Dataset Information

Effectiveness of Universal BCG Immunisation in Reducing the Incidence and Mortality of COVID-19: A Secondary Data Analysis.

ABSTRACT:

Background

Bacillus Calmette-Guérin (BCG) vaccine's immunomodulatory properties can protect against respiratory infections due to its nonspecific trained immunity of innate immune cells. The objective of the study is to assess the effectiveness of BCG in reducing incidence and morality of ongoing COVID-19 pandemic.

Methodology

The present study is a secondary data analysis to compare the incidence and mortality of COVID 19 among countries who had BCG vaccine in their national immunisation schedule with other group of countries who did not have. Twenty-six countries with BCG vaccinations and eleven countries without BCG vaccinations were included in the analysis. The analysis was performed using the glmer() function in R and a random effects logistic model to study the determinants of incidence and mortality.

Results

The association between the BCG vaccination and number of cases of COVID-19 was significant in the univariate analysis (p = 0.002), upon statistical adjustment; the random effects model shows there was no association between countries with BCG vaccine included in the national immunization schedule and the number of cases (p = 0.377). Similarly, BCG was not statistically associated with mortality (case fatality rate > 5%) with a p value of 0.443.

Conclusion

Considering single variable BCG vaccine, the incidence and mortality of COVID-19 was found to be significantly higher in non-BCG vaccinated countries while no significant association was observed when other determinants were included affecting the epidemiology of COVID-19. Additional research is recommended, to study the relationship between BCG and COVID-19.

SUBMITTER: Bhatia V

PROVIDER: S-EPMC10937066 | biostudies-literature | 2021 Mar-Apr

REPOSITORIES: biostudies-literature

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Publications

Effectiveness of Universal BCG Immunisation in Reducing the Incidence and Mortality of COVID-19: A Secondary Data Analysis.

Bhatia Vikas V Pragyan Parida Swayam S Prasad Sahoo Durgesh D Antony Annu A Mitra Arun A

Nigerian medical journal : journal of the Nigeria Medical Association 20210301 2

<h4>Background</h4>Bacillus Calmette-Guérin (BCG) vaccine's immunomodulatory properties can protect against respiratory infections due to its nonspecific trained immunity of innate immune cells. The objective of the study is to assess the effectiveness of BCG in reducing incidence and morality of ongoing COVID-19 pandemic.<h4>Methodology</h4>The present study is a secondary data analysis to compare the incidence and mortality of COVID 19 among countries who had BCG vaccine in their national immu ...[more]

PMID: 38505573

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Project description:BackgroundThe COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts.ObjectiveWe aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations.MethodsThis study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method.ResultsOur study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC.ConclusionsOur study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Project description:ObjectiveTo review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.DesignSystematic review and meta-analysis.Data sourcesMedline, Embase, CINAHL, Biosis, Joanna Briggs, Global Health, and World Health Organization COVID-19 database (preprints).Eligibility criteria for study selectionObservational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.Main outcome measuresThe main outcome measure was incidence of covid-19. Secondary outcomes included SARS-CoV-2 transmission and covid-19 mortality.Data synthesisDerSimonian Laird random effects meta-analysis was performed to investigate the effect of mask wearing, handwashing, and physical distancing measures on incidence of covid-19. Pooled effect estimates with corresponding 95% confidence intervals were computed, and heterogeneity among studies was assessed using Cochran's Q test and the I2 metrics, with two tailed P values.Results72 studies met the inclusion criteria, of which 35 evaluated individual public health measures and 37 assessed multiple public health measures as a "package of interventions." Eight of 35 studies were included in the meta-analysis, which indicated a reduction in incidence of covid-19 associated with handwashing (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I2=12%), mask wearing (0.47, 0.29 to 0.75, I2=84%), and physical distancing (0.75, 0.59 to 0.95, I2=87%). Owing to heterogeneity of the studies, meta-analysis was not possible for the outcomes of quarantine and isolation, universal lockdowns, and closures of borders, schools, and workplaces. The effects of these interventions were synthesised descriptively.ConclusionsThis systematic review and meta-analysis suggests that several personal protective and social measures, including handwashing, mask wearing, and physical distancing are associated with reductions in the incidence covid-19. Public health efforts to implement public health measures should consider community health and sociocultural needs, and future research is needed to better understand the effectiveness of public health measures in the context of covid-19 vaccination.Systematic review registrationPROSPERO CRD42020178692.

Dataset Information

Effectiveness of Universal BCG Immunisation in Reducing the Incidence and Mortality of COVID-19: A Secondary Data Analysis.

Background

Methodology

Results

Conclusion

Publications

Effectiveness of Universal BCG Immunisation in Reducing the Incidence and Mortality of COVID-19: A Secondary Data Analysis.

Similar Datasets

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