A Decision Rule Algorithm for the Detection of Patients With Hypertension Using Claims Data Publisher



Golestani A¹ ; Malekpour MR² ; Khosravi S¹ ; Rashidi MM¹ ; Ataei SMN¹ ; Nasehi MM^{3, 4} ; Rezaee M^{3, 5} ; Akbari Sari A⁶ ; Rezaei N^{1, 7} ; Farzadfar F^{1, 2} Show Affiliations
Source: Journal of Diabetes and Metabolic Disorders Published:2025

Abstract

Objectives: 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. Methods: 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. Results: 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%. Conclusion: 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 Author(s), under exclusive licence to Tehran University of Medical Sciences 2024.