Role of extreme weather events and El Niño Southern Oscillation on incidence of Enteric Fever in Ahmedabad and Surat, Gujarat, India

Effects of El Niño and the Positive Indian Ocean Dipole (+IOD) on Health, Food Security, Economics, and Conflict in Low- and Middle-Income Countries in the Indo-Pacific: A Systematic Review

Climate drivers such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can impact multiple sectors globally. We are currently witnessing the effects of these teleconnections against the backdrop of a changing climate. This systematic review takes stock of the available evidence on compounding and cascading effects of El Niño and the Positive IOD on health, economic, migration, conflicts, and nutrition outcomes in low- and middle-income countries from the Indo-Pacific region. The review sheds light on how effects vary between and within the considered countries and explores potential sources of heterogeneity. The search of studies was carried out in January 2024 in 12 major databases/search engines and 14 institutional websites, using English keywords, and paired by forward and backward citation tracking of the included studies. The review's inclusion criteria encompassed quantitative studies as long as they provide an estimate of relationship between the climate driver and outcome, and qualitative studies that aim to infer causation such as realist evaluation or process tracing. The analysis used a combination of meta-analysis with random-effects models, median effects from correlational and regression studies, and narrative synthesis. We found that El Niño is likely to decrease agricultural production and productivity at the Indo-Pacific level, although the analysed studies are highly diverse. The absence of evidence on the effects of the considered climate drivers on migration, conflict, food security and nutrition is an important evidence gap. We found limited evidence on the differential effects by El Niño's and +IOD's magnitude and no studies examining their combined impact or qualitative effectiveness studies. The high risk of bias detected across studies calls for more thorough attention to study design, conduct, and reporting in answering questions about effects. Despite remaining evidence gaps, this review highlights potential effects of El Niño and +IOD in the Indo-Pacific and underscores the need for context-specific policy responses to mitigate risks at local and regional levels. Caution is warranted in interpreting the overall findings given the generally high risk of bias of evidence.

A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens

Studies on climate variables and food pathogens are either pathogen- or region-specific, necessitating a consolidated view on the subject. This study aims to systematically review all studies on the association of ambient temperature and precipitation on the incidence of gastroenteritis and bacteraemia from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria species. PubMed, Ovid MEDLINE, Scopus, and Web of Science databases were searched up to 9 March 2023. We screened 3,204 articles for eligibility and included 83 studies in the review and three in the meta-analysis. Except for one study on Campylobacter, all showed a positive association between temperature and Salmonella, Shigella, Vibrio sp., and Campylobacter gastroenteritis. Similarly, most of the included studies showed that precipitation was positively associated with these conditions. These positive associations were found regardless of the effect measure chosen. The pooled incidence rate ratio (IRR) for the three studies that included bacteraemia from Campylobacter and Salmonella sp. was 1.05 (95 per cent confidence interval (95% CI): 1.03, 1.06) for extreme temperature and 1.09 (95% CI: 0.99, 1.19) for extreme precipitation. If current climate trends continue, our findings suggest these pathogens would increase patient morbidity, the need for hospitalization, and prolonged antibiotic courses.

Spatial analysis of food and water-borne diseases in Ahmedabad, India: Implications for urban public health planning

Spatial analysis of infectious diseases can play an important role in mapping the spread of diseases and can support policy making at local level. Moreover, identification of disease clusters based on local geography and landscape forms the basis for disease control and prevention. Therefore, this study aimed to examine the spatial-temporal variations, hotspot areas, and potential risk factors of infectious diseases (including Viral Hepatitis, Typhoid and Diarrhea) in Ahmedabad city of India. We used Moran's I and Local Indicators of Spatial Association (LISA) mapping to detect spatial clustering of diseases. Spatial and temporal regression analysis was used to identify the association between disease incidence and spatial risk factors. The Moran's I statistics identified presence of positive spatial autocorrelation within the considered diseases, with Moran's I from 0.09 for typhoid to 0.21 for diarrhea (p < 0.001). This indicates a clustering of affected wards for each disease, suggesting that cases were not randomly distributed across the city. LISA mapping demonstrated the clustering of hotspots in central regions of the city, especially towards the east of the river Sabarmati, highlighting key geographical areas with elevated disease risk. The spatial clusters of infectious diseases were consistently associated with slum population density and illiteracy. Furthermore, temporal analysis suggested illiteracy rates could increase risk of viral hepatitis by 13 % (95 % Confidence Interval (CI): 1.01-1.26) and of diarrhea by 18 % (95 % CI: 1.07-1.31). Significant inverse association was also seen between viral hepatitis incidence and the distance of wards from rivers. Conclusively, the study highlight the impact of socio-economic gradients, such as slum population density (indicative of poverty) and illiteracy, on the localized transmission of water and foodborne infections. The evident social stratification between impoverished and affluent households emerges as a notable contributing factor and a potential source of differences in the dynamics of infectious diseases in Ahmedabad.

Investigation of the Correlation between Enterovirus Infection and the Climate Factor Complex Including the Ping-Year Factor and El Niño-Southern Oscillation in Taiwan

Enterovirus infection and enterovirus infection with severe complications (EVSC) are critical issues in several aspects. However, there is no suitable predictive tool for these infections. A climate factor complex (CFC) containing several climate factors could provide more effective predictions. The ping-year factor (PYF) and El Niño-Southern Oscillation (ENSO) are possible CFCs. This study aimed to determine the relationship between these two CFCs and the incidence of enterovirus infection. Children aged 15 years and younger with enterovirus infection and/or EVSC were enrolled between 2007 and 2022. Each year was categorized into a ping-year or non-ping-year according to the PYF. Poisson regression was used to evaluate the associations between the PYF, ENSO, and the incidence of enterovirus infection. Compared to the ping-year group, the incidence rate of enterovirus infection, the incidence rate of EVSC, and the ratio of EVSC in the non-ping-year group were 1.24, 3.38, and 2.73 times higher, respectively (p < 0.001). For every one-unit increase in La Niña, the incidence rate of enterovirus infection decreased to 0.96 times (p < 0.001). Our study indicated that CFCs could be potential predictors for enterovirus infection, and the PYF was more suitable than ENSO. Further research is needed to improve the predictive model.

Fecal glucocorticoid metabolite levels in captive Indian leopards (Panthera pardus fusca) housed under three different enrichment regimes

Environmental enrichment improves the health and wellbeing of zoo animals. To test this hypothesis, we used Indian leopards (Panthera pardus fusca), one of the popular zoo animals, as a model organism to understand effects of active and passive enrichment elements on stress hormone levels of captive individuals. We included three enrichment categories, category 'A' (having both active: cage size of 1204 m3 with raised platforms and earthen flooring, and passive: controlled temperature, playback of forest sounds and sound proof glass to filter visitors' noise, enrichment elements), category 'B' (active enrichment type I, cage size of 264 m3 with air coolers), and category C (active enrichment type II, cage size of 517 m3 without air coolers) for leopards (n = 14) housed in two Indian zoos. We used a group-specific enzyme immunoassay to measure fecal glucocorticoid metabolites (fGCM) in captive leopards. For comparison, we analysed samples from free-ranging leopards, as well. fGCM levels (Mean±SEM) were 10.45±2.01 and 0.95±0.003 μg/g dry feces in captive and free-ranging leopards, respectively. Our results revealed that fGCM levels of leopards in categories B and C were significantly (P<0.05) different from each other, thus, indicating cage size (an active enrichment element) as an important factor in influencing the physiology of the sampled animals. Overall, the findings of the study will contribute towards informed policies for management of captive Indian leopards.

Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices

Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

El Niño Southern Oscillation, monsoon anomaly, and childhood diarrheal disease morbidity in Nepal

Climate change is adversely impacting the burden of diarrheal diseases. Despite significant reduction in global prevalence, diarrheal disease remains a leading cause of morbidity and mortality among young children in low- and middle-income countries. Previous studies have shown that diarrheal disease is associated with meteorological conditions but the role of large-scale climate phenomena such as El Niño-Southern Oscillation (ENSO) and monsoon anomaly is less understood. We obtained 13 years (2002-2014) of diarrheal disease data from Nepal and investigated how the disease rate is associated with phases of ENSO (El Niño, La Niña, vs. ENSO neutral) monsoon rainfall anomaly (below normal, above normal, vs. normal), and changes in timing of monsoon onset, and withdrawal (early, late, vs. normal). Monsoon season was associated with a 21% increase in diarrheal disease rates (Incident Rate Ratios [IRR]: 1.21; 95% CI: 1.16-1.27). El Niño was associated with an 8% reduction in risk while the La Niña was associated with a 32% increase in under-5 diarrheal disease rates. Likewise, higher-than-normal monsoon rainfall was associated with increased rates of diarrheal disease, with considerably higher rates observed in the mountain region (IRR 1.51, 95% CI: 1.19-1.92). Our findings suggest that under-5 diarrheal disease burden in Nepal is significantly influenced by ENSO and changes in seasonal monsoon dynamics. Since both ENSO phases and monsoon can be predicted with considerably longer lead time compared to weather, our findings will pave the way for the development of more effective early warning systems for climate sensitive infectious diseases.

The Evaluation of Temporal and Spatial Trends of Global Warming and Extreme Ocean Surface Temperatures: A Case Study of Canada

In recent years, extreme weather has frequently occurred worldwide and caused significant disasters, including large-scale forest fires, rare heat waves, heavy rains, floods, and tornadoes. Those have caused unprecedented losses of human lives and property in some countries, affecting the livelihoods of many people. Climate change and natural disasters are the two hotspots of scientific research today, and there is a certain degree of correlation between the two. Although countries worldwide have long known about climate change and its threats to human existence and have been discussing countermeasures, they have still not been able to carry out concerted and practical actions. The study takes Canada as an example, and selects six representative provinces to evaluate the temporal change characteristics of extreme temperature at different sites. We use MATLAB software to perform multiple linear regression, linear fitting methods, and Pearson correlation analysis to analyze spatial changes and time-space trends. The method studies the relationship between the emergence of extreme weather and climate change and uses the evolutionary game theory to explore whether there is any contradiction between global warming and extreme local cold. The study found: (i) The maximum temperature of most provinces in Canada will be constantly higher, and the minimum temperature will be lower. Generally speaking, the average temperature of each year is slowly decreasing. (ii) The average temperature data of British Columbia (Eastern Pacific) and Quebec (West Atlantic) show that ocean temperature has a specific effect on land temperature in surrounding areas. (iii) Pearson correlation analysis shows that the emergence of extreme weather is closely related to climate change. (iv) The evolution path of the two-party game shows that global warming and the occurrence of extreme local cold are not contradictory. Under the conditions, there is a certain degree of synchronization between the two, interacting and influencing each other.