A space-time multivariate Bayesian model to analyse road traffic accidents by severity

The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005–2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.Areti Boulieri acknowledges support from the National Institute for Health Research and the Medical Research Council Doctoral Training Partnership. Marta Blangiardo acknowledges support from the National Institute for Health Research and the Medical Research Council–Public Health England Centre for Environment and Health. Silvia Liverani acknowledges support from the Leverhulme Trust (grant ECF-2011-576)

Advances in spatiotemporal models for non-communicable disease surveillance

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public

Bayesian spatio-temporal hierarchical models for disease mapping and detection

In recent years, emerging computational algorithms have revolusionised the application of sophisticated Bayesian hierarchical models in numerous fields, including public health. Such models have become widespread in disease mapping and detection studies, where data of complex spatio-temporal structures arise, due to their flexibility to accommodate associated statistical challenges. This thesis contributes to the scientific literature by building upon existing disease mapping and detection spatio-temporal models within the Bayesian hierarchical framework in order to answer important epidemiological questions. Motivated by chronic respiratory diseases and road traffic accidents, two major public health concerns characterised by inherent geographical structures, three main objectives are established. First, a Bayesian model is proposed for the joint analysis of road traffic accidents by taking into account dependences across space, time and also between severity levels, in order to perform disease mapping and highlight increased risk of accident. Second, the chronic respiratory disease is investigated by using a recently proposed Bayesian detection model. To achieve this, three data sources are analysed: mortality, hospital admissions, and drug prescriptions, each capturing a different level of disease severity. Along with providing spatio-temporal patterns of the chronic respiratory disease, the detection of unusual areas in terms of temporal behaviour is enabled, thus providing insights for hypothesis generation. Following on from the previous work, a more flexible detection model is finally developed and evaluated through an extensive simulation study. Comparisons against the benchmark model are performed, suggesting great improvements of the proposed model. To illustrate the model, two case studies are carried out, on road traffic accidents and chronic obstructive pulmonary disease.Open Acces

A Bayesian mixture modelling approach for public health surveillance

Spatial monitoring of trends in health data plays an important part of public health surveillance. Most commonly, it is used to understand the etiology of a public health issue, to assess the impact of an intervention, or to provide detection of unusual behavior. In this article, we present a Bayesian mixture model for public health surveillance, which is able to provide estimates of the disease risk in space and time, and also to detect areas with unusual behavior. The model is designed to deal with a range of spatial and temporal patterns in the data, and with time series of different lengths. We carry out a simulation study to assess the performance of the model under different scenarios, and we compare it against a recently proposed Bayesian model for short time series. Finally, the proposed model is used for surveillance of road traffic accidents data in England over the years 2005–2015

A spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England

Objectives To estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation. Design We used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001–2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards. Results We confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level. Conclusions There are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies

Investigating trends in asthma and COPD through multiple data sources: A small area study.

This paper investigates trends in asthma and COPD by using multiple data sources to help understanding the relationships between disease prevalence, morbidity and mortality. GP drug prescriptions, hospital admissions, and deaths are analysed at clinical commissioning group (CCG) level in England from August 2010 to March 2011. A Bayesian hierarchical model is used for the analysis, which takes into account the complex space and time dependencies of asthma and COPD, while it is also able to detect unusual areas. Main findings show important discrepancies across the different data sources, reflecting the different groups of patients that are represented. In addition, the detection mechanism that is provided by the model, together with inference on the spatial, and temporal variation, provide a better picture of the respiratory health problem

Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic

Introduction: The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods: Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results: We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion: In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data