Abstract
Objectives
Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using ‘difference-in-differences’ (DiD).
Study design
A retrospective observational study was conducted using five media events in January–February 2020 in England on four routinely monitored syndromic surveillance indicators.
Methods
Dates ‘exposed’ to a media event were estimated using Google Trends internet search intensity data (terms = ‘coronavirus’ and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect.
Results
We estimated a four-fold increase in telehealth ‘cough’ calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called ‘superspreading event’ in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively.
Conclusions
We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.
Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using ‘difference-in-differences’ (DiD).
Study design
A retrospective observational study was conducted using five media events in January–February 2020 in England on four routinely monitored syndromic surveillance indicators.
Methods
Dates ‘exposed’ to a media event were estimated using Google Trends internet search intensity data (terms = ‘coronavirus’ and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect.
Results
We estimated a four-fold increase in telehealth ‘cough’ calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called ‘superspreading event’ in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively.
Conclusions
We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.
| Original language | English |
|---|---|
| Pages (from-to) | 132-137 |
| Number of pages | 6 |
| Journal | Public Health |
| Volume | 232 |
| Early online date | 22 May 2024 |
| DOIs | |
| Publication status | Published - 1 Jul 2024 |
Bibliographical note
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Humans
- COVID-19/epidemiology
- England/epidemiology
- Retrospective Studies
- Patient Acceptance of Health Care/statistics & numerical data
- Sentinel Surveillance
- SARS-CoV-2
- Mass Media/statistics & numerical data
- Pandemics
- Emergency Service, Hospital/statistics & numerical data
- Telemedicine/statistics & numerical data
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