Abstract

States have increasingly transitioned Medicaid enrollees with disabilities from fee-for-service (FFS) to Medicaid Managed Care (MMC), intending to reduce state Medicaid spending and to provide better access to health services. Yet, previous studies on the impact of MMC are limited and findings are inconsistent. We analyzed the impact of MMC on costs by tracking Illinois's Medicaid acute health services expenditures for adults with intellectual and developmental disabilities (IDD) living in the community (n = 1,216) before and after their transition to MMC. Results of the difference-in-differences (DID) regression analysis using an inverse propensity score weight (IPW) matched comparison group (n = 1,134) design suggest that there were no significant state Medicaid cost savings in transitioning people with IDD from FFS to MMC.

Medicaid is the single largest funding source of public services for persons with intellectual and developmental disabilities (IDD) in the United States (Braddock et al., 2015). It provides enrollees with IDD access to acute health services, such as physician services, hospitalizations, and prescription drugs (Kaiser Commission on Medicaid and the Uninsured, 2015a). Medicaid also provides facility-based services, most notably intermediate care facilities for individuals with developmental disabilities (ICF/DD), to adults with IDD who require ongoing medical and support services (Larson et al., 2016). Further, Medicaid offers Home and Community-Based Services (HCBS) waiver programs, alternatives to facility-based services that provide persons with IDD residential, day, and personal assistance services in community-based settings (Rizzolo, Friedman, Lulinski-Norris, & Braddock, 2013). Due to their intensive use of health and support services (Braddock et al., 2015; Kastner, Walsh, & Criscione, 1997), Medicaid spending by enrollees with IDD and other disabilities has been significantly higher than other groups of enrollees (Kaiser Commission on Medicaid and the Uninsured, 2015a).

Medicaid Managed Care (MMC) is one of the strategies that states have implemented with the goal of controlling ever-increasing health care costs and providing better services by changing payment methodologies for providers and service delivery systems for enrollees (Altman & Frist, 2015; Kaiser Commission on Medicaid and the Uninsured, 2015b; Lewin Group, 2009; Sparer, 2012). In the traditional fee-for-service (FFS) model, an enrollee self-selected any health service provider who was willing to accept Medicaid. The provider then submitted claims for reimbursement to the state for the specific services provided to the enrollee. Under the MMC model, states contract with managed care organizations (MCOs) for the delivery of health services through their network of providers, much like a managed health care plan under private health insurance. The MCOs use a combination of the assigned primary care physicians, referrals to specialists and other ancillary service providers, prior authorizations, utilization management services, and care coordination to control/monitor enrollees' access to services. The state pays the MCO a fixed monthly premium per enrollee, known as a capitation payment, and the MCO is expected to provide all necessary services for each enrollee. Thus, MMC allows states to better control costs compared with FFS, where health service utilization of enrollees, and therefore expenditures, vary each month. Capitation payments are more stable and easier to predict each month. Further, capitation payments incentivize MCOs to provide efficient services to each enrollee to control service cost in order to make a profit (Lewin Group, 2009), although implementation of medical loss ratios (MLR) requires MCOs to spend at least a pre-determined minimum percentage of premiums on direct services for enrollees.

A recent expansion of MMC to enrollees with disabilities across states was mainly attributable to the 2007–2009 economic recession and the 2010 passage of the Patient Protection and Affordable Care Act, both of which prompted states to more aggressively control Medicaid program costs than before (Kaiser Commission on Medicaid and the Uninsured, 2015b; Sommers, Kenney, & Epstein, 2014; Sparer, 2012). Medicaid costs increased as millions of Americans, mostly low-income families impacted by the recession, gained access to the program. At the same time, the recession resulted in a decline of tax revenues that limited states' capacity to finance the program (Kaiser Commission on Medicaid and the Uninsured, 2015b). Although stimulus funding under the American Recovery and Reinvestment Act of 2009 temporarily alleviated the impact of the recession (Synder, Rudowitz, Garfield, & Gordon, 2012), states needed to seek alternative funding as the act gradually phased out (National Council on Disability, 2013). The passage of the 2010 Patient Protection and Affordable Care Act was expected to further increase the number of low-income families enrolling in Medicaid and program costs (Sommers et al., 2014). Consequently, many states started to include persons with disabilities, a high-cost subset of enrollees, in MMC to better control costs and deliver services more efficiently (National Council on Disability, 2013; Sparer, 2012). As of July 2016, acute health services for persons with physical disabilities and those with mental health disabilities are each covered under mandatory MMC in 16 states. Those with IDD are included in mandatory MMC in 10 states. Twenty-two other states include acute health services for persons with IDD under MMC either voluntarily or based on specific eligibility (Smith et al., 2016).

Researchers reported mixed results on the cost savings of MMC specific to enrollees with disabilities (Burns, 2009; Caswell & Long, 2015; Duggan & Hayford, 2013; Shern et al., 2007). The Lewin Group (2009) found that MMC's cost savings impact was larger for enrollees who received Supplemental Security Income (SSI) than for other enrollees in Arizona, Kentucky, Oklahoma, Pennsylvania, and Texas. In a Florida study on enrollees with mental health disabilities, Shern et al. (2007) found that overall Medicaid expenditures for those under MMC were lower than those under FFS. Utilizing national population-based survey data, Burns (2009) studied health service expenditures for Medicaid enrollees with SSI in counties across 18 states on three models: FFS, mandatory MMC, and voluntary MMC. Contrary to the aforementioned studies, the researcher found no significant differences between the monthly total health expenditures for the two MMC models and that of FFS. Similar to their own findings on enrollees without disabilities, Caswell and Long (2015) reported that there was no significant relationship between the proportion of enrollees with SSI under MMC in the sampled county and their Medicaid health service expenditures. Thus, there was a lack of consensus among researchers if states would be able to contain their spending by expanding MMC to this high-cost subset of Medicaid enrollees. Further, there were only a few studies exploring impacts of MMC across subgroups of Medicaid enrollees with different disabilities.

In 2011, Illinois passed the Medicaid Reform Law (Public Act 096-1501) intended to reduce program costs while delivering better services and improving outcomes for people with disabilities (State of Illinois, 2011). The law required at least half of the state's Medicaid enrollees to be enrolled in systems of care coordination by January 2015. In order to help meet this requirement, Illinois initiated the integrated care program (ICP), a pilot program which transitioned seniors and persons with disabilities (SPD) Medicaid enrollees in a pilot region from FFS to MMC (State of Illinois, 2011). Under the ICP, 36,000 SPD enrollees ages 19 years or above, not dually eligible for Medicare, and residing in the Chicago suburbs (the pilot region), had their Medicaid services managed by one of two for-profit MCOs, selected via a competitive procurement process, starting in May 2011 (Illinois Department of Healthcare and Family Services, 2016).

The purpose of the present study was to examine how MMC impacted state Medicaid health service expenditures for people with IDD in the pilot region of ICP. Although researchers have studied Medicaid expenditures for enrollees with IDD (Braddock et al., 2015; Friedman & Rizzolo, 2016; Lakin et al., 2008; Lakin, Scott, Larson, & Salmi, 2010; Rizzolo et al., 2013), few focused on their health service expenditures. None specifically examined the implication of MMC for this group. Whether MMC would reduce state expenditures for enrollees with IDD compared with FFS is an important policy question for legislators, program administrators, and advocates across states. Providing empirical evidence on the financing of states' Medicaid health services for this population could highlight points of concern and facilitate targeted discussion among stakeholders on ways to improve the system.

Methods

Data

We obtained Medicaid enrollees' claims and administrative data from the state Medicaid agency as part of a larger project that evaluated ICP (Heller et al., 2015). These enrollees met the aforementioned ICP eligibility and resided either in the pilot region (i.e., Chicago suburbs) or a comparison region (i.e., the City of Chicago). We utilized the following variables in the data for the present study: 1) month-to-month Medicaid enrollment status; 2) demographics; 3) enrollment in long-term services and supports, including Home and Community-Based Services (HCBS) waivers; 4) primary diagnosis and up to 17 secondary diagnoses (i.e., International Classification of Diseases 9th revision [ICD 9] codes) associated with any claims filed before ICP started; 5) health service utilization before ICP started; 6) FFS payments to providers for each claim; and 7) monthly capitation payment to the MCOs for each enrollee.

Subjects

Any enrollee who had at least one claim that included one or more of the ICD-9 codes associated with IDD in the pre-ICP period were identified as subjects with IDD (see Appendix A for specific ICD-9 codes). We further restricted the sample to people who were ages 22 or above in July 2010 and enrolled in the Medicaid program continuously for 22 months before and after the implementation of ICP (described in a later section). We excluded enrollees who resided in an ICF/DD or a nursing facility. Subjects who resided in the pilot region in July 2010 were labeled the “treatment group,” and those who resided in the comparison region in July 2010 were labeled the “comparison group.” Using the intention-to-treat approach (Gupta, 2011), we kept subjects in the initially identified group even though they may have moved between the two regions during the study period.

State Health Service Expenditures

We developed a cost measure to track the state's monthly spending on health services for each subject using two variables in the data. First, we calculated the state's monthly reimbursement amount to providers by totaling FFS claims associated with acute health services for each subject. Examples of the health services included, but were not limited to, physician and specialist care, emergency care, hospitalization, laboratory and X-rays, pharmacy, mental health, and prescription drugs. State spending on long-term services and supports (LTSS) was excluded. Second, in the post-ICP period, we used the state's monthly capitation payment amount to MCOs for the treatment group who transitioned to MMC. We reduced the payment by $62.20 per subject per month to account for the MLR “pay back” that the MCOs returned to the state for not spending the predetermined minimum percentage of premiums (Heller et al., 2015).

SMART Act

In order to better understand the cost implications of ICP, we incorporated the impact of the state's Save Medicaid Access and Resource Together (SMART) Act (Public Act 097-0689) into our analysis. Implemented in July 2012, 7 months into the study's post-ICP period, the act either eliminated or restricted funding for some Medicaid health services including, for example, group psychotherapy, chiropractic services, nonemergency dental care, podiatry services for nondiabetics, and eyeglasses. It also reduced many provider reimbursement rates by an average of 2.7% (Heller et al., 2015; Illinois Department of Healthcare and Family Services, 2012). The act primarily impacted subjects in the comparison group who remained under FFS, as MCOs chose to maintain services at the pre-SMART Act levels in most cases (Heller et al., 2014).

Analysis

To assess the impact of MMC, we employed a quasi-experimental research design that simulated a pre- and post-test randomized control trial design in observational studies (Cook, 2015; see Table 1 for a graphical presentation of the design). The outcome variable was the state's previously described health service expenditures. The pre-intervention period included July 2010 to March 2011, 9 months immediately before implementation of ICP. The post-intervention period was the 13 months from January 2012 to January 2013. We excluded May 2011 to December 2011, the first 8 months of ICP implementation, from the post-intervention period because of progressive transition of enrollees to MMC and poor data quality during these months (Heller et al., 2015). We ended the post-intervention observation on January 2013 because costs for LTSS were included in the capitation payment to the MCOs starting in February 2013.

Table 1

Quasi-Experimental Research Design

Quasi-Experimental Research Design
Quasi-Experimental Research Design

We employed two statistical techniques, propensity score matching and difference-in-differences (DID) regression analysis, in order to adjust for differences between the treatment and comparison groups in the pre-intervention period and potential external impacts on the health services cost. To simulate the random assignment of subjects, which makes the treatment and comparison groups equivalent in the pre-intervention period, we constructed an inverse propensity score weight (IPW) using the following variables in the data set as covariates: subject's demographics, diagnoses related to disability, HCBS enrollment, state's health service spending, health conditions, and health service utilization during the pre-ICP period (Shadish & Steiner, 2010; Shadish, 2013; see Table 2 for a list of covariates). The propensity score was the probability of a comparison group member to represent the treatment group member given these covariates. We examined the overlap of the IPW between the two groups after the initial creation, and deleted unmatched subjects from both groups (Caliendo & Kopeinig, 2008). Then, we reconstructed the IPW with the same covariates to better match subjects across the two groups. The use of the IPW allowed the comparison group to statistically match the treatment group for these covariates and reduce the potential bias on the treatment effect due to the initial differences between the groups. The IPW-matched comparison group served as a proxy of the treatment group members if they didn't transition to MMC and remained under FFS in the DID analysis described in the following section.

Table 2

Covariates Used to Create Inverse Propensity Score Weight (IPW)

Covariates Used to Create Inverse Propensity Score Weight (IPW)
Covariates Used to Create Inverse Propensity Score Weight (IPW)

Panel data DID regression analysis was used to adjust for the external conditions that might impact the potential treatment effect (Dimick & Ryan, 2014; see Table 3 for the DID concept in a mathematical formula). Over time, the state's spending on Medicaid might be impacted by the changes in the economy, health care market, and state's administrative rules and regulations. Hence, it would be difficult to separate these “secular” trends from the ICP effect in a pre-/post-design. In the DID regression analysis, the average change in the comparison group between pre- and post-period was subtracted from the average change in the treatment group across the same two time periods. Our assumption was that secular trends would impact both the treatment and comparison group equally. Thus, any difference between the groups in their pre- and post-differences would likely be due to the intervention—transition to ICP in this case. This removed biases in post-period comparisons between the treatment and comparison group that could be the result of permanent differences between those groups, as well as biases from comparisons over time in the treatment group that could be influenced by secular trends. The following equation represents our DID regression model to estimate the effect of the ICP on the state's acute health services expenditures. The model allowed us to estimate the changes of the ICP impact with an additional impact of the SMART Act.

 
formula

Outcome variable, “Yit,” represented the state's health services expenditures for the subject “i” in month “t”. Independent variables, Treatmenti , PostICPt,, and PostSMARTt were binary dummy variables. Treatmenti represented whether the subjecti was expected to transition to MMC. It was set to 0 for the comparison group members and 1 for the treatment group members. PostICPt noted if the observed month was before or after the start of ICP. It was set to 0 for the nine months between July 2010 and March 2011, and 1 for the 13 months between January 2012 and January 2013. PostSMARTt referred to whether the observed month was before or after the implementation of the SMART Act. It was set to 0 for any months before or including June 2012, and 1 for the 7 months from July 2012 to January 2013. represented month fixed time effects which would impact both groups. Analyses were conducted using STATA Version 14 (StataCorp, 2016) and the level of significance was set at .05. The institutional review board of the first author's institution approved this study protocol.

Table 3

Difference-in-Differences Design

Difference-in-Differences Design
Difference-in-Differences Design

Results

Table 4 compares covariates in the pre-intervention period between the treatment (n = 1,216) and the comparison (n = 1,134) subjects with IDD. Columns 1 and 2 represent unweighted within-group proportions and counts. The largest race group in the treatment group was White (41.9%), and the majority of the comparison group was Black (54.9%). Almost one in every five subjects (18.7%) were Hispanic in the comparison group compared to one in every 10 (11.2%) in the treatment group. For both groups, the majority of subjects were under 40 years old. Breakdowns by the IDD diagnoses across the two groups were similar except for a moderate/severe level of ID diagnosis. The Developmental Disabilities (DD) waiver was the most frequently used HCBS waiver program (65.3% for the treatment group; 48.5% for the comparison group), followed by the Persons With Disabilities waiver (11.7% and 20.1%, respectively). On average, the state's total Medicaid health expenditures in the pre-intervention period was $7,851/per person for the treatment group, and $8,294 for the comparison group. There were few differences in health conditions and utilization of various services between the two groups. Column 3 represents a profile of the comparison group after applying the IPW. The weighted profile of the comparison group was more similar to the treatment group than the unweighted one. For example, the unweighted proportion of Whites in the unadjusted comparison group was 16.2%; it was adjusted to 42.2% using the IPW, which was almost identical to that of the treatment group (41.9%). Similarly, 34.3% of the weighted comparison group was Black (compared to 54.9% unadjusted), again similar to the 33.9% of the treatment group. This suggests the application of IPW successfully made the two groups similar enough to reduce the risk of biases in pre-existing group differences.

Table 4

Selected Baseline Covariates Comparison Between Treatment and Comparison Groups

Selected Baseline Covariates Comparison Between Treatment and Comparison Groups
Selected Baseline Covariates Comparison Between Treatment and Comparison Groups

Figure 1 graphically shows the state's health services expenditures, in the form of mean per member per month (pmpm) dollar, for the treatment (i.e., solid line) and the IPW-matched comparison group (i.e., dotted line) during the study periods. In the pre-intervention period, state spending for each group represented the amount of FFS reimbursement only. In the post-intervention period, state spending for the treatment group mostly consisted of the fixed capitation payment to MCOs. It also included a small amount of FFS reimbursements for subjects (n = 4) who moved from the suburbs to Chicago during this period and those who used services that had been carved out from the state's contract with the MCOs. Conversely, the state spending for the comparison group in the post-intervention period reflected mostly FFS reimbursement but included the small capitation payment for subjects (n = 14) who moved from Chicago to the suburbs. Note that state expenditures for subjects under FFS fluctuated from month to month. In contrast, state expenditures for subjects under MMC were more stable due to the fixed capitation payment.

Figure 1

State health service expenditures for persons with intellectual and developmental disabilities. IPW = inverse propensity score weight; ICP = integrated care program; SMART Act = Save Medicaid Access and Resource Together Act.

Figure 1

State health service expenditures for persons with intellectual and developmental disabilities. IPW = inverse propensity score weight; ICP = integrated care program; SMART Act = Save Medicaid Access and Resource Together Act.

Table 5 summarizes the estimated impact of the ICP and the SMART Act on the state's spending on health services for the treatment group using coefficients of the DID regression model using the IPW-matched comparison group. The treatment group member costed an average of $855/pmpm to the state prior to ICP implementation. It was estimated that implementation of ICP lowered the average state spending for the treatment group member by about $88/pmpm , a 10% reduction from the pre-ICP spending, compared to what it would have spent under the FFS model. The decline, however, didn't reach statistical significance, indicating that the transition of the treatment group to ICP did not have a significant impact on the state's Medicaid costs relative to the anticipated costs if the group remained under FFS. Conversely, the implementation of the SMART Act increased the state's spending for the treatment group member by $109/pmpm , a 13% increase from the pre-ICP spending. Again, the SMART Act impact was not statistically significant; the act didn't change the state spending significantly for the treatment group relative to what would have happened in the absence of ICP. Combining effects of both the ICP and the SMART Act, the net change of the state spending between pre- and post-ICP period was estimated to be a mere $21 pmpm or 2.5% of the pre-ICP spending. In summary, using the IPW-matched comparison group as a proxy of the treatment group if they didn't transition to MMC and remained under FFS in the post-ICP period, the transitioning enrollees with IDD to MMC did not significantly change the state's Medicaid health services spending.

Table 5

Effects of the ICP and the SMART Act on the Medicaid Acute Health Services Expenditures on Enrollees With Intellectual and Developmental Disabilities

Effects of the ICP and the SMART Act on the Medicaid Acute Health Services Expenditures on Enrollees With Intellectual and Developmental Disabilities
Effects of the ICP and the SMART Act on the Medicaid Acute Health Services Expenditures on Enrollees With Intellectual and Developmental Disabilities

Discussion

In the present study, we tracked the monthly state Medicaid health services expenditures for enrollees with IDD during their transition from FFS to MMC. Utilizing a quasi-experimental research design with the matched comparison group, we compared state expenditures on enrollees with IDD who transitioned to MMC with those of the same enrollees if they would have remained under FFS. To our knowledge, this is the first study examining the cost effect of MMC on this population. By examining the financial implications associated with MMC for the state, the present study provides unique and important policy-relevant information specific to this population.

Our finding was consistent with findings from two previous studies reporting no cost impact of MMC on health services expenditures on enrollees with disabilities (Burns, 2009; Caswell & Long, 2015). Although the strength of these studies was the use of national population-based sample survey data and the use of a statistical approach estimating the MMC impact, it obscured potential variations of MMC impact across different subpopulations with disabilities and different programmatic approaches of MMC implemented across sampled areas (Sparer, 2012). Using people with IDD as subjects, our study extended previous findings to a specific subgroup of the disability population. Future studies need to be conducted in states where enrollees with IDD were included in their MMC (Smith et al., 2016) to further extend the present finding to other program forms of MMC in different geographic areas. Given the diversity in health and support services needs across different subgroups within the larger group of enrollees with disabilities, similarly, studies examining the effects of MMC in each of these subgroups may need to be in place.

It was difficult to compare our finding with previous ones reporting cost savings of MMC (Lewin Group, 2009) because methodologies used in these studies diverged significantly from the one employed in the present study. First, similar to previously mentioned studies (Burns, 2009; Caswell & Long, 2015), findings from these studies were based on enrollees with disabilities in general. Second, some health services included in ICP were carved out from MMC in other studies (Lewin Group, 2009). For example, mental health services were carved out from MMC in Arizona and Kentucky; prescription drugs were carved out in Texas. Although our study didn't include state spending for LTSS, it was included in the Texas study. Third, the state MMC expenditures were compared with that of an unmatched comparison group. In the Arizona study, for example, the state expenditures under MMC were compared with that under FFS from other states (Lewin Group, 2009). Caution should be exercised when comparing results from the present study with these studies that were based on different forms of MMC and methodologies (Sparer, 2012).

Illinois's low FFS reimbursement rate could be a potential explanation for why the ICP didn't produce anticipated cost savings over FFS in the present study (Gupta, Yarbrough, Vujicic, Blatz, & Harrison, 2017; Nasseh, Vujicic, & Yarbrough, 2014; Zuckerman & Goin, 2012; Zuckerman, Skopec, & McCormack, 2014). When FFS reimbursement rates for providers are low, the state's health services spending on enrollees under FFS might be already at the bottom. In this environment, MMC was less likely to result in additional cost savings for states as there was little room for MCOs to bargain for lower prices with providers (Caswell & Long, 2015; Duggan & Hayford, 2013). Although lowering the capitation payment might produce the cost benefit of MMC, the state needs to keep the payment at a certain level so that MCOs would receive sufficient funds to meet their obligations to serve the target population (Dominiak & Libersky, 2016; Sparer, 2012). Illinois's Medicaid reimbursement rate was lower than the national average and in the lowest quartile among the 50 states and Washington, DC (Zuckerman & Goin, 2012). Even though the state set the capitation rate 3.9% below what it would have cost under FFS (Heller et al., 2014), it might have been too small to produce an observable cost impact on enrollees with IDD.

Implementation of the SMART Act, in the middle of our study period, made it difficult to estimate the MMC impact on the state's expenditures (Heller et al., 2014). The act resulted in FFS enrollees not being able to access certain services that MMC enrollees could still access starting July 2012. Similarly, it reduced the payment to providers for certain FFS-based services. These factors were threats to our research design using FFS enrollees in Chicago as a matched comparison group. After the implementation of the act, the group might not be considered as a proxy of the suburban group members if they didn't transition to MMC. Introduction of the act, with the primary goal of cost savings, could have interfered with the aim of MMC, which was to reduce state spending and improve access to health services simultaneously (Heller et al., 2014).

Although our finding indicated that ICP was cost-neutral relative to FFS, there were other benefits of the program. Utilizing the fixed capitation payment to MCOs, the state stabilized health services expenditures for the suburban group and increased its predictability. A predictable monthly payment would allow the state to better control its cash flow, which was one of the benefits of MMC and an important fiscal strategy for the state operating under a tight budget (Sparer, 2012). The state developed a comprehensive quality assurance system that monitored the MCO's health services delivery to make them accountable. The 39 quality measures used to monitor their performance included the Healthcare Effectiveness Data and Information Set (HEDIS) measures that covered a wide range, including a completion rate of health risk screenings and care plans of enrollees, impatient hospitalization use and outcomes, and prevention services provided, for example. Each MCO was evaluated annually for each measure. Hence, results of the larger ICP evaluation, including all the disability and aging groups, showed that MCOs improved the quality of services relative to FFS in most of the measures (Heller et al., 2015; Heller et al., 2014). These findings suggest that MMC successfully improved the quality of health services for enrollees.

The interpretation of the present findings is subject to several limitations. First, our study should be interpreted as a case study. Given the divergence of the Medicaid programs, types and geography of the MMC being implemented, and the target populations across states (Musumeci, 2014; Saucier, Kasten, Burwell, & Gold, 2012; Synder et al., 2012), our findings might have limited generalizability. Nonetheless, the paucity of the empirical data on Medicaid health expenditures on persons with IDD underscores the importance of monitoring the program across states, particularly states transitioning to the MMC model (Hall, Kurth, Chapman, & Shireman, 2015; HCBS Advocacy Coalition, 2015; National Council on Disability, 2013). Second, the present study left out another important benefit of MMC: improved access to quality health services (Hall et al., 2015; Sparer, 2012). There are preliminary findings suggesting that ICP produced positive changes in hospital-based health services utilization among enrollees with IDD who transitioned to MMC (Yamaki, Wing, Mitchell, Owen, & Heller, 2017). The current findings on the fiscal impact of MMC should be interpreted along with these findings on program aspects in order to understand the MMC impact on enrollees with IDD comprehensively. Third, our findings were limited to the early period of MMC implementation. Outside of our study period, the state implemented a couple of policy changes that impacted the state's Medicaid expenditures: reduction of the capitation payment to the MCOs (March 2013) and the reinstatement of some cost-cutting measures implemented through the SMART Act (July 2014; Heller et al., 2015; Heller et al., 2014). These changes would impact the cost savings effect of MMC in the long run. Fourth, we didn't examine the fiscal impact of MMC on LTSS, which constitute a large part of Medicaid expenditures for people with IDD (Lakin et al., 2008). Nationally, there are at least nine states that transferred LTSS for people with IDD to MMC (Dobson, Gibbs, Mosey, & Smith, 2017). Examining the financial and service implications of MMC on LTSS, particularly HCBS Developmental Disabilities (DD) waiver services, on these states may provide important program information to state program planners and advocates.

In light of the fact that almost 80% of public spending on IDD services is funded through Medicaid (Braddock et al., 2015), continuing efforts should be made to track the fiscal impact of MMC, on both the acute health services and LTSS, in the state Medicaid programs. It is imperative that policy makers and program administrators across states compare and contrast their MMC strategies and outcomes, and identify what works and what doesn't. Current lack of empirical data on MMC in general and on people with IDD specifically may prevent such a dialogue and hinder the ability to make evidence-based policy and program decisions across the states.

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Appendix A

ICD-9 Codes Used to Screen Individuals With Intellectual and Developmental Disabilities

ICD-9 Codes Used to Screen Individuals With Intellectual and Developmental Disabilities
ICD-9 Codes Used to Screen Individuals With Intellectual and Developmental Disabilities

Author notes

The contents of this article were developed under grants from the United States Department of Health and Human Services, Administration for Community Living (ACL), National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), Grants # 90RT5020-03-00 and 90RT5026-03-01. However, the content is solely the responsibility of the authors and do not necessarily represent the official views of the NIDILRR, and you should not assume endorsement by the Federal Government.