This study examines the spatial structure of children with cleft lip and palate (CLP) and its association with polluted areas in the Monterrey Metropolitan Area (MMA). The Nearest Neighbor Index (NNI) and the Spatial Statistical Scan (SaTScan) determined that the CLP cases are agglomerated in spatial clusters distributed in different areas of the city, some of them grouping up to 12 cases of CLP in a radius of 1.2 km. The application of the interpolation by empirical Bayesian kriging (EBK) and the inverse distance weighted (IDW) method showed that 95% of the cases have a spatial interaction with values of particulate matter (PM₁₀) of more than 50 points. The study also shows that 83% of the cases interacted with around 2000 annual tons of greenhouse gases. This study may contribute to other investigations applying techniques for the identification of environmental and genetic factors possibly associated with congenital malformations and for determining the influence of contaminating substances in the incidence of these diseases, particularly CLP.

Rising global temperatures and seawater temperatures have led to an increase in extreme weather patterns leading to droughts and floods. These natural phenomena, in turn, affect the supply of drinking water in some communities, which causes an increase in the prevalence of diseases related to the supply of drinking water. The objective of this work is to demonstrate the effects of global warming on human health in the population of Monterrey, Mexico after Hurricane Alex. We interpolated data using statistical downscaling of climate projection data for 2050 and 2080 and correlated it with disease occurrence. We found a remarkable rise in the incidence of transmissible infectious disease symptoms. Gastrointestinal symptoms predominated and were associated with drinking of contaminated water like tap water or water from communal mobile water tanks, probably because of the contamination of clean water, the disruption of water sanitation, and the inability to maintain home hygiene practices.

The proliferation of pollutants affects the world’s population, mainly those who live in large cities. Neurological and cardiovascular dysfunctions have a correlation with air particulate matter concentration, among other chronic diseases. Therefore, it is important to utilize different methods of analysis to build predictive models that can identify possible concentrations of contaminants in metropolitan areas. This work presents a methodology that will enable the forecasting of severe pollution contingencies using weather measurements as input variables. This predictive analytical technique combines several mathematical and statistical tools, which we refer to as predictive factor association (PFA). We perform principal component analysis on the samples to determine possible causal relationships and reduce dimensionality, resulting in orthogonal linear combinations of the variables called sample scores. For categorical variables, each linear combination probability is estimated using a window-based estimation technique or a machine learning algorithm to determine the class of a particular sample. For continuous variables, regression over the scores is carried out. The technique is used to predict environmental contingencies in Monterrey’s metropolitan area based on meteorological data with ${\mathrm{R}}^2$ between 0.7 and 0.8 and classification accuracies between 0.74 and 0.98.