Applied Health Economics and Health Policy最新文献

Objectives: Smoking is an important risk factor leading to many diseases, which brings substantial healthcare costs as well as indirect costs due to decreased productivity. This article aims to quantify the social costs of smoking in the Czech Republic in 2019.

Methods: The prevalence-based, cost-of-illness approach is used, which assesses the costs as the sum of direct (healthcare) costs and indirect costs (productivity losses due to mortality and morbidity). The costs of healthcare utilization and pharmacotherapy in direct costs, and the costs of absenteeism, presenteeism, and premature mortality in indirect costs, are included.

Results: Total costs of smoking in the Czech Republic in 2019 are estimated as 2110.6 million EUR (0.94% of GDP). Direct costs amounted to 537.0 million EUR (2.9% of health expenditures in 2019) and indirect costs were 1573.6 million EUR, mainly driven by the costs of premature mortality (1062.5 million EUR).

Conclusions: Despite the declining trend in the prevalence of smoking in the Czech Republic, the associated costs are considerable. Investments into strategies to reduce smoking continue to be needed.

Background: The use of robotic-assisted surgery (RAS) is growing rapidly. However, economic evaluation of this technology is challenging. This study aims to identify and discuss the different economic evaluation methods which have been used to evaluate RAS.

Method: This scoping review systematically searched PubMed and Embase from 2015 to 2023. We included economic evaluation studies comparing RAS versus laparoscopic or open surgery. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist was used to aid data extraction and was extended to cover additional features relevant to RAS, including learning curve, organisational impact, incremental innovation and dynamic pricing.

Results: A total of 50 economic evaluations of RAS were included. Cost-utility analysis (46%) was the most commonly applied economic evaluation method, followed by cost-consequence analysis (32%). The studies focused on the specialties of urology (42%), hepato-pancreato-biliary (20%), colorectal (14%) and gynaecology (6%). Distinctive features related to the assessment of RAS were under-addressed in economic evaluations. Only 40% of the included studies considered learning curve and organisational impact and less than 12% of the included studies reflected on incremental innovation and dynamic pricing.

Conclusions: This review found that some studies have incorporated challenges specific to RAS in their evaluations. However, most studies still lack key aspects of importance. In particular, studies rarely considered the ability of RAS platforms to be shared across multiple specialities. Incorporating these distinctive features offers an opportunity for economic evaluation to provide decision-makers with a more realistic assessment of the cost-effectiveness of this technology and to ensure its optimal utilisation in clinical practice.

Objectives: Caring for individuals with spinal muscular atrophy (SMA), a rare genetic disorder, poses tremendous challenges for the economy and healthcare system. This study evaluated the cost-utility of onasemnogene abeparvovec-xioi gene therapy and risdiplam for SMA in Thailand.

Methods: A Markov model was used to analyze the lifetime costs and outcomes of these treatments compared with standard of care for symptomatic SMA types 1 and 2-3. SMA type 1 patients were treated with one of either onasemnogene or risdiplam, while SMA types 2-3 patients received risdiplam. Data on disease progression and medical costs were sourced from hospital databases, while treatment efficacy was based on clinical trials. Interviews with patients and caregivers provided data on non-medical costs and utilities. Base case cost-effectiveness and sensitivity analyses were conducted, with the incremental cost-effectiveness ratio (ICER) calculated in US dollars (USD) per quality-adjusted life year (QALY) gained, against a willingness-to-pay threshold of 4444 USD/QALY gained.

Results: For SMA type 1, the ICERs for onasemnogene and risdiplam were 163,102 and 158,357 USD/QALY gained, respectively. For SMA types 2-3, the ICER for risdiplam was 496,704 USD/QALY gained.

Conclusions: While onasemnogene and risdiplam exceeded the value-for-money threshold of the Thai healthcare system, they yielded the highest QALY gains among all approved medications. Policy-makers should incorporate various pieces of evidence alongside the cost-effectiveness results for rare diseases with costly drugs. Additionally, cost-effectiveness findings are useful for price negotiations and alternative financial funding, which allows policy-makers to seek solutions to ensure patient access, aligning with universal health coverage principles in Thailand.

Introduction

Recent studies suggest that medical cannabis laws may contribute to a relative reduction in health insurance costs within the individual health insurance markets at the state level. We investigated the effects of adopting a medical cannabis law on the cost of employer-sponsored health insurance in the United States.

Methods

We analyzed state-level data from the Medical Expenditure Panel Survey–Insurance Component (MEPS-IC) Private Sector spanning from 2003 to 2022. The outcomes included log-transformed average total premium costs per employee for single, employee-plus-one, and family coverage plans. We utilized the Sun and Abraham (J Econometr 225(2):175–199, 2021) difference-in-difference (DiD) method, looking at the overall DiD and event-study DiD. Models were adjusted for various state-level demographics and dichotomous policy variables, including whether a state later adopted recreational cannabis, as well as time and unit fixed effects and population weights.

Results

For states that adopted a medical cannabis law, there was a significant decrease in the log average total premium per employee for single (−0.034, standard error [SE] 0.009 (−$238)) and employee-plus-one (−0.025, SE 0.009 (−$348)) coverage plans per year considering the first 10 years of policy change compared with states without such laws. Looking at the last 5 years of policy change, we saw increases in effect size and statistical significance. In-time placebo testing suggested model robustness. Under a hypothetical scenario where all 50 states adopted medical cannabis in 2022, we estimated that employers and employees could collectively save billions on healthcare coverage, potentially reducing healthcare expenditure's contribution to GDP by 0.65% in 2022.

Conclusion

Adoption of a medical cannabis law may contribute to decreases in healthcare costs. This phenomenon is likely a secondary effect and suggests positive externalities outside of medical cannabis patients.

Objective

This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus.

Methods

Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes.

Results

The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations.

Conclusions

For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.

Objective

We assess whether the preferences regarding dependency-related health states as stated by informal caregivers are aligned with those expressed by the general population.

Methods

The preferences of a sample of 139 Spanish informal caregivers of dependent patients are compared with those obtained via a sample of 312 persons, also from the Spanish general population. We assess 24 dependency states extracted from the DEP-6D using the time trade-off method. Descriptive statistics and regression methods are used to explore differences between the two samples.

Results

Mean difference tests establish that, for all but one of the 24 states, there are no significant differences between the samples. The estimated mean values ranged from − 0.64 to 0.60 for the caregiver sample and from − 0.60 to 0.65 for the general population sample, with a correlation of 0.96. On average, the classification of states as better or worse than dead matched in both samples (except for one state). Regression models also show that sample type does not have a significant average impact. After we introduce interaction effects, only the most severe level of two dimensions, cognitive problems and housework, result in significant differences—with the caregiver sample reporting higher values for the former, and lower values for the latter.

Conclusion

Caregivers and the general population exhibit quite similar preferences concerning dependency-related health states. This suggests that the results of cost-utility analyses, and the resource allocation decisions based on them, would likewise not be significantly affected by the preferences used to generate the weighting algorithm.

Background and objective: Patients may get more treatment options with off-label use of drugs while exposed to unknown risks of adverse events. Little is known about the public or demand-side perspective on off-label drug use, which is important to understand how to use off-label treatment and devise financial assistance. This study aimed to quantify public preference for off-label cancer treatment outcomes, process, and costs, and perceived importance of associated adverse events.

Methods: A discrete choice experiment and a best-worst scaling were conducted in Hong Kong in December 2022. Quota sampling was used to randomly select the study sample from a territory-wide panel of working-age adults. Preferences and willingness to pay (WTP) for treatment effectiveness, risk of adverse events, mode of drug administration, and availability of off-label treatment guidelines were estimated using a random parameter logit model and latent class model. The relative importance of different adverse events was elicited using Case 1 best-worst scaling.

Results: A total of 435 respondents provided valid responses. In the discrete choice experiment, the respondents indicated that extra overall survival as treatment effectiveness (WTP: HK$448,000/US$57,400 for 12-month vs 3-month extra survival) was the most important attribute for off-label drugs, followed by the risk of adverse events (WTP: HK$318,000/US$40,800 for 10% chance to have adverse event vs 55%), mode of drug administration (WTP: HK$42,000/US$5300 for oral intake vs injection), and availability of guidelines (WTP: HK$31,000/US$4000 for available versus not available). Four groups with distinct preferences were identified, including effectiveness oriented, off-label use refusal, oral intake oriented, and adverse event risk aversion. In the best-worse scaling, hypothyroidism, nausea/vomiting, and arthralgia/joint pain were the three most important adverse events based on the perceptions of respondents. Risk-averse respondents, who were identified from the discrete choice experiment, had different perceived importance of the adverse events compared with those with other preferences.

Conclusions: Knowing the preference and WTP for cancer treatment-related characteristics from a societal perspective facilitates doctors' communications with patients on decision making and treatment goal-setting for off-label treatment, and enables devising financial assistance for related treatments. This study also provides important insight to inform evaluations of public acceptance and information dissemination in drug development as well as future economic evaluations.

Objective: The aim of this study was to examine the association between characteristics of novel drugs and incremental health gains relative to standard of care, in terms of quality-adjusted life-years (QALYs).

Methods: This study's unit of analysis is the drug-indication pair. For pairs approved by the US FDA from 1999 to 2018, we quantified incremental health gains using QALYs from the published literature and characterized each pair's novelty in terms of a series of six binary (yes/no) characteristics of novel drugs given special consideration by Health Technology Assessment agencies: Novel mechanism of action, Indicated for a rare disease, Indicated for a pediatric population, Treats a serious condition, Offers meaningful improvement over available therapies, and Potential to address unmet clinical needs. We analyzed measures of bivariate association (Mann-Whitney U and Kolmogorov-Smirnov tests) and multivariable regression, accounting for the influence of multiple novelty characteristics simultaneously.

Results: Our sample of 146 drugs represents 21% of drugs approved the FDA in the time period (1999-2018). Median and mean QALY gains for 'novel' drug-indication pairs exceeded corresponding QALY gains for non-novel drug-indication pairs. For most comparisons, the bivariate relationships between QALY gains and novelty characteristics were significant at p < 0.05 except for novel mechanism of action (Kolmogorov-Smirnov test) and pediatric indication (both bivariate tests). Multivariable models revealed an independent association between novelty characteristics and QALY gain except for unmet clinical need and indicated for a rare disease.

Conclusions: Drugs with novelty characteristics conferred larger health gains than drugs without these characteristics in bivariate analysis, multivariable models, or both. Future research should examine other aspects of drug novelty, such as patient and health system costs and equitable access.

Background: Predictive biomarkers are intended to predict an individual's expected response to specific treatments. These are an important component of precision medicine. We explore measures of biomarker performance that are based on the expected probability of response to individual treatment conditional on biomarker status. We show how these measures can be used to establish thresholds at which testing strategies will be clinically superior.

Methods: We used a decision model to compare expected probabilities of response of treat-all and test-and-treat strategies. Based on this, R-Shiny-based apps were developed which produce plots of the threshold positive and negative predictive values or sensitivities and specificities above which a 'test-and-treat' strategy will outperform a 'treat-all' strategy. We present a case study using data on the use of RAS status to predict response to panitumumab in metastatic colorectal cancer.

Results: Where a companion diagnostic is predictive of response to one of the treatments being compared, it is possible to estimate threshold sensitivities and specificities above which a testing strategy will outperform a treat-all strategy, based only on the odds ratio of response. Where negative and positive predictive values were used, the threshold depended on the prevalence of the biomarker-positive patients.

Discussion: These intuitive performance measures for predictive biomarkers, based on expected response to individual treatments, can be used to identify promising candidate companion diagnostic tests and indicate the potential magnitude of the net benefit of testing.