{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:31:05Z","timestamp":1781555465300,"version":"3.54.5"},"reference-count":25,"publisher":"BMJ","issue":"6","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"unspecified","delay-in-days":25,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,6,11]]},"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Background rates are critical for contextualising safety signals arising from COVID-19-related interventions in investigational or real-world settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To estimate background rates of medical events of interest (MEI) for which COVID-19 infection and\/or COVID-19 interventions may be risk factors in two US claims databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting and participants<\/jats:title>\n                    <jats:p>This retrospective cohort study spans the pre-COVID-19 (2018\u20132019) and COVID-19 (2020\u20132021) periods. We constructed three cohorts, in each of Inovalon\/HealthVerity (Inovalon\/HV) and Optum databases: a COVID-19-positive adult cohort (2020\u20132021), a paediatric cohort (2018\u20132021) and a high-risk cohort (2018\u20132021) comprising patients at increased risk for severe COVID-19. Participants were indexed on the day they first qualified to enter each cohort during the study period. Background rates of 17 MEI were estimated per 1000 person-years (PY) with 95% CIs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main outcomes and measures<\/jats:title>\n                    <jats:p>Annual incidence rates (IRs) of 17 MEI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Overall, 758\u2009414 (COVID-19-positive adults; 57.8% women), 12\u2009513\u2009664 (high-risk adults; 56.8% women) and 8\u2009510\u2009627 (paediatric patients; 49.1% women) patients were identified in the HV database. IRs of MEI varied substantially by year, data source, study cohort and duration of follow-up. The IRs of MEI were highest among COVID-19-positive adults and lowest among paediatric patients. For example, IR of myocarditis\/pericarditis per 1000 PY was 3.0 (95% CI: 2.6 to 3.4) in the COVID-19-positive adult cohort vs 0.36 (95% CI: 0.34 to 0.37) among high-risk adults and 0.05 (95% CI: 0.05 to 0.06) among paediatric patients. In the COVID-19-positive adult cohort, we observed higher IRs during 90-day follow-up (eg, IR of acute myocardial infarction (AMI) 26.5 (95% CI: 25.3 to 27.7)) vs 365-day follow-up (eg, IR of AMI 20.0 (95% CI: 9.2 to 20.8)) and during 2020 compared with 2021. IRs were higher in the high-risk adult and paediatric populations during the pre-COVID-19 period than during the COVID-19 pandemic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Substantial variability was observed in IRs of MEI by study cohort, year, data source and follow-up duration. When generating background rates for contextualising safety signals from COVID-19 interventions, careful consideration must be given to the indicated subpopulation of interest, COVID-19-related temporal variations and data sources.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2024-098311","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T01:00:37Z","timestamp":1750986037000},"page":"e098311","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Background rates of medical events of interest before and during the COVID-19 pandemic: a longitudinal cohort study using claims data"],"prefix":"10.1136","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6729-1197","authenticated-orcid":false,"given":"Victor O","family":"Popoola","sequence":"first","affiliation":[{"name":"Regeneron Pharmaceuticals Inc, Tarrytown, New York, 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Inc, Tarrytown, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gulce","family":"Askin","sequence":"additional","affiliation":[{"name":"Aetion Inc, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Asubonteng","sequence":"additional","affiliation":[{"name":"Regeneron Pharmaceuticals Inc, Tarrytown, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clarke","family":"Patrone","sequence":"additional","affiliation":[{"name":"Aetion Inc, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Shao","sequence":"additional","affiliation":[{"name":"Regeneron Pharmaceuticals Inc, Tarrytown, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Szendrey","sequence":"additional","affiliation":[{"name":"Aetion Inc, New York, New York, 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