Worth the wait: The impact of government funding on hospital emergency waiting times

In the absence of a price mechanism, emergency department waiting times act as a rationing device to equate demand for treatment with available supply. Sustained increases to demand stemming from population growth, aging populations, and rising comorbidities has caused waiting times internationally to rise. This has resulted in increased calls for higher funding from governments and commitments from both state and national governments to address excessive waiting times. This paper aims to determine the effectiveness of government funding for improving the median waiting times for treatment and the proportion of patients seen within clinically recommended waiting times. For this purpose, an econometric analysis was conducted on a panel of data on Victorian local health networks over the period 2015–2018. This is supplemented with a discussion of the alternative measures which governments might take to both address demand for emergency treatment, and also ensure that waiting time reductions can be maintained over the long-term.     

Composite Measures for Rating Hospital Quality with Major Surgery

Objective

To assess the value of a novel composite measure for identifying the best hospitals for major procedures.

Data Source

We used national Medicare data for patients undergoing five high-risk surgical procedures between 2005 and 2008.

Study Design

For each procedure, we used empirical Bayes techniques to create a composite measure combining hospital volume, risk-adjusted mortality with the procedure of interest, risk-adjusted mortality with other related procedures, and other variables. Hospitals were ranked based on 2005–2006 data and placed in one of three groups: 1-star (bottom 20 percent), 2-star (middle 60 percent), and 3-star (top 20 percent). We assessed how well these ratings forecasted risk-adjusted mortality rates in the next 2 years (2007–2008), compared to other measures.

Principal Findings

For all five procedures, the composite measures based on 2005–2006 data performed well in predicting future hospital performance. Compared to 1-star hospitals, risk-adjusted mortality was much lower at 3-star hospitals for esophagectomy (6.7 versus 14.4 percent), pancreatectomy (4.7 versus 9.2 percent), coronary artery bypass surgery (2.6 versus 5.0 percent), aortic valve replacement (4.5 versus 8.5 percent), and percutaneous coronary interventions (2.4 versus 4.1 percent). Compared to individual surgical quality measures, the composite measures were better at forecasting future risk-adjusted mortality. These measures also outperformed the Center for Medicare and Medicaid Services (CMS) Hospital Compare ratings.

Conclusion

Composite measures of surgical quality are very effective at predicting hospital mortality rates with major procedures. Such measures would be more informative than existing quality indicators in helping patients and payers identify high-quality hospitals with specific procedures.     

Supplier-induced demand: re-examining identification and misspecification in cross-sectional analysis

This paper re-examines criticisms of cross-sectional methods used to test for supplier-induced demand (SID) and re-evaluates the empirical evidence using data from Australian medical services. Cross-sectional studies of SID have been criticised on two grounds. First, and most important, the inclusion of the doctor supply in the demand equation leads to an identification problem. This criticism is shown to be invalid, as the doctor supply variable is stochastic and depends upon a variety of other variables including the desirability of the location. Second, cross-sectional studies of SID fail diagnostic tests and produce artefactual findings due to model misspecification. Contrary to this, the re-evaluation of cross-sectional Australian data indicate that demand equations that do not include the doctor supply are misspecified. Empirical evidence from the re-evaluation of Australian medical services data supports the notion of SID. Demand and supply equations are well specified and have very good explanatory power. The demand equation is identified and the desirability of a location is an important predictor of the doctor supply. Results show an average price elasticity of demand of 0.22 and an average elasticity of demand with respect to the doctor supply of 0.46, with the impact of SID becoming stronger as the doctor supply rises. The conclusion we draw from this paper is that two of the main criticisms of the empirical evidence supporting the SID hypothesis have been inappropriately levelled at the methods used. More importantly, SID provides a satisfactory, and robust, explanation of the empirical data on the demand for medical services in Australia.     

A flexible model for the mean and variance functions,with application to medical cost data

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear relation with covariates. To tackle these issues, we consider an extension to generalized linear models by assuming nonlinear associations of covariates in the mean function and allowing the variance to be an unknown but smooth function of the mean. We make no further assumption on the distributional form. The unknown functions are described by penalized splines, and the estimation is carried out using nonparametric quasi‐likelihood. Simulation studies show the flexibility and advantages of our approach. We apply the model to the annual medical costs of heart failure patients in the clinical data repository at the University of Virginia Hospital System. Copyright © 2013 John Wiley & Sons, Ltd.     

Econometric analyses of national health expenditures: Can positive economics help to answer normative questions?

The size of national health care expenditure is an important research and policy issue. This paper reviews theoretical and empirical analyses of an implied optimal size for a health sector. Various economic theories are explicitly or implicitly invoked, but none is fully satisfactory. Theory provides, at best, a loose justification for empirical specifications of health sector behaviour. Nevertheless, this has a large and growing empirical research industry. The complexity of the issues provides an excuse for reliance on empirical analyses using ad hoc models. The paper analyses aggregate time-series data, using the cointegration approach, on health, health care expenditures and national income. Only one national model met both statistical criteria and showed a significant relationship: between potential life years lost and health care expenditure in the UK. The case for any general relationships remains unproven. There is no objective scientific method to determine optimal health expenditure, nor should we expect one. However, positive analyses can help with normative questions. A better understanding of health expenditure determination would arise from better specification of the relationships, perhaps by analysis at a lower level of aggregation.     

Do Physicians Always Flee from HMOs? New Results Using Dynamic Panel Estimation Methods

OBJECTIVE: To assess the impact of changes in relative health maintenance organization (HMO) penetration on changes in the physician-to-population ratio in California counties when changes in the economic conditions in California counties relative to the U.S. average are taken into account. DATA SOURCES: Data on physicians who practiced in California at any time from 1988 to 1998 were obtained from the AMA Masterfile. The analysis was restricted to active, patient care physicians, excluding medical residents. Data on other covariates in the model were obtained from the Bureau of Economic Analysis, InterStudy, the Area Resource File, and the California state government. Data were merged using county FIPS codes. STUDY DESIGN: Changes in the physician-to-population ratio in California counties include the effects of both intrastate migration and interstate migration. A reduced-form model was estimated using the Arellano-Bond dynamic panel estimator. Economic conditions in California relative to the U.S. were measured as the ratio of county-level real per capita income to national-level real per capita income. Relative HMO penetration in California was measured as the ratio of county-level HMO penetration to HMO penetration in the U.S. relative HMO penetration was instrumented using five identifying variables to address potential endogeneity. Omitted-variable bias was controlled for by first differencing the model. The model also incorporated eight other covariates that may be associated with the demand for physicians: the percentage of the population enrolled in Medicaid, beds in short-term hospitals per 100,000 population, the percentage of the population that is black, the percentage of the population that is Hispanic, the percentage of the population that is Asian, the percentage of the population that is below age 18, the percentage of the population that is aged 65 and older, and the percentage of the population that are new legal immigrants in a given year. All of the above variables were lagged one period. The lagged physician-to-population ratio was also included to control for the supply of physicians. Separate equations were estimated for primary care physicians and specialist physicians. PRINCIPAL FINDINGS: Changes in lagged relative HMO penetration are negatively associated with changes in specialist physicians per 100,000 population. However, this effect of HMO penetration is attenuated and at times reversed in areas where the magnitude of the difference in relative economic conditions is sufficiently large. We did not find any statistically significant effects for primary care physicians. CONCLUSIONS: Consistent with prior studies, we find that changes in physician supply are associated with changes in relative HMO penetration. Relative economic conditions are an important moderator of the effect of changes in relative HMO penetration on physician migration.     

A flexible model for correlated medical costs,with application to medical expenditure panel survey data

We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B‐splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method. Copyright © 2015 John Wiley & Sons, Ltd.     

Dental Health Personnel Planning: a Review of the Literature

Dental personnel planning is important in formulating policy in dental education, dental public health programs, and dental care delivery systems. The purpose of this literature review is to illustrate the use of dentist-to-population ratios, need-based models, and demand-based models in the determination of appropriate supply of dental personnel. A historical perspective is provided that demonstrates how political manipulation and subjectivity have characterized the use of these models by organized dentistry, the federal government, and others. The lack of pertinent data and the inability to predict economic, social, political, and epidemiologic trends weaken the applicability of each model in determining future personnel levels. Considering the long-term consequences of the use of each model in personnel planning, caution is urged in using any of the presently available models.