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

Tobacco endgame intervention impacts on health gains and Māori:non-Māori health inequity: a simulation study of the Aotearoa/New Zealand Tobacco Action Plan

BACKGROUND

The Aotearoa/New Zealand Government is aiming to end the tobacco epidemic and markedly reduce Māori:non-Māori health inequalities by legislating: (1) denicotinisation of retail tobacco, (2) 95% reduction in retail outlets and (c) a tobacco free-generation whereby people born after 2005 are unable to legally purchase tobacco. This paper estimates future smoking prevalence, mortality inequality and health-adjusted life year (HALY) impacts of these strategies.

METHODS

We used a Markov model to estimate future yearly smoking and vaping prevalence, linked to a proportional multistate life table model to estimate future mortality and HALYs.

RESULTS

The combined package of strategies (plus media promotion) reduced adult smoking prevalence from 31.8% in 2022 to 7.3% in 2025 for Māori, and 11.8% to 2.7% for non-Māori. The 5% smoking prevalence target was forecast to be achieved in 2026 and 2027 for Māori males and females, respectively.The HALY gains for the combined package over the population's remaining lifespan were estimated to be 594 000 (95% uncertainty interval (UI): 443 000 to 738 000; 3% discount rate). Denicotinisation alone achieved 97% of these HALYs, the retail strategy 19% and tobacco-free generation 12%.By 2040, the combined package was forcat to reduce the gap in Māori:non-Māori all-cause mortality rates for people 45+ years old by 22.9% (95% UI: 19.9% to 26.2%) for females and 9.6% (8.4% to 11.0%) for males.

CONCLUSION

A tobacco endgame strategy, especially denicotinisation, could deliver large health benefits and dramatically reduce health inequities between Māori and non-Māori in Aotearoa/New Zealand.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

OBJECTIVE

To systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.

DATA SOURCE AND STUDY SELECTION

Four databases were searched for modelling studies of e-cigarette use on population health published between 2010 and 2023. A total of 32 studies were included.

DATA EXTRACTION

Data on study characteristics, model attributes and estimates of population impacts including health outcomes and smoking prevalence were extracted from each article. The findings were synthesised narratively.

DATA SYNTHESIS

The introduction of e-cigarettes was predicted to lead to decreased smoking-related mortality, increased quality-adjusted life-years and reduced health system costs in 29 studies. Seventeen studies predicted a lower prevalence of cigarette smoking. Models that predicted negative population impacts assumed very high e-cigarette initiation rates among non-smokers and that e-cigarette use would discourage smoking cessation by a large margin. The majority of the studies were based on US population data and few studies included factors other than smoking status, such as jurisdictional tobacco control policies or social influence.

CONCLUSIONS

A population increase in e-cigarette use may result in lower smoking prevalence and reduced burden of disease in the long run, especially if their use can be restricted to assisting smoking cessation. Given the assumption-dependent nature of modelling outcomes, future modelling studies should consider incorporating different policy options in their projection exercises, using shorter time horizons and expanding their modelling to low-income and middle-income countries where smoking rates remain relatively high.

Prevention Lab: a predictive model for estimating the impact of prevention interventions in a simulated Italian cohort

BACKGROUND

A large fraction of the disease burden in the Italian population is due to behavioral risk factors. The objective of this work is to provide a tool to estimate the impact of preventive interventions that reduce the exposure to smoking and sedentary lifestyle of the Italian population, with the goal of selecting optimal interventions.

METHODS

We construct a Markovian model that simulates the state of each subject of the Italian population. The model predicts the distribution of subjects in each health status and risk factor status for every year of the simulation. Based on this distribution, the model provides a rich output summary, such as the number of incident and prevalent cases for each tracing disease and the Disability Adjusted Life Years (DALY), used to assess the impact of preventive interventions, and how this impact is shaped in time.

RESULTS

This paper focuses on the methodological aspects of the model. The proposed model is flexible and can be applied to estimate the impact of complex interventions on the two risk factors and adapted to consider different cohorts. We validate the model by simulating the evolution of the Italian population from 2009 to 2017 and comparing the output with historical data. Furthermore, as a case-study, we simulate a counterfactual scenario where both tobacco and sedentary lifestyle are eradicated from the Italian population in 2019 and estimate the impact of such intervention over the following 20 years.

CONCLUSIONS

We propose a Markovian model to estimate how interventions on smoking and sedentary lifestyle can affect the reduction of the disease burden, and validate the model on historical data. The model is flexible and allows to extend the analysis to consider more risk factors in future research. However, we are aware that, given the ever-increasing availability of data, it is necessary in the future to increase the complexity of the model, to be closer to reality and to provide decision-making support to the policy-makers.

Improving Prediction of Tobacco Use Over Time: Findings from Waves 1-4 of the Population Assessment of Tobacco and Health Study

INTRODUCTION

First-order Markov models assume future tobacco use behavior is dependent on current tobacco use and are often used to characterize patterns of tobacco use over time. Higher-order Markov models that assume future behavior is dependent on current and prior tobacco use may better estimate patterns of tobacco use.

AIMS AND METHODS

This study compared Markov models of different orders to examine whether incorporating information about tobacco use history improves model estimation of tobacco use and estimated tobacco use transition probabilities. We used data from four waves of the Population Assessment of Tobacco and Health Study. In each Wave, a participant was categorized into one of the following tobacco use states: never smoker, former smoker, menthol cigarette smoker, non-menthol cigarette smoker, or e-cigarette/dual user. We compared first-, second-, and third-order Markov models using multinomial logistic regression and estimated transition probabilities between tobacco use states.

`RESULTS

The third-order model was the best fit for the data. The percentage of former smokers, menthol cigarette smokers, non-menthol cigarette smokers, and e-cigarette/dual users in Wave 3 that remained in the same tobacco use state in Wave 4 ranged from 63.4% to 97.2%, 29.2% to 89.8%, 34.8% to 89.7%, and 20.5% to 80.0%, respectively, dependent on tobacco use history. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit.

CONCLUSIONS

Transition probabilities between tobacco use states varied widely depending on tobacco use history. Higher-order Markov models improve estimation of tobacco use over time and can inform understanding of trajectories of tobacco use behavior.

IMPLICATIONS

Findings from this study suggest that transition probabilities between tobacco use states vary widely depending on tobacco use history. Tobacco product users (cigarette or e-cigarette/dual users) who were in the same tobacco use state in the prior two years were least likely to quit. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit. Quitting smoking for at least two years is an important milestone in the process of cessation.

Economic effects for citizens and the government of a country-level tobacco endgame strategy: a modelling study

BACKGROUND

Aotearoa-New Zealand (A/NZ) was the first country to pass a comprehensive commercial tobacco endgame strategy into law. Key components include the denicotinisation of smoked tobacco products and a major reduction in tobacco retail outlets. Understanding the potential long-term economic impacts of such measures is important for government planning.

DESIGN

A tobacco policy simulation model that evaluated the health impacts of the A/NZ Smokefree Action Plan was extended to evaluate the economic effects from both government and citizen perspectives. Estimates were presented in 2021 US$, discounted at 3% per annum.

RESULTS

The modelled endgame policy package generates considerable growth in income for the A/NZ population with a total cumulative gain of US$31 billion by 2050. From a government perspective, increased superannuation payments and reduced tobacco excise tax revenue result in a negative net financial position and a cumulative shortfall of US$11.5 billion by 2050. In a sensitivity analysis considering future labour force changes, the government's cumulative net position remained negative by 2050, but only by US$1.9 billion.

CONCLUSIONS

A policy such as the A/NZ Smokefree Action Plan is likely to produce substantial economic benefits for citizens, and modest impacts on government finances related to reduced tobacco tax and increases in aged pensions due to increased life expectancy. Such costs can be anticipated and planned for and might be largely offset by future increases in the size of the labour force and the proportion of people 65+ years old working in the formal economy.

Modeling the future of tobacco control: Using SimSmoke to explore the feasibility of the tobacco endgame in Korea

INTRODUCTION

We used a simulation model to assess the feasibility of reaching the tobacco endgame target (reducing the smoking prevalence to below 5% by 2050) and explored potential implementation strategies.

METHODS

The impact of strengthened tobacco-control policies on smoking prevalence was analyzed using Korea SimSmoke, a discrete-time Markov process. We considered the effects of various scenarios from 2023 and predictions were conducted until 2050. To confirm the stability of the results, deterministic and probabilistic sensitivity analyses were carried out by increasing and decreasing parameter estimates.

RESULTS

The implementation of tobacco-control policies in accordance with the WHO MPOWER (Μonitor tobacco use and prevention policies; Protect people from tobacco smoke; Offer help to quit tobacco smoking; Warn of the dangers of tobacco; Enforce bans on tobacco advertising, promotion, and sponsorship; Raise taxes on tobacco) measures were insufficient to achieve the tobacco endgame objective of 5% by 2050. The overall predicted smoking prevalence in 2050 is 4.7% if all policies are fully implemented in accordance with the FCTC guidelines together with a complete ban on the sale of cigarettes to people born after 2003 and annual 10% increases in price. Sensitivity analyses using the varying policy effect assumptions demonstrated the robustness of the simulation results.

CONCLUSIONS

For a substantive reduction in smoking prevalence, it is essential to strongly implement the MPOWER strategy. Beyond this foundational step, the eradication of smoking requires a paradigm shift in the perception of conventional tobacco-control policies, including a tobacco-free generation strategy and radical increases in the price of tobacco products.

Prioritization of intervention domains to prevent cardiovascular disease: a country-level case study using global burden of disease and local data

AIM

We aimed to combine Global Burden of Disease (GBD) Study data and local data to identify the highest priority intervention domains for preventing cardiovascular disease (CVD) in the case study country of Aotearoa New Zealand (NZ).

METHODS

Risk factor data for CVD in NZ were extracted from the GBD using the "GBD Results Tool." We prioritized risk factor domains based on consideration of the size of the health burden (disability-adjusted life years [DALYs]) and then by the domain-specific interventions that delivered the highest health gains and cost-savings.

RESULTS

Based on the size of the CVD health burden in DALYs, the five top prioritized risk factor domains were: high systolic blood pressure (84,800 DALYs; 5400 deaths in 2019), then dietary risk factors, then high LDL cholesterol, then high BMI and then tobacco (30,400 DALYs; 1400 deaths). But if policy-makers aimed to maximize health gain and cost-savings from specific interventions that have been studied, then they would favor the dietary risk domain (e.g., a combined fruit and vegetable subsidy plus a sugar tax produced estimated lifetime savings of 894,000 health-adjusted life years and health system cost-savings of US$11.0 billion; both 3% discount rate). Other potential considerations for prioritization included the potential for total health gain that includes non-CVD health loss and potential for achieving relatively greater per capita health gain for Māori (Indigenous) to reduce health inequities.

CONCLUSIONS

We were able to show how CVD risk factor domains could be systematically prioritized using a mix of GBD and country-level data. Addressing high systolic blood pressure would be the top ranked domain if policy-makers focused just on the size of the health loss. But if policy-makers wished to maximize health gain and cost-savings using evaluated interventions, dietary interventions would be prioritized, e.g., food taxes and subsidies.