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

ABSTRACT: Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time.Models 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.Choice 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.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.

SUBMITTER: Baker J

PROVIDER: S-EPMC5576724 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Baker Jannah J White Nicole N Mengersen Kerrie K Rolfe Margaret M Morgan Geoffrey G GG

PloS one 20170830 8

<h4>Background</h4>Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time.<h4>Methods</h4>Models 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 congest ...[more]

PMID: 28854280

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Project description:BackgroundAboriginal and Torres Strait Islander people have high rates of avoidable hospital admissions for chronic conditions, however little is known about the frequency of avoidable admissions for this population. This study examined trends in avoidable admissions among Aboriginal and non-Aboriginal people with chronic conditions in New South Wales (NSW), Australia.MethodsA historical cohort analysis using de-identified linked administrative data of Aboriginal patients and an equal number of randomly sampled non-Aboriginal patients between 2005/06 to 2013/14. Eligible patients were admitted to a NSW public hospital and who had one or more of the following ambulatory care sensitive chronic conditions as a principal diagnosis: diabetic complications, asthma, angina, hypertension, congestive heart failure and/or chronic obstructive pulmonary disease. The primary outcomes were the number of avoidable admissions for an individual in each financial year, and whether an individual had three or more admissions compared with one to two avoidable admissions in each financial year. Poisson and logistic regression models and a test for differences in yearly trends were used to assess the frequency of avoidable admissions over time, adjusting for sociodemographic variables and restricted to those aged ≤75 years.ResultsOnce eligibility criteria had been applied, there were 27,467 avoidable admissions corresponding to 19,025 patients between 2005/06 to 2013/14 (71.2% Aboriginal; 28.8% non-Aboriginal). Aboriginal patients were 15% more likely than non-Aboriginal patients to have a higher number of avoidable admissions per financial year (IRR = 1.15; 95% CI: 1.11, 1.20). Aboriginal patients were almost twice as likely as non-Aboriginal patients to experience three or more avoidable admissions per financial year (OR = 1.90; 95% CI = 1.60, 2.26). There were no significant differences between Aboriginal and non-Aboriginal people in yearly trends for either the number of avoidable admissions, or whether or not an individual experienced three or more avoidable admissions per financial year (p = 0.859; 0.860 respectively).ConclusionAboriginal people were significantly more likely to experience frequent avoidable admissions over a nine-year period compared to non-Aboriginal people. These high rates reflect the need for further research into which interventions are able to successfully reduce avoidable admissions among Aboriginal people, and the importance of culturally appropriate community health care.

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Dataset Information

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

Publications

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

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