Determinants of Pneumonia mortality in Bogota, Colombia: A spatial econometrics approach.

Bogota, the capital and largest city of Colombia, constantly fights against easily transmitted and endemic-epidemic diseases that lead to enormous public health problems. Pneumonia is currently the leading cause of mortality attributable respiratory infections in the city. Its recurrence and impact have been partially explained by biological, medical, and behavioural factors. Against this background, this study investigates Pneumonia mortality rates in Bogota from 2004 and 2014. We identified a set of environmental, socioeconomic, behavioural, and medical care factors whose interaction in space could explain the occurrence and impact of the disease in the Iberoamerican city. We adopted a spatial autoregressive models framework to study the spatial dependence and heterogeneity of Pneumonia mortality rates associated with well-known risk factors. The results highlight the different types of spatial processes governing Pneumonia mortality. Furthermore, they demonstrate and quantify the driving factors that stimulate the spatial spread and clustering of mortality rates. Our study stresses the importance of spatial modelling of context-dependent diseases such as Pneumonia. Likewise, we emphasize the need to develop comprehensive public health policies that consider the space and contextual factors.

Spatially informed Bayesian neural network for neurodegenerative diseases classification.

Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three-dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision-making and can account for the heterogeneous medical presentations of neurodegenerative disorders.