Centre for Health Research, University of Western Sydney, Penrith, New South Wales, Australia.
PLoS One. 2014 Apr 15;9(4):e95029. doi: 10.1371/journal.pone.0095029. eCollection 2014.
Adjustment for the differing risk profiles of patients is essential to the use of administrative hospital data for epidemiological research. Smoking is an important factor to include in such adjustments, but the accuracy of the diagnostic codes denoting smoking in hospital records is unknown. The aims of this study were to measure the validity of current smoking and ever smoked status identified from diagnoses in hospital records using a range of algorithms, relative to self-reported smoking status; and to examine whether the misclassification of smoking identified through hospital data is differential or non-differential with respect to common exposures and outcomes. Data from the baseline questionnaire of the 45 and Up Study, completed by 267,153 residents of New South Wales (NSW), Australia, aged 45 years and older, were linked to the NSW Admitted Patient Data Collection. Patients who had been admitted to hospital for an overnight stay between 1 July 2005 and the date of completion of the questionnaire (1 January 2006 to 2 March 2009) were included. Smokers were identified by applying a range of algorithms to hospital admission histories, and compared against self-reported smoking in the questionnaire ('gold standard'). Sensitivities for current smoking ranged from 59% to 84%, while specificities were 94% to 98%. Sensitivities for ever smoked ranged from 45% to 74% and specificities were 93% to 97%. For the majority of algorithms, sensitivities and/or specificities differed significantly according to principal diagnosis, number of comorbidities, socioeconomic status, residential remoteness, Indigenous status, 28 day readmission and 365 day mortality. The identification of smoking through diagnoses in hospital data results in differential misclassification. Risk adjustment based on smoking identified from these data will yield potentially misleading results. Systematic capture of information about smoking in hospital records using a mandatory item would increase the utility of administrative data for epidemiological research.
调整患者不同的风险状况对于使用医院行政数据进行流行病学研究至关重要。吸烟是调整中需要纳入的一个重要因素,但医院记录中用于标识吸烟的诊断代码的准确性尚不清楚。本研究的目的是测量使用一系列算法从医院记录中确定的当前吸烟和曾经吸烟状况的准确性,这些算法与自我报告的吸烟状况相对应;并研究通过医院数据识别的吸烟错误分类是否与常见暴露和结局相关或不相关。澳大利亚新南威尔士州(NSW)年龄在 45 岁及以上的 267153 名居民完成了 45 岁及以上研究的基线问卷调查,该研究的数据与 NSW 住院患者数据采集相链接。凡在 2005 年 7 月 1 日至问卷完成日期(2006 年 1 月 1 日至 2009 年 3 月 2 日)之间住院过夜的患者都包含在研究中。通过将一系列算法应用于住院病史来识别吸烟者,并与问卷中的自我报告吸烟情况(“金标准”)进行比较。当前吸烟的敏感性范围为 59%至 84%,特异性为 94%至 98%。曾经吸烟的敏感性范围为 45%至 74%,特异性为 93%至 97%。对于大多数算法,敏感性和/或特异性根据主要诊断、合并症数量、社会经济地位、居住偏远程度、土著地位、28 天再入院和 365 天死亡率而显著不同。通过医院数据中的诊断来识别吸烟会导致错误分类存在差异。基于这些数据中识别出的吸烟情况进行风险调整可能会导致结果产生误导。通过使用强制性项目系统地收集医院记录中关于吸烟的信息,将提高行政数据在流行病学研究中的实用性。