METHODS: A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported).
RESULTS: Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39-3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.99) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 3.56, 95% CI, 1.62-7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number.
CONCLUSION: The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.
METHODS: Multistage sampling method was used to collect data (n = 210) from three unions of Satkhira District, Bangladesh. The dependent variable was the presence of COVID-19 related misconception (Yes, No) which was generated based on respondents' responses to a set of six questions on various types of misconception. Exposure variables were respondents' socio-demographic characteristics, mass media and social media exposure. Descriptive statistics were used to describe the characteristics of the respondents. Bivariate and multivariate logistic regression models were used to determine the factors associated with COVID-19 misconception.
RESULTS: More than half of the study respondents had one or more COVID-19 related misconception. Over 50% of the total respondents considered this disease as a punishment from God. Besides, many of the respondents reported that they do not think the virus causing COVID-19 is dangerous (59%) and it is a disease (19%). Around 7% reported they believe the virus is the part of a virus war (7.2%). The bivariate analysis found the presence of socio-demographic factors of the respondents, as well as the factors related to social and mass media, were significantly associated with the COVID-19's misconception. However, once all factors considered together in the multivariate model, misconception were found to be lower among secondary (AOR, 0.33, 95% CI: 0.13-0.84) and tertiary (AOR, 0.29, 95% CI: 0.09-0.92) educated respondents compared to the respondents with primary education.
CONCLUSION: This study obtained a very higher percentage of misconception about the COVID-19 among the respondents of Satkhira district in Bangladesh. This could be a potential challenge to fight against this pandemic which is now ongoing. Prioritizing mass and social media to disseminate evidence-based information as well as educate people about this disease are necessary.
METHODS: Data were derived from four waves of nationally representative Bangladesh Demographic and Health Survey (BDHS) conducted between 2004 and 2014. Rate of change analysis was used to calculate the average annual rate of increase in CS from 2004 to 2014, by socio-demographic categories. Multi-level logistic regression was used to identify the socio-demographic predictors of CS in a cross-sectional analysis of the 2014 BDHS data.
RESULT: CS rates increased from 3.5% in 2004 to 23% in 2014. The average annual rate of increase in CS was higher among women of advanced maternal age (≥35 years), urban areas, and relatively high socio-economic status; with higher education, and who regularly accessed antenatal services. The multi-level logistic regression model indicated that lower (≤19) and advanced maternal age (≥35), urban location, relatively high socio-economic status, higher education, birth of few children (≤2), antenatal healthcare visits, overweight or obese were the key factors associated with increased utilization of CS. Underweight was a protective factor for CS.
CONCLUSION: The use of CS has increased considerably in Bangladesh over the survey years. This rising trend and the risk of having CS vary significantly across regions and socio-economic status. Very high use of CS among women of relatively high socio-economic status and substantial urban-rural difference call for public awareness and practice guideline enforcement aimed at optimizing the use of CS.