Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.
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ABSTRACT:
SUBMITTER: Graves J
PROVIDER: S-EPMC9181506 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.
Graves John J Garbett Shawn S Zhou Zilu Z Schildcrout Jonathan S JS Peterson Josh J
Medical decision making : an international journal of the Society for Medical Decision Making 20210318 4
We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transitio ...[more]
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Project description:An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, patient care, and health outcomes in this context. This study compares the national policy approaches currently in development/use for health apps in nine countries. We used secondary data, combined with a detailed review of policy and regulatory documents, and interviews with key individuals and experts in the field of digital health policy to collect data about implemented and planned policies and initiatives. We found that most approaches aim for centralized pipelines for health app approvals, although some countries are adding decentralized elements. While the countries studied are taking diverse paths, there is nevertheless broad, international convergence in terms of requirements in the areas of transparency, health content, interoperability, and privacy and security. The sheer number of apps on the market in most countries represents a challenge for clinicians and patients. Our analyses of the relevant policies identified challenges in areas such as reimbursement, safety, and privacy and suggest that more regulatory work is needed in the areas of operationalization, implementation and international transferability of approvals. Cross-national efforts are needed around regulation and for countries to realize the benefits of these technologies.
| S-EPMC8933556 | biostudies-literature
Project description:ObjectiveThe goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.MethodsFor this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.ResultsOut of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.ConclusionOur review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.
| S-EPMC9869403 | biostudies-literature
Project description:BackgroundComputer models played an important role in the health care reform debate, and they will continue to be used during implementation. However, current models are limited by inputs, including available data. Aim. We review microsimulation and cell-based models. For each type of model, we discuss data requirements and other factors that may affect its scope. We also discuss how to improve models by changing data collection and data access procedures.Materials and methodsWe review the modeling literature, documentation on existing models, and data resources available to modelers. Results. Even with limitations, models can be a useful resource. However, limitations must be clearly communicated. Modeling approaches could be improved by enhancing existing longitudinal data, improving access to linked data, and developing data focused on health care providers.DiscussionLongitudinal datasets could be improved by standardizing questions across surveys or by fielding supplemental panels. Funding could be provided to identify causal parameters and to clarify ranges of effects reported in the literature. Finally, a forum for routine communication between modelers and policy makers could be established.ConclusionModeling can provide useful information for health care policy makers. Thus, investing in tools to improve modeling capabilities should be a high priority.
| S-EPMC2965891 | biostudies-literature
Project description:ObjectiveTo compare interactive fixed effects (IFE) and generalized synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture best practice tariffs introduced for hospitals in England in 2010.Data sourcesSimulations and Hospital Episode Statistics.Study designBest practice tariffs aimed to incentivize providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using difference-in-differences (DiD), synthetic control (SC), IFE, and GSC methods. We contrast the estimation methods' performance in a Monte Carlo simulation study.Principal findingsUnlike DiD, SC, and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of best practice tariffs led to a 5.9 (confidence interval: 2.0 to 9.9) percentage point increase in the proportion of patients having surgery within 48 hours and a statistically insignificant 0.6 (confidence interval: -1.4 to 0.4) percentage point reduction in 30-day mortality.ConclusionsThe GSC approach is an attractive method for health policy evaluation. We cannot be confident that best practice tariffs were effective.
| S-EPMC7080394 | biostudies-literature
Project description:BackgroundRegulatory analyses of air pollution policies require the use of concentration-response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter ≤2.5μm (PM2.5)]. Common practice by U.S. federal agencies involves using underlying health data and concentration-response functions that are not differentiated by racial/ethnic group.ObjectivesWe aim to explore the policy implications of using race/ethnicity-specific concentration-response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups.MethodsUsing new estimates from the epidemiological literature on race/ethnicity-specific concentration-response functions paired with race/ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards.ResultsUse of race/ethnicity-specific information increased PM2.5-related premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non-race/ethnicity-specific modeling approach. Standard approaches of using non-racial/ethnic specific information underestimate the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and overestimate the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach.DiscussionPolicy analyses incorporating race/ethnicity-specific concentration-response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of PM2.5 mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices. https://doi.org/10.1289/EHP9001.
| S-EPMC8653772 | biostudies-literature
Project description:PurposeHealth policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we developed a novel decision-analytic modeling framework to integrate social processes.MethodsWe evaluated a simplified decision problem using two models: a standard decision-analytic model and a model incorporating our social factors framework. The standard model simulated individuals transitioning through three disease natural history states-healthy, sick, and dead-without accounting for differential health system utilization. Our social factors framework incorporated heterogeneous health insurance coverage, which influenced disease progression and health system utilization. We assessed the impact of a new treatment on a cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-year-old adults. Main outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization over the lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios.ResultsIn the standard model, the new treatment increased life expectancy by 2.7 years for both non-Hispanic Black and non-Hispanic white adults, without affecting racial/ethnic gaps in life expectancy. However, incorporating known racial/ethnic disparities in health insurance coverage with the social factors framework led to smaller life expectancy gains for non-Hispanic Black adults (2.0 years) compared to non-Hispanic white adults (2.2 years), increasing racial/ethnic disparities in life expectancy.LimitationsThe availability of social factors and complexity of causal pathways between factors may pose challenges in applying our social factors framework.ConclusionsExcluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating a social factors framework enhances simulation models' effectiveness in evaluating interventions with health equity implications.
| S-EPMC11888512 | biostudies-literature
Project description:BackgroundThe Institute of Medicine recommends that public health agencies restrict the number and regulate the location of tobacco retailers as a means of reducing tobacco use. However, the best policy strategy for tobacco retailer reduction is unknown.PurposeThe purpose of this study is to test the percent reduction in the number and density of tobacco retailers in North Carolina resulting from three policies: (1) prohibiting sales of tobacco products in pharmacies or stores with a pharmacy counter, (2) restricting sales of tobacco products within 1000 ft of schools, and (3) regulating to 500 ft the minimum allowable distance between tobacco outlets.MethodsThis study uses data from two lists of tobacco retailers gathered in 2012, one at the statewide level, and another "gold standard" three-county list. Retailers near schools were identified using point and parcel boundaries in ArcMap. Python programming language generated a random lottery system to remove retailers within 500 ft of each other. Analyses were conducted in 2014.ResultsA minimum allowable distance policy had the single greatest impact and would reduce density by 22.1% at the state level, or 20.8% at the county level (range 16.6% to 27.9%). Both a pharmacy and near-schools ban together would reduce density by 29.3% at the state level, or 29.7% at the county level (range 26.3 to 35.6%).ConclusionsThe implementation of policies restricting tobacco sales in pharmacies, near schools, and/or in close proximity to another tobacco retailer would substantially reduce the number and density of tobacco retail outlets.
| S-EPMC4563823 | biostudies-literature
Project description:ImportanceDigital technology is part of everyday life. Digital interactions generate large amounts of data that can reveal information about the health of individual consumers (the digital health footprint).ObjectiveΤo describe health privacy challenges associated with digital technology.Design, setting, and participantsFor this qualitative study, In-depth, semistructured, qualitative interviews were conducted with 26 key experts from diverse fields in the US between January 1 and July 31, 2018. Open-ended questions and hypothetical scenarios were used to identify sources of digital information that contribute to consumers' health-relevant digital footprints and challenges for health privacy. Participants also completed a survey instrument on which they rated the health relatedness of digital data sources.Main outcomes and measuresHealth policy challenges associated with digital technology based on qualitative responses to expert interviews.ResultsAlthough experts' ratings of digital data sources suggested a possible distinction between health and nonhealth data, qualitative interviews uniformly indicated that all data can be health data, particularly when aggregated across sources and time. Five key characteristics of the digital health footprint were associated with health privacy policy challenges: invisibility (people are unaware of how their data are tracked), inaccuracy (data in the digital health footprint can be inaccurate), immortality (data have no expiration date and are aggregated over time), marketability (data have immense commercial value and are frequently bought and sold), and identifiability (individuals can be readily reidentified and anonymity is nearly impossible to achieve). There are virtually no regulatory structures in the US to protect health privacy in the context of the digital health footprint.Conclusions and relevanceThe findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections.
| S-EPMC7348687 | biostudies-literature
Project description:BackgroundThe need for more local technical capacity in Health Technology Assessment (HTA) is a leading challenge to its use in low- and middle-income countries. Zambia has been considering using HTA to support its universal health coverage initiative, which includes health benefits package design and implementation. This study assesses the local HTA capacity for the steering committee tasked with supporting the design and implementation of the national health benefits package in Zambia.MethodsThe study applied a cross-sectional web-based survey design and the consensus-based Checklist for Reporting of Survey Studies. Data were collected from the steering committee of the benefits package working group, tasked with leading the design process of the health benefits package using the Instrument for the Assessment of Skills to Conduct a Health Technology Assessment tool.ResultsThe majority of respondents had not served on a selection and reimbursement committee. Clinical effectiveness skills in structuring a search strategy, handling missing data, conducting qualitative evidence synthesis, and grading the certainty of evidence were low. Skills for leadership, networking, conflict management, and project coordination, public and patient involvement were mid-level to low. Most of the respondents were aware of ethical issues with health technologies. Health economics skills in economic evaluations and decision analytic modeling, equity and health system efficiency measurement, budget impact analysis, and quality of life were identified for capacity strengthening.ConclusionAvailable technical capacities to revise and implement the national benefits package were lower in health economics, synthesis for clinical effectiveness evidence, ethics, patient and public involvement, and soft skills, in that order.
| S-EPMC11894396 | biostudies-literature
Project description:Lifecycle considerations have always been part of health technology assessment (HTA). However, the concept of taking a fuller, more holistic "lifecycle approach" is gaining interest in the HTA community. The 2022 HTAi Global Policy Forum (GPF) discussed how adopting a lifecycle approach could promote stakeholder engagement and robust evidence generation, and whether it could enhance information sharing and transparency across stakeholder groups. This article summarizes the discussions held at the 2022 HTAi GPF and subsequent HTAi Annual Meeting panel session that debated some of the key challenges and opportunities, with particular focus on the pre- and postmarket and disinvestment phase activities. Core themes and recommendations identified that collaboration and patient involvement are happening but still needs to be strengthened, and moving to disease-based approaches may help, although individual contexts still need to be considered. Appropriately developed and mandated core outcome sets may help with information sharing and efficiency in all lifecycle activities. Further, methods for the appropriate use of big data and digital data collection should be developed and driven by the HTA community. The value of lifecycle activities should be reviewed; in particular, scientific advice appears valuable, but the magnitude of effect is somewhat unknown due to the challenges around the confidential nature of these activities. Not all lifecycle activities can be conducted for every technology, and while there is a move away from disinvestment phase activities, more structured prioritization criteria are required. This article ends with suggested next steps to bring forward some of the priority recommendations.
| S-EPMC11574536 | biostudies-literature