Mental Health during the COVID-19 Crisis in Africa: A Systematic Review and Meta-Analysis

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Abstract

We aim to provide a systematic review and meta-analysis of the prevalence rates of mental health symptoms among major African populations during the COVID-19 pandemic. We include articles from PubMed, Embase, Web of Science, PsycINFO, and medRxiv between 1 February 2020 and 6 February 2021, and pooled data using random-effects meta-analyses. We identify 28 studies and 32 independent samples from 12 African countries with a total of 15,071 participants. The pooled prevalence of anxiety was 37% in 27 studies, of depression was 45% in 24 studies, and of insomnia was 28% in 9 studies. The pooled prevalence rates of anxiety, depression, and insomnia in North Africa (44%, 55%, and 31%, respectively) are higher than those in Sub-Saharan Africa (31%, 30%, and 24%, respectively). We find (a) a scarcity of studies in several African countries with a high number of COVID-19 cases; (b) high heterogeneity among the studies; (c) the extent and pattern of prevalence of mental health symptoms in Africa is high and differs from elsewhere—more African adults suffer from depression rather than anxiety and insomnia during COVID 19 compared to adult populations in other countries/regions. Hence, our findings carry crucial implications and impact future research to enable evidence-based medicine in Africa.

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  1. SciScore for 10.1101/2021.04.19.21255755: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We performed a comprehensive literature search in the databases of PubMed, Embase, PsycINFO, Web of Science, and medRxiv from Feb 2020 to Feb 6th, 2021.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    PsycINFO
    suggested: (PsycINFO, RRID:SCR_014799)
    We used the search query, reported in Appendix 1, and entered these keywords in each database using Boolean operators within the titles, abstracts, keywords, and subject headings (for example, MeSH terms). 2.3 Eligibility Criteria: In this systematic review, we included original empirical studies that have examined the impact of the COVID-19 pandemic on adults’ mental health in African countries if they met the following criteria: We excluded studies based on the following criteria: A researcher (WX) contacted the authors of papers if they missed important information.
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    2.4 Screening and Data Extraction: One researcher (JC) exported the included articles from the databases into an EndNote library where we identified duplicates and then imported the articles to Rayyan for screening.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)

    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:
    4.3 Limitations and Future Work: Our meta-analysis is not free of limitations. First, the validity of our findings depends on the quality and reporting of the original studies included in the meta-analysis. The individual mental health studies employed a variety of instruments, cutoff scores, levels of cutoff scores to classify the severity of mental illness, and various reporting standards. For example, many studies report the overall prevalence rates without specifying which/how cutoff scores are utilized. While we have tried to reduce the additional noise and variance in the meta-analysis by paying extra attention to the severity, cutoff points, and the manners in which individual articles utilized and reported cutoff points, the profusion of different practices can lead to some biases and noise. Second, this systematic review identified empirical studies from only 12 out of the 48 Africa countries. On studies have appeared on the mental health of people in three quarters of African countries, even though those African countries are not immune to the COVID-19 pandemic. Hence, we call for research to investigate the mental health issues in Africa, the least studied continent with the majority of the countries without any studies. A possible reason is that we included articles published in English, which may result in some biases. Third, 96.7% of the studies included in this meta-analysis conducted cross-sectional surveys, whereas only 3.3% of the articles used cohort studie...

    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. SciScore for 10.1101/2021.02.01.21250929: (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 variableFurthermore, given the high degree of heterogeneity of the true differences in the effect sizes, we ran a meta-regression to regress the prevalence upon not only these three category variables (outcome, severity, and population) but also female proportion, data collection time, data collection location (Wuhan vs. non-Wuhan), sample size, and study quality.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 Data Sources and Search Strategy: We conducted a comprehensive literature search in the databases of PubMed, Embase, and Web of Science.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    2.2 Selection Criteria: The studies are included in our meta-analysis based on the following criteria: According, we excluded studies that meet the following criteria: We contacted the authors of papers that missed some critical information if the articles: 2.3 Selection Process and Data Extraction: The articles that passed the inclusion criteria were exported into an EndNote library where we identified duplications and then imported to Rayyan for screening.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)

    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:
    4.4 Study Limitations: This meta-analysis has a few limitations. First, the validity of our findings rests upon the quality and reporting of the original studies. As discussed before, individual mental health papers varied in their usage of instruments, cutoff scores, the use of cutoff scores to define mental issues, and the reporting standards. For example, the overall prevalence refers to “above the cutoff of mild” in some papers yet “above the cutoff of moderate” in other papers. Worse, many papers report the overall prevalence without specifying which/how cutoff scores are used. While we paid extra attention to the severity, the cutoff points, and the ways in which individual articles used this information, the multitude of varying practices contributes to additional noise and variance in the analysis. Second, since we included studies in English, which may result in some biases. Third, 96.2% of studies included in this meta-analysis were cross-sectional surveys, and we call for more cohort studies to examine the effect of time. Finally, we only focus on studies that collected data in China, and we call for future meta-analyses in other countries or regions as the COVID-19 crisis continues in most parts of the world. 4.5 Conclusion: Since the COVID-19 epidemic started in November 2019, hundreds of studies have documented the mental health of major populations by the key mental outcomes and varying levels of severity. This systematic review and meta-analysis synthesized th...

    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.