Literature DB >> 20211947

State variation in underinsurance among children with special health care needs in the United States.

Michael D Kogan1, Paul W Newacheck, Stephen J Blumberg, Kathleen M Heyman, Bonnie B Strickland, Gopal K Singh, Mary Beth Zeni.   

Abstract

OBJECTIVE: National attention has focused on providing health insurance coverage for children. Less awareness has been given to underinsurance, particularly for children with special health care needs (CSHCN). Defined as having inadequate benefits, underinsurance may be a particular problem for CSHCN because of their greater needs for medical care.
METHODS: We used the 2005-2006 National Survey of Children With Special Health Care Needs, a nationally representative study of >40,000 CSHCN, to address state variations in underinsurance. CSHCN with health insurance were considered underinsured when a parent reported that the child's insurance did not usually or always cover needed services and providers or reasonably cover costs. We calculated the unadjusted prevalence of underinsurance for each state. Using logistic regression, we estimated state-specific odds and prevalence for underinsurance after adjusting for poverty level, race/ethnicity, gender, family structure, language use, insurance type, and severity of child's health condition. We also conducted multilevel analyses incorporating state-level contextual data on Medicaid and the State Children's Health Insurance Program.
RESULTS: Bivariate and multivariate analyses indicated that CSHCN's state of residence had a strong association with insurance adequacy. State-level unadjusted underinsurance rates ranged from 24% (Hawaii) to 38% (Illinois). After multivariate adjustments, the range was largely unchanged: 23% (Hawaii) to 38% (New Jersey). Multilevel analyses indicated that Medicaid income eligibility levels were inversely associated with the odds of being underinsured.
CONCLUSIONS: The individual-level and macro-level factors examined only partly explain state variations in underinsurance. Furthermore, the macro-level factors explained only a small portion of the variance; however, other macro-level factors may be relevant for the observed patterns.

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Mesh:

Year:  2010        PMID: 20211947     DOI: 10.1542/peds.2009-1055

Source DB:  PubMed          Journal:  Pediatrics        ISSN: 0031-4005            Impact factor:   7.124


  10 in total

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  10 in total

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