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.

This article investigates the geographical spread of confirmed COVID-19 cases and deaths across municipalities in Mexico. It focuses on the spread dynamics and containment of the virus between Phase I (from March 23 to May 31, 2020) and Phase II (from June 1 to August 22, 2020) of the social distancing measures. It also examines municipal-level factors associated with cumulative COVID-19 cases and deaths to understand the spatial determinants of the pandemic. The analysis of the geographic pattern of the pandemic via spatial scan statistics revealed a fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the country’s center, whereas in Phase II, these clusters dispersed to the rest of the country. The regression results from the zero-inflated negative binomial regression analysis suggested that income inequality, the prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated with confirmed cases and deaths regardless of lockdown.

This article investigates the geographical spread of COVID-19 confirmed cases and deaths across municipalities in Mexico. It focuses on the spread dynamics between Phase I (from March 23th to May 31st, 2020) and II (from June 1st to August 22th, 2020) of the social distancing measures. It also examines municipal-level factors associated with cumulative COVID-19 cases and deaths to understand the spatial determinants of the pandemic. The analysis of the geographic pattern of the pandemic via Space-Time Scan Statistics (SaTScan) revealed fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the center of the country, while in Phase II, these clusters dispersed to the rest of the country. The regression results from the Zero-Inflated Negative Binomial Regression analysis suggested that income inequality, prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated to confirmed cases and deaths regardless of lockdown.

This research examines the spatial structure of a sample of breast cancer (BC) cases and their spatial interaction with contaminated areas in the Monterrey Metropolitan Area (MMA). By applying spatial statistical techniques that treat the space as a continuum, degrees of spatial concentration were determined for the different study groups, highlighting their concentration pattern. The results indicate that 65 percent of the BC sample had exposure to more than 56 points of PM 10 . Likewise, spatial clusters of BC cases of up to 39 cases were identified within a radius of 3.5 km, interacting spatially with environmental contamination sources, particularly with refineries, food processing plants, cement, and metals. This study can serve as a platform for other clinical research by identifying geographic clusters that can help focus health policy efforts.