Korthuis P Todd, Asch Steven, Mancewicz Martha, Shapiro Martin F, Mathews W Christopher, Cunningham William E, McCutchan J Allen, Gifford Allen, Lee Martin L, Bozzette Samuel A
Greater Los Angeles VA Health Care System, UCLA, Building 500, Room 3233, 11301 Wilshire Boulevard (111G), Los Angeles, CA 90073, USA.
Med Care. 2002 Dec;40(12):1270-82. doi: 10.1097/01.MLR.0000036410.86742.27.
Medication measurement is crucial in assessing quality for chronic conditions yet agreement of alternate data sources remains uncertain.
To evaluate medication agreement between interviews, medical records, and pharmacy data; to assess data source contribution to attributing medication exposure; and to describe the impact of combining data sources on models that predict medication use.
Prospective cohort study.
Probability sample of HIV-infected participants in the HIV Cost and Services Utilization Study.
Medications reported in 2267 interviews, 1936 medical records, and 457 pharmacy records were compared using crude agreement, kappa, and the proportion of average positive and negative agreement. The percent of medications reported in each source alone was used to assess their relative contribution to attributing exposure status. We performed weighted logistic regression in alternate data sources.
Kappa varied from 0.38 for nucleoside reverse transcriptase inhibitors to 0.70 for protease inhibitors, when comparing drug classes in interview versus medical record, interview versus pharmacy data, and medical record versus pharmacy data. The percentage of medications reported in medical records was greater than that reported in interviews or pharmacy data. Pharmacy data contributed little to the attribution of medication exposure. Adding medication data to interview data did not appreciably change analytic models predicting medication use.
For specific medications, agreement between alternative data sources is fair to substantial, but is lower for key drug classes. Relying on one data source may lead to misclassification of drug exposure status, but combining data sources does not change the results of analytic models predicting appropriate medication use.
药物计量对于评估慢性病的质量至关重要，但替代数据源之间的一致性仍不确定。
评估访谈、病历和药房数据之间的药物一致性；评估数据源对药物暴露归因的贡献；描述合并数据源对预测药物使用模型的影响。
前瞻性队列研究。
艾滋病成本与服务利用研究中感染艾滋病毒参与者的概率样本。
使用粗一致性、kappa以及平均阳性和阴性一致性比例，比较了2267份访谈、1936份病历和457份药房记录中报告的药物。单独在每个数据源中报告的药物百分比用于评估它们对暴露状态归因的相对贡献。我们在替代数据源中进行了加权逻辑回归。
在比较访谈与病历、访谈与药房数据以及病历与药房数据中的药物类别时，kappa值从核苷类逆转录酶抑制剂的0.38到蛋白酶抑制剂的0.70不等。病历中报告的药物百分比高于访谈或药房数据中报告的百分比。药房数据对药物暴露归因的贡献很小。将药物数据添加到访谈数据中并没有明显改变预测药物使用的分析模型。
对于特定药物，替代数据源之间的一致性从中等到高度，但关键药物类别的一致性较低。依赖单一数据源可能导致药物暴露状态的错误分类，但合并数据源并不会改变预测适当药物使用的分析模型的结果。
