Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Background: In Mexico, the COVID-19 pandemic led to preventative measures such as confinement and social interaction limitations that paradoxically may have aggravated healthcare access disparities for pregnant women and accentuated health system weaknesses addressing high-risk patients’ pregnancies. Our objective is to estimate the maternal mortality ratio in 1 year and analyze the clinical course of pregnant women hospitalized due to acute respiratory distress syndrome and COVID-19. Methods: A retrospective surveillance study of the national maternal mortality was performed from February 2020–February 2021 in Mexico related to COVID-19 cases in pregnant women, including their outcomes. Comparisons were made between patients who died and those who survived to identify prognostic factors and underlying health conditions distribution. Results: Maternal Mortality Ratio increased by 56.8% in the studied period, confirmed COVID-19 was the cause of 22.93% of cases. Additionally, unconfirmed cases represented 4.5% of all maternal deaths. Among hospitalized pregnant women with Acute Respiratory Distress Syndrome consistent with COVID-19, smoking and cardiovascular diseases were more common among patients who faced a fatal outcome. They were also more common in the age group of < 19 or > 38. In addition, pneumonia was associated with asthma and immune impairment, while diabetes and increased BMI increased the odds for death (Odds Ratio 2.30 and 1.70, respectively). Conclusions: Maternal Mortality Ratio in Mexico increased over 60% in 1 year during the pandemic; COVID-19 was linked to 25.4% of maternal deaths in the studied period. Lethality among pregnant women with a diagnosis of COVID-19 was 2.8%, and while asthma and immune impairment increased propensity for developing pneumonia, obesity and diabetes increased the odds for in-hospital death. Measures are needed to improve access to coordinated well-organized healthcare to reduce maternal deaths related to COVID-19 and pandemic collateral effects.

RRH: Rural and Remote Health. Published article number: 4701 - Emergency treatment for a venomous snakebite accident in rural southern Mexico

Objective: Describe the clinical and sociodemographic characteristics of maternal deaths that occurred in a hospital in Yucatán, Mexico, as well as propose recommendations to improve the care of pregnant women in order to help reduce in-hospital maternal mortality. Material and methods: Retrospective, cross-sectional study conducted in a second-level care concentration hospital. The hospital database of maternal deaths that occurred during the 2014–2018 period was reviewed, as well as clinical summaries, maternal death reports, maternal death ratification forms, and hospital records. Results: There were 54 maternal deaths, the year with the highest percentage was 2018 with 15 (27.8%) deaths. Most of the women resided in municipalities with low and very low degrees of marginalization 23 (42.6%); 48 (88.9%) were engaged in housework; 13 had a history of previous abortion (24.1%), with prenatal control 18 (33.3%), used some contraceptive method 13 (24.1%); and 21 (38.9%) presented some complication during pregnancy. Direct maternal deaths were mainly hypertensive disorders with 9 (32.1%) deaths. Indirect maternal deaths were mainly due to heart disease and arteriovenous disorders 6 (30.0%). The Hospital Mortality Committee issued 98 (55.1%) recommendations to improve emergency care, of which 32 (32. 6%) were related to the training of health personnel in the first level of care; and 22 (22.5%) for the second level of care. Conclusion: It is important to improve the care of the pregnant woman through the permanent training of health personnel to detect warning signs in the prenatal consultation and care of the obstetric emergency, in order to fully detect the problems that arise in the care chain of patients. Likewise, the training and updating of medical personnel for the correct completion of the basic causes of maternal death according to IDC-10 will be necessary for the correct certification of deaths.