Exploratory disease maps are designed to identify risk factors of disease and guide appropriate responses to disease and help-seeking behaviour. However, when produced using aggregate-level administrative units, as is standard practice, disease maps may mislead users due to the Modifiable Areal Unit Problem (MAUP). Smoothed maps of fine-resolution data mitigate the MAUP but may still obscure spatial patterns and features. To investigate these issues, we mapped rates of Mental HealthRelated Emergency Department (MHED) presentations in Perth, Western Australia, in 2018/19 using Australian Bureau of Statistics (ABS) Statistical Areas Level 2 (SA2) boundaries and a recent spatial smoothing technique: the Overlay Aggregation Method (OAM). Then, we investigated local variation in rates within high-rate regions delineated using both approaches. The SA2- and OAM-based maps identified two and five high-rate regions, respectively, with the latter not conforming to SA2 boundaries. Meanwhile, both sets of high-rate regions were found to comprise a select number of localised areas with exceptionally high rates. These results demonstrate how, due to the MAUP, disease maps that are produced using aggregate-level administrative units are unreliable as a basis for delineating geographic regions of interest for targeted interventions. Instead, reliance on such maps to guide responses may compromise the efficient and equitable delivery of healthcare. Detailed investigation of local variation in rates within high-rate regions identified using both administrative units and smoothing is required to improve hypothesis generation and the design of healthcare responses.

Precise population counts facilitate reliable estimation of differences in disease risk between population sub-groups or geographic regions, which informs rational public health decision-making. In Australia, Census population counts obtainable via the Australian Bureau of Statistics' (ABS') TableBuilder tool, which are widely used, are routinely perturbed to protect privacy. We examine this perturbation's impact on: the precision of TableBuilder population counts, and a geospatial analysis of high-risk foot (HRF) hospital admissions among Western Australian (WA) Indigenous people. The former is severely compromised, disproportionately so for Indigenous people and under the currently employed 'perturbation without additivity' (PWoA) approach. E.g., based on fine-resolution, PWoA-affected data, the Indigenous population total for 'Inner City' WA was >50% lower than its true total. Imprecise calculation of WA Indigenous HRF admission counts, and misrepresentation of high-rate regions of such admissions in Perth, resulted. An approach suggested to manage this - examination of coarse-resolution data - is inadequate, partly due the modifiable areal unit problem. However, a novel map overlay technique alleviates this issue. We recommend that the ABS review the PWoA approach and consider implementing different statistical disclosure control techniques or reverting to the previously employed 'perturbation with additivity' approach, prior to releasing data from the 2021 Australian Census.

Background In countries with high COVID-19 vaccination rates the SARS-CoV-2 Delta variant resulted in rapidly increasing case numbers. This study evaluated the use of non-pharmaceutical interventions (NPIs) coupled with alternative vaccination strategies to determine feasible Delta mitigation strategies for Australia. We aimed to understand the potential effectiveness of high vaccine coverage levels together with NPI physical distancing activation and to establish the benefit of adding children and adolescents to the vaccination program. Border closure limited SARS-CoV-2 transmission in Australia; however, slow vaccination uptake resulted in Delta outbreaks in the two largest cities and may continue as international travel increases. Methods An agent-based model was used to evaluate the potential reduction in the COVID-19 health burden resulting from alternative vaccination strategies. We assumed immunity was derived from vaccination with the BNT162b2 Pfizer BioNTech vaccine. Two age-specific vaccination strategies were evaluated, ages 5 and above, and 12 and above, and the health burden determined under alternative vaccine coverages, with/without activation of NPIs. Age-specific infections generated by the model, together with recent UK data, permitted reductions in the health burden to be quantified. Results Cases, hospitalisations and deaths are shown to reduce by (i) increasing coverage to include children aged 5 to 11 years, (ii) activating moderate NPI measures and/or (iii) increasing coverage levels above 80%. At 80% coverage, vaccinating ages 12 and above without NPIs is predicted to result in 1095 additional hospitalisations per million population; adding ages 5 and above reduces this to 996 per million population. Activating moderate NPIs reduces hospitalisations to 611 for ages 12 and over, and 382 per million for ages 5 and above. Alternatively, increasing coverage to 90% for those aged 12 and above is estimated to reduce hospitalisations to 616. Combining all three measures is shown to reduce cases to 158, hospitalisations to 1 and deaths to zero, per million population. Conclusions Delta variant outbreaks may be managed by vaccine coverage rates higher than 80% and activation of moderate NPI measures, preventing healthcare facilities from being overwhelmed. If 90% coverage cannot be achieved, including young children and adolescents in the vaccination program coupled with activation of moderate NPIs appears necessary to suppress future COVID-19 Delta-like transmission and prevent intensive care unit surge capacity from being exceeded.

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.

Misallocation of finite healthcare resources can occur when guided by maps that are produced using aggregate-level administrative units. Such maps are affected by the modifiable areal unit problem (MAUP), which describes how patterns may change depending on the particular choice of mapping unit. Smoothing can help avoid this unintended yet detrimental phenomenon. This paper compares the utility of aggregate-level administrative units and smoothing for exploring variation in lower urgency emergency department (ED) presentations across metropolitan Perth, Western Australia. Rates of such presentations were mapped using Australian Bureau of Statistics Statistical Areas Levels 1, 2, and 3 (SA1-3s) and the recently proposed Overlay Aggregation Method (OAM). Resulting maps were compared based on their ability to represent local variation in rates and optimise the targeting and logistical efficiency of geographically targeted resource allocation. SA1-level variation in rates was increasingly obscured by SA2s and SA3s. OAM helped avoid this pitfall, facilitating stable identification of SA1-resolution, high-rate regions while preserving privacy, mitigating the MAUP, and balancing the targeting and logistical efficiency of planned resource allocation to those regions. Routine application of smoothing can help avoid issues undermining maps of lower urgency ED presentations and other health outcomes that are based on aggregate-level administrative units.

Drought is thought to impact upon the mental health of agricultural communities, but studies of this relationship have reported inconsistent results. A source of inconsistency could be the aggregation of data by a single spatiotemporal unit of analysis, which induces the modifiable areal and temporal unit problems. To investigate this, mental health-related emergency department (MHED) presentations among residents of the Wheat Belt region of Western Australia, between 2002 and 2017, were examined. Average daily rainfall was used as a measure of drought. Associations between MHED presentations and rainfall were estimated based on various spatial aggregations of underlying data, at multiple temporal windows. Wide variation amongst results was observed. Despite this, two key features were found: Associations between MHED presentations and rainfall were generally positive when rainfall was measured in summer months (rate ratios up to 1.05 per 0.5 mm of daily rainfall) and generally negative when rainfall was measured in winter months (rate ratios as low as 0.96 per 0.5 mm of daily rainfall). These results demonstrate that the association between drought and mental health is quantifiable; however, the effect size is small and varies depending on the spatial and temporal arrangement of the underlying data. To improve understanding of this association, more studies should be undertaken with longer time spans and examining specific mental health outcomes, using a wide variety of spatiotemporal units.

Abstract 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.

There is a significant challenge in responding to second waves of COVID-19 cases, with governments being hesitant in introducing hard lockdown measures given the resulting economic impact. In addition, rising case numbers reflect an increase in coronavirus transmission some time previously, so timing of response measures is highly important. Australia experienced a second wave from June 2020 onwards, confined to greater Melbourne, with initial social distancing measures failing to reduce rapidly increasing case numbers. We conducted a detailed analysis of this outbreak, together with an evaluation of the effectiveness of alternative response strategies, to provide guidance to countries experiencing second waves of SARS-Cov-2 transmission. An individual-based transmission model was used to (1) describe a second-wave COVID-19 epidemic in Australia; (2) evaluate the impact of lockdown strategies used; and (3) evaluate effectiveness of alternative mitigation strategies. The model was calibrated using daily diagnosed case data prior to lockdown. Specific social distancing interventions were modelled by adjusting person-to-person contacts in mixing locations. Modelling earlier activation of lockdown measures are predicted to reduce total case numbers by more than 50%. Epidemic peaks and duration of the second wave were also shown to reduce. Our results suggest that activating lockdown measures when second-wave case numbers first indicated exponential growth, would have been highly effective in reducing COVID-19 cases. The model was shown to realistically predict the epidemic growth rate under the social distancing measures applied, validating the methods applied. The timing of social distancing activation is shown to be critical to their effectiveness. Data showing exponential rise in cases, doubling every 7–10 days, can be used to trigger early lockdown measures. Such measures are shown to be necessary to reduce daily and total case numbers, and the consequential health burden, so preventing health care facilities being overwhelmed. Early control of second wave resurgence potentially permits strict lockdown measures to be eased earlier.

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.

Background: New SARS-CoV-2 variants, such as B1.617.2 (Delta), are appearing that have higher transmissibility than the original Wuhan strain. Throughout 2020, the only responses available to mitigate SARS-CoV-2 transmission were non-pharmaceutical interventions such as social distancing, community lockdowns and quarantine measures. COVID-19 vaccines are now available and being administered, yet in countries with high vaccination rates, such as the United Kingdom and United States, a resurgence in COVID-19 cases has been observed and the Delta variant implicated. Of concern is the possibly lower efficacy of the ChAdOx1-S (AstraZeneca) vaccine against the Delta variant. We analysed alternative vaccination strategies to determine those most likely to mitigate future COVID-19 Delta outbreaks.Methods: We applied detailed agent-based community models to evaluate the impact of SARS-Cov-2 vaccination under a range of coverage levels, vaccine efficacies, and ages of those vaccinated. This was conducted with and without community lockdowns activated in response to significant outbreaks of highly transmissible SARS-CoV-2 variants. Critically, we determined whether vaccination in adolescents is required. We evaluated two vaccine types with different effectiveness potential against transmission.Findings: High vaccine efficacy and extremely high vaccination coverage of 90% of adults and adolescents was shown to be required to mitigate highly transmissible variants such as Delta without activation of strong lockdown measures, in contrast to the Alpha variant. We further demonstrated that inclusion of adolescent vaccination and use of mRNA vaccines (BNT162b2 Pfizer) to boost immunity levels among those previously vaccinated with the ChAdOx1-S (AstraZeneca) vaccine was effective in significantly reducing the scale of future COVID-19 outbreaks. Results suggest that greater than 70% vaccine coverage in those 12 years and older, together with a vaccine boosting regimen, would be sufficient to halt a rapidly growing Delta outbreak if coupled with early activation of moderate lockdown measures, which permit schools to remain open.Interpretation: Increasing the number of immune persons in the community by including adolescents in a vaccination program, and by applying "third dose" boosting vaccinations, may prevent B1.617.2 Delta outbreaks from rapidly growing without the need for strict lockdown measures. Under achievable 80% vaccination coverage moderate social distancing measures may be sufficient to prevent future large scale outbreaks. Compared to strict lockdown, such limited social distancing measures would substantially increase workplace numbers and allow schools to remain open, with attendant economic, mental and physical wellbeing, and educational benefits.Funding: The Department of Health, Western Australia (Future Health Research andInnovation Fund) and the Department of Health, Queensland, Australia.Declaration of Interest: GM reports research funding from the Department of Health, Queensland outside the submitted work. JC and DW declare no competing interests.

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.

Abstract Background: In countries with high COVID-19 vaccination rates the SARS-CoV-2 Delta variant has resulted in rapidly increasing case numbers. This study evaluated the use of non-pharmaceutical interventions (NPIs) coupled with alternative COVID-19 vaccination strategies to determine feasible Delta mitigation strategies for Australia. We aimed to understanding the interplay between high vaccine coverage levels and NPI physical distancing activation, and to establish the benefit of adding children and adolescents to the vaccination program. Border closure has limited SARS-CoV-2 transmission in Australia, however slow vaccination uptake resulted in Delta outbreaks in the two largest cities, and may continue once international travel resumes. Methods: An agent-based model was used to evaluate the potential reduction in the COVID-19 health burden resulting from alternative vaccination strategies. We assumed immunity was derived from vaccination with the BNT162b2 Pfizer BioNTech vaccine. Two age-specific vaccination strategies were evaluated, age 5 and above, and 12 and above, and the health burden determined under alternative coverages, with/without activation of NPIs. Age-specific infections generated by the model, together with recent UK data, permitted reductions in the health burden to be quantified. Results: Cases, hospitalisations and deaths are shown to reduce by: i) increasing coverage to include children aged 5 to 11 years, ii) activating moderate NPI measures, and/or iii) increasing coverage levels above 80%. At 80% coverage, vaccinating ages 12 plus without NPIs is predicted to give 1,162 hospitalisations; adding ages 5 and above gives 1,073 cases per million population. Activating moderate NPIs reduces hospitalisations to 705. Alternatively, increasing coverage to 90% reduces this to 684. Combining all three measures is shown to reduce cases to 398, hospitalisations to 2 and deaths to zero. Conclusions: Delta variant outbreaks may be successfully managed by an achievable 80% vaccine coverage rate and moderate NPI measures, allowing schools and many workplaces to remain open. This prevents use of hard lockdown measures, and consequential economic and societal damage. Activating moderate NPIs is shown to give a similar reduction in health burden as increasing coverage of ages 12 and above to 90%. If 90% coverage cannot be achieved, including children and adolescents in the vaccination program coupled with moderate NPIs appears necessary to contain future COVID-19 Delta transmission.

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.

Background There is a significant challenge in responding to second waves of COVID-19 cases, with governments being hesitant in introducing hard lockdown measures given the resulting economic impact. In addition, rising case numbers reflect an increase in coronavirus transmission some time previously, so timing of response measures is highly important. Australia experienced a second wave from June 2020 onwards, confined to greater Melbourne, with initial social distancing measures failing to reduce rapidly increasing case numbers. We conducted a detailed analysis of this outbreak, together with an evaluation of the effectiveness of alternative response strategies, to provide guidance to countries experiencing second waves of SARS-Cov-2 transmission. Method An individual-based transmission model was used to 1) describe a second-wave COVID-19 epidemic in Australia; 2) evaluate the impact of lockdown strategies used; and 3) evaluate effectiveness of alternative mitigation strategies. The model was calibrated using daily diagnosed case data prior to lockdown. Specific social distancing interventions were modelled by adjusting person-to-person contacts in mixing locations. Results Modelling earlier activation of lockdown measures are predicted to reduce total case numbers by more than 50%. Epidemic peaks and duration of the second wave were also shown to reduce. Our results suggest that activating lockdown measures when second-wave case numbers first indicated exponential growth, would have been highly effective in reducing COVID-19 cases. The model was shown to realistically predict the epidemic growth rate under the social distancing measures applied, validating the methods applied. Conclusions The timing of social distancing activation is shown to be critical to their effectiveness. Data showing exponential rise in cases, doubling every 7-10 days, can be used to trigger early lockdown measures. Such measures are shown to be necessary to reduce daily and total case numbers, and the consequential health burden, so preventing health care facilities being overwhelmed. Early control of second wave resurgence potentially permits strict lockdown measures to be eased earlier. All authors have seen and approved the manuscript. Research funding from Department of Health, Western Australia and Department of Health, Queensland is acknowledged. The authors confirm that these organisations had no influence on the submitted work, nor are there any competing interests. ### Competing Interest Statement All authors have seen and approved the manuscript. Research funding from Department of Health, Western Australia and Department of Health, Queensland is acknowledged. The authors confirm that these organisations had no influence on the submitted work, nor are there any competing interests. ### Funding Statement Research funding from Department of Health, Western Australia and Department of Health, Queensland is acknowledged. The authors confirm that these organisations had no influence on the submitted work, nor are there any competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: not applicable All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data contained in paper

Background Accurate 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). Aim To accurately map disease. Methods Using 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. Conclusion Health 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.

BACKGROUND:All analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of spatially aggregated data in all scientific disciplines. However, the impact of the MAUP is rarely considered, perhaps partly because it is still widely considered to be unsolvable.RESULTS:It was originally understood that a solution to the MAUP should constitute a comprehensive statistical framework describing the regularities in estimates of association observed at different combinations of spatial scale and zonation. Additionally, it has been debated how such a solution should incorporate the geographical characteristics of areal units (e.g. shape, size, and configuration), and in particular whether this can be achieved in a purely mathematical framework (i.e. independent of areal units). We argue that the consideration of areal units must form part of a solution to the MAUP, since the MAUP only manifests in their presence. Thus, we present a theoretical and statistical framework that incorporates the characteristics of areal units by combining estimates obtained from different scales and zonations. We show that associations estimated at scales larger than a minimal geographical unit of analysis are systematically biased from a true minimal-level effect, with different zonations generating uniquely biased estimates. Therefore, it is fundamentally erroneous to infer conclusions based on data that are spatially aggregated beyond the minimal level. Instead, researchers should measure and display information, estimate effects, and infer conclusions at the smallest possible meaningful geographical scale. The framework we develop facilitates this.CONCLUSIONS:The proposed framework represents a new minimum standard in the estimation of associations using spatially aggregated data, and a reference point against which previous findings and misconceptions related to the MAUP can be understood.

OBJECTIVE:To compare methods of assessment of the burden of primary care-type ED (PCTED) presentations against clinical assessment by general practitioners (GPs) in ED.METHODS:A cross-sectional study involving clinical assessment of patients presenting to four EDs in Western Australia. The GPs assessed patients who were likely to be discharged home from ED, and considered whether they could be managed in general practice. Patient presentations were defined by the GPs as: PCTED; PCTED if additional primary care resources were available; or not PCTED.RESULTS:GP researchers determined that 80% of patients assessed were PCTED presentations, with one-third of these considered PCTED presentations if additional resources were available. A high proportion of identified PCTED presentations included categories excluded by previous methods. Analysis of linked data found the cohort assessed to be of lower urgency, younger, and with a shorter length of stay than the average patient being discharged from ED. After accounting for potential bias, it is suggested that 20-40% of all ED presentations could be PCTED presentations.CONCLUSIONS:Previous methods determining the burden of PCTED presentations have not been validated. Many presentations excluded by previous methods were identified as manageable in general practice by GPs clinically assessing patients in ED. Improved validation of criteria used to identify PCTED presentations will enable appropriately designed interventions to reduce such events.

Objective To evaluate age, gender and disease-specific trends in ED for mental health presentations over 15 years. Methods The study population consisted of residents of metropolitan Perth, Western Australia, presenting to Perth ED between 1 July 2002 and 30 June 2017. Population rates of mental health-related ED presentations per year were calculated. Results Rates of mental health ED presentations are significantly increasing in the working-age population for those with stress and anxiety-related diagnoses, particularly in younger females, and also for alcohol-related presentations for those aged 10-49 years, particularly in males. Conclusion The present study demonstrates that increased rates of mental health-related ED presentations are driven by increased rates of presentation for stress and anxiety-related and alcohol-related presentations in both genders across the working-age population.

The aim of this study was to reconcile 3 approaches to calculating population attributable fractions and attributable burden percentage: the approach of Bruzzi et al. (Am J Epidemiol. 1985;122(5):904-914.), the maximum-likelihood method of Greenland and Drescher (Biometrics. 1993;49(3):865-872.), and the multivariable method of Tanuseputro et al. (Popul Health Metr. 2015;13:5.). Using data from a statewide point prevalence survey (Western Australian Point Prevalence Survey, 2014) linked to an administrative database, we compared estimates of attributable burden percentage obtained using the contrasting methods in 6 logistic models of health outcomes from the survey, estimating 95% confidence intervals using nonparametric and weighted bootstrap approaches. Our results show that instability can arise from the fundamental algebraic construction of Bruzzi's formula, and that this instability may substantially influence the calculation of attributable burden percentage and associated confidence intervals. These observations were confirmed in a simulation study. The algebraic reduction of Bruzzi's formula to the 2 alternative methods resulted in markedly more stable estimates for population attributable fraction and attributable burden percentage in cross-sectional studies and cohort designs with fixed follow-up time. We advocate the widespread implementation of the maximum-likelihood approach and the multivariable method.

Introduction The 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 Approach Using 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. Results The 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/Implications The 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.