Atkinson Mark D, Kennedy Jonathan I, John Ann, Lewis Keir E, Lyons Ronan A, Brophy Sinead T
Farr Institute, Swansea University Medical School, Swansea, SA2 8PP, UK.
Prince Philip Hospital, Hywel Dda Health Board, Llanelli, UK.
BMC Med Inform Decis Mak. 2017 Jan 5;17(1):2. doi: 10.1186/s12911-016-0400-6.
Patients' smoking status is routinely collected by General Practitioners (GP) in UK primary health care. There is an abundance of Read codes pertaining to smoking, including those relating to smoking cessation therapy, prescription, and administration codes, in addition to the more regularly employed smoking status codes. Large databases of primary care data are increasingly used for epidemiological analysis; smoking status is an important covariate in many such analyses. However, the variable definition is rarely documented in the literature.
The Secure Anonymised Information Linkage (SAIL) databank is a repository for a national collection of person-based anonymised health and socio-economic administrative data in Wales, UK. An exploration of GP smoking status data from the SAIL databank was carried out to explore the range of codes available and how they could be used in the identification of different categories of smokers, ex-smokers and never smokers. An algorithm was developed which addresses inconsistencies and changes in smoking status recording across the life course and compared with recorded smoking status as recorded in the Welsh Health Survey (WHS), 2013 and 2014 at individual level. However, the WHS could not be regarded as a "gold standard" for validation.
There were 6836 individuals in the linked dataset. Missing data were more common in GP records (6%) than in WHS (1.1%). Our algorithm assigns ex-smoker status to 34% of never-smokers, and detects 30% more smokers than are declared in the WHS data. When distinguishing between current smokers and non-smokers, the similarity between the WHS and GP data using the nearest date of comparison was κ = 0.78. When temporal conflicts had been accounted for, the similarity was κ = 0.64, showing the importance of addressing conflicts.
We present an algorithm for the identification of a patient's smoking status using GP self-reported data. We have included sufficient details to allow others to replicate this work, thus increasing the standards of documentation within this research area and assessment of smoking status in routine data.
在英国初级医疗保健中，全科医生（GP）会定期收集患者的吸烟状况。除了更常用的吸烟状况代码外，还有大量与吸烟相关的瑞德编码，包括与戒烟治疗、处方和用药编码有关的内容。初级保健数据的大型数据库越来越多地用于流行病学分析；吸烟状况是许多此类分析中的一个重要协变量。然而，变量定义在文献中很少有记录。
安全匿名信息链接（SAIL）数据库是英国威尔士一个基于个人的匿名健康和社会经济管理数据的国家集合库。对SAIL数据库中的全科医生吸烟状况数据进行了探索，以研究可用编码的范围以及如何将它们用于识别不同类别的吸烟者、已戒烟者和从不吸烟者。开发了一种算法，该算法解决了整个生命过程中吸烟状况记录的不一致性和变化问题，并在个体层面与2013年和2014年威尔士健康调查（WHS）中记录的吸烟状况进行了比较。然而，WHS不能被视为验证的“金标准”。
链接数据集中有6836人。全科医生记录中的缺失数据（6%）比WHS（1.1%）中更常见。我们的算法将34%的从不吸烟者判定为已戒烟者，并且检测出的吸烟者比WHS数据中申报的多30%。在区分当前吸烟者和非吸烟者时，使用最近比较日期的WHS和全科医生数据之间的相似性为κ = 0.78。在考虑了时间冲突后，相似性为κ = 0.64，这表明解决冲突的重要性。
我们提出了一种使用全科医生自我报告数据识别患者吸烟状况的算法。我们提供了足够的细节，以便其他人能够复制这项工作，从而提高该研究领域的文档标准以及常规数据中吸烟状况的评估。
