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To the Editor, We have read the systematic review and meta-analysis by Narapaka et al.,1 which provides insights regarding the psychological health outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among Indian healthcare workers (HCWs). This systematic review adds only two new studies to the existing meta-analysis, offering minimal updates to previous findings. Several methodological concerns need to be addressed to enhance the reliability and applicability of the results. Most importantly, we observed several data discrepancies between the analysis used and the data from the original studies. For instance, the authors reported 64 depression cases out of a total sample of 87 in the study by George et al.2 (2020). However, the original study reported that 64 out of the total number of participants completed the survey. Similarly, the authors have added the study by Simmi Gupta et al.3 (2020) two times in the meta-analyses. They have added cases of anxiety and depression in doctors separately along with HCWs overall. This caused duplication of the cases of doctors in the meta-analysis which led to an overestimation of pooled prevalence. These discrepancies and errors can introduce bias in the overall findings and compromise the rigor of the systematic review. The authors limited their search to PubMed and supplemented it with Google Scholar, although other databases were mentioned in the registered PROSPERO protocol. Deviations from the protocol should have been reported in the article. The search strategy appears to have omitted important keywords. Distinct keywords such as “COVID-19” or “SARS-CoV-2” could have been used to obtain more relevant studies. The use of an asterisk (*) with keywords and MeSH terms might have broadened the search scope. The authors mention using the Downs and Black checklist for the quality assessment of studies in their method section. However, the results of this assessment are not reported in the results section. Instead, the “JBI checklist status” is mentioned in “Table 1” and in the discussion section of the article. These discrepancies in this study should be noted. Critical appraisal of the included studies is an essential aspect of a systematic review. Subgroup analyses were conducted to explore heterogeneity in this systematic review. Meta-regression could have been a better alternative method for assessing the effect of continuous variables, such as age and sample size on prevalence, rather than categorizing these variables based on arbitrary cutoff points. Additionally, calculating prediction intervals would be beneficial for evaluating the heterogeneity, particularly for translating findings into real-world settings. Prediction intervals provide a range within which future study results are expected to fall, offering practical insights into the variability of outcomes across different contexts.4 The authors performed sensitivity analysis by omitting studies with smaller and larger sample sizes. However, A sensitivity analysis that omits low-quality studies should also be conducted to ensure the robustness of the findings since low-quality studies may introduce bias. Furthermore, the authors evaluated publication bias using funnel plots. There may have been mislabeling of the funnel plots: fourteen studies were included in the meta-analysis of depression, and twelve studies for anxiety. However, the funnel plots appear to show these and vice versa: twelve studies for depression and fourteen for anxiety. Employing Doi plots and the Luis Furuya-Kanamori index could enhance the evaluation of publication bias in prevalence meta-analysis compared to funnel plots and regression-based Egger tests.5 Systematic reviews and meta-analyses represent the pinnacle of evidence synthesis. They must adhere to the highest methodological standards to provide reliable evidence for clinical decision-making. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Shabil et al. (Thu,) studied this question.