Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique.

Bayesian spatial models are widely used to analyse data that arise in scientific disciplines such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo (MCMC) methods have been used to fit these type of models. However, these are highly computationally intensive methods that present a wide range of issues in terms of convergence and can become infeasible in big data problems. The integrated nested Laplace approximation (INLA) method is a computational less-intensive alternative to MCMC that allows us to perform approximate Bayesian inference in latent Gaussian models such as generalised linear mixed models and spatial and spatio-temporal models. This approach can be used in combination with the stochastic partial differential equation (SPDE) approach to analyse geostatistical data that have been collected at particular sites to predict the spatial process underlying the data as well as to assess the effect of covariates and model other sources of variability. Here we demonstrate how to fit a Bayesian spatial model using the INLA and SPDE approaches applied to freely available data of malaria prevalence and risk factors in Mozambique. We show how to fit and interpret the model to predict malaria risk and assess the effect of covariates using the R-INLA package, and provide the R code necessary to reproduce the results or to use it in other spatial applications.

Curation of an intensive care research dataset from routinely collected patient data in an NHS trust.

In this data note we provide the details of a research database of 4831 adult intensive care patients who were treated in the Bristol Royal Infirmary, UK between 2015 and 2019. The purposes of this publication are to describe the dataset for external researchers who may be interested in making use of it, and to detail the methods used to curate the dataset in order to help other intensive care units make secondary use of their routinely collected data. The curation involves linkage between two critical care datasets within our hospital and the accompanying code is available online. For reasons of data privacy the data cannot be shared without researchers obtaining appropriate ethical consents. In the future we hope to obtain a data sharing agreement in order to publicly share the de-identified data, and to link our data with other intensive care units who use a Philips clinical information system.