The Intergenerational Health and Mental Health Study (IHMHS) was the largest health study undertaken by the ABS in Australian history.

National health surveys are globally recognised for their significant role in monitoring population health status and health risk factors, including trends over time. The information collected in the IHMHS provides detailed insights into:

• the impact of mental and behavioural conditions and other long-term health conditions on people in Australia
• the use of health services and barriers to access
• risk factors underlying chronic conditions
• food consumption and physical activity
• undiagnosed health conditions and nutritional deficiencies
• biomedical factors that contribute to poor health outcomes
• lived experiences of suicide and related services.

Over 55,000 Australians took part in the IHMHS between 2020 and 2024. The information collected supports the creation, delivery and evaluation of health policies and research that will help Australians live longer, healthier lives.

The IHMHS covered all people in Australia, including people living in very remote areas and discrete Aboriginal and Torres Strait Islander communities. It was comprised of seven surveys:

• National Study of Mental Health and Wellbeing (NSMHW) 2020–22
• National Health Survey (NHS) 2022
• National Aboriginal and Torres Strait Islander Health Survey (NATSIHS) 2022–23
• National Nutrition and Physical Activity Survey (NNPAS) 2023
• National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (NATSINPAS) 2023
• National Health Measures Survey (NHMS) 2022–24
• National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS) 2022–24.

The NHMS 2022–24 was comprised of volunteering participants from either the NHS 2022 or the NNPAS 2023. The NATSIHMS 2022–24 was comprised of volunteering participants from either the NATSIHS 2022–23 or the NATSINPAS 2023.

There is no Aboriginal and Torres Strait Islander peoples component for the NSMHW 2020–22.

The ABS previously conducted a similar set of surveys as part of the Australian Health Survey 2011–13. This collection was similar in size and scope, but did not include a survey designed specifically to collect information on mental disorders and their impacts. For more information, see Australian Health Survey: Users' Guide 2011–13 (cat. no. 4363.0.55.001).

The ABS has also conducted some components of the IHMHS individually on previous occasions. The NHS was conducted on a 3-year cycle between 2001 and 2022. Prior to this, it was conducted in 1989–90 and 1995. The NATSIHS has been previously conducted in 2018–19, 2012–13, and 2004–05. The NHS 2001 also included an Aboriginal and Torres Strait Islander sample.

Other IHMHS surveys are not collected regularly. Nutrition and physical activity surveys (NNPAS and NATSINPAS), and health measures surveys (NHMS and NATSIHMS) were last conducted in 2011–12. The NSMHW was last conducted in 2007.

The National Health Survey 2022 was conducted from January 2022 to April 2023 and is the most recent in a series of Australia-wide health surveys. Data was collected people aged zero years and over across Australia, excluding very remote areas and discrete Aboriginal and Torres Strait Islander communities.

The survey focused on the health status of Australians and health-related aspects of their lifestyles. Information was collected on long-term and chronic health conditions, as well as health risk factors such as smoking and vaping, alcohol consumption, and physical activity.

Prescription medications data was sourced from the Pharmaceutical Benefits Scheme (PBS) through linkage to the Person Level Integrated Data Asset, instead of being collected directly from survey respondents. Appropriate permissions were obtained to source the PBS data for this purpose.

For key statistics, see National Health Survey, 2022.

For more information on the scope, geography, collection method, reporting guidelines used and history of changes, see National Health Survey methodology, 2022.
 

The National Aboriginal and Torres Strait Islander Health Survey (NATSIHS) 2022–23 was conducted from August 2022 to March 2024. Data was collected from Aboriginal and Torres Strait Islander people aged zero years and over around Australia, in both non-remote and remote areas, including discrete Aboriginal and Torres Strait Islander communities.

The survey focused on the health of Aboriginal and Torres Strait Islander people and was developed following extensive consultation to identify priority data requirements and data gaps. Information was collected on long-term and chronic health conditions, as well as health risk factors such as smoking and vaping, alcohol consumption, and physical activity.

The survey was designed to provide reliable estimates at the national and state/territory levels and by remoteness. Estimates for the Australian Capital Territory are not able to be published separately but are included in national estimates.

For main findings, see National Aboriginal and Torres Strait Islander Health Survey, 2022-23.

For more information on the scope, geography, collection method, reporting guidelines used and history of changes, see National Aboriginal and Torres Strait Islander Health Survey methodology, 2022-23.

The National Nutrition and Physical Activity Survey (NNPAS) 2023 was conducted from January 2023 to March 2024 and collected information from people aged two years and over across Australia, excluding very remote areas and discrete Aboriginal and Torres Strait Islander communities.

Information was collected about nutritional intakes (including foods, nutrients and dietary supplements consumed) using dietary recall and additional self-reported questions.

Dietary intake information was collected using Intake24, which is a dietary recall assessment tool that was modified to reflect Australian conditions. It involves a structured interview, where participants are asked to recall all the food and drinks they consumed in the 24 hours prior to interview.

Participants were asked to complete a second day of dietary recall eight days after their interview. This will support the analysis of:

• usual nutrient intakes
• food consumption against the 2013 Australian Dietary Guidelines.

Physical and sedentary activity information was collected from respondents using a combination of measured and self-reported data. To measure physical and sedentary activity, participants aged 5 years and over were asked to wear an activity wristband. Additional self-report questions were asked of participants about sleep. Children’s self-reported physical and sedentary activity information was also collected.

Similar nutrition and physical activity data was previously collected in the NNPAS 2011–13.

More information on NNPAS 2023 will become available when results are released.

The National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (NATSINPAS) 2023 was conducted from January 2023 to March 2024. Data was collected from Aboriginal and Torres Strait Islander people aged 2 years and over around Australia, in both non-remote and remote areas, including discrete Aboriginal and Torres Strait Islander communities.

The survey was developed following extensive consultation to identify priority data requirements and data gaps.

Information was collected about nutritional intakes (including foods, nutrients and dietary supplements consumed) of Aboriginal and Torres Strait Islander people. Additional information was collected on:

• access and barriers to healthy and nutritious foods
• access and barriers to drinking tap water
• influences on dietary choices.

Dietary intake information was collected using Intake24, which is a dietary recall assessment tool that was modified to reflect Australian conditions. It involves a structured interview, where participants are asked to recall all the food and drinks they consumed in the 24 hours prior to interview.

Physical and sedentary activity information was collected from respondents using a combination of measured and self-reported data. To measure physical and sedentary activity, participants aged 5 years and over were asked to wear an activity wristband. Additional self-report information was collected from participants on physical and sedentary activity, and sleep.

Similar nutrition and physical activity data was previously collected in the NATSINPAS 2012–13.

More information on NATSINPAS 2023 will become available when results are released.

The National Health Measures Survey (NHMS) 2022–24 was conducted from January 2022 to April 2024 and involved the collection of biomedical samples from participants aged 5 years and over across Australia, excluding very remote areas and discrete Aboriginal and Torres Strait Islander communities.

It measured specific biomarkers for chronic disease and nutrition status, from tests on blood and urine samples from volunteering participants selected in either the National Health Survey 2022 or the National Nutrition and Physical Activity Survey 2023.

Biomarkers collected include:

• chronic disease biomarkers, including tests for diabetes, cardiovascular disease, kidney disease and liver function
• nutrient biomarkers, including tests for iron, folate, vitamin B12, iodine, vitamin D, sodium and potassium levels.

In addition to chronic disease and nutrient biomarkers, the NHMS 2022–24 included tests for per- and polyfluoroalkyl substances, which are chemical contaminants found in the environment.

Participants’ self-reported information on health conditions and health risk factors, such as diet, physical activity and smoking, was taken from their responses in the other IHMHS surveys. For more information, see Overlap between surveys in the IHMHS.

This was the second time the ABS collected voluntary biomedical data in the NHMS. It was previously collected in 2011–13.

For main findings, see National Health Measures Survey, 2022–24.

For more information on the scope, geography, collection method, reporting guidelines used and history of changes, see National Health Measures Survey methodology, 2022–24.

The National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS) 2022–24 was conducted from August 2023 to April 2024 and involved the collection of biomedical samples from Aboriginal and Torres Strait Islander participants aged 5 years and over across Australia, in both non-remote and remote areas, including discrete Aboriginal and Torres Strait Islander communities.

It measured specific biomarkers for chronic disease and nutrition status, from tests on blood and urine samples from volunteering participants selected in either the National Aboriginal and Torres Strait Islander Health Survey 2022–23 or the National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey 2023.

Biomarkers collected include:

• chronic disease biomarkers, including tests for diabetes, cardiovascular disease, kidney disease and liver function
• nutrient biomarkers, including tests for iron, folate, vitamin B12, iodine, vitamin D, sodium and potassium levels.

Per- and polyfluoroalkyl substances were not tested in this survey following consultation with Aboriginal and Torres Strait Islander people.

Participants’ self-reported information on health conditions and health risk factors, such as diet, physical activity and smoking, was taken from their responses in the other IHMHS surveys. For more information, see Overlap between surveys in the IHMHS.

For main findings, see National Aboriginal and Torres Strait Islander Health Measures Survey, 2022–24.

For more information on the scope, geography, collection method, reporting guidelines used and history of changes, see National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

Several topics were collected in both the National Health Survey (NHS) 2022 and the National Nutrition and Physical Activity Survey (NNPAS) 2023. Content common to both surveys is therefore available in the National Health Measures Survey 2022–24. The following diagram shows the overlap in topics between the NHS 2022 and the NNPAS 2023.

Several topics were collected in both the National Aboriginal and Torres Strait Islander Health Survey (NATSIHS) 2022–23 and the National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (NATSINPAS) 2023. Content common to both surveys is therefore available in the National Aboriginal and Torres Strait Islander Health Measures Survey 2022–24. The following diagram shows the overlap in topics between the NATSIHS 2022–23 and the NATSINPAS 2023.

Whilst content for the NHS 2022 and the NATSIHS 2022–23 were designed separately, and responses were not combined, users may be interested in topics collected in both surveys. The diagram below compares the content between these surveys.

Whilst content for the NNPAS 2023 and the NATSINPAS 2023 were designed separately, and responses were not combined, users may be interested in topics collected in both surveys. The diagram below compares the content between these surveys.

The National Study of Mental Health and Wellbeing (NSMHW) 2020–22 was run with two cohorts and collected information about mental health prevalence in Australia for people aged 16–85 years. The first cohort was surveyed between December 2020 and July 2021. The second cohort was surveyed between December 2021 and October 2022.

The survey uses the Composite International Diagnostic Instrument (CIDI) 3.0, which is a diagnostic tool developed by the World Health Organization for assessing mental disorders. The CIDI asks respondents about symptoms and experiences over their lifetime as well as in the last 12 months and assesses these against diagnostic criteria for mental disorders.

As part of the NSMHW 2020–22, there was a 12-month follow-up phone interview survey for participants who agreed to participate. Respondents were asked about mental health service use and associated outcomes.

There is no Aboriginal and Torres Strait Islander peoples component for this study.

The ABS previously conducted this survey in 2007. The 2020–22 survey is broadly comparable with the 2007 cycle.

For main findings, see National Study of Mental Health and Wellbeing, 2020-2022.

For more information on the scope, geography, collection method, reporting guidelines used and history of changes, see National Study of Mental Health and Wellbeing methodology, 2020-2022.

The National Health Measures Study 2022–24 consists of two biomedical surveys:

• the National Health Measures Survey (NHMS) 2022–24
• the National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS) 2022–24. 

Both surveys measured specific biomarkers for chronic disease and nutrition status, by testing blood and urine samples from volunteering participants. 

People aged 12 years and over were asked to provide a blood and a urine sample. Children aged 5–11 years were asked to provide a urine sample only.

For more information about the scope of the NHMS 2022–24 and NATSIHMS 2022–24, see the National Health Measures Survey methodology, 2022–24 and National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

A biomarker refers to a measured biomedical characteristic, which may be used to indicate a health risk factor or condition. The biomarkers included in the National Health Measures Study were selected with guidance from a biomedical expert group established by the ABS.

Biomarkers collected in the IHMHS fall into three categories:

• chronic disease biomarkers, including tests for diabetes, cardiovascular disease, chronic kidney disease and liver function
• nutrient biomarkers, including tests for iron, folate, vitamin B12, iodine, vitamin D, sodium and potassium levels
• per- and polyfluoroalkyl substances (PFAS), which are chemical contaminants found in the environment (NHMS only).

The following table provides an overview of all IHMHS biomedical tests. PFAS tests were conducted in the NHMS component only following consultation with Aboriginal and Torres Strait Islander people. More information regarding the biomedical tests can be found in the relevant subsections.

Further information about test type, analyser system, and laboratory reference ranges can be found on the Downloads page.

Ethics approval for the NHMS 2022–24 was granted by the Bellberry Human Research Ethics Committee in December 2021. For more information, see the National Health Measures Survey methodology, 2022–24.

The NATSIHMS 2022–24 required ethical approval from the relevant Aboriginal and/or Torres Strait Islander Human Research Ethics Committee for the areas where participants were recruited. For more information, see the National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

Most blood and urine samples were collected at Sonic Healthcare Australia Pathology collection clinics or via a home visit. In the NATSIHMS 2022–24, alternative pathology collection services were arranged for Aboriginal and Torres Strait Islander people living in remote areas and discrete Indigenous communities. For more information, see the National Health Measures Survey methodology, 2022–24 and National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

Qualified phlebotomists collected fasting and non-fasting blood samples from persons aged 12 years and over into four collection tubes:

• 1 x 8.5ml SST gel tube for serum analysis
• 1 x 8.5ml SST gel tube for validation tests
• 1 x 4ml EDTA (Ethylene Diamine Tetra-acetic Acid) tube for whole blood analysis
• 1 x 4ml Fluoride oxalate tube for blood plasma analysis.

The order of priority for collecting blood samples was the 8.5ml SST tubes, the 4ml EDTA tube, followed by the 4 ml Fluoride oxalate tube. While participants were encouraged to fast, fasting was optional. Sonic Healthcare Australia Pathology collectors recorded the fasting status, date, and time of each collection.

A spot urine sample was obtained from participants aged 5 years and over.

The ABS established a mechanism with Sonic Healthcare Australia Pathology ensuring that participants and/or their nominated health professional were notified by Sonic Healthcare Australia Pathology of critical or clinically significant test results.

All samples were analysed at a central Sonic Healthcare Australia Pathology laboratory at Douglass Hanly Moir Pathology (DHM) in Sydney, Australia on machines accredited by the National Association of Testing Authorities, with the exception of the iodine test which was conducted by Sullivan Nicolaides Pathology in Queensland. DHM conducted Internal Quality Control (IQC) analysis for all the instruments used to conduct analysis on the blood and urine samples.

Periodic analysis of External Quality Assurance (EQA) samples provided by the Royal College of Pathologists of Australasia (RCPA) was conducted at DHM, with results independently assessed against set targets. The ABS monitored the analysis and delivery of results through specific quality measures. The results from the IQC and EQA reports indicate that the accuracy and precision of instruments used to analyse samples fell within expected limits against set targets.

Quality measures provided by Sonic Healthcare Australia Pathology measured performance criteria such as turnaround time (TAT). The TAT was used to assess any adverse impacts to the blood and urine samples due to time delays from collection to analysis.

Quality measure targets included:

• TAT from collection to analysis of less than 72 hours
• TAT from first analysis to final report of less than 96 hours for blood samples and less than 120 hours for urine samples (to accommodate for iodine analyses)
• final and complete results provided to the ABS within 7 days
• less than 1% of results received to the ABS with missing information or results
• less than 1% of errors from total collection to be reissued
• less than 1% of collected samples with insufficient volumes or missing tubes
• all quality assurance reports (100%) results delivered to the ABS within the required range/standard.

All analyses were carried out according to standard operating procedures set by Sonic Healthcare Australia Pathology. All results were routinely checked by the dedicated quality control laboratory technicians and clinically significant results were notified to the laboratory pathologist. Method coefficient of variations were obtained by an appropriate IQC programme for each machine based on the measurement of biomarker concentrations around the clinical laboratory cut-offs.

Analytical control of the imprecision (variability) of results was monitored and maintained using a multi-layered quality assurance system comprising of:

• IQC processes
• participation in an EQA Program
• the periodic review of analytical variability for each test.

The ABS is satisfied with the quality assurance system used by Sonic Healthcare Australia Pathology to ensure the reliability of results.

IQC processes were conducted by the DHM laboratory to monitor the performance of the methods and machines used to analyse the blood and urine samples and identify any analytical errors.

An EQA system assesses the overall performance of the laboratory and conducts method comparisons between laboratory test results. Whilst conducting this assessment, the EQA authority can identify problems with certain methods.

DHM subscribes to the Quality Assurance Program (QAP) that is run by the RCPA (RCPA n.d.). During the testing period, DHM received blind samples of varying concentration from RCPA at regular frequencies throughout the calendar year. These samples were analysed with the results sent back to RCPA and compared against other laboratories across Australia and New Zealand.

RCPA distributes reports to all participating laboratories annually reporting mean values for test methods, inter-laboratory measurements of precision and details of laboratory bias. DHM provided their RCPA QAP reports to the ABS to ensure confidence in the analytical methods and machines used to analyse samples.

The vitamin D Standardization Program (VDSP) was established by the US National Institute of Health (NIH) Office of Dietary Supplements, in collaboration with the Centers for Disease Control and Prevention and the National Institute for Standards and Technology in 2010. The aim of the VDSP is to internationally standardise the vitamin D analytical method and test the international differences and similarities in serum vitamin D (25-hydroxyvitamin D [25(OH)D]) distributions (NIH 2024, n.d.). Sonic Healthcare Australia Pathology used the VDSP recommended method of analysis for vitamin D.

National Institute of Health Office of Dietary Supplements (NIH) (2024), Vitamin D, NIH website, accessed 20/02/2025.

National Institute of Health Office of Dietary Supplements (NIH) (n.d.), Office of Dietary Supplements Vitamin D Initiative 2004-2018, NIH, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (n.d.), RCPA Quality Assurance Programs, RCPA Quality Assurance Programs website, accessed 20/02/2025.

The IHMHS is the second time the ABS has collected information on biomedical indicators. Biomedical indicators were first collected as part of the National Health Measures Survey (NHMS) 2011–12 and the National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS) 2012–13. In both collections, information was collected for the general population and for Aboriginal and Torres Strait Islander peoples. The ABS anticipates that users will have interest in comparing these collections to understand changes in biomedical information over time.

However, there are a range of factors that may impact the comparability of survey results across time. In some cases, it may be difficult to distinguish between changes due to methods, changes due to equipment updates, and/or changes due to biological trends. Users should be aware of these factors when comparing results between ABS biomedical collections. This page presents information on known changes in pathology test methodology and equipment between 2011–13 and 2022–24 for users looking to compare data across time. 

The approximately 10-year time period between the 2011–13 and 2022–24 biomedical collections significantly exceeds the life of most laboratory equipment. While the same pathology provider conducted the testing in 2011–13 and 2022–24 using a single laboratory, in some cases equipment for specific tests changed and/or the method of analysis changed in that time.

Verification and validation processes were conducted by the pathology provider each time a method and/or equipment was upgraded. Whilst these processes were robust, it was not always possible to directly verify current instrumentation and methods of analysis used in the 2022–24 against that used for in 2011–13 due to more than one change having been made. In addition to equipment changes, reagent lots may have changed, and calibrators and traceability may have improved. Internal procedures were in place for new batches of reagents and assays to check and adjust for results drift.

The precision of the methods (± x%) for each biomedical test needs to be considered when comparing results from old and new methods, in this case when interpreting observed changes in patient means between the two survey periods (i.e. whether observed changes are within the accepted precision range or not). New instruments tend to be more sensitive than older ones, with positive results at the lower end of the distribution that would not have been reported using older instruments.

To understand the impacts of changes in pathology methodology and equipment, the ABS commissioned expert advice on the equivalence of the individual tests in the 2022–24 with those undertaken in 2011–13. This work looked at the viability of using a range of various analytical tools to determine comparability of biomedical results over time. This included Quality Control material, Quality Assurance Program material and patient means data from the pathology provider.

Patient means data are routinely used by pathology companies to inform comparability of their results over time (e.g. mean or median fasting plasma glucose levels for patients attending pathology clinics). Patient means data is based on individual test results and cannot distinguish individual people. Patient means data were extracted by the pathology provider for each test from their database for comparable time periods (2011–13, 2022–24) and included patient numbers, means, medians, and standard deviations of the tested pathology by age and sex. Patients who attended the pathology clinic for the purposes of the ABS collections were excluded from patient means analysis.

The numbers of data points available for tests included in standard pathology tests for individuals (such as cholesterol and haemoglobin) were much higher than those for tests which are usually only reported on special request by a health professional for an individual or recorded through studies of population health (such as sodium, potassium, and iodine). Results indicated that patient means data were not normally distributed. As patient means data cannot distinguish individual people, it cannot identify whether a person has been tested multiple times for medical reasons in the period assessed or only once.

In determining the comparability of results over time for each test, the patient means data should not be given the same weight by the user as the instrument verification and validation results, because the populations from which they were drawn were different in the two time periods assessed.

Over time, there have been changes in clinical practice and government health policy that may influence the type of clients attending pathology clinics. For example, the HbA1c test for diabetes is now used by health professionals to diagnose diabetes as well as to assess longer term management of diabetes (WHO 2019; D’Emden et al. 2012; Australian Diabetes Society 2015).

The age at which diabetes screening occurs has been lowered since 2011–13. For the general population, screening should now be every 3 years starting at age 40, and for people with obesity or other risk factors, screening should be every 3 years, not limited by age (RACGP 2016, 2020; Bell et al. 2020). For Aboriginal and Torres Strait Islander peoples, the screening age was initially lowered from that for the general population based on evidence from previous ABS biomedical surveys, but a more recent recommendation from the Royal Australian College of General Practitioners was for annual diabetes screening from 18 years of age and from 10 years of age for children with one or more risk factors (Burrow and Ride 2016; RACGP 2016, 2024; NACCHO 2024).

Recommendations for requesting a vitamin D test have also changed since 2011–13. The vitamin D test is currently not routinely recommended for adults, children or healthy infants, and there are criteria for eligibility for coverage of the cost of the test by the Pharmaceutical Benefits Scheme for ‘at risk’ groups (RACGP 2024).

Changes to clinical practice can impact the patient population used to derive quality metrics like patient means, but the impact on these data sets is unknown. In some cases, for example HbA1c and serum ferritin, changes in patient means data between the two time periods assessed were different in magnitude and direction for different age and sex groups. This indicates a potential change in client population rather than changes in results due to instrument or method upgrades.

The tables below summarise changes in methods of analysis from 2011–13 to 2022–24, with comments on the equivalence of methods for consideration by users.

The topics in the NHMS 2022–24 and NATSIHMS 2022–24 are broadly comparable with their previous surveys. However, when comparing the 2011–13 and 2022–24 studies it is important to also consider impacts that changes to survey methodology and response rates may have on time series analysis. For more information, see the National Health Measures Survey methodology, 2022–24 and National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

Australian Diabetes Society (2015, updated 2023), Guidance concerning the use of glycated haemoglobin for the diagnosis of diabetes mellitus, Australian Diabetes Society, accessed 20/02/2025.

Bell K, Shaw JE, Maple-Brown L, Ferris W, Gray S, Murfet G, Flavel R, Maynard B, Ryrie H, Pritchard B, Freeman R, Gordan BA (2020), A position statement on screening and management of prediabetes in primary care in Australia, Diabetes Research and Clinical Practice, 164:108188, accessed 20/02/2025.

Burrow S, Ride K (2016), Review of diabetes among Aboriginal and Torres Strait Islander people, Australian Indigenous HealthInfoNet, accessed 20/02/2025.

d’Emden MC, Shaw JE, Colman PG, Colagiuri S, Twigg SM, Jones GRD, Goodall I, Schneider HG and Cheung NW (2012), The role of HbA1c in the diagnosis of diabetes mellitus in Australia, Medical Journal of Australia, 197(4):220-221, accessed 20/02/2025.

National Aboriginal Community Controlled Health Organisation (NACCHO) and The Royal Australian College of General Practitioners (RACGP) (2024), National guide to preventive healthcare for Aboriginal and Torres Strait Islander people: Recommendations. 4th edition., RACGP, accessed 20/02/2025.

Royal Australian College of General Practitioners (RACGP) and Diabetes Australia (2016), General practice management of type 2 diabetes: 2016–18 [PDF 3267 KB], RACGP, accessed 20/02/2025.

Royal Australian College of General Practitioners (RACGP) (2024), Management of type 2 diabetes: A handbook for general practice, RACGP, accessed 20/02/2025.

Royal Australian College of General Practitioners (RACGP) (2024), ‘Vitamin D testing’, First Do No Harm: a guide to choosing wisely in general practice, RACGP website, accessed 20/02/2025.

World Health Organization (WHO) (2019), Classification of diabetes mellitus, WHO, accessed 20/02/2025.

Cardiovascular disease (CVD) is an umbrella term that includes heart, stroke, and blood vessel diseases. In 2024, CVD contributed to almost 12% of the total burden of disease (AIHW 2024). and was attributed to one in four deaths. Ischaemic heart diseases and cerebrovascular diseases were the number one and number three leading causes of death in Australia in 2023, see Causes of Death, Australia, 2023. There are many risk factors for CVD including high cholesterol, high blood pressure, and smoking (AIHW 2024).

The indicators of CVD that were measured include:

• total cholesterol
• high density lipoprotein (HDL) cholesterol
• low density lipoprotein (LDL) cholesterol
• triglycerides.

These indicators, along with self-reported use of cholesterol medication, were used to derive dyslipidaemia status. Self-reported data on CVD was collected in the survey components of the IHMHS and can be used for comparison.

Cut-off reference values for normal and abnormal results were sourced from the current Royal College of Pathologists of Australasia Manual for pathology tests, which refers to the 2012 National Vascular Disease Prevention Alliance guidelines for the management of absolute risk of cardiovascular disease (NVDPA 2012; RCPA 2024). Results for CVD indicators were collected for children aged 12–17 years, but there are no agreed cut-offs for reporting these results as normal or abnormal.

Laboratory test information, including analysis methods and machines used to measure CVD biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on total cholesterol, HDL cholesterol, LDL cholesterols and triglycerides. Information on these biomedical indicators was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. Apolipoprotein B was measured in previous collections; however, it was not measured in the NHMS 2022–24 or the NATSIHMS 2022–24. For information on time series comparability, see Comparing biomedical collections over time.

Total cholesterol, HDL cholesterol, LDL cholesterols and triglycerides data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Cholesterol (a waxy, fat-like substance) is a lipid that is necessary to make hormones and vitamin D, and also assists in digestion. The body can produce its own cholesterol, and it is present in some foods. A person with high cholesterol can develop fatty deposits in their blood vessels increasing their risk of heart attacks or strokes (Heart Foundation 2024b). Total cholesterol includes both HDL and LDL cholesterol.

Total cholesterol results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Total cholesterol levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory using the cholesterol oxidase/peroxidase method. The total cholesterol test measures the combined amount of lipid (fat) components circulating in the blood at the time of the test, expressed as mmol/L.

The following cut-offs were used for serum total cholesterol:

• normal total cholesterol levels <5.5 mmol/L
• abnormal total cholesterol levels ≥5.5 mmol/L.

Therapeutic levels for treatment of individuals at high risk of cardiovascular disease used by health professionals are not the same as the reference ranges used to report pathology results for the general population with no risk factors. For example, the therapeutic level for total cholesterol for individuals being considered for lipid management due to high risk of cardiovascular disease is <4.0 mmol/L (CA/DHAC 2023; Heart Foundation 2024a).

Points to be considered when interpreting data for this topic include the following:

• Total cholesterol results do not confirm a specific diagnosis without consultation with a health professional.
• Age, sex and taking lipid lowering medications are all variables that may affect lipid and lipoprotein levels. As a result, the data should be interpreted with care.
• There are several different test methods for measuring total cholesterol, which may produce different results. The data from this topic should therefore be used with caution when comparing total cholesterol results from other studies using a different test method.
• Total cholesterol is not necessarily the best measure of risk for CVD in isolation. It is one component used in the 2023 Australian CVD risk calculator (CA/DHAC 2023).

High-density lipoprotein (HDL), sometimes known as 'good' cholesterol, picks up excess cholesterol in the blood and takes it to the liver where it is broken down. Low levels of HDL cholesterol may increase the risk of CVD (AIHW 2024; CA/DHAC 2023; Heart Foundation 2024b).

HDL cholesterol results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for the HDL test.

HDL cholesterol levels were measured at the DHM laboratory by the enzymatic method plus accelerator selective detergent. The HDL cholesterol test measures the amount of ‘good’ cholesterol circulating in the blood at the time of the test, expressed as mmol/L.

Two different cut-off points are available for HDL cholesterol for the general population, non-sex dependant and sex dependant (RCPA 2024). The following cut-offs were used for serum HDL cholesterol:

Points to be considered when interpreting data for this topic include the following:

• HDL cholesterol results do not confirm a specific diagnosis without consultation with a health professional.
• Age, sex and taking lipid lowering medications are all variables that may affect lipid and lipoprotein levels. As a result, the data should be interpreted with care.
• There are several different test methods for measuring HDL cholesterol, which may produce different results. The data from this topic should therefore be used with caution when comparing HDL cholesterol results from other studies using a different test method.
• Two cut-off points for reporting HDL levels have been provided, one that is non-sex dependant and one that is sex dependant. Users of the data may choose to use these different cut-off points for comparability with other health surveys.
• HDL cholesterol is not necessarily the best measure of risk for CVD in isolation. It is one component used in the 2023 CVD risk calculator (CA/DHAC 2023).

Low-density lipoprotein (LDL), sometimes known as ‘bad’ cholesterol, can leave fatty deposits in the blood vessels called plaque. Too much plaque leads to blockages that prevent the passage of blood flow. High levels of LDL cholesterol may increase the risk of cardiovascular disease (AIHW 2024; CA/DHAC 2023; Heart Foundation 2024b).

Points to be considered when interpreting data for this topic include the following:

• LDL cholesterol results do not confirm a specific diagnosis without consultation with a health professional.
• Age, sex and taking lipid lowering medications are all variables that may affect lipid and lipoprotein levels. As a result, the data should be interpreted with care.
• There are several different test methods for measuring LDL cholesterol and different equations for calculating LDL cholesterol levels, which may produce different results. The data from this topic should therefore be used with caution when comparing LDL cholesterol results from other studies using a different test method.
• Persons with a triglyceride level of ≥4.5 mmol/L were excluded from the LDL cholesterol data, as the inclusion of triglyceride levels ≥4.5 mmol/L (400 mg/100 ml) will result in the generation of a false LDL cholesterol result (Friedewald et al. 1972).

Triglycerides are lipids that circulate in the blood and are the most common type of fat in the body. High levels of triglycerides, alongside high LDL cholesterol or low HDL cholesterol, can increase a person’s risk of CVD (AIHW 2024; CA/DHAC 2023; Heart Foundation 2024b).

Triglyceride results were obtained for persons aged 12 years and over who provided a blood sample. Only persons who had fasted for 8 hours or more prior to their blood test were included in data analysis; however, all triglyceride results without this exclusion are available in the DataLab microdata.

Triglyceride levels were measured at the DHM laboratory, by the Lipase/Glycerol Kinase/GPO method. The triglyceride test measures the amount of triglycerides circulating in the blood at the time of the test, expressed as mmol/L.

The following cut-offs were used for serum triglyceride levels:

• normal triglyceride levels <2.0 mmol/L
• abnormal triglyceride levels ≥2.0 mmol/L.

Points to be considered when interpreting data for this topic include the following:

• Triglyceride results do not confirm a specific diagnosis without consultation with a health professional.
• Levels of triglycerides alone are not sufficient to assess the risk of CVD.
• Age, sex and taking lipid lowering medications are all variables that may affect lipid and lipoprotein levels. As a result, the data should be interpreted with care.
• Fasting over 8 hours is required for this biomarker to accurately assess the levels of lipids (fat) circulating in the blood.
• There are several different test methods for measuring triglycerides, which may produce different results. The data from this topic should therefore be used with caution when comparing triglyceride results from other studies using a different test method.

Dyslipidaemia is a term used to describe abnormal blood lipid levels (high or low). Blood lipids are fats in the blood and include cholesterol (a fatty substance produced by the liver) and triglycerides (a key component in metabolism) (AIHW 2017). Causes may be primary (genetic) or secondary (caused by lifestyle and other factors) (Pappan et al. 2024).  

Dyslipidaemia can contribute to the development of atherosclerosis, a build-up of fatty deposits in the blood vessels. This build-up increases the risk of several cardiovascular diseases, including coronary heart disease and stroke (AIHW 2017).

A person was classified as having dyslipidaemia if they met one or more of the following conditions:

• taking lipid-lowering medication
• total cholesterol ≥5.5 mmol/L
• HDL cholesterol <1.0 mmol/L for males and <1.3 mmol/L for females
• LDL cholesterol ≥3.5 mmol/L
• triglycerides ≥2.0 mmol/L.

This definition of dyslipidaemia was used in previous ABS biomedical collections. The inclusion of total, HDL, LDL cholesterol and triglycerides cut-offs in the definition is used elsewhere, though the cut-off values may vary with each country (ESC 2019; Pappan et al 2024). Other definitions for dyslipidaemia were previously used in Australia. For example, in 2012, the National Vascular Disease Prevention Alliance defined people with dyslipidaemia as those with low HDL cholesterol in combination with high triglycerides (NVDPA 2012).

Australian Institute of Health and Welfare (AIHW) (2017), ‘Abnormal blood lipids (dyslipidaemia)’, Risk factors to health, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2021), Australian Burden of Disease Study 2018: Interactive data on risk factor burden, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2024), Heart, stroke and vascular disease: Australian facts, AIHW, Australian Government, accessed 17/01/2025.

Commonwealth of Australia as represented by the Department of Health and Aged Care (CA/DHAC) (2023), Australian Guideline for assessing and managing cardiovascular disease risk, AusCVDRisk, Australian Government, accessed 20/02/2025.

European Society of Cardiology (ESC) (2019), 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk, European Heart Journal, 41(1):111-118, accessed 20/02/2025.

Friedwald WT, Levy RL, Fredrickson DS (1972), Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge, Clin Chem, 18(6):499-502, accessed 20/02/2025.

Heart Foundation (2024a), Practical guide to pharmacological lipid management, Heart Foundation, accessed 20/02/2025.

Heart Foundation (2024b), Blood cholesterol, Heart Foundation website, accessed 20/02/2025.

National Vascular Disease Prevention Alliance (NVDPA) (2012), Guidelines for the management of 
absolute cardiovascular disease risk [PDF 4458 KB], Heart Foundation, accessed 20/02/2025.

Pappan N, Awosika AO, Rehman A (2024), ‘Dyslipidaemia’, StatPearls, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2024), ‘Cholesterol’, RCPA Manual, RCPA website, accessed 20/02/2025.  

Chronic kidney disease (CKD) is characterised by a gradual loss of kidney function over time. This affects the kidney's ability to filter blood and leads to a build-up of waste and fluid inside the body. CKD can result in other problems such as high blood pressure, heart disease and anaemia (KHA 2023, 2024).

The biomarkers related to impairment of kidney function that were measured were:

• urinary albumin
• urinary creatinine
• serum creatinine.

From these results, the albumin/creatinine ratio (ACR) and the estimated glomerular filtration rate (eGFR) were calculated. Self-reported data on kidney disease was also collected in other survey components of the IHMHS.

It is important to note that while abnormal ACR or eGFR test results may indicate impaired kidney function, they cannot provide a diagnosis for CKD based on a single test alone. CKD can only be confirmed if abnormal results are detected for at least three months (KHA 2024).

Laboratory test information, including analysis methods and machines used to measure CKD biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on urinary albumin, urinary creatinine, ACR ratio, serum creatinine and eGFR. Information on these biomedical indicators was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Urinary albumin, urinary creatine, the ACR ratio, serum creatinine and eGFR data has been reported in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Creatinine is a by-product of muscle metabolism, which circulates around the body in the blood (KHA 2025a). Serum creatinine results were used to calculate eGFR.

Serum creatinine results were obtained for all persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Serum creatinine levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory, by the Enzymatic method traceable to isotope-dilution mass spectrometry. The serum creatinine test measures the amount of creatinine circulating in the blood at the time of the test.

There is no consensus on the epidemiological cut-off reference values for measuring creatinine in serum. As such, no cut-off points have been defined.

Albumin is a protein that is produced by the liver and is found in the bloodstream where it is important for maintaining fluid balance and carrying hormones, vitamins and enzymes throughout the body. As kidney function declines, albumin leaks into a person’s urine. Albuminuria is the term used when a person has abnormal levels of albumin in their urine (KHA 2025b). A urine albumin result is used with a urine creatinine result to calculate ACR.

Urinary albumin results were obtained for persons aged 5 years and over who provided a urine sample. Fasting was not required for this test.

Urinary albumin levels were measured at the DHM laboratory by the Turbidimetric/Immunoturbidimetric method. The albumin test measures the amount of albumin in the urine at the time of the test (spot test).

There is no consensus on the epidemiological cut-off reference values for measuring albumin, independently in urine. As such no cut-off points have been defined.

Points to be considered when interpreting data for this topic include the following:

• Albumin test results do not confirm a specific diagnosis without consultation with a health professional.
• There are several different test methods to measure albumin levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing albumin results from other studies using a different test method.

Creatinine is a by-product of muscle metabolism, which circulates around the body in the blood. Healthy kidneys filter creatinine out of the blood and it exits the body as a waste product in urine (KHA 2025a). A urinary creatinine result is used with a urinary albumin result to calculate ACR.

Urinary creatinine results were obtained for persons aged 5 years and over who provided a urine sample. Fasting was not required for this test.

Urinary creatinine levels were measured at the DHM laboratory by the Kinetic alkaline picrate method. The creatinine test measures the amount of creatine in the urine at the time of the test (spot test).

There is no consensus on the epidemiological cut-off reference values for measuring urinary creatinine. As such, no cut-off points have been defined.

Points to be considered when interpreting data for this topic include the following:

• Urinary creatinine test results do not confirm a specific diagnosis without consultation with a health professional.
• There are several different test methods to measure creatinine levels, and each test method may produce different results. The data from this topic should therefore be used with caution when comparing creatinine results from other studies using a different test method.
• There are issues with the validity of using urinary creatinine excretion as an index of muscle mass.

eGFR measures how well the kidneys filter waste from the blood and is considered the best overall measure of kidney function (KHA 2025a). eGFR helps determine if a person has kidney damage. A low filtration rate means that the kidneys are not working properly (KHA 2025a).

The eGFR was calculated using a person’s serum creatinine (SCr) result, age, and sex. The DHM laboratory used the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations (Levey et al. 2009; Johnson et al. 2012b; Levey and Eckfeldt 2017). These equations were also used in the NHMS 2011–12 and the NATSIHMS 2012–13. eGFR results were published only for people aged 18 years and over, as the CKD-EPI equations cannot be used for children without adjustments (Björk et al. 2021).

Cut-off reference values for normal and abnormal results were sourced from the CKD management guidelines by Kidney Health Australia (KHA 2024, 2025a). These guidelines are based on epidemiological data and publications of major clinical trials. The following cut-offs were used, where the result is expressed relative to a 'standard' body surface area of 1.73m²:

• normal eGFR levels ≥60 mL/min/1.73m²
• abnormal eGFR levels <60 mL/min/1.73m².
   

Points to be considered when interpreting data for this topic include the following:

• eGFR test results do not confirm a specific diagnosis without consultation with a health professional.
• There are several different test methods and equations to measure eGFR levels and they may produce different results. The data from this topic should therefore be used with caution when comparing eGFR results from other studies using a different test method or equation.
• The CKD-EPI equations are only validated for use with isotope dilution mass spectrometry. Traceable creatinine assays and coefficients for race are not included, therefore results should be interpreted with care when comparing this data with other sources (Delgado et al. 2021; Bailey and Farnsworth 2022; Fu et al. 2023).

ACR is measured by dividing the amount of albumin (a protein) in the urine by the amount of creatinine (a metabolism byproduct) in the urine. It is used to determine albuminuria, the presence of excessive amounts of the protein albumin in the urine (KHA 2023).

The degree of albuminuria relates to the severity of CKD and greater likelihood of progression to end stage CKD. Reduction in the amount of albuminuria is associated with improved outcomes (KHA 2025b, 2024; Johnson et al. 2012a).

Sex dependent cut-off reference values for albuminuria were sourced from the Royal College of Pathologists of Australasia manual (RCPA 2024). Cut-offs are based on epidemiological data and publications of major clinical trials (KHA 2024). The table below shows cut-offs used for albuminuria (ACR levels):

ACR was calculated for persons aged 5 years and over, who provided a urine sample. Results were only published for persons aged 18 years and over as there is no validated ACR level for determining impaired kidney function for children for spot urine samples.

Points to be considered when interpreting data for this topic include the following:

• ACR was calculated using urinary albumin and creatinine levels taken from a spot test only.
• ACR test results do not confirm a specific diagnosis without consultation with a health professional.
• Other factors such as urinary tract infections, high protein diets or heavy exercise within 24 hours of the test are known to affect ACR results (KHA 2024; Johnson et al. 2012a). As a result, the data should be interpreted with care.
• There are several different test methods for measuring urinary albumin and creatinine, which may produce different results. The data from this topic should be used with caution when comparing ACR results from other studies using different test methods or equation.

Impaired kidney function prevalence rates were derived using a combination of respondents’ eGFR and ACR. There are five stages of CKD, ranging in severity from Stage 1 to Stage 5. During stages 1 and 2, a person might not notice any symptoms, however at stage 5, they are generally reliant on dialysis or a kidney transplant to stay alive (KHA 2023).

Early stages of CKD are defined by abnormal ACR and normal eGFR test results. Later stages of CKD are defined by abnormal eGFR test results only. The following cut-offs were used for CKD stages:

Points to be considered in interpreting data for this topic include the following:

• While abnormal eGFR or ACR test results may indicate impaired kidney function, they cannot provide a diagnosis for CKD based on a single test alone. CKD can only be confirmed if albuminuria or eGFR of less than 60 mL/min/1.73m² is persistent for at least three months. For more information about these tests, see the relevant eGFR and ACR topic pages.
• Self-reported information about kidney disease was collected in other IHMHS components. Respondents were asked whether they had ever been told by a doctor or a nurse that they had kidney disease and whether they currently had the condition. Respondents with kidney disease were assumed to have the condition long-term.

Treatment for individuals with CKD by health professionals is determined by combining information on the CKD stages of disease based on eGFR results with ACR results and information on other conditions, noting these should be results over a 3-month period. Different stages of kidney function will require different management strategies (KHA 2024).

In general, those with an eGFR of ≥60 mL/min/1.73m² require monitoring unless haematuria (blood in the urine), structural or pathological abnormalities are present (Australian Urology Associates 2024). Progressive treatment is required for individuals as kidney function stage increases (KHA 2024).

Other factors considered by health professionals in managing CKD are:

• age
• blood pressure
• weight
• smoking
• fasting lipid levels
• cardiovascular risk
• oedema
• calcium and phosphate levels
• parathyroid hormone (if eGFR <45 mL/min/1.73 m²)
• diabetic status.

Management of early CKD includes steps to reduce cardiovascular disease risk (KHA 2024). For more information, refer to Australian CVD risk calculator.

Australian Urology Associates (AUA) (2024), Haematuria, AUA website, accessed 20/02/2025.

Bailey C, Farnsworth C (2022), Impact of switching from the 2009 to the 2021 CKD-EPI Equation for eGFR, American Journal of Clinical Pathology, 158:S1, accessed 20/02/2025.

Björk J, Nyman U, Larsson A, Delanaye P, Pottel H (2021), Estimation of the glomerular filtration rate in children and young adults by means of the CKD-EPI equation with age-adjusted creatinine values, Kidney International, 99(4):940–947, accessed 20/02/2025.

Delgado C, Baweja M, Burrows NR, Crews DC, Eneanya ND, Gadegbeku CA, Inker LA, Mendu ML, Miller WG, Moxey-Mims MM, Roberts GV, St. Peter WL, Warfield C, Powe NR (2021), Reassessing the Inclusion of Race in Diagnosing Kidney Diseases: An Interim Report From the NKF-ASN Task Force, American Journal of Kidney Disease, 78(1):103–115, accessed 20/02/2025.

Fu EL, Coresh J, Grams ME, Clase CM, Elinder C-G, Paik J, Ramspek CL, Lesley A. Inker LA, Levey AS, Dekker FW, Carrero JL (2023), Removing race from the CKD-EPI equation and its impact on prognosis in a predominantly White European population, Nephrology Dialysis Transplantation, 38(1):119–128, accessed 20/02/2025.

Johnson DW, Jones GRD, Mathew TH, Ludlow MJ, Chadban SJ, Usherwood T, Polkinghorne K, Colagiuri S, Jerums G, MacIsaac R, Martin H (2012a), Chronic kidney disease and measurement of albuminuria or proteinuria: a position statement, Australasian Proteinuria Consensus Working Group, Medical Journal of Australia, 197(4):224-225, accessed 20/02/2025.

Johnson DW, Jones GRD, Mathew TH, Ludlow MJ, Doogue MP, Jose MD, Langham RG, Lawton PD, McTaggart SJ, Peake MJ, Polkinghorne K, Usherwood (2012b), Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: new developments and revised recommendations, Australasian Creatinine Consensus Working Group, Medical Journal of Australia, 197(4):224-225, accessed 20/02/2025.

Kidney Health Australia (KHA) (2023), What is Chronic Kidney Disease?, KHA, accessed 20/02/2025.

Kidney Health Australia (KHA) (2024), Chronic Kidney Disease (CKD) Management in Primary Care (5th edition), KHA, accessed 20/02/2025.

Kidney Health Australia (KHA) (2025a), Estimated glomerular filtration rate (eGFR): Factsheet, KHA, accessed 20/02/2025.

Kidney Health Australia (KHA) (2025b), Albuminuria, KHA, accessed 20/02/2025.

Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro III AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD Epidemiology Collaboration (2009), A New Equation to Estimate Glomerular Filtration Rate, Annals of Internal Medicine, 150(9):604-612, accessed 20/02/2025.

Levey AS, Eckfeldt JH (2017), Estimating globular filtration rate using serum creatinine, Clinical Chemistry, 63(6):1161–1162, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2024), ’Albumin urine‘, RCPA Manual, RCPA website, accessed 20/02/2025.

Diabetes mellitus, or diabetes, is a chronic condition where there is too much glucose in the blood (hyperglycaemia). Glucose is a simple sugar that provides energy for the body. Diabetes is caused by the body not making enough insulin or not effectively using the insulin that it makes to convert glucose to energy. In the short term, high levels of glucose in the blood make you feel tired; the kidneys work hard to pass some of the excess glucose out through the urine which may lead to dehydration and feeling thirsty. If undiagnosed or poorly managed, diabetes can damage blood vessels and nerves and cause long term health complications including heart, kidney, eye and foot damage (WHO 2019, 2023; Diabetes Australia n.d.). The World Health Organization (WHO) describes diabetes or hyperglycaemic disorders by type: Type 1 (insulin dependent), Type 2 (insulin resistant), hybrid diabetes, unclassified diabetes and gestational diabetes (WHO 2019).

The indicators of diabetes that were measured were:

• fasting plasma glucose (FPG)
• glycated haemoglobin (HbA1c).

Self-reported data on diabetes was also collected in other components of the IHMHS. The biomedical results can be used together with the self-reported data to determine diabetes status and estimate disease prevalence rates.

Laboratory test information, including analysis methods and machines used to measure diabetes biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on fasting plasma glucose and HbA1c. Information on these biomedical indicators was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

FPG, diabetes status and HbA1c data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, definitions, assay and the instrument used, and any thresholds applied in the final analysis.

An FPG test measures how much glucose is in the bloodstream at a certain time of day after not eating for eight hours. Plasma glucose is converted into energy in the body by a hormone called insulin. However, people with diabetes do not produce sufficient insulin or do not use the insulin produced effectively to convert plasma glucose into energy. This means that a person with high levels of fasting plasma glucose likely has diabetes (Diabetes Australia n.d.; WHO 2023).

FPG results were obtained for persons aged 12 years and over who provided a blood sample and who had fasted for 8 hours or more prior to their blood test.

FPG levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory using the Hexokinase/G-6-PDH method, using EDTA tubes for blood collection. The FPG test measures the amount of glucose (sugar) circulating in the blood at the time of the test and is expressed as mmol/L.

Cut-off reference values for normal and abnormal results were sourced from WHO guidelines. These guidelines are based on epidemiological data and publications of major clinical trials (WHO 2006, 2019). The cut-off reference values are:

• has diabetes ≥7.0 mmol/L
• at high risk of diabetes 6.1–<7.0 mmol/L
• no diabetes <6.1 mmol/L.

Points to be considered when interpreting data for this topic include the following:

• FPG results do not confirm a specific diagnosis without consultation with a health professional.
• FPG results on their own do not satisfy the definition of diabetes prevalence in the IHMHS. For more information, refer to Diabetes prevalence.
• FPG results cannot distinguish between Type 1 and Type 2 diabetes.
• Impaired glucose tolerance (IGT) and impaired fasting glycaemia (IFG) are intermediate conditions in the transition between normality and diabetes. People with IGT or IFG are at high risk of progressing to Type 2 diabetes, although this is not inevitable.
• Other studies have reported fasting plasma glucose test results using the Oral Glucose Tolerance Test (OGTT). For individuals, the OGTT may be used to confirm diagnosis of diabetes.
• There are several different test methods for measuring FPG, which may produce different results. The data from this topic should therefore be used with caution when comparing fasting plasma glucose results from other studies using a different test method or equation.

HbA1c is haemoglobin that is bound to a glucose particle in the blood and is a measure of a person’s average blood glucose over the last two to three months. A test result for HbA1c does not indicate blood glucose levels at any one point. An HbA1c test is used to measure the level of control of diabetes in individuals or a population and may also be used to confirm a person has diabetes.

To confirm a diagnosis in asymptomatic individuals, an abnormal HbA1c result should be followed up with repeat testing on a subsequent day, unless two abnormal tests, such as FPG or an OGTT, are already available (WHO 2019; RACGP 2024). HbA1c may lack accuracy in certain cases, where FPG or OGTT can assist diagnosis (RACGP 2024).

HbA1c results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

HbA1c levels were measured at the DHM laboratory using the Sebia Capillary Electrophoresis method. The HbA1c test measures the average blood glucose over the life of a red blood cell. Results are reported as a percentage by the laboratory, and following conversion are expressed as mmol/mol haemoglobin.

Cut-off reference values for HbA1c were taken from WHO Guidelines and are based on epidemiological data and publications of major clinical trials (WHO 2011, 2019). The cut-offs are also referenced in the Australian Evidence-Based Clinical Guidelines for Diabetes (Australian Diabetes Society 2015).

Points to be considered when interpreting data for this topic include the following:

• HbA1c test results do not confirm a specific diagnosis without consultation with a health professional.
• HbA1c results on their own do not satisfy the definition of diabetes prevalence in the IHMHS. For more information, refer to Diabetes prevalence.
• HbA1c results cannot distinguish between Type 1 and Type 2 diabetes.
• Where HbA1c levels indicate a person is at risk of diabetes, prevention interventions might be considered such as lifestyle modifications to achieve HbA1c targets.
• Cut-off reference ranges are based on measuring chronic glycemia periodically as this test may be repeated every 3 or 6 months depending on HbA1c levels and upon consultation with a health professional.
• HbA1c levels are affected by conditions that affect red blood cell survival time or non-enzymatic glycation of haemoglobin (WHO 2011).
• There are several different test methods for measuring HbA1c, which may produce different results. The data from this topic should therefore be used with caution when comparing HbA1c results from other studies using a different test method or equation to convert units of measurement.

Diabetes prevalence was derived from IHMHS data using a combination of blood test results and self-reported information on diabetes diagnosis and medication use. Using self-reported information can differentiate between respondents with known diabetes, and those without a prior diagnosis (newly diagnosed diabetes). Three data items are available to users for assessing diabetes status based on different definitions: one for FPG as used in the NHMS 2011–13; one for HbA1c as defined in the 2019 WHO guidelines; and one combining the two test results.

Self-reported diabetes refers to people who reported having ever been told by a doctor or nurse that they had diabetes (including Type 1, Type 2, and type not known) and excludes gestational diabetes. For more information on self-reported diabetes, see the National Health Measures Survey methodology, 2022–24 and National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.

The total number of persons with diabetes is defined as the number of persons with known diabetes and newly diagnosed diabetes. Respondents without diabetes were separated into those at high risk of developing diabetes and those with no diabetes. Diabetes status definitions are outlined in the table below.

In the NHMS 2011–12 and the NATSIHMS 2012–13, only FPG results were used to determine ‘known’ or ‘newly diagnosed’ diabetes for the purpose of defining and reporting diabetes status. HbA1c was measured and primarily used as an indicator of the success or otherwise of diabetes management over a 3-month period (WHO 2011). Since then, it has been recognised that HbA1c results may be used to diagnose diabetes (WHO 2019; D’Emden et al. 2012; Australian Diabetes Society 2015).

For 2022–24, the ABS has released three different measures of diabetes status, this enables users to utilise which measure best fits their analysis. Caution must be taken to select the appropriate results when comparing the surveys over time.

Cut-offs for individual FPG and HbA1c data items are described in previous sections. Cut-offs for the combined item are shown in the table below.

The flowchart below illustrates the determination of diabetes status using test results, self-reported diabetes diagnosis, and medication use.

In the IHMHS, information is available on diabetes management for those with known diabetes (determined from self-reported diabetes and medication, fasting plasma glucose and/or HbA1c results).

HbA1c is the primary measure of how well a person is managing their diabetes. However, there are also other goals for optimum diabetes Type 2 management. The Royal Australian College of General Practitioners (RACGP) lists a number of goals for people with diabetes (RACGP 2024).

Goals for optimum diabetes management for individuals are also provided in the 2020 Australian Evidence-Based Clinical Guidelines for Diabetes (Living Evidence for Diabetes Consortium 2020).
The AUSDRISK Score tool developed in 2010 by the Baker Heart and Diabetes Research Institute on behalf of Australian, State and Territory governments predicts the likelihood of a person having Type 2 diabetes (Department of Health 2010). The information required to complete the tool to derive a score to determine the level of risk of Type 2 diabetes includes:

• age
• sex
• ethnicity
• country of birth
• family history of diabetes (Type 1 or 2)
• diagnosis of high blood glucose, any occasion
• currently taking medication for blood pressure
• smoker status (cigarettes and/or other tobacco products)
• frequency of eating vegetables and fruit (every day/not every day)
• amount of physical activity per week (2.5 hours a week/less than 2.5 hours)
• waist measurement.

Australian Diabetes Society (2015, updated 2023), Guidance concerning the use of glycated haemoglobin for the diagnosis of diabetes mellitus, Australian Diabetes Society, accessed 20/02/2025.

Burrow S, Ride K (2016), Review of diabetes among Aboriginal and Torres Strait Islander people, Australian Indigenous HealthInfoNet, accessed 20/02/2025.

Diabetes Australia (n.d.), What is Diabetes, Diabetes Australia website, accessed 20/02/2025.

Department of Health (2010), The Australian type 2 diabetes risk assessment tool (AUSDRISK), Department of Health and Aged Care, accessed 20/02/2025.

d’Emden MC, Shaw JE, Colman PG, Colagiuri S, Twigg SM, Jones GRD, Goodall I, Schneider HG and Cheung NW (2012), The role of HbA1c in the diagnosis of diabetes mellitus in Australia, Medical Journal of Australia, 197(4): 220-221, accessed 20/02/2025.

Hanas R, John G; International HbA1c Consensus Committee (2010), 2010 Consensus Statement on the Worldwide Standardization of the Hemoglobin A1C Measurement, Diabetes Care, 33(8):1903-1904, accessed 20/02/2025.

Jones GR, Barker G, Goodall I, Schneider HG, Shephard MD, Twigg SM (2011), Change of HbA1c reporting to the new SI units, Medical Journal of Australia, 195(1):45–46, accessed 20/02/2025.

Living Evidence for Diabetes Consortium (2020), Australian Evidence-Based Clinical Guidelines for Diabetes, Australian Diabetes Society, accessed 20/02/2025.

Royal Australian College of General Practitioners (RACGP) (2024), Management of type 2 diabetes: A handbook for general practice, RACGP, accessed 20/02/2025.

World Health Organization (WHO) (2006), Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2011), Use of Glycated Haemoglobin (HbA1c) in the Diagnosis of Diabetes Mellitus, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2019), Classification of diabetes mellitus, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2023), Diabetes, WHO website, accessed 20/02/2025.

The liver is an essential organ and regulates most chemical levels in the body. It has many functions, including removing toxins from the blood, processing nutrients, and regulating hormones (Liver Foundation 2022a).

A range of factors, including fatty liver disease (where fat accumulates in the liver), infections (hepatitis), liver cancer and excessive alcohol consumption can prevent the liver from performing these functions well and if left untreated, can lead to liver damage. When the liver is inflamed or damaged, enzymes leak from the liver cells into the bloodstream. These enzymes are also found in other organs, such as the heart and muscles, so high levels may not always be due to a liver problem (Liver Foundation 2022b).

The indicators of liver function that were measured were:

• alanine aminotransferase (ALT)
• gamma glutamyl transferase (GGT).

The IHMHS provides an objective measurement of the number of people in Australia with elevated ALT and GGT levels. While these tests cannot diagnose the presence of liver disease, elevated levels for either test may indicate impaired liver function (Coates 2011; Liver Foundation 2022b).

Laboratory test information, including analysis methods and machines used to measure liver function biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on ALT and GGT levels. Information on these biomedical indicators was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

ALT and GGT data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

ALT is an enzyme that is mainly found in your liver, but smaller amounts are found in your muscles, kidneys and other organs. Elevated levels of ALT are associated with liver damage and indicate a degree of liver inflammation (RCPA 2023).

ALT results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

ALT levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory by an Activated ALT assay. The ALT test measures the amount of ALT circulating in the blood at the time of the test, expressed as units per litre (U/L).

There is no consensus on the cut-off reference values for defining abnormal ALT levels for the Australian population, as there are currently several different methods that can be used to measure ALT. As such, cut-off reference values for normal and abnormal results were sourced from DHM laboratory reference ranges, taking the upper level of the normal range as the cut-off point.

The Royal College of Pathologists Australia (RCPA) uses a different reporting level for abnormal ALT. For adult females, the cut-off is >35 U/L. The cut-off for males is similar to above (>40 U/L) (RCPA 2024a).

Points to be considered when interpreting data for this topic include the following:

• ALT results do not confirm a specific diagnosis without consultation with a health professional.
• There are several different test methods for measuring ALT which may produce different results. The data from this topic should therefore be used with caution when comparing ALT results from other studies using a different test method.
• Liver disease is associated with many health concerns and elevated ALT levels are not only associated with liver damage, but also with Type 2 diabetes mellitus, cardiovascular disease, stroke and metabolic syndrome (Coates 2011).

Gamma-glutamyl transferase (GGT) is a common enzyme found in many of the body’s tissues and organs, primarily in the liver. GGT is located on the plasma membranes of most cells and organ tissues, but more commonly in hepatocytes, and is routinely used in clinical practice to help indicate liver injury and as a marker of excessive alcohol consumption. GGT is considered one of the more sensitive indicators of liver function and elevated levels indicate poor liver function (Kunotsor 2016; Coates 2011; Liver Foundation 2022b).

GGT results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

GGT levels were measured at the DHM laboratory by the Szasz l-gamma-glutamly-3-carboxyl-4-nitroanilide method. The GGT test measures the amount of GGT circulating in the blood at the time of the test, expressed as units per litre (U/L).

There is no consensus on the cut-off reference values for defining abnormal GGT levels for the Australian population, as there are currently several different methods that can be used to measure GGT. Reference values for normal and abnormal results were sourced from DHM laboratory reference ranges, taking the upper level of the normal range as the cut-off point.

For adults, the RCPA has a reporting level for GGT for abnormal levels for females of >35 U/L and for males of >50 U/L; the normal ranges of 5–35 U/L for females and 5–50 U/L for males are known as harmonised reference intervals, intended for use by all pathology laboratories in Australia (RCPA 2024b).

Points to be considered when interpreting data for this topic include the following:

• GGT results do not confirm a specific diagnosis without consultation with a health professional.
• There are several different test methods for measuring GGT, which may produce different results. The data from this topic should therefore be used with caution when comparing GGT results from other studies using a different test method.
• Liver disease is associated with many health concerns and elevated GGT levels are not only associated with liver damage, but also with Type 2 diabetes mellitus, cardiovascular disease, chronic alcohol abuse and pancreatic cancer (Coates 2011; Kunutsor 2016).

Coates P (2011), Liver function tests, Australian Journal for General Practitioners, 40(3):113-115, accessed 20/02/2025.

Kunutsor SK (2016), Gamma-glutamyltransferase – friend or foe within?, Liver International, 36(12):1723-1734, accessed 20/02/2025.

Liver Foundation (2022a), ‘About the liver’, Your Liver website (Liver Foundation), accessed 20/02/2025.

Liver Foundation (2022b), ‘Liver Tests Explained’, Your Liver website (Liver Foundation), accessed 20/02/2025.

Pathology Tests Explained (2023), Alanine Aminotransferase (ALT), Pathology Tests Explained website, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2024a), ’Alanine Aminotransferase‘, RCPA Manual, RCPA website, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2024b), ‘Gamma Glutamyltransferase’, RCPA Manual, RCPA website, accessed 20/02/2025.

Vitamin B9 (folate) and vitamin B12 are two essential nutrients with important functions in the body. Folate is essential for the formation of DNA, and without it, cells cannot divide. The need for folate is higher when the body is making new cells, such as during pregnancy (NHMRC 2013a). Vitamin B12 is an essential nutrient that is needed for making red blood cells, the production of DNA and maintenance of the nervous system. Vitamin B12 is important for folate status, as low levels of vitamin B12 can interfere with the body's ability to use folate (Gibson 2005; WHO 2008).

Laboratory test information, including analysis methods and machines used to measure folate and vitamin B12 biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on serum folate and vitamin B12. Both were previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Red cell folate (RCF) was measured in the NHMS 2011–12 and the NATSIHMS 2012–13, but not in 2022–24. The RCF assay used previously has been evaluated since that time and was considered by researchers to have overestimated folate status in the Australian population compared to other accepted methods of analysis (Hunt et al. 2020). Some of the factors that assist in interpreting RCF data were not collected in 2022–24, for example pregnancy and oral contraceptive use (Gibson 2005; WHO 2015b).

Serum folate and vitamin B12 data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Folate is a B group vitamin (B9) that the body uses to make DNA and other genetic material. It is essential for healthy growth and development, particularly for the foetus in the first 3 months of pregnancy (McNulty 2024; WHO 2015a). Folate cannot be made by the body and is found naturally in food, such as green leafy vegetables, fruits and grains. The term ‘folate’ can also be used to refer to folic acid, which is the synthetic form of folate added to food or used in dietary supplements (Gibson 2005).

Folate deficiency can lead to macrocytic anaemia. Macrocytic anaemia is a type of anaemia where the body produces red blood cells that are larger than normal. Symptoms include fatigue, irritability, weakness, and palpitations (Gibson 2005).

Recent intake of dietary folate can be determined by measuring serum folate in a blood sample. Serum folate levels are low in folate deficiency related anaemia. The World Health Organization (WHO) also recommends serum folate testing for folate status monitoring (WHO 2008, 2015a, 2015b). In Australia, the national monitoring of folate status program mandated the addition of folic acid to all non-organic bread making wheat flour in 2009. Biomedical measurements of folate status forms part of this monitoring program (AIHW 2011a, 2011b, 2016).

Serum folate results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Serum folate levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory by the competitive electro-chemiluminescence binding assay method (RCPA 2023a). The serum folate test measures the amount of folate circulating in the blood at the time of the test, expressed as nmol/L.

The reporting level for individuals for a serum folate value in the normal range from the laboratory test used was >7 nmol/L.

In 2012, WHO recommended that folate deficiency should be defined as serum folate concentrations of <10 nmol/L in population-based studies, which was supported in a 2015 update on folate.  This value is based on the point at which homocysteine concentrations begin to increase (WHO, 2015a, 2015b). High levels of circulating homocysteine are considered a functional indicator of folate deficiency and results from the inability of folate to donate the methyl group necessary to convert homocysteine to methionine (WHO 2015b). 

Points to be considered when interpreting data for this topic include the following:

• Serum folate results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• There are several different test methods to measure serum folate levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing serum folate results from other studies using a different test method.
   

Vitamin B12 is required for the synthesis of fatty acids in myelin and, in conjunction with folate, in DNA synthesis, red blood cell formation, brain function and when breaking down fatty and amino acids (NHMRC 2013b).

Adequate intake of vitamin B12 is essential for normal blood and neurological function. If left untreated, vitamin B12 deficiency can lead to anaemia, as well as nerve and brain damage. Vitamin B12 deficient anaemia has the same symptoms as folate deficient anaemia, as a result it cannot be diagnosed without a biomarker test (NHMRC 2013b).

Vitamin B12 results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Vitamin B12 levels were measured at the DHM laboratory by the competitive electro-chemiluminescence immunoassay (ECLIA) method (RCPA, 2023b). The vitamin B12 test measures the amount of vitamin B12 circulating in the blood at the time of the test, expressed as pmol/L.

There is no consensus on the epidemiological cut-off reference values for measuring serum vitamin B12 in the blood, as it depends on the assay used.

The reporting level for individuals for a serum vitamin B12 value in the normal range from the laboratory test used was >145 pmol/L.

A WHO consultation in 2008 proposed a vitamin B12 cut-off for deficiency of <150 pmol/L (plasma) for population studies (WHO 2008). This reporting level has also been referenced elsewhere and can be applied to serum or plasma vitamin B12 measurements (NIH 2024).

Points to be considered when interpreting data for this topic include the following:

• Vitamin B12 results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• There are several different test methods to measure vitamin B12 levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing vitamin B12 results from other studies using a different test method.
• Biomedical monitoring data on vitamin B12 status assists the monitoring of folate status program, as high folate intakes (>1000 µg/day) may mask B12 anaemia and delay the correct diagnosis of vitamin B12 deficiency in older people (WHO 2008).

Australian Institute of Health and Welfare (AIHW) (2011a), Mandatory folic acid and iodine fortification in Australia and New Zealand: baseline report for monitoring, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2011b), Mandatory folic acid and iodine fortification in Australia and New Zealand: supplement to the baseline report for monitoring, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2016), Monitoring the health impacts of mandatory folic acid and iodine fortification 2016, AIHW, Australian Government, accessed 20/02/2025.

Gibson RS (2005), Assessment of folate and Vitamin B12 status, Principles of Nutritional Assessment, 2nd ed, Oxford University Press.

Hunt SE, Netting MJ, Sullivan TR, Best KP, Houghton LA, Makrides M, Muhlhausler BM, Green TJ (2020), Red Blood Cell Folate Likely Overestimated in Australian National Survey: Implications for Neural Tube Defect Risk, Nutrients, 12(5):1283, accessed 20/02/2025.

McNulty H (2024), ‘Folate’, Principles of nutritional assessment: 3rd Edition, Nutritional Assessment website, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013b), ‘Vitamin B12’, Nutrient Reference Values for Australia and New Zealand, Eat For Health website, accessed 19/07/2023.

National Health and Medical Research Council (NHMRC) (2013a), ‘Folate’, Nutrient Reference Values for Australia and New Zealand, Eat For Health website, accessed 19/07/2023.

National Institutes of Health Office of Dietary Supplements (NIH) (2024), Vitamin B12, NIH website, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2023a), ‘Folate’, RCPA Manual, RCPA website, accessed 19/07/2023.

Royal College of Pathologists of Australasia (RCPA) (2023b), ‘Vitamin B12’, RCPA Manual, RCPA website, accessed 19/07/2023.

World Health Organization (WHO) (2008), Conclusions of a WHO technical consultation on folate and vitamin B12 deficiencies, Food and Nutrition Bulletin, 29(2 Suppl.):S238-S244, accessed 20/02/2025.

World Health Organization (WHO) (2015a), Guideline: Optimal serum and red blood cell folate concentrations in women of reproductive age for prevention of neural tube defects, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2015b), Serum and red blood cell folate concentrations for assessing folate status in populations, WHO, accessed 20/02/2025.

Iodine was one of the first trace elements to be identified as essential. It is an essential part of thyroid hormones that regulate normal growth and metabolism. Iodine plays a role in physical and mental development and adequate iodine intakes are important for young children and women of child-bearing age (Gibson 2005; WHO/UNICEF/ICCIDD 2007; WHO 2013; NHMRC 2013).

Iodine deficiency can lead to a range of conditions, including goitres, hypothyroidism, impaired development and in pregnant women, major impacts on foetal development (WHO/UNICEF/ICCIDD 2007; NHMRC 2013).

In Australia, legislation mandating the use of iodised salt in bread manufacturing (excluding organic bread) was introduced in 2009. Biomedical measurement of iodine levels in the population were used to monitor and report on the effectiveness of this standard (AIHW 2011a, b, 2016). The World Health Organization (WHO) recommends monitoring iodine status over time and, if necessary, adjusting the concentration of iodine in salt available for sale, particularly in countries that have strategies to reduce the sodium/salt content of the food supply (WHO 2022).

Laboratory test information, including analysis methods and machines used to measure iodine, is available from the Downloads page.

To enable population-level analysis, iodine tests were conducted on urine samples from persons aged 5 years and over. Fasting was not required for this test.

Iodine levels were measured at the Sullivan Nicolaides Pathology laboratory by the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) method. The iodine test measures the concentration of iodine in the urine that has been excreted from the body, at the time of the test (spot sample), expressed as μg/L. Spot urine samples are not a sufficient measure for individual status; however, these results can be used to assess the population iodine status (Gibson 2005; WHO/UNICEF/ ICCIDD 2007; WHO 2013).

Prior to use of the ICP-MS method, most studies used methods of analysis based on the manual spectrophotometric measurement of the Sandell–Kolthoff reduction reaction catalysed by iodine using different oxidising reagents in the initial digestion step (Jooste and Strydom 2010). The WHO and the International Council for Control of Iodine Deficiency Disorders (ICCIDD) determined a set of cut-offs for median urinary iodine concentration (UIC) using these methods to define if a population was iodine deficient (WHO/UNICEF/ICCIDD 2007; WHO 2013), outlined in the table below.

No cut-offs have been developed specifically for the new ICP-MS method. However, research studies indicate that within ranges of UIC 0 – <600 μg/L, the results from the ICP-MS and methods using the Sandell–Kolthoff reaction are comparable (Caldwell et al. 2003; Li et al. 2021). Therefore, the WHO cut-offs for median UIC were applied to the ICP-MS results in the to determine iodine deficiency for the Australian population (RCPA 2023). 

WHO considers a population iodine deficient if the median UIC is less than 100 μg/L (by definition, when the median is 100 μg/L, then 50% of the results will be lower than 100 μg/L). They also recommend that no more than 20% of the population have iodine concentrations below 50 μg/L (WHO/UNICEF/ICCIDD 2007; WHO 2013).

Points to be considered when interpreting data for this topic include the following:

• UIC from a single spot sample is not a sufficient measure to assess an individual's iodine status, thus comparing the iodine data items to other items at the individual level should be done with caution.
• UIC results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• There are several different test methods to measure iodine levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing iodine results from other studies using a different test method.

This is the second time the ABS has collected information on iodine levels. Iodine was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Iodine data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Australian Institute of Health and Welfare (AIHW) (2011a), Mandatory folic acid and iodine fortification in Australia and New Zealand: baseline report for monitoring, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2011b), Mandatory folic acid and iodine fortification in Australia and New Zealand: supplement to the baseline report for monitoring, AIHW, Australian Government, accessed 20/02/2025.

Australian Institute of Health and Welfare (AIHW) (2016), Monitoring the health impacts of mandatory folic acid and iodine fortification 2016, AIHW, Australian Government, accessed 20/02/2025.

Caldwell KL, Brook Maxwell C, Makhmudov A, Pino S, Braverman LE, Jones RL, Hollowell JG (2003), Use of Inductively Coupled Plasma Mass Spectrometry to Measure Urinary Iodine in NHANES 2000: Comparison with Previous Method, Clinical Chemistry, 49(6):1019-1021, accessed 20/02/2025.

Gibson RS (2005), Principles of Nutritional Assessment, 2nd edn, Oxford University Press.

Jooste PL, Strydom E (2010), Methods for determination of iodine in urine and salt, Best practice and Research Clinical Endocrinology and Metabolism; 24(1):77-88, accessed 20/02/2025.

Li M, Eastman CJ, Waite KV, Ma G, Zacharin MR, Topliss DJ, Harding PE, Walsh JP, Ward LC, Mortimer RH, Mackenzie EJ, Byth K and Doyle Z (2006), Are Australian children iodine deficient? Results of the Australian National Iodine Nutrition Study, Medical Journal of Australia, 184(4):165-169, accessed 20/02/2025.

Li M, Ma G, Guttikonda K, Boyages SC, Waite K and CJ Eastman (2001), Re-emergence of iodine deficiency in Australia, Asia Pacific Journal of Clinical Nutrition, 10(3):200-203, accessed 20/02/2025.

Li Y, Ding S, Han C, Liu A, Shan Z, Ten W, Mao J (2021), Concentration-dependent Differences in Urinary Iodine Measurements Between Inductively Coupled Plasma Mass Spectrometry and the Sandell-Kolthoff Method, Biological Trace Element Research, 199:2489–2495, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013), ‘Iodine’ Nutrient Reference Values for Australia and New Zealand, Eat for Health website, accessed 07/02/2024.

Royal College of Pathologists of Australasia (RCPA) (2023), ‘Iodine Urine’, RCPA Manual, RCPA website, last accessed 07/02/2024.

Singh GR, Davison B, Ma GY, Eastman J, Mackerras DEM (2019), Iodine status of Indigenous and non‐Indigenous young adults in the Top End, before and after mandatory fortification, Medical Journal of Australia, 210(3):121-125, accessed 20/02/2025.

World Health Organization (WHO) (2013), Urinary iodine concentrations for determining iodine status in populations, WHO, accessed 20/02/2025.

World Health Organization (WHO) (27 April 2022), Maternal, infant and young child nutrition: sustaining the elimination of iodine deficiency disorders, 75th World Health Assembly, accessed 20/02/2025.

World Health Organization/United Nations Children’s Emergency Fund/International Council for Control of Iodine Deficiency Disorders (WHO/UNICEF/ICCIDD) (2007), Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers, 3rd ed, WHO, accessed 20/02/2025.

Iron is an essential dietary mineral, with important functions including the production of haemoglobin, DNA synthesis and muscle metabolism. Almost two thirds of the body’s iron is found in haemoglobin, which transports oxygen to tissues around the body. The body cannot produce its own iron, so the body’s iron levels are reliant on dietary intake (NHMRC 2013; WHO 2020a, b; Gibson and Friel 2024).  

The main dietary sources of iron are meat, eggs, legumes, dark leafy greens, nuts and seeds. The 2013 National Health and Medical Research Council (NHMRC) Nutrient Reference Values for Australia and New Zealand for iron includes Adequate Intakes for infants, Estimated Average Requirements and Recommended Dietary Intakes for older infants, young children, and adults as individuals (NHMRC 2013).

Iron deficiency can lead to fatigue, tiredness, and decreased immunity. Iron deficiency is also the leading cause of anaemia, which is the most prevalent nutritional deficiency worldwide impacting an estimated 33% of the world’s population (WHO 2011; Gibson and Friel 2024).

Biomarkers that were collected to measure iron in the body were:

• haemoglobin (Hb)
• serum ferritin
• soluble transferrin receptor (sTfR).

To assist in interpreting ferritin levels, C-reactive protein (CRP) levels were also measured (to detect presence of inflammation). 

Laboratory test information, including analysis methods and machines used to measure iron biomarkers, is available from the Downloads page.

This is the second time the ABS has collected information on iron biomarkers (serum ferritin, CRP, sTfR and Hb). Information on these four biomarkers was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Iron biomarkers have been collected in other non-ABS surveys. However, caution must be taken when interpreting results for serum ferritin, CRP, sTfR and Hb due to the differences in scope, assay and the instrument used for each test, and any thresholds applied in the final analysis.

Ferritin is a blood protein that stores iron. In the body, small amounts of ferritin circulate in the blood and in most healthy persons the concentration of ferritin in blood is an effective measure of the body’s total iron stores. While normal ferritin concentrations vary by age and sex, a low ferritin concentration indicates iron deficiency. A low ferritin value is the first stage indicator of IDA. A high ferritin level suggests risk of iron overload, noting the cut-offs for risk of iron overload are different for ‘apparently healthy’ and ‘non-healthy’ individuals (WHO 2020a, b; Gibson and Friel 2024).

Ferritin concentrations are raised in inflammation (with or without infection), therefore, people with inflammation (defined in the IHMHS as a CRP level of >10 mg/L) are excluded from the published ferritin results. All serum ferritin results without this exclusion are available in the DataLab microdata products.

Serum ferritin results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Serum ferritin levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory, by an Ultrasensitive immunoturbidimetric assay. The ferritin test measures the amount of ferritin circulating in the blood at the time of the test, expressed as µg/L.

Levels of ferritin can be affected by certain health conditions, infection or inflammation. In 2020, the World Health Organization (WHO) released new guidelines for reporting serum ferritin levels that include ranges for ‘apparently healthy’ and ‘non-healthy’ individuals, summarised in the table below. WHO also recommends excluding individuals with elevated inflammatory markers, such as CRP, when analysing serum ferritin results because levels tend to increase when inflammation is present (WHO 2020a, b).

People with a CRP level >10 mg/L were excluded from serum ferritin analysis in line with WHO’s recommendation.

Points to be considered when interpreting data for this topic include the following:

• Low ferritin results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• Levels of ferritin can be affected by infection or inflammation. Therefore, people with inflammation, defined as a CRP of >10 mg/L, were excluded from the published ferritin results.
• There are several different test methods to measure ferritin levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing ferritin results from other studies that use a different test method.

CRP is an acute phase protein made by the liver. Acute phase proteins are a class of proteins in blood that change concentration in response to inflammation. As a result, CRP is a non-specific marker of inflammation and infection (WHO 2014a).

CRP is not used for screening in a clinical setting, but the measurement of CRP assists with interpreting the serum ferritin results that can be affected by inflammation (Gibson and Friel 2024).

CRP results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

CRP levels were measured at the DHM laboratory, by a Turbidimetric/Immunoturbidimetric assay. The CRP test measures the amount of CRP circulating in the blood at the time of the test, expressed as mg/L.

There is no consensus on the epidemiological cut-off reference values for measuring CRP. The test reference range for normal CRP levels is <5 mg/L, however research shows that CRP levels >10 mg/L are indicative of an acute infection or inflammation (WHO 2014a). The IHMHS defines CRP levels >10 mg/L as elevated CRP. People with elevated CRP are excluded from serum ferritin analysis.

CRP data is collected to assist in the interpretation of ferritin results and is not currently included in ABS publications. However, CRP results are available in DataLab microdata products.

Points to be considered when interpreting data for this topic include the following:

• CRP test results cannot be used for a specific diagnosis of deficiency, but in consultation with a health professional may be used to interpret serum ferritin results.
• There are several different test methods to measure CRP levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing CRP results from other studies using a different test method.

sTfR is an iron related protein that is important in the process of carrying iron to body cells. It can be used as a measure of iron levels and is not as affected by infection or inflammation to the extent of other measures, such as serum ferritin. sTfR levels are elevated if there is an increased demand for iron, that is, levels increase in IDA or when iron stores are low. When serum ferritin results indicate depleted iron stores, sTfR can be used to assess the severity of the iron depletion (WHO 2014b, Gibson and Friel 2024).

sTfR results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

sTfR levels were measured at the DHM laboratory, by a particle enhanced immunoturbidimetric assay. The sTfR test measures the amount of sTfRs circulating in the blood at the time of the test, expressed as mg/L.

There is no consensus on the epidemiological cut-off reference values for measuring sTfR in the blood. The cut-off values for reporting sTfR levels are based on laboratory ranges and should be considered along with serum ferritin results when assessing population iron status. This is done by assessing the proportion of the population below serum ferritin thresholds for iron deficiency along with the proportion of the population above sTfR cut-offs values (WHO 2014b).

Points to be considered when interpreting data for this topic include the following:

• sTfR test results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• There are several different test methods to measure sTfR levels and each test method may produce different results. The data from this topic should therefore be used with caution when comparing sTfR results from other studies using a different test method.

Hb is a protein found in red blood cells that helps transport oxygen from the lungs to the rest of the body. Iron is an essential part of the Hb molecule, this means that Hb concentration can be used to test for IDA (Pathology Tests Explained 2023).

Anaemia is caused by a decrease in either the number of red blood cells in the body or the quantity of haemoglobin within red blood cells. A reduction in either, means that the heart must work harder to ensure that muscles and organs get the oxygen they need (Gibson and Friel 2024).

Hb results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Hb levels were measured at the DHM laboratory, by a Spectrophotometric method after stabilisation with sodium laurel sulphate. The Hb test measures the amount of Hb circulating in the blood at the time of the test, expressed as g/L.

Abnormally low levels of Hb indicating a risk of IDA are defined differently for males and females, young people, and pregnant women. Cut-off reference values for normal and abnormally low (at risk of IDA) results were sourced from the WHO guidelines and presented in the table below (WHO 2011). These guidelines are based on epidemiological data and publications of major clinical trials.

Points to be considered when interpreting data for this topic include the following:

• Hb results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• Residential elevation above sea level and smoking are known to increase haemoglobin concentrations. Consequently, the prevalence of Hb deficiency may be underestimated in persons residing at high altitudes and among smokers when standard Hb cut-offs are applied (Gibson and Friel 2022).
• There are several different test methods for measuring Hb, which may produce different results. The data from this topic should therefore be used with caution when comparing Hb results from other studies using a different test method or equation.

Gibson RS, Friel JK (2024), ‘Iron’, Principles of Nutritional Assessment: 3rd Edition, Nutritional Assessment website, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013), ‘Iron’ Nutrient Reference Values for Australia and New Zealand, Eat for Health website, accessed 20/02/2025.

Pathology Tests Explained (2023), Haemoglobin, Pathology Tests Explained website, accessed 20/02/2025.

World Health Organization (WHO) (2011), Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2014a), C-reactive protein concentrations as a marker of inflammation or infection for interpreting biomarkers of micronutrient status, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2014b), Serum transferrin receptor levels for the assessment of iron status and iron deficiency in populations, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2020a), WHO guideline on use of ferritin concentrations to assess iron status in individuals and populations, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2020b), Serum ferritin concentrations for the assessment of iron status in individuals and populations: technical brief, WHO, accessed 20/02/2025.

Potassium is both a mineral and electrolyte that is essential for bodily function. It helps nerves, muscles and the heart to work properly (Gibson 2005). Potassium is found in varying amounts in most foods. Leafy green vegetables, root vegetables and vine fruit are all good sources of potassium. Legumes, tree fruits, milk, yoghurts, and meats are moderate sources of potassium (NHMRC 2013).

Research has shown that low levels of potassium can contribute to developing chronic diseases such as cardiovascular disease (CVD), diabetes, and chronic kidney disease (NHMRC 2013; WHO 2012a, 2023). In 2023, ischaemic heart diseases and cerebrovascular diseases were the number one and number three leading causes of death in Australia, see Causes of Death, Australia, 2023.

In 2023, the World Health Organization (WHO) confirmed previous statements that an increase in potassium intake from food reduces blood pressure and risk of cardiovascular disease, stroke and coronary heart disease in adults. WHO suggests a potassium intake from food of at least 3510 mg/day (90 mmol/day) for adults (WHO 2023). A biomedical measure of potassium intakes is considered useful in addition to estimated potassium intakes to assess trends in population urinary potassium levels.

Laboratory test information, including analysis methods and machines used to measure potassium biomarkers, is available from the Downloads page.

Potassium results were obtained for persons aged 5 years and over who provided a urine sample. Fasting was not required for this test.

Urinary potassium levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory using an integrated chip technology method. The potassium test measures the total amount of potassium in the urine that has been excreted from the body at the time of the test (spot test), expressed as mmol/L.

There is no consensus of epidemiological cut-off reference values for measuring potassium excretion from spot urine. As such no cut-off points have been defined.

While spot urine potassium tests have been used in international health surveys, it is recognised that it is not the most accurate method of estimating population potassium intakes. Interpretation of potassium test results is difficult as potassium levels in the urine are dependent on food intakes, kidney function, medications, blood pressure, sodium intake and excretion, and other factors (Mente et al. 2009).

A 24-hour urinary collection is widely considered to be the most accurate method of measuring potassium intakes as over 90% ingested potassium is excreted in urine within a 24-hour period (McLean 2014). In the IHMHS, a spot urine sample was used to test potassium content as it was not practical to collect urine samples from all study participants over a 24-hour period due to the high respondent burden and other method limitations (McLean 2014).

Potassium data is not currently included in ABS publications. However, potassium results are available in DataLab microdata products.

Points to be considered when interpreting data for this topic include the following:

• Urinary potassium results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• There are several different test methods to measure potassium levels, and each test method or collection method may produce different results. The data from this topic should therefore be used with caution when comparing potassium results from other studies using a different test approach.

This is the second time the ABS has collected information on potassium levels. Potassium was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Potassium data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Australian Institute of Health and Welfare (AIHW) (2024), Heart, stroke and vascular disease: Australian facts, AIHW, Australian Government, accessed 20/02/2025.

Gibson RS (2005), Principles of Nutritional Assessment, 2nd edn, Oxford University Press.

McLean RM (2014), Measuring Population Sodium Intake: A Review of Methods, Nutrients, 6(11):4651-4662, accessed 20/02/2025.

Mente A, Irvine EJ, Honey RJD, Logan AG (2009), Urinary Potassium Is a Clinically Useful Test to Detect a Poor Quality Diet, Journal of Nutrition, 139(4):743-749, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013), ‘Potassium’ Nutrient Reference Values for Australia and New Zealand, Eat for Health website, accessed 20/02/2025.

World Health Organization (WHO) (2012a), Guideline: potassium intake for adults and children, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2012b), Guideline: sodium intake for adults and children, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2023), Increasing potassium intake to reduce blood pressure and risk of cardiovascular diseases in adults, WHO website, accessed 20/02/2025. 

Sodium is an essential nutrient required by the body to regulate the body’s fluids. Its main function is to maintain water balance in the body. The level of sodium within the body may vary throughout the day and is dependent on a person’s dietary intake. The most common form of sodium in the everyday diet is salt which may be added during manufacturing, processing, cooking and preparing foods and drinks, and as salt at the table. Sodium may also be present naturally in foods, in food additives, dietary supplements and/or medicines (WHO 2012; NHMRC 2017).

There is strong evidence that there is a relationship between excessive sodium intake and blood pressure. Hypertension (high blood pressure) is a significant risk factor for cardiovascular disease. Research has shown that most people are consuming more sodium than is required (WHO 2012; NHMRC 2017).

A biomedical measure of sodium intakes is useful in addition to estimating sodium intakes from the diet. Estimated total sodium intakes from food and dietary supplements can be inaccurate as salt used in cooking and preparation of food or added at the table cannot be measured and food composition tables may not reflect the sodium content of all foods available in the food supply, particularly when reformulation programs are ongoing (McLean 2014; DHAC 2023).

Laboratory test information, including analysis methods and machines used to measure sodium, is available from the Downloads page.

Sodium results were obtained for persons aged 5 years and over who provided a urine sample. Fasting was not required for this test.

Urinary sodium levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory using an integrated chip technology method. The sodium test measures the total amount of sodium in the urine that has been excreted from the body at the time of the test (spot test), expressed as mmol/L.

There is no consensus of epidemiological cut-off reference values for reporting sodium excretion for spot urine collections. As such no cut-off points have been defined.

The WHO recommends using a 24-hour urine collection method for establishing a baseline assessment of sodium consumption (WHO/PANO 2011; WHO SEARO 2021). A 24-hour urinary collection is widely considered to be the most accurate method of measuring sodium intakes as over 90% ingested sodium is excreted in urine within a 24-hour period. The amount of sodium in the urine reflects what was eaten at the last meal and how much fluid an individual has drunk in the last 24 hours (McLean 2014). In the IHMHS, a spot urine sample was used to test sodium content as it was not practical to collect urine samples from all study participants over a 24-hour period due to the high respondent burden and other method limitations (McLean 2014). Although not ideal for estimating sodium intakes, spot urine sodium data can be used for the purposes of monitoring changes in population sodium intakes over time and has been used in other national biomedical surveys (McLean 2014; PHE 2020).

The 24-hour urine sodium excretion amount may be estimated from spot urine data but there are no position statements on the use of the available equations and formulas to estimate 24-hour sodium excretion from spot urine data for the Australian population to date. Several equations and formulas have been developed for this purpose elsewhere (WHO/PAHO 2011; McLean et al. 2018; He et al. 2019; Huang et al. 2020; WHO SEARO 2021).

Sodium data is not currently included in ABS publications. However, sodium results are available in DataLab microdata products.

Points to be considered when interpreting data for this topic include the following:

• Urinary sodium results do not indicate a specific diagnosis without consultation with a health professional.
• There are several different methods to measure urinary sodium levels, and each test method or collection method may produce different results. The data from this topic should therefore be used with caution when comparing sodium results from other studies using a different test method.
• The urinary excretion of sodium varies significantly with dietary intake and there is no consensus on methods to determine expected 24-hour values based on a spot urine sodium data. As such estimated 24-hour sodium excretion data has not been reported.

This is the second time the ABS has collected information on sodium levels. Urinary sodium data was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on time series comparability, see Comparing biomedical collections over time.

Sodium data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

Department of Health and Aged Care (DHAC) (2023), Partnership Reformulation Program, DHAC website, accessed 20/02/2025.

He FJ, Ma Y, Campbell NRC, MacGregor GA, Cogswell ME, Cook NR (2019), Formulas to Estimate Dietary Sodium Intake From Spot Urine Alter Sodium-Mortality Relationship, Hypertension, 74(3):572-580, accessed 20/02/2025.

Huang L, Trieu K, Yoshimura S, Neal B, Woodward M, Campbell NRC, Li Q, Lackland DT, Leung AA, Anderson CAM, MacGregor GA, He FJ. 2020. Effect of dose and duration of reduction in dietary sodium on blood pressure levels: systematic review and meta-analysis of randomised trials, BMJ, 368:m315, accessed 20/02/2025.

McLean RM (2014), Measuring Population Sodium Intake: A Review of Methods, Nutrients, 6(11):4651-4662, accessed 20/02/2025.

McLean RM, Williams SM, Te Morenga LA, Man JI (2018), Spot urine and 24-h diet recall estimates of dietary sodium intake from the 2008/09 New Zealand Adult Nutrition Survey: a comparison, European Journal of Clinical Nutrition, 72:1120-1127, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013), Nutrient Reference Values for Australia and New Zealand: Including Recommended Dietary Intakes, NHMRC, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2017), ‘Sodium’ Nutrient Reference Values for Australia and New Zealand, Eat for Health website, accessed 20/02/2025.

Public Health England (PHE) (2020), National Diet and Nutrition Survey: Assessment of salt intake from urinary sodium in adults (aged 19 to 64 years) in England, 2018 to 2019, National Diet and Nutrition Survey, GOV UK, accessed 20/02/2025.

World Health Organization (WHO) (2012), Guideline: sodium intake for adults and children, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2013), NCD Global Monitoring Framework, WHO, accessed 20/02/2025.

World Health Organization (WHO) (2023b), Sodium reduction, WHO website, accessed 20/02/2025.

World Health Organization (WHO) (2023a), WHO Global Report on sodium intake, WHO, accessed 20/02/2025.

World Health Organization South-East Asia Regional Office (WHO SEARO) (2021), Measurement of population sodium intakes, World Action on Salt, Sugar and Health, accessed 20/02/2025.

World Health Organization (WHO)/Pan American Health Organization (PAHO) Regional Expert Group for Cardiovascular Disease Prevention through Population-wide Dietary Salt Reduction (2011), Protocol for Population Level Sodium Determination in 24-Hour Urine Samples, PAHO, accessed 20/02/2025.

Vitamin D is a hormone that is essential for the body to absorb and retain calcium and phosphorus effectively, which is important for bone health and muscle function. The main source of vitamin D is exposure to sunlight, although small amounts can be obtained through some foods, such as fatty fish, eggs, UV light exposed mushrooms, red and organ meats, and in Australia from fortified margarine, breakfast cereals and milk products (Nowson et al. 2012; Whiting and Gibson 2024; Dunlop et al. 2023). The main consequence of long-term vitamin D deficiency is rickets in infants and children and osteopenia/osteoporosis (fragile bones) in adults (Whiting and Gibson 2024; NIH 2024).

Vitamin D is found in several forms in the body. The IHMHS included a test for vitamin D in the form of calcidiol (25(OH)D) that measured both vitamin D2 and vitamin D3.

Laboratory test information, including analysis methods and machines used to measure vitamin D, is available from the Downloads page.

Vitamin D results were obtained for persons aged 12 years and over who provided a blood sample. Fasting was not required for this test.

Serum vitamin D levels were measured at the Douglass Hanly Moir Pathology (DHM) laboratory using the Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) method. The vitamin D test used measures the total amount of vitamin D circulating in the blood at the time of the test, expressed as nmol/L.

Cut-offs for adequate and deficient levels of vitamin D were reported. These cut-offs are based on the current position statement on vitamin D and health in adults in Australia and New Zealand (Nowson et al. 2012; RCPA 2023a).  

The following cut-offs were used for serum vitamin D in the form of calcidiol.

• adequate levels ≥50 nmol/L
• deficiency <50 nmol/L

Additional cut-offs were applied to assess the severity of vitamin D deficiency:

• mild deficiency 30–49 nmol/L
• moderate deficiency 13–29 nmol/L
• severe deficiency <13 nmol/L

Users should note that the severe deficiency cut-off recommended in the Australian position statement is <12.5 nmol/L, but it was not possible to output data against this cut-off as the vitamin D data was only available in whole numbers. In most of the ABS output, the moderate and severe vitamin D deficiency categories were combined due to small numbers.

Points to consider when interpreting data for this topic include the following:

• Vitamin D test results do not confirm a specific diagnosis of deficiency without consultation with a health professional.
• All blood samples were analysed for vitamin D by the LC-MS/MS method using standard reference materials. The data from this topic should therefore be used with caution when comparing vitamin D results from other studies using a different test method, unless standardised against the LC-MC/MS method (Whiting and Gibson 2024).
• Vitamin D tests routinely conducted for individuals by pathology laboratories in Australia may only measure serum vitamin D in the form of 25(OH)D3 (RCPA 2023a, b).

This is the second time the ABS has collected information on vitamin D levels. Vitamin D was previously collected in the NHMS 2011–12 and the NATSIHMS 2012–13. For information on the potential for time series comparability, see Comparing biomedical collections over time.

Vitamin D data has been collected in other non-ABS surveys. However, caution must be taken when interpreting results due to the differences in scope, assay and the instrument used, and any thresholds applied in the final analysis.

The NHMS 2011–12 and the NATSIHMS 2012–13 were included in the US National Institute of Health (NIH) international Vitamin D Standardization Program (VSDP), which aimed to standardise the measurement of vitamin D across all laboratories to enable the transfer of findings to patient care and public health activities (NIH 2013, 2024, n.d.; Macova et al. 2019; Sempos et al. 2020; Cashman 2022).

Cashman KD (2022), 100 YEARS OF VITAMIN D: Global differences in vitamin D status and dietary intake: a review of the data, Endocrine Connections, 11(1):e210282, accessed 20/02/2025.

Dunlop E, Boorman JL, Hambridge HL, McNeill J, James AP, Kiely M, Nowson CA, Rangan a, Cunningham J, Adorno P, Atyeo P, Black LJ (2023), Evidence of low vitamin D intakes in the Australian population points to a need for data-driven nutrition policy for improving population vitamin D status, Journal of Human Nutrition and Dietetics, 36(1):203-215, accessed 20/02/2025.

Malcova E, Cheang P, Dunlop E , Sherriff JL, Lucas RM, Daly RM , Nowson CA & Black LJ (2019), Prevalence and predictors of vitamin D deficiency in a nationally representative sample of adults participating in the 2011–2013 Australian Health Survey, British Journal of Nutrition, 121(8):894–904, accessed 20/02/2025.

National Health and Medical Research Council (NHMRC) (2013), ‘Vitamin D’ Nutrient Reference Values for Australia and New Zealand, Eat for Health website, accessed 20/02/2025.

National Institutes of Health Office of Dietary Supplements (NIH) (2024), Vitamin D, NIH website, accessed 20/02/2025.

National Institutes of Health Office of Dietary Supplements (NIH) (n.d.), Office of Dietary Supplements Vitamin D Initiative 2004-2018, NIH, accessed 20/02/2025.

Nowson CA, McGrath JJ, Ebeling PR, Haikerwal A, Daly RM, Sanders KM Seibel MJ & Mason RS (2012), Vitamin D and health in adults in Australia and New Zealand: a position statement, Medical Journal of Australia, 196(11):686-687, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2023a), Position Statement: Use and Interpretation of Vitamin D testing, RCPA, accessed 20/02/2025.

Royal College of Pathologists of Australasia (RCPA) (2023), ‘Vitamin D’, RCPA Manual, RCPA website, last accessed 20/02/2025.

Sempos CT, Binkley S (2020), 25-Hydroxyvitamin D Assay Standardisation and Vitamin D Guidelines Paralysis, Public Health Nutrition, 23(7):1153-1164, accessed 20/02/2025.

Whiting RJ, Gibson RS 2024, ‘Vitamin D’, Principles of Nutritional Assessment: 3rd Edition, Nutritional Assessment website, accessed 20/02/2025.

To assist users who are interested in the presence of more than one condition, three comorbidity data items have been created for the biomedical collection in the IHMHS. These data items cover cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes. They have been calculated from both self-reported conditions and conditions based on biomedical data.

For the purposes of deriving the comorbidity data items, the following input definitions were used:

• CVD was calculated using only self-reported data. To have CVD, respondents needed to self-report one or more cardiovascular conditions from the ICD10-Mini classification. For more information, see the National Health Measures Survey methodology, 2022–24 and National Aboriginal and Torres Strait Islander Health Measures Survey methodology, 2022–24.
• CKD was calculated using only biomedical data. To have CKD, respondents needed to be classified as having Stage 1 – Stage 5 CKD. CKD stages are based on eGFR and albuminuria data items. For more information, refer to Chronic Kidney Disease (CKD) biomarkers.
• Diabetes prevalence was calculated using both biomedical and self-reported data. Three data items are available to assess diabetes status based on FPG or HbA1c results, or both, combined with self-reported diabetes and medication use. For more information on the diabetes prevalence items, refer to Diabetes biomarkers.

The populations for comorbidity data items depends on which biomedical tests were used and are presented in the table below. 

Using the input conditions, the following output categories were produced for each comorbidity item:

0.    Not applicable (not in the comorbidity item population)
1.    Diabetes only
2.    CKD only
3.    CVD (self-report) only
4.    Diabetes and CKD only
5.    Diabetes and CVD (self-report) only
6.    CKD and CVD (self-report) only
7.    Diabetes and CKD and CVD (self-report)
8.    Does not have diabetes or CKD or CVD (self-report).

In the comorbidity items, each respondent is allocated to only one category. Where people had a ‘not known’ or ‘unable to determine’ status for a biomedical input item, they were treated as not having that condition.

In order to create flexibility with the use of this item, categories 1 to 3 (which contain persons with only one condition) and category 8 (which contain persons with none of the conditions) were produced.

To produce frequencies based on different combinations of these conditions, users can collapse categories as required. For example, to determine those people with either Diabetes or CKD, combine categories 1, 2, 4, 5, 6 and 7. To determine those people who don't have CVD, combine categories 1, 2, 4 and 8.

Cardiovascular disease (CVD) is an umbrella term that includes heart, stroke, and blood vessel diseases. It is one of Australia’s largest health problems and a leading cause of death. An updated Australian 'Guideline for assessing and managing cardiovascular disease risk' was released in 2023. This update was released alongside a new Australian CVD risk calculator, by the Australian Heart Foundation with the Commonwealth of Australia (CA/DHAC 2023).

The Australian CVD risk calculator estimates the chance of an individual developing CVD within the next 5 years using data for multiple risk factors. Assessing CVD risk based on the combined effect of risk factors is more accurate than looking at risk factors individually and allows for more tailored risk factor management for each person (NVDPA 2012; Nelson 2020).

This page provides information for users who are interested in utilising the Australian CVD risk calculator when analysing results from the National Health Measures Survey (NHMS) and the National Aboriginal and Torres Strait Islander Health Measures Survey (NATSIHMS).

For more information on biomedical measures of CVD, refer to Cardiovascular disease (CVD) biomarkers.

The Australian CVD risk calculator produces estimated 5-year CVD risk scores, expressed as a percentage representing the person’s probability of dying or being hospitalised due to myocardial infarction, angina, other coronary heart disease, stroke, transient ischaemic attack, peripheral vascular disease, congestive heart failure or other ischaemic CVD-related conditions within the next 5 years.

At the time of publication, the ABS has not published CVD risk profiles from the IHMHS. However, the ABS recognises that some users may be interested in using the Australian CVD risk calculator to analyse results.

The applicable population for the Australian CVD risk calculator is persons aged 45–79 years and 30–79 years for Aboriginal and Torres Strait Islander peoples who agreed to participate in the NHMS or NATSIHMS and provided a blood sample. Persons aged 80 years and over or with known CVD should be excluded from data analysis.

To conduct analysis using the 2023 Australian CVD risk calculator, users will need to utilise both biomedical study data items and data from other IHMHS surveys. However, the IHMHS does not collect all the data inputs required for the Australian CVD risk calculator. A summary of available inputs is provided in the table below.

There is a diabetes-specific equation in the Australian CVD risk calculator that provides a more accurate estimate of CVD risk for people with Type 2 diabetes, but additional data inputs are required (see table above). The ABS advises that analysis using the diabetes-specific equation to assess the CVD risk for people with diabetes cannot be undertaken using IHMHS data as not all the required data inputs were collected.

Points to be considered when interpreting analysis with the 2023 Australian CVD risk calculator for this topic include the following:

• The calculator is not applicable for use with people with a high CVD risk (i.e. people with known CVD, chronic kidney disease or with a confirmed diagnosis of familial hypercholesterolaemia).
• Information was not collected on the history of atrial fibrillation, but this is an optional input into the calculator.
• Information on smoking status obtained from the IHMHS was not as detailed as required in the calculator so can only be used as a proxy measure.
• The equation on which the calculator is based has not been validated for people with Type 1 diabetes, or people aged 80 years and over.
• Whilst the biomedical indicators, uACR and eGFR, have been shown to independently improve prediction of cardiovascular events, they are only included as variables in the diabetes-specific equation for the calculator due to lack of availability of data in the reference population (New Zealand PREDICT cohort).
• In addition to physiological and lifestyle factors, socioeconomic status is also associated with increased CVD risk. Including socioeconomic status in risk prediction improves accuracy, compared with using risk factors alone.
• The data from this topic should be used with caution when comparing results from other studies using different biomedical test methods and/or CVD risk calculators.

The ABS did not publish results on CVD risk profiles using the 2012 Australian absolute CVD risk calculator in previous biomedical collections. However, CVD risk profiles were available in the DataLab microdata.

Data generated using the 2023 Australian CVD risk calculator should not be directly compared to that from the previous 2012 calculator as the input parameters are different and reference population has changed (CA/DHAC 2023). An overview of the difference between the two calculators is provided in the table below.

Commonwealth of Australia as represented by the Department of Health and Aged Care (CA/DHAC) (2023), Australian Guideline for assessing and managing cardiovascular disease risk, AusCVDRisk, Australian Government, accessed 20/02/2025.

National Institutes of Health (NIH) (n.d.), Framingham Heart Study: Laying the Foundation for Preventive Health Care, NIH, accessed 20/02/2025. 

National Vascular Disease Prevention Alliance (NVDPA) (2012), Guidelines for the management of absolute cardiovascular disease risk [PDF 4458 KB], Heart Foundation, accessed 20/02/2025.

Nelson MR (2020), Absolute cardiovascular disease risk and the use of the Australian cardiovascular disease risk calculator, Australian Journal of General Practice, 49(8):471–473, accessed 20/02/2025.

Wells S, Riddell T, Kerr A, Pylypchuk R, Chelimo C, Marshall R, Exeter DJ, Mehta S, Harrison J, Kyle C, Grey C, Metcalf, Warren J, Kenealy T, Drury PL, Harwood M, Bramley D, Gala G, Jackson R (2017), Cohort Profile: The PREDICT Cardiovascular Disease Cohort in New Zealand Primary Care, (PREDICT-CVD 19), International Journal of Epidemiology, 46(1):22, accessed 20/02/2025.