Drivers of change in risk factor attributable burden
This chapter presents new analyses undertaken using data from the Australian Burden of Disease Study 2015 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: methods and supplementary material 2015.
Between 2003 and 2015, there was an 11% increase in the total number of DALY attributed to all risk factors included in the Australian Burden of Disease Study (for those that were measured in both 2003 and 2015). Note this is different to changes in the age-standardised rate of attributable DALY per 1,000 population (which was 76.7 DALY per 1,000 in 2003 and 65.3 DALY per 1,000 in 2015).
The main factors contributing to the increase in the total number of attributable DALY were population growth (the Australian population increased by 21% between 2003 and 2015), 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 Table S2.
Four factors contributing to changes in attributable burden over time were included in these analyses:
- population growth—in Australia population size is increasing over time
- population ageing—in Australia the age structure of the population is changing, with the proportion of older people increasing over time
- risk factor exposure—changes in the prevalence of the risk factor in Australia
- 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 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 2015 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 2015. 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 2015 were chosen as the comparison time points to enable the longest possible time series.
The overall burden attributable to tobacco use rose 2.0% between 2003 and 2015. This increase is calculated from the change in the attributable burden (DALY) between 2003 (434,504 DALY, representing 10.5% of total burden) and 2015 (443,235 DALY, representing 9.3% of total DALY).
The change in tobacco use burden between 2003 and 2015 varied greatly by sex, with a 6.8% decrease in males and 17% increase in females. The main drivers of this change were increases in population and ageing, decreases in linked disease burden and decreases in exposure to tobacco use (for males).
To further understand the changes over time in burden attributed to tobacco use, changes due to the different types of exposures were broken down. The burden due to tobacco use was estimated from exposure to current tobacco use, past smoking and second-hand smoke.
These exposures are linked to different diseases because of differences in the time from exposure to developing the linked disease. For example, current tobacco use exposure is estimated from prevalence of current tobacco use (5-year lagged smoking rates) and is linked to diseases including coronary heart disease, type 2 diabetes, asthma and respiratory infections. Past smoking exposure is estimated from lung cancer mortality rates (using the smoking impact ratio) and is linked to cancers such as lung and bowel cancers as well as to chronic respiratory conditions, including COPD. For a full list of linked diseases by tobacco exposure type see Table S1.
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 females but not males.
Use the interactive text and graph below to explore the different drivers of change in burden attributable to tobacco use in Australia between 2003 and 2015. 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.
There was a 2% increase (8,731 DALYs) in total burden attributable to all types of tobacco use between 2003 and 2015, which was driven by a 19% increase due to population growth and an 11% increase due to population ageing.
Total burden attributable to past tobacco use increased by 15% (41,583 DALYs) between 2003 and 2015 which was driven by a 20% increase due to population growth and a 13% increase due to population ageing.
Total burden attributable due to current tobacco use and second hand smoke decreased by 20% (27,968 DALYs) and 57% (4,883 DALYs), respectively, between 2003 and 2015, due to changes in linked disease burden and risk factor exposure.
Among males, total burden attributable to all types of tobacco use decreased by 7%, compared with a 17% increase among females. The decrease in males was driven by a 21% decrease due to changes in linked disease burden and a 16% decrease due to changes in tobacco use exposure.
The overall burden attributable to alcohol use increased 9% between 2003 and 2015. This increase is calculated from the change in the attributable burden (DALY) between 2003 (195,622 DALY, representing 4.7% of total burden) and 2015 (213,705 DALY, representing 4.5% of total DALY). The main drivers of this change were increases in population and decreases in linked disease burden.
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 burden due to alcohol use was estimated from exposure to current alcohol use, former alcohol use and alcohol dependence. These exposures are linked to some different diseases because of differences in the time from exposure to developing the linked disease and the severity of the exposure. For example, a number of injuries are linked to current drinking but not former drinking, and alcohol use disorders, chronic liver disease and suicide are linked to alcohol dependence but not to current or former drinking. For a full list of linked diseases by type of alcohol exposure see Table S1.
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 2015. 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.
There was a 9% increase (18,314 DALYs) in total burden attributable to all types of alcohol use between 2003 and 2015 which was driven by a 20% increase due to population growth. Among females, total attributable burden due to all types of alcohol use increased by 11% (6,216 DALYs) compared with a 9% increase (12,098 DALYs) among males.
Total burden attributable to alcohol dependence increased by 26% (24,511 DALYs) between 2003 and 2015 which was driven by a 21% increase due to population growth.
Total attributable burden due to current alcohol use and former alcohol use decreased by 7% (5,475 DALYs) and 2% (722 DALYs), respectively, between 2003 and 2015, due to changes in linked disease burden.
Among males, total burden attributable to former alcohol use increased by 2% (278 DALYs) between 2003 and 2015 compared with a 6% decrease (1,000 DALYs) among females.
The overall burden attributable to overweight & obesity rose 27% between 2003 and 2015. This increase is calculated from the change in the attributable burden (DALY) between 2003 (313,434 DALY, representing 7.5% of total burden) and 2015 (399,419 DALY, representing 8.4% of total DALY). The main drivers of this change were increases in population and ageing, decreases in linked disease burden and increases in the prevalence of overweight and obesity.
To further understand the changes over time in burden attributed to overweight and obesity, the changes were broken down for the main linked disease groups. There was an increase in burden attributable to overweight and obesity for all linked disease groups with the exception of cardiovascular diseases, for which there was a decline. For a list of all specific diseases linked to overweight and obesity see Table S1).
Use the interactive text and graphs to explore the different drivers of change in burden attributable to overweight and obesity in Australia between 2003 and 2015. 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 and obesity.
There was a 27% increase (85,984 DALYs) in total burden attributable to overweight and obesity between 2003 and 2015. This was driven by a 22% increase due to population growth, an 11% increase due to population ageing, and an 11% increase due to changes in exposure to overweight and obesity.
Between 2003 and 2015, all disease groups linked to overweight and obesity increased in overall total burden except for cardiovascular diseases, which decreased by 8%. This was driven by a 47% decrease due to changes in linked disease burden.
Among linked disease groups, the greatest percent change in overall total burden attributable to overweight and obesity was a 163% increase in neurological conditions driven by increases due to changes in overweight and obesity exposure, linked disease burden, population growth and population ageing.
The overall burden attributable to high blood pressure fell 19% between 2003 and 2015. This decrease is calculated from the change in the attributable burden (DALY) between 2003 (338,032 DALY, representing 8.1% of total burden) and 2015 (273,894 DALY, representing 5.8% of total DALY). The main drivers of this change were increases in population and ageing, and decreases in linked disease burden and prevalence of high blood pressure.
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 which showed different patterns. There was an increase in burden attributed to high blood pressure for neurological conditions and kidney and urinary diseases, and a decrease for cardiovascular diseases. For a list of all specific diseases linked to high blood pressure see Table S1.
Use the interactive text and graphs to explore the different drivers of change in burden attributable to high blood pressure in Australia between 2003 and 2015. 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.
There was a 19% decrease (64,137 DALYs) in total burden attributable to high blood pressure between 2003 and 2015, which was driven by a 37% decrease due to changes in linked disease burden and 11% decrease due to changes in exposure to high blood pressure.
For the main disease groups linked to high blood pressure, there was an 80% and 54% overall increase in total burden attributable to high blood pressure among neurological conditions and kidney and urinary diseases, respectively. This was driven largely by increased linked disease burden, population growth and population ageing. Among cardiovascular diseases, total burden attributable to high blood pressure decreased by 23% overall which was driven by decreases due to changes in linked disease burden and changes to high blood pressure exposure.
The overall burden attributable to all dietary risks fell 11% between 2003 and 2015. This decrease is calculated from the change in the attributable burden (DALY) between 2003 (388,292 DALY, representing 9.3% of total burden) and 2015 (346,742 DALY, representing 7.3% of total DALY). This change was largely driven by 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 red meat has 2 linked diseases (bowel cancer and diabetes), which differ to the 2 linked diseases for diet low in vegetables (coronary heart disease and stroke). Diet high in sodium has the same 12 linked diseases as high blood pressure (5 of which overlap with those for other dietary risk factors) and diet high in sugar sweetened beverages has the same linked diseases as overweight and obesity. Coronary heart disease is linked to all dietary risk factors except for diet low in milk and diet high in red meat. For a full list of linked diseases by dietary risk see Table S1.
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 30% and diet high in red meat rose 28%. These differences were largely driven by changes in the risk factor exposure for each dietary risk.
Use the interactive text and graph below to explore the different drivers of change in burden attributable to dietary risk factors in Australia between 2003 and 2015. Estimates are displayed by sex, burden type (DALY, YLD or YLL) and individual dietary risk factor.
Diet high in sugar-sweetened beverages and diet high in sodium are not presented here as there is insufficient data available for the analysis required to inform trends in exposure to these risk factors.
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.
Total burden attributable to nearly all dietary risk factors decreased between 2003 and 2015, driven largely by decreases in linked disease burden. The greatest percent decrease by a dietary risk factor between 2003 and 2015 was seen in diets low in fish & seafood (40%, 3,642 DALYs), driven by a 42% decrease due to changes in linked disease burden and a 21% decrease due to changes in exposure to low fish & seafood diets.
Diets high in red meat and diets low in milk were responsible for the only increases in total attributable burden among all dietary risk factors. Between 2003 and 2015, total burden attributable to diets high in red meat and low in milk increased by 28% (2,964 DALYs) and 4% (433 DALYs), respectively, which was driven largely by population growth.
How to interpret the drivers of change over time analyses and charts
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 actual change in attributable burden between 2003 and 2015.
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.
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 Australia population size is increasing over time
- population ageing—in Australia the proportion of older people is increasing over time
- risk factor exposure—changes in the prevalence of exposure to the risk factor in Australia.
- 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 a 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 2015 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 2015 (B15)
P is the population size in 2003 (P03) or in 2015 (P15)
S is the population age structure in 2003 (S03) or in 2015 (S15)
R is the rate burden of diseases linked to risk factor in 2003 (R03) or in 2015 (R15)
F is the population attributable fraction of diseases linked to exposure in 2003 (F03) or in 2015 (F15)
The estimates were calculated using a statistical program developed by Dr Jinjing Li from the University of Canberra (Li 2017).
What are the benefits of the methods used in this analysis and how does this compare to a stepwise methodology?
The Das Gupta method for decomposing changes over time provides an indication of the proportionate impact of the specified factors (assuming any other unspecified factors are small and independent of the specific factors). The method distributes the interaction effects (such as the relationship between an ageing population and disease burden) between the factors in proportion to the strength of the main effects (Zhai et al. 2017).
It is also possible to decompose the changes over time in attributable burden using a stepwise methodology. This methodology was used in the Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015 report (Tables D12 & D13). The stepwise approach requires a logical order to be chosen for the factors to be included in the analyses and give a different result if the factors are included in a different order.
A comparison of results using the Das Gupta method and the stepwise method suggested no difference in the factors contributing the most to the change over time for each risk factor or in the direction of the change (increase or decrease) with the exception of the proportionate contribution due to risk factor exposure for alcohol use which was 1.2% using the Das Gupta method and –7.6% using the stepwise approach. There were also notable differences between the 2 methods in the proportionate contribution of changes due to risk factor exposure for overweight and obesity, and changes in linked disease burden for alcohol use and overweight and obesity (see Table S3). A comparison between the 2 methods could not be made for the individual dietary risk factors as the stepwise approach used in the Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015 report only included this analysis for all risk factors combined.
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.
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. doi: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.