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Original research
Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications
  1. Andrew T Levin1,2,
  2. Nana Owusu-Boaitey3,
  3. Sierra Pugh4,
  4. Bailey K Fosdick5,
  5. Anthony B Zwi6,
  6. Anup Malani7,
  7. Satej Soman8,
  8. Lonni Besançon9,
  9. Ilya Kashnitsky10,
  10. Sachin Ganesh11,
  11. Aloysius McLaughlin11,
  12. Gayeong Song11,
  13. Rine Uhm11,
  14. Daniel Herrera-Esposito12,
  15. Gustavo de los Campos13,
  16. Ana Carolina Peçanha Antonio14,
  17. Enyew Birru Tadese15,
  18. Gideon Meyerowitz-Katz16,17
  1. 1Economics, Dartmouth College, Hanover, New Hampshire, USA
  2. 2National Bureau for Economic Research, Cambridge, Massachusetts, USA
  3. 3School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
  4. 4Statistics, Colorado State University, Fort Collins, Colorado, USA
  5. 5Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
  6. 6School of Social Sciences, University of New South Wales, Sydney, New South Wales, Australia
  7. 7Law School, University of Chicago, Chicago, Illinois, USA
  8. 8Harris School of Public Policy, University of Chicago, Chicago, Illinois, USA
  9. 9Faculty of Information and Technology, Monash University, Clayton, Victoria, Australia
  10. 10Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, Odense, Denmark
  11. 11Department of Economics, Dartmouth College, Hanover, New Hampshire, USA
  12. 12Laboratorio de Neurociencias, Universidad de la República, Montevideo, Uruguay
  13. 13Department of Epidemiology & Biostatistics, Michigan State University, East Lansing, Michigan, USA
  14. 14Adult Intensive Care Unit, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
  15. 15Ethiopian Public Health Institute, Addis Ababa, Ethiopia
  16. 16Western Sydney Diabetes, Western Sydney Local Health District, Blacktown, New South Wales, Australia
  17. 17School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia
  1. Correspondence to Gideon Meyerowitz-Katz; gideon.meyerowitzkatz{at}health.nsw.gov.au

Abstract

Introduction The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries.

Methods We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible.

Results In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups.

Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure.

Conclusion The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

  • COVID-19
  • Epidemiology
  • Public Health
  • Systematic review
  • Serology

Data availability statement

Data are available in a public, open access repository.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjgh-2022-008477

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    Data availability statement

    Data are available in a public, open access repository.

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    Footnotes

    • Handling editor Seye Abimbola

    • Twitter @HEARDatUNSW, @anup_malani, @satejsoman, @AnaCarolPecanha, @Enyew54639156

    • Contributors ATL and GM-K initiated and provided leadership for the project, and act as guarantors for the project. BKF and SP designated the Bayesian statistical framework. NO-B took primary responsibility for the search procedures, and performed the review of assay characteristics and seroreversion. ATL and NO-B reviewed each of the studies identified in the initial screening, and assessed and applied the exclusion criteria. SS took the lead in designing the data management procedures and setting up the GitHub repository. LB has developed an interactive tool that will be linked to the GitHub repository. SG, AM, GS and RU assisted with data extraction and verification. ABZ, AM and IK reviewed the methodology and contributed to the discussion of key findings. DH-E, GdlC, ACPA and EBT contributed insights that reflected their experience with health issues in developing countries. GM-K drafted the main text; NO-B and SP drafted the supplementary materials. ATL was responsible for conducting the metaregressions and produced all the figures and tables included in the manuscript. ATL, GM-K, NO-B and SP edited the text of the manuscript and the supplementary materials.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Map disclaimer The inclusion of any map (including the depiction of any boundaries therein), or of any geographical or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability online repository https://covid-ifr.github.io/

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.