Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan
(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.
(Less)
- author
- Hara, Konan ; Tomio, Jun ; Svensson, Thomas LU ; Ohkuma, Rika ; Svensson, Akiko Kishi LU and Yamazaki, Tsutomu
- organization
- publishing date
- 2018-07
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Clinical Epidemiology
- volume
- 99
- pages
- 12 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:29548842
- 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
- 2024-04-15 04:28:45
@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}},
doi = {{10.1016/j.jclinepi.2018.03.004}},
volume = {{99}},
year = {{2018}},
}