Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia.

Jannah Baker,Kerrie Mengersen,Nicole White,Margaret Rolfe,Geoffrey G Morgan

doi:10.1371/journal.pone.0183653

Abstract

BackgroundThree variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time.MethodsModels are evaluated within the context of a case study examining hospitalisation rates for five chronic diseases for residents of a regional area in New South Wales: type II diabetes mellitus (DMII), chronic obstructive pulmonary disease (COPD), coronary arterial disease (CAD), hypertension (HT) and congestive heart failure (CHF) between 2001–2006. These represent ambulatory care sensitive (ACS) conditions, often used as a proxy for avoidable hospitalisations. Using a selected model, the effects of socio-economic status (SES) as a shared component are estimated and temporal patterns in the influence of the residual shared spatial component are examined.ResultsChoice of model depends upon the application. In the featured application, a model allowing for changing influence of the shared spatial component over time was found to have the best fit and was selected for further analyses. Hospitalisation rates were found to be increasing for COPD and DMII, decreasing for CHF and stable for CAD and HT. SES was substantively associated with hospitalisation rates, with differing degrees of influence for each disease. In general, most of the spatial variation in hospitalisation rates was explained by disease-specific spatial components, followed by the residual shared spatial component.ConclusionAppropriate selection of a joint disease model allows for the examination of temporal patterns of disease outcomes and shared underlying spatial factors, and distinction between different shared spatial factors.

Highlights

Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time
Hospitalisation rates were found to be increasing for chronic obstructive pulmonary disease (COPD) and Type II diabetes mellitus (DMII), decreasing for congestive heart failure (CHF) and stable for coronary arterial disease (CAD) and HT
Appropriate selection of a joint disease model allows for the examination of temporal patterns of disease outcomes and shared underlying spatial factors, and distinction between different shared spatial factors

Summary

Introduction

Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time. In Australia, diabetes complications are the leading cause of avoidable hospitalisation, and together with COPD and angina due to CAD account for almost one half (44.5%) of all avoidable hospital admissions [6]. Individuals of lower SES are more at risk for preventable hospitalisation even after adjusting for severity of illness [21]. To this end, accounting for area-level SES is important in joint disease spatiotemporal models exploring temporal trends in hospitalisation incidence for each ACS condition. Spatial studies have been performed in the US, UK and Europe to examine high-risk areas for avoidable hospitalisation, little is known about temporal trends in avoidable hospitalisation incidence in Australia specific to residential areas [20].

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