Association between community-based self-reported COVID-19 symptoms and social deprivation explored using symptom tracker apps: a repeated cross-sectional study in Northern Ireland

Jennifer M McKinley, David Cutting, Neil Anderson, Conor Graham, Brian Johnston, Ute Mueller, Peter M Atkinson, Hugo van Woerden, Declan T Bradley, Frank Kee

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Abstract

Objectives: The aim of the study was to investigate the spatial and temporal relationships between the prevalence of COVID-19 symptoms in the community-level and area-level social deprivation.

Design: Spatial mapping, generalised linear models, using time as a factor and spatial-lag models were used to explore the relationship between self-reported COVID-19 symptom prevalence as recorded through two smartphone symptom tracker apps and a range of socioeconomic factors using a repeated cross-sectional study design.

Setting: In the community in Northern Ireland, UK. The analysis period included the earliest stages of non-pharmaceutical interventions and societal restrictions or ‘lockdown’ in 2020.

Participants: Users of two smartphone symptom tracker apps recording self-reported health information who recorded their location as Northern Ireland, UK.

Primary outcome measures: Population standardised self-reported COVID-19 symptoms and correlation between population standardised self-reported COVID-19 symptoms and area-level characteristics from measures of multiple deprivation including employment levels and population housing density, derived as the mean number of residents per household for each census super output area.

Results: Higher self-reported prevalence of COVID-19 symptoms was associated with the most deprived areas (p
Original languageEnglish
Article numbere048333
Number of pages9
JournalBMJ Open
Volume11
Issue number6
DOIs
Publication statusPublished - 22 Jun 2021

Keywords

  • COVID-19 self-reported symptoms
  • social deprivation
  • population housing density
  • societal restrictions or ‘lockdown'
  • (spatial) regression

ASJC Scopus subject areas

  • Information Systems
  • Statistics and Probability
  • Health Informatics
  • Health Policy

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