Microclimate factors related to dengue virus burden clusters in two endemic towns of Mexico.

In dengue-endemic areas, transmission control is limited by the difficulty of achieving sufficient coverage and sustainability of interventions. To maximize the effectiveness of interventions, areas with higher transmission could be identified and prioritized. The aim was to identify burden clusters of Dengue virus (DENV) infection and evaluate their association with microclimatic factors in two endemic towns from southern Mexico. Information from a prospective population cohort study (2·5 years of follow-up) was used, microclimatic variables were calculated from satellite information, and a cross-sectional design was conducted to evaluate the relationship between the outcome and microclimatic variables in the five surveys. Spatial clustering was observed in specific geographic areas at different periods. Both, land surface temperature (aPR 0·945; IC95% 0·895-0·996) and soil humidity (aPR 3·018; IC95% 1·013-8·994), were independently associated with DENV burden clusters. These findings can help health authorities design focused dengue surveillance and control activities in dengue endemic areas.

House Condition Scoring Scale as a Risk Indicator of Infestation by Aedes in Two Mexican Localities.

Mexico has shown an increase in dengue incidence rates. There are factors related to the location that determine housing infestation by Aedes. This study aimed to determine factors associated with housing infestation by immature forms of Aedes spp. in the dengue endemic localities of Axochiapan and Tepalcingo, Mexico, from 2014 to 2016. A cohort study was carried out. Surveys and inspections of front- and backyards were conducted every 6 months, looking for immature forms of Aedes spp. A house condition scoring scale was developed using three variables (house maintenance, tidiness of the front- and backyards, and shading of the front- and backyards). Multiple and multilevel regression logistic analysis were conducted considering the housing infestation as the outcome and the household characteristics observed 6 months before the outcome as factors; this was adjusted by time (seasonal and cyclical variations of the vector). The infestation oscillated between 5.8% of the houses in the second semester of 2015 and 29.3% in the second semester of 2016. The factors directly associated with housing infestation by Aedes were the house condition score (adjusted odds ratio [aOR]: 1.64; 95% CI: 1.40-1.91) and the previous record of housing infestation (aOR: 2.99; 95% CI: 2.00-4.48). Moreover, the breeding-site elimination done by house residents reduced the housing infestation odds by 81% (95% CI: 25-95%). These factors were independent of the seasonal and cyclical variations of the vector. In conclusion, our findings could help to focalize antivectorial interventions in dengue-endemic regions with similar demographic and socioeconomic characteristics.

Risk Prediction of Death in Inpatient Adults With COVID-19 from Mexico.

Background: There is substantial variation in COVID-19 lethality across countries. In addition, in countries with populations with extreme economic inequalities, such as Mexico, there are regional and local differences in risk factors for COVID-19 death. The goal of this study was to test the hypothesis that the risk of death in Mexican COVID-19 patients was associated with the time between symptom onset and hospitalization and/or with the healthcare site. Also, death prognostic models were developed. Methods: The study included two COVID-19 inpatient cohorts, one prospective and one retrospective from Chiapas, Mexico. Demographic, clinical and laboratory variables were collected, and the diagnosis of SARS-CoV-2 infection was performed using RT-qPCR in samples collected seven days since symptom onset. The 30-day mortality, since symptom onset, was the outcome, and clinical variables at the first 48 hours of hospitalization were independent factors. Multivariate logistic regression analyses were conducted. Results: Of the 392 patients included, 233 died (59.4%). The time between symptom onset and hospitalization, the healthcare site and sex were not related to the 30-day mortality. Three death prognostic models were developed (AUC between 0.726 and 0.807). Age, LDH, AST, and lymphocyte count were included in all models, OSI-WHO Classification (Non-invasive ventilation or high-flow oxygen, and mechanical ventilation with or without organ support/ECMO) and leukocyte count in two models, and diabetes and diarrhea in one model. Conclusion: The population evaluated had underlying deteriorated health before COVID-19 compared with regional and country population. The factors that determine the COVID-19 mortality risk in a relatively healthy population are sex, age and comorbidities. However, as this study shows, when populations have underlying poor health, some of these factors lose their associations with mortality risk, and others become more important.