Traditionally the diagnosis of Metabolic syndrome (MetS) is binary (present/absent). The goal of this work is to propose a sex-specific continuous score to measure the severity of MetS in Mexican adults using waist circumference and body mass index as adiposity measures. MetSx-WC and MetSx-BMI indexes by sex were derived by confirmatory factor analysis (CFA) using data for 6567 adult participants of the National Health and Nutrition Survey 2018. The overall fit of the two proposed CFA models was excellent. We then validated these scores using a community-based health study of 862 university participants and determined that the reliability and strength of agreement between the MetSx-WC and MetSx-BMI scores were excellent. The ROC analysis of the resulting indexes indicates that they have excellent ability to discriminate a MetS classification according to the different criteria. The correlations of MetSx scores and surrogate markers of insulin resistance and obesity ranged from weak to strong. Subsequently, a retrospective study of 310 hospitalized patients with COVID-19 was used to determined that MetSx-BMI score was associated with the mortality of patients with COVID-19. The proposed indices provide a continuous measure in the identification of MetS risk in Mexican adults.

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The objective of this chapter is to present the methodology of some of the models used in the area of epidemiology, which are used to study, understand, model and predict diseases (infectious and non-infectious) occurring in a given region. These models, which belong to the area of geostatistics, are usually composed of a fixed part and a random part. The fixed part includes the explanatory variables of the model and the random part includes, in addition to the error term, a random term that generally has a multivariate Gaussian distribution. Based on the random effect, the spatial correlation (or covariance) structure of the data will be explained. In this way, the spatial variability of the data in the region of interest is accounted for, thus avoiding that this information is added to the model error term. The chapter begins by introducing Gaussian processes, and then looks at their inclusion in generalized spatial linear models, spatial survival analysis and finally in the generalized extreme value distribution for spatial data. The review also mentions some of the main packages that exist in the R statistical software and that help with the implementation of the mentioned spatial models.

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