Australian Aboriginal and Torres Strait Islander Health Survey: Biomedical Results methodology

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Reference period
2012-13 financial year
Released
24/08/2015

Introduction

This publication is the first release of information from the 2012–13 National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS), which forms part of the 2012–13 Australian Aboriginal and Torres Strait Islander Health Survey (AATSIHS).

For more information on the structure of the AATSIHS, see the Structure of the Australian Aboriginal and Torres Strait Islander Health Survey section of this publication. The following information focusses on the NATSIHMS component of the survey only.

All adults aged 18 years and over who participated in either the National Aboriginal and Torres Strait Islander Health Survey (NATSIHS) or the National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (NATSINPAS) were invited to participate in the voluntary NATSIHMS. The surveys took place throughout Australia from April 2012 to July 2013. Participants in the NATSIHMS voluntarily provided blood and urine samples, which were then analysed for specific biomarkers.

The 2012–13 NATSIHMS collected information about:

  • chronic disease biomarkers, including tests for diabetes, cholesterol, triglycerides, kidney disease and liver function
  • nutrient biomarkers, including tests for iron, folate, iodine, Vitamin B12 and Vitamin D.

See Summary of biomarkers for the list of tests conducted in the NATSIHMS.

In addition, the broader survey collected a wide range of information about selected health conditions, risk factors (for example, obesity) and demographic and socioeconomic factors, which can be analysed in relation to the NATSIHMS results.

The list of data items from the survey, as well as detailed information on the different tests used in the NATSIHMS, is available in the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2012–13 (cat. no. 4727.0.55.002).

Acknowledgements

The success of the 2012–13 AATSIHS was dependent on the very high level of cooperation received from Aboriginal and Torres Strait Islander Australians. Their continued cooperation is very much appreciated; without it, the range of statistics published by the ABS would not be possible. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

The 2012–13 AATSIHS was developed with the assistance of an advisory group comprised of experts on health issues, many of whom were Aboriginal and Torres Strait Islander people. The biomedical component was also developed with the assistance of several advisory groups and expert panels. Members of these groups were drawn from Commonwealth and state/territory government agencies, non-government organisations, relevant academic institutions and clinicians. The valuable contributions made by members of these groups are greatly appreciated.

Data collection

Scope of the survey

The 2012–13 NATSIHS and NATSINPAS included a combined sample of 8,237 private dwellings across Australia. Remote and non-remote areas in all states and territories were included, as were discrete Aboriginal and Torres Strait Islander communities.

The scope was all Aboriginal and Torres Strait Islander people who were usual residents of private dwellings in Australia. Usual residents are those who usually live in a particular dwelling and regard it as their own or main home.

Private dwellings are houses, flats, home units and any other structures used as private places of residence at the time of the survey. People usually resident in non-private dwellings, such as hotels, motels, hostels, hospitals, nursing homes, and short-stay caravan parks were not in scope. This may affect estimates of the number of people with some conditions; for example, conditions which may require periods of hospitalisation, such as kidney disease.

Further scope exclusions for this survey were:

  • Non-Indigenous persons
  • Non-Australian diplomats; diplomatic staff and members of their household
  • Members of non-Australian Defence forces stationed in Australia and their dependents
  • Overseas visitors.

All selected persons aged 18 years and over in both the NATSIHS and the NATSINPAS were then invited to participate in the voluntary NATSIHMS.

Data collection

The interview components of the NATSIHS and NATSINPAS were conducted under the Census and Statistics Act (CSA) 1905. The biomedical component was collected under the Privacy Act 1988 and were subject to ethics approval. Ethics approval for the NATSIHMS at the national level was sought and gained from Australian Government Department of Health and Ageing’s Departmental Ethics Committee.

Ethics approval for the NATSIHMS component was also required at the jurisdictional level for New South Wales, Western Australia, Northern Territory and for Queensland Health Service Districts. Ethics approval was sought and gained from the following Ethics Committees:

  • Aboriginal Health and Medical Research Council Ethics Committee in New South Wales
  • Aboriginal Health Research Ethics Committee in South Australia
  • Western Australian Aboriginal Health Ethics Committee in Western Australia
  • Western Australia Country Health Service (WACHS) Research Ethics Committee in Western Australia
  • Central Australian Human Research Ethics Committee in Northern Territory
  • Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research in Northern Territory
  • several Human Research Ethics Committees of Queensland Government Hospital and Health Services districts.

At the completion of NATSIHS and NATSINPAS questions, interviewers explained the voluntary NATSIHMS component and provided a written information sheet.

Informed consent was sought from adults through completion of a consent form. A copy of the consent form was left with the respondent. Those that agreed to take part were provided a referral form to complete (including whether specific medications or supplements were regularly taken) to provide to the collection clinic.

A follow-up reminder process was used for non-remote respondents who consented to the NATSIHMS but had not yet attended a collection clinic. This process took the form of phone calls or letters arranged ten days apart from the interview date. Home visits and temporary clinics were offered to participants in certain circumstances to maximise participation rates, particularly in remote areas and for those who were incapacitated. To reduce expenses for travel, child-care or time off work, all participants were able to claim a reimbursement of $50.

Most blood and urine samples were collected at Sonic Healthcare collection clinics or alternatively, via a home visit or temporary clinic held at Aboriginal Medical Services (AMS) using standard operating procedures for phlebotomy collection. In some areas, other pathology service providers were used (including IMVS Pathology for regional areas in South Australia and Northern Territory), but the same standard collection procedures were still used.

All blood and urine samples, with the exception of urinary Iodine analysis, which was conducted by Sullivan Nicolaides Pathology (SNP) in Queensland, were then analysed at a central laboratory at Douglass Hanly Moir (DHM) Pathology in Sydney, Australia on machines accredited by the National Association of Testing Authorities (NATA). DHM conducted Internal Quality Control (QC) analysis for all instruments used to conduct analysis on the samples. More information on NATSIHMS quality assurance methods and procedures is available in the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2011–13 (cat. no. 4727.0.55.002).

All participants were provided with a pathology report of their results either via post or through their local health service. Participants in non-remote areas could also nominate for their results to be sent to their regular doctor. In cases where the results were outside the normal range, participants were contacted by a qualified health professional and encouraged to discuss their results with their doctor. If the test results showed a significantly high or low result which was dangerous to the person's health, they were contacted immediately and advised on the best course of action.

Response rates

In the NATSIHS and NATSINPAS combined, there were a total of 8,237 households fully responding, giving a response rate of 79.5%. This resulted in a total of 12,947 persons in the sample aged 2 years and over.

Of the 8,157 respondents aged 18 years and over in the combined NATSIHS/NATSINPAS sample, 3,293 (40.4%) participated in the biomedical component. A higher level of response was achieved in remote areas (55.8%) than in non-remote areas (28.1%).

Table 1: Response rates, National Aboriginal and Torres Strait Islander Health Measures Survey, 2012-13
  Number of persons (no.)Proportion of persons (%)
 Fully responding interview (18+)8,157100.0
Did not proceed to biomedical componentNot offered(a)1151.4
Refused1,94723.9
Considering560.7
Gave consent but did not participate2,74633.7
Biomedical participants (18+)Urine sample provided3,10538.1
Fasting blood sample provided2,20027.0
Non-fasting blood sample provided1,06113.0
Total biomedical participants3,29340.4

(a) Biomedical component was not offered in proxy interviews for adults where the respondent was not present and communities where collection could not be arranged.

Table 2: Non-remote/remote response rates, National Aboriginal and Torres Strait Islander Health Measures Survey, 2012-13(a)
 Non-remoteRemote
 Number of persons (no.)Proportion of persons (%)Number of persons (no.)Proportion of persons (%)
Fully responding interview4,549100.03,608100.0
Did not proceed to biomedical component3,27071.91,59444.2
Biomedical participants1,27928.12,01455.8

(a) 18 years and over

In 2012–13, 77.6% of those who participated in the NATSIHMS had fasted. Data relating to fasting tests (for example, the fasting plasma glucose test) relate to the fasting population only. Analysis of the characteristics of people who fasted compared with those who did not fast showed no difference between fasters and non-fasters.

The following table compares characteristics of persons who participated in the NATSIHMS with those who participated in the NATSIHS and NATSINPAS combined.

Table 3: Comparisons between NATSIHMS and NATSIHS/NATSINPAS samples, persons aged 18 years and over, 2012-13
 Non-remoteRemoteTotal
 NATSIHMS (unweighted) (%)NATSIHS/NATSINPAS (unweighted) (%)NATSIHMS (unweighted) (%)NATSIHS/NATSINPAS (unweighted) (%)NATSIHMS (unweighted) (%)NATSIHS/NATSINPAS (unweighted) (%)
Married54.147.845.144.848.646.5
Has a non-school qualification53.948.732.433.140.841.8
In the Labour Force57.056.953.454.354.855.8
Self-reported diabetes15.213.723.621.320.317.0
Self-reported high cholesterol6.34.110.49.08.86.3
Excellent or Very Good self-assessed health32.134.432.733.532.534.0
Current daily smoker29.741.152.250.743.545.4
Overweight/obese77.871.767.467.671.469.8

More detailed information on response rates is available in the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2011–13 (cat. no. 4727.0.55.002)

Processing the data

Weighting, benchmarking and estimation

Weighting is a process of adjusting results from a sample survey to infer results for the in-scope total population. To do this, a weight is allocated to each sample person. The weight is a value which indicates how many population units are represented by the sample unit.

The first step in calculating weights for each person was to assign an initial weight, which was equal to the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 others). An adjustment was then incorporated into the weighting to account for Aboriginal and Torres Strait Islander persons not covered by the sample. For more information on undercoverage, see the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2012–13 (cat. no. 4727.0.55.002).

The weights are calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of sex by age by area of usual residence. Weights calibrated against population benchmarks compensate for over or under-enumeration of particular categories of persons and ensure that the survey estimates conform to the independently estimated distribution of the population by age, sex and area of usual residence, rather than to the distribution within the sample itself. The selection of benchmarks was chosen to maximise the accuracy of the estimates of biomedical characteristics, by reducing both random and systematic errors as much as possible.

The NATSIHMS results were benchmarked to the estimated Aboriginal and Torres Strait Islander resident population living in private dwellings at 30 June 2011. Excluded from these benchmarks were persons in non-private dwellings. The benchmarks, and hence the estimates from the survey, do not (and are not intended to) match estimates of the total Australian Aboriginal and Torres Strait Islander resident population obtained from other sources.

Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest. Estimates of non-person counts (for example, number of conditions) are obtained by multiplying the characteristic of interest with the weight of the reporting person and aggregating.

The weights for the NATSIHMS are different to the weights for the combined NATSIHS/NATSINPAS due to the differing response patterns between the surveys.

An investigation was undertaken to determine whether the accuracy of NATSIHMS estimates could be improved by weighting with any other variables collected in the NATSIHS and NATSINPAS, including smoking status, Body Mass Index, self-assessed health, employment status, marital status and blood pressure. While the use of some of these variables would have improved the accuracy of some NATSIHMS estimates (e.g. the use of smoker status in the weighting process would have ensured that totals relating to current daily smokers were identical in the NATSIHMS to those in the combined NATSIHS and NATSINPAS), they made no difference to the main variables of interest in the NATSIHMS (i.e. estimates of diabetes, cholesterol) and even in some cases increased the measure of sampling error or Relative Standard Error (RSE).

The decision to maximise the accuracy of these main variables of interest in the NATSIHMS by not including those other variables in the calculation of weights for the NATSIHMS means that, while variables collected in the NATSIHMS can be analysed with variables collected in either the NATSIHS and NATSINPAS, the NATSIHS and NATSINPAS should be used when reporting on the prevalence of these variables. For example, for self-reported medical conditions and risk factors such as smoking, the most accurate prevalences should be calculated using the combined NATSIHS and NATSINPAS sample.

Reliability of estimates

All sample surveys are subject to sampling and non-sampling error.

Sampling error is the difference between estimates, derived from a sample of persons, and the value that would have been produced if all persons in scope of the survey had been included. For more information refer to the Technical Note. Indications of the level of sampling error are given by the Relative Standard Error (RSE) and Margin of Error (MoE).

In this publication, estimates with an RSE of 25% to 50% are preceded by an asterisk (e.g. *3.4) to indicate that the estimate has a high level of sampling error relative to the size of the estimate, and should be used with caution. Estimates with an RSE over 50% are indicated by a double asterisk (e.g. **0.6) and are generally considered too unreliable for most purposes. These estimates can be used to aggregate with other estimates to reduce the overall sampling error.

The MoEs are provided for all proportions to assist users in assessing their reliability. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportions. The proportion combined with the MoE defines a range which is expected to include the true population value with a given level of confidence. This is known as the confidence interval. This range should be considered by users to inform decisions based on the proportion.

Non-sampling error may occur in any data collection, whether it is based on a sample or a full count such as a census. Non-sampling errors occur when survey processes work less effectively than intended. Sources of non-sampling error include non-response or missing test results, errors in reporting by respondents or in recording of answers by interviewers, and occasional errors in coding and processing data.

Non-response can affect the reliability of results and can introduce a bias. The magnitude of any bias depends on the rate of non-response and the extent of the difference between the characteristics of those people who responded to the survey and those who did not.

Results for biomarkers may vary depending on the type of test and assay used, as well as the type of machine used to analyse the blood and urine samples. Details around the procedures followed for each of the biomarkers in the NATSIHMS are outlined in the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2012–13 (cat. no. 4727.0.55.002).

In the NATSIHMS, month of collection was used to analyse the seasonal effects of Vitamin D deficiency. Although there were proportionally more people who had their blood samples taken in Spring than in Autumn, this only had a very small impact on the overall rate of Vitamin D deficiency at the population level.

Table 4: Distribution of the adult NATSIHMS sample by season
SeasonProportion of the sample (%)
Summer14.1
Autumn26.1
Winter21.9
Spring37.9

Rounding

Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals.

Proportions presented in this publication are based on unrounded figures. Calculations using rounded figures may differ from those published.

Data release

Reliability of estimates

Two types of errors are possible in an estimate based on a sample survey: sampling error and non-sampling error. The sampling error is a measure of the variability that occurs by chance because a sample, rather than the entire population, is surveyed. Since the estimates in this publication are based on information obtained from a sample of persons they are subject to sampling variability; that is, they may differ from the figures that would have been produced if all persons had been included in the survey. One measure of the likely difference is given by the standard error (SE). There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if all persons had been included, and about 19 chances in 20 that the difference will be less than two SEs.

Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate. The RSE is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer also to the size of the estimate.

\(R S E \%=\left(\frac{S E}{e s t i m a t e}\right) \times 100\)

RSEs for the published estimates are supplied in the online version of this publication on the ABS website.

The smaller the estimate the higher the RSE. Very small estimates are subject to such high SEs (relative to the size of the estimate) as to detract seriously from their value for most reasonable uses. In the tables in this publication, only estimates with RSEs less than 25% are considered sufficiently reliable for most purposes. However, estimates with larger RSEs, between 25% and less than 50% have been included and are preceded by an asterisk (e.g. *3.4) to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of 50% or more are preceded with a double asterisk (e.g. **0.6). Such estimates are considered unreliable for most purposes.

The imprecision due to sampling variability, which is measured by the SE, should not be confused with inaccuracies that may occur because of imperfections in reporting by interviewers and respondents and errors made in coding and processing of data. Inaccuracies of this kind are referred to as the non-sampling error, and they may occur in any enumeration, whether it be in a full count or only a sample. In practice, the potential for non-sampling error adds to the uncertainty of the estimates caused by sampling variability. However, it is not possible to quantify the non-sampling error.

Standard errors of proportions and percentages

Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator.

For proportions where the denominator is an estimate of the number of persons in a group and the numerator is the number of persons in a sub-group of the denominator group, the formula to approximate the RSE is given below. The formula is only valid when x is a subset of y.

\({RSE}\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}-[R S E(y)]^{2}}\)

For proportions where the denominator and numerator are independent estimates, for example a ratio of rates relating to two separate populations such as Aboriginal and Torres Strait Islander people and non-Indigenous people, the formula to approximate the RSE is given below. The formula is only valid when x and y are estimated from separate independent populations, and when the RSEs on x and y are small.

\({RSE}\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}+[R S E(y)]^{2}}\)

Comparison of estimates

Published estimates may also be used to calculate the difference between two survey estimates. Such an estimate is subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:

\(S E(x-y) \approx \sqrt{[S E(x)]^{2}+[S E(y)]^{2}}\)

While the above formula will be exact only for differences between separate and uncorrelated (unrelated) characteristics of sub-populations, it is expected that it will provide a reasonable approximation for all differences likely to be of interest in this publication.

Another measure is the Margin of Error (MoE), which describes the distance from the population value of the estimate at a given confidence level, and is specified at a given level of confidence. Confidence levels typically used are 90%, 95% and 99%. For example, at the 95% confidence level the MoE indicates that there are about 19 chances in 20 that the estimate will differ by less than the specified MoE from the population value (the figure obtained if all dwellings had been enumerated). The 95% MoE is calculated as 1.96 multiplied by the SE.

The 95% MoE can also be calculated from the RSE by:

\({MOE}(y) \approx \frac{R S E(y) \times y}{100} \times 1.96\)

The MoEs in this publication are calculated at the 95% confidence level. This can easily be converted to a 90% confidence level by multiplying the MoE by
\(1.645/1.96\)

or to a 99% confidence level by multiplying by a factor of

\(2.576/1.96\)

A confidence interval expresses the sampling error as a range in which the population value is expected to lie at a given level of confidence. The confidence interval can easily be constructed from the MoE of the same level of confidence by taking the estimate plus or minus the MoE of the estimate.

Example of interpretation of sampling error

Standard errors can be calculated using the estimates and the corresponding RSEs. For example, in this publication, the estimated proportion of Aboriginal and Torres Strait Islander females aged 18 years and over who have abnormal total cholesterol is 22.7%. The RSE for this estimate is 7.5%, and the SE is calculated by:

\(SE\ of\ estimate=\left(\frac{R S E}{100}\right)\times estimate\)

\(=\frac{0.0075}{22.7}\)

\(=1.7\)

Standard errors can also be calculated using the MoE. For example, the MoE for the estimate of the proportion of Aboriginal and Torres Strait Islander females aged 18 years and over who have abnormal total cholesterol is +/- 3.3 percentage points. The SE is calculated by:

\(SE\ of\ estimate=\left(\frac{M O E}{1.96}\right)\)

\(=\frac{3.3}{1.96}\)

\(=1.7\)

Note due to rounding the SE calculated from the RSE may be slightly different to the SE calculated from the MoE for the same estimate.

There are about 19 chances in 20 that the estimate of the proportion of Aboriginal and Torres Strait Islander females aged 18 years and over who have abnormal total cholesterol is within +/- 3.3 percentage points from the population value.

Similarly, there are about 19 chances in 20 that the proportions of Aboriginal and Torres Strait Islander females aged 18 years and over who have abnormal total cholesterol is within the confidence interval of 19.4.% to 26.0%.

Significance testing

For comparing estimates between surveys or between populations within a survey it is useful to determine whether apparent differences are 'real' differences between the corresponding population characteristics or simply the product of differences between the survey samples. One way to examine this is to determine whether the difference between the estimates is statistically significant. This is done by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below:

\(\left(\frac{|x-y|}{S E(x-y)}\right)\)

If the value of the statistic is greater than 1.96 then we may say there is good evidence of a statistically significant difference at 95% confidence levels between the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.

Confidentiality

The Census and Statistics Act, 1905 provides the authority for the ABS to collect statistical information, and requires that statistical output shall not be published or disseminated in a manner that is likely to enable the identification of a particular person or organisation. This requirement means that the ABS must take care and make assurances that any statistical information about individual respondents cannot be derived from published data.

Some techniques used to guard against identification or disclosure of confidential information in statistical tables are suppression of sensitive cells, random adjustments to cells with very small values, and aggregation of data. To protect confidentiality within this publication, some cell values may have been suppressed and are not available for publication but included in totals where applicable. As a result, sums of components may not add exactly to totals due to the confidentialisation of individual cells.

Products and services

Summary results from the NATSIHMS are available in spreadsheet form from the Data downloads of this release.

Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic areas selected to meet individual requirements. A list of data items is available from the Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2011–13 (cat. no. 4727.0.55.002).

Summary of biomarkers

Table 5: Summary of chronic disease biomarkers
 AgeTest typeFasting
Cardiovascular disease biomarkersTotal cholesterol18+BloodNo
High-density lipo-protein (HDL)18+BloodNo
Low-density lipo-protein (HDL)18+BloodYes
Triglycerides18+BloodYes
Diabetes biomarkersFasting plasma glucose18+BloodYes
Glycated haemoglobin (HbA1c)18+BloodNo
Kidney disease biomarkersAlbumin creatinine ration (ACR)18+UrineNo
Estimated glomerular filtration rate (eGFR)18+BloodNo
Liver function biomarkersAlanine aminotransferase (ALT)18+BloodNo
Gamma-glutamyl transferase (GGT)18+BloodNo
Tobacco useCotinine18+BloodNo
Table 6: Summary of nutrient biomarkers
 AgeTest typeFasting
Folate and vitamin B12Serum folate18+BloodNo
Serum vitamin B1218+BloodNo
IronSerum ferritin18+BloodNo
Inflammation marker (C-reactive protein (CRP))18+BloodNo
Serum transferrin receptor (sTfR)18+BloodNo
Haemoglobin (Hb)18+BloodNo
Vitamin DSerum 25-hydroxyvitamin D [25(OH)D]18+BloodNo
IodineIodine concentration18+UrineNo

Abbreviations

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Glossary

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