| アブストラクト | AIMS: To use association rule mining methods to investigate prescribing of smoking cessation medication in the UK primary care and to identify the characteristics of numerically important groups of patients who typically do, or do not, receive cessation therapy. DESIGN: An association rule mining study using The Health Improvement Network Database. SETTINGS AND PARTICIPANTS: 282 433 patients aged 16 years and over from 419 UK general practices, who were registered with the practice throughout 2008 and recorded as a current smoker during that year. OUTCOME: Prescription for any type of smoking cessation medications in 2008 (nicotine replacement therapy, bupropion or varenicline). VARIABLES: Age, gender, lifestyle indicators and co-morbidity. RESULTS: Of the current smokers, 37 731 (13.4%) were given prescriptions for smoking cessation treatment during 2008. Prescriptions were particularly likely to be given to women, those aged 31-60 years, and people with diagnoses of chronic obstructive pulmonary disease and depression. On the contrary, of patients with dementia, with alcohol intake over recommended levels, atrial fibrillation or chronic kidney disease was extremely unlikely to be prescribed a smoking cessation medication. However, the largest group of patients who did not receive therapy was young and otherwise healthy individuals. CONCLUSIONS: This novel approach identified sizeable and easily definable groups of patients who are systematically failing to receive support for smoking cessation in primary care. Association rule mining can be used to identify key numerically important groups at high or low risk of receiving treatment and hence potentially to improve healthcare delivery. |
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