Implications of Eligibility Category Churn for Pediatric Payment in Medicaid

Analyses of Ohio Medicaid claims data from 2013 to 2015 reveal that instability among eligibility categories is common and affects average capitation but not health service use.


Objectives: To describe the extent and implications of “churn” between different Medicaid eligibility classifications in a pediatric population: (1) aged, blind, and disabled (ABD) Medicaid eligibility, determined by disability status and family income; and (2) Healthy Start Medicaid eligibility, determined by family income alone.

Study Design: As a result of a 2013 policy change, children with ABD eligibility transitioned from fee-for-service to capitated care. We used Ohio Medicaid claims data from July 2013 through June 2015 to explore the relationships among instability in eligibility category, demographics, and utilization.

Methods: To examine the potential financial effect of categorical churn, an effective capitation rate was created to capture the proportion of the maximum potential capitation rate that was realized.

Results: More than 20% of children exited ABD-based eligibility at least once. Switching was associated with younger age and rural residence and was not associated with healthcare use.

Conclusions: Switching between eligibility categories is common and affects average capitation but not health service use.

Am J Manag Care. 2019;25(3):114-118Takeaway Points

Analyses of Ohio Medicaid claims data for children aged 3 to 16 years from 2013 to 2015 reveal that instability among eligibility categories is common and affects average capitation but not health service use.

  • The potential financial impact of churn should be considered when designing alternative payment models (APMs) to target high-cost populations.
  • In addition, the impact of churn should be considered when setting rates because the risk associated with revenue uncertainty may deter APM organizations from taking on risk-bearing contracts.
  • Policy changes that enhance categorical stability (eg, reduced frequency of redetermination) could reduce churn without adding payment system complexity.

In most states, children who qualify for Supplemental Security Income (SSI) under the Social Security Administration’s definition of disabled children also automatically qualify for Medicaid under the aged, blind, and disabled (ABD) eligibility category. Because of the complex needs of these children, state Medicaid programs have traditionally retained them in the fee-for-service (FFS) system rather than mandating them into managed care, accountable care organizations (ACOs), or other alternative payment models (APMs).1 Increasingly, states are rethinking this choice.2 There is a growing belief that the benefits of APMs may outweigh the perceived risks regarding inherent limitations on service access and provider networks.3,4 Rather than exclude Medicaid-eligible disabled children from APMs, states are instead experimenting with modified payment approaches, including differential experience-based capitation rates, that allow APMs to provide this patient population with healthcare services, care coordination, and social supports that meet their higher needs. Changes in Medicaid enrollment categories due to changes in children’s health conditions and their families’ financial conditions can, however, complicate these efforts. For example, data from Ohio—a state that transitioned children from FFS to an APM—show that in a 2-year period, more than 20% of children moved between higher and lower capitation rates, potentially blunting the impact of differential capitation (Figure).

In this case study, we explore the extent and impact of “churn” between different Medicaid eligibility classifications for enrollment reasons on the financial exposure of organizations that use APM models, drawing on the experience of Partners for Kids (PFK), an Ohio Medicaid ACO that serves approximately 330,000 Medicaid-covered children in 34 of Ohio’s 88 counties. PFK began enrolling children in Medicaid’s ABD eligibility classification as part of a policy change that ended the ABD managed care exclusion, effective July 2013.

In the time frame studied, July 2013 through June 2015, Ohio operated as a 209(b) state, which allowed the eligibility criteria for Medicaid ABD to be more restrictive than for SSI.5 Specifically, ABD eligibility required both an SSI determination and a family income level of less than 64% of the federal poverty level (FPL). This linking of disability status and income selects a very high-risk population, given research documenting that family poverty is associated with worse health status and higher health-related financial burden, even after controlling for insurance status.6-8

On a monthly basis, changes in household income could change categorical eligibility from ABD to Healthy Start, Ohio’s implementation of the State Children’s Health Insurance Program, which expands Medicaid to cover children 18 years or younger with family incomes up to 206% of the FPL. Changes in eligibility category generated changes in capitation, with the per-month experience-based capitation rate for ABD children being nearly 5 times the experience-based capitation rate for children who qualify for Healthy Start. The changes in capitation rates did not, however, change the services for which enrollees were eligible. Whether covered under ABD or Healthy Start, children had access to the full range of Medicaid-covered services and were eligible for care coordination appropriate for their clinical needs.


Data Sources and Study Population

Analyses used 2 years of Medicaid administrative claims data (July 1, 2013, to June 30, 2015). The study cohort included 7262 children aged 3 to 16 years who were enrolled in PFK as ABD in July 2013. Children younger than 3 years were excluded because they did not have sufficient claims history to calculate their medical complexity status. Those older than 16 years were excluded so that the full sample would be age eligible for Medicaid throughout the follow-up period. Enrollees whose initial entry to ABD occurred after July 2013 were also excluded from the analysis in order to focus on transitions over 2 years.


Categorical churn was defined as switching between ABD eligibility classification (SSI disabled and <64% FPL) and Healthy Start classification (≤206% FPL). Demographic correlates of churn were tested with a bivariate χ2 test. Medical complexity was calculated using the Pediatric Medical Complexity Algorithm, which classified enrollees’ conditions as complex chronic, noncomplex chronic, or noncomplex/nonchronic.9 In order to determine whether children affected by categorical churn had differential use of costly services, we tested the association between churn and the probabilities of emergency department (ED) use and inpatient use.

To examine the potential financial effect of categorical churn, we created an effective capitation rate (ECR) that represents the proportion of the maximum potential capitation rate that was realized. The ECR was calculated as:

We used a capitation adjustment of 0.21 to approximate PFK’s 4.79-fold differential between the capitation rates during the study period. Using this formula, a child covered for a full year as ABD would have an ECR of 1.0, meaning that PFK would receive 100% of the potential ABD capitation rate for that child. Alternatively, if that child experienced a 2-month switch to Healthy Start eligibility, the ECR would be 0.868 ([10 + (0.21 × 2)] / 12) and PFK would realize only 86.8% of the potential ABD capitation rate.


The mean (SD) age of the study cohort at the start of the observation period was 10.1 (3.8) years. Demographics are presented in Table 1.

By June 2015, 28% (n = 2052) of the cohort (N = 7262) were no longer in ABD coverage. Of those, 67% (n = 1377) remained in PFK but had switched to Healthy Start Medicaid and 33% (n = 675) were no longer in PFK. The latter category includes children who were still in Medicaid but no longer in the PFK coverage region and children who were no longer covered by Medicaid.

Overall, 22.3% of the cohort switched between ABD and Healthy Start at least once (mean, 1.17 switches; range, 1-5). Rural residence and younger age were both significantly associated with churn. Churn was consistent across all medical complexity levels (Table 1).

Churn was not associated with “any ED visit” or “any inpatient admission” in the 2-year follow-up period (Table 2); however, in children whose medical conditions were classified as noncomplex chronic, there was a marginally higher probability of ED and inpatient use among those with at least 1 eligibility change.

ECR and Utilization

The mean ECR was 0.925, reflecting that the realized financial capitation was 7.5 percentage points lower than it would have been without categorical churn. Examining the distribution, we see that, overall, 8.2% of children had an ECR between 80% and 99%, 5.2% between 60% and 79%, and 8.7% below 60%. This distribution was similar across levels of medical complexity (Table 3). Lower ECR was not associated with lower probability or lower count of ED visits or inpatient admissions (results not presented).


This case study demonstrates that categorical eligibility churn is extensive. Movement out of (or back into) ABD status was not associated with ED or inpatient use in the full sample, indicating that children who spend time in a non-ABD eligibility status are no less likely to use intensive services than their continuously ABD-enrolled peers. Utilization rates were at least as high and, in some cases, marginally higher in those who experienced churn compared with those with uninterrupted ABD eligibility status. This was most noticeable in children with noncomplex chronic diseases, including conditions such as asthma and attention-deficit/hyperactivity disorder, for which disease management and care coordination strategies could yield significant improvement in care.10,11

This relationship may reflect a heterogeneous relationship with the type of conditions included in the noncomplex chronic category. Noncomplex chronic conditions involving single-system diseases like asthma and diabetes may have health impacts that are more episodic in nature. Exacerbations or periods of better disease control for these conditions may be associated with family income reductions or improvements, respectively, and could thus spur eligibility changes, linking churn and healthcare use. In the other categories, the health status may be more stable (consistently higher in the complex category and consistently lower in the noncomplex nonchronic category) and, thus, changes in income would be less likely to be associated with changes in healthcare use.

Month-to-month eligibility category changes create, in effect, a blended capitation level and an environment in which the funding realized for this high-need population is somewhat less than the capitation rate set based on the experience. Findings, therefore, suggest that APMs taking on new high-cost populations should consider the potential financial impact of churn when designing targeted care models for these populations.

These results represent a single state and a unique period in which the state’s Medicaid program was implementing multiple process and system changes simultaneously with the studied policy change. As such, the results are not generalizable, but they do inform the conversation on the extension of managed care into traditionally carved-out pediatric populations. They suggest that rate setting should include consideration of the impact of categorical churn because the risk associated with revenue uncertainty may deter APM organizations from taking on risk-bearing contracts or, worse, encourage “cherry-picking” in enrollment, as was seen in the early days of Medicaid managed care programs.12 Beyond capitation, innovative payment models like bundled payments for noncapitated settings or complexity bonuses can also be implemented to limit the effect of eligibility instability. Alternatively, policy changes that enhance categorical stability (eg, reduced frequency of redetermination) could reduce churn without adding payment system complexity.

In the case of the population discussed in this study, the state Medicaid agency incorporated program design elements from the start to mitigate the potential dangers of enrolling the disabled population in Medicaid managed care. The state began risk adjusting capitation rates among the managed care entities 1 year after the children enrolled in ABD were mandated to enroll into PFK, allowing sufficient time for differences in the membership complexity to surface. The state’s 3 geographic contracting regions each contain 1 of the 3 largest cities in addition to rural counties, ensuring that APMs do not avoid rural populations.

Not only did the state Medicaid program attempt to address churn at the start, it transitioned from the 209(b) waiver program to section 1634 regulations in 2016. Under section 1634, ABD Medicaid eligibility is directly tied to disability and income level, without the need to calculate spend-down impacts. This administrative simplification has stabilized and expanded the ABD membership, thereby decreasing churn. Subsequent revisions of the program’s qualification hierarchy have also been enacted to reduce churn among membership.


Several limitations should be mentioned. As noted previously, these results are representative of a single organization in a single state, so they cannot be generalized. The period in which the ABD population policy change occurred overlapped with changes in the state’s implementation of a new Medicaid eligibility system and associated processes. These changes may have contributed to instability. We also note that detailed cost data for children in managed care are not currently available in the state claims data available for our analysis, so our results are limited to utilization. Analysis using cost data would allow for a more detailed consideration of the cost implications of instability. Finally, our analysis examined only the cohort of ABD-eligible children who rolled into the ACO at the onset of the policy change. We did not study the complementary population who started in the Healthy Start Medicaid program and migrated to the ABD program or ABD enrollees who were new to Medicaid after the definition of the cohort. As such, these results are only representative of the experiences of the defined cohort. The full utilization and financial implications may differ. This larger-scale research is underway.


As states continue to migrate traditionally carved-out pediatric populations, including children in foster care or juvenile detention, into APMs, it is valuable to understand potentially unexpected challenges faced by those that have blazed the trail. This case study gives one example regarding the importance of considering categorical instability and offers insight into the potential planning challenges in building fixed clinical support infrastructure for an “unfixed” population.Author Affiliations: Department of Pediatrics, The Ohio State University College of Medicine (DJC, KJK), Columbus, OH; Partners for Kids, Nationwide Children’s Hospital (SPG), Columbus, OH; Department of Health Policy and Management, The Gillings School of Global Public Health (MED, PHS), and The Cecil G. Sheps Center for Health Services Research (MED, PHS), University of North Carolina at Chapel Hill, Chapel Hill, NC; The Research Institute at Nationwide Children’s Hospital (EA), Columbus, OH; Division of Health Services Management and Policy, College of Public Health, The Ohio State University (WYX), Columbus, OH.

Source of Funding: This research was funded through Patient-Centered Outcomes Research Institute (PCORI) award #IHS-1310-07863. The views presented are solely the responsibility of the authors and do not necessarily represent the views of PCORI, its Board of Governors, or its Methodology Committee.

Author Disclosures: Drs Chisolm and Kelleher are employed by an academic medical center that is a partner in the accountable care organization (ACO) whose data are described, and Dr Gleeson is employed by that ACO. They receive no direct benefits from the findings. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DJC, SPG, KJK, WYX, PHS); acquisition of data (DJC, SPG, KJK); analysis and interpretation of data (DJC, SPG, EA, PHS); drafting of the manuscript (DJC, KJK, MED, PHS); critical revision of the manuscript for important intellectual content (DJC, SPG, KJK, MED, EA, WYX, PHS); statistical analysis (EA); obtaining funding (DJC, KJK, WYX, PHS); administrative, technical, or logistic support (KJK, MED); and supervision (DJC, KJK).

Address Correspondence to: Deena J. Chisolm, PhD, Department of Pediatrics, The Ohio State University College of Medicine, 700 Children’s Drive, RM FB3322, Columbus, OH 43205. Email:

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