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.
At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.