Applied Health Economics and Health Policy最新文献

Background

Inappropriate antibiotic use increases selective pressure, contributing to antimicrobial resistance. Point-of-care rapid diagnostic tests (RDTs) would be instrumental to better target antibiotic prescriptions, but widespread implementation of diagnostics for improved management of febrile illnesses is limited.

Objective

Our study aims to contribute to evidence-based guidance to inform policymakers on investment decisions regarding interventions that foster more appropriate antibiotic prescriptions, as well as to address the evidence gap on the potential clinical and economic impact of RDTs on antibiotic prescription.

Methods

A country-based cost-effectiveness model was developed for Burkina Faso, Ghana and Uganda. The decision tree model simulated seven test strategies for patients with febrile illness to assess the effect of different RDT combinations on antibiotic prescription rate (APR), costs and clinical outcomes. The incremental cost-effectiveness ratio (ICER) was expressed as the incremental cost per percentage point (ppt) reduction in APR.

Results

For Burkina Faso and Uganda, testing all patients with a malaria RDT was dominant compared to standard-of-care (SoC) (which included malaria testing). Expanding the test panel with a C-reactive protein (CRP) test resulted in an ICER of $ 0.03 and $ 0.08 per ppt reduction in APR for Burkina Faso and Uganda, respectively. For Ghana, the pairwise comparison with SoC—including malaria and complete blood count testing—indicates that both testing with malaria RDT only and malaria RDT + CRP are dominant.

Conclusion

The use of RDTs for patients with febrile illness could effectively reduce APR at minimal additional costs, provided diagnostic algorithms are adhered to. Complementing SoC with CRP testing may increase clinicians’ confidence in prescribing decisions and is a favourable strategy.

Background: In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling.

Methods: Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code.

Results: Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions.

Conclusions: This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Cost-utility analysis may not be sufficient to support reimbursement decisions when the assessed health intervention requires a large proportion of the healthcare budget or when the monetary healthcare budget is not the only resource constraint. Such cases include joint replacement, coronavirus disease 2019 (COVID-19) interventions and settings where all resources are constrained (e.g. post-COVID-19 or in low/middle-income countries). Using literature on health technology assessment, rationing and reimbursement in healthcare, we identified seven alternative frameworks for simultaneous decisions about (dis)investment and proposed modifications to deal with multiple resource constraints. These frameworks comprised constrained optimisation; cost-effectiveness league table; 'step-in-the-right-direction' approach; heuristics based on effective gradients; weighted cost-effectiveness ratios; multicriteria decision analysis (MCDA); and programme budgeting and marginal analysis (PBMA). We used numerical examples to demonstrate how five of these alternative frameworks would operate. The modified frameworks we propose could be used in local commissioning and/or health technology assessment to supplement standard cost-utility analysis for interventions that have large budget impact and/or are subject to additional constraints.

Purpose: To systematically summarise the recent literature on the cost and cost effectiveness of interventions implemented to reduce violence against women (VAW) and decision frameworks guiding resource allocation.

Method: A scoping review of scholarly and grey literature on the cost-effectiveness and/or resource allocation for interventions addressing intimate partner violence (IPV), dating violence and non-partner sexual violence perpetrated against women aged 15 years and over. All settings and contexts were eligible, with papers published in English between 2010 and March 2023 included.

Results: Nineteen papers fulfilled the inclusion criteria reporting the cost, cost savings and/or cost effectiveness of 24 interventions to prevent IPV and to a lesser extent, other forms of interpersonal violence. Among the 16 economic evaluation studies reviewed, four types of interventions were cost effective in multiple settings or studies, including community activism (Uganda, Ghana), gender transformative interventions with couples and individuals (Ethiopia, Rwanda), specific justice and law enforcement measures (USA) and a combined personnel training, support, and referral programme in General Practice in the UK. Other interventions were cost effective in a single study or had conflicting evidence. Three remaining papers conducted a partial evaluation or cost appraisal providing limited information on the cost or cost-savings of other implemented interventions. No frameworks on resource allocation for the prevention of VAW were identified.

Conclusion: While there is some evidence of cost effectiveness emerging for interventions implemented in specific contexts, overall, we find the recent evidence on costs and cost effectiveness of interventions for the prevention of VAW to be limited. Embedding economic evaluation in future effectiveness trials will build critical evidence needed to inform policy and resource allocation decisions based on the value-for-money of interventions. Modelling the benefits and costs of interventions to better understand the societal impacts of programmes at scale is a further research opportunity.

Objectives: The aim of this study was to review the current evaluation and funding processes for new drugs in different developed countries, to provide a comparative framework with detailed, homogeneous, and up-to-date information.

Methods: Scientific publications, reports and websites were reviewed between July and December 2021 using PubMed, Google Scholar, and grey literature sources. The main items searched were actors and processes, including timelines, characteristics of clinical and economic evaluations, participation of stakeholders, elements of price and reimbursement decisions, cost-effectiveness thresholds and specific funds. The analysed 13 countries were Australia, Canada, England, France, Germany, Italy, Japan, the Netherlands, Portugal, Scotland, South Korea, Spain and Sweden.

Results: Eight countries perform the assessment process separated from the pricing decision. Countries measure each drug's added therapeutic value through multi-attribute value scales, algorithms, non-prescriptive lists of criteria, or quality-adjusted life years (QALYs). Health technology assessment (HTA) methodologies differ in their outcome measures, elicitation techniques, comparators, and perspectives. The criteria used for pricing and reimbursement include humanistic, clinical, and economic aspects. Only Scotland, England, the Netherlands, Canada and Portugal use explicit efficiency thresholds. Health care professionals participate in all assessment committees, and patients are becoming increasingly involved in most countries. The official time from marketing authorisation to the completion of the evaluation and pricing processes varied from 126 to 540 days.

Conclusions: Most analysed countries show a trend towards value-based approaches that consider value for money to society, but also other economic, clinical, and humanistic criteria. Good practices included robustness, transparency, independence, and participation.

Background and objective: Androgen-deprivation therapy is the mainstay of treatment for patients with newly diagnosed metastatic hormone-sensitive prostate cancer (mHSPC). However, the intensification of treatment with either docetaxel or novel anti-androgens (abiraterone-acetate plus prednisone [AAP], enzalutamide, and apalutamide) is being recommended based on the improved clinical outcomes and quality of life among patients. This study aimed to determine the most cost-effective drug for treatment intensification for patients with mHSPC in India.

Methods: A Markov model was developed with four health states: progression-free survival, progressive disease, best supportive care, and death. Lifetime costs and consequences were estimated for four treatment sequences: AAP-first, enzalutamide-first, apalutamide-first, and docetaxel-first. Incremental cost per quality-adjusted life-year (QALY) gained with a given treatment option was compared against the next best alternative and assessed for cost effectiveness using a willingness to pay threshold of 1 × per capita gross domestic product in India.

Results: We estimated that the total lifetime cost per patient was ₹1,367,454 (US$17,487), ₹2,168,885 (US$27,735), ₹7,678,501 (US$98,190), and ₹1,358,746 (US$17,375) in the AAP-first, enzalutamide-first, apalutamide-first, and docetaxel-first treatment sequence, respectively. The mean quality-adjusted life-years lived per patient were 4.78, 5.03, 3.22, and 2.61, respectively. The AAP-first sequence incurs an incremental cost of ₹4014 (US$51) per quality-adjusted life-year gained as compared with the docetaxel-first sequence, with a 87% probability of being cost effective at the willingness-to-pay threshold of 1 × per-capita gross domestic product of India. The use of AAP-first also incurs an incremental net monetary benefit of ₹396,491 (US$5070) as compared with the docetaxel-first treatment sequence. Nearly a 48% reduction in the price of enzalutamide is required to make it a cost-effective treatment sequence as compared with AAP-first in India.

Conclusions: We concur with the inclusion of standard-dose AAP in India's publicly financed health insurance scheme for the intensification of treatment in mHSPC as it is the only cost-effective sequence among the various novel anti-androgens when compared with the docetaxel-first treatment sequence. Furthermore, a systematic reduction in the price of enzalutamide would further help to improve clinical outcomes among patients with mHSPC.

Objectives: There is increasing interest in expanding the elements of value to be considered when making health policy decisions. To help inform value frameworks, this study quantified preferences for disease attributes in a general public sample and examined which combination of attributes (disease profiles) are considered most important for research and treatment.

Methods: A discrete choice experiment (DCE) was conducted in a US general population sample, recruited through online consumer panels. Respondents were asked to select one of a set of health conditions they believed to be most important, characterized by attributes defined by a previous qualitative study: onset age; cause of disease; life expectancy; caregiver requirement; symptom burden (characterized by the Health Utilities Index with varying levels of ambulation independence, dexterity limitations, and degree of pain and discomfort); and disease prevalence. A fractional factorial DCE design was implemented using R, and 60 choice sets were generated (separated into blocks of 10 per participant). Data were analyzed using a mixed-logit regression model, and results used to assess the likelihood of preferring disease profiles. Based on individual attribute preferences, overall preferences for disease profiles, including a profile aligned with Duchenne muscular dystrophy (DMD), were compared.

Results: Fifty-two percent of respondents (n = 537) were female, and 70.6% were aged 18-54 years. Attributes considered most important were those related to life expectancy (odds ratio [OR], 95% confidence interval [CI] 1.88 [1.56-2.27] for a 50% reduction in remaining life expectancy vs no impact), and symptom burden (OR [95% CI] 1.84 [1.47-2.31] for severe vs mild burden). Greater importance was also found for pediatric onset, caregiver requirement, and diseases affecting more people. As an example of disease profile preferences, a DMD-like pediatric inherited disease with 50% reduction in life expectancy, extensive caregiver requirement, severe symptom burden, and 1:5000 prevalence had 2.37-fold higher odds of being selected as important versus an equivalent disease with adult onset and no life expectancy reduction.

Conclusions: Of disease attributes included in this DCE, respondents valued higher prevalence of disease, life expectancy and symptom burden as most important for prioritizing research and treatment. Based on expressed attribute preferences, a case study of an inherited pediatric disease involving substantial reductions to length and quality of life and requiring caregiver support has relatively high odds of being identified as important compared to diseases reflecting differing attribute profiles. These findings can help inform expansions of value frameworks by identifying important attributes from the societal perspective.

In Australia, local health services with allocated budgets manage public hospital services for defined geographical areas. The authors were embedded in a local health service for around 2 years and undertook a range of local level economic evaluations for which three decision contexts were defined: intervention development, post-implementation and prioritisation. Despite difficulties in estimating opportunity costs and in the relevance of portfolio-based prioritisation approaches, economic evaluation added value to local decision-making. Development-focused (ex ante) economic evaluations used expert elicitation and calibration methods to synthesise published evidence with local health systems data to evaluate interventions to prevent hospital acquired complications. The use of economic evaluation facilitated the implementation of interventions with additional resource requirements. Decision analytic models were used alongside the implementation of larger scale, more complex service interventions to estimate counterfactual patient pathways, costs and outcomes, providing a transparent alternative to the statistical analyses of intervention effects, which were subject to high risk of bias. Economic evaluations of more established services had less impact due to data limitations and lesser executive interest. Prioritisation-focused economic evaluations compared costs, outcomes and processes of care for defined patient populations across alternative local health services to identify, understand and quantify the effects of unwarranted variation to inform priority areas for improvement within individual local health services. The sustained use of local level economic evaluation could be supported by embedding health economists in local continuous improvement units, perhaps with an initial focus on supporting the development and evaluation of prioritised new service interventions. Shared resources and critical mass are important, which could be facilitated through groups of embedded economists with joint appointments between different local health services and the same academic institution.

Objective: About one-fifth of US adolescents experienced major depressive symptoms, but few studies have examined longitudinal trends of adolescents developing depression or recovering by demographic factors. We estimated new transition probability inputs, and then used them in a simulation model to project the epidemiologic burden and trajectory of depression of diverse adolescents by sex and race or ethnicity combinations.

Methods: Transition probabilities were first derived using parametric survival analysis of data from the National Longitudinal Study of Adolescent to Adult Health and then calibrated to cross-sectional data from the National Survey on Drug Use and Health. We developed a cohort state-transition model to simulate age-specific depression outcomes of US adolescents. A hypothetical adolescent cohort was modeled from 12-22 years with annual transitions. Model outcomes included proportions of youth experiencing depression, recovery, or depression-free cases and were reported for a US adolescent population by sex, race or ethnicity, and sex and race or ethnicity combinations.

Results: At 22 years of age, approximately 16% of adolescents had depression, 12% were in recovery, and 72% had never developed depression. Depression prevalence peaked around 16-17 years-old. Adolescents of multiracial or other race or ethnicity, White, American Indian or Alaska Native, and Hispanic, Latino, or Spanish descent were more likely to experience depression than other racial or ethnic groups. Depression trajectories generated by the model matched well with historical observational studies by sex and race or ethnicity, except for individuals from American Indian or Alaska Native and multiracial or other race or ethnicity backgrounds.

Conclusions: This study validated new transition probabilities for future use in decision models evaluating adolescent depression policies or interventions. Different sets of transition parameters by demographic factors (sex and race or ethnicity combinations) were generated to support future health equity research, including distributional cost-effectiveness analysis. Further data disaggregated with respect to race, ethnicity, religion, income, geography, gender identity, sexual orientation, and disability would be helpful to project accurate estimates for historically minoritized communities.

Background and objective: Improving health and economic equity are key objectives in priority setting, particularly in low-income and middle-income countries. This study aims to assess the distributional impacts of the Community-based Hypertension Improvement Project (ComHIP) on health and economic outcomes across wealth quintiles in Ghana.

Methods: We developed a decision analytical model to simulate a 30 million cohort of Ghanaians aged 15-49 years. The study specified health outcomes as the prevention of stroke cases and averting deaths among those with hypertension. Furthermore, we explored economic impacts, including savings in out-of-pocket costs for stroke patients and government spending. Financial risk protection against catastrophic and impoverishing health expenditures was also examined. We assessed these outcomes across wealth quintiles, and the corresponding concentration indexes (CIXs) were determined.

Results: It was estimated that ComHIP could prevent 1450 stroke cases and 564 related deaths annually. Health benefits were observed to be more significant among the wealthier quintiles (CIX 0.217), mainly attributed to a higher occurrence of hypertension within these groups. ComHIP was also projected to result in an annual saving of USD 49,885 in individuals' out-of-pocket costs (CIX 0.262) and USD 37,578 in government spending (CIX 0.146). These savings correspond to the prevention of 335 catastrophic health expenditure cases (CIX - 0.239) and 11 impoverishing health expenditure cases (CIX - 0.600).

Conclusions: While ComHIP provides greater health benefits to wealthier groups, it offers substantial financial risk protection for the less wealthy. This study highlights the importance of considering equity in both health and financial risk when making priority-setting decisions.