Health Plan Disenrollment in a Choice-Based Medicaid Managed Care Program

Auto-assignment of providers in Medicaid Managed Care and factors influencing seasonal flu vaccine uptake: a retrospective analysis

Objectives

Over 50 million people in the USA are enrolled in a Medicaid Managed Care plan. If they do not select a primary care provider, they are auto-assigned to one. The impact of auto-assignment has largely been understudied outside the context of patient satisfaction with the insurance plan. The purpose of the study was to explore the association between auto-assignment and flu vaccination use, which will contribute to our understanding of factors influencing the COVID-19 vaccine uptake.

Methods

Retrospective data from the Enterprise Data Warehouse of a health system were obtained for adult Medicaid enrolees assigned to a Midwestern health system in 2019. Descriptive statistics, independent t-tests and tetrachoric correlations were used to explore the relationship between auto-assignment and flu vaccine receipt among a large sample of Illinois residents (N = 7224). The sample was then divided into those who chose their provider (n = 6027) and those who were auto-assigned (n = 1197).

Key findings

Individuals who selected their provider were deemed to have flu vaccine coverage over those who were auto-assigned (33.2% vs. 6.6%). Furthermore, among those who were auto-assigned, age, number of office visits and having chronic morbidities, including chronic obstructive pulmonary disease (P < 0.01), diabetes (P < 0.01) and heart failure (P < 0.01), were positively associated with flu vaccine receipt.

Conclusions

Individuals who are auto-assigned to a primary care provider are less likely to be flu vaccine recipients than those who choose their provider. This suggests that auto-assignment is a risk factor that influences vaccine receipt. This research provides perspectives for outreach efforts that target individuals who are auto-assigned to a provider.

Informative censoring by health plan disenrollment among commercially insured adults

PURPOSE

Health plan disenrollment occurs frequently in commercial insurance claims databases. If individuals who disenroll are different from those who remain enrolled, informative censoring may bias descriptive statistics as well as estimates of causal effect. We explored whether patterns of disenrollment varied by patient or health plan characteristics.

METHODS

In a large cohort of commercially insured adults (2007-2013), we examined two primary outcomes: (a) within-year disenrollment between January 1 and December 30, which was considered to occur due to patient disenrollment from the health plan, and (b) end-of-year disenrollment on December 31, which was considered to occur due to either patient disenrollment from the health plan or withdrawal of the entire health plan from the commercial insurance database. In yearly cohorts, we identified factors independently associated with disenrollment by using log-binomial regression models to estimate risk ratios (RR) and 95% confidence intervals (CI).

RESULTS

Among 2 053 100 unique patient years, the annual proportion of within-year disenrollment remained steady across years (range, 13% to 14%) whereas the annual proportion of end-of-year disenrollment varied widely (range, 8% to 26%). Independent predictors of within-year disenrollment were related to health status, including age, comorbidities, frailty, hospitalization, emergency room visits, use of durable medical equipment, use of preventive care, and use of prescription medications. In contrast, independent predictors of end-of-year disenrollment were related to health plan characteristics including insurance plan type and geographic characteristics.

CONCLUSIONS

Differential risk of disenrollment suggests that analytic approaches to address selection bias should be considered in studies using commercial insurance databases.

Determinants of Voluntary National Health Insurance Drop-Out in Eastern Sudan

BACKGROUND

Low enrolment and high drop-out rates are common problems in voluntary health insurance schemes. Yet, most studies in this research area focus on community-based health insurance and enrolment, rather than drop-out.

OBJECTIVE

This study examines what causes informal sector families not to renew their voluntary National Health Insurance Fund (NHIF) health insurance membership in Eastern Sudan.

METHODS

Primary data from about 600 informal sector households that dropped out or remained insured, collected through a household survey conducted in March 2014, were used. Logistic regressions were employed to examine what determines drop-out of the voluntary NHIF scheme.

RESULTS

The logistic regression results are consistent with the existing literature and confirm the importance of household head, household and community characteristics. Notably, worse family health status and higher health care utilization decrease the probability of drop-out, which requires further analysis as it may indicate the problem of adverse selection and insufficient risk management. Most importantly, the results consistently show that household heads who are satisfied with health services and those who understand the main features of the voluntary NHIF scheme are less likely to drop out. Also, 30 % of drop-out households hold a social support card and reported that the social support scheme is the main reason for not renewing their voluntary NHIF health insurance membership as they qualify for sponsored NHIF health insurance membership.

CONCLUSIONS

This study shows that satisfaction with health services and knowledge of the health insurance scheme are important factors explaining drop-out of a national health insurance programme. The results suggest that education and information campaigns should be developed further to raise understanding of the NHIF voluntary scheme. In addition, information systems and coordination between the main agencies should be strengthened to reduce administrative costs and ensure policy coherence.

Continuity of Public Insurance Coverage

Publicly financed insurance programs are tasked with maintaining coverage for eligible children, but published measures to assess coverage have not been evaluated. Therefore, we sought to identify and categorize measures of health insurance continuity for children and adolescents. We conducted a systematic review of Medline and HealthStar databases, review of reference lists of eligible articles, and contact with experts. We categorized measures into 8 domains based on a conceptual framework. We identified 147 measures from 84 eligible articles. Most measures evaluated the following domains: always insured (41%), repeatedly uninsured (36%), and transition out of coverage (29%), while fewer assessed single gap in coverage, always uninsured, transition into coverage, change in coverage, and eligibility. Only 18% of measures assessed associations between continuity of coverage and child and adolescent health outcomes. These results suggest that a number of measures of continuity of coverage exist, but few measures have assessed impact on outcomes.

The stigma of public programs: does a separate S-CHIP program reduce it?

Previous studies suggest access to and satisfaction with care may be different for enrollees in S-CHIP and Medicaid, but it is unclear whether those differences are fully explained by socioeconomic characteristics of the enrollees. We analyze access and satisfaction of three groups of children: Medicaid enrolled, S-CHIP enrolled, and children who are income eligible for Medicaid but carry a card similar to the state's S-CHIP children's card. Both enrollees and providers may believe that these children are enrolled in S-CHIP despite the fact that reimbursement is through the state's Medicaid system. Results indicate that the same network of providers treat, or are perceived by families to treat, the three groups differently. They support the notion that some of the differences in satisfaction between S-CHIP and Medicaid enrollees are related to unmeasured characteristics (for example, income) of the families in the different programs, but that programmatic identity contributes substantially to differential care experience.