02779nas a2200349   4500000000100000008004100001653001100042653001700053653002000070653002000090653002000110653001800130653002000148653003000168653001500198653002000213653002800233653002400261653002200285653004300307653002900350100001700379700001700396700002100413700001900434700002000453245016200473300001300635490000700648520176000655022001402415       2017                            d10aHumans10aHypertension10aHospitalization10aChronic Disease10aNew South Wales10aHeart Failure10aData Collection10aDiabetes Mellitus, Type 210aAlgorithms10aAmbulatory Care10aCoronary Artery Disease10aModels, Theoretical10aPatient Admission10aPulmonary Disease, Chronic Obstructive10aSpatio-Temporal Analysis1 aBaker Jannah1 aWhite Nicole1 aMengersen Kerrie1 aRolfe Margaret1 aMorgan Geoffrey00aJoint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia.  ae01836530 v123 a<p><b>BACKGROUND: </b>Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>CONCLUSION: </b>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.</p>  a1932-6203