European Journal of Health Economics最新文献

In light of the persistent shortage of organ donations needed to save precious human lives, several countries have modified their organ donation laws by introducing an opt-out system, making every deceased a potential organ donor unless the person has objected. This study examines the impact of adopting opt-out on organ donation rates. Using a panel dataset covering a 21-year period, I apply a synthetic control approach to focus on countries that changed their prevailing organ donation legislation from opt-in to opt-out. I compare them to a synthetic counterfactual from countries that have kept their legislation the same since 1999. Synthetic control estimates show that Argentina and Wales achieved substantially higher organ donation rates with the shift from an opt-in to an opt-out system than without the reform taking place. My findings suggest that as one strategy among others, implementing opt-out cannot solve the organ shortage problem entirely but effectively contributes to reducing it considerably.

This paper analyses the structure of and variability in taxation and prescription drug distribution policies and quantifies the impact of such policies on the cost of prescription drugs to health systems in 35 countries. Taxes on prescription drugs remain highly prevalent (83% of the sample) although 63% of the sample countries implement a lower than standard VAT rate. Three remuneration types of the wholesale and retail distribution chain have been identified. Wholesale and retail distributors are remunerated on a regressive mark-up basis, which is price-dependent, although fixed fees and fixed percentages, which are non-price dependent, are also highly prevalent. Price component analysis for three groups of products classed as high-, medium- and low-priced suggests that mark-ups plus taxes varied significantly across countries and products, and ranged from 5% to 187% of ex-factory prices. Average margins also vary significantly by countries and products ranging 5-65% of retail prices. The cost of distribution and taxation contributes significantly to prescription drug costs for health systems. Although distribution chain remuneration raises efficiency and overall affordability questions, these need to be considered together with the regulatory framework shaping market structure of the distribution chain, as well as any prevailing horizontal and vertical integration policies. The overall cost of prescription drugs could be reduced immediately by eliminating taxation; this could go some way to alleviate fiscal pressures on health budgets, whilst avoiding resource re-allocation from health to other sectors.

Aims: This study aims to evaluate the cost effectiveness of genetic and genomic testing strategies for the diagnosis of rare developmental disorders in NHS Scotland.

Methods: Six genetic and genomic testing strategies were evaluated using a decision tree model. First-line, second-line and last-resort trio genome sequencing (GS), and second-line and last-resort trio exome sequencing (ES) were compared with standard genetic testing. The cost effectiveness of each strategy was expressed in terms of incremental cost per additional diagnosis. The impact of uncertainty on cost-effectiveness results was explored using deterministic and probabilistic sensitivity analysis.

Results: 2nd-line ES was a cost-saving option, increasing diagnostic yield by 13.9% and decreasing cost by £1027 per trio compared to standard genetic testing. Compared to ES, strategies involving GS increased costs significantly, with only a moderate or zero improvement in diagnostic yield. Sensitivity analysis indicated that significant reductions in cost or improvements in diagnostic yield are required before 1st-line GS becomes cost effective.

Conclusion: 2nd-line ES (after chromosomal microarray; replacing gene panel testing) for the diagnosis of developmental disorders is a cost-saving option for the Scottish NHS. Ongoing economic evaluation is required to monitor the evolving cost and diagnostic yield of GS and ES over time.

Background: In Jordan, no national value set is available for any preference-accompanied health utility measure.

Objective: This study aims to develop a value set for EQ-5D-3L based on the preferences of the Jordanian general population.

Methods: A representative sample of the Jordanian general population was obtained through quota sampling involving age, gender, and region. Participants aged above 18 years were interviewed via videoconferencing using the EuroQol Valuation Technology 2.1 protocol. Participants completed ten composite time trade-offs (cTTO) and ten discrete choice experiments (DCE) tasks. cTTO and DCE data were analyzed using linear and logistic regression models, respectively, and hybrid models were applied to the combined DCE and cTTO data.

Results: A total of 301 participants with complete data were included in the analysis. The sample was representative of the general population regarding region, age, and gender. All model types applied, that is, random intercept model, random intercept Tobit, linear model with correction for heteroskedasticity, Tobit with correction for heteroskedasticity, and all hybrid models, were statistically significant. They showed logical consistency in terms of higher utility decrements with more severe levels. The hybrid model corrected for heteroskedasticity was selected to construct the Jordanian EQ-5D-3L value set as it showed the best fit and lowest mean absolute error. The predicted value for the most severe health state (33333) was - 0.563. Utility decrements due to mobility had the largest weight, followed by anxiety/depression, while usual activities had the smallest weight.

Conclusion: This study provides the first EQ-5D-3L value set in the Middle East. The Jordanian EQ-5D-3L value set can now be used in health technology assessments for health policy planning by the Jordanian health sector's decision-makers.

The disparity in the distribution of COVID-19 vaccine has been recorded with more than 70% vaccination rate for high-income countries as compared to less than 40% for low-income countries. The low affordability of vaccines for the majority of low-income group invites the need for redistribution of vaccines. The disproportionate donation of vaccines across the globe motivates us to explore the incentive for the high-income group to redistribute. An exploratory analysis of cross-country COVID-19 vaccination distribution data shows that the countries which have received vaccines as donation has also contributed to vaccine wastage. This paper intends to provide a theoretical background for this counterintuitive observation using welfare analysis. We find that the market mechanism leads to a negative impact on global welfare due to redistribution. Only an invention with a defined redistribution mechanism may ensure an increase in global welfare. It is found that a critical value of redistribution mechanism reinforced by a minimum threshold level of income is essential to enhance welfare. The reduced form from the theoretical predictions is empirically validated with cross-country data on COVID-19 vaccination for all countries. As identified in theory, the internal support variables like political stability, government effectiveness, and health expenditure at the country level will impact global welfare. Therefore, when global cooperation is essential during a health crisis like COVID-19, improved internal coordination and intentions cannot be ignored.

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

This study evaluates a complex telemedicine-based intervention targeting patients with chronic health problems. Computer tablets and home telemonitoring devices are used by patients to report point-of-care measurements, e.g., blood pressure, blood glucose or oxygen saturation, and to answer health-related questions at a follow-up center. We designed a pragmatic randomized controlled trial to compare the telemedicine-based intervention with usual care in six local centers in Norway. The study outcomes included health-related quality of life (HRQoL) based on the EuroQol questionnaire (EQ-5D-5L), patient experiences, and utilization of healthcare. We also conducted a cost-benefit analysis to inform policy implementation, as well as a process evaluation (reported elsewhere). We used mixed methods to analyze data collected during the trial (health data, survey data and interviews with patients and health personnel) as well as data from national health registers. 735 patients were included during the period from February 2019 to June 2020. One year after inclusion, the effects on the use of healthcare services were mixed. The proportion of patients receiving home-based care services declined, but the number of GP contacts increased in the intervention group compared to the control group. Participants in the intervention group experienced improved HRQoL compared to the control group and were more satisfied with the follow-up of their health. The cost-benefit of the intervention depends largely on the design of the service and the value society places on improved safety and self-efficacy.

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observations) from the Finnish population in 2017. Using annual healthcare cost in 2017 as a response variable, we compare the performance of Random forest, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) to linear regression. As machine learning methods are often seen as unsuitable in risk adjustment applications because of their relative opaqueness, we also introduce visualizations from the machine learning literature to help interpret the contribution of individual variables to the prediction. Our results show that ensemble machine learning methods can improve predictive performance, with all of them significantly outperforming linear regression, and that a certain level of interpretation can be provided for them. We also find individual-level socioeconomic variables to improve prediction accuracy and that their effect is larger for machine learning methods. However, we find that the predictions used for funding allocations are sensitive to model selection, highlighting the need for comprehensive robustness testing when estimating risk adjustment models used in applications.

Objective: To estimate the cost-effectiveness of online behavioral interventions (EczemaCareOnline.org.uk) designed to support eczema self-care management for parents/carers and young people from an NHS perspective.

Methods: Two within-trial economic evaluations, using regression-based approaches, adjusting for baseline and pre-specified confounder variables, were undertaken alongside two independent, pragmatic, parallel group, unmasked randomized controlled trials, recruiting through primary care. Trial 1 recruited 340 parents/carers of children aged 0-12 years and Trial 2 337 young people aged 13-25 years with eczema scored ≥ 5 on Patient-Oriented Eczema Measure (POEM). Participants were randomized (1:1) to online intervention plus usual care or usual care alone. Resource use, collected via medical notes review, was valued using published unit costs in UK £Sterling 2021. Quality-of-life was elicited using proxy CHU-9D in Trial 1 and self-report EQ-5D-5L in Trial 2.

Results: The intervention was dominant (cost saving and more effective) with a high probability of cost-effectiveness (> 68%) in most analyses. The exception was the complete case cost-utility analysis for Trial 1 (omitting participants with children aged < 2), with adjusted incremental cost savings of -£34.15 (95% CI  - 104.54 to 36.24) and incremental QALYs of - 0.003 (95% CI  - 0.021 to 0.015) producing an incremental cost per QALY of £12,466. In the secondary combined (Trials 1 and 2) cost-effectiveness analysis, the adjusted incremental cost was -£20.35 (95% CI  - 55.41 to 14.70) with incremental success (≥ 2-point change on POEM) of 10.3% (95% CI 2.3-18.1%).

Conclusion: The free at point of use online eczema self-management intervention was low cost to run and cost-effective.

Trial registration: This trial was registered prospectively with the ISRCTN registry (ISRCTN79282252). URL www.EczemaCareOnline.org.uk .

Introduction: The composite time trade-off (cTTO) method is used as the primary method for valuing EQ-5D-5L health states, but it requires intensive interviewer training and stringent quality control, which increases the burden of conducting cTTO studies. In this study, two non-iterative variants of the TTO method, non-stopping TTO (nTTO) and open-ended TTO (oTTO), were tested head-to-head with the cTTO method aiming to reduce the administration burden.

Methods: 31 EQ-5D-5L health states from an orthogonal array was selected and valued by a general public sample in China. Data were collected by 7 interviewers with all interviewers performed an equal number of interviews using all three TTO methods. We compared the value distribution, logical consistency, administration burden, and modeling performance of these three TTO methods.

Results: In total, 422 participants participated in the valuation interviews, with 139 using the nTTO method, 140 using the oTTO method, and 143 using the cTTO method. Both oTTO and nTTO methods saved around 10 min for conducting an interview. The mean values of three methods were similar with each method showed different characteristics in their value distributions. cTTO outperformed the other two methods in terms of modeling performance.

Discussion: Both non-iterative TTO methods showed potential for valuing EQ-5D health states, although their data distributions and modeling performance were inferior to the cTTO method. The results of this study showed the potential of these two alternative non-iterative TTO methods.