The burden of occupational injury attributable to high temperatures in Australia, 2014-19: a retrospective observational study.

OBJECTIVES: To assess the population health impact of high temperatures on workplace health and safety by estimating the burden of heat-attributable occupational injury in Australia. STUDY DESIGN, SETTING: Retrospective observational study; estimation of burden of occupational injury in Australia attributable to high temperatures during 2014-19, based on Safe Work Australia (work-related traumatic injury fatalities and workers' compensation databases) and Australian Institute of Health and Welfare data (Australian Burden of Disease Study and National Hospital Morbidity databases), and a meta-analysis of climate zone-specific risk data. MAIN OUTCOME MEASURE: Burden of heat-attributable occupational injuries as disability-adjusted life years (DALYs), comprising the numbers of years of life lived with disability (YLDs) and years of life lost (YLLs), nationally, by Köppen-Geiger climate zone, and by state and territory. RESULTS: During 2014-19, an estimated 42 884 years of healthy life were lost to occupational injury, comprising 39 485 YLLs (92.1%) and 3399 YLDs (7.9%), at a rate of 0.80 DALYs per 1000 workers per year. A total of 967 occupational injury-related DALYs were attributable to heat (2.3% of occupational injury-related DALYs), comprising 890 YLLs (92%) and 77 YLDs (8%). By climate zone, the heat-attributable proportion was largest in the tropical Am (12 DALYs; 3.5%) and Aw zones (34 DALYs; 3.5%); by state and territory, the proportion was largest in New South Wales and Queensland (each 2.9%), which also included the largest numbers of heat-attributable occupational injury-related DALYs (NSW: 379 DALYs, 39% of national total; Queensland: 308 DALYs; 32%). CONCLUSION: An estimated 2.3% of the occupational injury burden in Australia is attributable to high ambient temperatures. To prevent this burden increasing with global warming, adaptive measures and industry-based policies are needed to safeguard workplace health and safety, particularly in heat-exposed industries, such as agriculture, transport, and construction.

The increasing significance of disease severity in a burden of disease framework.

Recent estimates have reiterated that non-fatal causes of disease, such as low back pain, headaches and depressive disorders, are amongst the leading causes of disability-adjusted life years (DALYs). For these causes, the contribution of years lived with disability (YLD) - put simply, ill-health - is what drives DALYs, not mortality. Being able to monitor trends in YLD closely is particularly relevant for countries that sit high on the socio-demographic spectrum of development, as it contributes more than half of all DALYs. There is a paucity of data on how the population-level occurrence of disease is distributed according to severity, and as such, the majority of global and national efforts in monitoring YLD lack the ability to differentiate changes in severity across time and location. This raises uncertainties in interpreting these findings without triangulation with other relevant data sources. Our commentary aims to bring this issue to the forefront for users of burden of disease estimates, as its impact is often easily overlooked as part of the fundamental process of generating DALY estimates. Moreover, the wider health harms of the COVID-19 pandemic have underlined the likelihood of latent and delayed demand in accessing vital health and care services that will ultimately lead to exacerbated disease severity and health outcomes. This places increased importance on attempts to be able to differentiate by both the occurrence and severity of disease.

Estimating the burden of disease attributable to high ambient temperature across climate zones: methodological framework with a case study.

BACKGROUND: With high temperature becoming an increasing health risk due to a changing climate, it is important to quantify the scale of the problem. However, estimating the burden of disease (BoD) attributable to high temperature can be challenging due to differences in risk patterns across geographical regions and data accessibility issues. METHODS: We present a methodological framework that uses Köppen-Geiger climate zones to refine exposure levels and quantifies the difference between the burden observed due to high temperatures and what would have been observed if the population had been exposed to the theoretical minimum risk exposure distribution (TMRED). Our proposed method aligned with the Australian Burden of Disease Study and included two parts: (i) estimation of the population attributable fractions (PAF); and then (ii) estimation of the BoD attributable to high temperature. We use suicide and self-inflicted injuries in Australia as an example, with most frequent temperatures (MFTs) as the minimum risk exposure threshold (TMRED). RESULTS: Our proposed framework to estimate the attributable BoD accounts for the importance of geographical variations of risk estimates between climate zones, and can be modified and adapted to other diseases and contexts that may be affected by high temperatures. CONCLUSIONS: As the heat-related BoD may continue to increase in the future, this method is useful in estimating burdens across climate zones. This work may have important implications for preventive health measures, by enhancing the reproducibility and transparency of BoD research.

Education-related inequalities in cause-specific mortality: first estimates for Australia using individual-level linked census and mortality data.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Value of a national burden-of-disease study: a comparison of estimates between the Australian Burden of Disease Study 2015 and the Global Burden of Disease Study 2017.

BACKGROUND: Estimates of burden of disease are important for monitoring population health, informing policy and service planning. Burden estimates for the same population can be reported differently by national studies [e.g. the Australian Burden of Disease Study (ABDS) and the Global Burden of Disease Study (GBDS)]. METHODS: Australian ABDS 2015 and GBDS 2017 burden estimates and methods for 2015 were compared. Years of life lost (YLL), years lived with disability (YLD) and disability-adjusted life years (DALY) measures were compared for overall burden and 'top 50' causes. Disease-category definitions (based on ICD-10), redistribution algorithms, data sources, disability weights, modelling methods and assumptions were reviewed. RESULTS: GBDS 2017 estimated higher totals than ABDS 2015 for YLL, YLD and DALY for Australia. YLL differences were mainly driven by differences in the allocation of deaths to disease categories and the redistribution of implausible causes of death. For YLD, the main drivers were data sources, severity distributions and modelling strategies. Most top-50 diseases for DALY had a similar YLL:YLD composition reported. CONCLUSIONS: Differences in the ABDS and GBDS estimates reflect the different purposes of local and international studies and differences in data and modelling strategies. The GBDS uses all available evidence and is useful for international comparisons. National studies such as the ABDS have the flexibility to meet local needs and often the advantage of access to unpublished data. It is important that all data sources, inputs and models be assessed for quality and appropriateness. As studies evolve, differences should be accounted for through increased transparency of data and methods.

History and development of national burden of disease assessment in Australia.

Australia's 1996 national burden of disease (BoD) study was one of the first in the world and updates have continued over the following two decades with the fifth study now underway. The studies adapt the global framework most recently implemented by the Global Burden of Disease Study and the World Health Organization to suit Australia's specific needs, producing estimates of fatal and non-fatal burden via the Disability Adjusted Life Year (DALY) metric, as well as attribution of the burden to many risk factors. Detailed Australian data are used with minimal reliance on modelling to fill data gaps. Comprehensive estimates are produced, including for the Indigenous population, for each of the eight states and territories, the five remoteness areas and five socioeconomic quintiles. A number of method developments have been made as part of these studies, including redistribution of deaths data and a detailed quality framework for describing the robustness of the underlying data and methods. Data and methods continue to be refined as part of the studies, and developments in global studies and other national studies are incorporated where appropriate.