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Climate change is closely monitored and numerous studies reports increasing air temperature and weather extremes across the globe. As a direct consequence of the increase of global temperature, the increased heat stress is becoming a global threat to public health. While most climate change and epidemiological studies focus on air temperature to explain the increasing risks, heat strain can be predicted using comprehensive indices such as Universal Thermal Climate Index (UTCI). The Asia–Pacific region is prone to thermal stress and the high population densities in the region impose high health risk. This study evaluated the air temperature and UTCI trends between 1990 and 2019 and found significant increasing trends for air temperature for the whole region while the increases of UTCI are not as pronounced and mainly found in the northern part of the region. These results indicate that even though air temperature is increasing, the risks of heat stress when assessed using UTCI may be alleviated by other factors. The associations between El Niño Southern Oscillation (ENSO) and heat stress was evaluated on a seasonal level and the strongest regional responses were found during December-January (DJF) and March–May (MAM).

 

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.

 

Tobacco use significantly influences the oral microbiome. However, less is known about how different tobacco products specifically impact the oral microbiome over time. To address this knowledge gap, we characterized the oral microbiome of cigarette users, smokeless tobacco users, and non-users over 4 months (four time points). Buccal swab and saliva samples (n = 611) were collected from 85 participants. DNA was extracted from all samples and sequencing was carried out on an Illumina MiSeq, targeting the V3–V4 region of the 16S rRNA gene. Cigarette and smokeless tobacco users had more diverse oral bacterial communities, including a higher relative abundance ofFirmicutesand a lower relative abundance ofProteobacteria, when compared to non-users. Non-users had a higher relative abundance ofActinomyces, Granulicatella, Haemophilus, Neisseria, Oribacterium, Prevotella, Pseudomonas, Rothia, andVeillonellain buccal swab samples, compared to tobacco users. While the most abundant bacterial genera were relatively constant over time, some species demonstrated significant shifts in relative abundance between the first and last time points. In addition, some opportunistic pathogens were detected among tobacco users includingNeisseria subflava, Bulleidia mooreiandPorphyromonas endodontalis. Overall, our results provide a more holistic understanding of the structure of oral bacterial communities in tobacco users compared to non-users.

 

Abstract In the Asia–Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990–2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June–August (JJA), and by precipitation frequency in December–February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March–May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region. 

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- 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% CI, 1.01-1.07). 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, 1.42) and a lesser effect at a 1-month lag (IRR=1.18; 95% CI, 1.06-1.31). 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, 1.02-1.12) to 1.18 (95% CI, 1.16-1.40).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. 

Advanced treated municipal wastewater is an important alternative water source for agricultural irrigation. However, the possible persistence of chemical and microbiological contaminants in these waters raise potential safety concerns with regard to reusing treated wastewater for food crop irrigation. Two low-cost and environmentally-friendly filter media, biochar (BC) and zero-valent iron (ZVI), have attracted great interest in terms of treating reused water. Here, we evaluated the efficacy of BC-, nanosilver-amended biochar- (Ag-BC) and ZVI-sand filters, in reducing contaminants of emerging concern (CECs),Escherichia coli (E. coli)and total bacterial diversity from wastewater effluent. Six experiments were conducted with control quartz sand and sand columns containing BC, Ag-BC, ZVI, BC with ZVI, or Ag-BC with ZVI. After filtration, Ag-BC, ZVI, BC with ZVI and Ag-BC with ZVI demonstrated more than 90% (> 1 log) removal ofE. colifrom wastewater samples, while BC, Ag-BC, BC with ZVI and Ag-BC with ZVI also demonstrated efficient removal of tested CECs. Lower bacterial diversity was also observed after filtration; however, differences were marginally significant. In addition, significantly (p < 0.05) higher bacterial diversity was observed in wastewater samples collected during warmer versus colder months. Leaching of silver ions occurred from Ag-BC columns; however, this was prevented through the addition of ZVI. In conclusion, our data suggest that the BC with ZVI and Ag-BC with ZVI sand filters, which demonstrated more than 99% removal of both CECs andE. coliwithout silver ion release, may be effective, low-cost options for decentralized treatment of reused wastewater.

Graphical Abstract

 

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.