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基于人群的行政数据的整合连续性护理措施可提高预测药物依从性模型的性能。

An integrated continuity of care measure improves performance in models predicting medication adherence using population-based administrative data.

机构信息

College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.

出版信息

PLoS One. 2022 Mar 3;17(3):e0264170. doi: 10.1371/journal.pone.0264170. eCollection 2022.

Abstract

OBJECTIVES

Continuity of care (COC) is considered an important determinant of medication adherence based on measures such as the usual provider continuity index (UPCI) that are derived exclusively from physician visit claims. This study aimed to: a) determine if high UPCI values predict physicians who deliver different clinical services; and b) compare UPCI with an integrated COC measure capturing physician visits, prescribing, and a complete medical examination in a multivariable model of patients receiving statin medications.

METHODS

This was a retrospective cohort study of new statin users between 2012 and 2017 in Saskatchewan, Canada. We calculated sensitivity/specificity of a high UPCI value for predicting physicians who were prescribers of statins and/or providers of complete medical examinations. Next, we used logistic regression models to test two measures of COC (high UPCI value or an integrated COC measure) on the outcome of optimal statin adherence (proportion of days covered ≥80%). The DeLong test was used to compare predictive performance of the two models.

RESULTS

Among 55,144 new statin users, a high UPCI was neither a sensitive or specific marker of physicians who prescribed statins or performed a complete medical examination. The integrated COC measure had a stronger association with optimal adherence [adjusted odds ratio (OR) = 1.56, 95% confidence interval (CI) 1.50 to 1.63] than UPCI (adjusted OR = 1.23, 95% CI 1.19 to 1.28), and improved predictive performance of the adherence model.

CONCLUSION

The number of physician visits alone appears to be insufficient to represent COC. An integrated measure improves predictive performance for optimal medication adherence in patients initiating statins.

摘要

目的

连续性护理(COC)被认为是药物依从性的一个重要决定因素,其依据是医生就诊次数连续性指数(UPCI)等指标,这些指标完全是根据医生就诊记录推算得出的。本研究旨在:a)确定 UPCI 值高是否能预测提供不同临床服务的医生;b)在他汀类药物治疗患者的多变量模型中,比较 UPCI 与综合 COC 指标,该指标涵盖了医生就诊、开处方和全面体检。

方法

这是一项在加拿大萨斯喀彻温省 2012 年至 2017 年间新使用他汀类药物的患者的回顾性队列研究。我们计算了高 UPCI 值预测他汀类药物处方医生和/或提供全面体检医生的灵敏度/特异性。接下来,我们使用逻辑回归模型测试两种 COC 指标(高 UPCI 值或综合 COC 指标)对他汀类药物最佳依从性(覆盖天数比例≥80%)的影响。使用 DeLong 检验比较两种模型的预测性能。

结果

在 55144 名新使用他汀类药物的患者中,高 UPCI 既不是预测医生开他汀类药物或进行全面体检的敏感或特异性标志物。综合 COC 指标与最佳依从性的相关性更强[调整后的优势比(OR)=1.56,95%置信区间(CI)1.50 至 1.63],高于 UPCI(调整后的 OR=1.23,95%CI 1.19 至 1.28),并提高了药物依从性模型的预测性能。

结论

单纯的就诊次数似乎不足以代表 COC。综合指标可提高他汀类药物起始治疗患者最佳药物依从性的预测性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b60/8893672/029c3b2b8a58/pone.0264170.g001.jpg

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