{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:48:25Z","timestamp":1791517705324,"version":"4.3.4"},"reference-count":21,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["K08AG055670"],"award-info":[{"award-number":["K08AG055670"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K24\u2010AR078959"],"award-info":[{"award-number":["K24\u2010AR078959"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100002491","name":"Bristol-Myers Squibb","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100002491","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008349","name":"Boehringer Ingelheim","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      Using the Mass General Brigham Research Patient Data Repository\u2010Medicare\u2010linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)\u20109 and ICD\u201010 eras. Patients were divided into two groups, with or without an obesity\u2010related ICD code in the 6\u00a0months before or after the BMI measurement date. We created two binary measures, first for composite overweight, obesity, or severe obesity (BMI \u226525\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ), and second for obesity or severe obesity (BMI \u226530\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ). We calculated accuracy measures (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) for each obesity category for the overall cohort, and stratified by type 2 diabetes and ICD\u2010code era.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The cohort included 73\u2009644 patients with a BMI measurement in 2014 or 2016, of whom 16\u2009280 had an obesity\u2010related ICD code. The specificity of obesity\u2010related ICD codes (ICD\u20109 and ICD\u201010) was 99.7% for underweight\/normal weight, 97.4% for overweight, 99.7% for obese and 98.9% for severely obese. For binary categories capturing BMI \u226525\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      and BMI \u226530\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      , specificity was 97.0% and 98.2%, and PPV was 86.9% and 97.3%. Sensitivity was low overall (&lt;40%). Codes for patients with type 2 diabetes and codes in the ICD\u201010 era had higher sensitivity, PPV and NPV.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Obesity\u2010related ICD codes can accurately identify patients with obesity in epidemiological studies using claims databases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.14512","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T06:16:43Z","timestamp":1627885003000},"page":"2623-2631","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Validation of obesity\u2010related diagnosis codes in claims data"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-3853","authenticated-orcid":false,"given":"Karine","family":"Suissa","sequence":"first","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Schneeweiss","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kueiyu Joshua","family":"Lin","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Medicine Massachusetts General Hospital and Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Brill","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seoyoung C.","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital and Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-9898","authenticated-orcid":false,"given":"Elisabetta","family":"Patorno","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc07-1934"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2018.0022"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.4617"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S214909"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0163278714553661"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000016438"},{"key":"e_1_2_9_8_1","article-title":"Calculating the benefits of a research patient data repository","volume":"1044","author":"Nalichowski R","year":"2006","journal-title":"AMIA Annu Symp 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ICD\u201010\u2010CM Official Guidelines for Coding and Reporting FY 2021 October 1 2020\u2013September 30 2021.https:\/\/www.who.int\/classifications\/icd\/factsheet\/en\/;www.cdc.gov\/nchs\/icd\/icd10cm.htm. 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