Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity.

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

Effectiveness of a school-based intervention on physical activity and screen time among adolescents.

OBJECTIVES: To evaluate the effectiveness of a multi-component school-based intervention on physical activity (PA) and screen time (ST) among urban adolescents in Bangladesh. DESIGN: Cluster-randomised controlled trial. METHODS: Eight high schools were randomly assigned to either intervention group (IG) or control group (CG). Participants (n = 160 per group, 40 school) were in grades 8-9. A 12-week multi-component intervention was developed based on the WHO's Health-Promoting Schools framework. The IG received weekly supervised circuit exercise (30 min/week), health education session (10 min/week) with health educational materials, and lunchtime sports activities (20 min/week). The main outcome measures included self-reported PA, ST, knowledge on PA and SB, which were assessed at baseline, 8 and 12 weeks. Repeated measures ANCOVA was used to evaluate the intervention effects. RESULTS: Total PA (MET-min/week) was increased from baseline to 8 and 12 weeks in the IG (3%-5%) but decreased in the CG (5%-3%) and significantly improved in the IG compared to the CG (p < 0.001) over time. Average ST (min/day) reduced from baseline to 8 and 12 weeks in the IG (28%-35%), while remained unchanged in the CG (6%-5%). The IG had a significantly lower average ST than the CG at 12 weeks. The average knowledge scores on PA and SB were significantly higher in the IG than the CG at 12 weeks (p < 0.001). CONCLUSIONS: Our intervention has demonstrated some promising effects on increasing PA, reducing ST, and improving PA and SB knowledge. This study underscores the need for a scaled-up evaluation in other locations including rural settings.

The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia.

INTRODUCTION: Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. METHODS: Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public's mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. RESULTS: Australians' mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. CONCLUSION: We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds.