Cost Effectiveness and Resource Allocation最新文献

Background: Management of COVID-19 patients with mild and moderate symptoms could be isolated at home isolation (HI), community isolation (CI) or hospitel. However, it was still unclear which strategy was more cost-effective. Therefore, this study was conducted to evaluate this.

Methods: This study used data from patients who initially stayed at HI, CI, and hospitel under supervision of Ramathibodi Hospital between April and October 2021. Outcomes of interest were hospitalisation and mortality. An incremental cost-effectiveness ratios (ICER) was calculated based on hospital perspective using home isolation as the reference.

Results: From 7,077 patients, 4,349 2,356, and 372 were admitted at hospitel, HI, and CI, respectively. Most patients were females (57.04%) and the mean age was 40.42 (SD = 16.15). Average durations of stay were 4.47, 3.35, and 3.91 days for HI, CI, and hospitel, respectively. The average cost per day for staying in these corresponding places were 24.22, 63.69, and 65.23 US$. For hospitalisation, the ICER for hospitel was at 41.93 US$ to avoid one hospitalisation in 1,000 patients when compared to HI, while CI had more cost, but less cases avoided. The ICER for hospitel and CI were at 46.21 and 866.17 US$ to avoid one death in 1,000 patients.

Conclusions: HI may be cost-effective isolated strategy for preventing hospitalisation and death in developing countries with limited resources.

Introduction: Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing.

Method: We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023.

Results: We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI.

Conclusions: To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

Background: In recent years, numerous guidelines and expert consensus have recommended the inclusion of digital technologies and products in cardiac rehabilitation. Digital therapeutics (DTx) is an evidence-based medicine that uses digital means for data collection and monitoring of indicators to control and optimize the treatment, management, and prevention of disease.

Objective: This study collected and reviewed real-world data and built a model using health economics assessment methods to analyze the potential cost-effectiveness of DTx applied to home-based cardiac rehabilitation for patients with chronic heart failure. From the perspective of medical and health decision-makers, the economic value of DTx is evaluated prospectively to provide the basis and reference for the application decision and promotion of DTx.

Methods: Markov models were constructed to simulate the outcomes of DTx for home-based cardiac rehabilitation (DT group) compared to conventional home-based cardiac rehabilitation (CH group) in patients with chronic heart failure. The model input parameters were clinical indicators and cost data. Outcome indicators were quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). The robustness of the evaluation methods and results was tested using sensitivity analyses. Clinical indicators, cost data, and health utility values were obtained from real-world data, including clinical study data, published literature, and public website information.

Results: The Markov model simulated a time span of 10 years, with a cycle set at one month, for 120 cycles. The results showed that the per capita cost of the CH group was 38,442.11 CNY/year, with a QALY of 0.7196 per person per year. The per capita cost of the DT group was 42,300.26 CNY/year, with a QALY of 0.81687 per person per year. The ICER per person was 39,663.5 CNY/QALY each year, which was below the willingness-to-pay threshold of 85,698 CNY (China's GDP per capita in 2022).

Conclusions: DTx for home-based cardiac rehabilitation is an extremely cost-effective rehabilitation option compared with conventional home-based cardiac rehabilitation. DTx for home-based cardiac rehabilitation is potentially valuable from the perspective of healthcare decision-makers.

Background: Chronic Kidney Disease (CKD) patients often require long-term care, and while Hemodialysis (HD) is the standard treatment, Comprehensive Conservative Care (CCC) is gaining popularity as an alternative. Economic evaluations comparing their cost-effectiveness are crucial. This study aims to perform a cost-utility analysis comparing HD and CCC using the EQ-5D-5L and ICECAP-O instruments to assessing healthcare interventions in CKD patients.

Methods: This short-term economic evaluation involved 183 participants (105 HD, 76 CCC) and collected data on demographics, comorbidities, laboratory results, treatment costs, and HRQoL measured by ICECAP-O and EQ-5D-5L. Incremental Cost-Effectiveness Ratios (ICERs) and Net Monetary Benefit (NMB) were calculated separately for each instrument, and Probabilistic Sensitivity Analysis (PSA) assessed uncertainty.

Results: CCC demonstrated significantly lower costs (mean difference $8,544.52) compared to HD. Both EQ-5D-5L and ICECAP-O indicated higher Quality-Adjusted Life Years (QALYs) for both groups, but the difference was not statistically significant (p > 0.05). CCC dominated HD in terms of HRQoL measures, with ICERs of -$141,742.67 (EQ-5D-5L) and -$4,272.26 (ICECAP-O). NMB was positive for CCC and negative for HD, highlighting its economic feasibility.

Conclusion: CCC proves a preferable and more cost-effective treatment option than HD for CKD patients aged 65 and above, regardless of the quality-of-life measure used for QALY calculations. Both EQ-5D-5L and ICECAP-O showed similar results in cost-utility analysis.

Objectives: Monetizing health has sparked controversy and has implications for pricing strategies of emerging health technologies. Medical insurance payers typically set up thresholds for quality-adjusted life years (QALY) gains based on health productivity and budget affordability, but they rarely consider patient willingness-to-pay (WTP). Our study aims to compare Chinese payer threshold and patient WTP toward QALY gain of advanced non-small cell lung cancer (NSCLC) and to inform a potential inclusion of patient WTP under more complex decision-making scenarios.

Methods: A regression model was constructed with cost as the independent variable and QALY as the dependent variable, where the regression coefficients reflect mean opportunity cost, and by transforming these coefficients, the payer threshold can be obtained. Patient WTP was elicited through a contingent valuation method survey. The robustness of the findings was examined through sensitivity analyses of model parameters and patient heterogeneity.

Results: The payer mean threshold in the base-case was estimated at 150,962 yuan (1.86 times per capita GDP, 95% CI 144,041-159,204). The two scenarios analysis generated by different utility inputs yielded thresholds of 112,324 yuan (1.39 times per capita GDP) and 111,824 yuan (1.38 times per capita GDP), respectively. The survey included 85 patients, with a mean WTP of 148,443 yuan (1.83 times per capita GDP, 95% CI 120,994-175,893) and median value was 106,667 yuan (1.32 times the GDP per capita). Due to the substantial degree of dispersion, the median was more representative. The payer threshold was found to have a high probability (98.5%) of falling within the range of 1-2 times per capita GDP, while the robustness of patient WTP was relatively weak.

Conclusions: In China, a country with a copayment system, payer threshold was higher than patient WTP, indicating that medical insurance holds significant decision-making authority, thus temporarily negating the need to consider patient WTP.

In recent years, international academics recognized that quality-adjusted life-years (QALYs) may not always fully capture the benefits produced by an intervention, and considered incorporating additional elements of value into cost-effectiveness analysis (CEA). Examples of these elements are adherence-improving factors, insurance value, value of hope, and real option value, which form the "value flower". In order to explore whether it is scientific and reasonable to incorporate additional elements into CEA, this paper focuses on what pharmacoeconomic evaluation should do and what it can do. By elaborating the connotation of value, the connotation of decision, and tracing the origin of pharmacoeconomic evaluation, we believe that it is unscientific and unreasonable to incorporate additional elements of value into CEA, which has exceeded the essential connotation and capability of pharmacoeconomic evaluation. The analysis results belong to the theoretical level, empirical test is needed to verify the correctness and scientificity of this conclusion in the future.

Dealing with randomness is a crucial aspect that cost-effectiveness analysis (CEA) tools need to address, but existing stochastic CEA tools have rarely examined risk and return from the perspective of population benefits, concerning the benefits of a group of individuals but not just a typical one. This paper proposes a stochastic CEA tool that supports medical decision-making from the perspective of population benefits of risk and return, the risk-adjusted incremental cost-effectiveness ratio (ICER). The tool has a traditional form of ICER but uses the cost under a risk-adjusted expectation. Theoretically, we prove that the tool can provide medical decisions trimming that promote the risk-return level on population benefits within any intervention structure and can also serve as a criterion for the optimal intervention structure. Numerical simulations within a framework of mean-variance support the conclusions in this paper.

Background: Hip fractures are a common and costly health problem, resulting in significant morbidity and mortality, as well as high costs for healthcare systems, especially for the elderly. Implementing surgical preventive strategies has the potential to improve the quality of life and reduce the burden on healthcare resources, particularly in the long term. However, there are currently limited guidelines for standardizing hip fracture prophylaxis practices.

Methods: This study used a cost-effectiveness analysis with a finite-state Markov model and cohort simulation to evaluate the primary and secondary surgical prevention of hip fractures in the elderly. Patients aged 60 to 90 years were simulated in two different models (A and B) to assess prevention at different levels. Model A assumed prophylaxis was performed during the fracture operation on the contralateral side, while Model B included individuals with high fracture risk factors. Costs were obtained from the Centers for Medicare & Medicaid Services, and transition probabilities and health state utilities were derived from available literature. The baseline assumption was a 10% reduction in fracture risk after prophylaxis. A sensitivity analysis was also conducted to assess the reliability and variability of the results.

Results: With a 10% fracture risk reduction, model A costs between $8,850 and $46,940 per quality-adjusted life-year ($/QALY). Additionally, it proved most cost-effective in the age range between 61 and 81 years. The sensitivity analysis established that a reduction of ≥ 2.8% is needed for prophylaxis to be definitely cost-effective. The cost-effectiveness at the secondary prevention level was most sensitive to the cost of the contralateral side's prophylaxis, the patient's age, and fracture treatment cost. For high-risk patients with no fracture history, the cost-effectiveness of a preventive strategy depends on their risk profile. In the baseline analysis, the incremental cost-effectiveness ratio at the primary prevention level varied between $11,000/QALY and $74,000/QALY, which is below the defined willingness to pay threshold.

Conclusion: Due to the high cost of hip fracture treatment and its increased morbidity, surgical prophylaxis strategies have demonstrated that they can significantly relieve the healthcare system. Various key assumptions facilitated the modeling, allowing for adequate room for uncertainty. Further research is needed to evaluate health-state-associated risks.

Background: The increasing global prevalence of atrial fibrillation (AF) has led to a growing demand for stroke prevention strategies, resulting in higher healthcare costs. High-quality economic evaluations of stroke prevention strategies can play a crucial role in maximising efficient allocation of resources. In this systematic review, we assessed the methodological quality of such economic evaluations.

Methods: We searched electronic databases of PubMed, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and Econ Lit to identify model-based economic evaluations comparing the left atrial appendage closure procedure (LAAC) and oral anticoagulants published in English since 2000. Data on study characteristics, model-based details, and analyses were collected. The methodological quality was evaluated using the modified Economic Evaluations Bias (ECOBIAS) checklist. For each of the 22 biases listed in this checklist, studies were categorised into one of four groups: low risk, partial risk, high risk due to inadequate reporting, or high risk. To gauge the overall quality of each study, we computed a composite score by assigning + 2, 0, - 1 and - 2 to each risk category, respectively.

Results: In our analysis of 12 studies, majority adopted a healthcare provider or payer perspective and employed Markov Models with the number of health states varying from 6 to 16. Cost-effectiveness results varied across studies. LAAC displayed a probability exceeding 50% of being the cost-effective option in six out of nine evaluations compared to warfarin, six out of eight evaluations when compared to dabigatran, in three out of five evaluations against apixaban, and in two out of three studies compared to rivaroxaban. The methodological quality scores for individual studies ranged from 10 to - 12 out of a possible 24. Most high-risk ratings were due to inadequate reporting, which was prevalent across various biases, including those related to data identification, baseline data, treatment effects, and data incorporation. Cost measurement omission bias and inefficient comparator bias were also common.

Conclusions: While most studies concluded LAAC to be the cost-effective strategy for stroke prevention in AF, shortcomings in methodological quality raise concerns about reliability and validity of results. Future evaluations, free of these shortcomings, can yield stronger policy evidence.

Background: Countries around the world are increasingly rethinking the design of their health benefit package to achieve universal health coverage. Countries can periodically revise their packages on the basis of sectoral cost-effectiveness analyses, i.e. by evaluating a broad set of services against a 'doing nothing' scenario using a budget constraint. Alternatively, they can use incremental cost-effectiveness analyses, i.e. to evaluate specific services against current practice using a threshold. In addition, countries may employ hybrid approaches which combines elements of sectoral and incremental cost-effectiveness analysis - a country may e.g. not evaluate the comprehensive set of all services but rather relatively small sets of services targeting a certain condition. However, there is little practical guidance for countries as to which kind of approach they should follow.

Methods: The present study was based on expert consultation. We refined the typology of approaches of cost-effectiveness analysis for benefit package design, identified factors that should be considered in the choice of approach, and developed recommendations. We reached consensus among experts over the course of several review rounds.

Results: Sectoral cost-effectiveness analysis is especially suited in contexts with large allocative inefficiencies in current service provision and can, in theory, realize large efficiency gains. However, it may be challenging to implement a comprehensive redesign of the package in practice. Incremental cost-effectiveness analysis is especially relevant in contexts where specific new services may impact the sustainability of the health system. It may potentially support efficiency improvement, but its focus has typically been on new services while existing inefficiencies remain unchallenged. The use of hybrid approach may be a way forward to address the strengths and weaknesses of sectoral and incremental analysis areas. Such analysis may be especially useful to target disease areas with suspected high inefficiencies in service provision, and would then make good use of the available research capacity and be politically rewarding. However, disease-specific analyses bear the risk of not addressing resource allocation inefficiencies across disease areas.

Conclusions: Countries should carefully select their approach of cost-effectiveness analyses for benefit package design, based on their decision-making context.