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Published on: September 26, 2018
A decision rule algorithm for the detection of patients with hypertension using claims data
Ali Golestani1, Mohammad-Reza Malekpour2, Sepehr Khosravi1
1Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Insights
This study developed a decision rule algorithm to identify hypertension patients in claims data, achieving 76.50% accuracy. The algorithm effectively uses prescription data for large-scale hypertension detection and care improvement.
Area of Science:
- Health Informatics
- Epidemiology
- Health Services Research
Background:
- Claims data is valuable for population health studies but often lacks diagnostic information.
- Identifying specific conditions like hypertension within claims data is challenging due to missing diagnosis codes.
Purpose of the Study:
- To develop and validate a decision rule algorithm for detecting hypertension patients using prescription data in claims.
- To leverage large-scale claims data for epidemiological surveillance of hypertension.
Main Methods:
- Retrospective analysis of Iran Health Insurance Organization (IHIO) data (2012-2016).
- Development of a 13-rule algorithm incorporating medication, age, sex, and physician specialty.
- Validation using sensitivity, specificity, PPV, NPV, and accuracy metrics.
Main Results:
- The algorithm identified 76.89% of patients receiving antihypertensive medication as having hypertension.
- High sensitivity (100%) and negative predictive value (100%) were observed.
- Overall accuracy was 76.50%, with a specificity of 48.91%.
Conclusions:
- The developed algorithm demonstrates robust performance for hypertension detection in claims data.
- This approach facilitates large-scale hypertension surveillance and can inform public health strategies.
- The algorithm aids policymakers and researchers in improving personalized care quality.
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.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s40200-024-01519-y.
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