{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:41:23Z","timestamp":1787013683168,"version":"build-2736575974"},"reference-count":37,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2018,3,11]],"date-time":"2018-03-11T00:00:00Z","timestamp":1520726400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01\u2010MH094290"],"award-info":[{"award-number":["R01\u2010MH094290"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009827","name":"Laura and John Arnold Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009827","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Services Research"],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Sources<\/jats:title>\n                    <jats:p>2011\u20132012 Truven MarketScan database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Principal Findings<\/jats:title>\n                    <jats:p>My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1475-6773.12848","type":"journal-article","created":{"date-parts":[[2018,3,11]],"date-time":"2018-03-11T22:46:37Z","timestamp":1520808397000},"page":"3836-3854","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending"],"prefix":"10.1111","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9076-8472","authenticated-orcid":false,"given":"Sherri","family":"Rose","sequence":"first","affiliation":[{"name":"Department of Health Care Policy Harvard Medical School Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,3,11]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"Health Research Data for the Real World: The MarketScan Databases","author":"Adamson D.","year":"2008"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3414"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_2_6_5_1","volume-title":"Classification and Regression Trees","author":"Breiman L.","year":"1984"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-EJS703"},{"key":"e_1_2_6_7_1","first-page":"1","volume-title":"Handbook of Health Economics","author":"Chernew M.","year":"2012"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1176\/ajp.2006.163.4.724"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-811325-7.00003-8"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1198\/tast.2009.08199"},{"key":"e_1_2_6_11_1","first-page":"26","article-title":"A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome","volume":"6","author":"Gruber S.","year":"2010","journal-title":"International Journal of Biostatistics"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v051.i13"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/hdy.1992.131"},{"key":"e_1_2_6_14_1","volume-title":"Risk Adjustment for Measuring Healthcare Outcomes","author":"Iezzoni L.","year":"1997"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/135.1.205"},{"issue":"3","key":"e_1_2_6_16_1","article-title":"The HHS\u2010HCC Risk Adjustment Model for Individual and Small Group Markets under the Affordable Care Act","volume":"4","author":"Kautter J.","year":"2014","journal-title":"Medicare & Medicaid Research Review"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-811325-7.00014-2"},{"key":"e_1_2_6_18_1","first-page":"25","article-title":"Super Learner","volume":"6","author":"Laan M.","year":"2007","journal-title":"Statistical Applications in Genetics and Molecular Biology"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/121.1.185"},{"key":"e_1_2_6_21_1","doi-asserted-by":"crossref","unstructured":"Layton T. 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