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Longitudinal Analysis of the Burden of Post-Acute Chikungunya-Associated Arthralgia in Children and Adults: A Prospective Cohort Study in Managua, Nicaragua (2014-2019).

Colin M WarnesFausto Bustos CarrilloJosé Victor ZambranaBrenda Lopez MercadoSonia ArguelloOscarlette AmpiéDamaris ColladoNery SanchezSergio OjedaGuillermina KuanAubree GordonAngel BalmasedaEva Harris
Published in: medRxiv : the preprint server for health sciences (2023)
Upon its emergence in the Americas in 2013, chikungunya virus spread rapidly, leading to >2 million suspected autochthonous cases between 2014-2015. Much of what we know about chikungunya is derived from adult populations, leading to gaps in guidelines to treat pediatric chikungunya. To address these gaps, we assembled a large cohort of both pediatric (n=612) and adult (n=158) laboratory-confirmed (n=682) or clinically/epidemiologically probable (n=88) chikungunya cases from two distinct epidemics in 2014 and 2015 in Managua, Nicaragua, followed these patients over a two-year timeline, and analyzed chikungunya-associated arthralgia using rigorous statistical approaches. Our analysis demonstrates that the pediatric (0-15 years old [y/o]) population faces a previously unappreciated high burden of post-acute chikungunya-associated arthralgia. Further, we observe post-acute arthralgia presents differently between pediatric and adult cases (16+ y/o). The difference between the two groups was evident when comparing distribution of polyarthralgia across the body parts and when analyzing the persistence of arthralgia in the post-acute phase (> 10 days post-fever onset). Using detailed longitudinal data, our findings provide insight into long-term chikungunya arthralgia across age, sex, body parts, and the different stages of chikungunya. We believe these findings will inform clinical guidelines regarding chikungunya-associated arthralgia across all ages.
Keyphrases
  • zika virus
  • aedes aegypti
  • dengue virus
  • liver failure
  • end stage renal disease
  • respiratory failure
  • machine learning
  • drug induced
  • peritoneal dialysis
  • prognostic factors
  • risk factors
  • intensive care unit