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      Effects of Medicaid Automatic Enrollment on Disparities in Insurance Coverage and Caregiver Burden for Children with Special Health Care Needs

      1 , 2
      Medical Care Research and Review
      SAGE Publications

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          Abstract

          We analyze how Medicaid automatic enrollment policies for children with special health care needs (CSHCN) who are enrolled in Supplemental Security Income (SSI) reduce disparities in health insurance coverage and caregiving burden. Using the 2009–2010 National Survey of Children with Special Health Care Needs, we implement a difference-in-differences regression model comparing insurance enrollment rates between CSHCN receiving SSI and CSHCN not receiving SSI, in states with and without automatic enrollment policies. We find that Medicaid automatic enrollment has a meaningful impact on insurance enrollment for low-income CSHCN who participate in SSI and can be an effective method for mitigating disparities in insurance coverage (reducing uninsurance by 38%). Medicaid automatic enrollment also reduces caregiver burden among socioeconomically disadvantaged families with CSHCN. The effects of these policies are largest families who might be on the margin of eligibility or who face high administrative burden.

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          Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?

          The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.
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            Racism and Health: Evidence and Needed Research

            In recent decades, there has been remarkable growth in scientific research examining the multiple ways in which racism can adversely affect health. This interest has been driven in part by the striking persistence of racial/ethnic inequities in health and the empirical evidence that indicates that socioeconomic factors alone do not account for racial/ethnic inequities in health. Racism is considered a fundamental cause of adverse health outcomes for racial/ethnic minorities and racial/ethnic inequities in health. This article provides an overview of the evidence linking the primary domains of racism—structural racism, cultural racism, and individual-level discrimination—to mental and physical health outcomes. For each mechanism, we describe key findings and identify priorities for future research. We also discuss evidence for interventions to reduce racism and describe research needed to advance knowledge in this area.
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              No Adjustments Are Needed for Multiple Comparisons

              Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that "chance" serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not be so reluctant to explore leads that may turn out to be wrong that they penalize themselves by missing possibly important findings.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Medical Care Research and Review
                Med Care Res Rev
                SAGE Publications
                1077-5587
                1552-6801
                February 2023
                July 05 2022
                February 2023
                : 80
                : 1
                : 65-78
                Affiliations
                [1 ]RAND, Arlington, VA, USA
                [2 ]RAND, Boston, MA, USA
                Article
                10.1177/10775587221106116
                35788159
                25ebcf14-32e1-462e-9a2f-1cabc24b02b5
                © 2023

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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