A comparison of four epidemic waves of COVID-19 in Malawi; an observational cohort study

  1. Catherine Anscombe
  2. Samantha Lissauer
  3. Herbert Thole
  4. Jamie Rylance
  5. Dingase Dula
  6. Mavis Menyere
  7. Belson Kutambe
  8. Charlotte van der Veer
  9. Tamara Phiri
  10. Ndaziona P. Banda
  11. Kwazizira S. Mndolo
  12. Kelvin Mponda
  13. Chimota Phiri
  14. Jane Mallewa
  15. Mulinda Nyirenda
  16. Grace Katha
  17. Henry Mwandumba
  18. Stephen B. Gordon
  19. Kondwani C. Jambo
  20. Jennifer Cornick
  21. Nicholas Feasey
  22. Kayla G. Barnes
  23. Ben Morton
  24. Philip M. Ashton
  25. Blantyre COVID-19 Consortium
  26. Wezzie Kalua
  27. Peter Mandala
  28. Barbara Katutula
  29. Rosaleen Ng’oma
  30. Steven Lanken
  31. Jacob Phulusa
  32. Mercy Mkandawire
  33. Sylvester Kaimba
  34. Sharon Nthala
  35. Edna Nsomba
  36. Lucy Keyala
  37. Beatrice Chinoko
  38. Markus Gmeiner
  39. Vella Kaudzu
  40. Bridget Freyne
  41. Todd D. Swarthout
  42. Pui-Ying Iroh Tam
  43. Simon Sichone
  44. Ajisa Ahmadu
  45. Grace Stima
  46. Mazuba Masina
  47. Oscar Kanjewa
  48. Vita Nyasulu
  49. End Chinyama
  50. Allan Zuza
  51. Brigitte Denis
  52. Evance Storey
  53. Nedson Bondera
  54. Danford Matchado
  55. Adams Chande
  56. Arthur Chingota
  57. Chimenya Ntwea
  58. Langford Mkandawire
  59. Chimwemwe Mhango
  60. Agness Lakudzala
  61. Mphatso Chaponda
  62. Percy Mwenechanya
  63. Leonard Mvaya
  64. Dumizulu Tembo
  65. Marc Y. R. Henrion
  66. James Chirombo
  67. Paul Kambiya
  68. Clemens Masesa
  69. Joel Gondwe

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Abstract

Background

Compared to the abundance of clinical and genomic information available on patients hospitalised with COVID-19 disease from high-income countries, there is a paucity of data from low-income countries. Our aim was to explore the relationship between viral lineage and patient outcome.

Methods

We enrolled a prospective observational cohort of adult patients hospitalised with PCR-confirmed COVID-19 disease between July 2020 and March 2022 from Blantyre, Malawi, covering four waves of SARS-CoV-2 infections. Clinical and diagnostic data were collected using an adapted ISARIC clinical characterization protocol for COVID-19. SARS-CoV-2 isolates were sequenced using the MinION™ in Blantyre.

Results

We enrolled 314 patients, good quality sequencing data was available for 55 patients. The sequencing data showed that 8 of 11 participants recruited in wave one had B.1 infections, 6/6 in wave two had Beta, 25/26 in wave three had Delta and 11/12 in wave four had Omicron. Patients infected during the Delta and Omicron waves reported fewer underlying chronic conditions and a shorter time to presentation. Significantly fewer patients required oxygen (22.7% [17/75] vs. 58.6% [140/239], p < 0.001) and steroids (38.7% [29/75] vs. 70.3% [167/239], p < 0.001) in the Omicron wave compared with the other waves. Multivariable logistic-regression demonstrated a trend toward increased mortality in the Delta wave (OR 4.99 [95% CI 1.0–25.0 p = 0.05) compared to the first wave of infection.

Conclusions

Our data show that each wave of patients hospitalised with SARS-CoV-2 was infected with a distinct viral variant. The clinical data suggests that patients with severe COVID-19 disease were more likely to die during the Delta wave.

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  1. Version published to 10.1186/s12879-022-07941-y
  2. Version published to 10.1101/2022.02.17.22269742v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.02.17.22269742: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: Patients with suspected or confirmed SARS-CoV-2 infection were approached for informed consent with an aim to recruit within 72 hours of hospital admission.
    IRB: Study protocols were approved by the Malawi National Health Science Research Committee (NHSRC, 20/02/2518 and 19/08/2246) and Liverpool School of Tropical Medicine Research Ethics Committee (LSTM REC, 20/026 and 19/017).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: Clinical data were analysed using Stata V15.1 (StataCorp, Stata Statistical Software:
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    Univariable and multivariable logistic regression analyses were fitted using the STATA “logistic” command to generate odds ratios and confidence intervals (data and code available in supplementary materials).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    Sequencing was carried out with the Oxford Nanopore Technologies MinION sequencer.
    Oxford Nanopore
    suggested: (Oxford Nanopore Technologies, RRID:SCR_003756)
    Analysis of SARS-CoV-2 sequencing data: Raw FAST5 data produced by the MinION were processed with Guppy v5.0.7.
    MinION
    suggested: (MinION, RRID:SCR_017985)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. Firstly, we produced a relatively small number of sequences. This was partly due to the limited number of patients recruited into the study during each wave but also because patients frequently presented with Ct values that were too high to produce good quality sequence data. Secondly, our observations are limited to a single centre in the Southern region of Malawi, however they appear to be broadly consistent with the national picture. Finally, we may not be capturing the full diversity of SARS-CoV-2 circulating in the community, as our sampling of hospitalised patients represents a considerable bias towards people with severe disease, and there is likely to be significant under ascertainment nationally (21). This inequity in the availability of clinical and preventative interventions was mirrored by the lack of timely sequencing data available to inform national public health measures and to contribute to international databases. The recent Omicron VOC was first described in South Africa in November 2021 because facilities were available to link clinical and laboratory observations – despite the barriers we faced, at the start of the fourth wave, we were able to confirm the presence of Omicron VOC within 4 weeks of its first detection globally and within three days of the swab being taken.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2022.02.17.22269742v1 on medRxiv