522914

research-article2014

INQXXX10.1177/0046958014522914INQUIRYWilk

Research Paper

Differential Responses among Primary Care Physicians to Varying Medicaid Fees

INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2013, Vol. 50(4) 296­–311 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0046958014522914 inq.sagepub.com

Adam S. Wilk, BA1

Abstract The Affordable Care Act (ACA) provided for significant increases in Medicaid fees for primary care services—up to 100 percent of Medicare fees for 2013 and 2014—to encourage increased Medicaid participation among primary care physicians (PCPs). In this study, I use non-linear multivariate regression techniques and data from nationally representative physician surveys and periodic Medicaid fee surveys to investigate heterogeneity in the effects of such increases. I find that the PCPs more responsive to Medicaid fee changes are those who see fewer Medicaid patients typically. I also estimate effects associated with Medicaid fee increases comparable with the ACA’s fee changes. Keywords Affordable Care Act, Medicaid, primary care

By 2022, there may be as many as 21 million new Medicaid and Children's Health Insurance Program enrollees (Holahan et al. 2012). Most will be enrolled over the next few years as states take advantage of the Affordable Care Act’s (ACA) enhanced Federal financing arrangements and expand their Medicaid populations to include nearly all individuals with incomes below 133 percent of the Federal Poverty Level. If all states were to do so, perhaps 70 percent of these new enrollees will have been previously uninsured (Holahan and Headen 2010). It is hoped that, as a result, many individuals and families will have significantly improved access to primary care and other important health care services. However, in recent years, fewer physicians indicate they are accepting new Medicaid patients in their practices (Decker 2012; 2013). Researchers often attribute this decline to low fees and increased administrative (e.g., delayed payments) and patient burdens associated with many Medicaid programs (Cunningham and Nichols 2005; Cunningham and O’Malley 2009; Davidson 1982; Decker 2007; Hadley 1979; Long 2013; Sloan, Mitchell, and Cromwell 1978). In

part to address concerns that Medicaid’s low fees may be insufficient to attract primary care physicians (PCPs) to care for expanding Medicaid populations (Miller 2013; Sommers, Swartz, and Epstein 2011), the ACA provided for federally subsidized increases in Medicaid fees for primary care services up to 100 percent of Medicare fees for 2013–2014. On average, this represented an increase of approximately 73 percent over 2012 fee levels across states, with significant variation across states (Zuckerman and Goin 2012). States expanding their Medicaid programs may be counting on these increases to help ensure new beneficiaries will have adequate access to care. The last two decades’ studies of PCP responses to varying Medicaid fee generosity have typically found that their impact on PCPs’ Medicaid 1

University of Michigan, School of Public Health, Ann Arbor, USA Corresponding Author: Adam S. Wilk, Department of Health Management and Policy, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48103, USA. Email: [email protected]

Wilk participation is small-to-moderate (Berman et al. 2002; Coburn, Long, and Marquis 1999; Fanning and de Alteriis 1993; Perloff, Kletke, and Fossett 1995; Perloff et al. 1997; Zuckerman et al. 2004). Notably, these studies’ results have been drawn from only moderate-sized physician samples, data from limited geographic areas where fee changes were relatively small, or data too old to reflect recent declines in physicians’ Medicaid participation. Decker (2012) found larger effects but relied on state-level data only. Researchers have also explored the effects of PCP- and practice-level characteristics on PCPs’ participation in Medicaid, reflecting heterogeneity among physicians (Decker 2012; Reschovsky and O’Malley 2008; Socha and Bech 2011; Tucker 2002). To date, researchers have not investigated to what extent and why such characteristics may predict different responses to varying Medicaid fee generosity. In this article, I identify how differences in states’ Medicaid fee generosity and key PCP- and group-level characteristics—including practice ownership, practice type, compensation arrangement, experience, and perceived quality—interact in determining the extent to which different PCP subgroups participate in Medicaid. To do so, I leverage a large, multi-year data set containing recent physician survey data, a wide range of Medicaid–Medicare fee ratios across states, and several market- and state-level factors. I apply the theoretical framework of Sloan, Mitchell, and Cromwell (1978) to interpret my findings. Data on actual physician responses to 2013’s fee increases will be neither complete nor reflective of PCPs’ steady-state responses until mid2014 at the earliest, and these responses will not be attributable solely to fee levels. Thus, my findings may be valuable to states evaluating how they might extend increased Medicaid primary care fees into 2015—when federal subsidies for them expire—and at which providers targeted incentives or outreach may be most needed as their Medicaid rolls expand.

Theoretical Framework Sloan, Mitchell, and Cromwell (1978) constructed a theoretical model predicting which PCPs would accept Medicaid patients and to

297 what extent. In this two-market model, profitmaximizing PCPs compare the incremental revenues and costs of accepting each additional Medicaid or non-Medicaid (i.e., privately insured, Medicare, or self-pay) patient into their panels. Sloan, Mitchell, and Cromwell assume PCPs have some price-setting power (e.g., with respect to patient cost-sharing) in the nonMedicaid market, which has a downward sloping demand curve—at least among non-Medicare patients—but no price-setting power in the Medicaid market, which has a fixed fee schedule. Revenues per privately insured or Medicare patient are significantly higher than revenues per Medicaid patient (for the same services), and average operating costs per patient may be higher per Medicaid patient due to increased administrative (e.g., delayed payments) and patient burdens. Therefore, we should expect PCPs’ panels to consist predominantly of non-Medicaid patients, and virtually all PCPs will have at least some non-Medicaid patients in their panels. Yet Sloan, Mitchell, and Cromwell argue not all PCPs will see non-Medicaid patients exclusively because of the increasing marginal operating costs of treating them—they describe increasing collection costs associated with increasing numbers of self-pay patients (likely to accrue bad debt for the PCPs). This model predicts that, for the average PCP, any factor improving the revenue-cost margin of accepting a new Medicaid patient—relative to that of accepting a new non-Medicaid patient— increases her propensity to accept new Medicaid patients. Likewise, at the margin, such factors increase the fraction of her panel consisting of Medicaid patients.1 This framework describes how an average PCP’s panel reaches an equilibrium payer mix, but it does not permit robust prediction of market-level measures of PCP participation in Medicaid because it does not account for heterogeneity across PCPs within a market. This heterogeneity may be important when projecting the effects of an increase in Medicaid fees locally, because different PCP and organizational factors may drive PCP groups to respond more or less strongly to this incentive. For example, different PCP groups might all participate relatively little in Medicaid but for different reasons. One group

298 has strong demand for its services among nonMedicaid patients. And others face steep marginal costs of treating Medicaid beneficiaries because the administrative burden (e.g., contesting denied payments) associated with their state’s Medicaid program is especially onerous, or because it is difficult for them to find local specialists willing to accept their referrals (as in Florida or New York; see Decker 2013). If these groups were presented with substantial increases in Medicaid fees, the first might respond more strongly than the others if the latter groups’ high marginal costs still exceeded the increased fees. One may observe the effects of this heterogeneity in mediating PCP responses to variation in Medicaid fee generosity by examining together the direct effects of the physician- and practicelevel factors of interest on Medicaid participation and the effects of their interactions with varying Medicaid fee levels. In particular, these interactions identify PCPs that are more or less responsive than average PCPs to differences in Medicaid fee generosity. For this reason, they should be of interest to policymakers and researchers observing variation in physician responses within and across states and evaluating the effects of 2013’s increases in Medicaid fees for primary care services on physician participation in Medicaid.

Empirical Framework I analyze the heterogeneous effects of Medicaid fees on PCPs’ Medicaid participation at the level of the individual PCP i in state s in the year t. In particular, I estimate the effects of relative generosity in Medicaid fees Pst on select indicators of the PCP’s participation in Medicaid Mist: first, the probability of accepting new Medicaid patients and, second, whether at least 2, 5, or 10 percent of practice revenues are from Medicaid (the 10th, 25th, and 50th percentiles, respectively, among responses from PCPs indicating they received non-zero revenues from Medicaid). I selected these levels for analysis because they reflect nontrivial Medicaid participation but are sufficiently low that small differences in fee levels would be unlikely to change significantly, if at all, the PCP’s estimate of the percent of revenues she receives from Medicaid; most of the variance observed should be attributable to differences in

INQUIRY 50(4) the fraction of the PCP’s care paid for by Medicaid rather than the fee differences.2 PCPs are defined to be physicians in general practice, internal medicine, or pediatrics. I assess how my models’ main effects are mediated by factors Xit associated with marginal revenues and costs. I estimate both the direct and the interaction effects of Xit (interacted with Pst) on each dependent variable, using the Stata command margins for the latter as described by Karaca-Mandic, Norton, and Dowd (2012).3 Among the covariates I consider are the PCP’s compensation arrangement (by salary vs. other) and practice ownership status (full or partial owner vs. neither). Provider organization administrators and practice-owning partners may not retain full authority to determine the payer mix of their group PCPs. In these cases, salaried PCPs and non-practice owners should be less attuned to the Medicaid–non-Medicaid fee differential than their peers because its linkage to their personal incomes is more indirect, operating through their employers’ practice-level bottom lines. Thus, on average, salaried PCPs and nonpractice-owning PCPs should be more likely to participate in Medicaid overall. Similarly, salaried PCPs and non-practice-owning PCPs should be less responsive to changes in Medicaid fee generosity than their non-salaried or practiceowning peers. The marginal administrative costs of treating additional non-Medicaid patients may be higher among certain PCP subgroups. Among these are PCPs more likely perceived as low quality or less desirable, such as PCPs not board-certified in their primary specialty and international medical graduates (IMGs; that is, graduated from medical school outside of the United States or Puerto Rico).4 In competitive markets, such PCPs may incur greater marginal administrative (e.g., marketing) costs when attracting non-Medicaid patients than their higher quality peers; this is because fewer employer or patient groups may choose to seek their care when board-certified or domestic medical graduates present an alternative. IMGs also may not respond to Medicaid fee incentives because of their visas’ work restrictions. The market barriers faced by these PCPs remain unchanged in the face of varying Medicaid fee generosity, and so they may be less

299

Wilk responsive than average PCPs to this incentive. Such dynamics would be reflected empirically by negatively signed direct and interaction effects of these covariates in predicting a PCP’s Medicaid participation.5 Local competition among providers—as measured through counts of all practicing physicians and general practice physicians per one thousand county residents—may significantly raise PCPs’ costs of attracting a desirable patient group, such as non-Medicaid patients typically and Medicaid patients when their fees are raised. Thus, PCPs in more competitive markets may participate more in Medicaid regardless of fee levels, but fee changes may not induce increased participation. By this reasoning, these indicators’ direct and interaction effects on Medicaid participation should be positively and negatively signed, respectively. Similarly, the constraints of tighter network and utilization management in Medicaid—as measured through managed care’s Medicaid market penetration at the state level— may raise PCPs’ costs of recruiting and serving Medicaid patients (Bindman, Yoon, and Grumbach 2003). Thus, managed care’s direct and interaction effects on Medicaid participation should both be negatively signed. However, because physicians locate their practices strategically, my examinations of competition and managed care may be subject to endogeneity. The practice-level Xit I examine is practice type, introduced as five dummy variables (solo- or twophysician practice, health maintenance organization (HMO), medical school–affiliated practice, hospital-based practice, and “other,” with groups of three or more physicians as reference). Due to poorer economies of scale, smaller practices should observe a more dramatic difference than larger practices in the operating costs of providing care to Medicaid (more administratively burdensome, on average) versus non-Medicaid patients (less burdensome), and so their PCPs should favor accepting non-Medicaid patients more, on average. Also, if these smaller practices are less bureaucratic and nimbler in their adjustments to policy changes, the effects of their indicators’ interactions with variation in Medicaid fee generosity will be positively signed and significant. Finally, I include numerous controls Cit at various levels of analysis. Among these are

physician-level measures of experience (years in practice) and capacity (accepting any new Medicare or privately insured patients) as well as county-level measures of market payer mix (number of Medicaid-eligible individuals per one thousand population) and demand for Medicaid primary care services (metro vs. non-metro area, total population, and local unemployment rate). My full ordinary least squares and logit regression models include sets of year fixed effects Yt to control for differences in average economic conditions and changes in Medicaid’s relative attractiveness to PCPs, and state fixed effects Ss to control for time-invariant differences across states in Medicaid eligibility criteria and benefit structures as well as unmeasured socioeconomic factors. Thus, these models may be represented as  β0 +β P Pst +β X X it    M ist = F  +β X × P X it × Pst  + εist ,  +βC Cit +Yt +S s   

(1)

where F represents the identity function for ordinary least squares regressions and the logistic function for logit regressions, eist represents the residual, and bX×P represents the parameters of primary interest. Alternative specifications are progressively additive: Specification I includes only the Medicaid–non-Medicaid fee ratios Pst, Specification II adds the covariates Xit and Cit, Specification III adds the interaction effects of interest, Specification IV adds year fixed effects, and Specification V includes both year and state fixed effects. My Specification IV results are preferred because of concerns about collinearity between state fixed effects and average differences between states in the fee ratios Pst across years: in some states, Medicaid fee generosity did not vary materially between 1993 and 2008 (e.g., Alabama’s Medicaid–Medicare fee ratios ranged between 77 and 84 percent, and Wisconsin’s ranged between 67 and 76 percent, whereas several states’ fee ratios varied by factors larger than two), and so these states’ experience would be largely washed out of my Specification V estimates of interest. As a sensitivity analysis comparable with Specification V, I introduce census region fixed effects in the place

300 of state fixed effects, thereby controlling for average region-level differences in socioeconomic factors, while not washing out all timeinvariant state-level variation. In all analyses, I cluster standard errors at the state level. I conduct my analyses using Stata/SE 13.1.

Data Sources This study relies on data gathered in four waves of the Community Tracking Study Physician Survey (CTSPS) from 1996, 1998, 2000, and 2004, as well as the 2008 Health Tracking Physician Survey (HTPS) (Center for Studying Health System Change). The CTSPS and HTPS are nationally representative surveys of non-federal physicians; they contain a great deal of information about the extent of physicians’ participation in Medicaid and other physician and practice characteristics. Because of a change in sampling frames, observations are more numerous in earlier survey waves. The survey items relevant to this study were retained in nearly identical formats across surveys such that only minor recoding was needed to ensure regression variables were populated for all study observations.6 Among these is the question: “Is your practice accepting all, most, some, or no new patients who are insured through MEDICAID, including Medicaid managed care patients?” I identify physicians accepting some, most, or all new Medicaid patients as accepting new Medicaid patients and ignore the residual heterogeneity to simplify my analysis. Average Medicaid–Medicare fee ratios for primary care services in 1993, 1998, 2003, and 2008 were obtained from published estimates based on the Urban Institute Physician Survey (Norton 1995; 1999; Zuckerman et al. 2004; Zuckerman, Williams, and Stockley 2009).7 This measure of Medicaid fee generosity was preferred to raw average Medicaid fees because the ratio better accounts for the opportunity costs of accepting Medicaid patients. The numerators and denominators of these ratios were calculated as averages across a set of primary care services, weighted by total expenditures on each service. While the sets of services identified as primary care and their associated weights vary across surveys, each ratio is reflective of expected

INQUIRY 50(4) payments a state’s PCPs would receive from Medicaid or Medicare for their most commonly provided services in a given year. For years in which these fee data do not coincide with the physician surveys, I interpolated fee ratio values using exponential trending. Use of alternative interpolation methods (including linear trending and selection of the nearest available value) did not meaningfully affect findings. This study’s state- and market-level independent variables are drawn from the 2009–2010 Area Resource File. I supplement this information with data on the proportion of states’ Medicaid beneficiaries enrolled in managed care as provided in Medicaid Analytic eXtract Chartbooks produced by the Centers for Medicare and Medicaid Services. As with the Medicaid fee ratio data, the proportion of Medicaid beneficiaries in managed care was interpolated for select years as needed. I include in the appendix a figure illustrating in which years key data were available or interpolated (see Figure A1).

Results After the exclusion of observations representing non-PCP physicians and those missing key data elements, the final sample used in each regression included nearly 24,300 physician-year observations.8 As presented in Table 1, 74.1 percent accepted new Medicaid patients, and 15.8 percent of respondents’ revenues came from Medicaid on average: 79.8 percent had at least 2 percent Medicaid revenues, 73.3 percent had at least 5 percent Medicaid revenues, and 55.4 percent had at least 10 percent Medicaid revenues. Around 34.4 percent of physicians were salaried, and just over half retained full or part ownership of their practices. Significant minorities were IMGs or were not board-certified in their primary specialty at the time of the survey. Most practiced in small, physician-led practices or larger groups. These statistics are reflective of the primary care provider population today, though recent trends in provider system integration have reduced the number of small, independent practices. The data also included substantial variation in states’ Medicaid fee generosity, which ranged from 24 to 146 percent of Medicare fees.

301

Wilk Table 1.  Study Sample Descriptive Statistics. Variables Dependent variables   Accepts new Medicaid patients   % practice revenues from Medicaid   ≥2% practice revenues from Medicaid   ≥5% practice revenues from Medicaid   ≥10% practice revenues from Medicaid Intervention variable   Average Medicaid–Medicare primary   Care fee ratio Physician- and practice-level factors  Salaried   Full or part owner of practice   Not board-certified in primary specialty   International medical graduate   Solo- or two-physician practice   Group of three or more physiciansa   Health maintenance organization based practice   Hospital-based practice   Medical school–affiliated practice   Other practice type Market-level factors   Total general practice physicians (per 1,000 population)   Total physicians (per 1,000 population)   % of Medicaid beneficiaries in managed care

M

SD

74.1% 15.8% 79.8% 73.3% 55.4%

  19.0%      

62.6%

20.8%  

34.4% 50.4% 17.0% 23.6% 35.4% 26.7% 6.9% 14.0% 5.3% 11.6%

                   

0.24 3.12 36.3%

0.11 1.95 21.8%

Note. Results presented for the N = 24,299 observations representing the sample analyzed under Specification I. a Reference group in regression analyses.

In Table 2, I present the average marginal effects of key model covariates on each dependent variable for logit regressions using Specifications IV and V. Results for Specifications I–III, presented in the appendix, and least squares results, available upon request, are broadly consistent with these. There is mixed evidence that Medicaid’s relative fee generosity is positively associated with the Medicaid participation of PCPs. Based on Specification IV results, all else equal, Medicaid–Medicare fee ratio increases of 1 percentage point are associated with increases in the proportion of PCPs accepting new Medicaid patients of about 0.15 percentage points (p = 0.073). I also estimate small, positive effects of changes in Medicaid’s relative fee generosity on the probability that the PCP’s practice receives at least 2 percent (0.13 percentage points, p = 0.048), 5 percent (0.21 percentage points, p = 0.011), and 10 percent (0.30

percentage points, p < 0.001) of its revenues from Medicaid. Overall, my estimates are smaller than most reported by other researchers. For each of these outcomes, the corresponding estimate in Specification V’s results—estimated with state fixed effects—is either not statistically significant or has the opposite sign. Thus, it appears average differences among states’ generosity in Medicaid fee levels were important for observing the effects I expect. Moreover, my null Specification V results are consistent with concerns about collinearity between states’ Medicaid fee generosity and fixed effects. My sensitivity analysis including census region fixed effects in the place of state fixed effects (results available upon request) also supports this inference because its results are very similar to those reported for Specification IV. I estimate that salaried physicians accept new Medicaid patients 4 percentage points more often

302 ≥5% practice revenue from Medicaid

≥10% practice revenue from Medicaid

−0.005 0.001* X X 24,277

0.006 0.000 X 24,297

0.158

0.046*** −0.033*** 0.003 0.082*** −0.042** 0.087*** 0.101*** 0.145*** 0.053***

0.041*** −0.038*** −0.005 0.077*** −0.054*** 0.086*** 0.096*** 0.147*** 0.049*** −0.066

0.000

0.001*

24,297

−0.002 0.000 X

0.049

0.003 −0.014 −0.020** 0.047*** −0.052*** 0.022 0.080*** 0.065*** 0.010

0.001**

−0.012*** 0.000 X X 24,277

0.299***

0.011* −0.011 −0.011 0.057*** −0.038*** 0.02 0.079*** 0.063*** 0.012

−0.001*

24,297

−0.002 0.001 X

0.059

0.016** −0.038*** −0.008 0.069*** −0.039*** 0.035 0.091*** 0.096*** 0.033**

0.002**

−0.014*** 0.001* X X 24,290

0.323***

0.025*** −0.036*** 0.001 0.082*** −0.024** 0.035 0.095*** 0.097*** 0.036**

−0.001**

24,297

−0.004 0.001 X

0.048

0.050*** −0.074*** 0.038*** 0.127*** 0.024 0.047* 0.130*** 0.213*** 0.120***

0.003***

−0.017*** 0.000 X X 24,290

0.289**

0.058*** −0.075*** 0.046*** 0.142*** 0.041** 0.045* 0.140*** 0.215*** 0.126***

−0.001

Specification IV Specification V Specification IV Specification V Specification IV Specification V Specification IV Specification V

≥2% practice revenue from Medicaid

Note. Reference group: physician not salaried, with no share of practice ownership, board-certified, domestic medical graduate, practice-type “Group with 3+ physicians,” in a large or small metropolitan area. Other covariates included in Specifications III–V: interaction effects (with Medicaid–Medicare fee ratios) of all covariates for which marginal effects are reported (and select other controls), years since began practice medicine, hours spent providing charity care last month, Medicaid-eligible individuals per one thousand population, unemployment rate, and total estimated population. International medical graduate status not known for two observations (dropped); twenty observations from two states dropped in Specification V regressions because of perfect prediction. *Statistically significant at 10 percent level. **Statistically significant at 5 percent level. ***Statistically significant at 1 percent level.

Intervention variable   Average Medicaid–Medicare primary care fee ratio Physician- and practice-level factors  Salaried   Full or part owner of practice   Not board-certified in primary specialty   International medical graduate   Solo- or two-physician practice   Health maintenance organization-based practice   Hospital-based practice   Medical school–affiliated practice   Other practice type Market-level factors   Total general practice physicians (per 1,000 population)   Total physicians (per 1,000 population)   % of Medicaid beneficiaries in managed care   Year fixed effects   State fixed effects n

 

Accepts new Medicaid patients

Table 2.  Predicting Medicaid Participation, Select Logit Regression Results, Average Marginal Effects, Specifications IV–V.

Wilk than physicians whose income is not fixed, all else equal, and it appears they also receive at least a small fraction (5 or 10 percent) of revenues from Medicaid more often. Similarly, IMG physicians (7.7–8.2 percentage points) and physicians practicing in HMOs (8.6–8.7 percentage points), hospital-based practices (9.6–10.1 percentage points), or medical school–affiliated practices (14.5–14.7 percentage points) also accept new Medicaid patients significantly more often than their peers, with estimates varying across specifications. Physicians retaining full or part ownership of their practices (3.3–3.8 percentage points) or practicing in one- or two-physician practices (4.2–5.4 percentage points) do so significantly less often. The same physician- and practice-level factors predict receiving at least a small proportion of practice revenues from Medicaid, with the largest and most significant effects observed on whether the physician’s practice receives at least 10 percent Medicaid revenues. My chosen market-level factors, which significantly predict accepting Medicaid patients only in my less preferred Specification V models, may be correlated with higher population density and reflect physician self-selection into areas where there are more (or fewer) Medicaid patients to serve. Notably, managed care’s Medicaid market penetration did not meaningfully predict PCPs’ participation in Medicaid. Results are broadly consistent with my hypotheses, though not all estimates were significant across models. I present in Table 3 the same models’ coefficient estimates for these covariates’ interactions with Medicaid–Medicare fee ratios. In my preferred model of the decision to accept new Medicaid patients, estimates of the interaction effects of a PCP’s salary status, practice ownership status, IMG status (Specification IV only), HMO affiliation, hospital-based practice status, and “other” practice type were all significant and negative. While fewer effect estimates were significant in my models predicting the receipt of at least some non-trivial proportion of practice revenues from Medicaid, several of the same factors’ interactions (as well as that of medical school affiliation) were significant and, again, negative. The magnitudes of these interaction effects were greatest for physicians in HMObased practices and hospital-based practices, two

303 of the PCP subgroups most disproportionately likely to accept new Medicaid patients, controlling for fee generosity. These findings suggest that the types of PCPs most accustomed to participate in Medicaid patients historically may be less responsive to Medicaid fee increases than was hoped, while PCPs whose Medicaid patient panels are smaller may be more responsive to increased fees than average. This evidence is consistent with the idea that PCPs typically seeing more Medicaid patients may already be serving as many as they are willing or able to care for, and the marginal costs of increasing capacity to accept additional Medicaid patients may be prohibitive despite dramatic fee increases, while the marginal costs of accepting additional Medicaid patients may be lower for PCPs typically seeing fewer of them. Because my data contain relatively few observations for physicians in state-years with Medicaid–Medicare fee ratios near or above 100 percent, I am unable to reliably project overall changes in Medicaid participation associated with increases in Medicaid fees to those levels. I obtain a more reliable estimate of the overall effects of these fee increases by predicting the Medicaid participation of PCPs in state-years with Medicaid–Medicare fee ratios at or below the 40th percentile (fee ratios ranging 24.0 to 59.0 percent) if fees were increased to the 75th percentile (74.8 percent fee ratio). I selected the 75th percentile to ensure my predictions were well within-sample. I selected the 40th percentile in specifying the baseline because, for PCPs facing these lower fee ratios, increases in fee ratios to the 75th percentile would be roughly equivalent in percentage or percentage point terms to the increases in Medicaid fees instituted by the ACA on average: Medicare fees for primary care services were approximately 52 percent or 34 fee ratio percentage points higher than average 2008 Medicaid fees (2008 being the final year of my data), and, relative to the Medicaid–Medicare fee ratio’s 75th percentile in my sample, such percent and percentage point differences approximately correspond to baselines at the 29th and 14th percentiles, respectively. Following this approach and using my preferred Specification IV logit model results, I estimate that Medicaid fee increases comparable

304 ≥5% practice revenue from Medicaid

≥10% practice revenue from Medicaid

−0.0004*** 0.0003 −0.0006* X X 24,277

−0.0001 0.0004 0.0006 X 24,297

−0.0007*** −0.0013*** 0.0002 −0.0004 −0.0003 −0.0022* −0.0016*** 0.0000 −0.0010

−0.0013*** −0.0012*** 0.0001 −0.0012** −0.0005 −0.0028** −0.0020** −0.0010 −0.0012*

24,297

0.0004 0.0006 X

−0.0001

−0.0007*** −0.0001 0.0001 −0.0008 −0.0005 −0.0019* −0.0014* −0.0014* −0.0003

0.0003 −0.0006* X X 24,277

−0.0004***

−0.0002 −0.0002 0.0001 0.0000 −0.0005 −0.0015 −0.0008* −0.0007 −0.0004

24,297

0.0004 0.0006 X

−0.0001

−0.0008*** 0.0000 0.0001 −0.0013*** −0.0007 −0.0025*** −0.0020** −0.0015** −0.0009

0.0003 −0.0006* X X 24,290

−0.0004***

−0.0002 −0.0003 0.0001 −0.0003 −0.0005 −0.0021** −0.0015** −0.0006 −0.0008

24,297

0.0004 0.0006 X

−0.0001

−0.0009* −0.0001 0.0002 −0.0009 −0.0005 −0.0025** −0.0027* −0.0008 −0.0015

&0.0003 −0.0006* X X 24,277

−0.0004***

−0.0004 −0.0002 0.0003 −0.0003 −0.0001 −0.0023* −0.0027** −0.0001 −0.0013

Specification IV Specification V Specification IV Specification V Specification IV Specification V Specification IV Specification V

≥2% practice revenue from Medicaid

Note. Reference group: physician not salaried, with no share of practice ownership, board-certified, domestic medical graduate, practice-type “Group with 3+ physicians,” in a large or small metropolitan area. Other covariates included in Specifications III–V: interaction effects (with Medicaid–Medicare fee ratios) of all covariates for which marginal effects are reported (and select other controls), years since began practice medicine, hours spent providing charity care last month, Medicaid-eligible individuals per one thousand population, unemployment rate, and total estimated population. International medical graduate status not known for two observations (dropped); twenty observations from two states dropped in Specification V regressions because of perfect prediction. a Estimated as the difference in the average marginal effects of increased Medicaid–Medicare fee ratios with all observations fixed at each factor’s 75th percentile and median. *Statistically significant at 10 percent level. **Statistically significant at 5 percent level. ***Statistically significant at 1 percent level.

Physician- and practice-level factors  Salaried   Full or part owner of practice   Not board-certified in primary specialty   International medical graduate   Solo- or two-physician practice   Health maintenance organization-based practice   Hospital-based practice   Medical school–affiliated practice   Other practice type Market-level factorsa   Total general practice physicians (per 1,000 population)   Total physicians (per 1,000 population)   % of Medicaid beneficiaries in managed care   Year fixed effects   State fixed effects n

 

Accepts new Medicaid patients

Table 3.  Predicting Medicaid Participation, Select Logit Regression Results, Interaction Effects Only, Specifications IV–V.

305

Wilk with those instituted by the ACA would be associated with an increase in the average PCP’s willingness to accept new Medicaid patients of about 5.3 percentage points and increases in the proportions of PCP practices receiving at least 2, 5, and 10 percent of revenues from Medicaid of about 6.5, 8.3, and 9.2 percentage points, respectively. While these increases in Medicaid participation are not insubstantial, they might have been significantly greater but for the organizational and market barriers I describe.

Limitations This study’s limitations include that it does not address the possibility that in 2015 the increased Medicaid fees will fall back to 2012 levels (or below) or physician responses to such reductions. The effects of the enacted fee increases’ temporary nature may vary meaningfully across PCPs if most believe future fees are an important determinant of panel management decisions today, but they hold different beliefs about how their Medicaid agencies or Congress will address the issue. Whether there exists such variation among physicians is not known. Endogeneity may affect some of this study’s parameter estimates. An important example is a form of reverse causality—Medicaid–Medicare fee ratios in a state may be determined as a function of historical levels of physician participation in Medicaid, which would be at least partially auto-correlated. One potentially important omitted variable is the relative reliability and timeliness of a state Medicaid agency’s claims processing team. Low-reliability states may need to pay higher fees to encourage physician participation in Medicaid to compensate for the administrative hassles. Another omitted variable is unmeasured health care needs among Medicaid beneficiaries. While physicians practicing in areas with elevated levels of need may feel compelled to accept and care for more Medicaid patients, local public health agencies, philanthropic organizations, or other entities would also be more likely to attempt to address needs there, thereby reducing the demand for physician services among Medicaid beneficiaries. Furthermore, those areas with elevated levels of need may be more likely to attract physicians and

provider organizations predisposed to serve such patients. It is not clear how unobservable provider characteristics driving this attraction may interact with varying Medicaid fee levels in determining physicians’ Medicaid participation. Because of these concerns, most of the results I describe may be best interpreted in terms of associations rather than determinations. Efforts to identify valid instruments for this study’s independent variables of interest or otherwise disentangle these relationships could be an important contribution. As the general equilibrium effects of the ACA’s many provisions are not fully captured here and may differ dramatically by state (Grogan 2011), one should not interpret this study’s estimates as quantitatively reflective of behavioral responses among any one state’s physicians in 2013–2014. Importantly, the ACA will significantly reduce Medicaid beneficiaries’ propensity to churn in and out of coverage. In 2014 and later years—because of the individual mandate and expanded Medicaid eligibility criteria in many states—when a beneficiary loses Medicaid eligibility, she will be significantly more likely to take up coverage, as through Health Insurance Exchanges, rather than to go uninsured. Thus many physicians, previously averse to treating Medicaid patients out of worry they could soon become uninsured, may become willing to participate in Medicaid, regardless of fee levels. A related consideration is that PCP groups more likely to serve Medicaid patients and those more likely to serve charity care patients have typically been distinct (Cunningham and Hadley 2008). As the uninsured population shrinks, PCPs historically dedicated to treating uninsured patients may adapt and begin treating Medicaid patients instead. Additional research focusing on the recent behavior of physicians providing charity care may aid in projecting their behavior under the ACA.

Conclusion The primary goal of this study was to identify physicians more or less responsive than average to changes in the relative generosity of Medicaid fees. I leverage variation in Medicaid-toMedicare primary care fee ratios between 1993

306 and 2008 and physician-level variation in the probability that a physician accepts new Medicaid patients and that her practice’s revenues from Medicaid exceed 2, 5, or 10 percent in multivariate least squares and logit models to help inform this question. Several alternative model specifications were employed to lend additional confidence about the validity of my parameter estimates and exploit the richness of the data sets used. Despite some lingering concerns about omitted variable bias and simultaneity in the determination of its key variables, this study offers evidence—based on an assessment of consistently negatively signed interaction effect estimates—that salaried physicians, IMGs, and physicians in HMOs or hospital-based practices may be less responsive (with respect to my four measures of Medicaid participation) than their peers to increased Medicaid fee generosity. These characteristics identify physician types more likely than average to participate in Medicaid independent of fee levels. It appears certain organizational considerations may be more predictive of Medicaid participation than fee levels. These findings are broadly consistent with my hypotheses—generated based on an application of Sloan, Mitchell, and Cromwell’s (1978) model of Medicaid participation—that PCPs experiencing disproportionately high administrative or operating costs when accepting or caring for Medicaid beneficiaries may be unwilling or unable to increase their participation in Medicaid despite increases in the relative generosity of

INQUIRY 50(4) Medicaid’s primary care fees. As Medicaid enrollments swell in the coming years, additional incentives or outreach may be necessary to realize desired improvements in new Medicaid patients’ access to primary care among provider types most likely to participate in Medicaid. However, perhaps counterintuitively, such efforts may be most effective if directed instead toward certain physicians less likely to serve Medicaid patients under historical fee schedules. Furthermore, my results suggest that states with PCPs more often practicing in HMOs or hospital-based practices (e.g., California) may be less well served by these fee increases than other states seeing similar relative fee increases; the same may be said of states with larger proportions of IMG physicians. States should account for their provider type mix when evaluating the effects of increased Medicaid fees on Medicaid participation among their PCPs, relative to those in other states; doing so may affect their debates regarding whether to extend the ACA’s fee increases into 2015 and beyond. To bring additional clarity to the interpretation of this study’s estimates, further explorations of some unmeasured factors—such as the administrative dependability of Medicaid agencies—are warranted. Nevertheless, this study presents valuable evidence that heterogeneity among PCPs may contribute significantly in determining divergent and, perhaps, unexpected patterns in their responses to the ACA’s legislated increases in Medicaid fees for primary care services.

307

≥5% practice revenue from Medicaid

≥10% practice revenue from Medicaid

24,297

0.002

24,297

24,297

0.000

0.000

0.051***

0.053***

0.005

0.098*** 0.146***

0.102*** 0.145***

0.004

0.076*** −0.052** 0.089***

0.083*** −0.055*** 0.091***

−0.082

0.043*** −0.038*** −0.003

0.042*** −0.038*** −0.003

−0.035

0.001*

0.001

24,297

0.002*

24,297

0.000

−0.004

0.106

0.013

0.086*** 0.067***

0.051*** −0.053*** 0.027

0.004 −0.016 −0.019*

0.001*

24,297

0.000

−0.002

0.041

0.011

0.080*** 0.065***

0.046*** −0.051*** 0.025

0.005 −0.014 −0.019*

0.001**

24,297

0.002**

24,297

0.001

−0.004

0.118

0.036**

0.097*** 0.096***

0.076*** −0.041*** 0.040

0.017** −0.040*** −0.007

0.002**

24,297

0.000

−0.002

0.052

0.033**

0.091*** 0.096***

0.069*** −0.039** 0.037*

0.018** −0.038*** −0.007

0.002***

24,297

0.003**

24,297

0.001

−0.005

0.088

0.120***

0.134*** 0.211***

0.132*** 0.020 0.042

0.049*** −0.074*** 0.038***

0.003***

24,297

0.000

−0.017

0.289

0.126***

0.140*** 0.215***

0.142*** 0.041 0.045*

0.058*** −0.075*** 0.046***

0.003***

Specification Specification Specification Specification Specification Specification Specification Specification Specification Specification Specification Specification I II III I II III I II III I II III

≥2% practice revenue from Medicaid

Note. Reference group: physician not salaried, with no share of practice ownership, board-certified, domestic medical graduate, practice-type “Group with 3+ physicians,” in a large or small metropolitan (non-rural) area. Other covariates included in Specifications II–III: years since began practice medicine, hours spent providing charity care last month, Medicaid-eligible individuals per one thousand population, unemployment rate, and total estimated population. Specification III also includes interaction effects (with Medicaid–Medicare fee ratios) of all covariates for which marginal effects are reported (and select other controls). International medical graduate status not known for two observations (dropped). *Statistically significant at 10 percent level. **Statistically significant at 5 percent level. ***Statistically significant at 1 percent level.

Intervention variable   Average Medicaid–Medicare    primary care fee ratio Physician- and practice-level factors  Salaried   Full or part owner of practice   Not board-certified in primary   specialty   International medical graduate   Solo- or two-physician practice   Health maintenance   organization-based practice   Hospital-based practice   Medical school–affiliated   practice   Other practice type Market-level factors   Total general practice physicians    (per 1,000 population)   Total physicians (per 1,000   population)   % of Medicaid beneficiaries in   managed care N

 

Accepts new Medicaid patients

Table A1.  Predicting Medicaid Participation, Select Logit Regression Results, Average Marginal Effects, Specifications I–III.

Results for Alternative Logit Model Specifications

Appendix

308

INQUIRY 50(4)

Table A2.  Predicting Medicaid Participation, Select Logit Regression Results, Interaction Effects Only, Specification III.

  Physician- and practice-level factors  Salaried   Full or part owner of practice   Not board-certified in primary   specialty   International medical graduate   Solo- or two-physician practice   Health maintenance   organization-based practice   Hospital-based practice   Medical school–affiliated practice   Other practice type Market-level factorsa   Total general practice physicians    (per 1,000 population)   Total physicians (per 1,000   population)   % of Medicaid beneficiaries in   managed care N

Accepts new Medicaid patients

≥2% practice revenue from Medicaid

≥5% practice revenue from Medicaid

≥10% practice revenue from Medicaid

Specification III

Specification III

Specification III

Specification III

−0.0012*** −0.0011*** 0.0001

−0.0007*** 0.0000 0.0002

−0.0008*** 0.0000 0.0001

−0.0009* −0.0001 0.0002

−0.0012** −0.0004 −0.0027**

−0.0008* −0.0005 −0.0018**

−0.0013*** −0.0007 −0.0024***

−0.0009 −0.0005 −0.0025**

−0.0019** −0.0009 −0.0010

−0.0013* −0.0014** −0.0003

−0.0020** −0.0015** −0.0009

−0.0027** −0.0008 −0.0014

−0.0001

−0.0001

−0.0001

−0.0001

0.0004

0.0004

0.0004

0.0004

0.0007

0.0007

0.0007

0.0007

24,297

24,297

24,297

24,297

Note. Reference group: physician not salaried, with no share of practice ownership, board-certified, domestic medical graduate, practice-type “Group with 3+ physicians,” in a large or small metropolitan (non-rural) area. Specifications I and II do not include interaction effects. Other covariates for Specification III results include interaction effects (with Medicaid– Medicare fee ratios) of all covariates for which marginal effects are reported (and select other controls), years since began practice medicine, hours spent providing charity care last month, Medicaid-eligible individuals per one thousand population, unemployment rate, and total estimated population. International medical graduate status not known for two observations (dropped). a Estimated as the difference in the average marginal effects of increased Medicaid–Medicare fee ratios with all observations fixed at each factor’s 75th percentile and median. *Statistically significant at 10 percent level. **Statistically significant at 5 percent level. ***Statistically significant at 1 percent level.

309

Wilk

Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Physician Survey Data Community Tracking Health Tracking Study Physician Survey Physician Survey

Medicaid Fee Data Urban Instute Physician Survey

State & Market-Level Covariates Medicaid Analyc Area Resource File eXtract Chartbooks

Key: Available Interpolated Not Available

Figure A1.  Year-to-year variation in availability of key data sets.

Acknowledgment The author wishes to acknowledge the valuable comments and input of Richard Hirth, Jack Wheeler, Joel Segel, Sayeh Nikpay, and Morris Hamilton III.

Declaration of Conflicting Interests The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author received no financial support for the research, authorship, and/or publication of this article.

Notes 1.

If we broaden our focus beyond strictly financial, physician-level considerations, we may also use this model to account for how other non-financial and practice-level factors influence a primary care physician’s (PCP) perception of the marginal benefits and costs of accepting Medicaid or nonMedicaid patients and likewise affect the PCP’s Medicaid participation outcomes. 2. An ideal dependent variable for this analysis, which is intended to capture in part the effects of changes in price on changes in quantity, would reflect only the fraction of the PCP’s patients (or

patient visits) enrolled in Medicaid and would not be calculated as a function of both the quantity of patients (or services provided to them) and the fees paid for their care. Such a variable is not available in large, nationally representative data sets. 3. In the context of larger physician groups, leadership issues recommendations about whether and to what extent the group’s PCPs should participate in Medicaid. Member PCPs may or may not adhere to those recommendations, though they may risk being disciplined when failing to do so. I include PCP- and practice-level factors in my analysis to account for incentives at both levels. 4. Temporarily licensed international medical graduate (IMG) physicians are not included in the study sample, and so the IMG status variable may be less reflective of quality than it would be if such physicians were also included. Moreover, Mick and Comfort (1997) found that evidence regarding a quality differential between internationally and domestically trained physicians was highly inconsistent, and so any quality-related effects I observe with respect to IMG status may be solely due to perceived, not actual, quality differences. 5. Casalino et al. (2007) noted, however, that the relationship between a PCP’s quality and her participation in Medicaid is complicated, because the Medicaid program’s lower fees may limit

310 the capital she could use to invest in medical devices, information technology, and other quality-improving assets. I include board certification status and IMG status in this study because they are less subject to concerns of reverse causality than most (perceived) quality indicators. 6. Further information about these surveys is available online, accessed February 13, 2012. http:// www.hschange.com/index.cgi?data=04 7. This study’s sample is limited to the forty-two states (including the District of Columbia) for which average Medicaid–Medicare fee ratios were available. 8. Applying additional sample exclusion criteria limiting the sample to one survey record per physician—including only each physician’s first survey response, including only one random survey response per physician, or including only those physicians who responded to one survey only— did not affect findings in supplemental analyses.

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