Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients
Background: Identifying appropriate comorbidity data sources is a key consideration in health services and outcomes research. Objective: Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the ho...
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Veröffentlicht in: | Medical care 2006-10, Vol.44 (10), p.921-928 |
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description | Background: Identifying appropriate comorbidity data sources is a key consideration in health services and outcomes research. Objective: Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the hospital record versus Medicare claims, both precancer diagnosis and associated with a cancer treatment-related index hospitalization. Methods: We used data from 1995 Surveillance, Epidemiology and End Results patterns of care studies for 1382 patients. Comorbid conditions were ascertained from the hospital record associated with the most definitive cancer treatment and Medicare claims. We calculated the prevalence for and assessed concordances among 12 conditions derived from the hospital record facesheet; full hospital record; Medicare claims precancer diagnosis, with and without a rule-out algorithm applied; and Medicare claims associated with an index hospitalization. Results: The proportion of patients with one or more comorbid conditions varied by data source, from 21% for the facesheet to 85% for prediagnosis Medicare claims without the rule-out algorithm. Condition prevalences were substantially lower for the facesheet compared with the full hospital record. For prediagnosis Medicare claims, condition prevalences were more than 1.7 times greater in the absence of an algorithm to screen for rule-out diagnoses. Measures assessing concordance between the full hospital record and prediagnosis Medicare claims (rule-out algorithm applied) showed modest agreement. Conclusions: The hospital record and Medicare claims are complementary data sources for identifying comorbid conditions. Comorbidity is greatly underascertained when derived only from the facesheet of the hospital record. Investigators using Part B Medicare claims to measure comorbidity should remove conditions that are listed for purposes of generating bills but are not true comorbidities. |
doi_str_mv | 10.1097/01.mlr.0000223480.52713.b9 |
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Objective: Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the hospital record versus Medicare claims, both precancer diagnosis and associated with a cancer treatment-related index hospitalization. Methods: We used data from 1995 Surveillance, Epidemiology and End Results patterns of care studies for 1382 patients. Comorbid conditions were ascertained from the hospital record associated with the most definitive cancer treatment and Medicare claims. We calculated the prevalence for and assessed concordances among 12 conditions derived from the hospital record facesheet; full hospital record; Medicare claims precancer diagnosis, with and without a rule-out algorithm applied; and Medicare claims associated with an index hospitalization. Results: The proportion of patients with one or more comorbid conditions varied by data source, from 21% for the facesheet to 85% for prediagnosis Medicare claims without the rule-out algorithm. Condition prevalences were substantially lower for the facesheet compared with the full hospital record. For prediagnosis Medicare claims, condition prevalences were more than 1.7 times greater in the absence of an algorithm to screen for rule-out diagnoses. Measures assessing concordance between the full hospital record and prediagnosis Medicare claims (rule-out algorithm applied) showed modest agreement. Conclusions: The hospital record and Medicare claims are complementary data sources for identifying comorbid conditions. Comorbidity is greatly underascertained when derived only from the facesheet of the hospital record. Investigators using Part B Medicare claims to measure comorbidity should remove conditions that are listed for purposes of generating bills but are not true comorbidities.</description><identifier>ISSN: 0025-7079</identifier><identifier>EISSN: 1537-1948</identifier><identifier>DOI: 10.1097/01.mlr.0000223480.52713.b9</identifier><identifier>PMID: 17001263</identifier><identifier>CODEN: MELAAD</identifier><language>eng</language><publisher>United States: Lippincott Williams & Wilkins</publisher><subject>Aged ; Aged, 80 and over ; Cancer ; Cohort Studies ; Comorbidity ; Comparative studies ; Data analysis ; Diabetes ; Female ; Hospital records ; Hospitalization ; Hospitals ; Humans ; Illnesses ; Insurance Claim Review - statistics & numerical data ; Insurance claims ; Lung diseases ; Male ; Medical Audit - statistics & numerical data ; Medical records ; Medicare ; Neoplasms ; Prophets ; Pulmonary heart disease ; SEER Program</subject><ispartof>Medical care, 2006-10, Vol.44 (10), p.921-928</ispartof><rights>Copyright © 2006 Lippincott Williams & Wilkins</rights><rights>2006 Lippincott Williams & Wilkins, Inc.</rights><rights>Copyright Lippincott Williams & Wilkins Oct 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3546-77bd4c2eed997106ec8191e697bc27f82a6be06b864443cb0f3a5ee3aad7b9f93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/41219541$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/41219541$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,27915,27916,58008,58241</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17001263$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Klabunde, Carrie N.</creatorcontrib><creatorcontrib>Harlan, Linda C.</creatorcontrib><creatorcontrib>Warren, Joan L.</creatorcontrib><title>Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients</title><title>Medical care</title><addtitle>Med Care</addtitle><description>Background: Identifying appropriate comorbidity data sources is a key consideration in health services and outcomes research. Objective: Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the hospital record versus Medicare claims, both precancer diagnosis and associated with a cancer treatment-related index hospitalization. Methods: We used data from 1995 Surveillance, Epidemiology and End Results patterns of care studies for 1382 patients. Comorbid conditions were ascertained from the hospital record associated with the most definitive cancer treatment and Medicare claims. We calculated the prevalence for and assessed concordances among 12 conditions derived from the hospital record facesheet; full hospital record; Medicare claims precancer diagnosis, with and without a rule-out algorithm applied; and Medicare claims associated with an index hospitalization. Results: The proportion of patients with one or more comorbid conditions varied by data source, from 21% for the facesheet to 85% for prediagnosis Medicare claims without the rule-out algorithm. Condition prevalences were substantially lower for the facesheet compared with the full hospital record. For prediagnosis Medicare claims, condition prevalences were more than 1.7 times greater in the absence of an algorithm to screen for rule-out diagnoses. Measures assessing concordance between the full hospital record and prediagnosis Medicare claims (rule-out algorithm applied) showed modest agreement. Conclusions: The hospital record and Medicare claims are complementary data sources for identifying comorbid conditions. Comorbidity is greatly underascertained when derived only from the facesheet of the hospital record. Investigators using Part B Medicare claims to measure comorbidity should remove conditions that are listed for purposes of generating bills but are not true comorbidities.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Cancer</subject><subject>Cohort Studies</subject><subject>Comorbidity</subject><subject>Comparative studies</subject><subject>Data analysis</subject><subject>Diabetes</subject><subject>Female</subject><subject>Hospital records</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Illnesses</subject><subject>Insurance Claim Review - statistics & numerical data</subject><subject>Insurance claims</subject><subject>Lung diseases</subject><subject>Male</subject><subject>Medical Audit - statistics & numerical data</subject><subject>Medical records</subject><subject>Medicare</subject><subject>Neoplasms</subject><subject>Prophets</subject><subject>Pulmonary heart disease</subject><subject>SEER Program</subject><issn>0025-7079</issn><issn>1537-1948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkUtv1TAQhS0EoreFnwCyumCX4FfsuLsqUIpUBOKxtmxnQn1J4ls7UdV_T9JcUQkv7Bn5O8cjH4TOKSkp0eo9oeXQp5IsizEualJWTFFeOv0M7WjFVUG1qJ-j3XJfFYoofYJOc94TQhWv2Et0QtVSM8l36PDBThb_iHPykHEXE_4CNs8pjL9xE4eYXGjD9HCBL9f2YFPIccSxw9cxH8Jke_wdfExtxnZsF20bvE2Am96GYfNr7Ogh4W92CjBO-RV60dk-w-vjeYZ-XX382VwXN18_fW4ubwrPKyELpVwrPANotVaUSPA11RSkVs4z1dXMSgdEuloKIbh3pOO2AuDWtsrpTvMz9G7zPaR4N0OezBCyh763I8Q5G1nXmhMqF_D8P3C__Ma4zGYYUULWgqzQxQb5FHNO0JlDCoNND4YSs4ZiCDVLKOYpFPMYinHrKG-PL8xugPZJekxhAcQG3Md-gpT_9PM9JHMLtp9uHy0rWZGCESLp2hXrtg71ZpPt8xTTP1tBGdWVoPwvKYuihQ</recordid><startdate>20061001</startdate><enddate>20061001</enddate><creator>Klabunde, Carrie N.</creator><creator>Harlan, Linda C.</creator><creator>Warren, Joan L.</creator><general>Lippincott Williams & Wilkins</general><general>Lippincott Williams & Wilkins, Inc</general><general>Lippincott Williams & Wilkins Ovid Technologies</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>K9.</scope><scope>7X8</scope></search><sort><creationdate>20061001</creationdate><title>Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients</title><author>Klabunde, Carrie N. ; Harlan, Linda C. ; Warren, Joan L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3546-77bd4c2eed997106ec8191e697bc27f82a6be06b864443cb0f3a5ee3aad7b9f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Cancer</topic><topic>Cohort Studies</topic><topic>Comorbidity</topic><topic>Comparative studies</topic><topic>Data analysis</topic><topic>Diabetes</topic><topic>Female</topic><topic>Hospital records</topic><topic>Hospitalization</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Illnesses</topic><topic>Insurance Claim Review - statistics & numerical data</topic><topic>Insurance claims</topic><topic>Lung diseases</topic><topic>Male</topic><topic>Medical Audit - statistics & numerical data</topic><topic>Medical records</topic><topic>Medicare</topic><topic>Neoplasms</topic><topic>Prophets</topic><topic>Pulmonary heart disease</topic><topic>SEER Program</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Klabunde, Carrie N.</creatorcontrib><creatorcontrib>Harlan, Linda C.</creatorcontrib><creatorcontrib>Warren, Joan L.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Medical care</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Klabunde, Carrie N.</au><au>Harlan, Linda C.</au><au>Warren, Joan L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients</atitle><jtitle>Medical care</jtitle><addtitle>Med Care</addtitle><date>2006-10-01</date><risdate>2006</risdate><volume>44</volume><issue>10</issue><spage>921</spage><epage>928</epage><pages>921-928</pages><issn>0025-7079</issn><eissn>1537-1948</eissn><coden>MELAAD</coden><abstract>Background: Identifying appropriate comorbidity data sources is a key consideration in health services and outcomes research. Objective: Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the hospital record versus Medicare claims, both precancer diagnosis and associated with a cancer treatment-related index hospitalization. Methods: We used data from 1995 Surveillance, Epidemiology and End Results patterns of care studies for 1382 patients. Comorbid conditions were ascertained from the hospital record associated with the most definitive cancer treatment and Medicare claims. We calculated the prevalence for and assessed concordances among 12 conditions derived from the hospital record facesheet; full hospital record; Medicare claims precancer diagnosis, with and without a rule-out algorithm applied; and Medicare claims associated with an index hospitalization. Results: The proportion of patients with one or more comorbid conditions varied by data source, from 21% for the facesheet to 85% for prediagnosis Medicare claims without the rule-out algorithm. Condition prevalences were substantially lower for the facesheet compared with the full hospital record. For prediagnosis Medicare claims, condition prevalences were more than 1.7 times greater in the absence of an algorithm to screen for rule-out diagnoses. Measures assessing concordance between the full hospital record and prediagnosis Medicare claims (rule-out algorithm applied) showed modest agreement. Conclusions: The hospital record and Medicare claims are complementary data sources for identifying comorbid conditions. Comorbidity is greatly underascertained when derived only from the facesheet of the hospital record. Investigators using Part B Medicare claims to measure comorbidity should remove conditions that are listed for purposes of generating bills but are not true comorbidities.</abstract><cop>United States</cop><pub>Lippincott Williams & Wilkins</pub><pmid>17001263</pmid><doi>10.1097/01.mlr.0000223480.52713.b9</doi><tpages>8</tpages></addata></record> |
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subjects | Aged Aged, 80 and over Cancer Cohort Studies Comorbidity Comparative studies Data analysis Diabetes Female Hospital records Hospitalization Hospitals Humans Illnesses Insurance Claim Review - statistics & numerical data Insurance claims Lung diseases Male Medical Audit - statistics & numerical data Medical records Medicare Neoplasms Prophets Pulmonary heart disease SEER Program |
title | Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients |
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