ArticlePloS one2017
Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research.International journal of environmental research and public health · 2023Pooled it
- A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research.International journal of environmental research and public health · 2023Pooled it
- Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Joint spatiotemporal modelling of tuberculosis and human immunodeficiency virus in Ethiopia using a Bayesian hierarchical approach.BMC public health · 2025Article
- The effect of primary care on potentially avoidable hospitalizations in France: a cross-sectional study.BMC health services research · 2020Article
- Article
- Risk factors for avoidable hospitalizations in Canada using national linked data: A retrospective cohort study.PloS one · 2020Article
- Spatio-Temporal Variation of Gender-Specific Hypertension Risk: Evidence from China.International journal of environmental research and public health · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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