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Health Production Function (health + production_function)
Selected AbstractsThe Effects of Uncertainty on the Demand for Health InsuranceJOURNAL OF RISK AND INSURANCE, Issue 1 2004atay Koç This article analyzes the effects of uncertainty and increases in risk aversion on the demand for health insurance using a theoretical model that highlights the interdependence between insurance and health care demand decisions. Two types of uncertainty faced by the individuals are examined. The first one is the uncertainty in the consumer's pretreatment health and the second is the uncertainty surrounding the productivity of health care. Comparative statics results are reported indicating the impact on the demand for insurance of shifts in the distributions of pretreatment health and productivity of health care in the form of first-order stochastic dominance, Rothschild,Stiglitz mean-preserving spreads, and second-order stochastic dominance. The demand for insurance increases in response to a Rothschild,Stiglitz increase in risk in the distribution of the pretreatment health provided that the health production function is in a special class and the price elasticity of health care is nondecreasing in the pretreatment health. Provided also that the demand for health care is own-price inelastic, the same conclusion is obtained when the uncertainty is about the productivity of health care. [source] Getting Real Performance Out of Pay-for-PerformanceTHE MILBANK QUARTERLY, Issue 3 2008SEAN NICHOLSON Context: Most private and public health insurers are implementing pay-for-performance (P4P) programs in an effort to improve the quality of medical care. This article offers a paradigm for evaluating how P4P programs should be structured and how effective they are likely to be. Methods: This article assesses the current comprehensiveness of evidence-based medicine by estimating the percentage of outpatient medical spending for eighteen medical processes recommended by the Institute of Medicine. Findings: Three conditions must be in place for outcomes-based P4P programs to improve the quality of care: (1) health insurers must not fully understand what medical processes improve health (i.e., the health production function); (2) providers must know more about the health production function than insurers do; and (3) health insurers must be able to measure a patient's risk-adjusted health. Only two of these conditions currently exist. Payers appear to have incomplete knowledge of the health production function, and providers appear to know more about the health production function than payers do, but accurate methods of adjusting the risk of a patient's health status are still being developed. Conclusions: This article concludes that in three general situations, P4P will have a different impact on quality and costs and so should be structured differently. When information about patients' health and the health production function is incomplete, as is currently the case, P4P payments should be kept small, should be based on outcomes rather than processes, and should target physicians' practices and health systems. As information improves, P4P incentive payments could be increased, and P4P may become more powerful. Ironically, once information becomes complete, P4P can be replaced entirely by "optimal fee-for-service." [source] Infant health production functions: what a difference the data makeHEALTH ECONOMICS, Issue 7 2009Nancy E. Reichman Abstract We examine the extent to which infant health production functions are sensitive to model specification and measurement error. We focus on the importance of typically unobserved but theoretically important variables (typically unobserved variables, TUVs), other non-standard covariates (NSCs), input reporting, and characterization of infant health. The TUVs represent wantedness, taste for risky behavior, and maternal health endowment. The NSCs include father characteristics. We estimate the effects of prenatal drug use, prenatal cigarette smoking, and first trimester prenatal care on birth weight, low birth weight, and a measure of abnormal infant health conditions. We compare estimates using self-reported inputs versus input measures that combine information from medical records and self-reports. We find that TUVs and NSCs are significantly associated with both inputs and outcomes, but that excluding them from infant health production functions does not appreciably affect the input estimates. However, using self-reported inputs leads to overestimated effects of inputs, particularly prenatal care, on outcomes, and using a direct measure of infant health does not always yield input estimates similar to those when using birth weight outcomes. The findings have implications for research, data collection, and public health policy. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||