Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative.
Hannah MandelYun YooAndrea J AllenSajjad AbedianZoe VerzaniElizabeth KarlsonLawrence Charles KleinmanPraveen MudumbiCarlos R OliveiraJennifer MuszynskiRachel GrossThomas CartonC KimEmily TaylorHeekyong ParkJasmin DiversJ Daniel KellyJonathan ArnoldCarol GearyChengxi ZangKelan TantisiraKyung RheeMichael KoropsakSindhu MohandasAndrew VaseyMark G WeinerAbu MosaMelissa HaendelChristopher G ChuteShawn N MurphyLisa O'BrienJacqueline SzmuszkoviczNicholas GütheJorge SantanaAliva DeAmanda BogieKatia HalabiLathika MohanrajPatricia KinserSamuel E PackardKatherine R TuttleLorna ThorpeRichard A MoffittPublished in: Research square (2024)
Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.
Keyphrases
- sars cov
- risk factors
- coronavirus disease
- electronic health record
- public health
- end stage renal disease
- respiratory syndrome coronavirus
- young adults
- ejection fraction
- machine learning
- chronic kidney disease
- newly diagnosed
- quality improvement
- prognostic factors
- deep learning
- liver failure
- big data
- dna methylation
- patient reported outcomes
- copy number
- genome wide
- intensive care unit
- respiratory failure
- cross sectional
- adverse drug