A model of factors determining levels of public support for health care policy in the United States

Public preferences for policy are formed in a little-understood process that is not adequately described by traditional economic theory of choice. In this paper I suggest that U.S. aggregate support for health reform can be modeled as tradeoffs among a small number of behavioral values and the stage of policy development. The theory underlying the model is based on Samuelson, et al.'s (1986) work and Wilke's (1991) elaboration of it as the Greed/Efficiency/Fairness (GEF) hypothesis of motivation in the management of resource dilemmas, and behavioral economics informed by Kahneman and Thaler's prospect theory. ^ The model developed in this paper employs ordered probit econometric techniques applied to data derived from U.S. polls taken from 1990 to mid-2003 that measured support for health reform proposals. Outcome data are four-tiered Likert counts; independent variables are dummies representing the presence or absence of operationalizations of each behavioral variable, along with an integer representing policy process stage. Marginal effects of each independent variable predict how support levels change on triggering that variable. Model estimation results indicate a vanishingly small likelihood that all coefficients are zero and all variables have signs expected from model theory. ^ Three hypotheses were tested: support will drain from health reform policy as it becomes increasingly well-articulated and approaches enactment; reforms appealing to fairness through universal health coverage will enjoy a higher degree of support than those targeted more narrowly; health reforms calling for government operation of the health finance system will achieve lower support than those that do not. Model results support the first and last hypotheses. Contrary to expectations, universal health care proposals did not provide incremental support beyond those targeted to “deserving” populations—children, elderly, working families. In addition, loss of autonomy (e.g. restrictions on choice of care giver) is found to be the “third rail” of health reform with significantly-reduced support. When applied to a hypothetical health reform in which an employer-mandated Medical Savings Account policy is the centerpiece, the model predicts support that may be insufficient to enactment. These results indicate that the method developed in the paper may prove valuable to health policy designers. ^

ECONOMIC ANALYSIS OF THE DEMAND FOR PRESCRIPTION MEDICINE IN THE UNITED STATES

The purpose of this study was to understand the role of principle economic, sociodemographic and health status factors in determining the likelihood and volume of prescription drug use. Econometric demand regression models were developed for this purpose. Ten explanatory variables were examined: family income, coinsurance rate, age, sex, race, household head education level, size of family, health status, number of medical visits, and type of provider seen during medical visits. The economic factors (family income and coinsurance) were given special emphasis in this study.^ The National Medical Care Utilization and Expenditure Survey (NMCUES) was the data source. The sample represented the civilian, noninstitutionalized residents of the United States in 1980. The sample method used in the survey was a stratified four-stage, area probability design. The sample was comprised of 6,600 households (17,123 individuals). The weighted sample provided the population estimates used in the analysis. Five repeated interviews were conducted with each household. The household survey provided detailed information on the United States health status, pattern of health care utilization, charges for services received, and methods of payments for 1980.^ The study provided evidence that economic factors influenced the use of prescription drugs, but the use was not highly responsive to family income and coinsurance for the levels examined. The elasticities for family income ranged from -.0002 to -.013 and coinsurance ranged from -.174 to -.108. Income has a greater influence on the likelihood of prescription drug use, and coinsurance rates had an impact on the amount spent on prescription drugs. The coinsurance effect was not examined for the likelihood of drug use due to limitations in the measurement of coinsurance. Health status appeared to overwhelm any effects which may be attributed to family income or coinsurance. The likelihood of prescription drug use was highly dependent on visits to medical providers. The volume of prescription drug use was highly dependent on the health status, age, and whether or not the individual saw a general practitioner. ^