Discussion

In this study population in 2008, about 13.4% of smokers received a smoking cessation medication, a substantially higher proportion than that recorded previously at 6.4% over 2 years from 2001 to 200317 but nevertheless a small proportion of all smokers. Given that 63% of smokers report readiness to quit and 25% make a quit attempt each year,13 it is thus evident that opportunities to support these attempts with cessation pharmacotherapy are being missed. Association rule mining identified a number of groups who were far more likely to receive prescriptions—particularly women aged between 31 and 60 years with a diagnosis of COPD and depression—and groups that were far less likely to receive prescriptions—particularly healthy young people and those with co-morbidity, particularly dementia, high alcohol intake, atrial fibrillation and chronic renal disease. A conventional logistic regression analysis showed broadly consistent findings, but the difference between the two approaches evident from the results is that association rule mining identifies specific, clinically and demographically identifiable categories of patients that can easily be targeted in clinical practice.

Our study relied on identifying current smokers by their Read codes, which we have previously shown to accurately reflect the prevalence of smoking found in national surveys,14 and in which we have demonstrated that smoking status is now updated more regularly.18 We included those recorded as current smokers in 2008 to ensure we were accurately identifying current smokers but with the result that our study excluded smokers whose smoking status was not recorded that year; while this may mean that our study population comprises relatively more of those who have their smoking status updated regularly such as those with chronic conditions, any such bias is relatively unlikely to have affected the associations that we have seen with prescribing in our study group. The data set does not capture non-prescription use of NRT, which accounts for about half of all cessation medication use in practice,19 and does not establish whether prescribed smoking cessation medication was used as part of a quit attempt or indeed whether it was used at all. We have chosen to analyse data on prescriptions rather than brief advice or referrals to Stop Smoking Services since the smoking cessation prescribing data in THIN are valid,20 while data on brief advice may be less reliable,21 but this study does identify patients in whom GPs have intervened in relation to smoking by providing a smoking cessation medication prescription and the characteristics of those who tend not to be treated. In our study population, over two-thirds of those prescribed medication were prescribed NRT, and analysis of those who did and did not receive an NRT prescription resulted in the same common features as those prescribed for all medication (data not shown); we have not carried out a data mining analysis in those receiving bupropion or varencline alone where numbers were smaller. Some of the discrepancy between the data mining and OR analyses is attributable to our decision to present only those rules including more than 500 cases; the determinations of support level or equivalently the minimum exposure group size is a crucial step in association rule mining. We explored in sensitivity analysis (data not presented) the effect of varying the minimum exposure group size and even at 50 rather than 500, the conclusions with respect to the common features of those who receive or do not receive prescriptions were not appreciably changed, although different specific characteristics of exposure groups were identified if the support level was varied. Nevertheless, this highlights an important difference between the two approaches. Conventional OR analyses do not typically select exposures on the basis of prevalence, and although it would be feasible to adopt such an approach, the association rule approach provides a simple means of identifying groups that are important in numerical terms, rather than exposures that are important in terms of strength. The association rules approach also has the potential advantage of approaching data with no prior hypothesis or null hypothesis, in what is in effect a structured exploration intended to identify discriminators that are important in terms of the number of patients they identify.

A previous study by Wilson et al 17 reported on the prescribing of smoking cessation treatments in UK general practice and factors that predicted their use from 2001 to 2003. We examined data for 2008, after the new GP contract released in 2004 and the introduction of a new smoking cessation drug, varenicline, at the end of 2006 and the introduction of smoke-free policy in 2007, all of which may have contributed to the increase in prescribing and which might have been expected to also impact on the characteristics of those who received prescriptions. We found a number of features that were in common with previous reports, including the higher likelihood that women would be prescribed smoking cessation drugs by their GPs, as were people aged between 31 and 60 years.17 ,22 Whether this reflects increased contact between GPs and these categories of people is not clear, but if contact alone was an important determinant of prescribing, then prescribing would generally be expected to be increased in most groups with chronic disease.

This was not however the case. The association rule mining approach identified that those with some co-morbidities, particularly COPD and depression, were more likely to receive prescriptions, while those with other co-morbidities of equally similar importance (atrial fibrillation and chronic renal disease in our study) were not. There is no obvious explanation for these biases in prescribing, and it is unclear whether the absence of prescribing was attributable to the GP not offering, the patients not accepting treatments or both of these options. Some doctors might regard cessation therapies to be relatively contraindicated by conditions such as hypertension or atrial fibrillation, patients that in our study were less likely to receive medication. However, while NRT is recommended to be used with caution in some conditions, for example, those with unstable hospitalised cardiovascular disease and in hepatic and renal impairment, most warnings associated with NRT also apply to continued cigarette smoking and the risks of continued smoking outweighs any risks of using nicotine preparations.23 There is also no obvious reason why prescribing should be biased in relation to dementia, obesity or alcohol intake.

That heavy smoking did not emerge from the association rule mining analysis as an important feature of those receiving prescriptions and, though significant, had a small effect size in the logistic regression analysis, was inconsistent with previous findings that cigarettes per day predict use of smoking cessation medications in those making a quit attempt.19 We also explored the effect of performing our analysis only in those recorded as heavy smokers but found that the patterns of those receiving or not receiving prescriptions were very similar to that seen in the whole patient population (data not shown). These findings may reflect the poor reliability of data on cigarette consumption collected in primary care, perhaps resulting from the fact that number of cigarettes per day is not required to be recorded under the Quality and Outcomes Framework. Overall, our results suggest that GPs do not use this information in their decision making.

Variations and inconsistencies are inevitable in clinical practice, and effective means of monitoring and correcting inappropriate biases in prescribing and delivery of other medical interventions are therefore important to ensure equitable delivery of services. This study illustrates how association rule mining approaches could be used in this context, namely by identifying groups that have been neglected or excluded from conventional service provision and providing an opportunity and means to set up a system whereby GPs are prompted to discuss a treatment or other type of care with patients in these groups. The strength of the association rule mining approach in relation to a more conventional logistic regression analysis is that it has identified sizeable groups that can easily be defined and identified for intervention at practice level, in real time to allow more focused and immediate correction of bias in service provision. Data mining methods thus provide a potentially very powerful means to monitor, intervene in and improve on the practice of care delivery at aggregate and individual practice level. Further work is now indicated to determine whether these findings can be applied to identify neglected groups in routine applications and thus improve on the delivery of this and other essential primary care interventions.