Health, economic, and equity consequences of future tobacco control policies in England: a microsimulation study.

BACKGROUND: Smoking still generates a huge, costly, and inequitable burden of disease. The UK tobacco-free generation target to reduce smoking prevalence to below 5% by 2030 will be missed if current trends continue. We aimed to determine whether additional policies could speed progress towards meeting the tobacco-free generation target. METHODS: We developed, calibrated, and validated a microsimulation model, IMPACTHINT simulating English adults aged 30-89 years from 2023 to 2072. The model included a detailed smoking history and quantified policy health outcomes including smoking prevalence and smoking-related diseases, economics, and equity. We simulated five scenarios: (1) baseline trends; (2) increasing the minimum age of access to tobacco to 21 years (MinAge21); (3) a 30% increase in tobacco duty (TaxUP); (4) improved smoking cessation services (ServicesUP); and (5) a combination of TaxUP and ServicesUP. We estimated the smoking prevalence, smoking-related diseases and cumulative cases prevented or postponed, and deaths. We evaluated the scenario cost-effectiveness from the societal perspective. Lastly, we analysed the results by deprivation quintile. We present in our findings cumulative cases prevented or postponed over 50 years. FINDINGS: None of the scenarios would reduce overall smoking prevalence to below 5% by 2030. However, that goal could be reached by 2035 under the TaxUP and the combination of TaxUP and ServicesUP scenarios, by 2037 under the ServicesUP scenario, or by 2038 under the MinAge21 and the baseline scenarios. By 2072, the combined scenario might reduce smoking-related diseases by 160 000 cases (95% CI 140 000-200 000), greatly exceeding the reductions by 140 000 cases (120 000-180 000) with TaxUP, 69 000 cases (53 000-86 000) with MinAge21, or 22 000 cases (14 000-31 000) with ServicesUP. Some 50% of all disease-years reduced by TaxUP would occur in the most deprived quintile. The most affluent quintile could reach the 5% goal sooner than the most deprived quintile (by 2032 for the least deprived vs 2038 for the most deprived), and it could reach the 5% target by 2030 under the combined TaxUP and ServicesUP scenario. Finally, all policies would save costs compared with the baseline trend. INTERPRETATION: Affluent groups will achieve the 5% tobacco-free goal a decade sooner than the most deprived. However, that goal could be achieved in all groups by 2035 through a 30% increase in tax and enhanced smoking cessation services. Our limitations included the uncertainties of any 50-year forecast. However, that long time-horizon can capture the potential policy benefits for younger age groups. FUNDING: Economic and Social Research Council.

Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework.

BACKGROUND: Policy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework. METHODS: We searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity. FINDINGS: We found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates. CONCLUSIONS: While fragments of good practices were widespread across the reviewed PSMs, only a few included a 'critical mass' of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.

Tobacco Control Policy Simulation Models: Protocol for a Systematic Methodological Review.

BACKGROUND: Tobacco control models are mathematical models predicting tobacco-related outcomes in defined populations. The policy simulation model is considered as a subcategory of tobacco control models simulating the potential outcomes of tobacco control policy options. However, we could not identify any existing tool specifically designed to assess the quality of tobacco control models. OBJECTIVE: The aims of this systematic methodology review are to: (1) identify best modeling practices, (2) highlight common pitfalls, and (3) develop recommendations to assess the quality of tobacco control policy simulation models. Crucially, these recommendations can empower model users to assess the quality of current and future modeling studies, potentially leading to better tobacco policy decision-making for the public. This protocol describes the planned systematic review stages, paper inclusion and exclusion criteria, data extraction, and analysis. METHODS: Two reviewers searched five databases (Embase, EconLit, PsycINFO, PubMed, and CINAHL Plus) to identify eligible studies published between July 2013 and August 2019. We included papers projecting tobacco-related outcomes with a focus on tobacco control policies in any population and setting. Eligible papers were independently screened by two reviewers. The data extraction form was designed and piloted to extract model structure, data sources, transparency, validation, and other qualities. We will use a narrative synthesis to present the results by summarizing model trends, analyzing model approaches, and reporting data input and result quality. We will propose recommendations to assess the quality of tobacco control policy simulation models using the findings from this review and related literature. RESULTS: Data collection is in progress. Results are expected to be completed and submitted for publication by April 2021. CONCLUSIONS: This systematic methodological review will summarize the best practices and pitfalls existing among tobacco control policy simulation models and present a recommendation list of a high-quality tobacco control simulation model. A more standardized and quality-assured tobacco control policy simulation model will benefit modelers, policymakers, and the public on both model building and decision making. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26854.

Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.

BACKGROUND: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.