The Australian National Health Survey (NHS), conducted every three years by the Australian Bureau of Statistics (ABS), provides a nationally representative profile of Australians' health and wellbeing. The 2017-18 survey collected self-reported data on health, demographics, and lifestyle factors from both adults and children, offering valuable insights into the nation's health status and behaviors through a comprehensive sampling design.

Proportion (%) of overweight or obese children aged 5–17 years, ACT and Australia, 2007–08, 2011–12 and 2014–15.

Source: ABS, National Health Survey, 2007–15

Proportion (%) of ACT young people aged 18–24 years who eat at least two serves of fruit and five serves of vegetables per day, 2007–08, 2011–2012 and 2014–15.

Source: ABS National Health Survey, 2007–15.

Note: * Estimate has a relative standard error greater than 50 per cent and is considered unreliable for general use. ^ Estimate has a relative standard error of 25–50 per cent and should be used with caution. The proportion of ACT young people (18–24 years) meeting the NHMRC Australian Dietary Guidelines for fruit and vegetables is a new indicator for 2018.

This dataset presents the statistics of prevalence for chronic kidney disease (CKD) distributed by age for the years between 2011-2012. The data is aggregated to 2015 Primary Health Network (PHN) geographic boundaries based on the 2011 Australian Statistical Geography Standard (ASGS). The chronic kidney disease (CKD) datasets were released by the Australian Institute of Health and Welfare (AIHW) to present data on prevalence, treatment (hospitalisations for CKD and dialysis) and deaths. It shows the distribution of the impact of CKD across Australia and visualises rates of CKD and the health and demographic profile for local areas. The data was modelled by the AIHW from the 2011-12 Australian Bureau of Statistics (ABS) Australian Health Survey (AHS): National Health Measures Survey.

This dataset presents the statistics of prevalence for chronic kidney disease (CKD) distributed by sex for the years between 2011-2012. The data is and is aggregated to the 2011 Public Health Information Development Unit (PHIDU) Population Health Areas (PHA), based on the 2011 Australian Statistical Geography Standard (ASGS). The CKD datasets were released by the Australian Institute of Health and Welfare (AIHW) to present data on prevalence, treatment (hospitalisations for CKD and dialysis) and deaths. It shows the distribution of the impact of CKD across Australia and visualises rates of CKD and the health and demographic profile for local areas. The data was modelled by the AIHW from the 2011-12 Australian Bureau of Statistics (ABS) Australian Health Survey (AHS): National Health Measures Survey.

This dataset presents the footprint of the crude percentage of adults who are overweight or obese. Adults were classified as overweight if their Body Mass Index (BMI) was greater than or equal to 25 and less than 30, or obese if their BMI was greater than or equal to 30. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the …Show full descriptionThis dataset presents the footprint of the crude percentage of adults who are overweight or obese. Adults were classified as overweight if their Body Mass Index (BMI) was greater than or equal to 25 and less than 30, or obese if their BMI was greater than or equal to 30. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial years of 2011-2012 and 2014-2015, and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data. Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

*Scales adjusted for age/sex distribution of PRISM population, factor loadings and standard deviation using Australian National Health Survey values.ABS. National Health Survey. SF-36 Population Norms Australia: Australian Bureau Statistics, Commonwealth of Australia Catalogue No. 4399.0; 1997.

Australia Food Emulsifier Market Size was valued at USD 372 Million in 2024 and is projected to reach USD 601 Million by 2032, growing at a CAGR of 6.2% from 2026 to 2032.

Key Market Drivers

Growing Health-Conscious Consumer Base: The growing health-conscious consumer base is driving the Australian Food Emulsifier Market. According to the ABS National Health Survey 2021-22, 47% of Australians are actively decreasing their fat intake, driving demand for reformulated, low-fat food products that use specialist emulsifiers. Furthermore, 56.7% of adults choosing healthier alternatives encourages the use of natural emulsifiers in food processing, which aligns with customer desires for clean-label, health-focused solutions.

This dataset, released July 2018, contains the community strength of areas based on Voluntary work for an organisation or group - people aged 15 years and over, 2016; Estimated number of people aged 18 years and over who did unpaid voluntary work in the last 12 months through an organisation (modelled estimates), 2014; Estimated number of people aged 18 years and over who are able to get support in times of crisis from people outside the household (modelled estimates), 2014; Estimated number of people aged 18 years and over (or their partner) who provide support to other relatives living outside the household (modelled estimates), 2014; Estimated number of people aged 18 years and over who disagree/strongly disagree with acceptance of other cultures (modelled estimates), 2014; Estimated number of people aged 18 years and over who, in the past 12 months, felt that they had experienced discrimination or have been treated unfairly by others (modelled estimates), 2014.

The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016; Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS from the 2014 General Social Survey; estimates at the LGA and PHN level were derived from the PHA estimates; Estimates for Quintiles and Remoteness Areas were compiled by PHIDU based on direct estimates from the 2014 General Social Survey, ABS Survey TableBuilder.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

The estimated number of people that had ‘poor' or ‘fair' health and those people as a rate of the total population. The data on which the estimates are based are self-reported responses, reported to interviewers in the 2014–15 National Health Survey. Respondents aged 15 years and over were asked to assess their health on a scale from ‘poor' to ‘excellent' (the scale was ‘poor', ‘fair', ‘good', ‘very good', or ‘excellent'). The data reported are the sum of responses categorised as ‘poor' or ‘fair' (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates.

This dataset, released July 2018, contains the community strength of areas based on Voluntary work for an organisation or group - people aged 15 years and over, 2016; Estimated number of people aged 18 years and over who did unpaid voluntary work in the last 12 months through an organisation (modelled estimates), 2014; Estimated number of people aged 18 years and over who are able to get support in times of crisis from people outside the household (modelled estimates), 2014; Estimated number of people aged 18 years and over (or their partner) who provide support to other relatives living outside the household (modelled estimates), 2014; Estimated number of people aged 18 years and over who disagree/strongly disagree with acceptance of other cultures (modelled estimates), 2014; Estimated number of people aged 18 years and over who, in the past 12 months, felt that they had experienced discrimination or have been treated unfairly by others (modelled estimates), 2014. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016; Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS from the 2014 General Social Survey; estimates at the LGA and PHN level were derived from the PHA estimates; Estimates for Quintiles and Remoteness Areas were compiled by PHIDU based on direct estimates from the 2014 General Social Survey, ABS Survey TableBuilder. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset presents the footprint of the crude percentage of adults who perform insufficient weekly physical activity. Insufficient physical activity is defined as those aged 18-64 years who did not complete over 150 minutes of physical activity, and at least 5 sessions over a week, and those aged 65+ years who did not complete 30 minutes of activity on at least 5 days in a week. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences.

• Adults are defined as persons aged 18 years and over.

• Values assigned to "n.p." in the original data have been removed from the data.

• Data for PHN107 should be interpreted with caution, as the estimates have a relative standard error of 25% to 50%.

• Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.

Abstract

The introduction of antiretroviral (ARV) therapy for the treatment of HIV infection is essential if life expectancy and quality are to be improved for the millions of Africans living with HIV/AIDS. Little was known about the consequences of ARV introduction for sexual risk behavior and HIV transmission.

This application proposed a behavior survey to be included in the new Welcome Trust Programme grant to the Karonga Prevention Study. The full grant proposal was submitted to the NHSRC and considered on 24th March 2006. The committee requested that separate protocols be provided for each activity. Further to this, the Census Sero-survey protocol was submitted and at the time of this application it was awaiting consideration (application ?419: 'Impact of HIV in northern Malawi in the era of antiretroviral therapy: Census sero-survey).

The Census sero-survey covered the population of southern Karonga district (a continuation of the demographic surveillance system established during the five year programme prior to the time of this application). It consisted of a population based HIV sero-survey within a demographic surveillance system. The HIV sero-survey included an HIV survey with voluntary counselling and testing for HIV (VCT). This application describes an extra module for the survey, a behavior survey, which had fit into the overall Census Sero-survey as follows:

                             Census Sero-survey

Demographic Surveillance (Annual recensus and socio-economic update of whole population) : - HIV Sero-survey (Eligible adults) - Proposed behavior survey - HIV survey (including VCT)

The HIV sero-survey was carried out in adults at baseline and after 4 years, with annual surveys in between for age groups at greatest risk. The proposed behavior survey was administered before the HIV survey and VCT, at each round.

Behavior data was intended to be used to measure changes in sexual behavior (especially risky sexual behavior), marital dynamics, behaviors such as condom use, and other determinants of HIV transmission such as partner concurrency and forced sex. This work provided useful information for national ARV delivery programmes, health education programmes, and HIV prevention programmes. Data on behavior change was also important in interpreting other research findings on the epidemiological and socio-demographic impacts of HIV.

                                 NHSRC Number: 419

Geographic coverage

Chilumba, Karonga district

Analysis unit

Individual

Universe

All adults aged 15 years and over and members of households located within the demographic surveillance area were eligible for inclusion in the behavior survey at baseline and after four years (changes were tested since baseline). There were annual behavior surveys in between for younger adults among whom the highest incidence of HIV was expected (the exact age range depended on results from the first survey).

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionnaires and modules are provided as external resources.

The purpose of this research was to examine individual differences related to fear of, perceived susceptibility to, and perceived severity of mpox as well as mpox knowledge, fear, perceived susceptibility, and perceived severity as predictors of vaccine intention in a national survey of U.S. adults (aged ≥18 years). Address-based sampling (ABS) methods were used to ensure full coverage of all households in the nation, reflecting the 2021 March Supplement of the Current Population Survey. Internet-based surveys were self-administered by Ipsos between September 16–26, 2022. N = 1018 participants completed the survey. The survey included items, based partially on the Health Belief Model, assessing vaccine intention (1 item; responses from 1 [Definitely not] to 5 [Definitely]), fear of mpox (7-item scale; α = .89; theoretical mean = 7–35), perceived susceptibility to mpox (3-item scale; α = .85; theoretical mean = 3–15), and perceived severity of mpox (4-item scale; α = .65; theoretical mean = 4–20). Higher scores indicate greater fear, susceptibility, and severity. One-way ANOVAs were run to examine mean score differences by demographic groups (e.g., gender, race/ethnicity, sexual orientation), and multiple regression analyses assessed the relationship between predictors (mpox knowledge, susceptibility/severity, fear) and a single outcome (vaccination intention), while controlling for demographic covariates. Sampling weights were applied to all analyses. Only 1.8% (n = 18) of respondents reported having received the mpox vaccine. While mpox vaccine intention was low (M = 2.09, SD = 0.99), overall differences between racial/ethnic, sexual orientation, education, and household income groups were statistically significant. Fear of mpox was very low (M = 13.13, SD = 5.33), and there were overall statistically significant differences in both fear and perceived severity among gender, race/ethnicity, sexual orientation, education, and household income groups. While respondents reported not feeling very susceptible to mpox (M = 5.77, SD = 2.50), they generally rated mpox as just above the theoretical mean in terms of severity (M = 11.01, SD = 2.85). Mpox knowledge, fear, severity, and susceptibility, as well as race/ethnicity, were all statistically significant predictors of intention to vaccinate, with susceptibility representing the strongest predictor. Overall, Americans’ vaccination for mpox/vaccine intent was low. Gay/lesbian and racial/ethnic minority respondents felt more susceptible to and viewed mpox more severely, compared with heterosexual and White respondents, respectively. These data may be used to tailor risk and prevention (e.g., vaccination) interventions, as cases continue to surge in the current global mpox outbreak. Greater perceptions of susceptibility, severity, and fear about mpox exist largely among minority populations. While public health messaging to promote mpox vaccination can focus on improving knowledge, as well as addressing fear and perceived severity of, and susceptibility to, mpox, such messages should be carefully crafted to prevent disproportionate negative effects on marginalized communities.

This dataset, released April 2017, contains an Estimated number of people aged 18 years and over whose household could raise $2,000 within a week (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income in the last 2 years (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months (modelled estimates), 2014. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Modelled by PHIDU based on the ABS General Social Survey, 2014. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released April 2017, contains an Estimated number of people aged 18 years and over whose household could raise $2,000 within a week (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income in the last 2 years (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months (modelled estimates), 2014. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Modelled by PHIDU based on the ABS General Social Survey, 2014. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released April 2017, contains an Estimated number of people aged 18 years and over whose household could raise $2,000 within a week (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income in the last 2 years (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months (modelled estimates), 2014. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Modelled by PHIDU based on the ABS General Social Survey, 2014.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released July 2018, contains Estimated number of people aged 18 years and over who often have a difficulty or cannot get to places neededwith transport, including housebound (modelled estimates), 2014; Estimated number of people aged 18 years and over who experienced a barrier to accessing healthcare whenneeded it in the last 12 months, with main reason being cost of service (modelled estimates), 2014. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: Modelled by PHIDU based on the ABS General Social Survey, 2014.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released July 2018, contains an Estimated number of people aged 18 years and over who often have difficulty or cannot get to places needed with transport, including housebound (modelled estimates), 2014; Estimated number of people aged 18 years and over who experienced a barrier to accessing healthcare when needed it in the last 12 months, with the main reason being the cost of service (modelled estimates), 2014. The data is by Local Government Area (LGA) 2016 geographic boundaries.

For more information please see the data source notes on the data.

Source: Modelled by PHIDU based on the ABS General Social Survey, 2014.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released July 2018, contains Estimated number of people aged 18 years and over who often have a difficulty or cannot get to places neededwith transport, including housebound (modelled estimates), 2014; Estimated number of people aged 18 years and over who experienced a barrier to accessing healthcare whenneeded it in the last 12 months, with main reason being cost of service (modelled estimates), 2014. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Modelled by PHIDU based on the ABS General Social Survey, 2014. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.