Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan

Konan Hara; Jun Tomio; Thomas Svensson; Rika Ohkuma; Akiko Kishi Svensson; Tsutomu Yamazaki

doi:10.1016/j.jclinepi.2018.03.004

Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan

J Clin Epidemiol. 2018 Jul:99:84-95. doi: 10.1016/j.jclinepi.2018.03.004. Epub 2018 Mar 14.

Authors

Konan Hara¹, Jun Tomio¹, Thomas Svensson², Rika Ohkuma³, Akiko Kishi Svensson⁴, Tsutomu Yamazaki⁵

Affiliations

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.

PMID: 29548842
DOI: 10.1016/j.jclinepi.2018.03.004

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.

Keywords: Administrative data; Algorithms; Association measures; Diabetes; Dyslipidemia; Hypertension.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Chronic Disease
Confidence Intervals
Data Collection / methods*
Data Collection / standards
Databases, Factual
Diabetes Mellitus / diagnosis*
Dyslipidemias / diagnosis*
Female
Health Benefit Plans, Employee / statistics & numerical data
Humans
Hypertension / diagnosis*
Insurance Claim Review*
Insurance, Pharmaceutical Services / statistics & numerical data
Japan
Male
Middle Aged
Reproducibility of Results
Sensitivity and Specificity