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. 2011 Feb;101(2):368-75.
doi: 10.2105/AJPH.2010.191569. Epub 2010 Dec 16.

Ongoing coverage for ongoing care: access, utilization, and out-of-pocket spending among uninsured working-aged adults with chronic health care needs

Stephen P Gulley  1 , Elizabeth K Rasch, Leighton Chan
Affiliations

Ongoing coverage for ongoing care: access, utilization, and out-of-pocket spending among uninsured working-aged adults with chronic health care needs

Stephen P Gulley et al. Am J Public Health. 2011 Feb.

Abstract

Objectives: We sought to determine how part-year and full-year gaps in health insurance coverage affected working-aged persons with chronic health care needs.

Methods: We conducted multivariate analyses of the 2002-2004 Medical Expenditure Panel Survey to compare access, utilization, and out-of-pocket spending burden among key groups of persons with chronic conditions and disabilities. The results are generalizable to the US community-dwelling population aged 18 to 64 years.

Results: Among 92 million adults with chronic conditions, 21% experienced at least 1 month uninsured during the average year (2002-2004). Among the 25 million persons reporting both chronic conditions and disabilities, 23% were uninsured during the average year. These gaps in coverage were associated with significantly higher levels of access problems, lower rates of ambulatory visits and prescription drug use, and higher levels of out-of-pocket spending.

Conclusions: Implementation of health care reform must focus not only on the prevention of chronic conditions and the expansion of insurance coverage but also on the long-term stability of the coverage to be offered.

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Figures

FIGURE 1
FIGURE 1
Percentages of US adults aged 18–64 years spending 15% or more of family income on health care services, by analytic group and insurance coverage status: Medical Expenditure Panel Survey, 2002–2004. Note. ADL = activities of daily living; IADL = instrumental activities of daily living. Data are pooled annual estimates. Covariate-controlled predicted marginal rates are presented. Predicted marginal estimates were computed from a logistic regression model fit on the basis of analytic group, insurance coverage status, the interaction of those 2 variables, and the following covariates: age, gender, race/ethnicity, education, marital status, and the number of persons in the health insurance eligibility unit.
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References

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