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Using individual-level administrative data, we investigate the spatial patterns of unexplained shares of health care expenditures (HCE) at the municipality level. The focus is on the elderly population in the Italian Region Friuli-Venezia Giulia observed over the period 2017-2019. The empirical analysis comprises two steps. First, random-effects two-part models are estimated to analyze the effect of age, morbidity, and death on the probability and amount of positive individual total HCE and its components. Second, the unexplained shares of HCE at the municipality level are examined to identify areas with under- or over-spending and substitution among services. Results confirm the existing findings on the determinants of HCE and reveal geographic patterns in the unexplained shares of expenditures. We identify clusters of municipalities with observed HCE higher than predicted for each type of service and clusters with substitution between home care and all other services. These findings are associated with the degree of urbanization of these areas and, consequently, with the ease of access to health care. This is crucial from a policy perspective, as it indicates specific policy targets for public health intervention.

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INTRODUCTION: Opioid use disorder (OUD) is highly prevalent among incarcerated populations, and the risk of fatal overdose following release from prison is substantial. Despite efficacy, few correctional facilities provide evidence-based addiction treatment. Extended-release injectable naltrexone (XR-NTX) administered prior to release from incarceration may improve health and economic outcomes. METHODS: We conducted an economic evaluation alongside a randomized controlled trial testing the effectiveness of XR-NTX before release from prison (n = 38) vs. XR-NTX referral after release (n = 48) of incarcerated participants with OUD, both groups continuing treatment at a community addiction treatment center. The incremental cost-effectiveness ratio (ICER) assessed the cost-effectiveness of XR-NTX before release compared to referral after release for three stakeholder perspectives at 12- and 24-week periods: state policymaker, health care sector, and societal. Effectiveness measures included quality-adjusted life-years (QALYs) and abstinent years from opioids. In addition, we categorized resources as OUD-related and non-OUD-related medical care, state transfer payments, and other societal costs (productivity, criminal justice resources, etc.). RESULTS: Results showed an association between XR-NTX and greater OUD-related costs and total costs from the state policymaker perspective. QALYs gained were positive but statistically insignificant between arms; however, results showed XR-NTX had an estimated 15.5 more days of opioid abstinence over 24 weeks and statistically significant at a 95 % confidence level based on the distribution of bootstrapped samples. We found that estimated ICERs to be > $500,000 per QALY for all stakeholder perspectives. For the abstinent-year effectiveness measure, we found XR-NTX before release to be cost-effective at a 95 % confidence level for willingness-to-pay values >$49,000 per abstinent-year, across all perspectives. CONCLUSIONS: XR-NTX administered to persons who are incarcerated with OUD before release may provide value for stakeholders and bridge a well-known treatment gap for this vulnerable population. Lower than expected participant engagement and missing data limit our results, and study outcomes may be sensitive to methods that address missing data if replicated.

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Reducing inequality is one of the current challenges that most societies are facing. Our aim was to analyze the evolution of inequalities in self-assessed health among older Europeans in a time period spanning the 2008 economic crisis and the COVID-19 health crisis. We used data from Waves 2, 4 and 8 of the Survey of Health, Ageing and Retirement in Europe. We used inequality indices that accept ordinal variables. Our empirical results suggest that average inequality declines over time. Gender significantly influences the results. Some of the countries with the highest level of inequality are Denmark and Sweden, and some with the lowest are Estonia and the Netherlands. Our results may be of interest for the development of public policies to reduce inequalities. Special attention should be paid to vulnerable groups, such as the elderly.

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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.

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AbstractSeveral statistical issues associated with health care costs, such as heteroscedasticity and severe skewness, make it challenging to estimate or predict medical costs. When the interest is modeling the mean cost, it is desirable to make no assumption on the density function or higher order moments. Another challenge in developing cost prediction models is the presence of many covariates, making it necessary to apply variable selection methods to achieve a balance of prediction accuracy and model simplicity. We propose Spike-or-Slab priors for Bayesian variable selection based on asymptotic normal estimates of the full model parameters that are consistent as long as the assumption on the mean cost is satisfied. In addition, the scope of model searching can be reduced by ranking the Z-statistics. This method possesses four advantages simultaneously: robust (due to avoiding assumptions on the density function or higher order moments), parsimonious (feature of variable selection), informative (due to its Bayesian flavor, which can compare posterior probabilities of candidate models) and efficient (by reducing model searching scope with the use of Z-ranking). We apply this method to the Medical Expenditure Panel Survey dataset.

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OBJECTIVES: This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. METHODS: The database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used for predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. RESULTS: Models based on multimorbidity-related variables better explained integrated healthcare expenditure. In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. CONCLUSIONS: The expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size.

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Health care expenditures and use are challenging to model because these dependent variables typically have distributions that are skewed with a large mass at zero. In this article, we describe estimation and interpretation of the effects of a natural experiment using two classes of nonlinear statistical models: one for health care expenditures and the other for counts of health care use. We extend prior analyses to test the effect of the ACA's young adult expansion on three different outcomes: total health care expenditures, office-based visits, and emergency department visits. Modeling the outcomes with a two-part or hurdle model, instead of a single-equation model, reveals that the ACA policy increased the number of office-based visits but decreased emergency department visits and overall spending.

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For many years, Pakistan has had a wide network of Basic Health Units spread across the country, but their utilization by the population in rural and peri-urban areas has remained low. As of 2004, in an attempt to improve the utilization and performance of these public primary healthcare facilities, the government has gradually started contracting-in intergovernmental organizations to manage these BHUs. Using five nationally representative household surveys conducted between 2001 and 2012, and exploiting the gradual roll-out of this reform to apply a difference-in-difference approach, we evaluate its impact on BHU utilization. We find that contracting of the BHU management did not have any effect on health care use generally in the population, but it did significantly increase the use of BHU for childhood diarrhoea for the poor (by 4% points) and rural (3% points) households. These increases were accompanied by lower rates of self-treatment and private facilities usage. We do not find any significant effects on the self-reported satisfaction with BHU utilization. Our findings contrast with earlier small-scale studies that reported larger effects of the contracting of primary care in Pakistan. We speculate that the modest additional budget, the limited management authority of the contracting agency and the lack of clear performance indicators are reasons for the small impact of the contracting reform. Apparently critical aspects of services delivery such as location of BHUs, ineffective referral system and medical practice variation in public and private sectors have contributed to the overall low utilization of BHUs, yet these were beyond the scope of the contracting reform.

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We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

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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.

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Objective: To plan the future for the establishment and development of health econometrics in China. Methods: To analyze the main current situation of the research on domestic and oversea health econometrics, and to look into the prospective future of its application field of public health in China. Results: Overseas studies on health economics have been relatively mature and extended to generate health econometrics as the independent discipline. However, the research on health economics in China is lagging, and on health econometrics even vacant. Conclusion: The results from foreign studies have revealed that this new discipline plays a unique and significant role in medical health research. Health econometrics occupies broad application prospect in the field of public health in China, which should be paid more attention and support.

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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.

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