Background: The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. Methods: To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. Results: We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. Conclusion: The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

Few studies have investigated whether SARS-CoV-2 infections increase the incidence of dengue haemorrhagic fever/shock syndrome (DHF/DSS) and/or severe dengue (SD) in dengue virus (DENV)-infected patients. This study was performed on a site with high incidences of classical dengue, but relatively few DHF/DSS or SD cases as defined by the WHO 1997 or 2009 criteria, respectively. Clinical, haematological/biochemical, and viral diagnostic data were collected from febrile patients before, during, and after the COVID-19 epidemic to assess whether (a) DENV-infected patients with prior SARS-CoV-2 infections or (b) DENV-SARS-CoV-2-co-infected patients had increased incidences of SD/DHF/DSS using logistic regression and machine learning models. Higher numbers of DHF/DSS/SD occurred during the COVID-19 epidemic, particularly in males and 18–40-year-olds. Significantly increased symptoms in the DENV-SARS-CoV-2-co-infected cases were (a) haemoconcentration (p < 0.0009) and hypotension (p < 0.0005) (DHF/DSS and SD criteria), (b) thrombocytopenia and mucosal bleeding (DHF/DSS-criteria), (c) abdominal pain, persistent vomiting, mucosal bleeding, and thrombocytopenia (SD warning signs) and (d) dyspnoea, but without fluid accumulation. DENV-infected patients with prior SARS-CoV-2 infections had significantly increased incidences of thrombocytopenia (DHF/DSS-criteria) and/or abdominal pain and persistent vomiting and also thrombocytopenia (SD warning signs), but without significant haemoconcentration or hypotension. DENV-SARS-CoV-2 co-infections significantly increased the incidence of DHF/DSS/SD, while DENV-infected patients with prior SARS-CoV-2 infections displayed significantly increased incidences of thrombocytopenia (DHF/DSS-criteria) and three important SD warning signs, which are therefore very important for health workers/clinicians in assessing patients’ DHF/DSS/SD risk factors and planning their optimal therapies.

Background: Armed conflicts, like the Russian invasion of Ukraine, devastated healthcare systems and amplified chronic disease burdens. This study aimed to unveil links between environmental and social factors and chronic illnesses during crises. Methods: This study uniquely combined individual-level data on chronic diseases, living conditions, and winter preparedness in Ukraine during crisis conditions. A multivariable logistic regression model, using age- and gender-adjusted analyses, revealed associations, while machine learning models employed rigorous feature selection and data balancing. Six algorithms predicted disease outcomes using validated metrics, and interpretable insights. Results: Surveying 1552 adults during the Russia-Ukraine war, we show that chronic disease prevalence is strongly linked to conflict-driven living conditions. Poor indoor environments disrupted access to medicines, food, transport, and care, and economic shocks consistently were consistently associated with higher disease prevalence/risk. Machine-learning models identified distinct environmental and socioeconomic predictors across hypertension, coronary heart disease, asthma, chronic obstructive pulmonary disease, and arthritis, enabling conflict-specific vulnerability mapping. Conclusion: This study reveals links among environment, socioeconomic status, and healthcare access in the context of chronic disease risks during armed conflicts, highlighting the need for targeted policy implementation to improve healthcare accessibility and reduce health disparities.