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Abstract
Background Policy makers need to be rapidly informed about the potential equity consequences of different COVID-19 strategies, alongside their broader health and economic impacts. While there are complex models to inform both potential health and macro-economic impact, there are few tools available to rapidly assess potential equity impacts of interventions.
Methods We created an economic model to simulate the impact of lockdown measures in Pakistan, Georgia, Chile, UK, the Philippines and South Africa. We consider impact of lockdown in terms of ability to socially distance, and income loss during lockdown, and tested the impact of assumptions on social protection coverage in a scenario analysis.
Results In all examined countries, socioeconomic status (SES) quintiles 1–3 were disproportionately more likely to experience income loss (70% of people) and inability to socially distance (68% of people) than higher SES quintiles. Improving social protection increased the percentage of the workforce able to socially distance from 48% (33%–60%) to 66% (44%–71%). We estimate the cost of this social protection would be equivalent to an average of 0.6% gross domestic product (0.1% Pakistan–1.1% Chile).
Conclusions We illustrate the potential for using publicly available data to rapidly assess the equity implications of social protection and non-pharmaceutical intervention policy. Social protection is likely to mitigate inequitable health and economic impacts of lockdown. Although social protection is usually targeted to the poorest, middle quintiles will likely also need support as they are most likely to suffer income losses and are disproportionately more exposed.
- COVID-19
- health economics
Data availability statement
Data sharing not applicable as no datasets generated and/or analysed for this study. This modelling study used published or publicly available data. The data used and the sources are described in this article and in the appendix. No primary data were collected for this study. Access to the model described in the study can be requested by contacting sedona.sweeney@lshtm.ac.uk.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
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Data availability statement
Data sharing not applicable as no datasets generated and/or analysed for this study. This modelling study used published or publicly available data. The data used and the sources are described in this article and in the appendix. No primary data were collected for this study. Access to the model described in the study can be requested by contacting sedona.sweeney@lshtm.ac.uk.
Supplementary materials
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Footnotes
Handling editor Edwine Barasa
TPJC, RE, MH, MJ, DM, NRN, SP, MQ, LS, ST-R and VV contributed equally.
Collaborators CHiL COVID Working Group: Fiammetta Bozzani, Nicholas G Davies, Henning Jensen, Marcus Keogh-Brown, Mishal Khan, Nichola Kitson, Nuru Saadi, Julia Shen (London School of Hygiene & Tropical Medicine, London, UK).
Contributors All authors contributed to study and model design. SS, TPJC, MH, MJ, DM, SP, MMQ, LS and VV analysed the data. All authors interpreted results, SS drafted the manuscript and all authors critically revised the manuscript and approved the final submitted version of the manuscript. Aside from SS and AV, the authors are listed alphabetically to reflect equal contribution.
Funding This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome (grant number 221303/Z/20/Z).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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