In Chile, the Metropolitan Region of Santiago (RMS) is exposed to several natural and anthropogenic hazards. This means that not only is there a constant need for healthcare, but also a significant increase whenever its inhabitants are affected by disasters. The RMS problem is not the lack of healthcare infrastructure; rather, the inequality in its spatial distribution, which does not consider the location of the most vulnerable population, who may have greater healthcare needs. In this paper, we have performed Pearson's correlation and multicollinearity analysis to select variables to include in the multiple regression analysis to identify the predictors of the number of healthcare facilities per commune in the RMS. Our research found that public healthcare facilities, average monthly income per person per commune, and population density predicts in a 74.1% the number of the total healthcare facilities per commune in the RMS. Network analysis allowed us to integrate distance-based and area-based approaches to spatially visualise the service area of the healthcare facilities in all the districts in the communes of the RMS according to three walking distances. Total coverage of service areas is observed only in 4% of the districts, while high and medium coverage is identified in 30%, low coverage is observed in 28% and 7% of districts are not covered at all. Those districts with low or non-coverage are mainly low-income and/or rural districts in the RMS communes.@en

During the 2012–2016 drought in La Guajira, Colombia, child mortality rates rose to 23.4 out of 1000. Most of these children belonged to the Wayuu indigenous community, the largest and one of the most vulnerable in Colombia. At the municipal level, this study found a significant positive correlation between the average child mortality rate and households with a monthly income of less than USD 100, the number of people without access to health insurance, being part of the indigenous population, being illiterate, lacking sewage systems, living in rural areas, and large households with members younger than 5 years old and older than 65 years old. No correlation was found with households without access to a water source. The stepwise regression analysis showed that households with a monthly income of less than USD 100, no members older than 65 years old, but several children younger than 5 years old, account for 90.4% of the child mortality rate. This study concludes that, if inhabitants had had better incomes or assets, as well as an adequate infrastructure, they could have faced the drought without the observed increase in child mortality.

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