Increased mortality among individuals hospitalised with COVID-19 during the second wave in South Africa

  1. Waasila Jassat
  2. Caroline Mudara
  3. Lovelyn Ozougwu
  4. Stefano Tempia
  5. Lucille Blumberg
  6. Mary-Ann Davies
  7. Yogan Pillay
  8. Terrence Carter
  9. Rams Morewane
  10. Milani Wolmarans
  11. Anne von Gottberg
  12. Jinal N. Bhiman
  13. Sibongile Walaza
  14. DATCOV Author Group
  15. Cheryl Cohen

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Abstract

Introduction

South Africa experienced its first wave of COVID-19 peaking in mid-July 2020 and a larger second wave peaking in January 2021, in which the SARS-CoV-2 501Y.V2 lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves of COVID-19.

Methods

We analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations. We defined four wave periods using incidence risk for hospitalisation, pre-wave 1, wave 1, pre-wave 2 and wave 2. We compared the characteristics of hospitalised COVID-19 cases in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using multivariable logistic regression.

Results

Peak rates of COVID-19 cases, admissions and in-hospital deaths in the second wave exceeded the rates in the first wave (138.1 versus 240.1; 16.7 versus 28.9; and 3.3 versus 7.1 respectively per 100,000 persons). The weekly average incidence risk increase in hospitalisation was 22% in wave 1 and 28% in wave 2 [ratio of growth rate in wave two compared to wave one: 1.04, 95% CI 1.04-1.05]. On multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave (adjusted OR 1.2, 95% CI 1.2-1.3). In-hospital case fatality-risk (CFR) increased in weeks of peak hospital occupancy, from 17.9% in weeks of low occupancy (<3,500 admissions) to 29.6% in weeks of very high occupancy (>12,500 admissions) (adjusted OR 1.5, 95% CI 1.4-1.5).

Compared to the first wave, individuals hospitalised in the second wave, were more likely to be older, 40-64 years [OR 1.1, 95% CI 1.0-1.1] and ≥65 years [OR 1.1, 95% CI 1.1-1.1] compared to <40 years; and admitted in the public sector [OR 2.2, 95% CI 1.7-2.8]; and less likely to have comorbidities [OR 0.5, 95% CI 0.5-0.5].

Conclusions

In South Africa, the second wave was associated with higher incidence and more rapid increase in hospitalisations, and increased in-hospital mortality. While some of this is explained by increasing pressure on the health system, a residual increase in mortality of hospitalised patients beyond this, could be related to the new lineage 501Y.V2.

RESEARCH IN CONTEXT

Evidence before this study

Most countries have reported higher numbers of COVID-19 cases in the second wave but lower case-fatality risk (CFR), in part due to new therapeutic interventions, increased testing and better prepared health systems. South Africa experienced its second wave which peaked in January 2021, in which the variant of concern, SARS-CoV-2 501Y.V2 predominated. New variants have been shown to be more transmissible and in the United Kingdom, to be associated with increased hospitalisation and mortality rates in people infected with variant B.1.1.7 compared to infection with non-B.1.1.7 viruses. There are currently limited data on the severity of lineage 501Y.V2.

Added value of this study

We analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations, comparing in-hospital mortality and other patient characteristics between the first and second waves of COVID-19. The study revealed that after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave. Our study also describes the demographic shift from the first to the second wave of COVID-19 in South Africa, and quantifies the impact of overwhelmed hospital capacity on in-hospital mortality.

Implications of all the available evidence

Our data suggest that the new lineage (501Y.V2) in South Africa may be associated with increased in-hospital mortality during the second wave. Our data should be interpreted with caution however as our analysis is based on a comparison of mortality in the first and second wave as a proxy for dominant lineage and we did not have individual-level data on lineage. Individual level studies comparing outcomes of people with and without the new lineage based on sequencing data are needed. To prevent high mortality in a potential third wave, we require a combination of strategies to slow the transmission of SARS-CoV-2, to spread out the peak of the epidemic, which would prevent hospital capacity from being breached.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.09.21253184: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableAccording to the 2016 Demographic and Health Survey 41% of adult women and 11% of men were obese, 46% of women and 44% of men had hypertension, and 13% of women and 8% of men had diabetes. (12) In 2019, there were 7.5 million people estimated to be living with HIV in South Africa, of which 2.3 million (31%) were not receiving treatment. (13) In 2018, 301,000 new cases of TB were diagnosed in South Africa.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: Strengths of this study include the use of comprehensive electronic health record data on all COVID-19 hospitalizations at all 641 public and private hospitals in the country, minimizing selection or surveillance bias and maximising generalizability. The study includes a diverse patient population, complete study outcomes, and a lengthy period of investigation of 11 months, with in-hospital follow-up until occurrence of discharge or death. Major limitations of this analysis were the lack of individual level lineage type data, and possible residual confounding as we could not adjust for several factors. We adjusted for COVID-19 admissions but were not able to adjust for weekly hospital admission volumes for persons under investigation (PUIs) and non-COVID-19 admissions. We are not able to adjust for differences between the first and second wave related to the level of national restrictions/lockdowns, and to individual preventive behaviours. The analysis includes only in-hospital deaths and any differences between the two waves in the proportions of patients who did not or could not access care and those that died outside of hospital are not accounted for in the analysis. There have been changes in treatment protocols with better COVID-19 treatment regimens including steroid use and high flow oxygen. These have likely decreased mortality rates as the epidemic progressed. The numbers of hospitals reporting to DATCOV increased in October 2020 and while ...

    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.

    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.

  2. Version published to 10.1101/2021.03.09.21253184v1 on medRxiv