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How previous epidemics enable timelier COVID-19 responses : an empirical study using organisational memory theory Tsuei, Sian Hsiang-Te
Abstract
Introduction There has been little systematic exploration into what affects timeliness of epidemic response, despite the potential for earlier responses to be more effective. Speculations have circulated that previous exposure to major epidemics helped health systems respond more quickly to COVID-19. This study leverages organisational memory theory to test whether health systems with any, more severe, or more recent exposure to major epidemics enacted timelier COVID-19 policy responses. Methods A data set was constructed cataloguing 846 policies across 178 health systems in total, 37 of which had major epidemics within the last 20 years. Hypothesis testing used OLS regressions with World Health Organization region fixed effects, controlling for several health system expenditure and political variables. Results Results show that exposure to any major epidemics was associated with providing earlier response in the following policy categories: all policies, surveillance/ response, distancing, and international travel policies. The effect was about 6–10 days earlier response. The significance of this variable was largely nullified with the addition of the other two independent variables. Neither total cases nor years since previous epidemics showed no statistical significance. Conclusion This study suggests that health systems may learn from past major epidemics. Policymakers ought to institutionalise lessons from COVID-19. Future studies can examine specific generalisable lessons and whether timelier responses correlated with lower health and economic impacts.
Item Metadata
Title |
How previous epidemics enable timelier COVID-19 responses : an empirical study using organisational memory theory
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Creator | |
Contributor | |
Publisher |
BMJ
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Date Issued |
2020-08-22
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Description |
Introduction There has been little systematic exploration
into what affects timeliness of epidemic response, despite
the potential for earlier responses to be more effective.
Speculations have circulated that previous exposure to
major epidemics helped health systems respond more
quickly to COVID-19. This study leverages organisational
memory theory to test whether health systems with any,
more severe, or more recent exposure to major epidemics
enacted timelier COVID-19 policy responses.
Methods A data set was constructed cataloguing
846 policies across 178 health systems in total, 37 of
which had major epidemics within the last 20 years.
Hypothesis testing used OLS regressions with World Health
Organization region fixed effects, controlling for several
health system expenditure and political variables.
Results Results show that exposure to any major
epidemics was associated with providing earlier response
in the following policy categories: all policies, surveillance/
response, distancing, and international travel policies.
The effect was about 6–10 days earlier response. The
significance of this variable was largely nullified with the
addition of the other two independent variables. Neither
total cases nor years since previous epidemics showed no
statistical significance.
Conclusion This study suggests that health systems may
learn from past major epidemics. Policymakers ought to
institutionalise lessons from COVID-19. Future studies
can examine specific generalisable lessons and whether
timelier responses correlated with lower health and
economic impacts.
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Genre | |
Type | |
Language |
eng
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Date Available |
2021-09-29
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution 4.0 International
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DOI |
10.14288/1.0402348
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URI | |
Affiliation | |
Citation |
Tsuei SH-T. How previous epidemics enable timelier COVID-19 responses: an empirical study using organisational memory theory. BMJ Global Health 2020;5:e003228.
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Publisher DOI |
10.1136/ bmjgh-2020-003228
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Peer Review Status |
Reviewed
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Scholarly Level |
Graduate
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Copyright Holder |
Authors
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Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution 4.0 International