Sarfati Diana, Hill Sarah, Purdie Gordon, Dennett Elizabeth, Blakely Tony
Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington South, New Zealand.
N Z Med J. 2010 Mar 5;123(1310):50-61.
This study aims to assess the quality of routinely collected comorbidity data in New Zealand which are increasingly used in health service planning and research.
Detailed medical notes-based comorbidity data from a cohort study of New Zealanders diagnosed with colon cancer in 1996-2003, were compared with routine hospital discharge data collected from the same patients using 1-year and 8-year lookback periods. We compared agreement between data sources for individual conditions, Charlson comorbidity index scores and total comorbidity counts using McNemar's p-test and the kappa statistic. We also assessed the association of comorbidity with all-cause survival using Cox proportional hazard models using data ascertained from the two sources.
Among these 569 patients, we found generally higher comorbidity was measured from notes than administrative data, with better comparability with an 8-year lookback period. Regardless of source of data, all measures of comorbidity significantly improved the ability of multivariable models to explain all-cause survival, but using both data sources combined resulted in better risk adjustment than either source separately.
While differences in medical notes and administrative comorbidity data exist, the latter provides a reasonably useful source of accessible information on comorbidity for risk adjustment particularly in multivariable models.
本研究旨在评估新西兰常规收集的共病数据的质量,这些数据在卫生服务规划和研究中越来越多地被使用。
将1996 - 2003年确诊为结肠癌的新西兰人群队列研究中基于详细医疗记录的共病数据,与使用1年和8年回顾期从同一患者收集的常规医院出院数据进行比较。我们使用McNemar p检验和kappa统计量,比较了个体疾病、查尔森共病指数评分和共病总数在数据源之间的一致性。我们还使用从这两个来源确定的数据,通过Cox比例风险模型评估了共病与全因生存的关联。
在这569名患者中,我们发现从医疗记录中测量的共病情况通常比行政数据更高,8年回顾期的可比性更好。无论数据来源如何,所有共病测量方法都显著提高了多变量模型解释全因生存的能力,但将两个数据源结合使用比单独使用任何一个数据源能实现更好的风险调整。
虽然医疗记录和行政共病数据存在差异,但后者为风险调整提供了一个相当有用的可获取的共病信息来源,特别是在多变量模型中。