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ABSTRACTObjectivesThe aim of this research is to explore current advances in data visualisation technology to improve the analysis of health service utilisation data to support decision making and equitable health care service provision.ApproachMulti-dimensional mapping allows for informative presentation of many types of spatial and geographical data. It allows for the visualisation, question, analysis, and interpretation of data to identify relationships, patterns, and trends. Such techniques can be used to identify patterns in healthcare utilisation relative to a point in time. This can be calendar time, where the event may be a change in services (such as the building of a new hospital) or a change in policy or practice across the system. Or it may be relative time, where the event is a transition in a patient's health or a new diagnosis.Patterns recognised through such data visualisations may be the result of apophenia, i.e. where patterns are observed in what is essentially random information. Thus, data visualisations such as these are a method to help generate hypotheses which can then be rigorously testing using statistical methods.The entire linked Western Australian Data Collections from 2002-2015 (including population-wide hospital admissions, emergency department presentations, cancer registry records, mental health care, maternity records, and mortality records) were examined. Both changes in the source (patient residential location) and target (location of service used) were visualised. Hypotheses generated were then statistically examined.ResultsThree analyses will be presented. Mean emergency department (ED) presentations follow a specific pattern of rising and falling in the years around a diagnosis of heart failure. The geospatial utilisation of ED over time will be visualised and areas of high and low use of ED will be identified. Mental healthcare involves both ED presentations and inpatient services. The geospatial pattern of ED and inpatient service use by mental health patients over time will be visualised and implications for provision of healthcare services discussed. Finally, in a specific current example, the closure of a metropolitan ED and the subsequent opening of a nearby ED has changed the local population's pattern of ED use. These changes will be visualised.ConclusionAdvanced methods of data visualisation improve the analysis of large population based datasets to support decision making and equitable health care service provision.

BackgroundAccurate disease mapping based on spatiotemporal data is an important aspect of public health surveillance, targeting interventions, and health service planning. This is achieved by public health surveillance organisations around the world through the construction of choropleth maps based on single spatiotemporal aggregations of finer resolution data. However, such maps are undermined by their dependence on the spatiotemporal units used. This dependence is described by the related modifiable areal and temporal unit problems (MAUP; MTUP), also known as change of support problems (COSPs).
AimTo accurately map disease.
MethodsUsing ischaemic stroke admissions and mental health-related ED presentations in metropolitan Perth between 2013 and 2016 as exemplars, we present a novel zonation overlay approach for disease mapping. This method involves aggregating fine resolution spatial data numerous times instead of just once, using the automated zonation construction software AZTool.
Results Through implementing the zonation overlay method in combination with a rolling window of time, both the MAUP and the MTUP may be overcome in the context of disease mapping. Furthermore, the AZTool zonations act as a geographical encryption key, allowing fine resolution, precise maps to be constructed while protecting the privacy of individuals.
ConclusionHealth surveillance organisations continue to produce single aggregation choropleth maps of disease, without acknowledging their limitations except in rare cases. Producing such maps and suggesting they should guide policy makers, while being aware of but not acknowledging the impact of COSPs, could be described as scientific malfeasance. However, assuming that most researchers producing such maps are not intending to mislead, we must conclude that COSPs are poorly understood and their impact underestimated. The zonation overlay method we describe can help alleviate the consequences of this continued practice.

IntroductionThe Modifiable Areal Unit Problem (MAUP) arises from the aggregation of data organized by spatially defined boundaries. Aggregated values are influenced by the shape (zone effect) and scale of the aggregated units. Aggregations of the same data using different zones or scales can give different analytical results, none reliable.
Objectives and ApproachUsing population-level administrative health data in Western Australia, the objectives were to: accurately measure the association between health service utilization and demographic, socio-economic, and service accessibility variables; and develop models to accurately forecast areas of high health service utilization into the future.
Multiple zone designs and aggregation scales were used to examine the impact of MAUP in association studies. These zone designs and scales were then used in all-subset model selection processes, combined with repeated k-fold cross-validation, to generate forecast maps of areas having high future rates of health service utilization.
ResultsThe impact of the MAUP and methods to reduce this bias in association studies will be presented, for both simple and complex model designs. Maps indicating gradients of predicted probabilities of high rate of health service demand in the future can be used to optimize the placement of services, through the use of catchment areas based on road-network travel distance and population distributions.
Conclusion/ImplicationsThe impact of the MAUP on the analysis of spatially-aggregated data has been considered intractable. However, methods to reduce the impact of the MAUP can improve policy and planning decisions based on such studies.

ABSTRACT
ObjectivesThe objectives of this project are to identify patients that can be recruited into specific interventions and the optimisation of the delivery of such interventions, in order to improve access to health services, equity of service delivery, and patient outcomes.ApproachThe entire linked Western Australian Data Collections from 2002-2015 (including population-wide hospital admissions, emergency department presentations, cancer registry records, mental health care, maternity records, and mortality records) were examined. To identify patients at risk, a definition of a 'PEAC' event was developed. This acronym reflects the following criteria for such events. First, the event should 'predictable', i.e. either able to be easily predicted, or able to predict subsequent poor patient outcomes (for example, death). Second, the event should be 'expensive', i.e. be associated with significantly increased levels of individual healthcare utilisation and/or mortality. Third, the impact of the event should be 'avoidable', i.e. an evidence-based intervention should exist that may delay or completely avoid the event, or reduce its sequelae. Fourth, the event should be 'cardinal', which in this context indicates that the event should be clearly and unambiguously defined and recognisable, and specific enough to assign an effective intervention. Once PEAC events were identified, geospatial and predictive modelling of future events were then used to inform clinical service delivery, alongside appropriate return-on-investment analysis to support intervention. Finally, the entire process was embedded within a learning health system, linking research, policy, and practice, to drive ongoing improvement.ResultsExemplar PEAC events will be described, including hospital admission events associated with chronic disease, mental health, and dental/oral health. The predictability of such events in individuals using statistical models fitted to the available administrative datasets will be presented, along with the sequelae of such events in terms of healthcare use and mortality. The optimisation of delivering interventions targeting PEAC events will be described, along with the process of translating findings into policy and practice within the context of a learning health system.ConclusionThe identification of PEAC events allows for targeted delivery of healthcare interventions in a manner that not only optimises access, equity, and outcomes, but also permits ongoing improvement of the health system.