A Spatial Analysis of Suicide Displacement at a High-Risk Cliff-Based Location Following Installation of a Means Restriction Initiative.

Means restriction interventions are recognised as highly effective for the deterrence of suicide attempts by jumping. While such interventions can lead to significant reductions in suicide, it is unclear whether these reductions represent a displacement effect, whereby individuals are instead choosing to attempt suicide at other nearby locations which offer the same means. The potential displacement of suicides as an unintended consequence of means restriction has been relatively unexplored to date. The only studies exploring displacement effects have focused on bridges, which are relatively easily contained sites; no studies have yet explored displacement effects at cliff-based high risk suicide locations (hotspots). Using Australian coronial data for the period of 2006-2019, we undertook joinpoint and kernel density analysis of suicides by jumping at a well-known cliff-based hotspot in Sydney, Australia, to determine whether there was evidence of displacement to local and broader surrounding cliffs following the installation of a multi-component harm minimization intervention (the Gap Park Masterplan). While slight decreases were noted in the immediate area subject to the structural intervention in the post-implementation period, alongside slight increases in the surrounding cliffs, there was no evidence for statistically significant changes. While kernel density analyses did not identify the emergence of any new hotspot locations in the post-implementation period, three existing hotspot sites of concern were found in our total area of interest, with greater than expected growth in the density of one of the hotspots. While we found no persuasive evidence of displacement, ongoing monitoring of the cliff-based location where the structural interventions were implemented is needed to ensure the ongoing safety of the area.

Hotel Suicides in Australia 2006-2017.

Background: This study investigated the frequency, characteristics, and geospatial clustering of hotel suicides in Australia to inform suicide prevention efforts. Aims: (1) To determine the proportion of suicide deaths that occurred in hotels, (2) to determine differences in demographic characteristics of hotel deaths compared to other locations, (3) to assess level of planning, and (4) to determine whether these deaths form geographic clusters amenable to targeted suicide prevention activities. Methods: Archival data on suicide mortality were used to examine associations between incident location (hotels, home, away from home), demographic characteristics, and suicide means. Kernel density visualization was used to assess geospatial clustering of hotel suicides, and the degree of planning involved was assessed using the modified Suicide Intent Scale. Results: Hotels accounted for 2% of all suicide deaths and 6.2% of suicides occurring away from home. Females were over-represented (p < .0001), as were deaths by drug overdoses (p < .0001) and falls (p < .0001). Approximately 40% of incidents occurred within seven geospatial clusters. 85% of those who died were state residents, with a median distance from home of 13.0 km. Most individuals checked in to the hotel alone, for short stays, and displayed a high degree of suicidal planning. Limitations: Coronial records had limited information on narrative circumstances of deaths; other indicators of risk may not have been identified. A comparison against a general population of hotel guests, rather than all other suicide deaths would be more useful in terms of preventative activities, however these data were not readily available. Conclusion: This study identified characteristics, behaviors, and geographic locations associated with hotel suicides to inform training of hotel staff to recognize and respond to signs of risk. Males of working age who live locally and arrive alone for short stays could be considered at a higher risk of suicide, and prevention efforts should be prioritized in the identified high-risk areas.

Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia.

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Spatial Errors in Automated Geocoding of Incident Locations in Australian Suicide Mortality Data.

BACKGROUND: There is increasing interest in the spatial analysis of suicide data to identify high-risk (often public) locations likely to benefit from access restriction measures. The identification of such locations, however, relies on accurately geocoded data. This study aims to examine the extent to which common completeness and positional spatial errors are present in suicide data due to the underlying geocoding process. METHODS: Using Australian suicide mortality data from the National Coronial Information System for the period of 2008-2017, we compared the custodian automated geocoding process to an alternate multiphase process. Descriptive and kernel density cluster analyses were conducted to ascertain data completeness (address matching rates) and positional accuracy (distance revised) differences between the two datasets. RESULTS: The alternate geocoding process initially improved address matching from 67.8% in the custodian dataset to 78.4%. Additional manual identification of nonaddress features (such as cliffs or bridges) improved overall match rates to 94.6%. Nearly half (49.2%) of nonresidential suicide locations were revised more than 1,000 m from data custodian coordinates. Spatial misattribution rates were greatest at the smallest levels of geography. Kernel density maps showed clear misidentification of hotspots relying solely on autogeocoded data. CONCLUSION: Suicide incidents that occur at nonresidential addresses are being erroneously geocoded to centralized fall-back locations in autogeocoding processes, which can lead to misidentification of suicide clusters. Our findings provide insights toward defining the nature of the problem and refining geocoding processes, so that suicide data can be used reliably for the detection of suicide hotspots. See video abstract at, http://links.lww.com/EDE/B862.

Spatial inequities of mental health nurses in rural and remote Australia.

Despite an increased burden from chronic mental health conditions, access to effective mental health services in rural and remote areas is limited, and these services remain spatially undefined. We examine the spatial availability of mental health nurses across local government areas in Australia and identify gaps in mental health service delivery capacity in a finer-grained level than the state/territory data. A spatial distribution of mental health nurses was conducted. We utilized the 2017 National Health Workforce Dataset which was aggregated to LGA level based on the 2018 Australian Bureau Statistics (ABS) Data. The availability of mental health nurses was measured using the full time equivalent (FTE) rates per 100 000 population. We calculated the proportion of LGAs with zero total FTE rates based on remoteness categories. We also compared the mean of total FTE rates based on remoteness categories using analysis of variance. A spatial distribution of mental health nurses was visualized using GIS software for total FTE rates. Our analysis included 544 LGA across Australia, with 24.8% being defined as remote and very remote. The mean total FTE for mental health nurses per 100 000 populations is 56.6 (±132.2) with a median of 17.4 (IQR: 61.8). A wide standard deviation reflects unequal distribution of mental health nurses across LGAs. The availability of total FTE rates for mental health nurses per 100 000 populations is significantly lower in remote and very remote LGAs in comparison with major cities. As many as 35.1% of LGAs across Australia have no FTE for mental health nurses with 46% are remote and very remote. Our study reflects the existing unequal distribution of mental health nurses between metropolitan/urban setting and rural and remote areas. We suggest three broad strategies to address these spatial inequities: improving supply and data information systems; revisiting task-shifting strategies, retraining the existing health workforce to develop skills necessary for mental health care to rural and remote communities; and incorporating the provision of mental health services within expanding innovative delivery models including consumer-led, telemedicine and community-based groups.

Spatial Analysis of the Neighborhood Risk Factors for Respiratory Health in the Australian Capital Territory (ACT): Implications for Emergency Planning.

The Australian Capital Territory (ACT) experienced the worst air quality in the world for several consecutive days following the 2019-2020 Australian bushfires. With a focus on asthma and Chronic Obstructive Pulmonary Disease (COPD), this retrospective study examined the neighborhood-level risk factors for these diseases from 2011 to 2013, including household distance to hospital emergency departments (ED) and general practices (GP) and area-level socioeconomic disadvantage and demographic characteristics at a high spatial resolution. Poisson and Geographically Weighted Poisson Regression (GWR) were compared to examine the need for spatially explicit models. GWR performed significantly better, with rates of both respiratory diseases positively associated with area-level socioeconomic disadvantage. Asthma rates were positively associated with increasing distance from a hospital. Increasing distance to GP was not associated with asthma or COPD rates. These results suggest that respiratory health improvements could be made by prioritizing areas of socioeconomic disadvantage. The ACT has a relatively high density of GP that is geographically well spaced. This distribution of GP could be leveraged to improve emergency response planning in the future.

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice.

The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential 'hotspots' of T2D risk were examined using Local Moran's I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

Beyond reasonable drought: hotspots reveal a link between the 'Big Dry' and cryptosporidiosis in Australia's Murray Darling Basin.

There is little evidence on how the health impacts of drought vary spatially and temporally. With a focus on waterborne cryptosporidiosis, we identify spatio-temporal hotspots and by using interrupted time series analysis, examine the impact of Australia's Big Dry (2001-2009) in these disease clusters in the Murray Darling Drainage Basin. Analyses revealed a statistically significant hotspot in the north of the Australian Capital Territory (ACT) and a hotspot in the north-eastern end of the basin in Queensland. After controlling for long-term trend and seasonality in cryptosporidiosis, interrupted time series analysis of reported cases in these hotspots indicated a statistically significant link with the Big Dry. In both areas, the end of the Big Dry was associated with a lower risk of reported cryptosporidiosis; in the ACT, the estimated relative risk (RR) was 0.16 (95% confidence interval: 0.07; 0.33), and in Queensland the RR was 0.42 (95% confidence interval: 0.19; 0.42). Although these data do not establish a causal association, this research highlights the potential for drought-related health risks.

Spatial clustering of fatal, and non-fatal, suicide in new South Wales, Australia: implications for evidence-based prevention.

BACKGROUND: Rates of suicide appear to be increasing, indicating a critical need for more effective prevention initiatives. To increase the efficacy of future prevention initiatives, we examined the spatial distribution of suicide deaths and suicide attempts in New South Wales (NSW), Australia, to identify where high incidence 'suicide clusters' were occurring. Such clusters represent candidate regions where intervention is critically needed, and likely to have the greatest impact, thus providing an evidence-base for the targeted prioritisation of resources. METHODS: Analysis is based on official suicide mortality statistics for NSW, provided by the Australian Bureau of Statistics, and hospital separations for non-fatal intentional self-harm, provided through the NSW Health Admitted Patient Data Collection at a Statistical Area 2 (SA2) geography. Geographical Information System (GIS) techniques were applied to detect suicide clusters occurring between 2005 and 2013 (aggregated), for persons aged over 5 years. The final dataset contained 5466 mortality and 86,017 non-fatal intentional self-harm cases. RESULTS: In total, 25 Local Government Areas were identified as primary or secondary likely candidate regions for intervention. Together, these regions contained approximately 200 SA2 level suicide clusters, which represented 46% (n = 39,869) of hospital separations and 43% (n = 2330) of suicide deaths between 2005 and 2013. These clusters primarily converged on the Eastern coastal fringe of NSW. CONCLUSIONS: Crude rates of suicide deaths and intentional self-harm differed at the Local Government Areas (LGA) level in NSW. There was a tendency for primary suicide clusters to occur within metropolitan and coastal regions, rather than rural areas. The findings demonstrate the importance of taking geographical variation of suicidal behaviour into account, prior to development and implementation of prevention initiatives, so that such initiatives can target key problem areas where they are likely to have maximal impact.

Community cardiovascular disease risk from cross-sectional general practice clinical data: a spatial analysis.

INTRODUCTION: Cardiovascular disease (CVD) continues to be a leading cause of illness and death among adults worldwide. The objective of this study was to calculate a CVD risk score from general practice (GP) clinical records and assess spatial variations of CVD risk in communities. METHODS: We used GP clinical data for 4,740 men and women aged 30 to 74 years with no history of CVD. A 10-year absolute CVD risk score was calculated based on the Framingham risk equation. The individual risk scores were aggregated within each Statistical Area Level One (SA1) to predict the level of CVD risk in that area. Finally, the pattern of CVD risk was visualized to highlight communities with high and low risk of CVD. RESULTS: The overall 10-year risk of CVD in our sample population was 14.6% (95% confidence interval [CI], 14.3%-14.9%). Of the 4,740 patients in our study, 26.7% were at high risk, 29.8% were at moderate risk, and 43.5% were at low risk for CVD over 10 years. The proportion of patients at high risk for CVD was significantly higher in the communities of low socioeconomic status. CONCLUSION: This study illustrates methods to further explore prevalence, location, and correlates of CVD to identify communities of high levels of unmet need for cardiovascular care and to enable geographic targeting of effective interventions for enhancing early and timely detection and management of CVD in those communities.

Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research.

BACKGROUND: General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times. METHODS: We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy. RESULTS: This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide. CONCLUSIONS AND IMPLICATIONS: The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner.

A brief report on Primary Care Service Area catchment geographies in New South Wales Australia.

BACKGROUND: To develop a method to use survey data to establish catchment areas of primary care or Primary Care Service Areas. Primary Care Service Areas are small areas, the majority of patients resident in which obtain their primary care services from within the geography. METHODS: The data are from a large health survey (n =267,153, year 2006-2009) linked to General Practitioner service use data (year 2002-2010) from New South Wales, Australia. Our methods broadly follow those used previously by researchers in the United States of America and Switzerland, with significant modifications to improve robustness. This algorithm allocates post code areas to Primary Care Service Areas that receive the plurality of patient visits from the post code area. RESULTS: Consistent with international findings the median Localization Index or the median percentage of patients that obtain their primary care from within a Primary Care Service Area is 55% with localization increasing with rurality. CONCLUSIONS: With the additional methodological refinements in this study, Australian Primary Care Service Areas have great potential to be of value to policymakers and researchers.

Undiagnosed diabetes from cross-sectional GP practice data: an approach to identify communities with high likelihood of undiagnosed diabetes.

OBJECTIVES: To estimate undiagnosed diabetes prevalence from general practitioner (GP) practice data and identify areas with high levels of undiagnosed and diagnosed diabetes. DESIGN: Data from the North-West Adelaide Health Survey (NWAHS) were used to develop a model which predicts total diabetes at a small area. This model was then applied to cross-sectional data from general practices to predict the total level of expected diabetes. The difference between total expected and already diagnosed diabetes was defined as undiagnosed diabetes prevalence and was estimated for each small area. The patterns of diagnosed and undiagnosed diabetes were mapped to highlight the areas of high prevalence. SETTING: North-West Adelaide, Australia. PARTICIPANTS: This study used two population samples-one from the de-identified GP practice data (n=9327 active patients, aged 18 years and over) and another from NWAHS (n=4056, aged 18 years and over). MAIN OUTCOME MEASURES: Total diabetes prevalence, diagnosed and undiagnosed diabetes prevalence at GP practice and Statistical Area Level 1. RESULTS: Overall, it was estimated that there was one case of undiagnosed diabetes for every 3-4 diagnosed cases among the 9327 active patients analysed. The highest prevalence of diagnosed diabetes was seen in areas of lower socioeconomic status. However, the prevalence of undiagnosed diabetes was substantially higher in the least disadvantaged areas. CONCLUSIONS: The method can be used to estimate population prevalence of diabetes from general practices wherever these data are available. This approach both flags the possibility that undiagnosed diabetes may be a problem of less disadvantaged social groups, and provides a tool to identify areas with high levels of unmet need for diabetes care which would enable policy makers to apply geographic targeting of effective interventions.

General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare.

BACKGROUND: Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere.Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. METHOD: We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. RESULTS: The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. CONCLUSION: In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets.

Social exclusion, deprivation and child health: a spatial analysis of ambulatory care sensitive conditions in children aged 0-4 years in Victoria, Australia.

Recent Australian policy initiatives regarding primary health care focus on planning services around community needs and delivering these at the local area. As in many other countries, there has also been a growing concern over social inequities in health outcomes. The aims of the analysis presented here were firstly to describe small area variations in hospital admissions for ambulatory care sensitive conditions (ACSC) among children aged 0-4 years between 2003 and 2009 in the state of Victoria, Australia, and secondly to explore the relationship of ACSC hospitalisations with socio-economic disadvantage using a comparative analysis of the Child Social Exclusion (CSE) index and the Composite Score of Deprivation (CSD). This is a cross sectional secondary data analysis, with data sourced from 2003 to 2009 ACSC data from the Victorian State Government Department of Health; the Australian Standard Geographical Classification of remoteness; the Australian 2006 Census of Population and Housing; and AMPCo General Practitioner data from 2010. The relationship between the indexes and child health outcomes was examined through bivariate analysis and visually through a series of maps. The results show there is significant variation in the geographical distribution of the relationship between ACSCs and socio-economic disadvantage, with both indexes capturing important social gradients in child health conditions. However, measures of access, such as geographical accessibility and workforce supply, detect additional small area variation in child health outcomes. This research has important implications for future primary health care policy and planning of services, as these findings confirm that not all areas are the same in terms of health outcomes, and there may be benefit in tailoring mechanisms for identifying areas of need depending on the outcome intended to be affected.