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Volumn 17, Issue 3, 1998, Pages 169-180

Access to Care: How Much Difference Does Medicaid Make?

(2)  Berk, Marc L a   Schur, Claudia L a  

a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; CHILD; CHILD HEALTH CARE; ECONOMICS; EPIDEMIOLOGY; FEMALE; HEALTH CARE DELIVERY; HEALTH CARE PLANNING; HUMAN; INSURANCE; MALE; MEDICAID; METHODOLOGY; MULTIVARIATE ANALYSIS; STATISTICS; UNITED STATES;

EID: 0032061646     PISSN: 02782715     EISSN: None     Source Type: Journal    
DOI: 10.1377/hlthaff.17.3.169     Document Type: Article
Times cited : (81)

References (32)
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    • note
    • Respondents with both private insurance and Medicaid were classified as having private coverage. Persons who reported private coverage but who had listed a "single-purpose" plan (for example, vision or dread disease) on the earlier NHIS interview were not included in the private category. After assignment of insurance status, unweighted sample sizes by insurance category were as follows: 2,239 persons with private insurance, 282 with Medicaid or other public coverage (not including Medicare or military coverage), and 415 persons who were uninsured Those with Medicare or military coverage (forty-one and fifty-five observations, respectively) were excluded from the analysis.
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    • note
    • Standard errors were computed with SUDAAN, which uses the Taylor series linearization method to account for the complex survey design. Tests of statistical significance were used to assess whether differences in population estimates exist at specified levels of confidence. Only differences significant at the .05 level are discussed in the text. To control for other confounding factors that influence access to health care, regression analyses were conducted as well. For the two dichotomous dependent variables, we used the SUDAAN procedure LOGISTIC to fit logistic regression models to binary data, estimating odds ratios and confidence levels for the parameters. For the physician visits equation, we used a negative binomial model based on a Poisson distribution. See J. Hausman, B. Hall, and Z. Griliches, "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica 52, no. 4 (1984): 909-938; and W.H. Green, Econometric Analysis (New York: Macmillan, 1990). The Poisson regression model, in contrast to a linear model, allows us to improve on the estimation by accounting for the characteristics found in the distribution of visits; specifically, we observe a large number of zeros, a large number of small values, and a variable that is discrete in nature. Although this model better reflects the observed distribution of visits, the estimation program does not allow for appropriate estimation of standard errors given the complex survey design. Because standard errors are understated, we apply a stricter test when interpreting results, considering as significant only those coefficients that are different from zero at a 99 percent confidence interval.
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    • Full regression results can be obtained from the authors at Project HOPE Center for Health Affairs, Suite 600, 7500 Old Georgetown Road, Bethesda, Maryland 20814.
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