Background Individual studies on the relations between ambient air pollution and the risk of stillbirth have provided contradictory results. We conducted a systematic review and meta-analysis to summarise the existing evidence. Methods We conducted a systematic search of three databases: PubMed, Scopus and Web of Science, from their time of inception to mid-April, 2015. Original studies of any epidemiological design were included. Data from eligible studies were extracted by two investigators. To calculate the summary effect estimates (EE), the random effects model was used with their corresponding 95% CI. Results 13 studies met the inclusion criteria. Although not reaching statistical significance, all the summary effect estimates for the risk of stillbirth were systematically elevated in relation to mean prenatal exposure to NO₂ per 10 ppb (EE=1.066, 95% CI 0.965 to 1.178, n=3), CO per 0.4 ppm (EE=1.025, 95% CI 0.985 to 1.066, n=3), SO₂ per 3 ppb (EE=1.022, 95% CI 0.984 to 1.062, n=3,), PM_2.5 per 4 μg/m³ (EE=1.021, 95% CI 0.996 to 1.046, n=2) and PM₁₀ per 10 μg/m³ (EE=1.014, 95% CI 0.948 to 1.085, n=2). The effect estimates for SO₂, CO, PM₁₀ and O₃ were highest for the third trimester exposure. Two time series studies used a lag term of not more than 6 days preceding stillbirth, and both found increased effect estimates for some pollutants. Conclusions The body of evidence suggests that exposure to ambient air pollution increases the risk of stillbirth. Further studies are needed to strengthen the evidence.

This article reports the intent to receive a SARS-COV-2 vaccine, its predictors and willingness to pay in Bangladesh. We carried out an online cross-sectional survey of 697 adults from the general population of Bangladesh in January 2021. A structured questionnaire was used to assess vaccination intent. The questionnaire included sociodemographic variables and health belief model constructs which may predict vaccination intent. Among the participants, 26% demonstrated a definite intent, 43% probable intent, 24% probable negative, and 7% a definite negative intention. Multivariable logistic regression analyses suggest an association between definite intent and previous COVID-19 infection (OR: 2.86; 95% CI: 1.71–4.78), perceiving COVID-19 as serious (OR: 1.93; 1.04–3.59), the belief that vaccination would make them feel less worried about catching COVID-19 (OR: 4.42; 2.25–8.68), and concerns about vaccine affordability (OR: 1.51; 1.01–2.25). Individuals afraid of the side effects (OR: 0.34; 0.21–0.53) and those who would take the vaccine if the vaccine were taken by many others (OR: 0.44; 0.29–0.67) are less likely to have a definite intent. A definite negative intent is associated with the concern that the vaccine may not be halal (OR: 2.03; 1.04–3.96). Furthermore, 68.4% are willing to pay for the vaccine. The median amount that they are willing to pay is USD 7.08. The study findings reveal that the definite intent to receive the SARS-CoV-2 vaccination among the general population varies depending on their COVID-19-related health beliefs and no significant association was found with sociodemographic variables.

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

Background: Bangladesh has been attracting international students with interests in various subjects recently. Every year students from different parts of the world come to study undergraduate and postgraduate courses, mostly at private universities in Bangladesh. This study evaluates the depression status among international students who are studying dentistry in Bangladesh. Methods: This cross-sectional survey was conducted among International undergraduate dental students who enrolled in the Bachelor of Dental Surgery program in nine public and private dental colleges in Bangladesh. Participants were selected using a convenience sampling method. A total of 206 students completed the survey where 78.5% of them were female students and 21.5% students were male, and a CES-D 10-item Likert scale questionnaire was used for data collection. The Cronbach alpha for the 10-item CES-D scale for this population is 0.812. Results: The majority of the students (79.5%) are below 24 years of age with a mean age of 23.22 years and standard deviation of 2.3, and are students who cannot communicate well in Bengali (Bangla), about 60% of them have experienced depression. About 77.3% (p < 0.00) of the international students having financial difficulties exhibited depression. The international students who went through financial problems were two times more likely to suffer from depression (OR = 2.38; p-value < 0.01). Conclusion: This study tried to highlight the struggles faced by international students in Bangladesh studying dentistry. It is evident from the findings that several factors influence students’ mental well-being during demanding dental education years.

There is accumulating evidence that prenatal exposure to air pollution disturbs fetal growth and development, but little is known about these effects in cold climates or their season-specific or joint effects. Our objective was to assess independent and joint effects of prenatal exposure to specific air pollutants on the risk of low birth weight (LBW). We utilized the 2568 children of the Espoo Cohort Study, born between 1984 and 1990 and living in the City of Espoo. We conducted stratified analyses for births during warm and cold seasons separately. We analyzed the effect estimates using multi-pollutant Poisson regression models with risk ratio (RR) as the measure of effect. The risk of LBW was related to exposure to CO (adjusted RR 1.44, 95% confidence interval [CI]: 1.04–2.00) and exposure to O₃ in the spring–summer season (1.82, 1.11–2.96). There was also evidence of synergistic effects between CO and O₃ (relative risk due to interaction (RERI), all year 1.08, 95% CI: 0.27–4.94, spring–summer 3.97, 2.17–25.85) and between PM_2.5 and O₃ (all year 0.72, −0.07–3.60, spring–summer 2.80, 1.36–19.88). We present new evidence of both independent and joint effects of prenatal exposure to low levels of air pollution in a cold climate on the risk of LBW.