South Asia, with more than one-fifth of the world’s population, is highly vulnerable to heatwaves and associated health consequences. The population experiences considerably higher residential vulnerability due to limited infrastructural capacities, economic resources, and health and environmental quality deficiencies. However, a limited number of studies are available from the region to account for the health effects of heatwaves. Therefore, this study has conducted a comprehensive review to characterize heatwaves across South Asian countries. The review explicitly identifies the population’s vulnerability to heatwaves during recent years and heatwave management policies in the region. The literature review suggests increased heat-related deaths in most South Asian countries, with few exceptions. In addition, the analysis of historical temperature records identified an upward trend in annual average temperature across the South Asian countries. The study highlights various heatwave definitions that have been used in the region to facilitate comparative evidence. The review of policies identified that only a few South Asian countries have functional heatwave management plans and majorly lack community and residential preparedness for heatwaves. Therefore, this study identifies potential community- and residential-based adaptation strategies to mitigate heat discomfort. As prospective solutions, the study recommends adaptation strategies such as blue–green spaces, indoor passive cooling, infrastructural adjustments, heat action plans, etc. However, such adaptation measures require a holistic amalgamation of different stakeholders to fabricate heatwave-resilient cities.

Background: In Low-and Middle-Income Countries, including India, consumers often purchase antibiotics over-the-counter (OTC) from retail pharmacies. This practice leads to the inappropriate use of antibiotics in the community which is an important driver for the development of antimicrobial resistance. A better understanding of consumers’ views towards this grave public health concern is critical to developing evidence-based intervention programs for awareness among the general population. Objective: To explore knowledge, practice and, behavior of consumers towards antibiotics, antibiotic use, antimicrobial resistance, purchasing behavior of consumers for antibiotics, and to gain insight which will help in developing evidence-based policy interventions. Methods: 72 in-depth consumer interviews were conducted in all 11 districts of the National Capital Territory of Delhi. The qualitative data were analyzed using thematic analysis. Results: Our study found that retail pharmacies were the first point of consultation for common ailments for patients/consumers once home remedies failed; they were largely unaware of the threat of antimicrobial resistance. Consumers’ knowledge of antibiotic use and about antimicrobial resistance was low, they used old prescriptions, and bought antibiotics OTC to save time and money. Despite the presence of regulations constituted to regulate the sale of antibiotics by the Government and the implementation of national campaigns, the practice of self-medication and behaviors such as OTC purchase, non-adherence to prescribed antibiotics was prevalent. Consumers perceive that antibiotics provide quick relief and accelerate the curing process and retail pharmacy shops try to protect their retail business interests by honoring old prescriptions and self-medication for antibiotics. Conclusions: The lack of awareness and insufficient knowledge about what antibiotics are and issues such as antimicrobial resistance or antibiotic resistance resulted in misuse of antibiotics by consumers. Limited access to public healthcare and affordability of private healthcare are factors that contribute towards the self-medication/OTC purchase of antibiotics. The regular misuse of antibiotics through irrational use reinforces the need for strong enactment of strategies like continuous community awareness campaigns. Mitigation efforts should focus upon educating consumers continuously and sustainably for the understanding of antibiotic misuse, antimicrobial resistance, and promote better compliance with regulations.

Background: Diarrhea remains a common infectious disease caused by various risk factors in developing countries. This study investigated the incidence rate and temporal associations between diarrhea and meteorological determinants in five regions of Surabaya, Indonesia. Method: Monthly diarrhea records from local governmental health facilities in Surabaya and monthly means of weather variables, including average temperature, precipitation, and relative humidity from Meteorology, Climatology, and Geophysical Agency were collected from January 2018 to September 2020. The generalized additive model was employed to quantify the time lag association between diarrhea risk and extremely low (5th percentile) and high (95th percentile) monthly weather variations in the north, central, west, south, and east regions of Surabaya (lag of 0–2 months). Result: The average incidence rate for diarrhea was 11.4 per 100,000 during the study period, with a higher incidence during rainy season (November to March) and in East Surabaya. This study showed that the weather condition with the lowest diarrhea risks varied with the region. The diarrhea risks were associated with extremely low and high temperatures, with the highest RR of 5.39 (95% CI 4.61, 6.17) in the east region, with 1 month of lag time following the extreme temperatures. Extremely low relative humidity increased the diarrhea risks in some regions of Surabaya, with the highest risk in the west region at lag 0 (RR = 2.13 (95% CI 1.79, 2.47)). Extremely high precipitation significantly affects the risk of diarrhea in the central region, at 0 months of lag time, with an RR of 3.05 (95% CI 2.09, 4.01). Conclusion: This study identified a high incidence of diarrhea in the rainy season and in the deficient developed regions of Surabaya, providing evidence that weather magnifies the adverse effects of inadequate environmental sanitation. This study suggests the local environmental and health sectors codevelop a weather-based early warning system and improve local sanitation practices as prevention measures in response to increasing risks of infectious diseases.

OBJECTIVES: Diarrheal disease continues to be a significant cause of morbidity and mortality. We investigated how anomalies in monthly average temperature, precipitation, and surface water storage (SWS) impacted bacterial, and viral diarrhea morbidity in Taiwan between 2004 and 2015.METHODS: A multivariate analysis using negative binomial generalized estimating equations was employed to quantify age-specific and cause-specific cases of diarrhea associated with anomalies in temperature, precipitation, and SWS.RESULTS: Temperature anomalies were associated with an elevated rate of all-cause infectious diarrhea at a lag of 2 months, with the highest risk observed in the under-5 age group (incidence rate ratio [IRR], 1.03, 95% confidence interval [CI], 1.01 to 1.07). Anomalies in SWS were associated with increased viral diarrhea rates, with the highest risk observed in the under-5 age group at a 2-month lag (IRR, 1.27; 95% CI, 1.14 to 1.42) and a lesser effect at a 1-month lag (IRR, 1.18; 95% CI, 1.06 to 1.31). Furthermore, cause-specific diarrheal diseases were significantly affected by extreme weather events in Taiwan. Both extremely cold and hot conditions were associated with an increased risk of all-cause infectious diarrhea regardless of age, with IRRs ranging from 1.03 (95% CI, 1.02 to 1.12) to 1.18 (95% CI, 1.16 to 1.40).CONCLUSIONS: The risk of all-cause infectious diarrhea was significantly associated with average temperature anomalies in the population aged under 5 years. Viral diarrhea was significantly associated with anomalies in SWS. Therefore, we recommend strategic planning and early warning systems as major solutions to improve resilience against climate change.

The quality of life (QOL) in rural communities is improved through electrification. Microgrids can provide electricity in areas where grid access to electricity is infeasible. Still, insufficient power capacity hinders the very progress that microgrids promote. Therefore, we propose a decision-making framework to manage power distribution based on its impact on the rural QOL. Parameters are examined in this paper to represent the QOL pertaining to water, safety, education, and leisure/social activities. Each parameter is evaluated based on condition, community importance, and energy dependence. A solution for power allocation is developed by executing the compromise decision support problem (cDSP) and exploring the solution space. Energy loads, such as those required for powering water pumps, streetlamps, and household devices, are prioritized in the context of the QOL. The technique also allows decision-makers to update the power distribution scheme as the dynamics between energy production and demand change over time. In this paper, we propose a framework for connecting QOL and power management. The flexibility of the approach is demonstrated using a problem with varying scenarios that may be time dependent. The work enables sustainable energy solutions that can evolve with community development.

Ongoing climate variability and change are increasing the burden of diarrhoeal disease worldwide. Meaningful early warning systems with adequate lead times (weeks to months) are needed to guide public health decision–making and enhance community resilience against health threats posed by climate change. Toward this goal, we trained various machine-learning models to predict diarrhoeal disease rates in Nepal (2002–2014), Taiwan (2008–2019), and Vietnam (2000–2015) using temperature, precipitation, previous disease rates, and El Niño Southern Oscillation phases. We also compared the performance of shallow time-series neural network (NN), Random Forest Regressor, artificial nn, gradient boosting regressor, and long short-term memory–based methods for their effectiveness in predicting diarrhoeal disease burden across multiple countries. We evaluated model performance using a test dataset and assessed the accuracy of predicted diarrhoeal disease incidence rates for the last year of available data in each district. Our results suggest that even in the absence of the most recent disease surveillance data, a likely scenario in most low- and middle-income countries, our NN-based early warning system using historical data performs reasonably well. However, future studies are needed to perform prospective evaluations of such early warning systems in real-world settings.