Singh Jasvinder A
Rheumatology Section, Medicine Service, Veterans Affairs (VA) Medical Center, and the Division of Rheumatology, Department of Medicine, University of Minnesota, Minneapolis, MN 55417, USA.
J Rheumatol. 2009 Sep;36(9):2000-8. doi: 10.3899/jrheum.090041. Epub 2009 Aug 14.
To study predictors of discordance between self-reported physician diagnosis and administrative database diagnosis of arthritis.
A cohort of all veterans who utilized Veterans Integrated Service Network (VISN)-13 medical facilities were mailed a questionnaire that included patient self-report of physician diagnosis of arthritis and questions regarding demographics, functional limitation, and SF-36V (a validated version of the Medical Outcomes Study Short-Form 36). Kappa coefficient was used to assess the extent of agreement between self-report of physician diagnosis and administrative database definitions that incorporated International Classification of Diseases (ICD) codes and use of medications for arthritis. We identified predictors of overall discordance between self-report and administrative database diagnosis using multivariable logistic regression analyses.
Among 70,334 eligible veterans surveyed, 19,749 subjects had an ICD diagnosis of arthritis in the administrative database in the year prior to the survey; 34,440 answered the arthritis question and 18,464 self-reported a physician diagnosis of arthritis. Kappa coefficient showed slight to fair agreement of 0.19-0.32 between self-report and administrative database definitions of arthritis. We found significantly higher overall discordance among veterans with more comorbidities, greater age, worse functional status, lower use of outpatient and inpatient services, lower education level, and among single medical-site users.
Low level of agreement between self-report and database diagnosis of arthritis and its significant association with patient demographic, clinical, and functional characteristics highlights the limitation of use of these strategies for identification of patients with arthritis in epidemiological studies.
研究自我报告的医生诊断与关节炎行政数据库诊断之间不一致的预测因素。
向所有使用退伍军人综合服务网络（VISN）-13医疗设施的退伍军人队列邮寄一份问卷，其中包括患者自我报告的医生对关节炎的诊断以及有关人口统计学、功能限制和SF-36V（医学结果研究简表36的有效版本）的问题。kappa系数用于评估医生诊断的自我报告与纳入国际疾病分类（ICD）代码和关节炎用药情况的行政数据库定义之间的一致程度。我们使用多变量逻辑回归分析确定了自我报告与行政数据库诊断之间总体不一致的预测因素。
在70334名符合条件的接受调查的退伍军人中，19749名受试者在调查前一年的行政数据库中有ICD诊断的关节炎；34440人回答了关节炎问题，18464人自我报告医生诊断为关节炎。kappa系数显示，自我报告与关节炎行政数据库定义之间的一致性为轻微至中等，为0.19-0.32。我们发现，合并症更多、年龄更大、功能状态更差、门诊和住院服务使用更少、教育水平更低的退伍军人以及单一医疗站点使用者之间的总体不一致性明显更高。
自我报告与关节炎数据库诊断之间的低一致性及其与患者人口统计学、临床和功能特征的显著关联凸显了在流行病学研究中使用这些策略识别关节炎患者的局限性。
