Konan Hara1, Jun Tomio1, Thomas Svensson2, Rika Ohkuma3, Akiko Kishi Svensson4, Tsutomu Yamazaki5. 1. Department of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. 2. Center of Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. 3. Center of Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. 4. Center of Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Clinical Research Support Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. Electronic address: Akiko-kishi@umin.ac.jp. 5. Center of Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Clinical Research Support Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Abstract
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.
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.