An analysis of the economic impact of smoking cessation in Europe

A range of potentially achievable smoking reductions was selected from a recent study commissioned by the UK National Institute for Health and Clinical Excellence [8]. This study reviewed the UK literature on smoking cessation interventions delivered by the National Health Service (NHS), in the workplace and by mass media, and developed a model to assess their cost effectiveness. This approach has an advantage over direct comparisons of cost effectiveness from published studies as the methods employed in the individual evaluations inevitably vary.

Instead, the review team applied a consistent methodology using data extracted from the studies in the review. This involved modelling a hypothetical cohort of 1000 smokers using the costs and cessation effects reported for each intervention together with consistently applied data on mortality by age, gender and smoking status, costs of smoking related diseases and the utilities (health related quality of life) associated with each disease. The resulting cost effectiveness ratios could thus be directly compared as differences would be due solely to differences in costs and effects rather than inconsistent evaluation methods. Table 1 shows the results for the least and most effective smoking cessation interventions included in the NICE exercise together with a mid-range intervention. Although different rates of reduction in smoking might be seen in other countries, these 3 rates (3%, 15% and 35%) were selected for the present exercise only to illustrate low, medium and high effects from smoking cessation interventions. They are of increasing intensity and increasing effectiveness in terms of reductions in smoking as compared with ‘no intervention’ in each case. All demonstrate dominance over ‘no intervention’ meaning that each is both more effective and less costly than doing nothing and hence is unambiguously more cost effective than doing nothing.

PREVENT is a Public Health model that links changes in risk factor exposure to changes in risk factor related disease specific outcomes and to changes in generic health outcomes [7]. Despite its venerable age, PREVENT is still being developed. Recently, the central algorithm that relates risk factor change to disease incidence change was modified from an age group perspective to a cohort perspective. This latest version of the PREVENT model was used to estimate reductions in incidence, mortality and health service costs of 4 smoking related diseases (lung cancer, coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD) and stroke) and health service costs.

The model was initially run for the UK where it was known that data were available and of good quality. These data, however, were not always available in the precise format required for the model. Statistical sources were used for data on population, live births, net migration, trends in smoking prevalence, total mortality, disease specific incidence, prevalence and mortality and total health service costs. Published studies were used for disease specific costs for lung cancer [9], COPD [10], CHD and stroke [11] and for relative risks for lung cancer and CHD [12], COPD [13] and stroke [14].

Data for the other European countries were collected using local investigators who were charged with identifying what data were available in that country. A common pro-forma was used by each local investigator to ensure that the same data were provided using common definitions. As this task involved considerable effort and commitment on the part of the local investigators, the quantity and quality of the data provided would inevitably be dependent at least to a degree on the time and effort that each was willing and able to devote to this task.

Where datasets were incomplete, Netherlands data were often used a basis for estimating proportions of data for other countries. The Netherlands was chosen for imputation purposes as the data supplied for that country were of higher quality than others in terms of providing what the PREVENT model requires. For example, while overall birth rate figures could have been obtained from publicly available datasets for countries that did not supply them, the birth rate input also required a breakdown according to different age groups, necessitating imputations that could only be taken from another country where these had been reported.

Additionally, if the incidence of a disease was not known but the prevalence was known, the ratio of incidence to prevalence for the Netherlands was used to estimate the unknown incidence. PREVENT requires these to be broken down by gender and age. Where only totals were provided, the age/gender breakdowns were similarly estimated based on Netherlands data. Where age but not gender breakdowns were supplied, quantities were split equally between male and female. Where only total health service cost figures were supplied, Netherlands rates were used to estimate age and gender breakdowns for total costs and individual disease costs. Netherlands cost data were imputed where total costs figures from individual countries were not provided.

The DISMOD2 model [15] was used to ensure that figures for each dataset were internally consistent. DISMOD2 is a software tool provided by the World Health Organisation that checks the internal consistency of epidemiological estimates of incidence, prevalence, duration, remission and case fatality for diseases. It requires a minimum of three input variables to be supplied. A remission rate of 0 is input to produce estimates for the datasets when fewer than three of the other variables have available data. For some countries, DISMOD2 estimated figures that were previously unknown, while for others, the figures were altered to ensure that internal consistency was valid.

The main study was undertaken prior to 2010 and based on availability of data, the base year in all cases was 2005. Results show the annual predicted reductions for the years 2010, 2020 and 2030 with reduced smoking in 2005, as compared with its predictions for those years without. Figures represent absolute values for each reported year and are not adjusted for population size. Ratios of predicted values in 2030 to those in 2010 show differences in the timing of effects between countries. All cost data were provided in Euros apart from Switzerland and the UK where conversions at 1 franc = €0.62 and £1 = €1.34were used (exchange rates on August 1, 2007). All costs are in 2005 prices.

Considerable care needs to be taken in interpreting these results which should be seen as illustrative. The completeness of data to meet the requirements of the PREVENT model varied considerably between countries with fairly heroic assumptions being required in some cases as shown in ‘Additional file 2’. Apart from the UK, datasets for all countries required some adjustment or imputation using data from another country. These varied from minimal, for example in the case of the Netherlands where all that was required was to apply age breakdowns from France to Netherlands birth rates, to severe, for example in the case of Austria, Portugal and Poland which did not provide any cost figures and where Netherlands costs were imputed. Clearly the accuracy of any prediction varies with the number and severity of adjustments and imputations required. The purpose of the study, however, was to illustrate how the effects of reduced smoking can differ between countries.

Results are reported as absolute values rather than as rates. Clearly the implications of any given reduction in the absolute number of new cases, deaths or health care costs have to be interpreted locally with regard to the size of the population. Converting values into rates, however, requires predicting population growth rates in addition to the predicting changes in the variables examined here. This would not have affected the main messages from the study.