Evidence-based research is used to generate, summarise, and understand the best available practices to inform decision-making. Systematic reviews (SR) and meta-analysis (MA) have become a valuable tool for these goals in public health, medicine and pharmaceutical research. MA is a statistical procedure for combining the results of multiple studies investigating a common intervention or issue to produce a pooled effect size and evaluate interventions' efficacy across studies. This article outlines the usefulness of systematic reviews and meta-analysis in medicine and public health. Steps in undertaking the systematic reviews and meta-analysis include forming a team, identifying and refining the research question, determining the inclusion and exclusion criteria, registering the SR and MA protocol, searching for the studies, selection of the studies, data extraction, data analysis and presenting the results. The review also outlines the issues that can impact meaningful meta-analysis. The heterogeneity in the included studies, conditions studied, interventions, and end-point measures is one of the major issues encountered in meta-analysis. Quantification of the heterogeneity can be done by I2 statistics and prediction intervals. Sub-group analysis, outlier detection followed by sensitivity analysis, and meta-regression can be applied to explore and reduce heterogeneity. Random effects model, Knapp-Hartung, likelihood estimates, and Bayesian models can be applied in a highly heterogenous meta-analysis.

Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India. The Google search terms used for the analysis were “coronavirus”, “COVID”, “COVID 19”, “corona”, and “virus”. GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms. “Coronavirus” and “corona” were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms “coronavirus” and “corona” was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term “coronavirus”, with 21 days, i.e., the search volume for “coronavirus” peaked 21 days before the peak number of cases reported by the disease surveillance system. Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.

Systematic review and meta-analyses (SRMA) form the cornerstone of evidence-based medicine and evidence-based public health. SRMA occupies the highest position in the evidence pyramid. Thus, researchers, clinicians, public health professionals, policymakers, and other stakeholders place much trust in the findings of an SRMA. So, these interpretations must be methodologically robust and statistically sound. No comprehensive and sound tutorial is available for meta-analysis of different effect sizes condensed in a single article. This is an easy yet comprehensive guide to fill this gap. It also incorporates some advanced but essential meta-analytical techniques. Everything has been explained simply in open-source and free software, i.e., R. This allows greater access by researchers from lower- and middle-income countries, ensuring inclusivity and equitable access to health research. Although the primary target readers are beginners of R, seasoned R users can also benefit from the advanced analyses introduced in the meta-analysis, thereby addressing the needs of both groups. It explains statistical concepts briefly as necessary and demonstrates their implementation. The code file for the demonstrated examples and the relevant data extraction sheets have been attached. Then, we have provided examples for reporting the results of a systematic review and meta-analysis, troubleshooting common mistakes, customising the output, changing the finer settings, and exploring other important aspects of meta-analysis. We have finally discussed other sources for further reading for those interested in delving into more advanced and complicated techniques.

Mpox (formerly known as monkeypox) is a global public health concern, given the recent outbreaks in non-endemic countries where little scientific evidence exists on the disease epidemiology. Oral lesions among mpox cases have been poorly reported. Our aim was to estimate the overall prevalence of oral manifestations among patients with mpox globally. In this systematic review and meta-analysis, an extensive literature search in PubMed, Scopus, Web of Science, Embase, Cochrane, and preprint servers (medRxiv, arXiv, bioRxiv, BioRN, ChiRxiv, ChiRN, and SSRN) and assessment of all published articles, conducted across the world, until Nov 15 2022 as per the PROSPERO registration protocol (CRD42022371249), was undertaken. Primary studies (case series, cross-sectional, retrospective, prospective designs) that reported the oral/oropharyngeal symptoms among laboratory-confirmed mpox cases were included. The characteristics of the study, information on the number of cases, and symptomatic status were extracted from the included studies. The quality of the included studies was assessed for bias. Random-effects meta-regression with DerSimonian & Laird estimator, and subgroup analyses were conducted using STATA (v17.0). The degree of funnel plot asymmetry was assessed using Egger's test when at least 10 estimates were available. The heterogeneity between studies was assessed using the I² statistic. The primary outcome was the pooled prevalence of oral manifestations in the examined population. 19 studies were included with 4042 laboratory-confirmed patients with mpox for qualitative and quantitative synthesis. The pooled prevalence of oral manifestations in the investigated population was 36.75% (95% confidence interval [CI]: 23.77-50.65). Heterogeneity was found to be high in the current meta-analysis (I² = 98.24%; p < 0.001). Subgroup analysis revealed a pooled prevalence of 39.96% (95% CI: 21.42-59.91) of sore throat, 24.80% (95% CI: 8.14-46.32) of mouth sore, 18.24% (95% CI: 0.34-52.54) of tonsillitis and 17.99% (95% CI: 15.66-20.43) of mouth rash from the included studies. Oral manifestations are common in patients with mpox, with variations in prevalence across the sites within the oral cavity, and geography of the studies. Healthcare workers should be wary of the oral signs and symptoms of mpox in endemic and high-risk areas. None.

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