While the GBD provides country-specific estimates and is important for international comparisons, several countries, including Australia, have undertaken their own national burden of disease studies.These country-specific studies offer particular advantages, including the capacity to add diseases and risk factors of national interest, calculate disease burden estimates for specific population groups and take advantage of detailed data and methodological approaches more appropriate to that country.Australia has a population of 25.2 million people.Overall, Australians have similar or better health than those in similarly developed countries, including a relatively high life expectancy and low smoking rate. 1 Recent years have seen improvements, such as in the number of years lived in full health, however, further improvements could be made, such as in the rate of obesity.Around 71% of people in Australia live in major cities and the remainder live in regional or remote areas.
Australian norms and structural analysis for the Harvard Group Scale of Hypnotic Susceptibility, Form A (HGSHS:A) are presented. Results relating to score distributions, item difficulty level, and reliability were considered for a large sample of Australian students (N = 4,752) obtained over eight years of testing at Macquarie University. The aggregated sample, which represents the largest normative study of the HGSHS:A undertaken to date, was compared to recent normative studies conducted in Australia, Canada, Germany, and Spain, using both English and non-English versions of the test. In general, the aggregated sample was consistent with other reference samples, and results indicated that the HGSHS:A continues to function well as an instrument for the initial screening of hypnotisability. Further, the emergence of a three-factor solution from the principal components analysis was also consistent with previous factor-analytic studies, and suggested that performance on this scale reflects three dimensions of hypnotic responding.
IntroductionOfficial Australian estimates of socioeconomic inequalities in cause-specific mortality have been based on area-level socioeconomic measures. Using area-level measures is known to underestimate inequalities.
Objectives and ApproachUsing recently released census linked to mortality data, we estimate education-related inequalities in cause-specific mortality for Australia. We used 2016 Australian Census and Death Registration data (2016-17) linked via a Person Linkage Spine (linkage rates: 92% and 97%, respectively) from the Multi-Agency Data Integration Project (MADIP). Education, from the Census, was categorised as low (no secondary school graduation or other qualification), intermediate (secondary graduation with/without other non-tertiary qualifications) and high (tertiary qualification). Cause of death was coded according to the underlying cause of death using the ICD-10. We used negative binomial regression to estimate relative rates (RR) for cause-specific mortality at ages 25-84 years, in the 12-months following Census, comparing low vs high education, separately by sex and 20-year age group, adjusting for age.
Results80,317 deaths occurred among 13,856,202 people. For those aged 25-44 years, relative inequalities were large for causes related to injury and smaller for lesspreventable deaths (e.g. for men, suicide RR=5.6, 95%CI: 4.1-7.5 and brain cancer RR=1.3, 0.6-3.1). For those aged 45-64, inequalities were large for causes related to health behaviours and amenable to medical intervention, e.g. lung cancer (men RR= 6.4, 4.7-8.8) and ischaemic heart disease (women RR=5.0, 3.2-7.7), and were small for less preventable causes e.g. brain cancer (women RR=0.9, 0.6-1.3). Patterns among those aged 65-84years were similar to those aged 45-64 years.
Conclusion / ImplicationsIn Australia, inequalities in mortality are substantial. Our findings highlight the health burden from inequalities, opportunities for prevention and provide insights on targets to effectively reduce them.
Abstract Background Four fifths of deaths in Australia involve multiple causes, but statistics typically use a single underlying cause of death (UC). The UC approach alone is insufficient for understanding the impact of non-underlying causes and identifying comorbid disease associations at death. Analysis of multiple causes of death (MC) is needed to measure the impact of all causes. We described MC patterns, considering cause-of-death coding and certification practices in Australia. Methods Using deaths registered in Australia from 2006 to 2017 (n = 1773525) coded to the International Classification of Diseases (ICD) and an extended classification (n = 136 causes) based on a World Health Organization short list, we described MCoD data by cause. Age-standardised rates based on UC and MC were compared using the standardised ratio of multiple to underlying causes (SRMU) to estimate the contribution of the cause to mortality compared to using the UC approach. Comorbidity was explored using the cause of death association indicator (CDAI) to compare the observed joint frequency of a contributory-underlying cause combined with expected frequency of the contributory cause (with any UC). Results On average 3.4 conditions caused each death and 24.4% of deaths had 5 plus causes. Largest SRMUs were for genitourinary diseases (8.0), blood diseases (7.8) and musculoskeletal conditions (6.7). CDAIs showed high associations between, for example, accidental alcohol and opioid poisoning, septicaemia and skin infections, and traumatic brain injury and falls. Conclusions MC indicators enhance measures of mortality and reassess the role of causes of death for descriptive and analytical epidemiology. Key messages This research demonstrates the value of MC analysis for Australian mortality data.
The World Health Organization's (WHO) 25X25 goal aims for a 25% relative reduction in premature death due to four non-communicable diseases (NCD4)-cancer, cardiovascular disease, chronic respiratory diseases and diabetes-by 2025 compared to 2010. This study aimed to quantify the premature mortality in the Australian population due to NCD4, quantify the variation in mortality rates by age and sex, predict the premature mortality due to NCD4 in 2025 and evaluate the progress towards the WHO 25X25 goal.
Abstract Key contact person Dr Grace Joshy, Fellow, Research School of Population Health, Australian National University. Focus and outcomes for participants Mortality statistics are typically based on a single underlying cause of death (UCoD), derived from multiple conditions on the death certificate, and have provided critical evidence for policy and practice for over a century. There have been radical shifts in patterns of death in the past couple of decades; deaths in older ages are increasingly from chronic and degenerative diseases. The relevance of assuming that a single disease is causing the death is diminishing, especially with an aging population structure and increasing life expectancy. This symposium will enable participants to understand the complexities associated with mortality reporting/coding, strengths and limitations of available statistical methods for using multiple causes of death (MCoD) and the importance of quantifying mortality incorporating MCoD. Rationale for the symposium, including for its inclusion in the Congress The use of a single UCoD rather than MCoD means that vast amounts of potentially useful data are largely ignored, which is likely to bias mortality estimates (including under- and over-reporting of the importance of certain causes of death). Despite global recognition of the urgent need to better integrate data on MCoD into mortality statistics, use of these data are challenging and limited. Complexities arise from the way mortality information is reported on death certificates and coded to form mortality collections; limited understanding of available statistical methods also adds to the complexity. International Classification of Diseases 10th Revision (ICD-10) has been translated into 43 languages and it is being used by over 100 countries to report mortality data, a primary indicator of health status. The 2018 release of the 11th revision of the International Classification of Diseases, enriching data on multiple parameters including comorbidity, confers further urgency and a unique opportunity to optimise the use of MCoD in mortality reporting. The World Congress of Epidemiology 2020 will provide a unique platform for wider discussions on the challenges and opportunities for using MCoD data. The symposium will provide a deeper understanding and enhanced the use of MCoD data. The speakers are engaged in cutting-edge NHMRC-funded research on mortality incorporating MCoD and development of novel statistical methods. Presentation program The symposium will feature presentations from six speakers. Names of presenters James Eynstone-Hinkins, Lauren Moran, Saliu Balogun, Karen Bishop, Margarita Moreno-Betancur, Grace Joshy
Abstract Background Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. To monitor socioeconomic inequalities in health, many high-income countries routinely report life expectancy by education level. However in Australia, education-related gaps in life expectancy are not routinely reported because, until recently, the data required to produce these estimates have not been available. Using newly linked, whole-of-population data, we estimated education-related inequalities in adult life expectancy in Australia. Methods Using data from 2016 Australian Census linked to 2016-17 Death Registrations, we estimated age-sex-education-specific mortality rates and used standard life table methodology to calculate life expectancy. For men and women separately, we estimated absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years according to education level (measured in five categories, from university qualification [highest] to no formal qualifications [lowest]). Results Data came from 14,565,910 Australian residents aged 25 years and older. At each age, those with lower levels of education had lower life expectancies. For men, the gap (highest vs. lowest level of education) was 9.1 (95 %CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. For women, the gap was 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences (comparing highest education level with each of the other levels) were larger for men than women and increased with age, but overall, revealed a 10–25 % reduction in life expectancy for those with the lowest compared to the highest education level. Conclusions Education-related inequalities in life expectancy from age 25 years in Australia are substantial, particularly for men. Those with the lowest education level have a life expectancy equivalent to the national average 15–20 years ago. These vast gaps indicate large potential for further gains in life expectancy at the national level and continuing opportunities to improve health equity.
Socioeconomic inequalities in mortality are evident in all high-income countries, and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide the first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons.We used Australian Census (2016) linked to 13 months of Death Registrations (2016-17). We estimated relative rates (RR) and rate differences (RD, per 100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high (tertiary) education for individual causes of death (among those aged 25-84 years) and grouped according to preventability (25-74 years), separately by sex and age group, adjusting for age, using negative binomial regression.Among 13.9 M people contributing 14 452 732 person-years, 84 743 deaths occurred. All-cause mortality rates among men and women aged 25-84 years with low education were 2.76 [95% confidence interval (CI): 2.61-2.91] and 2.13 (2.01-2.26) times the rates of those with high education, respectively. We observed inequalities in most causes of death in each age-sex group. Among men aged 25-44 years, relative and absolute inequalities were largest for injuries, e.g. transport accidents [RR = 10.1 (5.4-18.7), RD = 21.2 (14.5-27.9)]). Among those aged 45-64 years, inequalities were greatest for chronic diseases, e.g. lung cancer [men RR = 6.6 (4.9-8.9), RD = 57.7 (49.7-65.8)] and ischaemic heart disease [women RR = 5.8 (3.7-9.1), RD = 20.2 (15.8-24.6)], with similar patterns for people aged 65-84 years. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men.Australian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.
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