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Estimating premium sensitivity for children's public health insurance coverage: selection but no death spiral.

ABSTRACT: To estimate the effect of premium increases on the probability that near-poor and moderate-income children disenroll from public coverage.Enrollment, eligibility, and claims data for Georgia's PeachCare for Kids(™) (CHIP) program for multiple years.We exploited policy-induced variation in premiums generated by cross-sectional differences and changes over time in enrollee age, family size, and income to estimate the duration of enrollment as a function of the effective (per child) premium. We classify children as being of low, medium, or high illness severity.A dollar increase in the per-child premium is associated with a slight increase in a typical child's monthly probability of exiting coverage from 7.70 to 7.83 percent. Children with low illness severity have a significantly higher monthly baseline probability of exiting than children with medium or high illness severity, but the enrollment response to premium increases is similar across all three groups.Success in achieving coverage gains through public programs is tempered by persistent problems in maintaining enrollment, which is modestly affected by premium increases. Retention is subject to adverse selection problems, but premium increases do not appear to significantly magnify the selection problem in this case.

SUBMITTER: Marton J

PROVIDER: S-EPMC4369223 | biostudies-other | 2015 Apr

REPOSITORIES: biostudies-other

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Estimating premium sensitivity for children's public health insurance coverage: selection but no death spiral.

Marton James J Ketsche Patricia G PG Snyder Angela A Adams E Kathleen EK Zhou Mei M

Health services research 20140815 2

<h4>Objective</h4>To estimate the effect of premium increases on the probability that near-poor and moderate-income children disenroll from public coverage.<h4>Data sources</h4>Enrollment, eligibility, and claims data for Georgia's PeachCare for Kids(™) (CHIP) program for multiple years.<h4>Study design</h4>We exploited policy-induced variation in premiums generated by cross-sectional differences and changes over time in enrollee age, family size, and income to estimate the duration of enrollmen ...[more]

PMID: 25130764

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Project description:BACKGROUND:Children with health insurance have increased access to healthcare and receive higher quality care. However, despite recent initiatives expanding children's coverage, many remain uninsured. New technologies present opportunities for helping clinics provide enrollment support for patients. We developed and tested electronic health record (EHR)-based tools to help clinics provide children's insurance assistance. METHODS:We used mixed methods to understand tool adoption, and to assess impact of tool use on insurance coverage, healthcare utilization, and receipt of recommended care. We conducted intent-to-treat (ITT) analyses comparing pediatric patients in 4 intervention clinics (n?=?15,024) to those at 4 matched control clinics (n?=?12,227). We conducted effect-of-treatment-on-the-treated (ETOT) analyses comparing intervention clinic patients with tool use (n?=?2240) to intervention clinic patients without tool use (n?=?12,784). RESULTS:Tools were used for only 15% of eligible patients. Qualitative data indicated that tool adoption was limited by: (1) concurrent initiatives that duplicated the work associated with the tools, and (2) inability to obtain accurate insurance coverage data and end dates. The ITT analyses showed that intervention clinic patients had higher odds of gaining insurance coverage (adjusted odds ratio [aOR]?=?1.32, 95% confidence interval [95%CI] 1.14-1.51) and lower odds of losing coverage (aOR?=?0.77, 95%CI 0.68-0.88), compared to control clinic patients. Similarly, ETOT findings showed that intervention clinic patients with tool use had higher odds of gaining insurance (aOR?=?1.83, 95%CI 1.64-2.04) and lower odds of losing coverage (aOR?=?0.70, 95%CI 0.53-0.91), compared to patients without tool use. The ETOT analyses also showed higher rates of receipt of return visits, well-child visits, and several immunizations among patients for whom the tools were used. CONCLUSIONS:This pragmatic trial, the first to evaluate EHR-based insurance assistance tools, suggests that it is feasible to create and implement tools that help clinics provide insurance enrollment support to pediatric patients. While ITT findings were limited by low rates of tool use, ITT and ETOT findings suggest tool use was associated with better odds of gaining and keeping coverage. Further, ETOT findings suggest that use of such tools may positively impact healthcare utilization and quality of pediatric care. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02298361 ; retrospectively registered on November 5, 2014.

Project description:In the United States, health insurance is not universal. Observational studies show an association between uninsured parents and children. This association persisted even after expansions in child-only public health insurance. Oregon's randomized Medicaid expansion for adults, known as the Oregon Experiment, created a rare opportunity to assess causality between parent and child coverage.To estimate the effect on a child's health insurance coverage status when (1) a parent randomly gains access to health insurance and (2) a parent obtains coverage.Oregon Experiment randomized natural experiment assessing the results of Oregon's 2008 Medicaid expansion. We used generalized estimating equation models to examine the longitudinal effect of a parent randomly selected to apply for Medicaid on their child's Medicaid or Children's Health Insurance Program (CHIP) coverage (intent-to-treat analyses). We used per-protocol analyses to understand the impact on children's coverage when a parent was randomly selected to apply for and obtained Medicaid. Participants included 14409 children aged 2 to 18 years whose parents participated in the Oregon Experiment.For intent-to-treat analyses, the date a parent was selected to apply for Medicaid was considered the date the child was exposed to the intervention. In per-protocol analyses, exposure was defined as whether a selected parent obtained Medicaid.Children's Medicaid or CHIP coverage, assessed monthly and in 6-month intervals relative to their parent's selection date.In the immediate period after selection, children whose parents were selected to apply significantly increased from 3830 (61.4%) to 4152 (66.6%) compared with a nonsignificant change from 5049 (61.8%) to 5044 (61.7%) for children whose parents were not selected to apply. Children whose parents were randomly selected to apply for Medicaid had 18% higher odds of being covered in the first 6 months after parent's selection compared with children whose parents were not selected (adjusted odds ratio [AOR]=1.18; 95% CI, 1.10-1.27). The effect remained significant during months 7 to 12 (AOR=1.11; 95% CI, 1.03-1.19); months 13 to 18 showed a positive but not significant effect (AOR=1.07; 95% CI, 0.99-1.14). Children whose parents were selected and obtained coverage had more than double the odds of having coverage compared with children whose parents were not selected and did not gain coverage (AOR=2.37; 95% CI, 2.14-2.64).Children's odds of having Medicaid or CHIP coverage increased when their parents were randomly selected to apply for Medicaid. Children whose parents were selected and subsequently obtained coverage benefited most. This study demonstrates a causal link between parents' access to Medicaid coverage and their children's coverage.

Project description:ObjectiveTo improve understanding of the relationship between lack of insurance and risk of subsequent mortality.Data sourcesAdults who reported being uninsured or privately insured in the National Health Interview Survey from 1986 to 2000 were followed prospectively for mortality from initial interview through 2002. Baseline information was obtained on 672,526 respondents, age 18-64 at the time of the interview. Follow-up information on vital status was obtained for 643,001 (96 percent) of these respondents, with approximately 5.4 million person-years of follow-up.Study designRelationships between insurance status and subsequent mortality are examined using Cox proportional hazard survival analysis.Principal findingsAdjusted for demographic, health status, and health behavior characteristics, the risk of subsequent mortality is no different for uninsured respondents than for those covered by employer-sponsored group insurance at baseline (hazard ratio 1.03, 95 percent confidence interval [CI], 0.95-1.12). Omitting health status as a control variable increases the estimated hazard ratio to 1.10 (95 percent CI, 1.03-1.19). Also omitting smoking status and body mass index increases the hazard ratio to 1.20 (95 percent CI, 1.15-1.24). The estimated association between lack of insurance and mortality is not larger among disadvantaged subgroups; when the analysis is restricted to amenable causes of death; when the follow-up period is shortened (to increase the likelihood of comparing the continuously insured and continuously uninsured); and does not change after people turn 65 and gain Medicare coverage.ConclusionsThe Institute of Medicine's estimate that lack of insurance leads to 18,000 excess deaths each year is almost certainly incorrect. It is not possible to draw firm causal inferences from the results of observational analyses, but there is little evidence to suggest that extending insurance coverage to all adults would have a large effect on the number of deaths in the United States.

Project description:IntroductionHealth insurance has been found to increase healthcare utilisation and reduce catastrophic health expenditures in a number of countries; however, coverage is often unequally distributed among populations. The sociodemographic patterns of health insurance in Namibia are not fully understood. We aimed to assess the prevalence of health insurance, the relation between health insurance and health service utilisation and to explore the sociodemographic factors associated with health insurance in Namibia. Such findings may help to inform health policy to improve financial access to healthcare in the country.MethodsUsing data on 14,443 individuals, aged 15 to 64 years, from the 2013 Namibia Demographic and Health Survey, the association between health insurance and health service utilisation was investigated using multivariable mixed effects Poisson regression analyses, adjusted for sociodemographic covariates and regional, enumeration area and household clustering. Multivariable mixed effects Poisson regression analyses were also conducted to explore the association between key sociodemographic factors and health insurance, adjusted for covariates and clustering. Effect modification by sex, education level and wealth quintile was also explored.ResultsJust 17.5% of this population were insured (men: 20.2%; women: 16.2%). In fully-adjusted analyses, education was significantly positively associated with health insurance, independent of other sociodemographic factors (higher education RR: 3.98; 95% CI: 3.11-5.10; p < 0.001). Female sex (RR: 0.83; 95% CI: 0.74-0.94; p = 0.003) and wealth (highest wealth quintile RR: 13.47; 95% CI: 9.06-20.04; p < 0.001) were also independently associated with insurance. There was a complex interaction between sex, education and wealth in the context of health insurance. With increasing education level, women were more likely to be insured (p for interaction < 0.001), and education had a greater impact on the likelihood of health insurance in lower wealth quintiles.ConclusionsIn this population, health insurance was associated with health service utilisation but insurance coverage was low, and was independently associated with sex, education and wealth. Education may play a key role in health insurance coverage, especially for women and the less wealthy. These findings may help to inform the targeting of strategies to improve financial protection from healthcare-associated costs in Namibia.

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Estimating premium sensitivity for children's public health insurance coverage: selection but no death spiral.

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Estimating premium sensitivity for children's public health insurance coverage: selection but no death spiral.

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