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Published on: February 22, 2018
Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected
Konan Hara1, Jun Tomio1, Thomas Svensson2
1Department of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
Insights
Claims-based algorithms (CBAs) show moderate to high accuracy in identifying hypertension and diabetes, but lower accuracy for dyslipidemia. This study validates CBAs using health screening data for improved health condition identification.
Area of Science:
- Health Informatics
- Epidemiology
- Biostatistics
Background:
- Claims data are extensively utilized in medical research.
- The accuracy of claims-based algorithms (CBAs) for identifying chronic conditions requires validation.
- Health screening results serve as a reliable gold standard for condition identification.
Purpose of the Study:
- To assess the validity of CBAs for identifying common chronic conditions.
- To compare the performance of various CBAs against health screening data.
- To establish a framework for validating CBAs using routinely collected data.
Main Methods:
- Utilized a large longitudinal claims database (n=523,267).
- Employed annual health screening results as the gold standard for defining hypertension, diabetes, and dyslipidemia.
- Compared diagnostic and medication code-based CBAs against the gold standard.
Main Results:
- CBAs demonstrated high specificity (≥97.2%) for all conditions.
- Sensitivity for hypertension and diabetes was substantial (74.5% and 78.6%, respectively).
- Sensitivity for dyslipidemia was lower (34.5%), and remained adequate for hypertension and diabetes when not limited to primary care.
Conclusions:
- The developed framework provides a basis for assessing CBA validity using routinely collected data.
- CBAs are valuable tools for identifying hypertension and diabetes in large populations.
- Further refinement of CBAs is needed for accurate dyslipidemia identification.
Objectives:
Although claims data are widely used in medical research, their ability to identify persons' health-related conditions has not been fully justified. We assessed the validity of claims-based algorithms (CBAs) for identifying people with common chronic conditions in a large population using annual health screening results as the gold standard.
Study Design And Setting:
Using a longitudinal claims database (n = 523,267) combined with annual health screening results, we defined the people with hypertension, diabetes, and/or dyslipidemia by applying health screening results as their gold standard and compared them against various CBAs.
Results:
By using diagnostic and medication code-based CBAs, sensitivity and specificity were 74.5% (95% confidence interval [CI], 74.2%-74.8%) and 98.2% (98.2%-98.3%) for hypertension, 78.6% (77.3%-79.8%) and 99.6% (99.5%-99.6%) for diabetes, and 34.5% (34.2%-34.7%) and 97.2% (97.2%-97.3%) for dyslipidemia, respectively. Sensitivity did not decrease substantially for hypertension (65.2% [95% CI, 64.9%-65.5%]) and diabetes (73.0% [71.7%-74.2%]) when we used the same CBAs without limiting to primary care settings.
Conclusion:
We used regularly collected data to obtain CBA association measures, which are applicable to a wide range of populations. Our framework can be a basis of the validity assessment of CBAs for identifying persons' health-related conditions with regularly collected data.
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