This is the first study that produces a large number of data relating to health-related quality of life (more precisely, the 'utility' values) by smoking status (i.e. extent of smoking) in English general population. Paucity of this kind of data has left economic evaluation researchers very limited choice in modelling the cost-effectiveness of interventions which affect the recipients' smoking status (e.g. tobacco control) or are affected by it (e.g. treatment of lung cancer). There are very few studies that provide some estimates of utility values by smoking status [11, 33, 45, 46]. Our study differs from this in several ways - our sample size is much larger, we are able to provide granularity in estimates, and the estimates reflect the 'net' effect of smoking controlling for other important covariates including socioeconomics which has been considered by some authors as having more impact on HRQoL than smoking status itself . Both the values themselves and the methods with which such values are estimated in this paper are deemed more robust.
Before we discuss the implications of the estimated 'utility' values for economic evaluations, it is important to examine some methodological issues. First, the 'utility' values are based on EQ-5D mean tariff which is not the 'valuation' of individual's health state per se but a reflection of it derived from a standard formula (extrapolated from the original UK valuation exercise) applied to the EQ-5D descriptive system . This is a generic problem of EQ-5D . As long as EQ-5D descriptive system remains one of the recommended tools for use in economic evaluations , this issue is of less relevance in the context of this paper.
The second issue relates to how best one could model EQ-5D tariff. We used three different estimators and found that despite the difficulty in translating the unique features of general population tariff data under OLS assumptions, OLS predictions were not only consistent with the observed values but were also more useful than those from Tobit and CLAD in terms of measuring 'utility loss' across different smoking profiles. These predictions could be valuable inputs to estimate QALYs in economic evaluation of different interventions, including tobacco control policies. In addition, the literature suggests that Tobit and CLAD estimations, although may perform well in specific patient-group data, do differ from the OLS in general population and are biased [34, 35]. Note however that many earlier studies, mostly based on patient group data as opposed to population data as in our case, have resorted to OLS or its variants [25, 30] and therefore it is not unreasonable to present predictions based on OLS estimator. Although there have been some very recent efforts to look at alternative ways in which health utility data could be modelled [24, 36, 37, 48] which is yet to be scrutinized closely by modelling community, we emphasize that future research continues to propose and debate estimation strategies that would take into account all the unique features of EQ-5D tariff data.
The third issue is the nature of the utility values. Our results are based on the general population as opposed to patient group and therefore the utility values are that of the general population, and not that of the specific patient population. This may have implications for economic modelling based on a cohort with specific disease conditions. However, it is important to note that the methods with which these values are derived reflect the net losses in utilities due to the degree of smoking. That is, these losses have already been adjusted for one's biology, clinical conditions, lifestyles and socioeconomics (see Additional file 4 for the impact of limiting conditions). It is then up to the economic modeller to decide appropriate states in the Markov model that allows the use of such data in a specific patient group.
There are a number of implications of our findings. The most resounding conclusion that can be drawn from this study is that smoking is significantly associated with HRQoL in English general population. This is consistent with studies reported from similar high-income, industrialized countries such as Spain , Finland , Australia , the Netherlands , USA [46, 53], Denmark , and also with earlier UK studies [11, 45, 55]. In quantitative terms, moving from never-smoking to heavy-smoking profile leads to a utility loss of 0.0516. Likewise, supporting heavy-smokers to quit by various support mechanisms will lead to a utility gain of 0.0347. When applied at the population level, these small gains could translate into significant economic returns as explained below.
Our findings suggest that the more frequently one smokes, the worse quality of life they could expect from their smoking habit- regardless of other biological, clinical, lifestyle and socioeconomics. The absolute difference of 0.0347 in EQ-5D tariff between current heavy-smokers and ex-regular smoker is remarkable. Putting this into perspectives, there are currently 10 million smokers in England  of which according to our own data 27% (2.7 million) can be classified as heavy-smokers. Assuming that 6.4% of these smokers would receive a nicotine replacement therapy (NRT) prescription , 16% of whom will successfully quit at the end of the year , and conservatively assuming no deaths occurring in this group in this one year, this smoking cessation alone would save about 1000 QALYs at the end of the first year. If the NICE threshold for a QALY is used to value these benefits, NRT prescription alone could potentially save between £20-30 million (minus the costs of NRT provision) in one year.
Using the data provided in Table 3, a number of such policy simulations can be performed. Furthermore, these data can inform more robust economic evaluations of interventions that affect population smoking status (e.g. tobacco control) or are affected by it (e.g. lung cancer treatment). In particular, the information in the changes in utility due to smoking status in the population group that has no longstanding illness is valuable. By providing the utility values by age and gender, our estimates provides much more flexibility for cost-effectiveness researchers to model QALYs, compared to, for instance, the Scottish study  which provide a single estimate: the difference in ex-smokers and smokers (-0.0347) only. Our values are robust in the sense that they represent the net utility loss due to smoking. The standard deviation attached to each estimate will allow the modellers enough room to assess the uncertainty around their QALYs figures.
Finally, although the main driver of the paper is to provide utility values to inform the future economic evaluations, the findings around the EQ-5D domains warrant some interesting discussions. The fact that the frequency of severe conditions in our sample in all five domains was less than 4% mirrors the concern that EQ-5D is less able to pick up severe conditions . Thus, combining 'some' and 'severe' problems into one category to model the effect that smoking status has on each of the five domains is not unreasonable. The findings that the degree of smoking, particularly more than 20 or more cigarettes a day, affects all domains is consistent with a priori expectations but what is new is the quantification of differential effect this has on the domains.
The fact that being a heavy-smoker is associated with 86% more likelihood of reporting some/severe problems in anxiety/depression compared with 42% in usual activity, coupled with the finding that quitting smoking (e.g. in the case of ex-regular smokers) does not affect self-care and usual activity but it continues to affect the other three domains, has two immediate implications for cessation interventions: (a) in order to improve quality of life among quitters, cessation services may need to combine other forms of support, e.g. facilitate access to mental health services; and (b) anxiety/depression and mobility are the two domains on which cessation services can have the greatest impact. Putting this into perspectives, encouraging heavy-smokers quit by various support mechanisms will lead to a massive 70% reduction in them reporting some/severe problems in anxiety/depression (49% in mobility). This is an important aspect to be communicated as an individual benefit of smokers in quitting campaigns. This is also supported by studies on the basic association of nicotine and anxiety/depression [58, 59]. However, in order to assure sustained abstinence, it may be necessary to address/monitor mental health of those who attempt to quit right at the time of the intervention and beyond.