Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

The application of artificial intelligence in health financing: a scoping review

INTRODUCTION

Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing.

METHOD

We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023.

RESULTS

We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI.

CONCLUSIONS

To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

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.

Sleep disorders among patients suffering from road traffic injuries in an urban setting of Vietnam: an exploratory study

Sleep quality is an important indicator of treatment outcome for patients with traffic accident injuries. In Vietnam, the impacts of injury on sleep status are usually amplified in urban areas due to disproportionate distribution of mental care services between the city and less developed settings. Our study investigated deterioration in sleep quality and identified associated demographic factors among traffic injury patients in an small urban setting of Vietnam. A cross-sectional study was conducted among 408 patients in one provincial hospital and five district hospitals in Thai Binh, Vietnam from October to December 2018. A structured questionnaire was designed based on 3 standardized scales: Health-related Quality of Life, the Pittsburgh Sleep Quality Index and the Kessler Scale. Face-to-face interviews and medical records were conducted by trained health professionals on patients hospitalized in the Trauma-Orthopedic/Burn Department and Surgery and General Department. About 16.9% of respondents had sleep disturbances, and there was a statistically significant difference between age group (p < 0.01), education level (p < 0.01), and monthly household income (p < 0.01) between participants who with and without sleep disturbances. Furthermore, more than half (50.7%) of respondents sleep less than 5 h per day, while 18.7% of the sampled also reported that the habitual sleep efficiency was below 85%. Current results indicated that people being female, suffering from traumatic brain injury, being comatose at hospitalization, and having higher psychological distress scores were more likely to suffer from sleep problems. Our study is one of the first evidence in Vietnam to assess sleep disturbances in road traffic injury patients and their correlated factors. It is important to identify patients who are at risk of sleep disturbances based on socio-demographic and clinical characteristics, as well as psychological distress status. Therefore, a holistic approach should be taken to include sleep quality and psychological state in the treatment process and outcome assessment for road traffic injury patients.

Motivational support intervention to reduce smoking and increase physical activity in smokers not ready to quit: the TARS RCT

Background

Physical activity can support smoking cessation for smokers wanting to quit, but there have been no studies on supporting smokers wanting only to reduce. More broadly, the effect of motivational support for such smokers is unclear.

Objectives

The objectives were to determine if motivational support to increase physical activity and reduce smoking for smokers not wanting to immediately quit helps reduce smoking and increase abstinence and physical activity, and to determine if this intervention is cost-effective.

Design

This was a multicentred, two-arm, parallel-group, randomised (1 : 1) controlled superiority trial with accompanying trial-based and model-based economic evaluations, and a process evaluation.

Setting and participants

Participants from health and other community settings in four English cities received either the intervention (n = 457) or usual support (n = 458).

Intervention

The intervention consisted of up to eight face-to-face or telephone behavioural support sessions to reduce smoking and increase physical activity.

Main outcome measures

The main outcome measures were carbon monoxide-verified 6- and 12-month floating prolonged abstinence (primary outcome), self-reported number of cigarettes smoked per day, number of quit attempts and carbon monoxide-verified abstinence at 3 and 9 months. Furthermore, self-reported (3 and 9 months) and accelerometer-recorded (3 months) physical activity data were gathered. Process items, intervention costs and cost-effectiveness were also assessed.

Results

The average age of the sample was 49.8 years, and participants were predominantly from areas with socioeconomic deprivation and were moderately heavy smokers. The intervention was delivered with good fidelity. Few participants achieved carbon monoxide-verified 6-month prolonged abstinence [nine (2.0%) in the intervention group and four (0.9%) in the control group; adjusted odds ratio 2.30 (95% confidence interval 0.70 to 7.56)] or 12-month prolonged abstinence [six (1.3%) in the intervention group and one (0.2%) in the control group; adjusted odds ratio 6.33 (95% confidence interval 0.76 to 53.10)]. At 3 months, the intervention participants smoked fewer cigarettes than the control participants (21.1 vs. 26.8 per day). Intervention participants were more likely to reduce cigarettes by ≥ 50% by 3 months [18.9% vs. 10.5%; adjusted odds ratio 1.98 (95% confidence interval 1.35 to 2.90] and 9 months [14.4% vs. 10.0%; adjusted odds ratio 1.52 (95% confidence interval 1.01 to 2.29)], and reported more moderate-to-vigorous physical activity at 3 months [adjusted weekly mean difference of 81.61 minutes (95% confidence interval 28.75 to 134.47 minutes)], but not at 9 months. Increased physical activity did not mediate intervention effects on smoking. The intervention positively influenced most smoking and physical activity beliefs, with some intervention effects mediating changes in smoking and physical activity outcomes. The average intervention cost was estimated to be £239.18 per person, with an overall additional cost of £173.50 (95% confidence interval -£353.82 to £513.77) when considering intervention and health-care costs. The 1.1% absolute between-group difference in carbon monoxide-verified 6-month prolonged abstinence provided a small gain in lifetime quality-adjusted life-years (0.006), and a minimal saving in lifetime health-care costs (net saving £236).

Conclusions

There was no evidence that behavioural support for smoking reduction and increased physical activity led to meaningful increases in prolonged abstinence among smokers with no immediate plans to quit smoking. The intervention is not cost-effective.

Limitations

Prolonged abstinence rates were much lower than expected, meaning that the trial was underpowered to provide confidence that the intervention doubled prolonged abstinence.

Future work

Further research should explore the effects of the present intervention to support smokers who want to reduce prior to quitting, and/or extend the support available for prolonged reduction and abstinence.

Trial registration

This trial is registered as ISRCTN47776579.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 4. See the NIHR Journals Library website for further project information.

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.

Cost-Effectiveness Analyses of Lung Cancer Screening Using Low-Dose Computed Tomography: A Systematic Review Assessing Strategy Comparison and Risk Stratification

OBJECTIVES

Our first study objective was to assess the range of lung cancer screening intervals compared within cost-effectiveness analyses (CEAs) of low-dose computed tomography (LDCT) and to examine the implications for the strategies identified as optimally cost effective; the second objective was to examine if and how risk subgroup-specific policies were considered.

METHODS

PubMed, Embase and Web of Science were searched for model-based CEAs of LDCT lung screening. The retrieved studies were assessed to examine if the analyses considered sufficient strategy variation to permit incremental estimation of cost effectiveness. Regarding risk selection, we examined if analyses considered alternative risk strata in separate analyses or as alternative risk-based eligibility criteria for screening.

RESULTS

The search identified 33 eligible CEAs, 23 of which only considered one screening frequency. Of the 10 analyses considering multiple screening intervals, only 4 included intervals longer than 2 years. Within the 10 studies considering multiple intervals, the optimal policy choice would differ in 5 if biennial intervals or longer had not been considered. Nineteen studies conducted risk subgroup analyses, 12 of which assumed that subgroup-specific policies were possible and 7 of which assumed that a common screening policy applies to all those screened.

CONCLUSIONS

The comparison of multiple strategies is recognised as good practice in CEA when seeking optimal policies. Studies that do include multiple intervals indicate that screening intervals longer than 1 year can be relevant. The omission of intervals of 2 years or longer from CEAs of LDCT screening could lead to the adoption of sub-optimal policies. There also is scope for greater consideration of risk-stratified policies which tailor screening intensity to estimated disease risk. Policy makers should take care when interpreting current evidence before implementing lung screening.

Costing the outpatient rehabilitation services: time-driven activity-based costing approach

Background

Considering the importance of healthcare services costing in resource allocation, the aim of this study is to calculate the cost of rehabilitation services in an outpatient rehabilitation clinic in Tehran, Iran.

Methods

The data for this study were categorised as financial data and information about the process of rehabilitation services. The first category was extracted from the financial documents and the second was obtained by observation of patient flow and interviews with the clinic staff in 2016. The cost of rehabilitation services has been estimated using the time-driven activity-based costing approach.

Results

The findings show that the cost of physical occupational therapy in the Asma rehabilitation center was $18.79 per unit of service. This amount for speech therapy services was $17.23 to $19.40, taking into account the difference in the quality of the service delivered. The cost of mental health occupational therapy service was between $19.46 and $23.57. Comparing the cost of these services with the government’s tariffs makes it clear that there is a huge gap.

Conclusion

The limited number of patients referred to the center makes the cost of one unit of rehabilitation services much higher than the official tariffs. This is true for almost all similar institutions and makes the profitability of small rehabilitation institutions extremely unstable. Therefore, proper marketing for rehabilitation services by promoting patient referral links with larger healthcare centers and reallocation of resources to the formation of integrated rehabilitation complexes can play a significant role in their profitability.

Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples

Burden of Disease studies-such as the Global Burden of Disease (GBD) Study-quantify health loss in disability-adjusted life-years. However, these studies stop short of quantifying the future impact of interventions that shift risk factor distributions, allowing for trends and time lags. This methodology paper explains how proportional multistate lifetable (PMSLT) modelling quantifies intervention impacts, using comparisons between three tobacco control case studies [eradication of tobacco, tobacco-free generation i.e. the age at which tobacco can be legally purchased is lifted by 1 year of age for each calendar year) and tobacco tax]. We also illustrate the importance of epidemiological specification of business-as-usual in the comparator arm that the intervention acts on, by demonstrating variations in simulated health gains when incorrectly: (i) assuming no decreasing trend in tobacco prevalence; and (ii) not including time lags from quitting tobacco to changing disease incidence. In conjunction with increasing availability of baseline and forecast demographic and epidemiological data, PMSLT modelling is well suited to future multiple country comparisons to better inform national, regional and global prioritization of preventive interventions. To facilitate use of PMSLT, we introduce a Python-based modelling framework and associated tools that facilitate the construction, calibration and analysis of PMSLT models.

Simulating future public health benefits of tobacco control interventions: a systematic review of models

BACKGROUND

To prioritise tobacco control interventions, simulating their health impacts is valuable. We undertook a systematic review of tobacco intervention simulation models to assess model structure and input variations that may render model outputs non-comparable.

METHODS

We applied a Medline search with keywords intersecting modelling and tobacco. Papers were limited to those modelling health outputs (eg, mortality, health-adjusted life years), and at least two of cancer, cardiovascular and respiratory diseases. Data were extracted for each simulation model with ≥3 arising papers, including: model type, untimed or with time steps and trends in business-as-usual (BAU) tobacco prevalence and epidemiology.

RESULTS

Of 1911 papers, 186 met the inclusion criteria, including 13 eligible simulation models. The SimSmoke model had the largest number of publications (n=46), followed by Benefits of Smoking Cessation on Outcomes (n=12) and Tobacco Policy Model (n=10). Two of 13 models only estimated deaths averted, 1 had no time steps, 5 had no future trends in BAU tobacco prevalence, 9 had no future trends in BAU disease epidemiology and 7 had no time lags from quitting tobacco to reversal of health harm.

CONCLUSIONS

Considerable heterogeneity exists in simulation models, making outputs substantively non-comparable between models. Ranking of interventions by one model may be valid. However, this may not be true if, for example, interventions that differentially affect age groups (eg, a tobacco-free generation policy vs increased cessation among adults) do not account for plausible future trends. Greater standardisation of model structures and outputs will allow comparison across models and countries, and for comparisons of the impact of tobacco control interventions with other preventive interventions.

Future smoking prevalence by socioeconomic status in England: a computational modelling study

BACKGROUND

The difference in smoking across socioeconomic groups is a major cause of health inequality. This study projected future smoking prevalence by socioeconomic status, and revealed what is needed to achieve the tobacco-free ambition (TFA) by 2030 in England.

METHODS

Using data from multiple sources, the adult (≥18 years) population in England was separated into subgroups by smoking and highest educational qualification (HEQ). A discrete time state-transition model was used to project future smoking prevalence by HEQ deterministically and stochastically.

RESULTS

In a status quo scenario, smoking prevalence in England is projected to be 10.8% (95% uncertainty interval: 9.1% to 12.9%) by 2022, 7.8% (5.5% to 11.0%) by 2030 and 6.0% (3.7% to 9.6%) by 2040. The absolute difference in smoking rate between low and high HEQ is reduced from 12.2% in 2016 to 7.9% by 2030, but the relative inequality (low/high HEQ ratio) is increased from 2.48 in 2016 to 3.06 by 2030. When applying 2016 initiation/relapse rates, achievement of the TFA target requires no changes to future cessation rates among adults with high qualifications, but increased rates of 37% and 149%, respectively, in adults with intermediate and low qualifications.

CONCLUSIONS

If the current trends continue, smoking prevalence in England is projected to decline in the future, but with substantial differences across socioeconomic groups. Absolute inequalities in smoking are likely to decline and relative inequalities in smoking are likely to increase in future. The achievement of England's TFA will require the reduction of both absolute and relative inequalities in smoking by socioeconomic status.

Prospective impact of tobacco eradication and overweight and obesity eradication on future morbidity and health-adjusted life expectancy: simulation study

BACKGROUND

Interventions that reduce morbidity, in addition to mortality, warrant prioritisation. It is important to understand the magnitude of potential morbidity and health gains from changing risk factor distributions. We quantified the impact of tobacco compared with overweight/obesity eradication on future morbidity and health-adjusted life expectancy (HALE) for the New Zealand population alive in 2011.

METHODS

Business-as-usual (BAU) future smoking rates were set based on past falling rates, but we assumed no future change in Body Mass Index (BMI) distribution, given historic trends. Population impact fractions and the percentage reduction in incidence rates for 16 tobacco-related and 14 overweight/obesity-related diseases (allowing for time lags) were calculated using the difference between BAU and eradication risk factor scenarios combined with tobacco and BMI incidence rate ratios. We used two multistate lifetable models to estimate HALE changes over the remaining lifespan and morbidity rate changes 30 years hence.

RESULTS

HALE gains always exceeded life expectancy (LE) gains for overweight/obesity eradication (ie, absolute compression of morbidity), but for eradication of tobacco, the pattern was mixed. For example, among 32-year-olds in 2011, overweight/obesity eradication increased HALE by 2.06 years and LE by 1.21 years, compared with 0.54 and 0.50 years for tobacco eradication.Morbidity rate reductions 30 years into the future were considerably greater for overweight/obesity eradication (eg, a 15.8% reduction for 72-year-olds in 2041, or the cohort that was aged 42 years in 2011) than for tobacco eradication (2.7%). The same rate of morbidity experienced at age 65 years under BAU was deferred by 5 years with overweight/obesity eradication.

CONCLUSIONS

Preventive programmes that reduce overweight and obesity have strong potential to reduce or compress morbidity, improving the average health status of ageing populations. This paper simulated eradication of tobacco and overweight/obesity; actual interventions will have lesser health impacts, but the relativities of morbidity to mortality gains should be similar.

Impact of increasing tobacco taxes on working-age adults: short-term health gain, health equity and cost savings

OBJECTIVE

The health gains and cost savings from tobacco tax increase peak many decades into the future. Policy-makers may take a shorter-term perspective and be particularly interested in the health of working-age adults (given their role in economic productivity). Therefore, we estimated the impact of tobacco taxes in this population within a 10-year horizon.

METHODS

As per previous modelling work, we used a multistate life table model with 16 tobacco-related diseases in parallel, parameterised with rich national data by sex, age and ethnicity. The intervention modelled was 10% annual increases in tobacco tax from 2011 to 2020 in the New Zealand population (n=4.4 million in 2011). The perspective was that of the health system, and the discount rate used was 3%.

RESULTS

For this 10-year time horizon, the total health gain from the tobacco tax in discounted quality-adjusted life years (QALYs) in the 20-65 year age group (age at QALY accrual) was 180 QALYs or 1.6% of the lifetime QALYs gained in this age group (11 300 QALYs). Nevertheless, for this short time horizon: (1) cost savings in this group amounted to NZ$10.6 million (equivalent to US$7.1 million; 95% uncertainty interval: NZ$6.0 million to NZ$17.7 million); and (2) around two-thirds of the QALY gains for all ages occurred in the 20-65 year age group. Focusing on just the preretirement and postretirement ages, the QALY gains in each of the 60-64 and 65-69 year olds were 11.5% and 10.6%, respectively, of the 268 total QALYs gained for all age groups in 2011-2020.

CONCLUSIONS

The majority of the health benefit over a 10-year horizon from increasing tobacco taxes is accrued in the working-age population (20-65 years). There remains a need for more work on the associated productivity benefits of such health gains.

Smoking Cessation: A Comparison of Two Model Structures

BACKGROUND

Most economic evaluations of smoking cessation interventions have used cohort state-transition models. Discrete event simulations (DESs) have been proposed as a superior approach.

OBJECTIVE

We developed a state-transition model and a DES using the discretely integrated condition event (DICE) framework and compared the cost-effectiveness results. We performed scenario analysis using the DES to explore the impact of alternative assumptions.

METHODS

The models estimated the costs and quality-adjusted life years (QALYs) for the intervention and comparator from the perspective of the UK National Health Service and Personal Social Services over a lifetime horizon. The models considered five comorbidities: chronic obstructive pulmonary disease, myocardial infarction, coronary heart disease, stroke and lung cancer. The state-transition model used prevalence data, and the DES used incidence. The costs and utility inputs were the same between two models and consistent with those used in previous analyses for the National Institute for Health and Care Excellence.

RESULTS

In the state-transition model, the intervention produced an additional 0.16 QALYs at a cost of £540, leading to an incremental cost-effectiveness ratio (ICER) of £3438. The comparable DES scenario produced an ICER of £5577. The ICER for the DES increased to £18,354 when long-term relapse was included.

CONCLUSIONS

The model structures themselves did not influence smoking cessation cost-effectiveness results, but long-term assumptions did. When there is variation in long-term predictions between interventions, economic models need a structure that can reflect this.

Modeling the impact of changes in tobacco use on individual disease risks

Reduced Risk Products (RRPs) do not burn tobacco and produce lower levels of toxicants than in cigarette smoke. The long-term effects of using RRPs on health are difficult to assess in a pre-market setting and a modeling approach is required to quantify harm reduction. The Population Health Impact Model (Weitkunat et al., 2015) follows a hypothetical population of individuals over time, creating their tobacco use histories and, based on these, estimating relative and absolute risks of lung cancer, ischemic heart disease, stroke and chronic obstructive pulmonary disease. Linking the tobacco use to the risk profile allow us to assess how the relative and absolute risks of these diseases vary between individuals aged 20, 30, 40 or 50 at baseline who have never smoked or who initiated smoking at 19 years old and either continued to smoke, quit smoking, or switched to RRPs with varying degrees of harm reduction. The simulations suggest that, for smokers in their 20s-30s quitting, or switching to RRP primarily prevents the accrual of risk, while in their 40s-50s it reduces risk. Though tobacco prevention has been the primary approach to limit smoking-related diseases, RRPs can also substantially reduce risks in individuals who do not quit.

Modelling the implications of reducing smoking prevalence: the public health and economic benefits of achieving a 'tobacco-free' UK

INTRODUCTION

Smoking is still the most preventable cause of cancer, and a leading cause of premature mortality and health inequalities in the UK. This study modelled the health and economic impacts of achieving a 'tobacco-free' ambition (TFA) where, by 2035, less than 5% of the population smoke tobacco across all socioeconomic groups.

METHODS

A non-linear multivariate regression model was fitted to cross-sectional smoking data to create projections to 2035. These projections were used to predict the future incidence and costs of 17 smoking-related diseases using a microsimulation approach. The health and economic impacts of achieving a TFA were evaluated against a predicted baseline scenario, where current smoking trends continue.

RESULTS

If trends continue, the prevalence of smoking in the UK was projected to be 10% by 2035-well above a TFA. If this ambition were achieved by 2035, it could mean 97 300 +/- 5 300 new cases of smoking-related diseases are avoided by 2035 (tobacco-related cancers: 35 900+/- 4 100; chronic obstructive pulmonary disease: 29 000 +/- 2 700; stroke: 24 900 +/- 2 700; coronary heart disease: 7600 +/- 2 700), including around 12 350 diseases avoided in 2035 alone. The consequence of this health improvement is predicted to avoid £67 +/- 8 million in direct National Health Service and social care costs, and £548 million in non-health costs, in 2035 alone.

CONCLUSION

These findings strengthen the case to set bold targets on long-term declines in smoking prevalence to achieve a tobacco 'endgame'. Results demonstrate the health and economic benefits that meeting a TFA can achieve over just 20 years. Effective ambitions and policy interventions are needed to reduce the disease and economic burden of smoking.

Tobacco retail outlet restrictions: health and cost impacts from multistate life-table modelling in a national population

BACKGROUND

Since there is some evidence that the density and distribution of tobacco retail outlets may influence smoking behaviours, we aimed to estimate the impacts of 4 tobacco outlet reduction interventions in a country with a smoke-free goal: New Zealand (NZ).

METHODS

A multistate life-table model of 16 tobacco-related diseases, using national data by sex, age and ethnicity, was used to estimate quality-adjusted life years (QALYs) gained and net costs over the remainder of the 2011 NZ population's lifetime. The outlet reduction interventions assumed that increased travel costs can be operationalised as equivalent to price increases in tobacco.

RESULTS

All 4 modelled interventions led to reductions of >89% of current tobacco outlets after the 10-year phase-in process. The most effective intervention limited sales to half of liquor stores (and nowhere else) at 129 000 QALYs gained over the lifetime of the population (95% UI: 74 100 to 212 000, undiscounted). The per capita QALY gains were up to 5 times greater for Māori (indigenous population) compared to non-Māori. All interventions were cost-saving to the health system, with the largest saving for the liquor store only intervention: US$1.23 billion (95% UI: $0.70 to $2.00 billion, undiscounted).

CONCLUSIONS

These tobacco outlet reductions reduced smoking prevalence, achieved health gains and saved health system costs. Effects would be larger if outlet reductions have additional spill-over effects (eg, smoking denormalisation). While these interventions were not as effective as tobacco tax increases (using the same model), these and other strategies could be combined to maximise health gain and to maximise cost-savings to the health system.

Effectiveness of a hospital-initiated smoking cessation programme: 2-year health and healthcare outcomes

Background

Tobacco-related illnesses are leading causes of death and healthcare use. Our objective was to determine whether implementation of a hospital-initiated smoking cessation intervention would reduce mortality and downstream healthcare usage.

Methods

A 2-group effectiveness study was completed comparing patients who received the ‘Ottawa Model’ for Smoking Cessation intervention (n=726) to usual care controls (n=641). Participants were current smokers, >17 years old, and recruited during admission to 1 of 14 participating hospitals in Ontario, Canada. Baseline data were linked to healthcare administrative data. Competing-risks regression analysis was used to compare outcomes between groups.

Results

The intervention group experienced significantly lower rates of all-cause readmissions, smoking-related readmissions, and all-cause emergency department (ED) visits at all time points. The largest absolute risk reductions (ARR) were observed for all-cause readmissions at 30 days (13.3% vs 7.1%; ARR, 6.1% (2.9% to 9.3%); p<0.001), 1 year (38.4% vs 26.7%; ARR, 11.7% (6.7% to 16.6%); p<0.001), and 2 years (45.2% vs 33.6%; ARR, 11.6% (6.5% to 16.8%); p<0.001). The greatest reduction in risk of all-cause ED visits was at 30 days (20.9% vs 16.4%; ARR, 4.5% (0.4% to 8.7%); p=0.03). Reduction in mortality was not evident at 30 days, but significant reductions were observed by year 1 (11.4% vs 5.4%; ARR 6.0% (3.1% to 9.0%); p<0.001) and year 2 (15.1% vs 7.9%; ARR, 7.3% (3.9% to 10.7%); p<0.001).

Conclusions

Considering the relatively low cost, greater adoption of hospital-initiated tobacco cessation interventions should be considered to improve patient outcomes and decrease subsequent healthcare usage.

Choosing an epidemiological model structure for the economic evaluation of non-communicable disease public health interventions

Non-communicable diseases are the leading global causes of mortality and morbidity. Growing pressures on health services and on social care have led to increasing calls for a greater emphasis to be placed on prevention. In order for decisionmakers to make informed judgements about how to best spend finite public health resources, they must be able to quantify the anticipated costs, benefits, and opportunity costs of each prevention option available. This review presents a taxonomy of epidemiological model structures and applies it to the economic evaluation of public health interventions for non-communicable diseases. Through a novel discussion of the pros and cons of model structures and examples of their application to public health interventions, it suggests that individual-level models may be better than population-level models for estimating the effects of population heterogeneity. Furthermore, model structures allowing for interactions between populations, their environment, and time are often better suited to complex multifaceted interventions. Other influences on the choice of model structure include time and available resources, and the availability and relevance of previously developed models. This review will help guide modelers in the emerging field of public health economic modeling of non-communicable diseases.

Cost-Effectiveness Analysis of Smoking Cessation Interventions in Japan Using a Discrete-Event Simulation

BACKGROUND

Smoking cessation medications have been shown to yield higher success rates and sustained abstinence than unassisted quit attempts. In Japan, the treatments available include nicotine replacement therapy (NRT) and varenicline; however, unassisted attempts to quit smoking remain common.

OBJECTIVE

The objective of this study was to compare the health and economic consequences in Japan of using pharmacotherapy to support smoking cessation with unassisted attempts and the current mix of strategies used.

METHODS

A discrete-event simulation that models lifetime quitting behaviour and includes multiple quit attempts (MQAs) and relapses was adapted for these analyses. The risk of developing smoking-related diseases is estimated based on the duration of abstinence. Data collected from a survey conducted in Japan were used to determine the interventions selected by smokers initiating a quit attempt and the time between MQAs. Direct and indirect costs are assessed (expressed in 2014 Japanese Yen).

RESULTS

Using pharmacotherapy (NRT or varenicline) to support quit attempts proved to be dominant when compared with unassisted attempts or the current mix of strategies (most are unassisted). The results of stratified analyses by age imply that smoking cessation improves health outcomes across all generations. Indirect costs due to premature death leading to lost wages are an important component of the total costs, exceeding the direct medical cost estimates.

CONCLUSIONS

Increased utilisation of smoking cessation pharmacotherapy to support quit attempts is predicted to lead to an increase in the number of smokers achieving abstinence, and provide improvements in health outcomes over a lifetime with no additional costs.

Health, Health Inequality, and Cost Impacts of Annual Increases in Tobacco Tax: Multistate Life Table Modeling in New Zealand

BACKGROUND

Countries are increasingly considering how to reduce or even end tobacco consumption, and raising tobacco taxes is a potential strategy to achieve these goals. We estimated the impacts on health, health inequalities, and health system costs of ongoing tobacco tax increases (10% annually from 2011 to 2031, compared to no tax increases from 2011 ["business as usual," BAU]), in a country (New Zealand) with large ethnic inequalities in smoking-related and noncommunicable disease (NCD) burden.

METHODS AND FINDINGS

We modeled 16 tobacco-related diseases in parallel, using rich national data by sex, age, and ethnicity, to estimate undiscounted quality-adjusted life-years (QALYs) gained and net health system costs over the remaining life of the 2011 population (n = 4.4 million). A total of 260,000 (95% uncertainty interval [UI]: 155,000-419,000) QALYs were gained among the 2011 cohort exposed to annual tobacco tax increases, compared to BAU, and cost savings were US$2,550 million (95% UI: US$1,480 to US$4,000). QALY gains and cost savings took 50 y to peak, owing to such factors as the price sensitivity of youth and young adult smokers. The QALY gains per capita were 3.7 times greater for Māori (indigenous population) compared to non-Māori because of higher background smoking prevalence and price sensitivity in Māori. Health inequalities measured by differences in 45+ y-old standardized mortality rates between Māori and non-Māori were projected to be 2.31% (95% UI: 1.49% to 3.41%) less in 2041 with ongoing tax rises, compared to BAU. Percentage reductions in inequalities in 2041 were maximal for 45-64-y-old women (3.01%). As with all such modeling, there were limitations pertaining to the model structure and input parameters.

CONCLUSIONS

Ongoing tobacco tax increases deliver sizeable health gains and health sector cost savings and are likely to reduce health inequalities. However, if policy makers are to achieve more rapid reductions in the NCD burden and health inequalities, they will also need to complement tobacco tax increases with additional tobacco control interventions focused on cessation.

Reduction of risk of dying from tobacco-related diseases after quitting smoking in Italy

AIMS AND BACKGROUND

The aims of this paper are to compute the risks of dying of ischemic heart disease (IHD), lung cancer (LC), stroke, and chronic obstructive pulmonary disease (COPD) for Italian smokers by gender, age and daily number of cigarettes smoked, and to estimate the benefit of stopping smoking in terms of risk reduction.

METHODS

Life tables by sex and smoking status were computed for each smoking-related disease based on Italian smoking data, and risk charts with 10-year probabilities of death were computed for never, current and former smokers.

RESULTS

Men aged 45-49 years, current smokers, have a 8, 10, 3 and 1 in 1,000 chance of dying of IHD, LC, stroke and COPD, respectively, whereas women with the same characteristics have a 2, 6, 3 and 1 in 1,000 chance, respectively, for all smokers combined, i.e., independent of the smoking intensity. The risk reduction rates from quitting smoking are remarkable: a man who quits smoking at 45-49 years can reduce the risk of dying of IHD, LC, stroke and COPD in the next 10 years by 43%, 53%, 57% and 55%, respectively; a woman by 49%, 49%, 59% and 57%, respectively.

CONCLUSIONS

Estimates of risk reduction by quitting smoking are useful to provide a sounder scientific basis for public health messages and clinical advice.

Modeling the potential effects of new tobacco products and policies: a dynamic population model for multiple product use and harm

BACKGROUND

Recent declines in US cigarette smoking prevalence have coincided with increases in use of other tobacco products. Multiple product tobacco models can help assess the population health impacts associated with use of a wide range of tobacco products.

METHODS AND FINDINGS

We present a multi-state, dynamical systems population structure model that can be used to assess the effects of tobacco product use behaviors on population health. The model incorporates transition behaviors, such as initiation, cessation, switching, and dual use, related to the use of multiple products. The model tracks product use prevalence and mortality attributable to tobacco use for the overall population and by sex and age group. The model can also be used to estimate differences in these outcomes between scenarios by varying input parameter values. We demonstrate model capabilities by projecting future cigarette smoking prevalence and smoking-attributable mortality and then simulating the effects of introduction of a hypothetical new lower-risk tobacco product under a variety of assumptions about product use. Sensitivity analyses were conducted to examine the range of population impacts that could occur due to differences in input values for product use and risk. We demonstrate that potential benefits from cigarette smokers switching to the lower-risk product can be offset over time through increased initiation of this product. Model results show that population health benefits are particularly sensitive to product risks and initiation, switching, and dual use behaviors.

CONCLUSION

Our model incorporates the variety of tobacco use behaviors and risks that occur with multiple products. As such, it can evaluate the population health impacts associated with the introduction of new tobacco products or policies that may result in product switching or dual use. Further model development will include refinement of data inputs for non-cigarette tobacco products and inclusion of health outcomes such as morbidity and disability.

Quality of Life and Time to Death

This article explores the implications of the relation between quality of life (QoL) and time to death (TTD) for economic evaluations of preventive interventions. By using health survey data on QoL for the general Dutch population linked to the mortality registry, we quantify the magnitude of this relationship. For addressing specific features of the nonstandard QoL distribution such as boundness, skewness, and heteroscedasticity, we modeled QoL using a generalized additive model for location, scale, and shape (GAMLSS) with a β inflated outcome distribution. Our empirical results indicate that QoL decreases when approaching death, suggesting that there is a strong relationship between TTD and QoL. Predictions of different regression models revealed that ignoring this relationship results in an underestimation of the quality-adjusted life year (QALY) gains for preventive interventions. The underestimation ranged between 3% and 7% and depended on age, the number of years gained from the intervention, and the discount rate used.

Design of the INTEGRATE study: effectiveness and cost-effectiveness of a cardiometabolic risk assessment and treatment program integrated in primary care

Background

The increasing prevalence of cardiometabolic disease (CMD) in combination with an ageing population is a major public health problem. Early detection and management of individuals at risk for CMD is required to prevent future health problems with associated costs. General practice is the optimal health care setting to accomplish this goal. Prevention programs for identification and treatment of patients with an increased risk for CMD in primary care have been proven feasible. However, the effectiveness and cost-effectiveness have yet to be demonstrated. The ‘Personalized Prevention Approach for CardioMetabolic Risk’ (PPA CMR) is such a prevention program. The objective of the INTEGRATE study is to investigate the effectiveness and cost-effectiveness of PPA CMR, as well as to establish determinants for participation and compliance.

Methods

The INTEGRATE study is designed as a stepped-wedge randomized controlled trial with a waiting list control group. In approximately 40 general practices, all enlisted patients without CMD aged 45–70 years, are invited to participate in PPA CMR. After an online risk estimation, patients with a score above risk threshold are invited to the GP for additional measurements, detailed risk profiling and tailored treatment of risk factors through medication and/or lifestyle counseling. At baseline and after twelve months of follow-up lifestyle, health and work status of all participants are established with online questionnaires. Additionally after twelve months, we will determine health care utilization, costs of PPA CMR and compliance. Primary endpoints are the number of newly detected patients with CMD and changes in individual risk factors between the intervention and waiting list control group. Medical data will be extracted from the GPs’ electronic medical records. In order to assess factors related to participation, we will send questionnaires to non-participants and assess characteristics of participating practices. For all participants, additional demographic characteristics will be available through Statistics Netherlands.

Discussion

The INTEGRATE study will provide insight into the effectiveness and cost-effectiveness of PPA CMR as well as determinants for participation and compliance, which represents essential information to guide further large-scale implementation of primary prevention programs for CMD.

Trial registration number

NTR4277, The Netherlands National Trial Register, 26-11-2013.

Life gain in Italian smokers who quit

This study aims to estimate the number of life years gained with quitting smoking in Italian smokers of both sexes, by number of cigarettes smoked per day (cig/day) and age at cessation. All-cause mortality tables by age, sex and smoking status were computed, based on Italian smoking data, and the survival curves of former and current smokers were compared. The more cig/day a man/woman smokes, and the younger his/her age of quitting smoking, the more years of life he/she gains with cessation. In fact, cessation at age 30, 40, 50, or 60 years gained, respectively, about 7, 7, 6, or 5, and 5, 5, 4, or 3 years of life, respectively, for men and women that smoked 10–19 cig/day. The gain in life years was higher for heavy smokers (9 years for >20 cig/day) and lower for light smokers (4 years for 1–9 cig/day). Consistently with prospective studies conducted worldwide, quitting smoking increases life expectancy regardless of age, gender and number of cig/day. The estimates of the number of years of life that could be gained by quitting smoking, when computed specifically for a single smoker, could be used by physicians and health professionals to promote a quit attempt.

Smoking cessation treatment and outcomes patterns simulation: a new framework for evaluating the potential health and economic impact of smoking cessation interventions

BACKGROUND

Most existing models of smoking cessation treatments have considered a single quit attempt when modelling long-term outcomes.

OBJECTIVE

To develop a model to simulate smokers over their lifetimes accounting for multiple quit attempts and relapses which will allow for prediction of the long-term health and economic impact of smoking cessation strategies.

METHODS

A discrete event simulation (DES) that models individuals' life course of smoking behaviours, attempts to quit, and the cumulative impact on health and economic outcomes was developed. Each individual is assigned one of the available strategies used to support each quit attempt; the outcome of each attempt, time to relapses if abstinence is achieved, and time between quit attempts is tracked. Based on each individual's smoking or abstinence patterns, the risk of developing diseases associated with smoking (chronic obstructive pulmonary disease, lung cancer, myocardial infarction and stroke) is determined and the corresponding costs, changes to mortality, and quality of life assigned. Direct costs are assessed from the perspective of a comprehensive US healthcare payer ($US, 2012 values). Quit attempt strategies that can be evaluated in the current simulation include unassisted quit attempts, brief counselling, behavioural modification therapy, nicotine replacement therapy, bupropion, and varenicline, with the selection of strategies and time between quit attempts based on equations derived from survey data. Equations predicting the success of quit attempts as well as the short-term probability of relapse were derived from five varenicline clinical trials.

RESULTS

Concordance between the five trials and predictions from the simulation on abstinence at 12 months was high, indicating that the equations predicting success and relapse in the first year following a quit attempt were reliable. Predictions allowing for only a single quit attempt versus unrestricted attempts demonstrate important differences, with the single quit attempt simulation predicting 19 % more smoking-related diseases and 10 % higher costs associated with smoking-related diseases. Differences are most prominent in predictions of the time that individuals abstain from smoking: 13.2 years on average over a lifetime allowing for multiple quit attempts, versus only 1.2 years with single quit attempts. Differences in abstinence time estimates become substantial only 5 years into the simulation. In the multiple quit attempt simulations, younger individuals survived longer, yet had lower lifetime smoking-related disease and total costs, while the opposite was true for those with high levels of nicotine dependence.

CONCLUSION

By allowing for multiple quit attempts over the course of individuals' lives, the simulation can provide more reliable estimates on the health and economic impact of interventions designed to increase abstinence from smoking. Furthermore, the individual nature of the simulation allows for evaluation of outcomes in populations with different baseline profiles. DES provides a framework for comprehensive and appropriate predictions when applied to smoking cessation over smoker lifetimes.

A dynamic population model for estimating all-cause mortality due to lifetime exposure history

We developed a comprehensive, flexible dynamic model that estimates all-cause mortality for a hypothetical cohort. All model input is user-specified. In the base case, members of the cohort may be exposed to a high risk product as they age. The counterfactual scenario includes exposure to both a high risk and a lower risk product. The model sorts the population into age and exposure categories, and applies the appropriate mortality rates to each category. The model tracks individual exposure histories, and estimates, at the end of each modeled age category, the number of survivors in the two exposure scenarios (base case and counterfactual), and the difference between them. Markov Chain Monte Carlo techniques are used to estimate the variability of the results. Model output was compared against US and Swedish life tables using population-specific tobacco exposure transition probabilities derived from the literature, and it produced similar survival estimates.

Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study

Background

Counseling in combination with pedometer use has proven to be effective in increasing physical activity and improving health outcomes. We investigated the cost-effectiveness of this intervention targeted at one million insufficiently active adults who visit their general practitioner in the Netherlands.

Methods

We used the RIVM chronic disease model to estimate the long-term effects of increased physical activity on the future health care costs and quality adjusted life years (QALY) gained, from a health care perspective.

Results

The intervention resulted in almost 6000 people shifting to more favorable physical-activity levels, and in 5100 life years and 6100 QALYs gained, at an additional total cost of EUR 67.6 million. The incremental cost-effectiveness ratio (ICER) was EUR 13,200 per life year gained and EUR 11,100 per QALY gained. The intervention has a probability of 0.66 to be cost-effective if a QALY gained is valued at the Dutch informal threshold for cost-effectiveness of preventive intervention of EUR 20,000. A sensitivity analysis showed substantial uncertainty of ICER values.

Conclusion

Counseling in combination with pedometer use aiming to increase physical activity may be a cost-effective intervention. However, the intervention only yields relatively small health benefits in the Netherlands.

Uncertainty Analysis in Population-Based Disease Microsimulation Models

Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.

Estimating the probabilities of making a smoking quit attempt in Italy: stall in smoking cessation levels, 1986-2009

Background

No data on annual smoking cessation probability (i.e., the probability of successfully quit in a given year) are available for Italy at a population level. Mathematical models typically used to estimate smoking cessation probabilities do not account for smoking relapse. In this paper, we developed a mathematical model to estimate annual quitting probabilities, taking into account smoking relapse and time since cessation.

Methods

We developed a dynamic model describing the evolution of current, former, and never smokers. We estimated probabilities of smoking cessation by fitting the model with observed smoking prevalence in Italy, 1986-2009.

Results

Annual cessation probabilities were higher than 5% only in elderly persons and in women aged < 30 years, while in adults aged 30-49 and 50-59 cessations were about 2% and 3-5%, respectively. Most of quit probabilities stalled from 1986 to 2009.

Conclusions

Over the last 20 years, cessation probabilities among Italian smokers, particularly for those aged 30-59 years, have been very low and stalled. Quitting in Italy is considered as a practicable strategy only by women in the age of pregnancy and by elderly persons, when it’s likely that symptoms of tobacco-related diseases have already appeared. In order to increase cessation probabilities, smoking cessation treatment policies (introducing total reimbursement of cessation treatments, with a further development of quitlines and smoking cessation services) should be empowered and a country-wide mass media campaign targeting smokers aged 30-59 years and focusing on promotion of quitting should be implemented.

The effect of tobacco control policies on smoking prevalence and smoking-attributable deaths. Findings from the Netherlands SimSmoke Tobacco Control Policy Simulation Model

AIM

To develop a simulation model projecting the effect of tobacco control policies in the Netherlands on smoking prevalence and smoking-attributable deaths.

DESIGN, SETTING AND PARTICIPANTS

Netherlands SimSmoke-an adapted version of the SimSmoke simulation model of tobacco control policy-uses population, smoking rates and tobacco control policy data for the Netherlands to predict the effect of seven types of policies: taxes, smoke-free legislation, mass media, advertising bans, health warnings, cessation treatment and youth access policies.

MEASUREMENTS

Outcome measures were smoking prevalence and smoking-attributable deaths.

FINDINGS

With a comprehensive set of policies, as recommended by MPOWER, smoking prevalence can be decreased by as much as 21% in the first year, increasing to a 35% reduction in the next 20 years and almost 40% by 30 years. By 2040, 7706 deaths can be averted in that year alone with the stronger set of policies. Without effective tobacco control policies, almost a million lives will be lost to tobacco-related diseases between 2011 and 2040. Of those, 145,000 can be saved with a comprehensive tobacco control package.

CONCLUSIONS

Smoking prevalence and smoking-attributable deaths in the Netherlands can be reduced substantially through tax increases, smoke-free legislation, high-intensity media campaigns, stronger advertising bans and health warnings, comprehensive cessation treatment and youth access laws. The implementation of these FCTC/MPOWER recommended policies could be expected to show similar or even larger relative reductions in smoking prevalence in other countries which currently have weak policies.

Estimating net transition probabilities from cross-sectional data with application to risk factors in chronic disease modeling

A problem occurring in chronic disease modeling is the estimation of transition probabilities of moving from one state of a categorical risk factor to another. Transitions could be obtained from a cohort study, but often such data may not be available. However, under the assumption that transitions remain stable over time, age specific cross-sectional prevalence data could be used instead. Problems that then arise are parameter identifiability and the fact that age dependent cross-sectional data are often noisy or are given in age intervals. In this paper we propose a method to estimate so-called net annual transition probabilities from cross-sectional data, including their uncertainties. Net transitions only describe the net inflow or outflow into a certain risk factor state at a certain age. Our approach consists of two steps: first, smooth the data using multinomial P-splines, second, from these data estimate net transition probabilities. This second step can be formulated as a transportation problem, which is solved using the simplex algorithm from linear programming theory. A sensible specification of the cost matrix is crucial to get meaningful results. Uncertainties are assessed by parametric bootstrapping. We illustrate our method using data on body mass index. We conclude that this method provides a flexible way of estimating net transitions and that the use of net transitions has implications for model dynamics, for example when modeling interventions.

Developing and applying a stochastic dynamic population model for chronic obstructive pulmonary disease

OBJECTIVES

To develop a stochastic population model of disease progression in chronic obstructive pulmonary disease (COPD) that includes the effects of COPD exacerbations on health-related quality of life, costs, disease progression, and mortality and can be used to assess the effects of a wide range of interventions.

METHODS

The model is a multistate Markov model with time varying transition rates specified by age, sex, smoking status, COPD disease severity, and/or exacerbation type. The model simulates annual changes in COPD prevalence due to COPD incidence, exacerbations, disease progression (annual decline in the forced expiratory volume in 1 second as percentage of the predicted value), and mortality. The main outcome variables are quality-adjusted life years, total exacerbations, and COPD-related health care costs. Exacerbation-related input parameters were based on quantitative meta-analysis. All important model parameters are entered into the model as probability distributions. To illustrate the potential use of the model, costs and effects were calculated for 3-year implementation of three different COPD interventions, one pharmacologic, one on smoking cessation, and one on pulmonary rehabilitation using a time horizon of 10 years for reporting outcomes.

RESULTS

Compared with minimal treatment the cost/quality-adjusted life year was €8,300 for the pharmacologic intervention, €10,800 for the smoking cessation therapy, €8,700 for the combination of the pharmacologic intervention and the smoking cessation therapy, and €17,200 for the pulmonary rehabilitation program. The probability of the interventions to be cost-effective at a ceiling ratio of €20,000 varied from 58% to 100%.

CONCLUSIONS

The COPD model provides policy makers with information about the long-term costs and effects of interventions over the entire chain of care, from primary prevention to care for very severe COPD and includes uncertainty around the outcomes.

Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

Current prediction models for risk of cardiovascular disease (CVD) incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting.

The risk of developing a first CVD event was evaluated using a Cox’s model for participants in the Framingham offspring cohort who attended the fourth examination (1988–92) between the ages of 30 and 74 years and were free of CVD (n=3751). The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and pack-years among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.

Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention

BACKGROUND

Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients.

METHODS

A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed.

RESULTS

Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients.

CONCLUSIONS

Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.

A discrete-event simulation of smoking-cessation strategies based on varenicline pivotal trial data

BACKGROUND

Smoking is the leading cause of preventable death in the US. While one in five individuals smoke, and 70% of these indicate a desire to quit, <5% of unaided quit attempts succeed. Cessation aids can double or triple the odds of successfully quitting. Models of smoking-cessation behaviour can elucidate the implications of individual abstinence patterns to allow better tailoring of quit attempts to an individual's characteristics.

OBJECTIVE

The objectives of this study were to develop and validate a discrete-event simulation (DES) to evaluate the benefits of smoking abstinence using data from the pooled pivotal clinical trials of varenicline versus bupropion or placebo for smoking cessation and to provide a foundation for the development of a lifetime smoking-cessation model.

METHODS

The DES model simulated the outcome of a single smoking-cessation attempt over 1 year, in accordance with the clinical trial timeframes. Pharmaceutical costs were assessed from the perspective of a healthcare payer. The model randomly sampled patient profiles from the pooled varenicline clinical trials. All patients were physically and mentally healthy adult smokers who were motivated to quit abruptly. The model allowed for comparisons of up to five distinct treatment approaches for smoking cessation. In the current analyses, three interventions corresponding to the clinical trials were evaluated, which included brief counselling plus varenicline 1.0 mg twice daily (bid) or bupropion SR 150 mg bid versus placebo (i.e. brief counselling only). The treatment periods in the clinical trials were 12 weeks (target quit date: day 8), with a 40-week non-treatment follow-up, and counselling continuing over the entire 52-week period in all treatment groups. The main outcome modelled was the continuous abstinence rate (CAR; defined as complete abstinence from smoking and confirmed by exhaled carbon monoxide ≤ 10 ppm) at end of treatment (weeks 9-12) and long-term follow-up (weeks 9-52), and total time abstinent from smoking over the course of 52 weeks. The model also evaluated costs and cost-effectiveness outcomes.

RESULTS

For the varenicline, bupropion and placebo cohorts, respectively, the model predicted CARs for weeks 9-12 of 44.3%, 30.4% and 18.6% compared with observed rates of 44.0%, 29.7% and 17.7%; over weeks 9-52, predicted CARs in the model compared with observed rates in the pooled clinical studies were 22.9%, 16.4% and 9.4% versus 22.4%, 15.4% and 9.3%, respectively. Total mean abstinence times accrued in the model varenicline, bupropion and placebo groups, respectively, were 3.6, 2.6 and 1.5 months and total pharmaceutical treatment costs were $US261, $US442 and $US0 (year 2008 values) over the 1-year model period. Using cost per abstinent-month achieved as a measure of cost effectiveness, varenicline dominated bupropion and yielded an incremental cost-effectiveness ratio of $US124 compared with placebo.

CONCLUSION

The model accurately replicated abstinence patterns observed in the clinical trial data using individualized predictions and indicated that varenicline was more effective and may be less costly than bupropion. This simulation incorporated individual predictions of abstinence and relapse, and provides a framework for lifetime modelling that considers multiple quit attempts over time in diverse patient populations using a variety of quit attempt strategies.

Economic evaluation and the postponement of health care costs

The inclusion of medical costs in life years gained in economic evaluations of health care technologies has long been controversial. Arguments in favour of the inclusion of such costs are gaining support, which shifts the question from whether to how to include these costs. This paper elaborates on the issue how to include cost in life years gained in cost effectiveness analysis given the current practice of economic evaluations in which costs of related diseases are included. We combine insights from the theoretical literature on the inclusion of unrelated medical costs in life years gained with insights from the so-called 'red herring' literature. It is argued that for most interventions it would be incorrect to simply add all medical costs in life years gained to an ICER, even when these are corrected for postponement of the expensive last year of life. This is the case since some of the postponement mechanism is already captured in the unadjusted ICER by modelling the costs of related diseases. Using the example of smoking cessation, we illustrate the differences and similarities between different approaches. The paper concludes with a discussion about the proper way to account for medical costs in life years gained in economic evaluations.

If you try to stop smoking, should we pay for it? The cost-utility of reimbursing smoking cessation support in the Netherlands

BACKGROUND

Smoking cessation can be encouraged by reimbursing the costs of smoking cessation support (SCS). The short-term efficiency of reimbursement has been evaluated previously. However, a thorough estimate of the long-term cost-utility is lacking.

OBJECTIVES

To evaluate long-term effects of reimbursement of SCS.

METHODS

Results from a randomized controlled trial were extrapolated to long-term outcomes in terms of health care costs and (quality adjusted) life years (QALY) gained, using the Chronic Disease Model. Our first scenario was no reimbursement. In a second scenario, the short-term cessation rates from the trial were extrapolated directly. Sensitivity analyses were based on the trial's confidence intervals. In the third scenario the additional use of SCS as found in the trial was combined with cessation rates from international meta-analyses.

RESULTS

Intervention costs per QALY gained compared to the reference scenario were approximately euro1200 extrapolating the trial effects directly, and euro4200 when combining the trial's use of SCS with the cessation rates from the literature. Taking all health care effects into account, even costs in life years gained, resulted in an estimated incremental cost-utility of euro4500 and euro7400, respectively. In both scenarios costs per QALY remained below euro16 000 in sensitivity analyses using a life-time horizon.

CONCLUSIONS

Extrapolating the higher use of SCS due to reimbursement led to more successful quitters and a gain in life years and QALYs. Accounting for overheads, administration costs and the costs of SCS, these health gains could be obtained at relatively low cost, even when including costs in life years gained. Hence, reimbursement of SCS seems to be cost-effective from a health care perspective.

Standard tool for quantification in health impact assessment a review

BACKGROUND

The health impact assessment (HIA) of policy proposals is becoming common practice. HIA represents a broad approach with quantification of the impact of policy options at its core. However, no standard tool is available and it remains unclear whether any current model can serve as a standard for the field.

PURPOSE

The aim of this study is to assess whether already existing models can be used as a standard tool for the quantification step in an HIA.

METHODS

A search in 2008 identified 20 models for HIA, of which six are sufficiently generic to allow for various and multiple diseases and different risk factors: Age-Related Morbidity and Death Analysis, Global Burden of Disease, Population Health Modeling, PREVENT, Proportional Life Table Method, and the National Institute for Public Health and the Environment (the Netherlands) Chronic Disease Model. These were evaluated along three proposed model structure criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) developed to address the needs and requirements of the HIA framework.

RESULTS

Of the six generic models investigated, none fulfills all the proposed criteria as a standard HIA tool. The models are either technically advanced with no or limited accessibility, or they are accessible but oversimplified.

CONCLUSIONS

Further work on models for HIA with equal emphasis on technical appropriateness, availability of data, and end-user-friendly implementation is warranted if the field is to move forward.

Reduction of smoking in Dutch adolescents over the past decade and its health gains: a repeated cross-sectional study

BACKGROUND

Smoking is the main preventable lifestyle-related risk factor threatening human health. In this study, time trends in smoking behaviour between 1996 and 2005 among adolescents enrolled in secondary school were assessed.

METHODS

In 1996, 2001 and 2005, a survey was conducted in the south-eastern region of the Netherlands. All students in second and fourth year of secondary education (1996: n = 20 000; 2001: n = 27 500; 2005: n = 24 000) were asked to complete a questionnaire about their smoking behaviour. A simulation model was used to estimate lifetime health gains related to the observed trends.

RESULTS

In 1996, 2001 and 2005, the number of questionnaires analysed were 13 554 (68%), 20 767 (76%) and 17 896 (75%), respectively. The results show a decrease in 'ever smoking' as well as 'current smoking' between 1996 and 2005. Among second year high school students, current smoking prevalence decreased from 22.2% in 1996 to 8.0% in 2005 (P(trend) < 0.001). Among fourth year students, current smoking declined from 37.5% in 1996 to 22.0% in 2005 (P(trend) < 0.001). Time trends were not influenced by gender or educational level. Model projections show that if these students not take up smoking later in life, 11 500 new cases of COPD, 3400 new cases of lung cancer and 1800 new cases of myocardial infarction could be prevented for the Dutch 13-year-olds.

CONCLUSION

This study found that, in the past decade, smoking prevalence among adolescents has declined by almost 50%, potentially resulting in a considerable reduction in new cases of COPD or lung cancer.

Cost-effectiveness of an opportunistic screening programme and brief intervention for excessive alcohol use in primary care

BACKGROUND

Effective prevention of excessive alcohol use has the potential to reduce the public burden of disease considerably. We investigated the cost-effectiveness of Screening and Brief Intervention (SBI) for excessive alcohol use in primary care in the Netherlands, which is targeted at early detection and treatment of 'at-risk' drinkers.

METHODOLOGY AND RESULTS

We compared a SBI scenario (opportunistic screening and brief intervention for 'at-risk' drinkers) in general practices with the current practice scenario (no SBI) in The Netherlands. We used the RIVM Chronic Disease Model (CDM) to extrapolate from decreased alcohol consumption to effects on health care costs and Quality Adjusted Life Years (QALYs) gained. Probabilistic sensitivity analysis was employed to study the effect of uncertainty in the model parameters. In total, 56,000 QALYs were gained at an additional cost of 298,000,000 euros due to providing alcohol SBI in the target population, resulting in a cost-effectiveness ratio of 5,400 euros per QALY gained.

CONCLUSION

Prevention of excessive alcohol use by implementing SBI for excessive alcohol use in primary care settings appears to be cost-effective.

The cost-effectiveness of increasing alcohol taxes: a modelling study

BACKGROUND

Excessive alcohol use increases risks of chronic diseases such as coronary heart disease and several types of cancer, with associated losses of quality of life and life-years. Alcohol taxes can be considered as a public health instrument as they are known to be able to decrease alcohol consumption. In this paper, we estimate the cost-effectiveness of an alcohol tax increase for the entire Dutch population from a health-care perspective focusing on health benefits and health-care costs in alcohol users.

METHODS

The chronic disease model of the National Institute for Public Health and the Environment was used to extrapolate from decreased alcohol consumption due to tax increases to effects on health-care costs, life-years gained and quality-adjusted life-years gained, A Dutch scenario in which tax increases for beer are planned, and a Swedish scenario representing one of the highest alcohol taxes in Europe, were compared with current practice in the Netherlands. To estimate cost-effectiveness ratios, yearly differences in model outcomes between intervention and current practice scenarios were discounted and added over the time horizon of 100 years to find net present values for incremental life-years gained, quality-adjusted life-years gained, and health-care costs.

RESULTS

In the Swedish scenario, many more quality-adjusted life-years were gained than in the Dutch scenario, but both scenarios had almost equal incremental cost-effectiveness ratios: 5100 euros per quality-adjusted life-year and 5300 euros per quality-adjusted life-year, respectively.

CONCLUSION

Focusing on health-care costs and health consequences for drinkers, an alcohol tax increase is a cost-effective policy instrument.