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"Cost-effectiveness"
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From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects
Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non-randomized studies to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention: pulmonary artery catheterization.
Journal Article
Sponsorship bias in oncology cost effectiveness analysis
Cost effectiveness analysis (CEA) has been increasingly used to inform cancer treatment coverage policy making worldwide. The primary objective of this study was to assess the association between industry sponsorship and CEA results in oncology.
All CEAs in oncology used incremental cost per quality-adjusted life year (QALY) as health effect identified from the Tufts CEA Registry since 1976 was analyzed. Descriptive analyses were performed to present and compare the characteristics of CEA funded by industry and non-industry. Robust logistic regression was performed to assess the relationship between the industry sponsorship and cost effective conclusion over a wide range of threshold values.
A total of 1537 CEAs in oncology published from 1976 to 2021 were included. There were 387 (25.2%) with the industry sponsorship. CEAs sponsored by the industry were more likely to report ICERs below $50,000/QALY (adjusted odds ratio (OR), 1.91, 95% confidence interval (CI), 1.45-2.51, P < 0.001), $100,000/QALY (2.74, 1.98-3.79, P < 0.001), and $150,000/QALY (3.53, 2.37-5.27, P < 0.001) than studies without industry sponsorship.
Our study suggests that there has been a significant sponsorship bias in CEAs in oncology. This bias could have a profound implication on drug pricing and coverage policy making.
Journal Article
Priority Threat Management for biodiversity conservation
Threats to biodiversity and the integrity of ecological systems are escalating globally, both within and outside of protected areas. Decision makers have inadequate resources to manage all threats and typically lack information on the likely outcomes and cost‐effectiveness of possible management strategies. Priority Threat Management (PTM) is an emerging approach designed to address this challenge, by defining and appraising cost‐effective strategies for mitigating threats to biodiversity across regions. The scientific and practical impacts of PTM are increasing, with a growing number of case study applications across the globe.
Here, we provide guidance and resource material for conducting the PTM process based on our experience delivering six large‐scale projects across Australia and Canada. Our handbook describes the four stages of PTM: scoping and planning; defining and collecting key elements; analysing the cost‐effectiveness of strategies; and communicating and integrating recommendations. We summarise critical tips, strengths, and limitations and scope for possible enhancements of the approach.
Priority Threat Management harnesses scientific and expert‐derived information to prioritise management strategies based on their benefit to biodiversity, management costs and feasibility. The approach involves collaboration with key experts and stakeholders in a region to improve knowledge sharing and conservation support. The PTM approach identifies sets of regional level strategies that together provide the greatest benefits for multiple species under a limited budget, which can be used to inform existing processes for decision‐making.
The PTM approach applies some generalisations in management strategies and resolution, in order to address complex challenges. Further developments of the approach include testing in a greater range of socioecological systems with adaptations that cater for multiobjective decisions.
Synthesis and applications. Priority Threat Management is a decision science approach that brings people together to define and prioritise strategies for managing threats to biodiversity across broad regions. It delivers a prospectus for investment in the biodiversity of a region that is transparent, repeatable, participatory, and based on the best available information. Our handbook provides the necessary guidance and resources for expanding the Priority Threat Management approach to new locations, contexts, and challenges.
Priority Threat Management is a decision science approach that brings people together to define and prioritise strategies for managing threats to biodiversity across broad regions. It delivers a prospectus for investment in the biodiversity of a region that is transparent, repeatable, participatory, and based on the best available information. Our handbook provides the necessary guidance and resources for expanding the Priority Threat Management approach to new locations, contexts, and challenges.
Journal Article
Estimating a cost-effectiveness threshold for health care decision-making in South Africa
Abstract
Cost-effectiveness thresholds are important decision rules that determine whether health interventions represent good value for money. In low- and middle-income countries, the World Health Organization (WHO) one to three times per capita gross domestic product (GDP) per disability-adjusted life years (DALYs) averted has been the most widely used threshold for informing resource allocation decisions. However, in 2016, the WHO withdrew recommendations for using this threshold, creating a significant vacuum in South Africa and many countries that rely on results of cost-effectiveness analyses for making resource allocation decisions. This study estimates a cost-effectiveness threshold that reflects the health opportunity cost of health spending in South Africa using a three-step approach. First, marginal returns to health spending was estimated as health spending elasticity for crude death rates using a fixed effect estimation approach. Second, the opportunity cost of health spending was estimated as DALYs averted. Finally, a cost per DALY averted threshold was estimated as the inverse of the marginal product of health spending. We show that 1% of total health spending in 2015 (equivalent to approximately ZAR 1.54 billion/USD 120.7 million) averted 1050 deaths, 34 180 years of life lost, 5880 years lived with disability and 40 055 DALYs. The cost-effectiveness threshold was estimated at approximately ZAR 38 500 (USD 3015) per DALY averted, ∼53% of South Africa’s per capita GDP in 2015 (ZAR 72 700/USD 5700) and lower than the previously recommended one to three times per capita GDP. As South Africa moves towards implementing universal health coverage reforms through National Health Insurance by 2025, the adoption of a threshold that reflects health opportunity costs will be crucial for ensuring efficiency in the allocation of scarce resources. This study provides useful insight into the magnitude of the health opportunity cost of health spending in South Africa and highlights the need for further research.
Journal Article
The Short-Run and Long-Run Effects of Behavioral Interventions: Experimental Evidence from Energy Conservation
We document three remarkable features of the Opower program, in which social comparison-based home energy reports are repeatedly mailed to more than six million households nationwide. First, initial reports cause high-frequency \"action and backsliding,\" but these cycles attenuate over time. Second, if reports are discontinued after two years, effects are relatively persistent, decaying at 10-20 percent per year. Third, consumers are slow to habituate: they continue to respond to repeated treatment even after two years. We show that the previous conservative assumptions about post-intervention persistence had dramatically understated cost effectiveness and illustrate how empirical estimates can optimize program design.
Journal Article