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Comparison of Long COVID-19 Caused by Different SARS-CoV-2 Strains: A Systematic Review and Meta-Analysis

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Listed:
  • Min Du

    (Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China)

  • Yirui Ma

    (Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China)

  • Jie Deng

    (Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China)

  • Min Liu

    (Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China)

  • Jue Liu

    (Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China
    Institute for Global Health and Development, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing 100871, China
    Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases Group, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China
    Key Laboratory of Reproductive Health, National Health and Family Planning Commission of the People’s Republic of China, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China)

Abstract

Although many studies of long COVID-19 were reported, there was a lack of systematic research which assessed the differences of long COVID-19 in regard to what unique SARS-CoV-2 strains caused it. As such, this systematic review and meta-analysis aims to evaluate the characteristics of long COVID-19 that is caused by different SARS-CoV-2 strains. We systematically searched the PubMed, EMBASE, and ScienceDirect databases in order to find cohort studies of long COVID-19 as defined by the WHO (Geneva, Switzerland). The main outcomes were in determining the percentages of long COVID-19 among patients who were infected with different SARS-CoV-2 strains. Further, this study was registered in PROSPERO (CRD42022339964). A total of 51 studies with 33,573 patients was included, of which three studies possessed the Alpha and Delta variants, and five studies possessed the Omicron variant. The highest pooled estimate of long COVID-19 was found in the CT abnormalities (60.5%; 95% CI: 40.4%, 80.6%) for the wild-type strain; fatigue (66.1%; 95% CI: 42.2%, 89.9%) for the Alpha variant; and ≥1 general symptoms (28.4%; 95% CI: 7.9%, 49.0%) for the Omicron variant. The pooled estimates of ≥1 general symptoms (65.8%; 95% CI: 47.7%, 83.9%) and fatigue were the highest symptoms found among patients infected with the Alpha variant, followed by the wild-type strain, and then the Omicron variant. The pooled estimate of myalgia was highest among patients infected with the Omicron variant (11.7%; 95%: 8.3%, 15.1%), compared with those infected with the wild-type strain (9.4%; 95%: 6.3%, 12.5%). The pooled estimate of sleep difficulty was lowest among the patients infected with the Delta variant (2.5%; 95%: 0.2%, 4.9%) when compared with those infected with the wild-type strain (24.5%; 95%: 17.5%, 31.5%) and the Omicron variant (18.7%; 95%: 1.0%, 36.5%). The findings of this study suggest that there is no significant difference between long COVID-19 that has been caused by different strains, except in certain general symptoms (i.e., in the Alpha or Omicron variant) and in sleep difficulty (i.e., the wild-type strain). In the context of the ongoing COVID-19 pandemic and its emerging variants, directing more attention to long COVID-19 that is caused by unique strains, as well as implementing targeted intervention measures to address it are vital.

Suggested Citation

  • Min Du & Yirui Ma & Jie Deng & Min Liu & Jue Liu, 2022. "Comparison of Long COVID-19 Caused by Different SARS-CoV-2 Strains: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(23), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16010-:d:989396
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    References listed on IDEAS

    as
    1. Yirui Ma & Jie Deng & Qiao Liu & Min Du & Min Liu & Jue Liu, 2022. "Long-Term Consequences of COVID-19 at 6 Months and Above: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(11), pages 1-16, June.
    2. Bao V. Duong & Puchanee Larpruenrudee & Tianxin Fang & Sheikh I. Hossain & Suvash C. Saha & Yuantong Gu & Mohammad S. Islam, 2022. "Is the SARS CoV-2 Omicron Variant Deadlier and More Transmissible Than Delta Variant?," IJERPH, MDPI, vol. 19(8), pages 1-25, April.
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    1. Weipeng Fang & Changwei Qin & Dan Zhou & Jian Yin & Zhongmin Liu & Xianjun Guan, 2023. "The Impact of Cooperative Behavior between Social Organizations during the COVID-19 Pandemic Outbreak in Shanghai: A Simulation Approach," IJERPH, MDPI, vol. 20(2), pages 1-12, January.
    2. Claire E. Hastie & David J. Lowe & Andrew McAuley & Nicholas L. Mills & Andrew J. Winter & Corri Black & Janet T. Scott & Catherine A. O’Donnell & David N. Blane & Susan Browne & Tracy R. Ibbotson & J, 2023. "True prevalence of long-COVID in a nationwide, population cohort study," Nature Communications, Nature, vol. 14(1), pages 1-6, December.
    3. Jacqueline A. Krysa & Sidney Horlick & Kiran Pohar Manhas & Katharina Kovacs Burns & Mikayla Buell & Maria J. Santana & Kristine Russell & Elizabeth Papathanassoglou & Chester Ho, 2023. "Accessing Care Services for Long COVID Sufferers in Alberta, Canada: A Random, Cross-Sectional Survey Study," IJERPH, MDPI, vol. 20(15), pages 1-14, July.
    4. Christina J. Atchison & Bethan Davies & Emily Cooper & Adam Lound & Matthew Whitaker & Adam Hampshire & Adriana Azor & Christl A. Donnelly & Marc Chadeau-Hyam & Graham S. Cooke & Helen Ward & Paul Ell, 2023. "Long-term health impacts of COVID-19 among 242,712 adults in England," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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