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

Hara, Konan; Tomio, Jun; Svensson, Thomas LU ; Ohkuma, Rika; Svensson, Akiko Kishi and Yamazaki, Tsutomu (2018) In Journal of Clinical Epidemiology 99. p.84-95
Abstract

OBJECTIVE: 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... (More)

OBJECTIVE: 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 employed 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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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Clinical Epidemiology
volume
99
pages
12 pages
publisher
Elsevier
external identifiers
  • scopus:85045056295
ISSN
1878-5921
DOI
10.1016/j.jclinepi.2018.03.004
language
English
LU publication?
yes
id
06538c26-d6d9-463a-b27f-ad8f90cc5194
date added to LUP
2018-03-28 02:46:55
date last changed
2019-04-10 04:05:34
@article{06538c26-d6d9-463a-b27f-ad8f90cc5194,
  abstract     = {<p>OBJECTIVE: 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.</p><p>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.</p><p>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.</p><p>CONCLUSION: We employed 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.</p>},
  author       = {Hara, Konan and Tomio, Jun and Svensson, Thomas and Ohkuma, Rika and Svensson, Akiko Kishi and Yamazaki, Tsutomu},
  issn         = {1878-5921},
  language     = {eng},
  pages        = {84--95},
  publisher    = {Elsevier},
  series       = {Journal of Clinical Epidemiology},
  title        = {Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan},
  url          = {http://dx.doi.org/10.1016/j.jclinepi.2018.03.004},
  volume       = {99},
  year         = {2018},
}