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Knowledge barriers in a national symptomatic-COVID-19 testing programme.
Graham, Mark S; May, Anna; Varsavsky, Thomas; Sudre, Carole H; Murray, Benjamin; Kläser, Kerstin; Antonelli, Michela; Canas, Liane S; Molteni, Erika; Modat, Marc; Cardoso, M Jorge; Drew, David A; Nguyen, Long H; Rader, Benjamin; Hu, Christina; Capdevila, Joan; Hammers, Alexander; Chan, Andrew T; Wolf, Jonathan; Brownstein, John S; Spector, Tim D; Ourselin, Sebastien; Steves, Claire J; Astley, Christina M.
Afiliación
  • Graham MS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • May A; Zoe Global Limited, London, United Kingdom.
  • Varsavsky T; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Sudre CH; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Murray B; Department of Population Science and Experimental Medicine, MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom.
  • Kläser K; Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.
  • Antonelli M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Canas LS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Molteni E; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Modat M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Cardoso MJ; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Drew DA; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Nguyen LH; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Rader B; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America.
  • Hu C; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America.
  • Capdevila J; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States of America.
  • Hammers A; Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States of America.
  • Chan AT; Zoe Global Limited, London, United Kingdom.
  • Wolf J; Zoe Global Limited, London, United Kingdom.
  • Brownstein JS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Spector TD; King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, London, United Kingdom.
  • Ourselin S; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America.
  • Steves CJ; Zoe Global Limited, London, United Kingdom.
  • Astley CM; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States of America.
PLOS Glob Public Health ; 2(1): e0000028, 2022.
Article en En | MEDLINE | ID: mdl-36962066
Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. We analysed a cohort of untested symptomatic app users (N = 1,237), nested in the Zoe COVID Symptom Study (Zoe, N = 4,394,948); and symptomatic respondents who wanted, but did not have a test (N = 1,956), drawn from a University of Maryland survey administered to Facebook users (The Global COVID-19 Trends and Impact Survey [CTIS], N = 775,746). The proportion tested among individuals with incident test-qualifying symptoms rose from ~20% to ~75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (72.9% vs 84.6% p<0.001), or short vs long symptom duration (69.9% vs 85.4% p<0.001). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR = 0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-CTIS respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR = 1.207 [1.129-1.292]) and for every 4-years fewer in education (OR = 0.685 [0.599-0.783]). Despite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ~25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages. The testing gap may be ever larger in countries that do not have extensive, free testing, as the UK does.

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLOS Glob Public Health Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLOS Glob Public Health Año: 2022 Tipo del documento: Article