Australian Institute of Health and Welfare (2021) Australian Burden of Disease Study: Methods and supplementary material 2018, AIHW, Australian Government, accessed 27 September 2022.
Australian Institute of Health and Welfare. (2021). Australian Burden of Disease Study: Methods and supplementary material 2018. Retrieved from https://pp.aihw.gov.au/reports/burden-of-disease/abds-methods-supplementary-material-2018
Australian Burden of Disease Study: Methods and supplementary material 2018. Australian Institute of Health and Welfare, 24 November 2021, https://pp.aihw.gov.au/reports/burden-of-disease/abds-methods-supplementary-material-2018
Australian Institute of Health and Welfare. Australian Burden of Disease Study: Methods and supplementary material 2018 [Internet]. Canberra: Australian Institute of Health and Welfare, 2021 [cited 2022 Sep. 27]. Available from: https://pp.aihw.gov.au/reports/burden-of-disease/abds-methods-supplementary-material-2018
Australian Institute of Health and Welfare (AIHW) 2021, Australian Burden of Disease Study: Methods and supplementary material 2018, viewed 27 September 2022, https://pp.aihw.gov.au/reports/burden-of-disease/abds-methods-supplementary-material-2018
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In light of the assessments of measuring uncertainty described previously, the Expert Advisory Group for the ABDS 2011 concluded that this was beyond the scope and resources of the project. However, they supported the need for clearly defined indicators to accompany each set of estimates (DALY, YLL, YLD and attributable burden) to provide users with guidance on the quality of the data underpinning the estimate, and to inform interpretation. Such indicators should inform users not only of the type of data used to derive the estimate, but also its coverage and any transformations required to produce inputs suitable to the YLL, YLD, DALY and risk factor attribution estimation process.
To help users understand the potential sources of uncertainty associated with the estimates, the 2-dimensional index developed for the ABDS 2011 was used for the ABDS 2018 burden estimates. This index was derived based on:
These dimensions are explained in greater detail in the following section.
The index was designed to help users understand the reliability and limitations of the estimates, especially which patterns and differences were likely to be genuine, and which could be influenced by uncertainties in the data or methods that made them less reliable. The higher the index the more relevant and accurate the estimate was.
To be useful in assessing the impact of different data sources and transformation methods, the final index also took into account the contribution of the underlying data to the overall estimate. For example, a particular data source might have contributed a large proportion of the overall YLD for a single disease, while another might have only contributed a small proportion.
Based on the processes required to produce the various estimates for burden of disease, and the experience of the ABDS project team in collating and analysing data for this purpose, the following key assumptions and core dimensions were developed to provide users with a succinct and coherent assessment of the quality of the estimates.
To create the index, all standard inputs, methods and assumptions underpinning the estimates were referred to the Australian Burden of Disease Expert Advisory Group and/or disease and risk factor experts for review. Assumptions on which this framework was based include:
This dimension refers to the data used to generate the estimate, and includes concepts of data quality, currency and coverage, and suitability to the model being used. These were drawn together into a single score of 5 to 1, as outlined in Table 5.1. The higher the score the more relevant, current and complete the data.
All input data to the ABDS were required to meet quality guidelines endorsed by the study’s Expert Advisory Group and Indigenous Reference Group to ensure that the highest quality data available were included in the study (see Additional material in Overarching methods and choices for ABDS 2018). However, there was still a wide variability of data reliability. This approach facilitated comparison between data sourced from:
Generally, higher scores were given to Australia-wide unit record or survey data, and lower scores to small surveys and epidemiological studies or international data of limited generalisability.
Data currency refers to how close in time the data were to the reference year. The ABDS 2018 aimed to source data as close to the reference year as possible. While this was possible for most key data sources, it was not possible for all data sources. Data for conditions that are known to be stable over short periods of time were considered current if referring to within 2 years of the reference date (for example, cancer incidence data). Data for conditions that varied from year to year, such as some infectious diseases, were considered current if specific to the reference year.
Data coverage refers to the proportion of the population covered by the data. For example, national versus sub-national, or all age groups versus particular age groups. Generally, the wider the coverage, the higher the score.
Data specificity refers to the suitability of the data to the condition and measure being analysed. Specificity depended very much on the relationship between the condition and the data source. For example:
for survey data, clinically diagnosed conditions scored higher than self-reported conditions.
Current data from one of the following: fully enumerated disease register (such as a cancer register) or administrative data, unlinked hospitalisation data for condition with a high likelihood of hospitalisation or national Australian survey (such as the AHS) of either (a) diagnostically confirmed conditions/sequelae or (b) established high correlation between self-report and clinical diagnosis specific to the population with no major variability due to small numbers.
No severity distribution needed, or high-quality empirical data on this distribution were available.
Same as ‘5’ BUT not fully enumerated with either known gaps in coverage or not diagnostically confirmed or within 2 years of the reference date or there was some variability due to small numbers (for example, a particular age group) or had high RSEs or severity not available.
It was also used for estimates with components that scored between 5 and 3.
Same as ‘4’ BUT with medium specificity of the data source to the condition/sequela being estimated. For example:
Also, data were from a single, large area (more than 1 state/territory) Australian study of very good quality or from a systemic meta-analysis that could be generalised or from a review of Australian studies with medium currency.
It was also used for estimates with components that scored between 4 and 2.
Data were from one of the following: small Australian studies of good quality, small international area study with good sampling that could be generalised to the Australian population, a systematic and meta-analysis that could be generalised, a review of Australian and/or international (for example, other high-income countries) studies. Additionally, the data source was specific to the condition/sequela being estimated and either the data were collected less than 5 years previously for a disease or condition that had a known trend of changing over time or data were collected more than 5 years previously for a disease or condition that had a known trend of not changing over time.
It was also used for estimates with components that scored between 3 and 1.
Data were from one of the following: a small Australian study and refers to data more than 5 years from the reference year for a disease or condition that has a known or unknown trend of changing over time, a small number of overseas research studies of questionable generalisability to the Australian context or a secondary data source for indirect prevalence estimates.
This dimension refers to the methods used to transform the data to generate the estimate. It included processes used to fill data gaps, such as:
As for Dimension I, these were also drawn together into a single score of 5 to 1, as outlined in Table 5.2.
Data were directly applied to the model and minimal or no extra modelling was required.
Severity distribution (if required) was obtained directly from the data..
Rates were projected to the reference year, taking into account changes in underlying trend, and applied to reference population/broad sex or age distributions were converted to 5-year age groups using trend analyses/pooled data from multiple years or sources with comparable definitions/ratios of related and primary data (for example, incidence-to-separations ratio from 1 state) applied to primary data (for example, applied to national separations data). Severity distribution (if required) was obtained from an Australian study.
One of the following transformations was used: rates from another year were applied to the same population for the reference year not accounting for any change in the underlying trend, rates from another population were applied to the reference population for the reference year where there was evidence or expert advice supporting no difference in the underlying prevalence between populations/age or sex distribution from alternative (but relevant) data source applied to the base data, pooled data from multiple sources with differing definitions after standardisation, applied New Zealand Burden of Disease prevalence rates or severity distributions based on linked data, severity distribution obtained from international studies similar to Australia (such as other high-income countries or GBD high-income severity distribution)/ratios of related and similarly defined secondary data (for example, incidence-to-separations ratio) applied to primary data (for example, prevalence).
One of the following transformations was used: other epidemiological measures were modelled to produce the estimates; indirect modelling methods were used, including indirect modelling of prevalence from other measures, such as incidence, mortality, and so on; GBD global severity distribution was used.
Transformations were done using one of the following: inference of distributions from other slightly related data sources; based on expert advice only; indirect modelling methods where the data source had an inconsistent definition of the condition, had a low coverage factor or data were not within 5 years of the reference year; or the severity distribution from another disease or condition was applied as a proxy.
The ABDS quality index operated at the disease or risk factor level, and was applied to the YLL, YLD and attributable burden for the 2018 national estimates. The quality of DALY estimates is the weighted average of the YLL and YLD estimate.
The index was built from the lowest level of estimate using the 2 dimensions outlined previously, weighted for the contribution to the overall disease-level estimate, as follows:
The index for each dimension is derived and reported separately for YLD and risk factors (see tables below) to help interpret results.
Each dimension was scored from 5 to 1. Although these are linear units, it should not be assumed that each score is proportionally equal. This was dealt with by scaling, as follows:
Each score was weighted by the proportion it contributed to the estimate in question. As the maximum score for a disease was 500 (that is, score of 5 contributing to 100% of the estimate) and the minimum 100 (a score of 1 contributing 100%), this was divided by 5 to give an overall score in the range 20–100.
This overall score was then divided into an index (A–E) for Dimension I/Dimension II, as follows:
A. 90 or more (highly relevant/accurate—estimate was derived from comprehensive and highly relevant data/little or no data transformation was required)
B. 75 to less than 90 (relevant/accurate)
C. 45 to less than 75 (moderately relevant/accurate—estimate was derived from reasonably comprehensive and relevant data/moderate transformations required, taking into account known trends in the underlying data, such as over time or age-distributions)
D. 30 to less than 45 (somewhat relevant/accurate)
E. Less than 30 (questionable relevance/accuracy—use with caution, as estimate was derived from less comprehensive or relevant data/moderate transformations required with trends unknown or unaccounted for).
The data and methods used for 2018 estimates underpinned the sub-national, 2015, 2011 and 2003 estimates, so the quality of these estimates must be considered together with the broad sub-national, 2015, 2011 and 2003 methods described in Overarching methods and choices for ABDS 2018, and the specific details described in Disease specific methods and Risk factor specific methods.
Using the ABDS quality index, the mortality data were considered to be comprehensive and relevant with little or no transformation required other than the redistribution of a small proportion of deaths that were not considered appropriate for burden of disease analyses (see Years of life lost (YLL)). Therefore, all fatal burden estimates are highly indicative of the YLL due to these diseases. One exception to this is the fatal injury burden by nature of injury, as injury-related deaths are classified by the external cause—subsequent mapping was needed to estimate the fatal burden by nature.
The table below lists the quality index for YLD assigned to each disease, and a concise summary of any data issues. Each rating must be interpreted carefully together with the statement accompanying the index and the disease specific methods described in Disease specific methods. Care is needed when using estimates that have a rating of D or E, which are considered to be somewhat relevant/accurate or of questionable dependability, respectively.
The quality index ratings for risk factor estimates, and a summary of key data issues and gaps are listed in the table below. For each risk factor, it was only possible to rate the quality of the data used to estimate the direct PAFs or the exposure data used to calculate the PAFs. Many other inputs (such as relative risks) were included in these calculations, but it was not feasible in the scope of this project to determine the quality of these inputs.
For risk factors with multiple measures of exposure such as tobacco use, the quality measures have been summarised to reflect the measures with the most attributable burden. Each rating should be interpreted together with the statement accompanying the index and the risk factor-specific methods described in Risk factor specific methods.
This interactive data visualisation reports on the quality information regarding the non-fatal burden estimates of each disease and injury for the national population and for the Aboriginal and Torres Strait Islander population. The specific disease or injury can be selected by the user. There are 2 sections – the first section displays the quality information of the estimates for the national Australian population, the second section displays the quality information of the estimates for the Aboriginal and Torres Strait Islander population. For each disease and injury, there are two scores – one for data and one for methods. Each score is a whole number out of 5. There is also a description of the data and methods used to obtain the non-fatal burden estimate.
Recent, relevant, fully enumerated data of high quality data specific to the Australian population. Where severity is required, this is derived from the same data source.
Minimal or no extra modelling; estimate was derived directly from source data
Relevant, high quality data however data is either not fully enumerated, is non-specific to the population, has high variability, is not derived from the reference year or where severity is required it is not available. This may also be a combination of a 5 and 3 star rating.
Modelling such as disaggregating broad age groups into finer age groupings or applying person: separation hospitalisation ratios from linked data to non-linked, however the modelling is minimal and primarily specific to the population condition-specific and is evidence based.This may also be a combination of a 5 and 3 star rating.
Relevant, high quality data however for the condition required it has either medium specificity, derived from a single smaller-scale Australian study or is from a generalisable review or meta-analyses. This may also be a combination of a 4 and 2 star rating.
Assumptions to be made as there is no information to model trends, or modelling was required using methods which were not specific to the population or were from various sources with differing definitions for the condition. This may also be a combination of a 4 and 2 star rating.
A small good-quality Australian/ International study/ Review or meta-analyses generalisable to the Australian population that may not be recent or has low specificity for that condition. This may also be a combination of a 3 and 1 star rating.
Indirect modelling methods based on evidence which was; less than 5 years from the reference year, non-specific to the the condition or population or inferences were made from related data with medium specificity. This may also be a combination of a 3 and 1 star rating.
A small Australian study more than 5 years old from the reference year with questionable applicability/ an international study with questionable generalisability to the Australian population or is indirect and from a secondary data source.
Indirect modelling methods based on evidence which was either; more than 5 years old to the reference year, non-specific to the condition or population or inferences were made from slightly related data.
This interactive data visualisation reports on the quality information regarding the risk factor exposure data estimates for the national population and for the Aboriginal and Torres Strait Islander population. The specific risk factor can be selected by the user. There are 2 sections – the first section displays the quality information of the risk factor estimates for the national Australian population, the second section displays the quality information of the risk factor estimates for the Aboriginal and Torres Strait Islander population. For each risk factor, there are two scores – one for data and one for methods. Each score is a whole number out of 5. There is also a description of the data and methods used to obtain risk factor exposure data.
Recent, relevant, fully enumerated data of high quality with either diagnostically confirmed exposure; or established high correlation between self-report and clinical diagnosis of exposure specific to the Australian population.
Relevant, high quality data however data is either not fully enumerated, not diagnostically confirmed, is non-specific to the population, has high variability, is not derived from the reference year. This may also be a combination of a 5 and 3 star rating.
Modelling such as disaggregating broad age groups into finer age groupings or to project estimates to the reference year, however the modelling is minimal and primarily specific to the population exposure-specific and is evidence based. This may also be a combination of a 5 and 3 star rating.
Relevant, high quality data however for the exposure required it has either medium specificity to exposure, derived from a single smaller-scale Australian study or is from a generalisable review or meta-analyses. This may also be a combination of a 4 and 2 star rating.
Assumptions to be made as there is no information to model trends, or modelling was required using methods which were not specific to the population. This may also be a combination of a 4 and 2 star rating.
A small good-quality Australian/ International study/ Review or meta-analyses generalisable to the Australian population that may not be recent or has low specificity for that exposure. This may also be a combination of a 3 and 1 star rating.
Indirect modelling methods based on evidence which was; less than 5 years from the reference year, non-specific to the exposure or population or inferences were made from related data with medium specificity. This may also be a combination of a 3 and 1 star rating.
A small Australian study more than 5 years old from the reference year with questionable applicability/ an international study with questionable generalisability to the Australian population or is indirect and from a secondary data source.
Indirect modelling methods based on evidence which was either; more than 5 years old to the reference year, non-specific to the exposure or population or inferences were made from slightly related data.
Two commonly used measures of reliability considered by the study to describe the overall quality of estimates were:
uncertainty analysis—this provides a measure of the ‘precision’ of the estimate, including how much the true value might differ from the estimate (for example, by using 95% CIs). These are estimated based on the underlying data using well‑established statistical techniques that measure random variation in the data, but do not measure variation in the model and assumptions to which the data are applied
scenario testing—this provides a measure of how much the estimate might vary if certain parameters in the model underpinning the estimate differed (for example, if the duration of a disease was longer or shorter) or if the data applied to the model varied, but it does not measure differences that might be due to random variation in the underlying data.
Using case studies of mortality (national and Indigenous), cancer and chronic kidney disease, the ABDS project team considered 2 approaches to estimate uncertainty: direct calculation and simulation.
Both the direct-calculation approach and the simulation approach required some information about the uncertainty around the input data. The information might take various forms, ranging from an explicitly estimated statistical distribution to a general indication of, for example, the variance (breadth of scatter) around the input data. If only the latter were available, then some plausible statistical distribution (consistent with that variance) needed to be assumed or imposed.
Obtaining information about uncertainty for the inputs (even for a single disease or injury) might require a complex investigation or brave assumptions, particularly for input data drawn from registries or administrative data. Obtaining such information across the whole spectrum of diseases and injuries is a major research problem requiring subject matter expertise, and was outside the scope of this project.
In concept, this approach entails 4 steps:
Even if the information for the first step were obtainable, the third step is feasible only in the case of some relatively straightforward transformations and some well-understood input distributions. That is why the GBD and other investigators that have provided uncertainty intervals have generally relied upon simulation.
In concept, this approach requires 5 steps, although the actual sequence of computations is generally different, but has been laid out this way for clarity:
Subject to accomplishing the large prior task of ascertaining statistical distributions for the inputs, this was considered a feasible approach. The methods are fairly well understood and software tools can be used for the computations (such as WinBUGS, a statistical software for Bayesian analysis using Markov chain Monte Carlo methods, developed by the BUGS Project, a team of United Kingdom researchers at the MRC Biostatistics Unit, Cambridge, and Imperial College School of Medicine, London). Nevertheless, implementing the approach across the whole of ABDS, and validating the findings, was estimated to involve a large volume of work that might have exceeded what was required to generate the actual estimates.
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