{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T20:23:28Z","timestamp":1787257408714,"version":"build-2736575974"},"reference-count":23,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"2","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["jheor.org"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The evolving obesity treatment landscape requires examining clinical and economic outcomes, but ICD-10-CM coding inconsistencies hinder patient identification in claims databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"objectives\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to validate obesity diagnosis codes and identify factors influencing their utilization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study utilized claims and clinical data from the Healthcare Integrated Research Database (HIRD\u00ae) to identify adults with a BMI of 30 kg\/m\u00b2 or higher, spanning the period from June 2021 to October 2022. The study examined the use of ICD-10-CM diagnosis codes for obesity across two distinct timeframes: a 6-month period before and after the index date for prevalence and modeling purposes, and a 60-day window around the index date for validity calculations. For validation, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ICD-10-CM codes were measured, classifying patients as true or false positives or negatives based on their BMI and coding presence. Multivariable logistic regression was used to identify factors predicting obesity coding, guided by clinical reasoning, statistical results, and refined through backward elimination regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 2\u2009218\u2009115 patients with available BMI data during the relevant period, 966\u2009427 (44%) had a BMI of 30 kg\/m\u00b2 or above. The study identified 380,606 true positives, 17\u2009422 false positives, 1\u2009234\u2009266 true negatives, and 585\u2009821 false negatives, resulting in a PPV of 95.6%, a NPV of 67.8%, a specificity of 98.6%, and a sensitivity of 39.4%. The prevalence of obesity coding was 54%. Increased likelihood of coding was associated with being female, over the age of 24, non-White, insured by Medicare Advantage, having lower socioeconomic status, higher BMI, and receiving weight management interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"conclusion\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ICD-10-CM codes demonstrate high specificity and PPV but low sensitivity, indicating that while coded cases are likely true obesity, many patients with obesity are not captured in claims.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.36469\/001c.164460","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T19:23:56Z","timestamp":1787253836000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":0,"title":["Validity and Predictors of Medical Claims Coding for the Identification of Patients with Obesity"],"prefix":"10.36469","volume":"13","author":[{"given":"Effie L.","family":"Kuti","sequence":"first","affiliation":[{"name":"Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma","family":"Richard","sequence":"additional","affiliation":[{"name":"Carelon Research Inc., Wilmington, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Schott","sequence":"additional","affiliation":[{"name":"Carelon Research Inc., Wilmington, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher L.","family":"Crowe","sequence":"additional","affiliation":[{"name":"Carelon Research Inc., Wilmington, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Willey","sequence":"additional","affiliation":[{"name":"Carelon Research Inc., Wilmington, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bonnie","family":"Donato","sequence":"additional","affiliation":[{"name":"Boehringer Ingelheim, Ridgefield, Connecticut, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22273","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"ref1","article-title":"Creation of New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators for Medicare Beneficiaries: final report, sub-task 21","author":"A. 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