Standard error estimation in meta-analysis of studies reporting medians.

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

BACKGROUND: COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19. METHODS: This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies. RESULTS: Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73). DISCUSSION: This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.

Facility-based directly observed therapy (DOT) for tuberculosis during COVID-19: A community perspective.

Facility-based directly observed therapy (DOT) has been the standard for treating people with TB since the early 1990s. As the commitment to promote a people-centred model of care for TB grows, the use of facility-based DOT has been questioned as issues of freedom, privacy, and human rights have been raised. The disruptions caused by the COVID-19 pandemic and ensuing lockdown measures have fast-tracked the need to find alternative methods to provide treatment to people with TB. In this study, we present quantitative and qualitative findings from a global community-based survey on the challenges of administering facility-based DOT during a pandemic as well as potential alternatives. Our results found that decreased access to transportation, the fear of COVID-19, stigmatization due to overlapping symptoms, and punitive measures against quarantine violations have made it difficult for persons with TB to receive treatment at facilities, particularly in low-resource settings. Potential replacements included greater focus on community-based DOT, home delivery of treatment, multi-month dispensing, and video DOT strategies. Our study highlights the need for TB programs to re-evaluate their approach to providing treatment to people with TB, and that these changes must be made in consultation with people affected by TB and TB survivors to provide a true people-centred model of care.