{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T21:31:05Z","timestamp":1791581465354,"version":"4.3.4"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:00:00Z","timestamp":1611187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    To investigate healthcare costs and contributors to costs for multiple chronic conditions (MCCs), common clusters of conditions and their impact on cost and utilization.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    This was a cross-sectional analysis of US financial claims data representative of the US population, including Medicare, Medicaid, and Commercial insurance claims in 2015. Outcome measures included healthcare costs and contributors; ranking of clusters of conditions according to frequency, strength of association and unsupervised (k-means) analysis; the impact of clustering on costs and contributors to costs.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Of 1,878,951 patients, 931,045(49.6%) had MCCs, 56.5% weighted to the US population. Mean age was 53.0 years (SD16.7); 393,121(42.20%) were male. Mean annual healthcare spending was $12,601, ranging from $4,385 (2 conditions) to $33,874 (11 conditions), with spending increasing by 22-fold for inpatient services, 6-fold for outpatient services, 4.5-fold for generic drugs, and 4.2-fold for branded drugs. Cluster ranking using the 3 methodologies yielded similar results: highest ranked clusters included metabolic syndrome (12.2% of US insured patients), age related diseases (7.7%), renal failure (5.6%), respiratory disorders (4.5%), cardiovascular disease(CVD) (4.3%), cancers (4.1\u20134.3%), mental health-related clusters (1.0\u20131.5%), and HIV\/AIDS (0.2%). Highest spending was in HIV\/AIDS clusters ($48,293), mental health-related clusters ($38,952\u2013$40,637), renal disease ($38,551), and CVD ($37,155); with 89.9% of spending on outpatient and inpatient care combined, and 10.1% on medication.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion and Relevance:<\/jats:bold>\n                    Over 57% of insured patients in the US may have MCCs. MCC Clustering is frequent and is associated with healthcare utilization. The findings favor health system redesign toward a multiple condition approach for clusters of chronic conditions, alongside other cost-containment measures for MCCs.\n                  <\/jats:p>","DOI":"10.3389\/fpubh.2020.607528","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T05:37:56Z","timestamp":1611207476000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Clustering and Healthcare Costs With Multiple Chronic Conditions in a US Study"],"prefix":"10.3389","volume":"8","author":[{"given":"Cother","family":"Hajat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yakir","family":"Siegal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amalia","family":"Adler-Waxman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e102149","DOI":"10.1371\/journal.pone.0102149","article-title":"Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies","volume":"9","author":"Violan","year":"2014","journal-title":"PLoS ONE"},{"key":"B2","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.arr.2011.03.003","article-title":"Aging with multimorbidity: a systematic review of the literature","volume":"10","author":"Marengoni","year":"2011","journal-title":"Ageing Res Rev"},{"key":"B3","doi-asserted-by":"publisher","first-page":"143","DOI":"10.2147\/RMHP.S97248","article-title":"Multimorbidity in chronic disease: impact on health care resources and costs","volume":"9","author":"McPhail","year":"2016","journal-title":"Risk Manage Healthc Policy"},{"key":"B4","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/1477-7525-7-82","article-title":"O'Donnell BE, Dean D. 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