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Impact of the NHS Stop Smoking Services on Smoking Prevalence in England: a Simulation Modelling Evaluation Fujian Song,‍ ‍ 1 Tim Elwell-Sutton,2 Felix Naughton3

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Impact of the NHS Stop Smoking Services on Smoking Prevalence in England: a Simulation Modelling Evaluation Fujian Song,‍ ‍ 1 Tim Elwell-Sutton,2 Felix Naughton3 Tob Control: first published as 10.1136/tobaccocontrol-2018-054879 on 5 April 2019. Downloaded from Research paper Impact of the NHS Stop Smoking Services on smoking prevalence in England: a simulation modelling evaluation Fujian Song, 1 Tim Elwell-Sutton,2 Felix Naughton3 ► Additional material is ABStract The UK is considered to be in a leading position published online only. To view Background The English National Health Service in Europe regarding the implementation of tobacco please visit the journal online 6 (http:// dx. doi. org/ 10. 1136/ NHS Stop Smoking Services (SSS), established in 2001, control measures. Smoking prevalence among tobaccocontrol- 2018- 054879). were the first such services in the world. An appropriate adults in the UK has declined from 27% in 2000 to evaluation of the SSS has national and international 16% in 2016.7 As part of a comprehensive tobacco 1 Norwich Medical School, significance. This modelling study sought to evaluate the control strategy, National Health Service (NHS) University of East Anglia, Stop Smoking Services (SSS) were launched in Norwich, UK impact of the SSS on changes in smoking prevalence in 2Public Health, Thurrock Council, England. Health Action Zones in 1999 in England, and were 8 Civic Offices, Essex, UK Methods A discrete time state-transition model rolled out to all English Health Authorities in 2001. 3 School of Health Sciences, was developed to simulate changes in smoking Smokers who are motivated to quit are referred by University of East Anglia, other health professionals or themselves to the SSS. Norwich, UK status among the adult population in England during 2001–2016. Input parameters were based on data from The recommended total contact time for each client national statistics, population representative surveys at SSS is 1.5 hours from pre-quit preparation to 4 Correspondence to 9 Dr Fujian Song, Norwich and published literature. The main outcome was the weeks after quitting. Evidence-based behavioural Medical School, University of percentage point reduction in smoking prevalence and pharmacological interventions are provided by East Anglia, Norwich NR4 7TJ, attributable to the SSS. smoking cessation specialists, or delivered by staff UK; fujian. song@ uea. ac. uk Results Smoking prevalence was reduced by 10.8 % in primary care, pharmacy or dental practice who Received 7 December 2018 in absolute terms during 2001–2016 in England, and have received smoking cessation training. Revised 31 January 2019 15.3 % of the reduction could be attributable to the SSS. Previous studies have evaluated the effects of 8 Accepted 9 February 2019 The percentage point reduction in smoking prevalence the SSS on short-term quitting and long-term 10 11 each year was on average 0.72%, and 0.11 % could be success rates. Other than a study using data 12 attributable to the SSS. The proportion of SSS supported from the SSS in one region in 2004, the impact quit attempts increased from 5.5 % in 2001, to as high of the SSS on smoking prevalence in England has as 18.9 % in 2011, and then reduced to 8.2 % in 2016. not been comprehensively quantified. As the first Quit attempts with SSS support had a higher success such services in the world, an appropriate evalua- rate than those without SSS support (15.1% vs 11.3%). tion of the SSS will provide empirical evidence for http://tobaccocontrol.bmj.com/ Smoking prevalence in England continued to decline the development of tobacco control strategies in after the SSS was much reduced from 2013 onwards. the UK and other countries. Based on official statis- Conclusions Approximately 15% of the percentage tical data and published literature, we developed point reduction in smoking prevalence during 2001– a computational simulation model to quantify the 2016 in England may be attributable to the NHS SSS, impact of the SSS on smoking prevalence between although uncertainty remains regarding the actual 2001 and 2016 in England. impact of the formal smoking cessation services. METHODS Model framework A discrete time state-transition model13 was devel- on October 2, 2021 by guest. Protected copyright. INTRODUCTION oped to simulate smoking prevalence among the ► http:// dx. doi. org/ 10.1136/ Cigarette smoking remains a leading cause of avoid- adult population (≥16 years old) in England 1 tobaccocontrol- 2018- 054586. able deaths worldwide. According to the WHO between 2001 and 2016. The population was wit Framework Convention on Tobacco Control, separated into subgroups by age, sex and smoking comprehensive tobacco control measures include status. Smoking status was classified as never cigarette taxes, smoke-free laws, public informa- smokers, current smokers and former smokers. As © Author(s) (or their tion campaigns, advertising bans, health warning the cycle length of time was 1 year in simulation employer(s)) 2019. Re-use and cessation treatment.2 Observational evidence modelling, current smokers in this study included permitted under CC BY-NC. No indicates that the implementation of key tobacco those who quit smoking less than 12 months commercial re-use. See rights control measures is associated with increased cessa- and permissions. Published ago. Former smokers were defined as those who by BMJ. tion rates and reduced smoking prevalence at the stopped smoking for at least 12 months and were population level.3 4 However, the relative impact further classified according to time since cessation. To cite: Song F, Elwell- of each of the key tobacco control measures on a The simulation was started using the adult popu- Sutton T, Naughton F. 5 Tob Control Epub ahead of population’s smoking prevalence remains unclear, lation structure of 2001. Population subgroups by print: [please include Day although different interventions, simultaneously age and gender were updated annually according Month Year]. doi:10.1136/ implemented, may interact in complex ways and to the probability of death and smoking status tobaccocontrol-2018-054879 have synergistic effect. transition. Song F, et al. Tob Control 2019;0:1–7. doi:10.1136/tobaccocontrol-2018-054879 1 Tob Control: first published as 10.1136/tobaccocontrol-2018-054879 on 5 April 2019. Downloaded from Research paper Figure 1 Diagram of possible transitions across smoking status. D0,t, D1,t and D2,t, the number of deaths among never, current and former smokers in year T; N0,t, N1,t and N2,t, the number of never, current and former smokers in year t, respectively; Ptkup, the probability of taking up smoking in never http://tobaccocontrol.bmj.com/ smokers; Pquit, the probability of quitting for current smokers; Prelp, the probability of returning to smoking among former smokers; QUIT, the number of quitters; RELP, the number of former smokers who start to smoke again; TKUP, the number of new take-ups. Detailed methods for the simulation modelling are provided To estimate the size of the population at the start of the year in online supplementary appendix 1. The transition between 2001, we subtracted half of the difference between population smoking status over time was based on (1) the probability of estimates for 2000 and 2001 from the mid-year population current smokers who quit smoking, (2) the probability of former in 2001. The prevalence rates for never, current and former smokers who start to smoke regularly again and (3) the proba- smokers were based on data from General Lifestyle Surveys in bility of never smokers who start to smoke regularly. Figure 1 the UK.7 15 Smoking prevalence was gender-specific and age-spe- shows the possible transitions across never, current and former cific. Some of the fluctuation in the reported smoking prevalence smokers between year (ie, at the beginning of the year) and year rates during 2001–2016 was probably as a result of sampling on October 2, 2021 by guest. Protected copyright. t+1. Never smokers in year t may remain as never smokers or errors. To facilitate simulation modelling, the observed age-spe- may start smoking. The number of never smokers in year t+1 cific and sex-specific smoking prevalence rates over time were equals the number of never smokers in year t minus deaths and smoothed by simple linear regression (online supplementary file new take-ups. Similarly, current smokers in year t may remain 1). as current smokers or may have stopped smoking by the begin- Relative risks for death by smoking status were based on data ning of year t+1. The number of current smokers in year t+1 summarised by Vugrin et al.16 New smoking take-ups included equals the number of current smokers in year t minus deaths 16-year-old smokers, which were exogenously added to the and quitters, plus new take-ups in year t. Finally, the number simulated population. Then, we estimated the additional number of former smokers at the beginning of year t+1 is calculated by of new take-ups among people aged 16–22 years in line with the subtracting deaths and relapsers from, and adding new quitters observed increase in current and former smokers between two to, the number of former smokers in year t. Exogenous inputs adjacent years (for details, see online supplementary file 1). The to the model included the number of people aged 16 and net model allowed for relapse, with rates declining with the number immigrants, along with their smoking status. of years quit, and it was assumed that relapse rates among those who quit for ≥12 months were not affected by the SSS. The risk Data sources and parameter estimation of smoking relapse for former smokers was estimated according The 2001 mid-year population and death rates by age and sex in to the relapse curve developed by Stapleton and West.17 The England were obtained from the Office for National Statistics.14 rate of quit attempts among current smokers was estimated 2 Song F, et al.
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