Golestani Ali, Malekpour Mohammad-Reza, Khosravi Sepehr, Rashidi Mohammad-Mahdi, Ataei Seyed Mohammad-Navid, Nasehi Mohammad Mahdi, Rezaee Mehdi, Akbari Sari Ali, Rezaei Negar, Farzadfar Farshad
Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
J Diabetes Metab Disord. 2024 Dec 20;24(1):21. doi: 10.1007/s40200-024-01519-y. eCollection 2025 Jun.
Claims data covers a large population and can be utilized for various epidemiological and economic purposes. However, the diagnosis of prescriptions is not determined in the claims data of many countries. This study aimed to develop a decision rule algorithm using prescriptions to detect patients with hypertension in claims data.
In this retrospective study, all Iran Health Insurance Organization (IHIO)-insured patients from 24 provinces between 2012 and 2016 were analyzed. A list of available antihypertensive drugs was generated and a literature review and an exploratory analysis were performed for identifying additional usages. An algorithm with 13 decision rules, using variables including prescribed medications, age, sex, and physician specialty, was developed and validated.
Among all the patients in the IHIO database, a total of 4,590,486 received at least one antihypertensive medication, with a total of 79,975,134 prescriptions issued. The algorithm detected that 76.89% of patients had hypertension. Among 20.43% of all prescriptions the algorithm detected as issued for hypertension, mainly were prescribed by general practitioners (55.78%) and hypertension specialists (30.42%). The validity assessment of the algorithm showed a sensitivity of 100.00%, specificity of 48.91%, positive predictive value of 69.68%, negative predictive value of 100.00%, and accuracy of 76.50%.
The algorithm demonstrated good performance in detecting patients with hypertension using claims data. Considering the large-scale and passively aggregated nature of claims data compared to other surveillance surveys, applying the developed algorithm could assist policymakers, insurers, and researchers in formulating strategies to enhance the quality of personalized care.
The online version contains supplementary material available at 10.1007/s40200-024-01519-y.
索赔数据涵盖大量人群，可用于各种流行病学和经济目的。然而，许多国家的索赔数据中并未确定处方的诊断情况。本研究旨在开发一种利用处方的决策规则算法，以在索赔数据中检测高血压患者。
在这项回顾性研究中，分析了2012年至2016年间来自24个省份的所有伊朗健康保险组织（IHIO）参保患者。生成了可用抗高血压药物清单，并进行了文献综述和探索性分析以确定其他用途。开发并验证了一种具有13条决策规则的算法，该算法使用包括处方药物、年龄、性别和医生专业等变量。
在IHIO数据库中的所有患者中，共有4590486人接受了至少一种抗高血压药物治疗，共开出了79975134张处方。该算法检测出76.89%的患者患有高血压。在该算法检测为用于高血压的所有处方中，20.43%主要由全科医生（55.78%）和高血压专科医生（30.42%）开出。该算法的有效性评估显示，灵敏度为100.00%，特异度为48.91%，阳性预测值为69.68%，阴性预测值为100.00%，准确率为76.50%。
该算法在利用索赔数据检测高血压患者方面表现良好。考虑到与其他监测调查相比，索赔数据具有大规模和被动汇总的性质，应用所开发的算法可以帮助政策制定者、保险公司和研究人员制定提高个性化医疗质量的策略。
在线版本包含可在10.1007/s40200-024-01519-y获取的补充材料。
