A geospatial examination of specialist care accessibility and impact on health outcomes for patients with acute traumatic spinal cord injury in New South Wales, Australia: a population record linkage study.

BACKGROUND: Timely treatment is essential for achieving optimal outcomes after traumatic spinal cord injury (TSCI), and expeditious transfer to a specialist spinal cord injury unit (SCIU) is recommended within 24 h from injury. Previous research in New South Wales (NSW) found only 57% of TSCI patients were admitted to SCIU for acute post-injury care; 73% transferred within 24 h from injury. We evaluated pre-hospital and inter-hospital transfer practices to better understand the post-injury care pathways impact on patient outcomes and highlight areas in the health service pathway that may benefit from improvement. METHODS: This record linkage study included administrative pre-hospital (Ambulance), admissions (Admitted Patients) and costs data obtained from the Centre for Health Record Linkage, NSW. All patients aged ≥16 years with incident TSCI in NSW (2013-2016) were included. We investigated impacts of geographical disparities on pre-hospital and inter-hospital transport decisions from injury location using geospatial methods. Outcomes assessed included time to SCIU, surgery and the impact of these variables on the experience of inpatient complications. RESULTS: Inclusion criteria identified 316 patients, geospatial analysis showed that over half (53%, n = 168) of all patients were injured within 60 min road travel of a SCIU, yet only 28.6% (n = 48) were directly transferred to a SCIU. Patients were more likely to experience direct transfer to a SCIU without comorbid trauma (p < 0.01) but higher ICISS (p < 0.001), cervical injury (p < 0.01), and transferred by air-ambulance (p < 0.01). Indirect transfer to SCIU was more likely with two or more additional traumatic injuries (p < 0.01) or incomplete injury (p < 0.01). Patients not admitted to SCIU at all were older (p = 0.05) with lower levels of injury (p < 0.01). Direct transfers received earlier operative intervention (median (IQR) 12.9(7.9) hours), compared with patients transferred indirectly to SCIU (median (IQR) 19.5(18.9) hours), and had lower risk of complications (OR 3.2 v 1.4, p < 0.001). Complications included pressure injury, deep vein thrombosis, urinary infection, among others. CONCLUSIONS: Getting patients with acute TSCI patients to the right place at the right time is dependent on numerous factors; some are still being triaged directly to non-trauma services which delays specialist and surgical care and increases complication risks. The higher rates of complication following delayed transfer to a SCIU should motivate health service policy makers to investigate reasons for this practice and consent to improvement strategies. More stringent adherence to recommended guidelines would prioritise direct SCIU transfer for patients injured within 60 min radius, enabling the benefits of specialised care.

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps.

BACKGROUND: In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. RESULTS: We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. CONCLUSIONS: The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

Hospital admissions and emergency department presentations for dental conditions indicate access to hospital, rather than poor access to dental health care in the community.

High rates of dental-related potentially preventable hospitalisations are thought to reflect poor access to non-hospital dental services. The association between accessibility (geographic and financial) to non-hospital dentists and potentially preventable hospitalisations was examined in Western Australia. Areas with persistently high rates of dental-related potentially preventable hospitalisations and emergency department (ED) presentations were mapped. Statistical models examined factors associated with these events. Persistently high rates of dental-related potentially preventable hospitalisations were clustered in metropolitan areas that were socioeconomically advantaged and had more dentists per capita (RR 1.06, 95% CI 1.04-1.08) after adjusting for age, sex, socioeconomics, and Aboriginality. Persistently high rates of ED presentations were clustered in socioeconomically disadvantaged areas near metropolitan EDs and with fewer dentists per capita (RR 0.91, 0.88-0.94). A positive association between dental-related potentially preventable hospitalisations and poor (financial or geographic) access to dentists was not found. Rather, rates of such events were positively associated with socioeconomic advantage, plus greater access to hospitals and non-hospital dental services. Furthermore, ED presentations for dental conditions are inappropriate indicators of poor access to non-hospital dental services because of their relationship with hospital proximity. Health service planners and policymakers should pursue alternative indicators of dental service accessibility.

Improving the Efficiency of Geographic Target Regions for Healthcare Interventions.

Appropriate prioritisation of geographic target regions (TRs) for healthcare interventions is critical to ensure the efficient distribution of finite healthcare resources. In delineating TRs, both 'targeting efficiency', i.e., the return on intervention investment, and logistical factors, e.g., the number of TRs, are important. However, existing approaches to delineate TRs disproportionately prioritise targeting efficiency. To address this, we explored the utility of a method found within conservation planning: the software Marxan and an extension, MinPatch ('Marxan + MinPatch'), with comparison to a new method we introduce: the Spatial Targeting Algorithm (STA). Using both simulated and real-world data, we demonstrate superior performance of the STA over Marxan + MinPatch, both with respect to targeting efficiency and with respect to adequate consideration of logistical factors. For example, by design, and unlike Marxan + MinPatch, the STA allows for user-specification of a desired number of TRs. More broadly, we find that, while Marxan + MinPatch does consider logistical factors, it also suffers from several limitations, including, but not limited to, the requirement to apply two separate software tools, which is burdensome. Given these results, we suggest that the STA could reasonably be applied to help prevent inefficiencies arising due to targeting of interventions using currently available approaches.

Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation.

Long-term future prediction of geographic areas with high rates of potentially preventable hospitalisations (PPHs) among residents, or "hotspots", is critical to ensure the effective location of place-based health service interventions. This is because such interventions are typically expensive and take time to develop, implement, and take effect, and hotspots often regress to the mean. Using spatially aggregated, longitudinal administrative health data, we introduce a method to make such predictions. The proposed method combines all subset model selection with a novel formulation of repeated k-fold cross-validation in developing optimal models. We illustrate its application predicting three-year future hotspots for four PPHs in an Australian context: type II diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and "high risk foot". In these examples, optimal models are selected through maximising positive predictive value while maintaining sensitivity above a user-specified minimum threshold. We compare the model's performance to that of two alternative methods commonly used in practice, i.e., prediction of future hotspots based on either: (i) current hotspots, or (ii) past persistent hotspots. In doing so, we demonstrate favourable performance of our method, including with respect to its ability to flexibly optimise various different metrics. Accordingly, we suggest that our method might effectively be used to assist health planners predict excess future demand of health services and prioritise placement of interventions. Furthermore, it could be used to predict future hotspots of non-health events, e.g., in criminology.

Impact of the modifiable areal unit problem in assessing determinants of emergency department demand.

OBJECTIVE: To examine the impact of the modifiable areal unit problem (MAUP) in an investigation of factors associated with ED demand in Perth, Western Australia, in 2016. Furthermore, to advocate a means of avoiding this impact. METHODS: ED presentations were classified as: urgent medical, non-urgent medical, urgent trauma or non-urgent trauma. In each group, sex-stratified, age-adjusted multivariate associations with socio-economic status and distance to the nearest ED and general practitioner (GP) were estimated. Modelling was undertaken using different sets of spatial units: Australian Bureau of Statistics (ABS) Statistical Areas Level 1 (SA1s) and numerous aggregate-level zonations of SA1s (ABS SA2s and others). RESULTS: Estimates obtained using the different units often varied widely: for seven (30%) of 24 strata defined by combinations of sex, ED type and covariate, the smallest and largest effect sizes differed in terms of direction; further, for 11 (65%) of the remaining 17 strata, the largest effect size was at least twice as high as the smallest. This demonstrates the MAUP's impact and that analyses based on a single set of spatial units are unreliable. To resolve the observed variation, we highlight the SA1-level estimates. CONCLUSIONS: When formulating interventions targeting reduced ED utilisation, policy planners should be guided by evidence based on analysis of appropriate spatial units. This ideal is undermined by the widespread lack of acknowledgement of the MAUP in studies examining drivers of ED demand using spatially aggregated data. To avoid the MAUP, only estimates obtained through examining a minimal geographic unit should be relied upon.