Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates
Background. Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may h...
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Veröffentlicht in: | Clinical infectious diseases 2015-10, Vol.61 (8), p.1235-1243 |
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description | Background. Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions. Methods. Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. Demographics, comorbidities, and socioeconomic status were entered into a hierarchical generalized linear mixed model predicting all-cause and infection-related readmissions. Crude verses adjusted hospital rankings were compared using Cohen's kappa. Results. We assessed 30-day readmission rates from 323 hospitals, accounting for 213 879 194 post-discharge person-days of follow-up. Infection-related readmissions represented 28% of all readmissions and were associated with discharging a high proportion of patients to skilled nursing facilities. Hospitals serving populations with high proportions of males, comorbidities, prolonged length of stay, and populations living in a federal poverty area, had higher all-cause and infection-related readmission rates. Academic hospitals had higher all-cause and infection-related readmission rates (odds ratio 1.24 and 1.15, respectively). When comparing adjusted vs crude hospital rankings for infection-related readmission rates, adjustment revealed 31% of hospitals changed performance category for infection-related readmissions. Conclusions. Infection-related readmissions accounted for nearly 30% of all-cause readmissions. High hospital infection-related readmissions were associated with serving a high proportion of patients with comorbidities, long lengths of stay, discharge to skilled nursing facility, and those living in federal poverty areas. Preventability of these infections needs to be assessed. |
doi_str_mv | 10.1093/cid/civ539 |
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Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions. Methods. Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. Demographics, comorbidities, and socioeconomic status were entered into a hierarchical generalized linear mixed model predicting all-cause and infection-related readmissions. Crude verses adjusted hospital rankings were compared using Cohen's kappa. Results. We assessed 30-day readmission rates from 323 hospitals, accounting for 213 879 194 post-discharge person-days of follow-up. Infection-related readmissions represented 28% of all readmissions and were associated with discharging a high proportion of patients to skilled nursing facilities. Hospitals serving populations with high proportions of males, comorbidities, prolonged length of stay, and populations living in a federal poverty area, had higher all-cause and infection-related readmission rates. Academic hospitals had higher all-cause and infection-related readmission rates (odds ratio 1.24 and 1.15, respectively). When comparing adjusted vs crude hospital rankings for infection-related readmission rates, adjustment revealed 31% of hospitals changed performance category for infection-related readmissions. Conclusions. Infection-related readmissions accounted for nearly 30% of all-cause readmissions. High hospital infection-related readmissions were associated with serving a high proportion of patients with comorbidities, long lengths of stay, discharge to skilled nursing facility, and those living in federal poverty areas. Preventability of these infections needs to be assessed.</description><identifier>ISSN: 1058-4838</identifier><identifier>EISSN: 1537-6591</identifier><identifier>DOI: 10.1093/cid/civ539</identifier><identifier>PMID: 26129752</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject><![CDATA[and Commentaries ; ARTICLES AND COMMENTARIES ; California - epidemiology ; Cohort Studies ; Communicable Diseases - epidemiology ; Comorbidity ; Diagnosis-Related Groups ; Hospitalization - economics ; Hospitalization - statistics & numerical data ; Humans ; Intervention ; Length of Stay - economics ; Length of Stay - statistics & numerical data ; Nosocomial infections ; Odds Ratio ; Patient admissions ; Patient Discharge - statistics & numerical data ; Patient Readmission - economics ; Patient Readmission - statistics & numerical data ; Poverty ; Poverty - statistics & numerical data ; Retrospective Studies ; Risk Factors ; Skilled Nursing Facilities - standards ; Skilled Nursing Facilities - statistics & numerical data ; Socioeconomic Factors]]></subject><ispartof>Clinical infectious diseases, 2015-10, Vol.61 (8), p.1235-1243</ispartof><rights>Copyright © 2015 Oxford University Press on behalf of the Infectious Diseases Society of America</rights><rights>The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.</rights><rights>Copyright Oxford University Press, UK Oct 15, 2015</rights><rights>The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: . 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-81594436921f2bbf39ce3bce24f041577d1fae6121af1ad1e1b552603376a2733</citedby><cites>FETCH-LOGICAL-c428t-81594436921f2bbf39ce3bce24f041577d1fae6121af1ad1e1b552603376a2733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26368659$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26368659$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,780,784,803,885,27924,27925,58017,58250</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26129752$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gohil, Shruti K.</creatorcontrib><creatorcontrib>Datta, Rupak</creatorcontrib><creatorcontrib>Cao, Chenghua</creatorcontrib><creatorcontrib>Phelan, Michael J.</creatorcontrib><creatorcontrib>Nguyen, Vinh</creatorcontrib><creatorcontrib>Rowther, Armaan A.</creatorcontrib><creatorcontrib>Huang, Susan S.</creatorcontrib><title>Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates</title><title>Clinical infectious diseases</title><addtitle>Clin Infect Dis</addtitle><description>Background. Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions. Methods. Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. Demographics, comorbidities, and socioeconomic status were entered into a hierarchical generalized linear mixed model predicting all-cause and infection-related readmissions. Crude verses adjusted hospital rankings were compared using Cohen's kappa. Results. We assessed 30-day readmission rates from 323 hospitals, accounting for 213 879 194 post-discharge person-days of follow-up. Infection-related readmissions represented 28% of all readmissions and were associated with discharging a high proportion of patients to skilled nursing facilities. Hospitals serving populations with high proportions of males, comorbidities, prolonged length of stay, and populations living in a federal poverty area, had higher all-cause and infection-related readmission rates. Academic hospitals had higher all-cause and infection-related readmission rates (odds ratio 1.24 and 1.15, respectively). When comparing adjusted vs crude hospital rankings for infection-related readmission rates, adjustment revealed 31% of hospitals changed performance category for infection-related readmissions. Conclusions. Infection-related readmissions accounted for nearly 30% of all-cause readmissions. High hospital infection-related readmissions were associated with serving a high proportion of patients with comorbidities, long lengths of stay, discharge to skilled nursing facility, and those living in federal poverty areas. Preventability of these infections needs to be assessed.</description><subject>and Commentaries</subject><subject>ARTICLES AND COMMENTARIES</subject><subject>California - epidemiology</subject><subject>Cohort Studies</subject><subject>Communicable Diseases - epidemiology</subject><subject>Comorbidity</subject><subject>Diagnosis-Related Groups</subject><subject>Hospitalization - economics</subject><subject>Hospitalization - statistics & numerical data</subject><subject>Humans</subject><subject>Intervention</subject><subject>Length of Stay - economics</subject><subject>Length of Stay - statistics & numerical data</subject><subject>Nosocomial infections</subject><subject>Odds Ratio</subject><subject>Patient admissions</subject><subject>Patient Discharge - statistics & numerical data</subject><subject>Patient Readmission - economics</subject><subject>Patient Readmission - statistics & numerical data</subject><subject>Poverty</subject><subject>Poverty - statistics & numerical data</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Skilled Nursing Facilities - standards</subject><subject>Skilled Nursing Facilities - statistics & numerical data</subject><subject>Socioeconomic Factors</subject><issn>1058-4838</issn><issn>1537-6591</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdks-LEzEcxYMo7lq9eFcCXkQ2mm8ymclchKX-2MKKUvQcMplkTUkns8lMsSf_dVO6llVISOB98nhfXhB6DvQt0Ja_M74veyd4-wCdg-ANqUULD8udCkkqyeUZepLzhlIAScVjdMZqYG0j2Dn6vdqO2kw4OnwV8-gnHfC3OM5BTz4OeKmzJV_8rwu8GkyYez_cFHln07S_wEU_vbkMgSz1nC3WQ19gZ83BgKxtcbI95pR80Hu8trrf-pwP3usi5KfokdMh22d35wL9-PTx-_KKXH_9vFpeXhNTMTkRCaKtKl63DBzrOsdbY3lnLKscrUA0TQ9O2zIVaAe6BwudEKymnDe1Zg3nC_T-6DvO3db2xg5T0kGNyW912quovfpXGfxPdRN3qhKSl1UMXt8ZpHg72zypMoexIejBxjkraKBhtZC0Luir_9BNnNNQxjtQLQVelWQL9OZImRRzTtadwgBVh15V6VUdey3wy_vxT-jfIgvw4ghs8hTTPZ3XsvwG_geM8Kix</recordid><startdate>20151015</startdate><enddate>20151015</enddate><creator>Gohil, Shruti K.</creator><creator>Datta, Rupak</creator><creator>Cao, Chenghua</creator><creator>Phelan, Michael J.</creator><creator>Nguyen, Vinh</creator><creator>Rowther, Armaan A.</creator><creator>Huang, Susan S.</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7T2</scope><scope>7T7</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20151015</creationdate><title>Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates</title><author>Gohil, Shruti K. ; Datta, Rupak ; Cao, Chenghua ; Phelan, Michael J. ; Nguyen, Vinh ; Rowther, Armaan A. ; Huang, Susan S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-81594436921f2bbf39ce3bce24f041577d1fae6121af1ad1e1b552603376a2733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>and Commentaries</topic><topic>ARTICLES AND COMMENTARIES</topic><topic>California - epidemiology</topic><topic>Cohort Studies</topic><topic>Communicable Diseases - epidemiology</topic><topic>Comorbidity</topic><topic>Diagnosis-Related Groups</topic><topic>Hospitalization - economics</topic><topic>Hospitalization - statistics & numerical data</topic><topic>Humans</topic><topic>Intervention</topic><topic>Length of Stay - economics</topic><topic>Length of Stay - statistics & numerical data</topic><topic>Nosocomial infections</topic><topic>Odds Ratio</topic><topic>Patient admissions</topic><topic>Patient Discharge - statistics & numerical data</topic><topic>Patient Readmission - economics</topic><topic>Patient Readmission - statistics & numerical data</topic><topic>Poverty</topic><topic>Poverty - statistics & numerical data</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Skilled Nursing Facilities - standards</topic><topic>Skilled Nursing Facilities - statistics & numerical data</topic><topic>Socioeconomic Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gohil, Shruti K.</creatorcontrib><creatorcontrib>Datta, Rupak</creatorcontrib><creatorcontrib>Cao, Chenghua</creatorcontrib><creatorcontrib>Phelan, Michael J.</creatorcontrib><creatorcontrib>Nguyen, Vinh</creatorcontrib><creatorcontrib>Rowther, Armaan A.</creatorcontrib><creatorcontrib>Huang, Susan S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Clinical infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gohil, Shruti K.</au><au>Datta, Rupak</au><au>Cao, Chenghua</au><au>Phelan, Michael J.</au><au>Nguyen, Vinh</au><au>Rowther, Armaan A.</au><au>Huang, Susan S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates</atitle><jtitle>Clinical infectious diseases</jtitle><addtitle>Clin Infect Dis</addtitle><date>2015-10-15</date><risdate>2015</risdate><volume>61</volume><issue>8</issue><spage>1235</spage><epage>1243</epage><pages>1235-1243</pages><issn>1058-4838</issn><eissn>1537-6591</eissn><abstract>Background. Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions. Methods. Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. Demographics, comorbidities, and socioeconomic status were entered into a hierarchical generalized linear mixed model predicting all-cause and infection-related readmissions. Crude verses adjusted hospital rankings were compared using Cohen's kappa. Results. We assessed 30-day readmission rates from 323 hospitals, accounting for 213 879 194 post-discharge person-days of follow-up. Infection-related readmissions represented 28% of all readmissions and were associated with discharging a high proportion of patients to skilled nursing facilities. Hospitals serving populations with high proportions of males, comorbidities, prolonged length of stay, and populations living in a federal poverty area, had higher all-cause and infection-related readmission rates. Academic hospitals had higher all-cause and infection-related readmission rates (odds ratio 1.24 and 1.15, respectively). When comparing adjusted vs crude hospital rankings for infection-related readmission rates, adjustment revealed 31% of hospitals changed performance category for infection-related readmissions. Conclusions. Infection-related readmissions accounted for nearly 30% of all-cause readmissions. High hospital infection-related readmissions were associated with serving a high proportion of patients with comorbidities, long lengths of stay, discharge to skilled nursing facility, and those living in federal poverty areas. Preventability of these infections needs to be assessed.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>26129752</pmid><doi>10.1093/cid/civ539</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | and Commentaries ARTICLES AND COMMENTARIES California - epidemiology Cohort Studies Communicable Diseases - epidemiology Comorbidity Diagnosis-Related Groups Hospitalization - economics Hospitalization - statistics & numerical data Humans Intervention Length of Stay - economics Length of Stay - statistics & numerical data Nosocomial infections Odds Ratio Patient admissions Patient Discharge - statistics & numerical data Patient Readmission - economics Patient Readmission - statistics & numerical data Poverty Poverty - statistics & numerical data Retrospective Studies Risk Factors Skilled Nursing Facilities - standards Skilled Nursing Facilities - statistics & numerical data Socioeconomic Factors |
title | Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates |
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