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Medicaid Managed Care Reduces Readmissions for Youths With Type 1 Diabetes
Kathleen Healy-Collier, CSSBB, DHA; Walter J. Jones, PhD; James E. Shmerling, DHA, FACHE; Kenneth R. Robertson, MD, MBA; and Robert J. Ferry, Jr, MD, FAAP
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Medicaid Managed Care Reduces Readmissions for Youths With Type 1 Diabetes

Kathleen Healy-Collier, CSSBB, DHA; Walter J. Jones, PhD; James E. Shmerling, DHA, FACHE; Kenneth R. Robertson, MD, MBA; and Robert J. Ferry, Jr, MD, FAAP
An analysis of the largest cohort available reveals that youths with type 1 diabetes, on a Medicaid managed care plan, are less likely to be readmitted within 90 days of discharge.
A surprising finding of the present CHA study was that youths with T1D were not more likely to be readmitted for DKA based on their primary insurance type. This unexpected observation may reflect that patients who are not on a managed care product are more likely to use the hospital and/or the emergency department (ED) as a primary care setting compared with those on a managed care Medicaid plan. The likelihood of seeking healthcare at an inappropriate setting might be greater for those patients not on managed care plans. On the other hand, however, patients with DKA may simply be more likely to present at a hospital once they are clinically compromised, regardless of insurance type. 

A particular strength of our study was the inclusion of inpatient, observation, and ED readmissions. This approach enabled deeper analysis of the type of services provided, both on initial visit (adjusted for severity) and on return. Other studies of readmission have focused only on initial inpatient hospitalization and/or ED encounters, which do not account for patients who leave the ED nor those who were on observation status.6,24 Moreover, prior studies have not considered those who returned to the hospital but were not admitted as inpatients.25,26 Our present study concurs with other reports suggesting that race is the most significant factor with respect to diabetic readmissions, although age and gender are also significant risk contributors.25 These observations support the need for interventions focused on specific races and age groups, early diabetic education, and improved engagement of the family in outpatient settings.22

Copious literature validates readmission as one proxy indicator for quality of diabetes care and affirms that specific clinical pathways can prevent hospital readmissions.27-30 A high readmission rate reflects the quality of the initial hospitalization and underscores areas needing improvement, especially for those with DKA.31,32 A health plan’s expectations of providers directly influence the key interventions cited nationally as best practices for diabetes care.33,34 Nearly all hospitals in our CHA study displayed significant variation of readmission rates, both for overall diabetes and for DKA-specific etiologies. Connecticut, Indiana, and Utah displayed the largest observed variation in days between readmissions in this analysis; however, only 1 free-standing children’s hospital exists within each of these states, so these results should not infer a causal relationship with the state Medicaid plan structure. Future insights might be gleaned from states presenting a greater number of hospitals per state to evaluate (eg, TX, FL, CA, TN). Future studies could compare the managed care relationship across states with a larger sample of children’s hospitals.

For most states, the CHA-participating hospitals capture the majority of pediatric admissions for subspecialty care.21 Incomplete access to data by state limits our analysis, because PHIS data only represent children encountered at one of the participating CHA children’s hospitals. PHIS for CHA cannot capture all admissions for youths with diabetes within these states. Lag time prior to accessing these data also contributes to this ascertainment bias. However, the available PHIS data were well vetted by consistent data submissions by each participating hospital during 2008 through 2011. Using PHIS data, Stone et al recently reported primary payer status as an independent predictor of risk-adjusted postoperative mortality, morbidity, and resource utilization among pediatric surgical patients.35

Limitations

A potential limitation of this study relates to interpretation of “managed care” across institutions. Although the PHIS data dictionary defines managed care, each individual hospital can independently interpret this concept due to variation in Medicaid programs nationally (eg, level of benefits, number of providers by plan, access to specialists and subspecialists, and the amount of patient/family coinsurance). Many nondiabetic reasons for readmissions were not assessable with the available data set. Additionally, our study could not directly test for educational level of the parents, family income levels (although assumed to be less than the minimum federal poverty threshold in order to acquire Medicaid), or other socioeconomic contributors. Therefore, the observed results do not necessarily indicate poor care quality for those hospitals with higher readmissions. Our observations do affirm successful access to care for many health plan formats. Managed care plans can provide greater support for addressing specific social variables, such as parental support and education, socioeconomic indicators, racial disparities, school system support, and insurance continuity for youths with diabetes.36-41 Medicaid plans address many of these challenges and have reduced readmissions unrelated to diabetes.24,42

The American Diabetes Association has successfully promoted enacting laws to require state-regulated health insurers to cover diabetes supplies. Opposition persists to such statutes because of concern about increased overall system costs, but public health insurance has been shown to improve pediatric diabetes management by increasing access to critical supplies and equipment (eg, glucose monitoring strips and insulin pen devices).43 Studying patients with diabetes in 31 health plans across the United States, Roski et al observed extreme variations in resource utilization, yet minor variance in quality outcomes.44 Analyzing data from 42 CHA hospitals in the PHIS database, Feudtner et al recently reported that better-performing hospitals (as defined by the Commonwealth Fund) were more likely to have higher readmission rates.45 Saha et al observed that preventable hospitalizations for adults with diabetes increased with improved access to care.46 Even if such data imply that utilization of particular resources does not affect readmission outcomes, the direct benefits to patients from access to appropriate care cannot be understated. The present CHA study design could not estimate additional—and potentially significant—cost savings for families related to reduced absenteeism (from work or school) and reduced commuting time between their homes and the hospitals due to lower readmission rates.22

Finally, the retrospective design limits generalization. The inability to fully control an outpatient environment precludes conclusions applicable across all Medicaid programs, even for a cohort as specific as youths with T1D. Differences in populations, provider access, eligibility criteria, and plan benefits represent major variables; we attempted to account for demographic differences, but unique variables by plan could not be controlled retrospectively. Moreover, it is not feasible to control prospectively for these differences between states, so one should not assume that individual case studies for one state will yield the same result elsewhere.

CONCLUSIONS
After adjusting for severity, youths with T1D on Medicaid managed care in this national CHA cohort were significantly less likely to be readmitted within 90 days. Being in a managed care plan resulted in substantial cost savings during 2008 through 2011, despite large variation across 26 states with respect to readmission rates. Policy makers should consider these data when structuring and monitoring state Medicaid products.16 Policy makers should explore specific successes with managed care tools (eg, health information technology, case management) to improve adherence to evidence-based practice for T1D as approaches to reduce preventable readmissions.22,24 As national efforts evolve to develop “Medicare type solutions” for chronic pediatric disease management, this CHA study could guide ACA implementation and Medicaid expansion to develop and leverage managed care tools that reduce expensive readmissions.

Acknowledgments

Dr Healy-Collier deeply thanks the CHA (formerly National Association of Children’s Hospitals and Related Institutions) for providing data. Portions of this work were presented in abstract form at the American Public Health Association 141st Annual Meeting and Expo, on November 4, 2013, at Boston, MA. 

Author Affiliations: Brookwood Medical Center (KH-C), Birmingham AL; Health Administration & Policy, College of Health Professions, Medical University of South Carolina (WJJ), Charleston SC; Connecticut Children’s Medical Center (JES), Denver CO; Le Bonheur Children’s Hospital (KRR), Memphis, TN; Division of Pediatric Endocrinology (RJF), and Department of Pediatrics (KRR), University of Tennessee Health Science Center, Memphis, TN; Department of Psychology, University of Memphis (RJF), Memphis, TN.

Source of Funding: NIH U01 DK085465 and Juvenile Diabetes Research Foundation International grant 1-2011-597 (to RJF).

Author Disclosures: Dr Ferry discloses receipt of unrelated research support from Takeda, Bristol-Myers Squibb, and Eli Lilly. Dr Healy-Collier conceived the study, obtained institutional review board approval, researched data, contributed to discussion, and wrote the manuscript. Drs Healy-Collier and Ferry are the guarantors of this work and, as such, had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. 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 (RJF, KH-C, WJJ, KRR, JES); acquisition of data (KH-C); analysis and interpretation of data (RJF, KH-C, KRR, JES); drafting of the manuscript (WJJ, RJF, KH-C, JES); critical revision of the manuscript for important intellectual content (RJF, WJJ); statistical analysis (KH-C); provision of patients or study materials (RJF); obtaining funding (RJF); administrative, technical, or logistic support (KH-C, WJJ, RJF); and supervision (WJJ, RJF, KRR, JES).

Address correspondence to: Robert Ferry, Jr, MD, 858 Madison Ave, MSB 501A, Memphis, TN 38103-3409. E-mail bob@uthsc.edu.
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