% in the second half of 2013 to 4.2% in the first half of 2014, and then remained in the 6% to 7% range for the rest of the study period. The percentage with a PC visit within 180 days of enrollment followed a similar pattern. In contrast, the percentage with a PC visit within 365 days recovered from the decline in the first half of 2014 but then ended the study period much lower.

Although the percentage of new enrollees with a PC visit declined in the first half of 2014, the total number increased due to the surge in enrollment. For example, the number of new enrollee visits within 90 days nearly doubled from 6085 (8% of 80,809) in the second half of 2013 to 11,556 (4% of 273,203) in the first half of 2014. For new enrollee visits within 180 and 365 days, the numbers more than doubled between the second half of 2013 and the first half of 2014.

Figure 4 shows the percentage of individuals who remained enrolled in Medicaid/CHIP at 90, 180, and 365 days. The percentage remaining enrolled at 365 days reached its peak (86.8%) for those initially enrolled in the first half of 2014; it then declined slightly for individuals enrolled in subsequent post-ACA half-years but remained above pre-ACA levels through the second half of 2016. Continued enrollment at 90 and 180 days exhibited a similar though less dramatic pattern, with continued enrollment at 90 days reaching its peak (98.4%) in the second half of 2014. Despite differences in overall levels of PC visits and enrollment retention, trends in PC utilization and enrollment patterns for new enrollees did not vary by eligibility group (eAppendix Tables 1 and 2).

Figure 5 shows trends in PC utilization among established enrollees and includes 2-period moving averages to smooth out seasonal variation. Overall, total PC visits per established enrollee declined by 12.5% from a mean of 1.4 in the pre-ACA period to 1.2 in the post-ACA period. Most of this decline was driven by a reduction in the percentage of enrollees with any PC visits rather than the number of visits among enrollees with at least 1 visit. In terms of pre- vs post-ACA averages, the former declined by 8.7% whereas the latter declined by 4.1%. These patterns are not affected by adjustment for patient demographics, risk scores, or length of Medicaid enrollment in each half-year (eAppendix Table 3). Except for dual-eligible patients, these patterns are similar for all Medicaid/CHIP eligibility groups (eAppendix Table 4).

DISCUSSION

After ACA implementation, New Jersey experienced significant growth in its Medicaid/CHIP population. Although new enrollment was concentrated among the ACA expansion population, part of the increase was driven by new enrollees in other eligibility categories who may have been influenced by the ACA’s publicity and outreach activities along with its mandated coverage requirement. These activities may have also influenced the greater retention observed among new enrollees in the early ACA period. Although there was an increase in the number of participating PC providers in the postexpansion period, this increase lagged behind enrollment growth leading to a decline in providers per enrollee.

Among new enrollees, there was a marked drop in PC utilization in the first half of 2014 that quickly reversed in subsequent periods. Although the percentage of new enrollees with PC visits declined in the first half of 2014, the total number of such visits delivered by PC providers to new enrollees at that time still expanded dramatically due to the surge in enrollment.

Utilization by new enrollees appears to have generated negative spillover effects on established enrollees, who experienced a downward trend in PC utilization. This trend was driven somewhat more by a decline in the percentage of individuals with any visits than the number of visits among those with at least 1 visit. Individuals with multiple visits may have had a greater need for care and, therefore, PC providers gave them greater priority when scheduling visits under emerging conditions of excess demand. These utilization patterns for established patients were not sensitive to adjustments for risk scores, demographics, or length of enrollment.

The enrollment surge at the beginning of 2014 can be viewed as a shock to the PC delivery system, leading to more enrollees per provider and causing excess demand that led to reductions (or possibly delays) in the receipt of care. For new enrollees, the shock was short lived, as PC utilization quickly returned to previous or higher levels. For established patients, the effects of the enrollment shock leveled off but did not reverse. In meeting the demand for PC among new enrollees, PC providers may have limited the availability of services for some established enrollees. It is not knowable from our data, however, whether this lower utilization represented a reduction in needed care or more careful prioritization of visits to ensure that high-value services were maintained and negative effects on patients were avoided. It is notable that the patterns observed across nearly all Medicaid/CHIP eligibility groups did not appear among dual eligible patients, who by virtue of their Medicare enrollment have access to a wider set of PC providers and plausibly different utilization patterns as a result.

Although New Jersey did not continue the PC payment bump in 2015, the state increased PC payment to Medicaid/CHIP providers through Medicaid health maintenance organization contracts in 2016 and 2017, which led to a rising index of PC prices during the time of our study period.32 We have not measured it directly, but it is possible that without this rising price index, PC provider participation may have flattened or reduced while enrollment grew, leading to spillover effects that would have been larger and overall PC use that would have been smaller.

It would be useful to contrast the New Jersey experience for new and established enrollees during different phases of Medicaid expansion with other expansion states. These contrasts would provide information about how to optimize PC as significant numbers of new patients are enrolled in insurance coverage. Key issues include balancing service to new and established enrollees and the role of prices paid in limiting potential negative spillover effects. The findings in this paper also provide an illustration of the possible effects that current nonexpansion states might experience if they were to expand Medicaid.

Limitations

This study is subject to some limitations. First, the New Jersey experience, although illustrative of how PC delivery can adjust to a surge in enrollment, may not generalize to all other states. Second, the analysis is largely descriptive and therefore does not isolate causal impacts of the ACA. Third, this analysis does not address whether any changes occurred in the duration, quality, or outcomes of PC visits or effects beyond the Medicaid/CHIP population.

CONCLUSIONS

This study provides a statewide view on how PC delivery systems can adjust to a rapid surge in demand for services as a result of expanding public insurance coverage. It makes unique contributions to the literature on the effects of coverage expansion by distinguishing between initial vs longer-term effects and experiences of new vs established enrollees. In the New Jersey case, bottlenecks initially appeared in the delivery of PC visits for new enrollees. But although there was a decline in the number of PC providers per enrollee, these bottlenecks quickly reversed, suggesting that PC providers were ultimately able to accommodate the higher level of demand from new enrollees. For established enrollees, there was a slight downward trend in use of PC services, suggesting that services to new enrollees may have reduced the availability of services somewhat for some established enrollees. Patterns of PC utilization were not affected by any observable changes in patient demographics, risk scores, or length of Medicaid enrollment. Ultimately, Medicaid expansion in New Jersey did not appear to cause widespread shortages, as some had feared, in the availability of PC. However, the small decline in service use by established enrollees suggests that any further expansion of Medicaid eligibility (eg, through a Medicaid buy-in) may lead to shortages, unless further expansion is paired with specific efforts to expand PC supply.

Acknowledgments

The work presented in this report was conducted at the request of the New Jersey Department of Human Services, Division of Medical Assistance and Health Services. Any opinions expressed in this report are those of the author and do not necessarily represent the view of the New Jersey Department of Human Services. The research was conducted with financial support from the Robert Wood Johnson Foundation (grant #75142).

The author acknowledges assistance with data assembly from Jose Nova and David Goldin and assistance with literature search and table and chart formatting from Daniel Wilhite.


Author Affiliations: MedStar Health Research Institute, Hyattsville, MD; Department of Plastic and Reconstructive Surgery, Georgetown University School of Medicine, Washington, DC.

Source of Funding: The research was conducted with financial support from the Robert Wood Johnson Foundation (grant #75142).

Author Disclosures: The author reports 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; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; obtaining funding; administrative, technical, or logistic support; and supervision.

Address Correspondence to: Derek DeLia, PhD, MedStar Health Research Institute, 6525 Belcrest Rd, Rm 714, Hyattsville, MD 20782. Email: Derek.M.DeLia@medstar.net.

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