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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.

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.

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.

A number of small area geographies are used in Australia to investigate primary care relevant outcomes/behaviours and to manage the supply of Primary Care Providers (PCP) that influence these outcomes. However, very little research exists on the choice of a small area geography suitable for these purposes. We evaluated a large basket of Australian small area geographies to determine which geography is optimal for investigating relationships between PCP supply and the use of PCP services. We used linked data to evaluate the relationship between PCP supply and the likelihood of a patient visiting a PCP, after adjusting for individual level covariates. PCP supply was measured at different geographies including Local Government Areas (LGAs), Primary Health Networks (PHNs), Statistical Areas-1/2/3 and Remoteness Areas. Overall, the strongest relationships between PCP density and PCP use were found when LGAs were used to measure PCP density. Large geographies such as PHNs also detected strong relationships while custom built geographies such as Primary Care Service Areas were not significantly better than the rest. Existing geographies such as LGAs may be suitable for investigating the effect of PCP supply at state or national scales.

Primary Care is fundamental to a well-functioning health system. Various geographical small areas including specialized geographies such as Primary Care Service Areas (PCSAs) are used to measure primary care relevant outcomes and services, or to target interventions. PCSAs are small areas, the majority of patients resident in which obtain their primary care services from within the geography. The extent of this self-sufficiency of use is measured by the Localization Index (LI). PCSAs have been built in the US, Australia and Switzerland using an allocation algorithm, which, while simple and easy to implement, may require the use of various ad-hoc parameters. In this article we propose an optimization based approach to creating PCSAs, - an approach which has previously been used to generate labour flow regions in Ireland. The approach is data driven, thus requiring a minimal number of ad-hoc parameters. We compared the resulting PCSAs (or `rPCSAs') with PCSAs generated using the traditional allocation algorithm. We found that rPCSAs were generally larger, offered greater LIs and reflected patient travel patterns better than traditional PCSAs. Accounting for the larger size of rPCSAs showed that rPCSAs offered better LIs than similar sized traditional PCSAs.

The appropriate delivery of primary care services, an important policy imperative in many developed nations, is contingent on defining appropriate geographies to which these services are delivered. Primary Care Service Area (PCSA) geographies have been created in some countries to facilitate primary care policy making and have been utilized in a large body of research. In spite of their extensive use across rural and urban settings, the usefulness of PCSAs has not been evaluated. In this study, for the first time we put PCSAs to the test by comparing them to another small area geography - Postal Areas, and by exploring their usefulness in measuring relationships between Primary Care Practitioner supply and use. We find while PCSAs are better than Postal Areas in measuring relationships between General Practitioner supply and visits by patients, this relationship shows some heterogeneity across areas.

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.

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.

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.

Contains fulltext : 154830.pdf (Publisher’s version ) (Open Access) 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.

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. Electronic supplementary material The online version of this article (doi:10.1186/1476-072X-13-38) contains supplementary material, which is available to authorized users.

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.

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.