Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records.
Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records.
复制标题
DOI：
10.1186/s12911-016-0400-6
复制
发表时间：
2017-01-05
影响因子：
3.5
通讯作者：
DEMISTIFY Research Group
中科院分区：
文献类型：
--
作者：
Atkinson MD;Kennedy JI;John A;Lewis KE;Lyons RA;Brophy ST;DEMISTIFY Research Group
英国初级卫生保健中的全科医生（GP）定期收集患者的吸烟状况。除了更经常使用的吸烟状态代码外，还有大量与吸烟相关的Read代码，包括与戒烟治疗、处方和管理代码相关的代码。大型初级保健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重要协变量。然而，在文献中很少记录变量定义。安全匿名信息链接（SAIL）数据库是英国威尔士的一个国家级匿名健康和社会经济管理数据库。对SAIL数据库中的GP吸烟状况数据进行了探索，以探索可用代码的范围以及如何将其用于识别不同类别的吸烟者，戒烟者和从不吸烟者。开发了一种算法，解决了整个生命过程中吸烟状态记录的不一致性和变化，并与2013年和2014年威尔士健康调查（WHS）中记录的个人吸烟状态进行了比较。然而，WHS不能被视为验证的“黄金标准”。在链接的数据集中有6836个个体。GP记录中缺失数据（6%）比WHS（1.1%）更常见。我们的算法将34%的从不吸烟者指定为既往吸烟者，并检测到的吸烟者比WHS数据中声明的多30%。当区分当前吸烟者和非吸烟者时，使用最近比较日期的WHS和GP数据之间的相似性为κ = 0.78。当考虑时间冲突时，相似度为κ = 0.64，表明解决冲突的重要性。我们提出了一种算法，用于识别患者的吸烟状态使用GP自我报告的数据。我们已经包括了足够的细节，使其他人能够复制这项工作，从而提高了该研究领域内的文件标准和常规数据中吸烟状况的评估。本文的在线版本（doi：10.1186/s12911-016-0400-6）包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/s12911-016-0400-6) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
13.8
作者：
Neimann, Andrea L.;Shin, Daniel B.;Gelfand, Joel M.
通讯作者：
Gelfand, Joel M.
影响因子：
2.1
作者：
Delaney, Joseph A C;Daskalopoulou, Stella S;Suissa, Samy
通讯作者：
Suissa, Samy
影响因子：
3.5
作者：
Lyons RA;Jones KH;John G;Brooks CJ;Verplancke JP;Ford DV;Brown G;Leake K
通讯作者：
Leake K
DOI：
10.1016/j.ijmedinf.2004.02.002
发表时间：
2004-06-15
影响因子：
4.9
作者：
Carey, IM;Cook, DG;Hilton, SR
通讯作者：
Hilton, SR
影响因子：
2.6
作者：
Lewis, JD;Brensinger, C
通讯作者：
Brensinger, C