Australian Burden of Disease Study 2024

The impact of alcohol use is presented for people of all ages. In people aged under 15, burden is only attributed to the linked disease alcohol use disorders. The burden attributable to the remaining linked diseases was estimated in people aged 15 and over. Note that the risk factor is alcohol use while alcohol use disorders is a linked disease. The burden attributable to this risk factor was calculated as described in detail in the AIHW publication Impact of alcohol and illicit drug use on the burden of disease and injury in Australia: Australian Burden of Disease Study 2011 (AIHW 2018).

Population attributable fractions calculated with direct evidence

In ABDS 2024, the PAFs for chronic liver disease were estimated from the proportion represented by chronic liver disease due to alcohol of all chronic liver disease burden, as estimated for Australia by the GBD 2021 (GBD 2021 Risk Factor Collaborators 2024). The same method was used to estimate the PAFs for liver cancer. The burden of alcohol dependence (the linked disease) was entirely attributed to alcohol use (the risk factor).

Direct evidence was used to derive the PAFs for accidental poisoning linked to alcohol use, using the mention of specific drugs recorded in the National Mortality Database (NMD) 2022 as described by the AIHW (2018).

Population attributable fractions estimated using comparative risk assessment

The proportions of the Australian population who are current drinkers, former drinkers or never drank alcohol were sourced from self-reported data in the National Drug Strategy Household Survey (NDSHS) 2022–2023. However, the amount of alcohol self-reported to be consumed by current drinkers in this and other surveys is known to be an underestimate of actual consumption (Rehm et al. 2010).

To overcome this, alcohol sales data were used to inflate the survey estimates. The total volume of alcohol sold in Australia was sourced from the apparent consumption of alcohol (ABS 2019). Self-reported daily consumption from the NDSHS, by age and sex, was inflated to match alcohol sales data in each reference year, based on the methods described by Rehm et al. (2010). The inflation factors used for the 2018, 2015, 2011 and 2003 reference years were the same as those used in ABDS 2018. In ABDS 2024, self-reported daily consumption from the NDSHS was inflated by the same rate as used for 2018, as sales data was not available for 2022–2023 to match data from the NDSHS 2022–2023. 

Among current drinkers, the mean number of standard drinks self‑reported per day was converted into litres of self-reported alcohol consumption for that year. In 2024, the inflation factor was estimated to be 1.46. The proportion of self-reported lifetime abstainers and ex-drinkers from the NDSHS was assumed to be accurate.

Following methods used in Rehm et al. (2010) and in the GBD 2010, 80% of the alcohol available nationally was assumed to have been consumed (Lim et al. 2012). Only a proportion (80%) of alcohol sold in Australia was used, because the total figure includes alcohol discarded due to changes in stocks, alcohol consumed by overseas travellers, alcohol that has been stored or cellared, and alcohol that has been used to prepare food or discarded as waste.

The adjusted litres of alcohol consumed nationally were distributed among self-reported current drinkers using a 2-parameter gamma distribution, which has been found to be the best model to shift the distribution of survey data to fit sales data (Rehm et al. 2010). While this approach brings self-reported alcohol consumption in line with known alcohol sales, a limitation is that it assumes the degree of under-reporting of alcohol consumption is uniform across all age and sex groups. This distribution was used to estimate the proportion of the population who consumed alcohol in categories relevant to the relative risks.

ABS (Australian Bureau of Statistics) (2019) Apparent alcohol consumption, Australia, 2017–18, ABS, Australian Government, accessed 10 October 2020.

AIHW (Australian Institute of Health and Welfare) (2018). Impact of alcohol and illicit drug use on the burden of disease and injury in Australia: Australian Burden of Disease Study 2011, AIHW, Australian Government, accessed 12 November 2024. 

GBD 2021 Risk Factors Collaborators (2024) ‘Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021’, The Lancet, 403:2162–2203. doi:10.1016/S0140-6736(24)00933-4.

Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H et al. (2012) ‘A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010’, Lancet, 380(9859):2224–60.

Rehm J, Kehoe T, Gmel G, Stinson F, Grant B and Gmel G (2010) ‘Statistical modelling of volume of alcohol exposure for epidemiological studies of population health: the US example’, Population Health Metrics, 8(3):1–12, doi:10.1186/1478-7954-8-3.

The burden for bullying victimisation was estimated in people aged 10 to 24. The methods used for ABDS 2024 are the same as those described in detail in Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021).

Population attributable fractions estimated by comparative risk assessment

Prevalence and relative risks were taken from a recent systematic review and meta-analysis of bullying victimisation among Australian children and adolescents (Jadambaa et al. 2019a, 2019b) using the comparative risk assessment methodology to estimate PAFs for anxiety disorders and depressive disorders. As no data were available to inform trends, the same PAFs were applied to each reference year of this study.

References

AIHW (Australian Institute of Health and Welfare) (2021) Australian Burden of Disease Study: Methods and supplementary material 2018, AIHW, Australian Government, accessed 11 October 2024.

Jadambaa A, Thomas HJ, Scott JG, Graves N, Brain D and Pacella R (2019a) ‘Prevalence of traditional bullying and cyberbullying among children and adolescents in Australia: A systematic review and meta-analysis’, Australian & New Zealand Journal of Psychiatry, 53:878–888, doi: 10.1177/000486741984639. 

Jadambaa A, Thomas HJ, Scott JG, Graves N, Brain D and Pacella R (2019b), ‘The contribution of bullying victimisation to the burden of anxiety and depressive disorders in Australia’, Epidemiology and Psychiatric Sciences, 29:1–23, doi: 10.1017/S2045796019000489.

Child abuse & neglect included emotional, physical, sexual abuse and neglect. The burden of child abuse & neglect was estimated in people aged 5 and over. 

Exposure and population attributable fractions estimates

Exposure and PAFs were estimated by Moore et al. (2015) by age and sex. As no data were available to inform trends, the same PAFs were applied to each reference year of this study: 2003, 2011, 2015, 2018 and 2024. Additional detail can be found in Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021).

Table 5.3: Child abuse & neglect risk model parameters

References

AIHW (Australian Institute of Health and Welfare) (2021) Australian Burden of Disease Study: Methods and supplementary material 2018, AIHW, Australian Government, accessed 11 October 2024.

Moore SE, Scott JG, Ferrari AJ, Mills R, Dunne MP, Erskine HE, Devries KM, Degenhardt L, Vos T, Whiteford HA, McCarthy M and Norman RE (2015) ‘Burden attributable to child maltreatment in Australia’, Child Abuse & Neglect, 48:208–20, doi:10.1016/j.chiabu.2015.05.006.

The burden attributable to dietary risk factors was estimated in people aged 25 and over.

It should be noted that the methods used in the ABDS 2024 to calculate attributable burden due to dietary risk factors do not align with current Australian dietary guidelines. This is because the methods are designed to align with TMREDs and relative risks sourced from the Global Burden of Disease Study (GBD 2019 Risk Factors Collaborators 2020, see Dietary risk model parameters in the table below). For information on recommended food choices, see the Australian Dietary Guidelines (NHMRC 2021).

The risk factors included were based on the AIHW review of evidence from the GBD 2019 (which included 15 dietary risk factors) and other systematic reviews from authoritative sources that have also assessed the impact of dietary risk factors on health. 

Due to methodological differences, methods for diet high in sodium are discussed in a separate sub-section.

Dietary risks included

The same dietary risk factors were included as in the ABDS 2018.

Population attributable fractions estimated using comparative risk assessment

The risk factors estimated using the comparative risk assessment were diet low in fruit, vegetables, wholegrains, legumes, nuts and seeds, milk, fish and seafood, and polyunsaturated fats; and diet high in red meat, processed meat and sugar-sweetened beverages.

Exposure estimate

There was no new data available to update exposure estimates for ABDS 2024. As such PAFs from ABDS 2018 were carried forward to ABDS 2024 and applied to updated estimates of disease burden.

Exposure estimates and PAFs for ABDS 2018 were based on the National Nutrition and Physical Activity Survey (NNPAS) part of the AHS 2011–12 (ABS 2013), which collected food intake data (through a 24-hour recall) from participants for 2 days. For details on the methods used to estimate exposure and PAFs for the dietary risk factors in ABDS 2018 see: Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021). 

Table 5.4: Dietary risk model parameters

2018, 2015, 2011 and 2003 estimates

Estimates for 2018 were based on the methods using the AHS 2011–12 data as described above.

Exposure to these dietary risk factors over time was calculated by comparing the mean exposure from the NHS 1995 and the mean exposure from the AHS 2011–12 by age. Unit record level data from the AHS 2011–12 were adjusted by the percentage change from 2011 to 2003 and 2011 to 2015 in these data sources to estimate the distribution of dietary intake in those reference years. 

Dietary risks mediated through other risk factors - Diet high in sodium

Diet high in sodium was measured by the amount it mediated blood pressure. The methods for this risk factor use comparative risk assessment and are based on the GBD 2019.

The attributable burden for diet high in sodium was calculated from a model of the impact of current sodium consumption on blood pressure levels in Australia. The model estimates the blood pressure distribution of Australians if no sodium above the TMRED was consumed. The main data source used was the AHS 2011–12.

As there were no new data available for ABDS 2024, PAFs from ABDS 2018 were carried forward to ABDS 2024 and applied to updated estimates of disease burden. 

For detail on the methods used to calculate PAFs for 2018 for this risk factor see Australian Burden of Disease Study: methods and supplementary material 2018.

The impact of illicit drug use was estimated in people aged 15 and over. The burden attributable to this risk factor was calculated as described in detail in the AIHW publication Impact of alcohol and illicit drug use on the burden of disease and injury in Australia: Australian Burden of Disease Study 2011 (AIHW 2018).

Population attributable fractions estimated using direct evidence

There was limited new data available to update direct PAFs for illicit drug use for ABDS 2024 (with the exception of accidental poisoning). As such, PAFs from ABDS 2018 were largely carried forward to ABDS 2024 and applied to updated estimates of burden of disease. A summary of the methods used for ABDS 2018 is provided below. For further detail see Australian Burden of Disease Study: methods and supplementary material 2018.

Unsafe injecting practices

PAFs for the linked diseases for unsafe injecting practices (chronic liver disease, hepatitis B hepatitis C, HIV/AIDS and liver cancer) were calculated from the National Notifiable Diseases Surveillance System (NNDSS) data published in the annual surveillance reports by The Kirby Institute (The Kirby Institute 2018).

HIV/AIDS

For HIV/AIDS, direct PAFs were calculated from the estimated proportion of diagnosed HIV cases in 2018 who were exposed to unsafe injecting practices regardless of sexual behaviour.

Acute hepatitis B and C

For acute hepatitis B and hepatitis C, the direct PAFs were calculated from the estimated proportion of newly acquired hepatitis B or hepatitis C infections in 2018 who were exposed to unsafe injecting practices regardless of sexual behaviour.

Chronic liver disease and liver cancer

Chronic hepatitis C infection

The rates of decompensated cirrhosis (chronic liver disease), hepatocellular carcinoma (liver cancer) and liver transplants due to hepatitis C are published by The Kirby Institute. These were multiplied by the earliest year of exposure data estimates available to determine the proportion of hepatitis C related morbidity due to unsafe injecting practices.

The proportion of chronic liver disease and liver cancer due to unsafe injecting practices was then estimated by quantifying the rate of hepatitis C related morbidity from the total prevalence for liver cancer and chronic liver disease in 2018.

Chronic hepatitis B infection

The Kirby Institute reported that 5.7% of people living with chronic hepatitis B in 2017 and 2015 had acquired this condition through unsafe injecting practices (The Kirby Institute 2016, 2018). This is similar to Australian estimates reported by other published studies for the years 2011 (5.7%) and the year 2000 (4.7%) (MacLachlan et al. 2013; O’Sullivan et al. 2004).

The proportion of these chronic outcomes being chronic liver disease or liver cancer was then estimated using total disease prevalence data from the ABDS 2018.

Accidental poisoning

The direct PAFs for accidental poisoning linked to specific illicit drugs were estimated using the number of deaths due to accidental poisoning with a mention of each drug type compared with the total number of accidental poisoning deaths in 2022 (latest year available) in the National Mortality Database (NMD). These methods are described in more detail in the section on alcohol use. The PAFs were also applied to non-fatal burden due to accidental poisoning.

Illicit drug dependence

All of the burden due to drug use disorders (including amphetamine, cannabis, cocaine, opioid and other illicit drug use disorders) was attributable to illicit drug use (a PAF of 1).

Population attributable fractions estimated using comparative risk assessment

There are 2 types of exposure to drug use estimated for the risk factor illicit drug use: drug dependence and driving under the influence of illicit drugs. Estimates of the exposure to drug dependence are sourced from prevalence estimates for the relevant drug use disorder from the ABDS 2024. Exposure to drug dependence—not drug use—was used in this study.

Exposure to driving under the influence of illicit drugs was estimated from the National Drug Strategy Household Survey (NDSHS) 2022–2023—specifically, the proportion of the population that responded yes to the question: ‘In the last 12 months did you undertake the activity—drove a motor vehicle—while under the influence of or affected by illicit drugs?’ Data were further provided by the type of drug used while driving.

Including drug driving by drug type is an improvement from the methods used for ABDS 2018, where the type of drug used while driving was estimated based on the relative prevalence of the use of different drugs self-reported in the NDSHS. Drug driving by drug type, and broad age group, in NDSHS 2022–2023 was applied to all years in the ABDS 2024 (2003, 2011, 2015, 2018 and 2024). Data from NDSHS 2022–2023 were applied to all years as this year of data was the most complete. While expected patterns of driving under the influence of different types of illicit drugs may be expected to change over time, estimates from NDSHS 2022–2023 were considered conservative as self-reported driving under the influence of illicit drugs appears to have been decreasing over time (where time series data is available) (AIHW 2024). The type of drug used while driving was measured independently, and no adjustment was made for those who may have driven under the influence of multiple drug types. 

Table 5.6: Illicit drug use risk model parameters

2018, 2015, 2011 and 2003 estimates

The burden attributable to illicit drug use from drug driving for 2018, 2015, 2011 and 2003 used the NDSHS 2022–2023, described above. 

The burden attributable to illicit drug use in 2015, 2011 and 2003 was further estimated using the Kirby Institute data, using the same methods as for 2018 and 2024 (described above).

References

AIHW (Australian Institute of Health and Welfare) (2018) Impact of alcohol and illicit drug use on the burden of disease and injury in Australia: Australian Burden of Disease Study 2011, AIHW, Australian Government, accessed 12 November 2024.

AIHW (2024) National Drug Strategy Household Survey 2022–2023, AIHW, Australian Government, accessed 12 November 2024.

MacLachlan JH, Allard N, Towell V and Cowie BC (2013) ‘The burden of chronic hepatitis B virus infection in Australia, 2011’, Australian and New Zealand Journal of Public Health, 37(5):416–22, doi:10.1111/1753-6405.12049.

O’Sullivan BG, Gidding HF, Law M, Kaldor JM, Gilbert GL and Dore GJ (2004) ‘Estimates of chronic hepatitis B virus infection in Australia, 2000’, Australian and New Zealand Journal of Public Health, 28(3):212–16, doi: 10.1111/j.1467-842x.2004.tb00697.x.

The Kirby Institute (2016) HIV, viral hepatitis and sexually transmissible infections in Australia: Annual surveillance report 2016, The Kirby Institute, UNSW, accessed 19 November 2024.

The Kirby Institute (2018) HIV, viral hepatitis and sexually transmissible infections in Australia: Annual surveillance report 2018, The Kirby Institute, UNSW, accessed 19 November 2024.

The burden of intimate partner violence was estimated in women aged 15 and over.

The burden was estimated as described further in the report Examination of the burden of disease of intimate partner violence against women in 2011: Final report (Ayre et al. 2016).

This risk factor was estimated in women only as the evidence in the literature used to inform the linked diseases and relative risks was not available for men (AIHW unpublished; Ayre et al. 2016; GBD 2019 Risk Factor Collaborators 2020). Available data on male victims of violence can be found in Australian Burden of Disease Study: Impact and causes of illness and death in Australia 2018.

Population attributable fractions estimated with direct evidence

Homicide and violence linked to intimate partner violence was estimated using direct evidence from the National Homicide Monitoring Program (NHMP; Miles & Bricknell 2024) for fatal burden, which estimated that 57% of homicides in females were due to an intimate partner in 2022–23. NHMP data on victim-offender relationship is collected for cleared incidents only. NHMP data for 2022–23 on victim-offender relationship exclude Western Australia, but this is expected to have minimal impact on the results.

Non-fatal burden from homicide and violence due to an intimate partner was estimated directly from the National Hospital Morbidity Database (NHMD), using the proportion of hospitalisations (with any principal diagnosis) with an external cause related to assault by an intimate partner (ICD-10-AM codes X85–Y09 with a fifth digit of 0).

Population attributable fractions estimated with comparative risk assessment

Exposure to intimate partner violence data were sourced from the ABS Personal Safety Survey (PSS) 2021–22 (ABS 2023). It was based on survey respondents aged 18 and over who self-reported intimate partner violence from a cohabiting partner from the age of 15 onwards (ABS 2024). PSS 2021–22 data were used to estimate a PAF which was applied to 2024 burden of disease estimates. 

Multiple definitions of exposure to intimate partner violence exist to reflect the complexity of violence against women. This study has been able to include emotional, physical and sexual intimate partner violence by a cohabiting current or previous intimate partner. It was not possible to estimate violence by a non-cohabiting current or previous intimate partner. This is because the PSS 2021–22 did not include an estimate of emotional abuse by non-cohabiting partners (ABS 2023).

While the PSS 2021–22 is comparable to previous surveys, no nowcasting was applied to this risk factor. This is because the PAF for physical and sexual intimate partner violence was stable with little benefit from nowcasting, while a conservative approach was taken for emotional partner violence, taking into consideration the expanded definition of partner emotional abuse compared with previous surveys (ABDS 2023).

Table 5.7: Intimate partner violence risk model parameters

Burden due to physical inactivity was estimated in people aged 20 and over.

Population attributable fractions estimated by comparative risk assessment

Population exposure to physical inactivity was treated as a categorical variable. The categories describe the range of total activity per week, as measured by the total metabolic equivalent of tasks (METs). This measure encompasses the rate of energy expenditure, with one (1) MET equivalent to 1 kcal/kg/hr, which is about the energy expended in sitting. The higher the MET, the greater the energy expended. The calculation of METs requires the input of:

• time spent undertaking the activity in 1 week (T)
• intensity score for that specific activity (I).

The total MET for each activity is calculated as:

MET = T x I

In this study, the total MET score describes the total rate of energy expended across 4 activity domains: leisure, transportation, occupational, and household. The categories included:

• Fewer than 600 METs per week
• 600–1,199 METs per week
• 1,200–1,799 METs per week
• 1,800–2,399 METs per week
• 2,400–2,999 METs per week
• 3,000–3,599 METs per week
• 3,600–4,199 METs per week
• 4,200 METs and over per week.

These categories align with relative risks provided by the GBD 2019. 

For ABDS 2024, the METs for leisure, walking for transport and occupational activity were estimated from the number of self‑reported minutes spent in each activity per week, multiplied by the intensity scores as provided by the AHS 2011–12, using the National Health Survey 2022 (ABS 2023). Estimates from the NHS 2022 were used directly for 2024, rather than nowcast to 2024 using trend information from previous surveys as in ABDS 2018. This is because while the NHS 2022 is generally comparable to NHS 17–18, there were major updates to the physical inactivity module (ABS 2022) and a conversative approach (using NHS 2022 estimates directly) was taken. 

The METs for household chores, gardening and strengthening and toning were calculated in the same way as ABDS 2018, as no new appropriate data were available. For further detail see: Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021). 

Prevalence was estimated from the proportion of people within each activity category once the METs from each domain were summed.

Table 5.8: Physical inactivity risk model parameters

2018, 2015, 2011 and 2003 estimates

As the relevant NHS data had not changed for earlier reference years, the estimated PAFs for 2018, 2015, 2011 and 2003 were the same as those estimated for ABDS 2018, for further detail see Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021).

References

The impact of tobacco use captures the burden attributable to current smoking, past smoking (in people aged 30 and over) and exposure to second-hand smoke in the home (in people of all ages). Note that due to a current lack of input data appropriate for burden of disease analysis, the ABDS 2024 does not include vaping in the tobacco use risk factor. These estimates may be revised in the future, as more data becomes available.

Population attributable fractions estimated using comparative risk assessment

Linked diseases and relative risks

Linked diseases and relative risks were sourced from the GBD 2016 (GBD 2016 Risk Factor Collaborators 2017). More detail on the methods are described further in the report Burden of tobacco use in Australia: Australian Burden of Disease Study 2015 (AIHW 2019).

Exposure estimates

The National Drug Strategy Household Survey (NDSHS) 2019 was used to estimate the proportion of the population who currently smoke (5-year lagged). Using these data for current smoking allows for a 5-year lag between exposure and these disease outcomes. Current smoking (5-year lagged) was linked to cardiovascular diseases, diabetes, asthma and respiratory infections. Exposure to current tobacco smoking (5-year lagged) was calculated from the proportion of individuals in the NDSHS 2019 who reported smoking daily, weekly or less than weekly.

The NDSHS 2022–2023 was used to estimate the proportion of non-smokers exposed to environmental tobacco in the home (second-hand smoke).

Due to the much longer lag between smoking and the incidence of cancers and chronic respiratory conditions, as well as consistent reductions in smoking rates over recent decades, the tobacco attributable burden for those disease outcomes cannot be estimated from data on the current or recent prevalence. For these conditions, the ‘smoking impact ratio’ (described by Peto et al. 1992) was used as an indirect method to estimate the accumulated risk from tobacco smoking. Lung cancer mortality in 2022 (by age and sex) from the National Mortality Database (NMD) was compared with lung cancer mortality rates among a cohort of people who smoke tobacco and never-smokers in the United States (Peto et al. 1992). The excess mortality seen in the Australian population, compared with this cohort of non-smokers, is used to determine the proportion of the population living with accumulated tobacco risk. The burden attributable to past smoking was estimated in people aged 40 and over because the small number of lung cancer deaths observed in those aged 30–39 resulted in unreliable PAFs.

Table 5.9: Tobacco risk model parameters

2018, 2015, 2011 and 2003 estimates

The NDSHS 2013 was used to estimate the proportion of the population who smoke tobacco (5‑year lagged) for 2018. The NDSHS 2019 was used to estimate the proportion of non‑smokers exposed to second-hand smoke. The NMD 2018 was used to estimate lung cancer mortality.

National exposure estimates for 2015, 2011 and 2003 were calculated from the earlier iterations of the same surveys used for the 2018 estimates and followed the same method.

References

AIHW (Australian Institute of Health and Welfare) (2019) Burden of tobacco use in Australia: Australian Burden of Disease Study 2015, AIHW, Australian Government, accessed 12 November 2024.

GBD 2016 Risk Factors Collaborators (2017) ‘Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016’, The Lancet, 390:1345–422, doi: 10.1016/S0140-6736(17)32366-8.

Peto R, Boreham J, Lopez AD, Thun M and Heath C (1992) ‘Mortality from tobacco in developed countries: indirect estimation from national vital statistics’, The Lancet, 339(8804):1268–78, doi: 10.1016/0140-6736(92)91600-d.

This risk factor was estimated in people aged 15 and over using direct evidence.

Population attributable fractions estimated using direct evidence

The entire burden of cervical cancer, chlamydia, gonorrhoea, syphilis and other sexually transmitted infections was attributed to unsafe sex; therefore, a PAF of 1 was used.

PAFs were estimated directly for chronic liver disease, hepatitis B, hepatitis C, HIV/AIDS, and liver cancer from the National Notifiable Diseases Surveillance Scheme (NNDSS) data published in annual surveillance reports by The Kirby Institute (The Kirby Institute 2018). There was no new data available to update exposure estimates for ABDS 2024, and PAFs from ABDS 2018 were carried forward to ABDS 2024 and applied to updated estimates of burden of disease. The methods for calculating these direct PAFs are described in more detail in Australian Burden of Disease Study: methods and supplementary material 2018 (AIHW 2021).

Table 5.10: Unsafe sex risk model parameters

2018, 2015, 2011 and 2003 estimates

Methods for estimating exposure and calculating the PAFs in 2018 were used to produce 2015, 2011 and 2003 estimates. Data from the NNDSS published in the annual surveillance reports by The Kirby Institute were used to calculate PAFs for unsafe sex (The Kirby Institute 2004, 2012, 2013, 2016).

References

AIHW (Australian Institute of Health and Welfare) (2021) Australian Burden of Disease Study: Methods and supplementary material 2018, AIHW, Australian Government, accessed 11 October 2024.

GBD 2021 Risk Factors Collaborators (2024) ‘Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021’, The Lancet, 403:2162–2203. doi:10.1016/S0140-6736(24)00933-4.

The Kirby Institute (2004) HIV, viral hepatitis and sexually transmitted infections in Australia: annual surveillance report 2004, The Kirby Institute, UNSW, accessed 19 November 2024.

The Kirby Institute (2012) HIV, viral hepatitis and sexually transmitted infections in Australia: annual surveillance report 2012, The Kirby Institute, UNSW, accessed 19 November 2024.

The Kirby Institute (2013) National blood-borne virus and sexually transmissible infections surveillance and monitoring report 2013, The Kirby Institute, UNSW, accessed 19 November 2024.

The Kirby Institute (2016) HIV, viral hepatitis and sexually transmissible infections in Australia: Annual surveillance report 2016, The Kirby Institute, UNSW, accessed 19 November 2024.

The Kirby Institute (2018) HIV, viral hepatitis and sexually transmissible infections in Australia: Annual surveillance report 2018, The Kirby Institute, UNSW, accessed 19 November 2024.

The burden attributable to high blood plasma glucose was estimated in people of all ages. The risk factor includes estimates of the burden due to intermediate hyperglycaemia and diabetes. 

Burden due to this risk factor was not estimated for the 2003 reference year as there were no data on trends of blood plasma glucose between 2003 and 2011.

Population attributable fractions using direct evidence

All types of diabetes were entirely attributable to high blood plasma glucose (PAF of 1) as high blood plasma glucose is a diagnostic criteria for all types of diabetes.

Chronic kidney disease due to high blood plasma glucose

The method for attributing the amount of chronic kidney disease due to diabetes is based on the GBD 2021 and involves a 2-step approach:

1. The proportion of the GBD cause ‘chronic kidney disease due to diabetes’ of the total GBD cause ‘chronic kidney disease’ in the GBD 2021 (13% in males and 12% in females) was used to estimate the direct PAF of chronic kidney disease due to high blood plasma glucose (GBD 2021 Risk Factors Collaborators 2024).
2. Exposure to high blood plasma glucose is linked to the remaining amount of chronic kidney disease burden not attributed in step 1 as described later in this section. Part of this remaining proportion is attributed to high blood plasma glucose, using the comparative risk assessment method.

Population attributable fractions using comparative risk assessment

Exposure estimates

Exposure to high plasma glucose included 2 parts: the population distribution of blood plasma glucose levels (continuous risk model) and the proportion of the population with diabetes (categorical risk model). Each of these exposures was linked to different diseases (see High blood plasma glucose risk model parameters below).

To estimate and report the burden attributable by intermediate hyperglycaemia and diabetes, the continuous distribution of high blood plasma glucose was divided into the following categories:

• exposure to 4.9 to 6.9 mmol/L high plasma glucose was attributable to intermediate hyperglycaemia. This range was defined by the GBD TMRED of 4.9 mmol/L and expert advice for the 6.9 mmol/L cut-off.
• burden due to blood plasma glucose of 7 mmol/L or more was attributable to diabetes in addition to the attributable burden estimated from exposure to diabetes.

High blood plasma glucose

Age- and sex-specific data were extracted in the finest possible increments from a continuous fasting blood plasma glucose distribution for the Australian population from the Australian Health Survey (AHS) 2011–12 (ABS 2013). As no data were available to inform trends, this estimate was also applied in 2015, 2018 and 2024.

Diabetes

The prevalence of diabetes was based on the prevalence of type 1, type 2 and other diabetes in from ABDS 2024. All types of diabetes are included because people exposed to all types of diabetes are at risk of the disease outcomes identified, and the risk factor is modifiable.

Table 5.11: High blood plasma glucose risk model parameters

2018, 2015, 2011 estimates

The prevalence of high blood plasma glucose in 2011 was estimated using measured data from the AHS 2011–12. These estimates were also applied for 2015, 2018 and 2024.

It was not possible to estimate this risk factor in 2003 because there were no data available to estimate the trend in high blood plasma glucose.

References

ABS (Australian Bureau of Statistics) (2013) Microdata: Australian Health Survey, National Health Survey, 2011–12 [DataLab], accessed 13 April 2021. 

GBD 2021 Risk Factors Collaborators (2024) ‘Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021’, The Lancet 403:2162–2203. doi:10.1016/S0140-6736(24)00933-4.

The burden attributable to high blood pressure was estimated in people aged 25 and over.

Population attributable fractions estimated with comparative risk assessment

Age- and sex-specific data were extracted in the finest possible increments from a continuous systolic blood pressure distribution for the Australian population based on blood pressure measurements from the National Health Survey (NHS) 2022 (ABS 2023).

The burden of dementia attributable to high blood pressure was calculated as described in detail in the AIHW publication Contribution of vascular diseases and risk factors to the burden of dementia in Australia: Australian Burden of Disease Study 2011 (AIHW 2016), based on exposure to high blood pressure in midlife (defined for this analysis as aged 35–64).

The 2022 estimates were incorporated alongside earlier estimates (2003, 2011, 2015 and 2018) to nowcast a PAF for 2024.

Table 5.12: High blood pressure risk model parameters

2018, 2015, 2011 and 2003 estimates

Exposure data for 2015 and 2018 was sourced from the NHS 2014–15 and NHS 2017–18 (ABS 2016, 2018), respectively, using the same method as for 2024. For 2011, data were sourced directly from the AHS 2011–12 (ABS 2013).

For 2003 estimates, the exposure to high blood pressure in 2003 was calculated by comparing the mean exposure from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) 1999–2000 and the mean exposure from the AHS 2011–12 by age and sex (Begg et al. 2007). Record level data from the AHS 2011–12 were adjusted by the percentage change in the mean from 2011 to 2003. The adjusted unit record data were used to estimate the distribution of exposure to high blood pressure in 2003.

References

ABS (Australian Bureau of Statistics) (2013) Microdata: Australian Health Survey, National Health Survey, 2011-12 [DataLab], accessed 26 April 2024. 

ABS (2016) Microdata: National Health Survey, 2014-15 [DataLab], accessed 26 April 2024. 

ABS (2018) Microdata: National Health Survey, 2017-18 [DataLab], accessed 26 April 2024. 

ABS (2023) Microdata: National Health Survey, 2022 [DataLab], accessed 26 April 2024. 

AIHW (Australian Institute of Health and Welfare) (2016) Contribution of vascular diseases and risk factors to the burden of dementia in Australia: Australian Burden of Disease Study 2011, AIHW, Australian Government, accessed 9 September 2024.

Begg S, Vos T, Barker B, Stevenson C, Stanley L & Lopez AD (2007) The burden of disease and injury in Australia 2003, AIHW, Australian Government, accessed 30 September 2024.

The burden attributable to high cholesterol was estimated in people aged 25 and over. 

Population attributable fractions

There was no new trend data available to update exposure estimates for ABDS 2024. PAFs from ABDS 2018 were carried forward to ABDS 2024 and applied to updated estimates of burden of disease.

Age- and sex-specific data were extracted in the finest possible increments from a continuous measured low-density lipoprotein (LDL) cholesterol distribution for the Australian population from the Australian Health Survey (AHS) 2011–12 (ABS 2013).

The exposure to high cholesterol in 2018 (also used for 2024) was calculated by comparing the mean exposure of total cholesterol from the AusDiab 1999–2000 and the mean exposure from the AHS 2011–12 by age and sex (Begg et al. 2007). Record level data from the AHS 2011–12 were adjusted by the percentage change in the mean that would be expected between the years 2011 to 2018. The adjusted unit record data were used to estimate the distribution of exposure to high cholesterol in 2018 and 2024.

Table 5.13: High cholesterol risk model parameters

2018, 2015, 2011 and 2003 estimates

The prevalence of total cholesterol for 2011 was estimated using data from the AHS 2011–12.

The same trend described here for 2018 was used to estimate prevalence in total cholesterol in 2015 and 2003.

References

ABS (Australian Bureau of Statistics) (2013) Microdata: Australian Health Survey, National Health Survey, 2011–12 [DataLab], accessed 19 May 2020. 

Begg S, Vos T, Barker B, Stevenson C, Stanley L & Lopez AD (2007) The burden of disease and injury in Australia 2003, AIHW, Australian Government, accessed 30 September 2024.

The burden attributable to impaired kidney function is presented for people of all ages. In people under 25, attributable burden is only attributed to chronic kidney disease. Attributable burden for all other linked diseases was estimated in people aged 25 and over.

Population attributable fractions

Exposure estimates

Chronic kidney disease stages 1–3

There was no new trend data available to update exposure estimates for ABDS 2024. PAFs from ABDS 2018 were carried forward to ABDS 2024 and applied to updated estimates of burden of disease.

PAFs from ABDS 2018 were calculated by extracting age- and sex-specific data in the finest possible increments from the estimate of stages 1, 2 and 3 chronic kidney disease for the Australian population from the Australian Health Survey (AHS) 2011–12 (ABS 2013).

To estimate prevalence in the year 2018 (also used for 2024), the AIHW analysis of trends in stages 3–5 chronic kidney disease prevalence from the 1999–2000 AusDiab compared with the AHS 2011–12 in the broad age groups was used (AIHW 2018). The age and sex distribution for stage 3 chronic kidney disease were further refined using the age and sex of people who were hospitalised for stage 3 chronic kidney disease (N18.3) in 2018.

Chronic kidney disease stages 4–5

The prevalence of stage 4 and 5 (end-stage) chronic kidney disease was estimated as the prevalence for the relevant sequelae (stage 4 chronic kidney disease, end-stage chronic kidney disease treated with dialysis or transplant) for the cause chronic kidney disease in the ABDS 2024. The methods for these sequelae are described for the cause chronic kidney disease.

Table 5.14: Impaired kidney function risk model parameters

2018, 2015, 2011 and 2003 estimates

Chronic kidney disease stages 1–3

The prevalence of stages 1, 2 and 3 chronic kidney disease for 2011 was estimated using data from the AHS 2011–12. To estimate prevalence in the years 2003 and 2015, the AIHW analysis of trends in stages 3–5 chronic kidney disease prevalence from the 1999–2000 AusDiab compared with the AHS 2011–12 in the broad age groups was used (AIHW 2018). The same PAFs were used for 2018 and 2024 (described above).

Chronic kidney disease stages 4–5

The prevalence of stages 4–5 chronic kidney disease was sourced as described for the cause chronic kidney disease for the ABDS 2018 (see Disease-specific methods - morbidity).

References

ABS (Australian Bureau of Statistics) (2013) Microdata: Australian Health Survey, National Health Survey, 2011–12 [DataLab], accessed 25 February 2021. 

AIHW (Australian Institute of Health and Welfare) (2018) Chronic kidney disease prevalence among Australian adults over time, AIHW, Australian Government, accessed 30 September 2024.

The burden attributable to iron deficiency was estimated for people of all ages. Iron deficiency anaemia is the only disease linked to iron deficiency and was 100% attributable to this risk factor (PAF of 1). The method was the same for all years in the study.

Table 5.15: Iron deficiency risk model parameters

Burden due to low birth weight & short gestation was estimated in people of all ages. This risk factor represents the combined impact of being born of low weight and/or prematurely and not as separate risk factors. It should be noted that the methods, including the TMREDs, used in the ABDS 2024 to calculate attributable burden due to low birthweight and short gestation do not align with other definitions of low birthweight (usually <2500g) or premature birth (usually <37 weeks gestation). Due to data limitations, this risk factor was only estimated for the 2018 and 2024 reference years.

Population attributable fractions estimated by comparative risk assessment

Exposure estimates

Exposure estimates were obtained using the National Perinatal Data Collection (NPDC), which contains data on all live births and stillbirths of at least 20 weeks gestation or 400 grams birthweight, and the National Perinatal Mortality Data Collection (NPMDC) which contains data on all stillbirths and neonatal deaths of at least 20 weeks gestation or 400 grams birthweight. For more information please read the latest NPDC data quality statement.

The number of live births within each category of birthweight and gestational age (to represent the early neonatal period, 0–6 days), and the number of living babies within each category of birthweight and gestation age at 7 days (to represent the late neonatal period, 7–28 days), was calculated so as to correspond with relative risks provided by the GBD 2021 (see Table 5.16).

Table 5.16: Categories of exposure to gestation and birthweight and TMREDs from GBD 2021 study

Source: GBD 2021.

PAFs were estimated using the comparative risk assessment method using NPDC and NPMDC exposure data from 2016–2018 (for 2018 reference year) and 2019–2021 (the latest available at the time of analysis, for 2024 reference year). PAFs were scaled down using the proportion of deaths in the early and late neonatal period out of all deaths among children less than one year old, to match the age groups used in burden of disease estimates.

Relative risks

Relative risks and linked diseases were obtained from the GBD 2021 though, following expert advice, not all were deemed appropriate within the Australian context. Pre-term birth and low birthweight complications was the only linked disease that was attributed entirely to the risk factor and applied to people of all ages.

Table 5.17: Low birthweight & short gestation risk model parameters

References

GBD 2021 Risk Factors Collaborators (2024) ‘Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021’, The Lancet 403:2162–2203. doi:10.1016/S0140-6736(24)00933-4.

The burden attributable to low bone mineral density was measured in people aged 40 and over. 

Population attributable fractions

Self-reported prevalence of osteoporosis underestimates the true community prevalence of the condition, as many individuals with low bone mineral density display no overt symptoms and are therefore undiagnosed.

The PAFs from ABDS 2018 (and earlier years) were also used for ABDS 2024 as there was no new data available. Exposure data on measured bone mineral density (the gold standard for measuring osteoporosis) were sourced from the 2001–06 wave of the Geelong Osteoporosis Study (Henry et al. 2010). Mean bone mineral density at the femoral neck, by age and sex, was used to model exposure distributions, assuming a normal distribution and following methods described by Sànchez-Riera et al. (2014).

Table 5.18: Low bone mineral density risk model parameters

2018, 2015, 2011 and 2003 estimates

Methods for estimating exposure and calculating the PAFs for the 2018, 2015, 2011 and 2003 reference year were the same as those used for 2024.

References

Henry MJ, Pasco JA, Korn S, Gibson JE, Kotowicz MA and Nicholson GC (2010) ‘Bone mineral density reference ranges for Australian men: Geelong Osteoporosis Study’, Osteoporosis International 21(6):909–17. doi:10.1007/s00198-009-1042-7.

Looker AC, Borrud LG, Hughes JP, Fan B, Shepherd JA and Melton LJ (2012) ‘Lumbar spine and proximal femur bone mineral density, bone mineral content, and bone area: United States 2005–2008’, Vital Health Statistics 11(251):1-132.

Sànchez-Riera L, Carnahan E, Vos T, Veerman L, Norman R, Lim SS, Hoy D, Smith E, Wilson N, Nolla JM, Chen JS, Macara M, Kamalaraj N, Li Y, Kok C, Santos-Hernandez C and March L (2014) ‘The global burden attributable to low bone mineral density’, Annals of the Rheumatic Diseases 73:1635–45. doi:10.1136/annrheumdis-2013-204320.

The burden due to overweight (including obesity) was estimated in people aged 5 and over. The methods used for this risk factor are described in detail in the AIHW publication Impact of overweight and obesity as a risk factor for chronic conditions: Australian Burden of Disease Study (AIHW 2017). 

Population attributable fractions estimated with comparative risk assessment

Exposure estimates

Age- and sex-specific data were extracted in the finest possible increments from a continuous high body mass distribution for the Australian population based on measurements of height and weight from the National Health Survey (NHS) 2022 (ABS 2023). For children and adolescents aged 5–14, age- and sex-specific BMI cut-off levels indicating overweight (including obesity) were derived from the study by Cole et al. (2000).

Relative risks

The relative risks used were largely based on those published by the GBD 2019, including atrial fibrillation & flutter, cataract, non-Hodgkin lymphoma and multiple myeloma. Other relative risks were based on work by the AIHW (AIHW 2017). For dementia and gallbladder and bile duct disease, relative risks from the GBD 2019 were used instead of relative risks from the AIHW as they were based on a more recent meta-analysis.

Population attributable fractions

Updated body mass index (BMI) data from the National Health Survey (NHS) 2022 was used to estimate a PAF for 2022. This 2022 estimate was incorporated alongside earlier estimates (2003, 2011, 2015 and 2018) to nowcast a PAF for 2024. Mediated PAFs were also nowcast, where relevant.

Table 5.19: Overweight (including obesity) risk model parameters

2018, 2015, 2011 and 2003 estimates

Exposure for 2011, 2015 and 2018 were estimated as described above, using data from the AHS 2011–12, NHS 2014–15 and NHS 2017–18, respectively (ABS 2013, 2016, 2018).

For people aged 20 and over, prevalence by BMI category, age and sex was estimated for the time-point 2003 by using the trends in the prevalence of the distribution of BMI from the 3 successive health surveys (the NHS 2007–08, the AHS 2011–12 and the NHS 2014–15) as described in AIHW 2017.

For people aged 5–19, prevalence by BMI category, age and sex was estimated for the time-point 2003 using the NHS 2007–08.

References

ABS (Australian Bureau of Statistics) (2013) Microdata: Australian Health Survey, National Health Survey, 2011–12 [DataLab], accessed 26 April 2024. 

ABS (2016) Microdata: National Health Survey, 2014–15 [DataLab], accessed 26 April 2024. 

ABS (2018) Microdata: National Health Survey, 2017–18 [DataLab], accessed 26 April 2024. 

ABS (2023) Microdata: National Health Survey, 2022 [DataLab], accessed 26 April 2024. 

AIHW (2017) Impact of overweight and obesity as a risk factor for chronic conditions: Australian Burden of Disease Study, AIHW, Australian Government, accessed 30 September 2024.

Cole TJ, Bellizzi MC, Flegal KM & Dietz WH (2000) ‘Establishing a standard definition for child overweight and obesity worldwide: international survey’, British Medical Journal, 320:1240–3. doi:10.1136/bmj.320.7244.1240.

The burden attributable to air pollution in Australia was estimated by considering the annual average concentration of particulate matter of aerodynamic diameter <2.5 μg (PM_2.5). Burden attributable to coronary heart disease and stroke is only estimated for people aged 25 and over, while burden attributable to the remaining linked diseases (COPD, lower respiratory infections, lung cancer and type 2 diabetes) is estimated for all ages. Estimates are provided for 2015, 2018 and 2024 only.

The analysis for this risk factor was undertaken using estimates obtained directly from the University of Sydney (L Knibbs, personal communication, 2024), as the most recent update of the validated PM_2.5 predictions described by Knibbs et al. (2018). Note that these are gridded as approximately 100 m raster cells for all of Australia, and incorporate minor technical improvements related to the underlying predictor data sets, as needed.

The development of the original mesh block-based exposure estimates described by Knibbs et al. (2018) were supported by the Centre for Air Quality and Health Research and Evaluation (CAR), an NHMRC Centre of Research Excellence (APP1030259).

The estimates are presently available with thanks to the successor Centres of Research Excellence (CRE) for Safe Air (CSA) via the Clean Air Research Data and Analysis Tools (CARDAT) platform (https://cardat.github.io). It is anticipated that the PM_2.5 rasters used in ABDS 2024 will be available on CARDAT in the near future. 

CARDAT is supported by The Centre for Safe Air (https://safeair.org.au/) which is currently supported by the National Health and Medical Research Council (2015584) and the Australian Research Data Commons (ARDC) AirHealth Data Bridges project (https://doi.org/10.47486/PS022).

Population attributable fractions estimated using comparative risk assessment

PM_2.5 are particles suspended in the air with a diameter in a specified size range, 0–2.5 microns. Average annual PM_2.5 raster data from the Centre for Safe Air was estimated using satellite data, calibrated by ground monitoring stations, for 2015, 2018 and 2021 (latest available data, applied to 2024 reference year). 

These raster data were used to estimate average PM_2.5 levels for mesh blocks, then geographic correspondence files were used to convert the mesh block data to SA2 geography. The SA2 data were aggregated using population weights for each age and sex cohort to estimate national exposure to air pollution by age and sex.

Air pollution modelling, including satellite-based modelling used here, as well as fixed site ground based measurement networks, are limited in that they reflect air pollution concentrations, rather than actual personal exposures to air pollution. However, satellite-based modelling has advantages over previous methods using monitoring stations in that estimates are based on measurements which sample the geographic extent of Australia much more comprehensively and are calibrated using ground monitoring stations. There may still be variation in estimated levels of air pollution and actual levels realised. There is also likely to be a substantial amount of variation between sites in the amount of time that people generally spend outside, being exposed to air pollution. While outside air pollution can move into indoor spaces, contributing to exposure, indoor air pollution is not currently captured in this study. This will be considered for inclusion in future studies if available input data is available.

Table 5.20: Air pollution risk model parameters

References

Knibbs LD, Van Donkelaar A, Martin RV, Bechle MJ, Brauer M, Cohen DD, Cowie CT, Dirgawati M, Guo Y, Hanigan IC and Johnston FH (2018) ‘Satellite-based land-use regression for continental-scale long-term ambient PM2.5 exposure assessment in Australia’, Environmental science & technology, 52(21):12445–12455, doi:10.1021/acs.est.8b02328.

The impact of occupational exposures and hazards was estimated in people aged 15 and over. Occupational exposures and hazards captured the impact of exposure to 12 carcinogens (asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica and sulphuric acid), asthmagens, noise, ergonomic stressors, injury, particulate matter, and gases and fumes in the workplace.

Population attributable fractions from direct evidence

The PAFs for injuries were estimated directly from data collected by Safe Work Australia. For all other disease outcomes, the PAFs were estimated from exposure to working in various industries or occupations.

All pneumoconiosis was attributable to occupational exposure as informed by expert advice (T Driscoll, personal communication, 24 June 2016). As per the disease group methods, pneumoconiosis was split into its component sequelae of silicosis, asbestosis and other pneumoconiosis for ABDS 2024.

For injuries, direct evidence was sourced from Safe Work Australia, including data on the number of deaths occurring at work in 2022 (Safe Work Australia 2024a) and the number of workers’ compensation injury claims annually in 2022–23 (preliminary data, Safe Work Australia 2024b). Counts of deaths and injuries, with disaggregation by age, sex and nature or external cause of injury, were used to directly calculate PAFs. Where the full distribution of counts by age and sex were not available due to small numbers, the disaggregation was estimated using the age and sex distribution of available occupational injuries in that year. 

The PAFs for fatal burden were estimated by the number of deaths occurring at work compared with the total number of deaths due to injuries in the broader population in the same year.

The data for non-fatal burden are limited in that serious workers’ compensation claims will capture only injuries that require more than 1 week away from work, by definition. This means that some work-related injuries or illnesses that have a significant and ongoing impact on workers, such as permanent impairment, are not captured in this data. They will also not include people who are self-employed. These PAFs were estimated for people aged 15 and over.

The PAFs for non-fatal burden were estimated by the number of injuries reported at work in 2022–23 preliminary data from Safe Work Australia divided by the incidence of admitted and non-admitted injury hospitalisations and emergency department presentations in the National Hospital Morbidity Database (NHMD) in 2022.

Population attributable fractions by comparative risk assessment

Exposure estimates

The number of people working in Australia—the economically active population—by age, sex and industry or occupation, was estimated from the Labour Force Survey (ABS 2024).

Industry

Exposure to working in certain types of industry was linked to various cancers, hearing loss and COPD (see Occupational exposure risk model parameters below). This is because working in these industries is known to expose a proportion of the workforce to carcinogens, noise, particulate matter, gases and fumes as estimated by the Carcinogen Exposure (CAREX) research project (Kauppinen et al. 2000).

The working population was distributed across 9 broad industry types (agriculture, hunting, forestry and fishing; mining and quarrying; wholesale, retail trade, restaurants and hotels; manufacturing; electricity, gas and water; transport, storage and communication; construction; finance, insurance, real estate and business services; community, social and personal services) based on the 2021 Census of Population and Housing.

A severity distribution from the GBD 2010 was applied to obtain the proportion of people working in these industries exposed to high and low levels of noise, and to high and low levels of particulate matter, gases and fumes. The PAFs were calculated for people aged 15–74.

To account for the latency period between exposure and the symptoms of cancer, an ‘occupational turnover rate’ was applied to the number of people working in these industries. The occupational turnover rate adjusts for annual worker turnover, mortality rates and past trends by industry, to estimate past exposure to carcinogens in each industry. These factors are based on trends observed in the United Kingdom.

Data from the Carcinogen Exposure (CAREX) research project produces estimates of the proportion of workers in each industry who will be exposed to specific carcinogens (Kauppinen et al. 2000). These proportions, which are based on data from the European Union and Canada, are then applied for each of the industries described earlier. The PAFs for carcinogens were calculated for people aged over 15.

Occupation

Exposure to types of occupations was linked to asthma and low back pain (see occupational exposure risk model parameters below). This is because working in these occupations is known to expose a proportion of the workforce to asthmagens and ergonomic stressors.

The number of working people was apportioned by 8 broad occupational groups (professional, technical and related workers; administrative and managerial workers; clerical and related workers; sales workers; service workers; agricultural, animal husbandry and forestry workers; fishermen and hunters; production and related workers; transport equipment operators and labourers) based on the 2021 Census of Population and Housing (ABS 2022).

Exposure to working in these occupations was used to estimate the PAFs in people aged 15–64 and no severity distribution was applied.

Table 5.21: Occupational exposure risk model parameters

2018, 2015, 2011 and 2003 estimates

Methods for estimating exposure and calculating the PAFs in 2024 were followed for 2018, 2015 and 2011 estimates. Due to data availability, exposure estimates from 2011 were applied to 2003 population data for all occupational exposures except fatal injuries. The working population was estimated from the Labour Force Survey (ABS 2024) and disaggregated by occupation and industry using the 2016 and 2011 Census of Population and Housing (ABS 2017; ABS 2013).

References

ABS (Australian Bureau of Statistics) (2013) Census of Population and Housing 2011 [Census TableBuilder], accessed March 2024.

ABS (2017) Census of Population and Housing 2016 [Census TableBuilder], accessed March 2024.

ABS (2022) Census of Population and Housing 2021 [Census TableBuilder], accessed March 2024.

ABS (2024) Labour force, Australia, July 2024 [website], accessed 17 September 2024.

Kauppinen T, Toikkanen J, Pedersen D, Young R, Ahrens W, Boffetta P, Hansen J, Kromhout H, Maqueda Blasco J, Mirabelli D, de la Orden-Rivera V, Pannett B, Plato N, Savela A, Vincent R and Kogevinas M (2000) ‘Occupational exposure to carcinogens in the European Union’, Occupational and Environmental Medicine, 57(1):10–18, doi:10.1136/oem.57.1.10.

Safe Work Australia (2024a) Work-related traumatic injury fatalities, Australia 2022, Safe Work Australia, Australian Government, accessed 18 October 2024.

Safe Work Australia (2024b) Australian Workers’ Compensation Statistics 2022–23 (preliminary), Safe Work Australia, Australian Government, accessed 18 October 2024.

The burden attributable to ultraviolet (UV) radiation exposure was estimated in people of all ages using direct evidence. The main source of UV exposure is the sun. The direct PAFs used here represent a proportion of current burden due to past and current UV exposure in the population.

Population attributable fractions using direct evidence

Direct PAFs sourced from Olsen et al. (2015) were used to estimate burden attributable to high UV exposure. Olsen et al. (2015) estimated the PAFs for melanoma (0.633) and non-melanoma skin cancer (0.994) due to ambient UV exposure in Australia by comparing the incidence of these linked diseases in Australian residents compared with minimally sun exposed populations (the UK for melanoma, Scandinavia for non-melanoma skin cancers). 

Note that the PAFs used for ABDS 2024 are based on a more recent study compared with the PAFs used for ABDS 2018 (see Australian Burden of Disease Study: methods and supplementary material 2018). 

Table 5.22: UV sun exposure risk model parameters

2018, 2015, 2011 and 2003 estimates

The same PAFs were used in 2018, 2015, 2011 and 2003.

References

Olsen CM, Wilson LF, Green AC, Bain CJ, Fritschi L, Neale RE and Whiteman DC (2015) 'Cancers in Australia attributable to exposure to solar ultraviolet radiation and prevented by regular sunscreen use', Australia and New Zealand Journal Public Health, 39:471–476, doi:10.1111/1753-6405.12470.

AIHW (Australian Institute of Health and Welfare) (2021a) Australian Burden of Disease Study: impact and causes of illness and death in Australia 2018, AIHW, Australian Government, accessed 11 October 2024, doi:10.25816/5ps1-j259.

AIHW (2021b) Australian Burden of Disease Study: Methods and supplementary material 2018, AIHW, Australian Government, accessed 11 October 2024.

GBD 2019 Risk Factors Collaborators (2020) ‘Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019’, The Lancet, 396:1223–249, doi: 10.1016/S0140-6736(20)30752-2.