Drivers of change in risk factor attributable burden
This webpage presents analyses undertaken using data from the Australian Burden of Disease Study 2018: Impact and causes of illness and death in Aboriginal and Torres Strait Islander people to explore the different drivers of change over time in burden of disease attributable to 5 selected risk factors: tobacco use, alcohol use, overweight and obesity, high blood pressure and dietary risk factors.
The disease burden attributed to a selected risk factor is referred to as ‘attributable burden’ and is measured in terms of Disability Adjusted Life Years (or DALY). It reflects the reduction in fatal burden (measured by years of life lost (YLL)) and non-fatal burden (measured by years lived with disability (YLD)) that would have occurred if exposure to the risk factor had been avoided or reduced to its lowest level. For more information on how attributable burden is calculated, see Australian Burden of Disease Study 2018: methods and supplementary material.
Between 2003 and 2018, there was a 59% increase among Indigenous Australians in the total number of DALY attributable to all risk factors included in the Australian Burden of Disease Study (for those that were measured in both 2003 and 2018). Note this is different to changes in the age-standardised rate of attributable DALY per 1,000 population (which decreased by 10%, from 217.0 DALY per 1,000 in 2003 to 195.3 DALY per 1,000 in 2018).
The main factors contributing to the increase in the total number of attributable DALY were population growth, population ageing, changes to exposure to the risk factor in the population, and changes in the amount of burden for diseases linked to each risk factor. These 4 factors and the method used to estimate the contribution of each to changes in attributable burden, are explained further in the box below.
Key results from these analyses for the 5 selected risk factors can be found in the data visualisations below and in the ABDS 2018 Drivers of change in risk factor attributable burden data table.
Four factors contributing to changes in attributable burden among Indigenous Australians over time were included in these analyses:
- population growth – the size of the Aboriginal and Torres Strait Islander population is increasing over time
- population ageing – the age structure of the Aboriginal and Torres Strait Islander population is changing, with the proportion of older people increasing over time
- risk factor exposure – changes in the prevalence of the risk factor among Aboriginal and Torres Strait Islander population
- changes in linked disease burden – changes in the overall burden for those diseases or injuries that are linked to the selected risk factor. These may be influenced by changes in diagnosis, treatment or health intervention (resulting in changes in disease prevalence or severity), as well as changes in other risk factors. For example, increases in overweight and obesity may have some impact on coronary heart disease burden, which is also linked to tobacco use. See Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden data table for a list of all linked diseases for the 5 risk factors included in this analysis.
These factors were selected as they are the main drivers of trends in attributable burden examined in global burden of disease studies and are measurable with available data. In this analysis, the contribution of each of the 4 factors to the change in fatal, non-fatal and total attributable burden between 2003 and 2018 were estimated using methods developed by Das Gupta (Das Gupta 1993). This method considers the size of each factor and the interactions between them.
Each factor may cause burden to rise (indicated by a positive factor of change) or fall (a negative factor of change) over time. The sum of the effect of all factors represents the overall change in burden between 2003 and 2018. This is expressed as the amount of change (DALY) or as a percentage of the change due to the factor. Although 2011 data are also available, 2003 and 2018 were chosen as the comparison time points to enable the longest possible time series.
The figure below is provided to help readers interpret the analyses and charts presented in this web release. Each factor included in the drivers of change over time analyses (population growth, population ageing, risk exposure and linked disease burden) may cause the attributable burden from a risk factor to rise (indicated by a positive percent change) or fall (a negative percentage change) over time. The sum of the effect of all factors represents the overall change in attributable burden between 2003 and 2018.
Put simply, these analyses show that if the overall attributable burden due to a risk factor is increasing (i.e. getting worse), we can see which factors are most responsible for this increase and target policy and program responses accordingly. Secondly, it also gives us additional information on those risk factors for which burden is decreasing (i.e. getting better) and whether there are still factors (e.g. increasing exposure or linked disease burden) which could be targeted and result in further improvements in the attributable burden for that risk factor.
The total burden attributable to tobacco use among Indigenous Australians increased 71% between 2018 and 2003. This is calculated from the change in the attributable burden (DALY) between 2003 (16,638 DALY, representing 10% of total burden) and 2018 (28,514 DALY, representing 11.9% of total burden). For the purposes of this decomposition analysis, the burden attributable to exposure to second-hand smoke was not able to be included. Therefore the analysis below considers only the burden attributable to direct use of tobacco, that is, current and past smoking. For direct tobacco use, the attributable burden increased by 66% between 2003 and 2018.
The change in the direct tobacco use burden between 2003 and 2018 varied by sex, with a 58% increase in the number of attributable DALY for Indigenous males and a 76% increase in Indigenous females. The main drivers of this change were increases in population size, ageing and exposure to tobacco use and decreases in the linked disease burden.
To further understand the changes over time in burden attributed to tobacco use, the changes were broken down for the main linked disease groups. There was an increase in burden attributable due to tobacco use for cancer and other neoplasms, endocrine disorders, musculoskeletal diseases, neurological conditions and respiratory diseases. The attributable burden for hearing and vision disorders and infectious diseases decreased, and the attributable burden for gastrointestinal disorders and cardiovascular diseases did not change substantially.
For a full list of linked diseases by tobacco exposure type see Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden for Indigenous Australians data table.
The change in burden over time shows a very different pattern between current and past tobacco use. For example, changes in exposure to current tobacco use decreased attributable burden in both males and females, while changes in exposure to past tobacco use contributed to a large rise in attributable burden in both males and females.
Use the interactive text and graph below to explore the different drivers of change in burden attributable to tobacco use among Indigenous Australians between 2003 and 2018. Estimates are displayed by sex, type of burden (DALY, YLD or YLL) and type of exposure to tobacco. For more interactive data on the burden due to this risk factor and changes in the age-standardised rates of attributable burden over time, see Tobacco use.
This interactive data visualisation describes drivers of change in tobacco use attributable burden between 2003 and 2018 due to population growth, population ageing, linked disease burden and risk factor exposure among Indigenous Australians. There are 3 sections which can be customised to report data according to type of burden, sex and type of tobacco use. The first section is a text box which reports the percentage and amount of change in attributable burden due to each driver of change for the selected tobacco use type, sex and type of burden. The second section is a stacked horizontal bar chart which reports the percentage change in attributable burden by sex and driver of change for the selected tobacco use type and type of burden. Markers are superimposed on the bar chart to show the overall percentage change. The third section is a stacked horizontal bar chart which reports the percentage change in attributable burden for all types of tobacco use by disease group and driver of change for the selected sex and type of burden. Markers are superimposed on the bar chart to show overall percentage changes.
The overall burden attributable to alcohol use increased 44% between 2003 and 2018. This increase is calculated from the change in the attributable burden (DALY) between 2003 (17,404 DALY, representing 10.4% of total burden) and 2018 (25,085 DALY, representing 10.5% of total DALY). The main drivers of this change were increases in population size.
To further understand the changes over time in burden attributed to alcohol use, the changes due to different types of exposures were broken down. The overall burden due to alcohol use was estimated from exposure to current alcohol use, former alcohol use and alcohol dependence. The changes were also broken down for the main linked disease groups. There was an increase in burden attributable to alcohol use for all linked disease groups.
For a full list of linked diseases by type of alcohol exposure see Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden for Indigenous Australians data table. Use the interactive text and graph below to explore the different drivers of change in burden attributable to alcohol use in Australia between 2003 and 2018. Estimates are displayed by sex, burden type (DALY, YLD or YLL) and type of exposure to alcohol.
For more interactive data on the burden due to this risk factor and changes in the age-standardised rates of attributable burden over time, see Alcohol use.
This interactive data visualisation describes drivers of change in alcohol use attributable burden between 2003 and 2018 due to population growth, population ageing, linked disease burden and risk factor exposure among Indigenous Australians. There are 3 sections which can be customised to report data according to type of burden, sex and type of alcohol use. The first section is a text box which reports the percentage and amount of change in attributable burden due to each driver of change for the selected alcohol use type, sex and type of burden. The second section is a stacked horizontal bar chart which reports the percentage change in attributable burden by sex and driver of change for the selected alcohol use type and type of burden. Markers are superimposed on the bar chart to show the overall percentage change. The third section is a stacked horizontal bar chart which reports the percentage change in attributable burden for all types of alcohol use by disease group and driver of change for the selected sex and type of burden. Markers are superimposed on the bar chart to show overall percentage changes.
The overall burden attributable to overweight (including obesity) rose 73% between 2003 and 2018. This increase is calculated from the change in the attributable burden (DALY) between 2003 (13,494 DALY, representing 8.1% of total burden) and 2018 (23,338 DALY, representing 9.7% of total burden). The main drivers of this change were increases in population size, ageing and exposure to overweight, and decreases in the linked disease burden.
To further understand the changes over time in burden attributed to overweight (including obesity), the changes were broken down for the main linked disease groups. There was an increase in burden attributable to overweight (including obesity) for all linked disease groups. For a list of all specific diseases linked to overweight (including obesity) see Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden for Indigenous Australians data table.
Use the interactive text and graphs to explore the different drivers of change in burden attributable to overweight (including obesity) among Indigenous Australia between 2003 and 2018. Estimates are displayed by sex, burden type (DALY, YLD or YLL) and linked disease group.
For more interactive data on the burden due to this risk factor and changes in the age-standardised rates of attributable burden over time, see Overweight (including obesity).
This interactive data visualisation describes drivers of change in overweight (including obesity) attributable burden between 2003 and 2018 due to population growth, population ageing, linked disease burden and risk factor exposure among Indigenous Australians. There are 3 sections which can be customised to report data according to type of burden and sex. The first section is a text box which reports the percentage and amount of change in attributable burden due to each driver of change for the selected sex and type of burden. The second section is a stacked horizontal bar chart which reports the percentage change in attributable burden by sex and driver of change for the selected type of burden. Markers are superimposed on the bar chart to show the overall percentage change. The third section is a stacked horizontal bar chart which reports the percentage change in attributable burden by disease group and driver of change for the selected sex and type of burden. Markers are superimposed on the bar chart to show overall percentage changes.
The overall burden attributable to high blood pressure rose 8% between 2003 and 2018. This increase is calculated from the change in the attributable burden (DALY) between 2003 (9,639 DALY, representing 5.8% of total burden) and 2018 (10,382 DALY, representing 4.3% of total burden). The main drivers of this change were increases in population growth and ageing and decreases in linked disease burden.
To further understand the changes over time in burden attributed to high blood pressure, the changes were broken down for the main linked disease groups. There was an increase in attributable burden for neurological conditions and kidney and urinary diseases and a small decrease in the attributable burden for cardiovascular diseases. For a list of all specific diseases linked to high blood pressure see Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden for Indigenous Australians data table.
Use the interactive text and graphs to explore the different drivers of change in burden attributable to high blood pressure among Indigenous Australians between 2003 and 2018. Estimates are displayed by sex and burden type (DALY, YLD or YLL).
For more interactive data on the burden due to this risk factor and changes in the age-standardised rates of attributable burden over time, see High blood pressure.
This interactive data visualisation describes drivers of change in high blood pressure attributable burden between 2003 and 2018 due to population growth, population ageing, linked disease burden and risk factor exposure among Indigenous Australians. There are 3 sections which can be customised to report data according to type of burden and sex. The first section is a text box which reports the percentage and amount of change in attributable burden due to each driver of change for the selected sex and type of burden. The second section is a stacked horizontal bar chart which reports the percentage change in attributable burden by sex and driver of change for the selected type of burden. Markers are superimposed on the bar chart to show the overall percentage change. The third section is a stacked horizontal bar chart which reports the percentage change in attributable burden by disease group and driver of change for the selected sex and type of burden. Markers are superimposed on the bar chart to show overall percentage changes.
The overall burden attributable to all dietary risks increased by 15% between 2003 and 2018. This increase is calculated from the change in the attributable burden (DALY) between 2003 (13,028 DALY, representing 7.8% of total burden) and 2018 (14,940 DALY, representing 6.2% of total DALY). The change was largely a combination of increases due to population growth and ageing, and a reduction in the amount of burden due to diseases linked to the dietary risk factors.
To better understand the changes over time in burden attributed to dietary risk factors, the changes were broken down for each individual dietary risk factor, which each have their own linked diseases (some of which overlap and some are different). For example, diet high in sugar-sweetened beverages has 2 linked diseases (type 2 diabetes and coronary heart disease), which differ to the 3 linked diseases for diet low in vegetables (oesophageal cancer, coronary heart disease and stroke). Coronary heart disease is linked to all dietary risk factors except for diet low in milk. For a full list of linked diseases by dietary risk see Table S1 in the ABDS 2018 Drivers of change in risk factor attributable burden for Indigenous Australians data table.
The change in attributable burden over time varied by individual dietary risk factor. For example, burden attributed to a diet low in nuts and seeds fell 2% and burden due to a diet high in processed meat rose 4%.
Use the interactive text and graph below to explore the different drivers of change in burden attributable to dietary risk factors among Indigenous Australians between 2003 and 2018. Estimates are displayed by sex, burden type (DALY, YLD or YLL) and individual dietary risk factor.
Diet high in sodium is not presented here as there is insufficient data available for the analysis required to inform trends in exposure to this risk factor.
For more interactive data on the burden due to this risk factor and changes in the age-standardised rates of attributable burden over time, see Dietary risk factors.
This interactive data visualisation describes drivers of change in dietary risk attributable burden between 2003 and 2018 due to population growth, population ageing, linked disease burden and risk factor exposure among Indigenous Australians. There are 3 sections which can be customised to report data according to type of burden, sex and type of dietary risk. The first section is a text box which reports the percentage and amount of change in attributable burden due to each driver of change for the selected dietary risk type, sex and type of burden. The second section is a stacked horizontal bar chart which reports the percentage change in attributable burden by dietary risk and driver of change for the selected sex and type of burden. Markers are superimposed on the bar chart to show overall percentage changes.
How are the estimates of drivers of change over time calculated?
The Das Gupta method was used to decompose the changes in burden attributable to each risk factor into 4 additive components (Das Gupta 1993). Using a series of scenarios this method calculates the effect of each factor on the changes over time by assuming that all other factors, except the factor under consideration, remain the same at both time points.
The change in overall attributable burden is decomposed into changes due to:
- population growth – in the Aboriginal and Torres Strait Islander population size is increasing over time
- population ageing – the age structure of the Aboriginal and Torres Strait Islander population is changing, with the proportion of older people increasing over time
- risk factor exposure – changes in the prevalence of exposure to the risk factor among Aboriginal and Torres Strait Islander population
- changes in linked disease burden – changes in the overall burden for those diseases or injuries that are linked to the selected risk factor. This may be influenced by changes in diagnosis, treatment or health intervention (resulting in changes in disease prevalence or severity), as well as changes in other risk factors. For example, increases in overweight and obesity may have some impact on coronary heart disease burden which is also linked to tobacco use.
Attributable burden is estimated as the product of these 4 factors using the formula when examining burden by type of exposure to the risk factor:
where
Bt is the amount of burden (DALY, YLL or YLD) attributable to a particular risk factor at time point t.
i is a type of exposure to the risk factor such as current tobacco use
n is all types of exposure included in the estimate for the risk factor
j is an age and sex group
m is all age and sex groups included (males and females aged 0 to 100+)
t is a time point
Pt is the total population size at time t
Sijt is the share of the population in age and sex group i at the time t
Rijt is the rate burden of diseases linked to exposure i in the age and sex group j at the time t.
Fijt is the population attributable fraction of diseases linked to exposure i in age and sex group j at the time t.
∑ is the sum of all of the types of exposures i and all of the age and sex groups j
Attributable burden is estimated as the product of these 4 factors using the formula when examining burden by linked disease group:
where
Bt is the amount of burden (DALY, YLL or YLD) attributable to a particular risk factor at time point t.
k is a disease group of the burden linked to the risk factor
o is all disease groups of diseases linked to the risk factor
j is an age and sex group
m is an age and sex groups included (males and females aged 0 to 100+)
t is a time point
Pt is the total population size at time t
Sijt is the share of the population in age and sex group i at the time t
Rijt is the rate burden of disease group k linked to the risk factor in the age and sex group j at the time t.
Fijt is the population attributable fraction for disease group k in age and sex group j at the time t.
∑ is the sum of all of the disease groups k and all of the age and sex groups j
The effect of each of the 4 factors – population size, population ageing, linked disease burden and risk factor exposure – using this method on the change in attributable burden between 2003 and 2018 is calculated as:
where
EA is the effect of factor A (population size, population ageing, linked disease burden and risk factor exposure)
B is the amount of burden (DALY) attributable to the risk factor in 2003 (B03) in 2018 (B18)
P is the population size in 2003 (P03) or in 2018 (P18)
S is the population age structure in 2003 (S03) or in 2018 (S18)
R is the rate burden of diseases linked to risk factor in 2003 (R03) or in 2018 (R18)
F is the population attributable fraction of diseases linked to exposure in 2003 (F03) or in 2018 (F18)
The estimates were calculated using a statistical program developed by Dr Jinjing Li from the University of Canberra (Li 2017).
What are the limitations of the methods used in this analysis?
Only factors that could be easily measured (population ageing, population growth, changes in disease/injury and changes in risk factor exposure) were included in these analyses. However, these are considered to be among the most important drivers of change in attributable burden over time. It is not possible to include other factors in the analyses such as socioeconomic status that may also have an impact on changes in attributable burden over time as they are not able to be quantified.
How do these estimates of drivers of change compare to age-standardised rates?
Both age-standardised rates (which use a 'standard' population to produce rates that can be compared independent of the age structure of the study population(s)) and the drivers of change estimates presented here are methods used to compare rates over time, while taking into account the differing age structures (population ageing) of the population over time.
The percent change in age-standardised rates of attributable burden over time is somewhat comparable to the measure of percent change due to the amount of linked disease burden in the drivers of change estimates. However, the advantage of the drivers of change estimates is that they provide an indication of the proportionate impact of each of the specified factors, not just the change in age standardised population rates. A disadvantage of age-standardised rates is that they are only useful for the purposes of comparison with other standardised rates which have used the same reference population. Once standardised, the rates no longer reflect the actual rate observed in the population.
References
Das Gupta P 1993. Standardization and decomposition of rates: a user's manual. U.S. Bureau of the Census, Current Population Reports, Series P23-186. Washington, DC: U.S. Government Printing Office.
Li J 2017. Rate decomposition for aggregate data using Das Gupta’s method. The Stata Journal 17(2): 490–502. https://doi.org/10.1177%2F1536867X1701700213
Zhai T, Goss J, Li J 2017. Main drivers of health expenditure growth in China: a decomposition analysis. BMC Health Services Research 17(1): 185. https://doi.org/10.1186/s12913-017-2119-1
Acknowledgments
The authors would like to acknowledge John Goss and Jinjing Li from the University of Canberra for providing us with an analytical tool and advice to be able to break down results over time, using the method developed by Prithwis Das Gupta.