Climate change poses severe consequences, particularly in sub-Saharan Africa, where poverty rates may escalate by 2050 without significant climate and development action. The health impacts are diverse, encompassing communicable and non-communicable diseases. Mozambique, a climate-vulnerable nation, has experienced significant natural disasters in the past 42 years, impacting its health system. This study aims to assess Mozambique’s health sector’s vulnerability and adaptation needs to climate change. Following a methodology proposed by the World Health Organization and the Intergovernmental Panel for Climate Change, a six-step vulnerability and adaptation assessment was conducted to conduct the Health Vulnerability Index (HVI) for Mozambique’s regions $(n=161)$. The HVI integrates historical climate, epidemiological, and socio-economic data at the district level, and was computed using exposure, sensitivity, and adaptive capacity dimensions. The results revealed spatial patterns in exposure to climate variables, extreme weather events, and variations in sensitivity and adaptive capacity across the country. The HVI mirrored the exposure findings. Notably, high vulnerability was observed in several districts, while major urban centers displayed lower vulnerability. These findings highlight the country’s vulnerability to climate change and underscore the potential for adverse impacts on livelihoods, the economy, and human health. The study provides a foundation for developing strategies and adaptation actions.

Stroke is one of the most common causes of disability and death worldwide. With the rapidly growing stroke survivor population, it is crucial to identify an effective method for their healthcare. Recovery from stroke is followed by physiotherapy to promote rehabilitation. Task-oriented circuit training is designed to improve stroke patients’ overall functioning during rehabilitation. This research aims to assess the effectiveness of task-oriented circuit training compared with conventional physiotherapy. The investigators have planned an 8-week parallel, two-arm, prospective, randomised clinical study. Participants will be enrolled from eight branches of the centre for the rehabilitation of the paralysed (CRP). We have planned to recruit 506 stroke survivors via a 1:1 random assignment procedure for 24 months. As a main objective, the Action Arm Research Test and the Timed Up and Go will be used to test upper and lower limb motor function. The secondary objectives will include daily living and balance activities, which will be evaluated using the Barthel Index and the Berg Balance Scale. The post-test and follow-up data will be collected after 8 and 12 weeks. The final analysis will include dropouts and treatment side effects. This study has been granted ethical approval by the Ethics Review Committee of the CRP (CRP-R&E-0401-357)). All activities and interventions will be carried out following the Helsinki Declaration of 2020. The findings will be published in peer-reviewed journals and disseminated at international conferences. Trial registration number: CTRI/2023/09/057907 (21 September 2023) (Prospectively registered).

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh’s 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city’s high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect.

Aim Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. Methods Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. Results The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus’s survival and transmission. Conclusions Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.