{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:33:32Z","timestamp":1782632012077,"version":"3.54.5"},"reference-count":21,"publisher":"American Diabetes Association","issue":"2","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/www.diabetesjournals.org\/content\/license"}],"funder":[{"DOI":"10.13039\/100000130","name":"National Center for Chronic Disease Prevention and Health Promotion","doi-asserted-by":"publisher","award":["200-2016-92270"],"award-info":[{"award-number":["200-2016-92270"]}],"id":[{"id":"10.13039\/100000130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["diabetesjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,2,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>To estimate the health utility impact of diabetes-related complications in a large, longitudinal U.S. sample of people with type 2 diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESEARCH DESIGN AND METHODS<\/jats:title>\n                    <jats:p>We combined Health Utilities Index Mark 3 data on patients with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Look AHEAD (Action for Health in Diabetes) trials and their follow-on studies. Complications were classified as events if they occurred in the year preceding the utility measurement; otherwise, they were classified as a history of the complication. We estimated utility decrements associated with complications using a fixed-effects regression model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Our sample included 15,252 persons with an average follow-up of 8.2 years and a total of 128,873 person-visit observations. The largest, statistically significant (P &amp;lt; 0.05) health utility decrements were for stroke (event, \u22120.109; history, \u22120.051), amputation (event, \u22120.092; history, \u22120.150), congestive heart failure (event, \u22120.051; history, \u22120.041), dialysis (event, \u22120.039), estimated glomerular filtration rate (eGFR) &amp;lt;30 mL\/min\/1.73 m2 (event, \u22120.043; history, \u22120.025), angina (history, \u22120.028), and myocardial infarction (MI) (event, \u22120.028). There were smaller effects for laser photocoagulation and eGFR &amp;lt;60 mL\/min\/1.73 m2. Decrements for dialysis history, angina event, MI history, revascularization event, revascularization history, laser photocoagulation event, and hypoglycemia were not significant (P \u2265 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>With use of a large study sample and a longitudinal design, our estimated health utility scores are expected to be largely unbiased. Estimates can be used to describe the health utility impact of diabetes complications, improve cost-effectiveness models, and inform diabetes policies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2337\/dc20-1207","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T22:31:20Z","timestamp":1607121080000},"page":"381-389","update-policy":"https:\/\/doi.org\/10.2337\/ada-journal-policies","source":"Crossref","is-referenced-by-count":11,"title":["Patient Health Utility Equations for a Type 2 Diabetes Model"],"prefix":"10.2337","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3512-7397","authenticated-orcid":false,"given":"Simon J.","family":"Neuwahl","sequence":"first","affiliation":[{"name":"RTI International, Durham, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8429-6301","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Centers for Disease Control and Prevention, Atlanta, GA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9712-5963","authenticated-orcid":false,"given":"Haiying","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[{"name":"Centers for Disease Control and Prevention, Atlanta, GA"},{"name":"Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8775-7417","authenticated-orcid":false,"given":"Michael","family":"Laxy","sequence":"additional","affiliation":[{"name":"Centers for Disease Control and Prevention, Atlanta, GA"},{"name":"Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany"},{"name":"German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany"},{"name":"Institute of Health Economics and Health Care Management, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea M.","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy E.","family":"Craven","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and 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