Publication Type: Paper/Poster Proposal
Abstract: Surveys attempting to measure the number of insured consistently find that
respondents underreport Medicaid coverage. In a study of Minnesota Medicaid recipients, Call, et. al., for example, find a number of reasons for the undercount and conclude that the undercount has a negligible impact on estimates of the uninsured for Minnesota. However, they also note that the study may not generalize to other locations with very different populations. In this study, we consider Medicaid underreporting in Louisiana and its implications for survey estimates of uninsured adults and children. Data for the analysis are based on a random sample of Medicaid recipients in Louisiana, allowing comparisons of reported insurance status for these Medicaid recipients. The paper will also consider the implications of Medicaid underreporting on state-level and national-level estimates (CPS data) of Louisiana's uninsured population. As a final note, with its unique charity hospital system and high poverty level among Louisiana natives, Louisiana provides an interesting and unique context for examining the problem of Medicaid underreporting.

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Lack of direct public support for initiatives related to increasing equality for health care is closely connected to the lack of public knowledge about who the uninsured actually are. Through the use of both public opinion polls and the Current Population Survey, it will be shown that the American public is not well-informed enough about the problems of the uninsured to make it highly visible to push for either 1); a more exhaustive employer-offered coverage; or; 2) a national health plan. One of the main barriers to appropriate public discourse on health insurance policies is that the public is generally not aware that the majority of those without coverage are employed—and not in poverty—possibly due to perceptions of class. Because of this, proper policy directives aimed towards the correct uninsured population are lacking. The numbers of those employed without health insurance are continuing to grow, and will have longer-term effects in the form of worse health outcomes and income disparities. An intrinsic component of discourse on class and the uninsured is to create an atmosphere where the public is adequately informed about who the uninsured are and what the feasible options are to address this growing problem.

Publication Type: Abstract
Abstract: Author: Núria Mas, IESE Business School

Title: Managed Care and the Safety Net. More Pain for the Uninsured?

Rationale: Health care insurance in the US has changed enormously in the last 20 years, since managed care radically altered incentives to providers. The impact of the managed care boom has been subject to close scrutiny. However, most of the literature has concentrated either in the role of managed care in bringing efficiency gains to the health care market or on studying its effect on utilization and quality of care for its enrollees. Research analyzing the impact of managed care on the uninsured and on safety net hospitals has been minimal.

Objectives: This paper contributes to fill this gap and analyses the impact of managed care on access to care and quality of care for the uninsured.

Methodology: The United States has always relied on an institutional safety net to meet the basic health care needs of the uninsured. Traditionally, hospitals in the US had been able to finance charity care through a complex system of cross-subsidies where privately insured patients were charged higher prices. By imposing stricter financial restrictions to hospitals and doctors, managed care reduced prices charged to their insured patients, making it very difficult for hospitals to obtain excess funds for their uncompensated care. In this paper we test the hypothesis that this increased financial pressure has worsened both access to care and quality of care for the uninsured, by affecting the safety net hospitals. We expand Frank and Salkever´s (1991) model to analyze hospitals´ decisions to provide charity care and use a probit model to test the results empirically. We use hospital data from the American Hospital Association and patient data from the Office of Statewide Healthcare Planning and Development (OSHPD). Managed care data is obtained from the Area Resource File.

Results: First, using US data, our results show that manage care has increased safety net hospital closures and it has encouraged the termination of those services (ER, obstetrics, etc) most frequently used by the uninsured, hence negatively affecting their access to care. Second, our results confirm that uninsured patients shift toward government hospitals. The results also seem to point toward the possibility of a trade-off between the number of charity care patients and the quality of care provided. Finally, we directly analyze the impact of managed care on health outcomes for the uninsured (as measured by the probability of dying after a heart attack). Our results indicate that managed care penetration has a negative impact on the health of charity care patients and also of those that go to government hospitals.

Conclusions: the results of this paper confirm that the impact of managed care goes beyond its effect on its enrollees and on efficiency. They also have important policy implications since the gap between socioeconomic groups could widen if the uninsured not only see their access to care reduced, but also go to hospitals which quality is declining.

Publication Type: Abstract
Abstract: Policymakers have long been interested in identifying the number of people lacking health insurance, the consequences of uninsurance for access to health care, and the potential cost of covering the uninsured. Household surveys provide the primary source of information about the uninsured, but many validation studies find some respondents misreport insurance status. Reporting errors may lead to inaccurate estimates of the number of uninsured and bias estimates of the effects of insurance on access, use, and costs. Using data from the 1996 Medical Expenditure Panel Survey (MEPS), we investigate what can be learned in the presence of arbitrary health insurance reporting errors about (a) the gap between the insured and uninsured in the use of health services and (b) the impact of universal insurance coverage on the use of services. We exploit information from insurance cards, policy booklets, and follow-back interviews with employers and insurance companies to construct validation data for a nonrandom portion of the sample. There are 18,851 nonelderly in the 1996 MEPS, and about two-thirds of them have evidence from at least one source validating their reported insurance status. Extending the theoretical literature on nonparametric bounds and treatment effects, we formally characterize the identification problem and assess the identifying power of a variety of verification, monotonicity, and independence assumptions. Molinari’s (2002) “missing treatments” bounds are tightened when there is information about the potential degree of reporting error within the subpopulation without validation data. For some results, we estimate rates of false negatives and false positives on a small subsample whose employers responded to the follow-back interviews and use them to extrapolate potential rates of reporting errors to the third of the sample that does not have validation data. Under a set of relatively strong nonparametric assumptions, we preliminarily estimate that coverage the uninsured would increase the proportion of the population using health care in a month by no more than 2.5 percentage points for adults (an 11% increase) and by no more than 0.8 percentage points for children (a 4.8% increase).

Publication Type: Paper/Poster Proposal
Abstract: Missing data in the form of item or entire survey module non-response is a common problem in survey research (Groves, Dillman, Eltinge, and Little 2001). Approximately 10 percent of the Current Population Survey (CPS) sample refuses to take the demographic supplement. These “full supplement” refusals have the variable values for the entire demographic supplement imputed. Although properly specified imputation can alter basic distributional summary statistics (e.g., means, rates and variances) from the statistics calculated using complete cases only, it should not transform the relationships among variables. In other words, imputation should not create significant correlations between variables that were not there before the imputation, nor should it reduce the magnitude of significant correlations between variables that were there prior to imputation. With this in mind, there are two questions we attempt to answer in this analysis: 1) Is there a difference between the imputed cases and the non-imputed cases with respect to health insurance coverage? 2) Does hot deck imputation create a significant bias in health insurance coverage estimates? In the 2003 Current Population Survey’s Demographic Supplement, 59.7 percent of 18-64 year old adults have commercial health insurance coverage if they have the full supplement imputed. However, 72.4 percent of the non-full supplement imputations have commercial health insurance coverage. Furthermore, full supplement imputations have a 26.7 percent uninsurance rate while all other 18-64 year old adults in the CPS have an uninsurance rate of 14.6 percent. We examine the relationships among key correlates to see whether this difference is due to the characteristics of the full supplement imputations or is due to the Census Bureau’s missing data imputation routines introducing/reducing relationships among variables.