{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:16:57Z","timestamp":1787062617307,"version":"build-2736575974"},"reference-count":29,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T00:00:00Z","timestamp":1532995200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","award":["R01HS023796"],"award-info":[{"award-number":["R01HS023796"]}],"id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Academic Emergency Medicine"],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The objective was to describe and validate construction of a population\u2010based, longitudinal cohort of injured older adults from 9\u20101\u20101 call to 1\u2010year follow\u2010up using existing data sources, probabilistic linkage, and multiple imputation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This was a descriptive cohort study conducted in seven counties in Oregon and Washington from January 1, 2011, through December 31, 2011, with follow\u2010up through December 31, 2012. The primary cohort included all injured adults \u2265 65 years served by 44 emergency medical services (\n                      <jats:styled-content style=\"fixed-case\">EMS<\/jats:styled-content>\n                      ) agencies. We used nine existing databases to assemble the cohort, including\n                      <jats:styled-content style=\"fixed-case\">EMS<\/jats:styled-content>\n                      data, two state trauma registries, two state discharge databases, two state vital statistics databases, the Oregon Physician Order for Life\u2010Sustaining Treatment registry, and Medicare claims data. We matched data files using probabilistic linkage and handled missing values with multiple imputation. We independently validated data processes using 1,350 randomly sampled records for probabilistic linkage and 3,140 randomly sampled records for variables created from existing data sources.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      There were 15,649 injured older adults in the primary cohort, with 13,661 (87.3%) total matched records and 9,337 (59.7%) matches to the index\n                      <jats:styled-content style=\"fixed-case\">ED<\/jats:styled-content>\n                      \/hospital visit. The sensitivity of linkage was 99.9% (95% confidence interval [\n                      <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                      ]\u00a0= 99.3%\u2013100%) for any match and 98.3% (95%\n                      <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                      \u00a0= 96.2%\u201399.4%) for index event matches. The specificity of linkage was 95.7% (95%\n                      <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                      \u00a0= 93.7%\u201397.2%) for any match and 100% (95%\n                      <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                      \u00a0= 99.2%\u2013100%) for index event matches. Name, date of birth, home zip code, age, and hospital had the highest yield for linkage. Patients with matched records tended to be higher acuity than unmatched patients, suggesting selection bias if unmatched patients were excluded. Compared to hand\u2010abstracted values, the sensitivity of electronically derived variables ranged from 18.2% (abdominal\u2010pelvic Abbreviated Injury Scale score \u2265 3) to 97.4% (in\u2010hospital mortality), with specificity of 88.0% to 99.8%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A population\u2010based emergency care cohort with long\u2010term outcomes can be constructed from existing data sources with high accuracy and reasonable validity of resulting variables.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/acem.13512","type":"journal-article","created":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T18:53:06Z","timestamp":1530643986000},"page":"1268-1283","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Building A Longitudinal Cohort From 9\u20101\u20101 to 1\u2010Year Using Existing Data Sources, Probabilistic Linkage, and Multiple Imputation: A Validation Study"],"prefix":"10.1111","volume":"25","author":[{"given":"Craig D.","family":"Newgard","sequence":"first","affiliation":[{"name":"Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health &amp; Science University Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Malveau","sequence":"additional","affiliation":[{"name":"Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health &amp; Science University Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana","family":"Zive","sequence":"additional","affiliation":[{"name":"Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health &amp; Science University Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Lupton","sequence":"additional","affiliation":[{"name":"Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health &amp; Science University Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amber","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health &amp; Science University Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,7,31]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"What is Big Data?Available at:https:\/\/datascience.nih.gov\/bd2k\/about\/what. 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