Chapters authored
The Basics of Structural Equations in Medicine and Health Sciences
Structural Equation Models (SEM) are very useful and, with a wide range of practical applications in many fields of science, in medicine and health sciences, have increased interest in their usefulness. This chapter is divided into three sections. The first includes concepts, notation, and theoretical aspects of SEM, such as path diagrams, measurement model, confirmatory factor analysis, structural regression, and identification model. In addition, it includes some simple examples applied to health sciences. The second section deals with the estimation and evaluation of the model. On the first topic, the methods of Maximum Likelihood (ML), Generalized Least Squares, Unweighted Least Squares, and ML with robust standard errors are addressed, as well as alternative methods to the problem of violations of the multivariate normality assumption. On the second topic, some goodness of fit statistics of the estimated model are defined, such as the chi-square statistic, Root Mean Square Error of Approximation, Tucker-Lewis Index, Comparative Fit Index, Standardized Root Mean Square Residual, and Goodness of Fit Index. The last section deals with SEM example and its implementation using the lavaan library of R software.
Part of the book: Recent Advances in Medical Statistics
Spatial Statistics in Vector-Borne Diseases
Vector-borne diseases are those caused by the bite of an infected arthropod, such as the Aedes aegypti mosquito, which can infect humans with dengue or Zika. Spatial statistics is an interesting tool that is currently implemented to predict and analyze the behavior of biological systems or natural phenomena. In this chapter, fundamental characteristics of spatial statistics are presented and its application in epidemiology is exemplified by presenting a study on the prediction of the dispersion of dengue disease in Chiapas, Mexico. A total of 573 confirmed dengue cases (CDCs) were studied over the period of January–August 2019. As part of the spatial modeling, the existence of spatial correlation in CDCs was verified with the Moran index (MI) and subsequently the spatial correlation structure was identified with the mean squarer normalized error (MSNE) criterion. A Generalized Linear Spatial Model (GLSM) was used to model the CDCs. CDCs were found to be spatially correlated, and this can be explained by a Matérn covariance function. Finally, the explanatory variables were maximum environmental temperature, altitude, average monthly rainfall, and patient age. The prediction model shows the importance of considering these variables for the prevention of future CDCs in vulnerable areas of Chiapas.
Part of the book: Recent Advances in Medical Statistics