Kroeker Kristine, Widdifield Jessica, Muthukumarana Saman, Jiang Depeng, Lix Lisa M
Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
Institute for Clinical Evaluative Sciences, Toronto, Canada.
BMJ Open. 2017 Jun 23;7(6):e016173. doi: 10.1136/bmjopen-2017-016173.
This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study.
Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data. We considered: (A) separate univariate models for sensitivity and specificity, (B) univariate model for Youden's summary index and (C) bivariate (ie, joint) mixed-effects model for sensitivity and specificity. Model covariates included the number of diagnoses in physician, hospital and emergency department records, physician diagnosis observation time, duration of time between physician diagnoses and number of RA-related prescription medication records.
The most common case definition attributes were: 1+ hospital diagnosis (65%), 2+ physician diagnoses (43%), 1+ specialist physician diagnosis (51%) and 2+ years of physician diagnosis observation time (27%). Statistically significant improvements in sensitivity and/or specificity for separate univariate models were associated with (all p values <0.01): 2+ and 3+ physician diagnoses, unlimited physician diagnosis observation time, 1+ specialist physician diagnosis and 1+ RA-related prescription medication records (65+ years only). The bivariate model produced similar results. Youden's index was associated with these same case definition criteria, except for the length of the physician diagnosis observation time.
A model-based method provides valuable empirical evidence to aid in selecting a definition(s) for ascertaining diagnosed disease cases from administrative health data. The choice between univariate and bivariate models depends on the goals of the validation study and number of case definitions.
本研究提出一种基于模型的方法，以促进从行政卫生数据验证研究中选择疾病病例定义。该方法在类风湿性关节炎（RA）验证研究中得到了验证。
数据来自148种用于在医院、医生和处方药物行政数据中确定RA病例的定义。我们考虑了：（A）用于敏感性和特异性的单独单变量模型，（B）用于尤登综合指数的单变量模型，以及（C）用于敏感性和特异性的双变量（即联合）混合效应模型。模型协变量包括医生、医院和急诊科记录中的诊断数量、医生诊断观察时间、医生诊断与RA相关处方药物记录数量之间的时间间隔。
最常见的病例定义属性为：1次及以上医院诊断（65%）、2次及以上医生诊断（43%）、1次及以上专科医生诊断（51%）以及2年及以上医生诊断观察时间（27%）。单独单变量模型在敏感性和/或特异性方面的统计学显著改善与以下因素相关（所有p值<0.01）：2次及以上和3次及以上医生诊断、无限制的医生诊断观察时间、1次及以上专科医生诊断以及1次及以上RA相关处方药物记录（仅65岁及以上）。双变量模型产生了类似的结果。尤登指数与这些相同的病例定义标准相关，但医生诊断观察时间长度除外。
基于模型的方法为从行政卫生数据中确定已诊断疾病病例的定义选择提供了有价值的实证证据。单变量模型和双变量模型之间的选择取决于验证研究的目标和病例定义的数量。
