Australian Bureau Of Statistics Research Articles

Modelling Tourism in Australia Based on Periodic Autoregressive Time Series Models

Tourism has become very important in international commerce and also represents one of the main sources of income for some developing countries in the world. It is the act of people travelling and staying in a place outside their usual environment for leisure, business or other purposes, and this may include sightseeing, camping, retreats, etc Tourism is one of the key economic growth contributors and has contributed towards complete growth and development of Australia by bringing numerous economic value and benefits to her, and also building her brand value, image and identity. This paper seeks to generate a periodic autoregressive PAR model that could be used to make reliable forecast for tourism in Australia. Periodic autoregressive models are for seasonally observed data, particularly quarterly and monthly. Its’ parameters take different values across the seasons. The data used in this work was extracted from the official website of the Australian Bureau of Statistics (ABS), (www.abs.gov.au). It consists of monthly historical data of the number of short term visitors in Australia from January 1998 to December 2017 and was analysed using R- statistical software. The result revealed the order of the PAR model, and also verified that there is periodic variation (periodicity) in the tourism data. It was also verified that there is existence of single unit root and periodic integration which led to the fitting of periodic integrated autoregressive (PIAR(2)) model as the suitable model for the Australian tourism data. Finally, the residual generated from the model was subjected to statistical test and it showed a white noise behavior. Based on these findings, it is concluded that periodic autoregressive time series model can be used to generate reliable forecast for Australian tourism data. Further research on the stochastic nature and seasonality of Australian tourism data was recommended.

Exploring the Impact of Remoteness and Socio-Economic Status on Child and Adolescent Injury-Related Mortality in Australia

Injuries are a leading cause of harm for children. This study explores the impact of determinants of health on children (0–19 years) injury-related mortality (namely remoteness and socio-economic disadvantage, calculated using the index of relative socio-economic advantage and disadvantage (IRSAD)). Cause of death data from the Australian Bureau of Statistics were sourced for children in Australia between 1 July 2007 to 30 June 2017. Fifteen injury categories (ICD-10-AM external cause codes) were used. Burden and trends by injury mechanism were explored. A total of 5153 children died; with road traffic incidents (3.39 per 100,000 population), intentional self-harm (2.46) and drowning (0.72) being the leading mechanisms. Female fatality rates in very remote areas (8.73) were nine times higher than in major cities (Relative Risk [RR] = 8.73; 95% Confidence Interval [95% CI]: 4.23–18.00). Fatality rates increased with remoteness; very remote areas recording an injury-related fatality rated six times (RR = 5.84; 95% CI: 3.76–9.12) that of major city residents. Accidental poisoning and intentional self-harm fatalities were more likely in high IRSAD areas, while road traffic fatalities were more likely in low and mid socio-economic areas (X2 = 69.1; p < 0.001). People residing in regional and remote areas and from low socio-economic backgrounds already face significant health and lifestyle challenges associated with disadvantage. It is time to invest in injury prevention interventions for these populations, as well as upstream policy strategies to minimize any further preventable loss of life.

Chinese–international intimate relations: An Australian case study of migrant marriage and divorce

This article analyses trends in Chinese–international marriages and divorces, using Australia, a major migrant-receiving country, as a comparative case study. In exploring the recent rise of ‘Chinese–foreign’ marriage in the People’s Republic of China (PRC), we show that Chinese–international marriage within mainland China is a small, gendered phenomenon that largely involves Chinese women marrying men from other Asian societies. By examining unique data published by the Australian Bureau of Statistics, we reveal that most marriages involving PRC-born people in contemporary Australia are between two people born in China. But the displacement of Chinese intimate relationships to a non-Asian country results in significant behavioural divergences from couples ‘at home’, especially regarding prior cohabitation. Marriages solely involving PRC-born couples in Australia are also typically less enduring than marriages to non-Chinese. We argue that these differences underscore the roles of country-specific immigration policies and labour mobility patterns in shaping unpredicted family formation behaviour.

Creating Additional Census Income Topics from Linked Administrative Data

IntroductionThe Census is the largest statistical collection undertaken by the Australian Bureau of Statistics (ABS), with its data critical to informing the planning and delivery of Government and community services. While the Census measure of income supports a wide range of analysis, demand exists for additional income topics to complement and extend the range of socio economic analysis that can be undertaken. The ABS has recently developed three experimental income topics for the 2016 Census using linked administrative data: main source of income; main source of government payments; and previous financial year income.&#x0D; Objectives and ApproachThis research utilised administrative data integrated by the ABS for the Multi-Agency Data Integration Project (MADIP). Taxation data from the Australian Taxation Office (ATO) and social security data from the Department of Social Services (DSS) were used in conjunction with the 2016 Census data to derive the additional topics.&#x0D; ResultsOverall, the three measures compare relatively closely to similar measures from the ABS Survey of Income and Housing (SIH). The ‘Main source of income’ and ‘Main source of government payments’ measures exhibit similar distributions to those from the SIH. The ‘Previous financial year income’ measures compare more closely with Census and SIH at the higher end of the income distribution, with some differences apparent at the lower end of the income distribution.&#x0D; Conclusion / ImplicationsThis work demonstrates the potential to supplement and enhance existing Census topics with linked administrative data. Further research, development and consultation with data users and the Australian community is needed.

Use of Data Linkage Method to Compile Life Tables for Aboriginal and Torres Strait Islander Australians

IntroductionAlmost all deaths of Aboriginal and Torres Strait Islander Australians are registered, but some are not identified and recorded as such when registered. Therefore, registration data underestimates the actual number of deaths and death rates of Aboriginal and Torres Strait Islander Australians, and consequently their application can result in overestimates of life expectancy.&#x0D; Hence registered deaths needs to be adjusted before deriving life tables and life expectancy estimates.&#x0D; Objectives and ApproachObjective: To develop a data-driven method for estimating factors for adjusting registered deaths.&#x0D; Approach: The Australian Bureau of Statistics (ABS) linked Census records to all deaths that were registered within a year following the Census. Adjustment factors were estimated by directly comparing Indigenous status of a person reported in Census and death registration data. The ABS then used an established and widely used method to compile life tables, by making up-front adjustments to registered deaths by the factors derived from the Census and deaths linked dataset.&#x0D; ResultsAboriginal and Torres Strait Islander deaths identification rate in registration data was estimated to be 89% at the national level in 2016-2017. Over the same period, identification rates varied considerably by state/territory and ranged between 0.69%-1.04%. It clearly showed the need to adjust for under/over identification in Aboriginal and Torres Strait Islander death registrations.&#x0D; Conclusion / ImplicationsStatistical data linkage methods can successfully be used to improve Aboriginal and Torres Strait Islander Australians life expectancy estimates. This has implications for accurately measuring and monitoring the Council of Australian Governments’ ‘Closing the Gap’ target in life expectancy estimates between Aboriginal and Torres Strait Islander and non-Indigenous populations.

Five Key Lessons for A Cross-Jurisdictional Data Linkage Project Win-Win

IntroductionThe Coordination of Health Care study was developed by the Australian Institute of Health and Welfare in partnership with the Australian Bureau of Statistics (ABS) to fill an important data gap relating to patient-reported experiences of continuity of care across Australia.&#x0D; Objectives and approachThe 2016 Survey of Health Care was administered by the ABS. Over 35,000 responses were weighted to population benchmarks before being linked toadministrative datasets including hospital and emergency department care, Medicare Benefits Schedule and Pharmaceutical Benefits Scheme.&#x0D; These linked datasets aim to provide a comprehensive picture of patient journeys in relation to patient-reported experiences of continuity of care.&#x0D; The cross-jurisdictional data linkage spanned state and federal agencies, data linkage units and state health departments. Approvals across 10 different ethicscommittees were sought. Study management included governance and compliance with state and federal privacy legislation, negotiation of safe transfer of sensitive information across organisations, data interrogation, understanding data quality and study limitations.&#x0D; Specific challenges included sensitivities in response to requested data variables, obtaining private hospital data, understanding state based legislation, particularly in changes to guardianship and consent, working across jurisdictions to meet requirements and communicating linkage protocol.&#x0D; ResultsAll hospital linked datasets were received in early 2020 and currently being validated. Analysis of linked data will investigate continuity themes on low urgencycare and potentially preventable hospitalisations.&#x0D; ConclusionFive key lessons learned from the cross-jurisdictional data linkage:&#x0D; &#x0D; Engage with jurisdictional-based organisations early. Leveraging existing committees in the first instance can help.&#x0D; Be specific in documentation and approach.&#x0D; Subtly, every state is different. Local context and state legislation matter.&#x0D; Governance ethics and compliance take time. Get started early.&#x0D; Liaise broadly, across and between organisations.&#x0D;

Demonstrating the Utility of New Linked National Health Survey and Administrative Data: Secondary CVD Prevention in The Community

IntroductionCardiovascular events are largely preventable though access to timely quality primary health care and use of guideline-recommended medication. However, around half of Australians with cardiovascular disease (CVD) are not receiving best practice treatment. This study aims to identify factors associated with under treatment, using National Health Survey (NHS) linked for the first time to administrative health data.&#x0D; Objectives and ApproachParticipants with self-reported CVD in the NHS 2014-15 were included in the study, with data linked to Medicare (MBS) and pharmaceutical (PBS) data by the Australian Bureau of Statistics through the Multi-Agency Data Integration Project (MADIP). Use of primary and specialist ambulatory care and blood pressure- and lipid-lowering medications and their relation to sociodemographic and health characteristics were quantified using logistic and Poisson regression analyses.&#x0D; Results1100 participants with self-reported CVD were available for analysis, with linkage rates for NHS data to a Person Linkage Spine of 95%. We will present our findings from adjusted regression models (incomplete at time of abstract submission).&#x0D; Conclusion / ImplicationsThe nationally representative linked data developed under this project provides a unique opportunity to quantify and identify points to improve access to best practice CVD care, with the ultimate aim of preventing secondary CVD events in the population. Findings will also inform optimal use of MADIP data by the research community in order to answer questions of national importance and provide robust evidence to drive improvement in health and health care.

Assessing the accuracy of individual link with varying block sizes and cutoff values using MaCSim approach

Record linkage is the process of matching together records from different data sources that belong to the same entity. Record linkage is increasingly being used by many organizations including statistical, health, government etc. to link administrative, survey, and other files to create a robust file for more comprehensive analysis. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or compare between methods with respect to accuracy. In this paper, we evaluate the accuracy of individual link using varying block sizes and different cutoff values by utilizing a Markov Chain based Monte Carlo simulation approach (MaCSim). MaCSim utilizes two linked files to create an agreement matrix. The agreement matrix is simulated to generate re-sampled versions of the agreement matrix. A defined linking method is used in each simulation to link the files and the accuracy of the linking method is assessed. The aim of this paper is to facilitate optimal choice of block size and cutoff value to achieve high accuracy in terms of minimizing average False Discovery Rate and False Negative Rate. The analyses have been performed using a synthetic dataset provided by the Australian Bureau of Statistics (ABS) and indicated promising results.

GEOGRAPHIC INFORMATION SYSTEM FOR DROUGHT RISK MAPPING IN AUSTRALIA – DROUGHT RISK ANALYSER WEB APP

Abstract. Australia frequently experiences extended periods of severe droughts which have a significant negative impact on populations and economy. To improve preparedness for drought, decision-support tools which provide comprehensive information about current dry conditions are essential. In this paper, we present a conceptual design for a Drought Risk Analyser (DRA) – web-based information App for drought risk mapping developed using geographic information system (GIS). The developed DRA is based on combining Drought Hazard/Vulnerability/Exposure Indices (DHI, DVI and DEI respectively) into a final Drought Risk Index (DRI) for total of 542 Local Government Areas (LGA) in Australia. Drought indicators selected to compute drought hazard – the Standardised Precipitation Index (SPI), the Vegetation Health Index (VHI) and Soil Moisture – were obtained through the World Meteorological Organization (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) international initiative. Australian Bureau of Statistics (ABS) census data were used to develop the drought-related population vulnerability index – DVI. Australian national Digital Elevation Model and catchment scale land use data were used to calculate the DEI. Implemented functionality of the designed DRA is illustrated using a case study for the 2019 drought in Australia. The DRA App will be beneficial for Australian farmers and rural communities to assist with decision making, as well as for LGA planners to gain insights on current state of drought risk at both local and national levels. The developed methodology of using space-based observations for assessing drought hazard could be applied for developing similar web-based information tools in drought-prone areas of other countries.

COVID‐19 and the immediate impact on young people and employment in Australia: A gendered analysis

Young people have been regarded as “not at risk” of coronavirus, but what about the economic impact of social distancing and “lockdowns” arising from the pandemic? How have young women fared in comparison to young men? To address these questions, this study draws upon data from the Australian Bureau of Statistics, comparing young men and women and their older counterparts. The study also uses the Global Financial Crisis as a reference point to compare the immediate impact of COVID‐19 on young people. Results suggest that young people have been significantly impacted by COVID‐19 compared to older Australians. Young women in particular are being exposed to the economic fallout, especially those women in their 20s, wanting more work and more hours. COVID‐19 threatens to erode some of the gains women have made in recent decades in terms of participation unless governments act to provide opportunities and support for young women.

Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study.

To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia. A population-based, cross-sectional national health survey on mental health and its risk factors across Australia. National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS). Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons. Patient-reported mental disorder outcomes. Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders. Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups. The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.

Dengue in Queensland, Australia

BackgroundDengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia.Methods Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of 1st January 2010 to 31st December 2015 were obtained for each statistical local area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.Results In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data.ConclusionsClimate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to focus mosquito control activities in the most needed areas and will help in designing effective local dengue control programs.

Heatwave-health vulnerability in Adelaide: Analysis of mortality and morbidity outcomes

Background: Heatwaves are of increasing concern due to its impacts on population health (mortality/morbidity). As heatwave-impacts varies between populations and locations, evidence concerning the places and people vulnerable at a local level is critical for effective policy, planning, and interventions, especially in a warming climate. This study part of a larger national research aimed to investigate heatwave-associated mortality and morbidity (using general practitioner-GP visits) in Adelaide and identify the drivers of heatwave-vulnerability. Methods: Data on mortality (2007-2017), GP visits (2011-2016), heatwaves (Excess Heat Factor-EHF), and vulnerability factors (socio-demographic, health-risk, and environmental) were obtained from the Australian Bureau of Statistics, Bureau of Meteorology, and Geoscience Australia. Heatwave effects on mortality and morbidity outcomes at the Statistical Area Level 2 (SA2) geography (representing suburbs) were estimated using a case-crossover design. Results are reported as relative risks (RRs) in heatwaves periods compared with non-heatwave periods. Effect modification by individual- and area-level factors were also assessed. Results: Mortality increased during heatwaves (RR 1.08; 95%CI: 1.04-1.12) but varied spatially. Higher RRs were found in areas with a greater proportion of renters, inaccessibilities (access to vehicle/internet), less vegetation, newer houses, and prevalence of respiratory and psychological diseases. Risks were elevated among individuals with limited English-speaking ability, diabetes, asthma/chronic obstructive pulmonary diseases and those using antidepressants, anxiolytics and sedative medications. Preliminary analysis of GP visits with EHF reveals a significant decrease in GP visits (RR 0.98; 95%CI:0.98-0.99) during low-intensity heatwaves, which increased during severe/extreme heatwaves (RR 1.14; 95%CI: 1.13-1.15). Analysis of the individual- and area-level drivers of heatwave-related GP visits is ongoing. Conclusions: Our results, leveraging a substantive person and area-level dataset, show that heatwaves increase mortality and place a greater burden on primary care services (GPs) in Adelaide. Attention to the spectrum of identified heatwave-vulnerability drivers is key for developing effective and efficient prevention strategies.

The Geography of Bankruptcy in Australia

This study analyzes a unique data set to explore geographic variations in bankruptcy across Australia, drawing upon United States research that points to striking differences between urban and rural bankruptcies. The U.S. research finds that rural debtors enter bankruptcy in much more severe financial distress than their urban counterparts. The present study draws upon data obtained from the Australian Financial Security Authority, as well as data gathered by the Australian Bureau of Statistics. It finds that, compared with debtors in regional areas, debtors in major cities earn higher incomes, are more likely to be employed and more likely to cite the excessive use of credit, rather than unemployment, as the cause of their financial problems. In most respects, however, it finds that differences between Australian bankruptcies in urban and non-urban locations are neither consistent nor pronounced. It concludes that broad generalizations about financial hardship in regional areas cannot do justice to the complex geography of bankruptcy in Australia. In this sense, the study poses a contrast to the U.S. research, which identifies stark differences between urban and rural debtors. It offers a nuanced account, one that links bankruptcy rates to localized factors such as housing prices and the impact of specific industries, such as mining.

Travel Connectivity and Infectious Disease Transmission Risks in Oceania

Background: The increase in international travel brought about by globalisation has enabled the rapid spread of emerging pathogens with epidemic and pandemic potential. While travel connectivity-based assessments may help understand patterns of travel network-mediated epidemics, such approaches are rarely carried out in sufficient detail for Oceania where air travel is the dominant method of transportation between countries. Methods: Travel data from the Australian Bureau of Statistics, Stats NZ, and the United Nations World Tourism Organization websites were used to calculate travel volumes in 2018 within Oceania and between Oceania and the rest of the world. The Infectious Disease Vulnerability Index (IDVI) was incorporated into the analysis as an indicator of each country’s capacity to contain an outbreak. Travel networks were developed to assess the introduction of infectious diseases to and from Oceania, the spread of infectious diseases within Oceania and the spread of infectious diseases between each PICT and their most connected countries. Findings: Oceania and countries within the continent were highly connected to countries in Asia, Europe, and North America. Australia, New Zealand and several PICTs were highly connected to the United States of America and the United Kingdom (least vulnerable countries for outbreaks based on the IDVI), and to China (intermediate low vulnerable country). High variability was also observed between the PICTS in the geographical distribution of their international connections. The PICTs with the highest number of such connections were Fiji, French Polynesia, Guam, and Papua New Guinea. Interpretation: Travel connectivity assessments may help to stratify more accurately outbreak risks in countries depending on disease transmission in other parts of the world. This information is essential to track future requirements for scaling up and targeting outbreak surveillance and control strategies in Oceania. Funding Statement: LFK was supported by Australian National Health and Medical Research Council Early Career Fellowships (APP1158469). Declaration of Interests: The authors declare no competing interests.

How strong is the housing assets pillar of funding for residential aged care?

To explore reasons for the gap between the perception that high home ownership provides a strong pillar of funding for aged care accommodation and the actuality of half of those in residential care having this cost met by the Accommodation Supplement. Review of data from Australian Bureau of Statistics and administrative sources, and recent research studies. Trends in payment methods show continuing reliance on the Accommodation Supplement. Reasons are found in patterns of home ownership at older ages, changes in tenure and living arrangements over the age range, and increasing use of the exchange value of housing assets. Policy tensions arise between protecting access for low means residents and requiring those who are able to pay to do so. The housing assets pillar at advanced ages is not as strong as early in retirement and makes it increasingly unreliable as a source of funding.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis

BackgroundDengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia. MethodsData on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1st, 2010 to December 31st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. ResultsIn the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data. ConclusionsClimate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Heatwave-related Mortality in Australia: Who's impacted the most?

Abstract Background The impacts of heatwaves on the risk of mortality have been well-documented worldwide. However, impacts are not equally spread across the population with certain subgroups and locations affected more than others, warranting local evidence to guide and improve prevention and adaptation strategies. The objectives of this study were to identify the person and area-level socio-demographic, health, and environmental factors that modify the heatwave-mortality association in Australia. Methods Warm-season (October-March) mortality (2007-2017) were obtained from the Australian Bureau of Statistics. Heatwaves were defined using Excess Heat Factor, a normalised metric of heatwave severity. A time-stratified case-crossover design was used to model heatwave-mortality associations at the Statistical Areas 2 (SA2) spatial unit. Effect modification by person and area-level factors was assessed using interaction terms. Results Nationally, mortality increased by 2% (Relative Risk-RR 1.02; 95%CI: 1.01-1.03) during heatwaves with 1418 excess deaths (95%CI: 723-2113). But impacts varied with the highest effect observed in Adelaide (RR 1.08; 95%CI: 1.04-1.12) and Regional Tasmania (RR 1.11; 95%CI: 1.04-1.18). A gradient of impact was found within locations, for example, vulnerable SA2s in Adelaide were featured by a higher proportion of people in rental housing, inaccessibilities (vehicle and internet), low vegetation, newer houses, and a prevalence of respiratory and psychological diseases. Person-level factors included those: renting privately, with a low English-speaking ability, with chronic health conditions (diabetes, asthma/chronic obstructive pulmonary diseases) and using antidepressants, anxiolytics and sedative medications. Conclusions Our results, leveraging person and area-level linked data, highlight the need to consider contextual and individual risk factors and the importance of developing place-based targeted interventions to reduce heatwave health impacts. Key messages Heatwaves increase mortality in Australia. Heatwave-vulnerability is determined by individual and community-level factors.

Managing technology effectively in large public organisations

Technology is one of the most significant and important resources of large organisations. However, to realise the opportunities provided by technology, it must be managed well to ensure its benefits are timely and procured at reasonable cost. The executive team must be involved in the management of technology. They do not need to be technology experts but they do need to have some basic understanding of technology and the opportunities it provides. Further, they need to ensure that projects utilising technology are business driven not technology driven; understand the challenges and the risks and how these should be managed; and understand the externalities that might have an impact. This article is written from the perspective of the former chief executive of the Australian Bureau of Statistics. It identifies and discusses the principles that executive management in a large organisation should follow to ensure technology is managed well.

Reporting of alcohol as a contributor to death in Australian national suicide statistics and its relationship to post-mortem alcohol concentrations.

To describe the assignment of International Classification of Disease (ICD)-10 alcohol codes as underlying or contributory causes of death by the Australian Bureau of Statistics during mortality coding for suicides according to the blood alcohol concentration (BAC) detected at autopsy. Population-based case-series descriptive analysis. Data for all alcohol-related (Alc+) suicide deaths (aged 15+) in Australia from 2010-2015 (n=3132) from the National Coronial Information System. Alc+suicides were categorised as those with a post-mortem BAC≥0.05g/100mL. The outcome variable was whether the case was assigned an ICD-10 alcohol code (F10.0-F10.9, R78.0, T51, X45 and/or X65). We estimated OR for the assignment of codes in Alc+suicides using BAC as the key predictor. We also examined several covariates that have been implicated in the risk of Alc+suicides. An ICD-10 alcohol code was assigned during the mortality coding process in 47.6% (n=1491) of Alc+suicides. Higher BAC was associated with higher odds of having a code assigned; cases with a BAC over 0.20g/100mL over were twice as likely to have an alcohol code assigned (adjusted OR [AOR]=2.06, 95% CI = 1.59, 2.67) compared with cases with a BAC of 0.050-0.075g/100mL. Compared with New South Wales, higher likelihood of code assignment was found in Northern Territory (AOR=3.85, 95% CI = 2.32, 6.63) and Western Australia (AOR=2.89, 95% CI = 2.27, 3.68). Compared with 15-24year olds, 25-44 (AOR=0.79, 95% CI = 0.63, 0.99) and 65-84year olds (AOR=0.63, 95% CI = 0.43, 0.93) were less likely to have a code assigned. An ICD-10 alcohol code was not assigned as an underlying or contributory cause of death in over half of suicides in Australia (2010-2015) with a BAC≥0.05g/100mL. The higher the BAC detected at autopsy, the more likely cases were to be assigned an alcohol code during the mortality coding process.