Cost-effectiveness of extended-release injectable naltrexone among incarcerated persons with opioid use disorder before release from prison versus after release

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.

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.

A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs

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.