Latest release

National Health Survey: First Results methodology

Reference period
2017-18
Released
12/12/2018
Next release Unknown
First release

Explanatory notes

Introduction

1 This publication presents key indicators from the 2017-18 National Health Survey (NHS), including information on:

  • the health status of the population, including long-term health conditions;
  • health risk factors such as smoking, Body Mass Index, diet, exercise and alcohol consumption; and
  • demographic and socioeconomic characteristics.
     

2 The 2017-18 NHS was conducted throughout Australia from July 2017 to June 2018. Previous surveys were conducted in 1989-90, 1995, 2001, 2004-05, 2007-08, 2011-12 and 2014-15. Health surveys conducted by the ABS in 1977-78 and 1983, while not part of the NHS series, also collected similar information.

Scope of the survey

3 The NHS was conducted from a sample of approximately 21,300 people in 16,400 private dwellings across Australia.

4 Urban and rural areas in all states and territories were included, while Very Remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities were excluded. These exclusions are unlikely to affect national estimates, and will only have a minor effect on aggregate estimates produced for individual states and territories, excepting the Northern Territory where the population living in Very Remote areas accounts for around 20.3% of persons.

5 Non-private dwellings such as hotels, motels, hospitals, nursing homes and short-stay caravan parks were excluded from the survey. This may affect estimates of the number of people with some long-term health conditions (for example, conditions which may require periods of hospitalisation or long term care).

6 Within each selected dwelling, one adult (18 years and over) and one child (0-17 years) were randomly selected for inclusion in the survey. This sub-sampling within households enabled more information to be collected from each respondent than would have been possible had all usual residents of selected dwellings been included in the survey. For the purposes of the NHS, a household was defined as one or more persons, at least one of whom is aged 18 years and over, usually resident in the same private dwelling.

7 The following groups were excluded from the survey:

  • certain diplomatic personnel of overseas governments, customarily excluded from the Census and estimated resident population;
  • persons whose usual place of residence was outside Australia;
  • members of non-Australian Defence forces (and their dependents) stationed in Australia; and
  • visitors to private dwellings.
     

Sample design

8 Dwellings were selected at random using a multistage area sample of private dwellings. The initial sample selected for the survey consisted of approximately 25,109 dwellings. This was reduced to a sample of 21,544 after sample loss (for example, households selected in the survey which had no residents in scope of the survey, vacant or derelict buildings, buildings under construction). Of those remaining dwellings, 16,384 (or 76.1%) were fully or adequately responding, yielding a total sample for the survey of 21,315 persons.

Approached sample, final sample and response rates

 NSWVic.QldSAWATas.NTACTAust.
Households approached
(after sample loss)
4 537
3 420
4 412
1 981
2 223
1 778
1 828
1 365
21 544
Households in sample
3 272
2 614
3 365
1 659
1 656
1 606
1 091
1 121
16 384
Response rate (%)
72.1
76.4
76.3
83.8
74.5
90.3
59.7
82.1
76.1
Persons in sample
4 273
3 419
4 412
2 056
2 168
2 016
1 479
1 492
21 315
 

9 To take account of possible seasonal effects on health characteristics, the sample was spread across the 12-month enumeration period. Analysis of previous health surveys has shown no seasonal bias across key estimates.

Data collection

10 Trained ABS interviewers conducted personal interviews with selected residents in sampled dwellings. One adult (aged 18 years and over) in each dwelling was selected and interviewed about their own health characteristics as well as information about the household (for example, income of other household members). An adult, nominated by the household, was interviewed about one child in the household. Some children aged 15-17 years may have been personally interviewed with parental consent.

Weighting, benchmarking and estimation

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

12 The first step in calculating weights for each person was to assign an initial weight, which was equal to the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 others). An adjustment was then made to these initial weights to account for the time period in which a person was assigned to be enumerated.

13 The weights are calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of sex by age by area of usual residence. Weights calibrated against population benchmarks in this way compensate for over or under-enumeration of particular categories of persons and ensure that the survey estimates conform to the independently estimated distribution of the population by age, sex and area of usual residence, rather than to the distribution within the sample itself. 

14 The NHS was benchmarked to the estimated resident population living in private dwellings in non-Very Remote areas of Australia at 31 December 2017. Excluded from these benchmarks were persons living in discrete Aboriginal and Torres Strait Islander communities. The benchmarks, and hence the estimates from the survey, do not (and are not intended to) match estimates of the total Australian resident population (which include persons living in Very Remote areas or in non-private dwellings, such as hotels) obtained from other sources.

15 In 2017-18, data from the NHS and the Survey of Income and Housing (SIH) was combined to produce the National Health Survey and Survey of Income and Housing pooled dataset (NHS/SIH) and enable more accurate smoker status estimates. This dataset was also benchmarked to the above population to produce weights for this dataset. In addition, to preserve consistency between the two datasets, the NHS data was also benchmarked to the pooled NHS/SIH dataset by age, sex, area of usual residence and smoker status. This means that unperturbed smoker estimates will be identical between the NHS data and the NHS/SIH data at these cross-classifications.

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

Reliability of estimates

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

18 Sampling error is the difference between estimates, derived from a sample of persons, and the value that would have been produced if all persons in scope of the survey had been included. Indications of the level of sampling error are given by the Relative Standard Error (RSE) and 95% Margin of Error (MoE). For more information refer to the Technical Note - Reliability of Estimates.

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

20 Margins of Error are provided for proportions to assist users in assessing the reliability of these data. Estimates of proportions with an MoE more than 10% are annotated to indicate they are subject to high sample variability and particular consideration should be given to the MoE when using these estimates. Depending on how the estimate is to be used, an MoE greater than 10% may be considered too large to inform decisions. In addition, estimates with a corresponding standard 95% confidence interval that includes 0% or 100% are annotated with a # to indicate that they are usually considered unreliable for most purposes.

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

22 Non-response occurs when people are unable to or do not cooperate, or cannot be contacted. Non-response can affect the reliability of results and can introduce a bias. The magnitude of any bias depends on the rate of non-response and the extent of the difference between the characteristics of those people who responded to the survey and those who did not. 

23 In the 2017-18 NHS, measurements of height, weight and waist circumference were taken of respondents aged 2 years and over, while blood pressure was also measured for adult respondents (aged 18 years and over). While these items had relatively high non-response rates, analysis indicated no bias existed in the non-responding population. Imputation was used to obtain values for respondents for whom physical measurements were not taken. For more information see Appendix 2: Physical measurements in the 2017-18 National Health Survey.

24 The following methods were adopted to reduce the level and impact of non-response:

  • face-to-face interviews with respondents;
  • the use of proxy interviews in cases where language difficulties were encountered, noting the interpreter was typically a family member;
  • follow-up of respondents if there was initially no response; and
  • weighting to population benchmarks to reduce non-response bias;
     

Interpretation of results

25 Care has been taken to ensure that results are as accurate as possible. This includes thorough design and testing of the questionnaire, interviews being conducted by trained ABS Interviewers, and quality control procedures throughout data collection, processing and output. There remain, however, other factors which may have affected the reliability of results, and for which no specific adjustments can be made. The following factors should be considered when interpreting these estimates:

  • Information recorded in the survey is essentially 'as reported' by respondents, and hence may differ from information available from other sources or collected using different methodology; for example, information about health conditions is self-reported and, while not directly based on a diagnosis by a medical practitioner in the survey, respondents were asked whether they had ever been told by a doctor or nurse that they had a particular health condition. Conditions which have a greater effect on people's wellbeing or lifestyle, or those specifically mentioned in survey questions, are expected in general to have been better reported than others;
  • Some respondents may have provided responses that they felt were expected, rather than those that accurately reflected their own situation. Every effort has been made to minimise such bias through the development and use of appropriate survey methodology;
  • Results from previous surveys indicate a tendency for respondents to under-report consumption of alcohol; and
  • Under-reporting of young persons identifying as current smokers may have occurred due to social pressures, particularly in cases where other household members were present at the interview.
     

Comparability with previous National Health Surveys

26 Data for 2017-18 are comparable with earlier surveys, with some exceptions:

  • In 2017-18 an additional example "32. Learning difficulties, including dyslexia" was added to Prompt Card O2, for the Mental, Cognitive and Behavioural Conditions module. All other items remain the same and have been coded consistently with 2014-15 (as above);
  • In NHS 2017-18 a shorter version of the standard ABS Income questionnaire module for Household Surveys for the collection of 'Total Personal Income' and 'Total Household Income' was introduced. Overall data for 'Total Personal Income' and 'Total Household Income' is comparable between NHS 2017-18 and NHS 2014-15, however the breakdown by type of government pension is not available for NHS 2017-18 .
  • A new module regarding clients of the Department of Veterans' Affairs (DVA) has been added to NHS 2017-18, this should not be confused with the item used in previous NHS which presented data regarding persons who hold a "DVA Health Card";
  • Age ranges for two Disability items have been changed. In NHS 2014-15 the Item 'Whether has an education restriction' was limited to those aged 5 to 20 years, for 2017-18 this age range is persons aged 4 or more years, recognising that an education restriction can exist outside of school years and be life long. Similarly, the age range for 'Whether has an employment restriction' has been changed from 15 to 64 years to persons aged 15 years or more. Apart from this, the items are consistent with previous NHS;
  • New scales allowing measurements of up to 200kg were used in NHS 2017-18. In addition, a new stadiometer was used to measure height for greater accuracy. For this reason, an additional height measure was taken to analyse variation for Quality Assurance. Despite these changes, the estimates are considered comparable with 2014-15.
  • In line with Census 2016 a number of standard classifications used in the NHS have been updated in 2017-18, these include: Standard Australian Classification of Countries (SACC), 2016 (Country of Birth), Australian Standard Classification of Languages (ASCL) 2016 (Main language spoken at home), Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 - Coder 2018 (Industry of Main Job) and ANZSCO - Occupation Classification and Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia 2016. More information about these can be found on the ABS website. http://www.abs.gov.au/classifications
     

27 In 2014-15 and 2017-18, a module specifically dedicated to mental and behavioural conditions was included in the NHS to collect information on cognitive, organic and behavioural conditions. In previous NHS cycles, mental and behavioural conditions were collected in a module that included a wide range of long-term health conditions. The number of persons who reported having a mental and behavioural condition in 2014-15 increased from the 2011-12 NHS, potentially due to the greater prominence of mental and behavioural conditions in the new module. Data on mental and behavioural conditions for 2014-15 and 2017-18 are therefore not comparable with data in previous National Health Surveys.

28 Estimates of people with mental or behavioural conditions from the NHS will differ from those obtained from a diagnostic tool such as that used in the 2007 National Survey of Mental Health and Wellbeing.

29 For the 2017-18 NHS cycle, the smoking questionnaire module was used in both the NHS and the 2017-18 Survey of Income and Housing (SIH) to produce a larger sample size for more accurate smoker status estimates. The pooled dataset is known as the National Health Survey and Survey of Income and Housing (NHIH) and will contain data items common to both NHS and SIH such as age, sex, country of birth and those from the smoking module. In this publication, this pooled dataset is used whenever possible to produce estimates with smaller errors. The NHS dataset is used for items collected only in the NHS for example smoking status by BMI. The following table compares results produced from the NHIH and the NHS 2017-18 on its own. Note that the pooled dataset was used solely for smoker status and not consumption of cigarettes.

Smoking status, NHIH and NHS, 2017-18

National Health Survey and Survey of Income and Housing (pooled dataset)

 Estimate ('000)RSE of Estimate Proportion (%)MoE of Proportion 
Smoker status     
   Current smoker    
      Daily 
2 567.0
1.7
13.8
0.5
      Other (a) 
258.6
5.9
1.4
0.2
      Total current smoker
2 824.8
1.5
15.1
0.4
   Ex-smoker
5 440.8
0.8
29.2
0.5
   Never smoked
10 388.1
0.5
55.7
0.5
   Total persons aged 18
    years and over (b)
18 654.2
0.0
100.0
0.0

National Health Survey, 2017-18

 Estimate ('000)RSE of Estimate Proportion (%)MoE of Proportion 
Smoker status     
   Current smoker    
      Daily 
2 568.1
1.7
13.8
0.5
      Other (a) 
278.0
9.4
1.5
0.3
      Total current smoker
2 840.3
1.6
15.2
0.5
   Ex-smoker
5 585.6
1.3
29.9
0.8
   Never smoked
10 227.3
0.8
54.8
0.9
   Total persons aged 18
    years and over (b)
18 656.2
0.0
100.0
0.0
a. Includes current smoker weekly (at least once a week, but not daily) and current smoker less than weekly.
b. Discrepancy between 'Total persons aged 18 years and over' are due to random adjustments to avoid the release of confidential data.
 

30 When interpreting changes over time or differences between population groups (for example, between males and females), reliability of estimates should be taken into account. All comparisons in this publication were tested for statistical significance at the 95% level of confidence; for more information see Technical Note - Reliability of Estimates.

Classifications

31 Long-term health conditions reported by respondents in the NHS are presented using a classification originally developed for the 2001 NHS by the Family Medicine Research Centre, University of Sydney, in conjunction with the ABS. The classification is based on the 10th revision of the International Classification of Diseases (ICD) and is used for all years from 2001 to 2017-18.

32 Country of birth is classified to the Standard Australian Classification of Countries (cat. no. 1269.0).

33 Main language spoken at home is classified according to the Australian Standard Classification of Languages (cat. no. 1267.0).

34 Descriptions of data items such as Body Mass Index and the Kessler Psychological Distress Scale (K10) are included in the Glossary to this publication.

Confidentiality

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

36 To minimise the risk of identifying individuals in aggregate statistics, a technique known as perturbation is used to randomly adjust cell values. Perturbation involves a small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. After perturbation, a given published cell value will be consistent across all tables. However, adding up cell values to derive a total will not necessarily give the same result as published totals. 

37 Perturbation has been applied to 2014–15 and 2017–18 data. Data from previous NHS presented in this publication have not been perturbed, but have been confidentialised if required using suppression of cells.

Rounding

38 Estimates presented in this publication have been rounded. 

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

Acknowledgements

40 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated; without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

Products and services

41 Summary results from the NHS are available in spreadsheet form from the 'Data downloads' section in this release. The statistics presented are only a selection of the information collected. 

42 For users who wish to undertake more detailed analysis, a TableBuilder product for the 2017-18 NHS is expected to be available in the second quarter of 2019. TableBuilder is an online tool for creating tables from ABS survey data, where variables can be selected for cross-tabulation. It has been developed to complement the existing suite of ABS microdata products and services including Census TableBuilder and CURFs. Further information about ABS microdata, including conditions of use, is available via the Microdata section on the ABS website.

43 Customised tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic areas selected to meet individual requirements.

Related publications

44 Current publications and other products released by the ABS are listed on the ABS website. The ABS also issues a daily Release Advice on the website which details products to be released in the week ahead.

Appendix 1 - sample counts and estimates

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The following tables present sample counts and weighted estimates for the 2017-18 National Health Survey.

Sample counts and weighted estimates, Australia

 PERSONS IN SAMPLEWEIGHTED ESTIMATE
 MalesFemalesPersonsMalesFemalesPersons
Age group (years)
no.
no.
no.
'000
'000
'000
0-4
770
728
1 498
797.9
752.6
1 549.4
5-9
670
648
1 318
812.2
768.6
1 588.0
10-14
632
619
1 251
748.4
712.5
1 461.0
15-19
593
606
1 199
785.6
719.4
1 497.7
20-24
407
446
853
789.7
797.0
1 590.7
25-29
554
591
1 145
904.9
762.2
1 661.3
30-34
651
780
1 431
884.3
1 069.7
1 951.4
35-39
675
792
1 467
847.6
824.1
1 676.2
40-44
632
745
1 377
747.7
806.9
1 547.9
45-49
705
747
1 452
812.6
783.7
1 596.2
50-54
625
723
1 348
719.4
824.9
1 543.2
55-59
667
736
1 403
687.3
749.6
1 434.9
60-64
643
743
1 386
680.9
694.4
1 376.4
65-69
630
663
1 293
566.7
575.4
1 142.1
70-74
525
638
1 163
487.4
539.5
1 029.8
75-79
320
440
760
308.1
361.9
672.5
80-84
216
330
546
206.1
240.8
449.5
85 years and over
171
254
425
146.3
183.0
332.7
Total all ages
10 086
11 229
21 315
11 935.2
12 165.3
24 105.3
 

Sample counts and weighted estimates, states and territories

Age group (years)NSWVic.QldSAWATas.NTACTAust.
 PERSONS IN SAMPLE (no.)
0-17
1 003
808
1 050
398
513
411
391
371
4 945
18-64
2 344
1 932
2 544
1 189
1 221
1 119
947
887
12 183
65 years and over
926
679
818
469
434
486
141
234
4 187
Total all ages
4 273
3 419
4 412
2 056
2 168
2 016
1 479
1 492
21 315
 WEIGHTED ESTIMATES ('000)
0-17
1 733.0
1 390.7
1 131.2
361.7
581.1
112.0
43.3
92.8
5 445.7
18-64
4 819.9
3 982.0
2 965.7
1 027.2
1 554.8
305.0
118.8
260.7
15 030.0
65 years and over
1 190.9
915.7
707.9
295.8
346.9
96.2
14.9
49.2
3 618.8
Total all ages
7 762.4
6 280.6
4 810.8
1 680.3
2 482.2
512.6
176.9
402.3
24 105.3
 

Sample counts and weighted estimates, remoteness areas

 PERSONS IN SAMPLEWEIGHTED ESTIMATE
 
Males
Females
Persons
Males
Females
Persons
Age group (years)
no.
no.
no.
'000
'000
'000
 MAJOR CITIES OF AUSTRALIA
0-17
1 600
1 589
3 189
2 017.9
1 921.5
3 937.0
18-64
3 494
4 076
7 570
5 527.1
5 656.7
11 182.7
65 years and over
1 068
1 385
2 453
1 132.2
1 266.2
2 397.7
Total all ages
6 162
7 050
13 212
8 678.8
8 840.3
17 523.6
 INNER REGIONAL AUSTRALIA
0-17
446
414
860
508.3
472.0
975.8
18-64
1 066
1 240
2 306
1 211.3
1 312.0
2 524.6
65 years and over
442
572
1 014
386.5
415.8
802.3
Total all ages
1 954
2 226
4 180
2 104.9
2 202.3
4 306.3
 OUTER REGIONAL AND REMOTE AUSTRALIA
0-17
464
432
896
273.9
260.9
531.2
18-64
1 154
1 153
2 307
675.4
647.0
1 324.2
65 years and over
352
368
720
198.9
222.4
419.5
Total all ages
1 970
1 953
3 923
1 149.3
1 128.5
2 274.9
 

Sample counts and weighted estimates, pooled National Health Survey and Survey of Income and Housing (NHIH) dataset

Age group (years)NSWVic.QldSAWATas.NTACTAust.
 PERSONS IN SAMPLE (no.)
15-64
6 278
5 856
5 779
4 320
4 795
3 484
1 937
2 231
34 680
65 years and over
2 037
1 796
1 615
1 513
1 317
1 188
260
495
10 221
Total 18 years and over
7 945
7 326
7 049
5 568
5 818
4 453
2 076
2 608
42 843
Total 15 years and over
8 315
7 652
7 394
5 833
6 112
4 672
2 197
2 726
44 901
 WEIGHTED ESTIMATES ('000)
15-64
5 092.2
4 189.8
3 145.5
1 083.9
1 642.3
323.6
125.0
273.6
15 881.0
65 years and over
1 195.3
915.7
710.5
293.3
345.3
96.3
14.9
49.7
3 621.4
Total 18 years and over
6 020.3
4 892.2
3 675.3
1 319.9
1 901.9
401.2
133.9
310.3
18 654.2
Total 15 years and over
6 290.1
5 105.6
3 856.7
1 377.8
1 989.1
419.8
139.8
324.1
19 503.7

Appendix 2 - physical measurements

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In the 2017-18 National Health Survey (NHS), voluntary measurements of height, weight and waist circumference were collected from respondents aged 2 years and over, whilst voluntary blood pressure measurements were also collected from adult respondents (aged 18 years and over). These measurements provide information on overweight and obesity (using Body Mass Index (BMI)), risk of developing chronic disease, and high blood pressure amongst the Australian population.

Physical measurement variables have a relatively high rate of non-response, compared to other variables, due to respondent sensitivities and the voluntary nature of these questions.Non-response rates for physical measurements were higher in 2017-18 than in the 2014-15 NHS, for example, the non-response for BMI for adults in 2017-18 was 33.8% compared with 26.8% in 2014-15.

Non-response rates for the physical measurement data items are shown in the table below.

Non-repsonse rates for physical measurements, 2017-18 National Health Survey

Age group
(years)
Total
persons
in
sample
Non-response rates
Body Mass
Index(a)
Waist
circumference
Blood
pressure
 no.%%%
Children    
2–4
841
44.4
48.2
. .
5–7
804
44.8
47.0
. .
8–11
1 004
41.5
43.0
. .
12–15
1 025
43.5
45.2
. .
16–17
614
46.9
47.4
. .
Total 2–17 years
4 288
43.9
45.9
. .
Adults    
18–24
1 174
31.8
33.5
31.1
25–34
2 576
32.7
34.2
30.0
35–44
2 844
32.9
34.7
31.2
45–54
2 800
36.3
37.3
34.3
55–64
2 789
34.4
36.2
31.7
65–74
2 456
32.6
34.6
30.1
75 years and over
1 731
34.7
36.9
32.9
Total 18 years
and over
16 370
33.8
35.4
31.6
. . not applicable
 

In addition to the voluntary measured items, respondents in the 2017-18 NHS were also asked to self report their height and weight measurements. Self reported measurements were not collected in the 2014-15 NHS. The majority of measured BMI non-respondents (76% of adults and 61% of children) provided self-reported height and weight measurements. This provides valuable information about the height and weight that can be used in assisting in the imputation for those with missing values. A future article will contain detailed analysis of the comparison between self report and measured height and weight.

In both the 2014-15 NHS and the 2017-18 NHS, missing values were imputed using the 'hot decking' imputation method. In this method, a record with a missing response (the 'recipient') receives the response of another similar record (the 'donor'). A number of characteristics with which to match recipients to donors were used. For adults they were:

  • age group
  • sex
  • part of state (capital city and balance of state)
  • self perceived body mass (underweight, acceptable, or overweight)
  • level of exercise (sedentary, low, moderate or high)
  • whether or not has high cholesterol (as a long-term health condition)
  • self reported BMI category (calculated from self reported height and weight)
     

For example, a female recipient aged 35-39 years who lives in a capital city, has a selfreported BMI category of overweight (calculated using self reported height and weight), has a self perceived body mass of healthy, has high cholesterol and lives a sedentary lifestyle will match to a donor record who has the same profile (female, 35-39, self-reports as overweight, etc).

For BMI, around 86% of imputed records with self-reported BMI used all seven variables to match to a donor record. The remaining 14% could not be matched using all seven variables and were therefore matched using fewer variables. For example, around 7% of imputed records with self-reported BMI were matched to donors by age group, sex, self-reported BMI, self perceived body mass, level of exercise and cholesterol.

For children 2-14 years, age group, sex, self reported BMI and part of state were used as imputation variables, while for 15-17 year olds, level of exercise and self perceived body mass (only if a person answered for themselves) were also used as imputation variables, due to the other variables not being collected for children aged 2-17 years.

For analysis purposes, the 2017-18 NHS data was processed using both the 2017-18 imputation method and the 2014-15 imputation method. The key difference between these two imputation methods was the addition of a characteristic (ie self-reported BMI category) with which to match imputation recipients to donorsThe table below shows that the results are comparable with a sufficiently small impact. This indicates that time series changes between 2014-15 NHS and 2017-18 NHS are unlikely to be due to a change in the imputation method. 

Measured and imputed body mass index results for 2017-18 National Health Survey, by imputation method(a)

Body Mass Index categoryMeasured onlyMeasured and Imputed
Using 2017-18 imputation methodUsing 2014-15 imputation method
2-17
years 
2-17
years
18 years
and over
18 years
and over
2-17
years
2-17
years 
18 years
and over
18 years
and over
2-17
years 
2-17
years 
18 years
and over
18 years
and over
 (no.)(%)(no.)(%)(no.)(%)(no.)(%)(no.)(%)(no.)(%)
Underweight
174
7.2%
123
1.1%
325
7.6%
189
1.2%
317
7.4%
194
1.2%
Normal
1604
66.7%
3370
31.1%
2846
66.4%
4968
30.3%
2827
66.1%
5052
30.9%
Overweight
424
17.6%
3903
36.0%
758
17.7%
5837
35.7%
765
17.9%
5853
35.8%
Obese
202
8.4%
3446
31.8%
359
8.4%
5376
32.8%
368
8.6%
5271
32.2%
Total
2404
100.0%
10842
100.0%
4288
100.0%
16370
100.0%
4277
100.0%
16370
100.0%
Total overweight/obese
626
26.0%
7349
67.8%
1117
26.0%
11213
68.5%
1133
26.5%
11124
68.0%
  Whether measured
Measured
2404
56.1%
10842
66.2%
..
..
..
..
..
..
..
..
Not measured
1884
43.9%
5528
33.8%
..
..
..
..
..
..
..
..
Total
4288
100.0%
16370
100.0%
..
..
..
..
..
..
..
..
.. not applicable
a. Using National Health Survey 2017-18 unweighted sample counts
 

Physical measurement data (BMI, waist circumference and blood pressure) from NHS 2017-18 are of suitable quality and are directly comparable to 2014-15. For comparisons to earlier years, the ABS recommends using proportion comparisons only as imputation was not used on the physical measurement data prior to 2014-15 NHS.

Appendix 3 - self-reported height and weight

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Sources

Data presented in the analysis of self-reported and measured data are from:

  • 2007-08 and 2017-18 National Health Survey (NHS)
  • 1995 National Nutrition Survey (NNS) which is a sub-sample of the 1995 NHS.
     

Definitions

Adult

A respondent aged 18 years or over.

Body Mass Index

Body Mass Index (BMI) is a simple index of weight-for-height that is commonly used to classify underweight, normal weight, overweight and obesity. It is calculated from height and weight information, using the formula weight (kg) divided by the square of height (m) and is expressed in kilograms per metres squared.

Table 1 - Body Mass Index, adults

CategoryRange
UnderweightLess than 18.50
Normal range18.50 — 24.99
Overweight25.00 — 29.99
Obesity class I30.00 — 34.99
Obesity class II35.00 — 39.99
Obesity class III40.00 or more

Separate BMI classifications were produced for children. BMI scores were created in the same manner described above but also took into account the age and sex of the child. There are different cutoffs for BMI categories (underweight/normal combined, overweight or obese) for male and female children. These categories differ to the categories used in the adult BMI classification and follow the scale provided in Cole TJ, Bellizzi MC, Flegal KM and Dietz WH, Establishing a standard definition for child overweight and obesity worldwide: international survey, BMJ 2000; 320. See Appendix 4 in NHS 17-18 Users' Guide for more details.

Child

A person aged 0-17 years.

Outliers

Observations which appear inconsistent within the dataset.

Treatment of outliers

Routine processing of the NHS involved conservative editing of the most extreme outliers. However, such editing may not detect all of the inconsistent records (given the number of possible interrelated variables that could be examined). In the analysis of self-reported and measured height and weight data, further data ‘cleaning’ was applied to exclude any records with large disparities, which were likely to be a result of reporting or data entry errors. In the feature article analysis, if the difference between self-reported and measured height or weight (expressed as a percentage) was greater than 4 standard deviations from the group mean, then the record was excluded from analysis.

Respondent sample

In 2017-18, 57.5% of respondents agreed to provide both measured and self-reported data and were included in analysis. A comparison between those included and excluded (i.e. those who provided self-reported and measured height and weight versus those who did not) showed no substantial differences between the two groups (see table 1 below).Therefore, for the purpose of the report, the sample analysed was representative of the whole 2017-18 NHS respondent sample (see table 1 below).

Table 2 - sample characteristics from NHS and report subsample, proportion of persons 18 years and over(a)

 Included in
report
analysis(b)
Provided
self-reported
data only
Index of Relative Socio-Economic Disadvantage(c)  
    First Quintile  15.617.6
    Second Quintile20.720.5
    Third Quintile20.120.6
    Fourth Quintile21.620.7
    Fifth Quintile22.120.6
Self-perceived body mass(d)  
    Acceptable weight  57.853.9
    Underweight3.83.5
    Overweight38.538.2
Level of highest educational attainment  
    Bachelor/post-graduate degree32.029.2
    Advanced diploma/Diploma/Certificate III/IV31.831.1
    Year 12 or equivalent14.215.1
    Below Year 1218.120.9
Smoking status  
    Current daily smoker(e) 11.713.8
Self-assessed health status  
    Excellent21.120.2
    Very Good37.335.4
    Good28.429.1
    Fair10.311.3
    Poor2.93.9
Age group (years)  
    18-2411.412.0
    25-3420.219.4
    35-4417.817.3
    45-5416.316.8
    55-6415.515.1
    65-7411.811.6
    75 years and over7.07.8
a. Cells in this table containing data have been randomly adjusted to avoid the release of confidential data. Discrepancies may occur between sums of the component items and totals.
b. Adults providing both self-reported and measured height and weight.
c. A lower Index of Disadvantage quintile (e.g. the first quintile) indicates relatively greater disadvantage and a lack of advantage in general. A higher Index of Disadvantage (e.g. the fifth quintile) indicates a relative lack of disadvantage and greater advantage in general. See Index of Relative Socio-Economic Disadvantage in the Glossary.
d. Excludes pregnant persons.
e. In 2017-18, data from NHS and Survey of Income and Housing (SIH) have been combined to create a much larger sample which will allow for a more accurate smoker status estimate.. 
 

Imputed data

As standard practise, an imputation (editing) method was applied for respondents that opted not to be measured (33.8% in 2017-18) to derive height and weight data. However, since the feature article focused on measured and self-reported data only, respondents that had imputed height and weight values were excluded from the analysis. See Appendix 2 for more details about imputation methods.

Non-response

The feature article analysed reporting trends for the 57.5% of survey respondents who agreed to be measured and who self-reported their height and weight. Without data from the remaining survey respondents it is difficult to determine whether those who volunteered and those who did not would differ when it comes to BMI, or reporting accuracy. To this end, there was a third group of respondents of interest who provided self-reported height and weight, but no measured data. When comparing self-reported BMI between the group analysed in the feature article, and the self-reported only group, the latter had a higher average self-reported BMI (see table 2 below). More research is needed to determine potential differences between those who opt to provide self-reported data only, those who also agree to be measured, and those who provide neither.

Table 3 - self-reported BMI by respondent compliance, average BMI

 Included in report analysis(a)Provided self-reported data only
Males26.628.2
Females25.627.5
Persons26.927.8
a. Persons who provided both measured and self-reported height and weight.
b. Persons who provided self-reported height and weight data but not measured.

Appendix 4 - modelled estimates for small areas

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1 Introduction

This publication contains modelled estimates of health conditions for small areas based on data from the 2017-18 National Health Survey (NHS), 2018 Estimated Resident Population (ERP) sourced from Regional Population Growth, Australia, 2017-18 (Cat. No. 3218.0), the 2016 ABS Census of Population and Housing, and aggregate administrative data sources.

These modelled estimates for small areas were produced as part of the ABS response to measure the impact of coronavirus (COVID-19) in collaboration with the Commonwealth Department of Health. A series of interactive maps examining the geographic distribution of people with a range of chronic health conditions, across several age groups have been produced based on these modelled estimates and were released on 7 April 2020.

2 Methodology used

A modelled estimate can be interpreted as the expected prevalence of a health condition for an area in Australia based on the demographic information available for that area. The process of producing modelled small area estimates for health conditions measured in the NHS consisted of the following components, described in detail in sections 2.1 to 2.8:

  1. Identification of the outcome variables
  2. Identification of the geographical areas
  3. Selection of the predictor variables
  4. Scoping the data
  5. Creation of binary and proportion variables
  6. Aggregating observations and merging datasets
  7. Model selection
  8. Assessment of the modelled estimates
     

2.1 Identification of the outcome variables

From the NHS, modelled small area estimates (counts, proportions, measure of error) have been produced for persons with chronic conditions and comorbidities, considered by the World Health Organization to be at higher risk from COVID-19. The outcome variables are:

  • asthma, by age group
  • diabetes mellitus, by age group
  • heart, stroke and vascular disease, by age group
  • three or more chronic conditions, by age group
     

For age groups:

  • all ages
  • 60 years and over
  • 70 years and over
     

For more information about the outcome variables, including definitions, see the comments in each sheet of Table 33: Small area estimates available in the Data downloads, the Glossary, or Explanatory Notes of this page.

2.2 Identification of the geographical areas

The modelled estimates for small areas have been produced at the Statistical Area Level 2 (SA2) for each jurisdiction, with the exception of:

  • areas classified as very remote
  • areas classified as discrete Aboriginal and Torres Strait Islander communities
  • areas that had an adjusted 2018 ERP of zero residents, or a 2016 Census population of zero residents
     

2.3 Selection of the predictor variables

In order to predict outcome variables, predictor variables are required on both the NHS dataset and a small area dataset containing population, Census, and administrative data. Predictor variables were created if data were available for small areas for all of urban, rural, and remote Australia and if there was an expectation that they might be good predictors of the outcome variables.

For age and sex predictor variables, data at the small area level were obtained from ABS ERP data from Regional Population Growth, Australia, 2017-18 (Cat. No. 3218.0). This is described below in section 2.4.

For other demographic variables collected in the NHS, data at the small area level were obtained from the 2016 Census of Population and Housing, as this was the most up-to-date comprehensive source of demographic data due to the depth of information at small geographical levels.

Additional variables that were available at the small area but not collected in the NHS were also included in the model. These variables included other demographic variables on the Census, geographic variables, and variables from administrative sources.

Predictor variables that relate to the geographical areas where people reside included:

  • remoteness area
  • socio-economic indexes for areas (SEIFAs) – population-weighted deciles at the Statistical Area Level 1 (SA1) level
  • state and territory
  • section of state (major urban/other urban/bounded locality/rural balance)
  • Greater Capital City Statistical Area (GCCSA)/balance of state
  • design area type (categorises inner city, large and small urban towns, rural towns and remote areas within states and territories for designing the sample of the NHS)
     

Sources of geographical area data included:

Predictor variables from administrative data sources are described in the following table:

Type of dataData source
Birth registrations in 2017ABS, Births, Australia, 2017 (Cat. No. 3301.0)
Death registrations in 2017ABS, Deaths, Australia, 2017 (Cat. No. 3302.0)
Immigration within Australia and overseas migration in 2016-17
Population density in 2017
Dwelling transfers and median sale prices in 2016
ABS, Data by Region, 2013-18 (Cat. No. 1410.0)
Personal income tax data for employee earnings, investment, own business or superannuation income in 2012-13ABS, Estimates of Personal Income for Small Areas, 2012-13 (Cat. No. 6524.0.55.002)
Recipients of age pensions, disability support pensions, Newstart allowances, carer allowances, health care cards, pensioner cards, Family tax benefits and other benefits in the quarter to March 2016Department of Social Services (DSS) Payment Demographic Data (DSS is now known as Services Australia)
Attendance at public hospitals for various conditions and procedures (2016-17)
Deaths from selected causes (2011 to 2015)
Home and Community Care Program (HACC) clients (2012-13)
Participation in vocational education and training (2015)
Development of children (2015)
Immunisations (2011 to 2016)
Bowel cancer screening (2012-13)
Public Health Information Development Unit (PHIDU) May 2019 release http://phidu.torrens.edu.au
Use of services from the Medicare Benefits Schedule (MBS) and transactions from the Pharmaceutical Benefits Scheme (PBS) in 2016.Multi Agency Data Integration Project (MADIP) (Cat. No. 1700.0)
 

Within most types of predictor variables (as discussed above), several separate categories or data items were included. The variables considered for inclusion in the model are listed in the Predictor Variables tab of Table 33: Small area estimates available in the Data downloads.

2.4 Scoping the data

The modelled estimates for small areas are applicable to persons who were usual residents of private dwellings to match the scope of the NHS. They exclude:

  • non-private dwellings, for example hospitals and aged care facilities
  • areas classified as very remote
  • areas classified as discrete Aboriginal and Torres Strait Islander communities
     

The base data source used to compile the modelled small area estimates was the ABS Estimated Resident Population (ERP) data from Regional Population Growth, Australia, 2017-18 (Cat. No. 3218.0). Adjustments were made to the ERP data, by using ratios of private to non-private dwellings, calculated from the 2016 Census to match the scope of the NHS, and then summed to the NHS population state by age by sex estimates. These are the ‘population denominator’ estimates included in Table 33: Small area estimates available in the Data downloads. It is important to note that these population estimates are not official estimates and were created solely for analysis of the NHS modelled small area estimates and will not match other population data at the SA2 geography level.

Adjustments were also made to the Census data, specifically the predictor variables obtained from the Census to match the scope of the NHS. Persons residing in non-private dwellings were easily removed from the small area dataset using persons’ dwelling type available on the Census datasets for respondents at home on Census night. However, for persons who were not at home on Census night, information is not collected to determine if the dwelling they usually reside in is a private or non-private dwelling; therefore, their records were deleted from the small area dataset. This data adjustment assumes that the people who were away from home on Census night and live in private dwellings have the same health characteristics as the people who were at home in a private dwelling.

To further match NHS scope, removal of very remote areas and discrete Aboriginal and Torres Strait Islander communities from the ERP and Census datasets was approximately done by deleting persons residing in SA2s that had more than 20% of their population classified as very remote or in discrete Aboriginal and Torres Strait Islander communities.

Additional exclusions that were applied to the data included:

  • small area locations (SA2s) with zero residents in the 2016 Census
  • small area locations (SA2s) with an adjusted ERP of zero residents
  • residents of Other Territories
  • foreign diplomatic personnel and their families were excluded from the modelled estimates because they are not included in Australia’s ERP, the Census or the NHS
     

See the SA2s Excluded tab within Table 33: Small area estimates available in the Data downloads for the full list of SA2s not included in the modelled estimates.

While out of scope for the NHS, members of non-Australian defence forces (and their dependents) stationed in Australia were unable to be removed from the modelled estimates because they could not be identified in Australia’s 2018 ERP.

2.5 Creation of binary and proportion variables

On the NHS dataset outcome variables were created as binary variables to make them suitable for the type of modelling undertaken (logistic regression). On both the NHS and the small area datasets, predictor variables that were categorical were also created as binary variables. An observation took the value of 1 if an individual had a characteristic of interest and 0 otherwise. For example:

  • in the case of asthma, the outcome variable for asthma took the value of 1 if an individual had asthma and 0 if the individual did not have asthma,
  • in the case of labour force status, the predictor variable for employed took the value of 1 if an individual was employed and 0 if the individual was unemployed, not in the labour force or aged 0-14 years.
     

Variables on the small area dataset sourced from administrative data were converted to proportions of their areas’ population with the characteristic of interest. For example a person can live in an area with a proportion of its population receiving a disability support pension.

In addition, binary variables were created on the small area dataset denoting ranges of the characteristic of interest. For example: for fertility rate, a binary variable was created to denote whether the person lived in an area with a fertility rate between 2 and 2.5.

2.6 Aggregating observations and merging datasets

All data sources were aggregated to a fixed structure (cross classification cell groups) including several levels of geography, five year age group and sex. This decreases the size of the datasets (especially the Census dataset) to increase the efficiency of the modelling process.

The Census, adjusted ERP and administrative datasets were then merged into one small area dataset.

2.7 Model selection

Models were created for each outcome variable independently. For example, a different model is created and selected for heart, stroke and vascular disease than for asthma. However within each outcome variable the same model is used for each output classification, for example geography, age group, and sex.

The model selection method uses the prepared dataset to measure the relationship between the outcome variable and possible predictor variables to determine one set of significant predictor variables. This method assumes that the relationships observed in the survey data at State and National levels also hold at the small area level. The significant predictor variables for each model are listed in the Predictor Variables tab of Table 33: Small area estimates available in the Data downloads.

Random effects logistic regression models are used for each outcome variable. As part of any model selection process an appropriate significance level must be chosen for determining which predictor variables to include in the models. The 0.05 (95%) level is most commonly used; however, due to NHS’ relatively large sample sizes, the Bayesian Information Criterion (BIC) was used to reduce the risk of over-fitting.

To verify that the model adequately predicted the outcome variable, the models were applied to small area data, summed to create Australia level modelled estimates and compared with reliable direct survey weighted estimates. This property is known as model additivity. Where model additivity was not similar, additional predictor variables were included in the model until suitable model additivity was achieved.

Using the selected model for each outcome variable, a mixed estimate comprised of modelled and survey data is then produced for each small area output classification (SA2 by age group). A mixed/composite estimate reflects the best trade-off between the accuracy of the direct survey weighted estimate and the error associated with the modelled estimate. For a small area that happens to have a low sampling error (because of a large sample size within that small area, for example), more weight will be given to the direct estimate when calculating a modelled estimate for that small area. On the other hand, for a small area with high sampling error, more weight will be given to the model based prediction as this will be more reliable in calculating the modelled estimate for that small area. This takes advantage of what is known about the small area location from the survey to improve the modelled estimates.

The modelled estimates are then adjusted so that they sum to national direct survey estimates. The associated errors resulting from the modelling process, which improve on direct survey estimates’ errors, were not adjusted.

The modelled estimates in Table 33: Small area estimates available in the Data downloads are in the form of counts (number of persons) and their relative error for each small area location. Prevalence proportions (percentage of population at risk in each small area) have also been calculated. The denominators used in the calculation of proportions at risk were the unofficial population estimates for each SA2 (based on adjusted ERP) described above in section 2.4.

To mitigate against the identification of survey respondents, modelled estimates have been confidentialised to ensure they meet ABS requirements for confidentiality. Small area locations (SA2s) with populations or modelled counts that didn’t meet the confidentiality rules have modelled estimates comprised solely of the modelled component, rather than the mixed/composite estimator described above. This means that no sampled contribution is included in such modelled estimates, regardless of whether sample exists in these small areas.

2.8 Assessment of the modelled estimates

Various measures were taken to examine the modelled estimates. Modelled estimates were compared with direct survey estimates from the NHS for areas that were sampled. For the survey estimates, 95% Confidence Intervals (CIs) were calculated. These were plotted against the modelled estimates to see if the majority of modelled rates fell within the CIs of the NHS estimates.

Relative root mean squared errors (RRMSEs) (described in section 3.5) of the modelled estimates were examined to ensure that the majority were of suitable quality.

The number, range, and applicability of predictor variables included in the models used to create the small area estimates were also considered.

Finally, comparisons among the small area estimates and choropleth maps were produced to assess whether the modelled estimates aligned with expectations.

Please see section 5 for a quality summary for the modelled small area estimates.

3 Accuracy of results

The process undertaken in producing modelled estimates overcomes much of the volatility at the SA2 level caused by sampling error. However, it should be remembered that the modelled estimates produced are still subject to errors.

The errors associated with the modelled small area estimates fall into four categories, as follows:

  1. sampling error
  2. non-sampling error
  3. modelling error
  4. prediction error
     

These errors are combined into an overall measure of accuracy, the relative root mean squared error (RRMSE), described in section 3.5.

3.1 Sampling error

Sampling error is introduced into estimates because the NHS data were collected from only a sample of dwellings. Therefore, they are subject to sampling variability; that is, modelled estimates may differ from those that would have been produced if all dwellings had been included in NHS. Furthermore, the smaller the sample obtained within a small area, the greater the sampling error associated with that small area's modelled estimates will be.

3.2 Non-sampling error

The imprecision due to sampling error should not be confused with inaccuracies that may occur because of imperfections in reporting by respondents and recording by interviewers, and errors made in coding and processing data. Inaccuracies of this kind are referred to as non-sampling error, and they occur in any enumeration, whether it be a full count (Census) or a sample. Unlike the other sources of error, non-sampling error is not measurable and therefore isn’t accounted for in the measured error (direct or modelled) that accompanies ABS estimates. Every effort is made to reduce non-sampling error to a minimum by careful design of questionnaires, intensive training and supervision of interviewers, and rigorous procedures.

3.3 Modelling error

Modelling error is introduced by model misspecification. This can occur when the choice of model is incorrect, a key predictor variable is left out or an inappropriate predictor variable is included. Therefore, the variables chosen in the models may result in incorrect modelled estimates for certain small areas, particularly those unusually small areas that do not follow the typical associations between the available predictor variables and the health conditions being modelled. The models that have been chosen have been tested against a range of possible alternative models; however, they are only the most preferred models subject to available data at the time.

3.4 Prediction error

A strong model does not guarantee statistically accurate modelled estimates. Prediction error is a measure of the statistical accuracy of the predictions made to produce the modelled small area estimates.

3.5 Relative Root Mean Squared Error (RRMSE)

A measure of the quality of the modelled estimates is the RRMSE. The RRMSE is primarily a measure of prediction error but in its calculation it also inherits some aspects of modelling and sampling error. The RRMSE generally decreases as the population size increases, and is used to assess the reliability of modelled estimates.

As a general rule of thumb, estimates with RRMSEs less than 25% are considered reliable for most purposes, estimates with RRMSEs between 25% and 50% should be used with caution and estimates with RRMSEs greater than 50% are considered too unreliable for general use.

4 Using modelled estimates

The small area modelled estimates can be interpreted as the expected prevalence of a health condition for a typical area in Australia with the same characteristics. For some small area location (SA2s), there will be differences between the modelled estimates and the actual number of people with the characteristic of interest. One explanation for this is that significant local information about particular small areas exists but has not been collected for all areas and cannot be incorporated into the models. This sort of information is usually not measurable, and relies on local or expert knowledge.

Small area modelled estimates should be viewed as a tool that when used in conjunction with local area knowledge as well as the consideration of the modelled estimates reliability, can provide useful information that can assist in making decisions for small geographic areas. Care needs to be taken to ensure decisions are not based on inaccurate estimates. The provided modelled small area estimates can be aggregated to larger regions (such as regional planning regions) to help improve decision making. Small area estimates can be aggregated together using an approximation formula outlined in section 6. Aggregation of small areas should be done taking into account local knowledge about these areas.

Alternatively, a range of small area estimates for larger areas were produced by the ABS in collaboration with the Public Health Information Development Unit (PHIDU), using the same method and models described in this document. For further information see PHIDU's social health atlas.

5 Quality summary for modelled estimates

The quality of the modelled estimates were assessed according to the following criteria:

  1. median RRMSE, as a measure of prediction accuracy
  2. consistency with national direct survey estimates. For example, whether modelled estimates for heart, stroke and vascular disease increased proportionally with age
  3. the number, range, and applicability of predictor variables included in the models
     

These culminated in an overall reliability assessment, which has three categories:

  • reliable, meaning the modelled estimates are suitable for general use
  • less reliable, meaning the modelled estimates should be used with caution
  • unreliable, meaning the modelled estimates are unsuitable for general use
     

Reliability assessment table - SA2 estimates

Outcome variable
Median RRMSE
(all ages estimates)
Consistency with
National data
Number and
range of
predictor
variables
Overall
reliability
estimate
Asthma14.7% - ReliableReliableReliableReliable
Diabetes mellitus17.9% - ReliableReliableReliableReliable
Heart, stroke, and vascular disease27.0% - Less reliableReliableReliableLess reliable
Three or more chronic conditions6.6% - ReliableReliableReliableReliable
 

Estimating aggregated areas

The following formulas describe the estimation of aggregated areas. This may be done for one of two reasons:

  1. Estimates are required for a bespoke small area of interest
  2. Where the error (RRMSE) for an area is unacceptably high aggregating areas can decrease the error
     

Note that the error formula is an approximation only, and that these should only be used where alternative modelled estimates are not available. Aggregation of the modelled small area estimates to large geographies such as capital city or state/territory level is not recommended. If you require capital city or state/territory level data for the characteristics of health conditions provided here at small area level, then use of NHS published data (or use of the TableBuilder product) is recommended.

The following formula is used to estimate the count for an aggregated area.

\(\Large{Count_{\text{aggregated area}}=\sum\limits_{SA2}Count_{SA2}}\)

The following formula may be used to approximate the RRMSE for an aggregated area.

\(\Large{RRMSE_{\text{aggregated area}}=\frac{\sqrt{\sum_{SA2}({Count_{SA2}}^2\times {RRMSE_{SA2}}^2)}}{Count_{\text{aggregated area}}}}\)

Technical note - reliability of estimates

Reliability of estimates

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

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

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

3 RSEs for published estimates are supplied in Excel data tables, available via the Data downloads section.

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

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

Standard errors of proportions and percentages

6 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. For proportions where the denominator is an estimate of the number of persons in a group and the numerator is the number of persons in a sub-group of the denominator group, the formula to approximate the RSE is given below. The formula is only valid when x is a subset of y.
 
\(\Large R S E\left(\frac{x}{y}\right)=\sqrt{R S E(x)^{2-} R S E(y)^{2}}\)

Comparison of estimates

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

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

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

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

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

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

11 The MOEs in this publication are calculated at the 95% confidence level. This can easily be converted to a 90% confidence level by multiplying the MOE by:

\(\Large\frac{1.645}{1.96}\)

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

\(\Large\frac{2.576}{1.96}\)

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

Significance testing

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

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

where

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

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

Glossary

Show all

Definitions used in the National Health Survey (NHS) are not necessarily identical to those used for similar items in other collections.

Adequate consumption of fruit and vegetables

A balanced diet, including sufficient fruit and vegetables, reduces a person's risk of developing conditions such as heart disease and diabetes. The National Health and Medical Research Council's (NHMRC) 2013 Australian Dietary Guidelines recommend a minimum number of serves of fruit and vegetables each day, depending on a person's age and sex, to ensure good nutrition and health. Adequacy of intake (consumption) is based on whether a respondent's reported usual daily intake in serves of fruit or vegetables meets or exceeds each recommendation. More information about the guidelines can be found under Usual daily intake of fruit and Usual daily intake of vegetables in this glossary.

Adult

A respondent aged 18 years or over.

Age standardisation

Age standardisation is a way of allowing comparisons between two or more populations with different age structures, in order to remove age as a factor when examining relationships between variables. For example, the age structure of the population of Australia is changing over time. As the prevalence of a particular health condition (for example, arthritis) may be related to age, any increase in the proportion of people with that health condition over time may be due to real increases in prevalence or to changes in the age structure of the population over time or to both. Age standardising removes the effect of age in assessing change over time or between different populations.

Proportions quoted in commentary in this publication are not age standardised, however, proportions presented in Tables 1 and 2 include age standardised rates. Data are age standardised to the 2001 Australian population.

Alcohol consumption risk level

Alcohol consumption risk levels in the National Health Survey: First Results, 2017-18 (cat. no. 4364.0.55.001) have been assessed using the 2009 National Health and Medical Research Council (NHMRC) guidelines for the consumption of alcohol.

The 2009 lifetime risk guideline (guideline 1) was assessed using average daily consumption of alcohol for persons aged 15 years and over, derived from the type, brand, number and serving sizes of beverages consumed on the three most recent days of the week prior to interview, in conjunction with the total number of days alcohol was consumed in the week prior to interview.

The 2009 single occasion risk guideline (guideline 2) was assessed using questions on the number of times in the last 12 months a person's consumption exceeded specified levels.

The NHMRC drinking guidelines provide two universal guidelines for adults, one for children and young people and one for pregnant and breast feeding women. The following table outlines the risk level for each group. The NHMRC drinking guidelines advise that for anyone under the age of 18, not consuming alcohol is the safest option. However this population group has been assessed in the NHS against the universal guidelines for adults, that is guideline 1 and 2. This allows an assessment of the levels of risky drinking for this age group for both single occasion and lifetime risk.

2009 NHMRC Guidelines(a)(b)

 Level of risk 
Does not exceed guidelineExceeds guideline
Guideline 1 - Lifetime risk
up to and including 2 standard drinks
more than 2 standard drinks
Guideline 2 - Single occasion risk
up to and including 4 standard drinks
more than 4 standard drinks (c)
Guideline 3 - Children and young people
No drinking is the safest option
Alcohol consumed
Guideline 4 - Pregnant and breast feeding women
No drinking is the safest option
Alcohol consumed
a. One standard drink contains 12.5 mLs of alcohol.
b. Guidelines relate to both males and females.
c. On at least one occasion in the last 12 months.
 

Alcohol consumption status information was also collected for persons who did not consume any alcohol in the 7 days prior to interview, categorised as:

  • Last consumed more than one week to less than 12 months ago;
  • Last consumed 12 months or more ago; and
  • Never consumed.
     

For more detailed information on the 2009 NHMRC guidelines, see the Australian Guidelines to Reduce Health Risks from Drinking Alcohol and Frequently Asked Questions.

For a detailed explanation of the method used to measure alcohol consumption in ABS health surveys, see Alcohol Consumption in Australia: A Snapshot, 2007-08 (cat. no. 4832.0.55.001).

Arthritis

Arthritis is characterised by an inflammation of the joints often resulting in pain, stiffness, disability and deformity.

Asthma

A chronic disease marked by episodes of wheezing, chest tightness and shortness of breath associated with widespread narrowing of the airways within the lungs and obstruction of airflow. To be current, symptoms of asthma or treatment for asthma must have occurred in the last 12 months.

Accessibility/Remoteness Index of Australia

Accessibility/Remoteness Index of Australia (ARIA) was developed by the Commonwealth Department of Health and Aging (DoHA) and the National Key Centre for Social Applications of Geographic Information Systems (GISCA). ARIA measures the remoteness of a point based on the physical road distance to the nearest Urban Centre in each of five size classes. For more information on how ARIA is defined see Information Paper: ABS Views on Remoteness, 2001 (cat. no. 1244.0) and Information Paper: Outcomes of ABS Views on Remoteness Consultation, Australia, Jun 2001 (cat. no. 1244.0.00.001). Also refer to Census Geography Paper 03/01 - ASGC Remoteness Classification - Purpose and Use, available from the ABS website.

Anatomical Therapeutic Chemical Classification System (ATC)

The ATC system classifies therapeutic drugs, to enable drug utilisation research and improve the quality of drug use. Drugs are divided into different groups according to the organ or system they act on as well as their chemical, pharmacological and therapeutic properties.

ASGC and ASGS Remoteness Structure

The Remoteness Structure for the Australian Standard Geographical Classification (ASGC) 2006 and the Australian Statistical Geography Standard (ASGS) 2016, has 5 categories based on an aggregation of geographical areas which share common characteristics of remoteness, determined in the context of Australia as a whole. The criteria for these categories are based on the Accessibility/Remoteness Index of Australia (ARIA). For more details, see Accessibility/Remoteness Index of Australia definition above and the ASGC page on the ABS website.

Australian Dietary Guidelines

The National Health and Medical Research Council (NHMRC) 2013 Australian Dietary Guidelines use the best available scientific evidence to provide information on the types and amounts of foods, food groups, and dietary patterns that aim to:

  • Promote health and wellbeing
  • Reduce the risk of diet-related conditions
  • Reduce the risk of chronic disease.
     

The Guidelines are for use by health professionals, policy makers, educators, food manufacturers, food retailers and researchers.

The content of the Australian Dietary Guidelines applies to all healthy Australians, as well as those with common diet-related risk factors such as being overweight. They do not apply to people who need special dietary advice for a medical condition, or to the frail elderly.

See Usual daily intake of fruit and Usual daily intake of vegetables.

Australian Health Survey (AHS)

The Australian Health Survey 2011-13 was composed of three separate surveys:

  • National Health Survey (NHS) 2011-12
  • National Nutrition and Physical Activity Survey (NNPAS) 2011-12
  • National Health Measures Survey (NHMS) 2011-12.
     

Australia's Physical Activity and Sedentary Behaviour Guidelines

The 2014 Guidelines recommend that:

  • Young people (13-17 years) accumulate at least 60 minutes of moderate to vigorous physical activity everyday, from a variety of activities including some vigorous.
  • Adults (18-64 years) should be active most days of the week, accumulate 150 to 300 minutes moderate intensity physical activity or 75 to 150 minutes of vigorous intensity physical activity (or an equivalent combination each week), and do muscle strengthening activities on at least two days each week.
  • Older Australians (65 years and over) should accumulate at least 30 minutes of moderate intensity physical activity on most, preferably all, days.
     

For more information, see Australia's Physical Activity and Sedentary Behaviour Guidelines.

Back problems

'Back problems (dorsopathies)' include sciatica, disc disorders, back pain/problems not elsewhere classified and curvature of the spine. Publications prior to 2014-15 defined 'Back problems' as including only disc disorders and back pain/problems not elsewhere classified.

Blood pressure

See High blood pressure, Diastolic blood pressure and Systolic blood pressure.

Bodily pain

Indication of the severity of any bodily pain that the respondent had experienced (from any and all causes) during the last 4 weeks. This is a self-assessment from the SF36 international instrument. Data was collected from respondents aged 18 years and over.

For more information about the SF36, see: 36-Item Short Form Survey (SF-36)

Body Mass Index

Body Mass Index (BMI) is a simple index of weight-for-height that is commonly used to classify underweight, normal weight, overweight and obesity. It is calculated from height and weight information, using the formula weight (kg) divided by the square of height (m). In the 2017-18 NHS, respondents were also asked to self report their height and weight. To produce a measure of the prevalence of underweight, normal weight, overweight or obesity in adults, BMI values are grouped according to the table below.

Body Mass Index, adults

CategoryRange
UnderweightLess than 18.50
Normal range18.50 —24.99
Overweight25.00 — 29.99
Obese I30.00 — 34.99
Obesity class II35.00 — 39.99
Obesity class III40.00 or more

Separate BMI classifications were produced for children. BMI scores were created in the same manner described above but also took into account the age and sex of the child. There are different cutoffs for BMI categories (underweight/normal combined, overweight or obese) for male and female children. These categories differ to the categories used in the adult BMI classification and follow the scale provided in Cole TJ, Bellizzi MC, Flegal KM and Dietz WH, Establishing a standard definition for child overweight and obesity worldwide: international survey, BMJ 2000; 320.

Cancer (malignant neoplasms)

Cancer is a condition in which the body's cells grow and spread in an uncontrolled manner. A cancerous cell can arise from almost any cell, and therefore cancer can be found almost anywhere in the body.

Child

A person aged 0-17 years.

Chronic conditions

Tables 1, 2, 18 and 19 present data on a subset of long-term health conditions, referred to as chronic diseases. These consist of:

  • Arthritis;
  • Asthma;
  • Back problems (dorsopathies);
  • Cancer (malignant neoplasms);
  • Chronic obstructive pulmonary disease (COPD);
  • Diabetes mellitus;
  • Heart, stroke and vascular disease;
  • Kidney disease;
  • Mental and behavioural conditions; and
  • Osteoporosis,
     

and are selected for reporting because they are mostly common, pose significant health problems, have been the focus of recent population health surveillance efforts, and action can be taken to prevent their occurrence.

In this publication, persons were included in estimates when they reported that their condition was current and long-term; that is, their condition was current at the time of interview and had lasted, or was expected to last, 6 months or more. In 2014-15 and 2017-18, estimates also included persons who reported they had diabetes mellitus, angina, heart attack, other ischaemic heart diseases, stroke or other cerebrovascular diseases, but that these conditions were not current and long-term at the time of interview.

Chronic obstructive pulmonary disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a collective term for a group of conditions that include emphysema, chronic bronchitis and chronic asthma that is not fully reversible. Emphysema and chronic bronchitis are the two most common forms of COPD.

Conditions

Health conditions reported by respondents in the NHS are presented using a classification originally developed for the 2001 NHS by the Family Medicine Research Centre, University of Sydney, in conjunction with the ABS. The classification is based on the 10th revision of the International Classification of Diseases (ICD) and is used for all years from 2001 to 2017-18. See also Long-term health condition.

Current daily smoker

A current daily smoker is a respondent who reported at the time of interview that they regularly smoked one or more cigarettes, cigars or pipes per day. See also Smoker status.

Deafness

Includes partial or total loss of hearing.

Department of Veterans' Affairs (DVA) client

Refers to those receiving benefits from the Department of Veterans' Affairs.

Note that many people beyond former Australian Defence Force (ADF) members may qualify for a benefit or support from the Department of Veterans’ Affairs, including:

  • British, Commonwealth and Allied forces veterans who served in conflicts in which Australia was involved.
  • Former serving members (including reservists and cadets).
  • Current serving members of the ADF.
  • Partner/Spouse of an eligible member/veteran.
  • Widow/Widower of an eligible member/veteran.
  • Children of an eligible member/veteran.
  • Police officers who served in a declared peacekeeping force.
     

Diabetes mellitus

A chronic condition in which blood glucose levels become too high due to the body producing little or no insulin, or not responding to insulin properly.

Data on diabetes refers to persons who reported having been told by a doctor or nurse that they had diabetes (including persons who were not ever told or not known), irrespective of whether the person considered their diabetes to be current or long-term. This definition was first used for estimates of diabetes in Australian Health Survey: Updated Results, 2011-12 (cat. no. 4364.0.55.003). Estimates of diabetes for all years in the National Health Survey: First Results, 2017-18 (cat. no. 4364.0.55.001), are presented using this definition. In earlier publications prior to National Health Survey: First Results, 2014-15, persons who had reported having diabetes, but that it was not current, were not included.

Diastolic blood pressure

Measures the pressure in the arteries as the heart relaxes before the next beat. It is the lower number of the blood pressure reading.

Diet drinks

In the National Health Survey: First Results, 2017-18 (cat. no. 4364.0.55.001) selected diet drinks include diet soft drink, cordials, sports drinks or energy drinks. They are sweetened with artificial sweeteners rather than sugar. This definition includes diet soft drinks in ready to drink alcoholic beverages and excludes non-diet drinks, fruit juice, flavoured milk, water or flavoured water, or coffee/tea flavoured with sugar replacements like 'Equal'.

Disability status

A disability or restrictive long-term health condition exists if a limitation, restriction, impairment, disease or disorder has lasted, or is expected to last, for six months or more, which restricts everyday activities.

A disability or restrictive long-term condition is classified by whether or not a person has a specific limitation or restriction. The specific limitation or restriction is further classified by whether the limitation or restriction is a limitation in core activities, or a schooling/employment restriction only.

There are five levels of activity limitation (profound, severe, moderate, mild and school/employment restriction only). These are based on whether a person needs help, has difficulty, or uses aids or equipment with any core activities (mobility, self-care and communication). A person's overall level of core activity limitation is determined by their highest level of limitation in any of these activities.

Employed

Persons aged 15 years and over who had a job or business, or who undertook work without pay in a family business for a minimum of one hour per week. Includes persons who were absent from a job or business. See also Unemployed and Not in the labour force.

Exercise

Physical activity (exercise only) which consists of four domains, walking for transport, walking for fitness, sport or recreation, moderate exercise and vigorous exercise, which was undertaken in the last week.

Family

Two or more persons, one of whom is at least 15 years of age, who are related by blood, marriage (registered or de facto), adoption, step or fostering; and who are usually resident in the same household. The basis of a family is formed by identifying the presence of a couple relationship, lone parent-child relationship or other blood relationship. Some households will, therefore, contain more than one family.

Family composition

The differentiation of families based on the presence or absence of couple relationships, parent-child relationships, child dependency relationships or other blood relationships, in that order of preference.

Family composition of household

Refers to the composition of the household to which the respondent belongs to. In this publication households are categorised as persons living alone, couple only, couple with child(ren), and other households.

Hayfever and allergic rhinitis

An allergic inflammation of the nasal airways occurring when an allergen, such as pollen or dust, is inhaled by an individual with a sensitised immune system. When caused specifically by grass pollens it is known as 'hayfever'.

Heart, stroke and vascular conditions (heart disease)

In the National Health Survey: First Results, 2017-18 (cat. no. 4364.0.55.001), data on heart, stroke and vascular disease refers to persons who reported having been told by a doctor or nurse that they had any of a range of circulatory conditions comprising:

  • Ischaemic heart diseases (angina, heart attack and other ischaemic heart diseases);
  • Cerebrovascular diseases (stroke and other cerebrovascular diseases);
  • Oedema;
  • Heart failure; and
  • Diseases of the arteries, arterioles and capillaries.
     

and that their condition was current and long-term; that is, their condition was current at the time of interview and had lasted, or was expected to last, 6 months or more.

However, all persons who reported having ischaemic heart diseases cerebrovascular diseases, heart failure and rheumatic heart disease are included, even if they were not reported to be current and long-term at the time of interview. These conditions are automatically considered to be current and long term. Estimates of heart, stroke and vascular disease for 2007-08, 2011-12, 2014-15 and 2017-18 in this publication are presented using this definition. There is limited comparability between 2007-08 and previous years due to a change in derivation methodology in 2007-08.

Health risk factors

Specific lifestyle and related factors impacting on health, including:

  • Tobacco smoking;
  • Alcohol consumption;
  • Sugar sweetened and diet drinks;
  • Exercise;
  • Body Mass Index;
  • Waist circumference;
  • Dietary behaviour; and
  • Blood pressure.
     

High blood pressure

In the National Health Survey 2017-18, persons aged 18 years and over could consent to having a blood pressure measurement taken at the time of the interview. Participants who recorded a systolic blood pressure reading 140mmHg or greater were counted as having a high blood pressure reading. Note that this only referred to the measurement at the time of the interview and does not necessarily indicate a chronic condition. For this survey, this is distinguished from 'Hypertension' which was self reported as a long term health condition.

For more information, see hypertension.

Household

A household is defined as one or more persons, at least one of whom is at least 15 years of age, usually resident in the same private dwelling. In this survey, only households with at least one adult (aged 18 years and over) were included.

High Sugar Levels

High sugar levels in blood or urine.

Hypertension

Hypertension (commonly known as high blood pressure) is a condition in which blood pressure in the arteries is elevated, requiring the heart to work harder than normal to circulate blood throughout the body. Hypertension is a major risk factor for hypertensive heart disease, strokes, myocardial infarction (heart attacks) and chronic kidney disease as well as several other medical conditions.

Information on hypertension/high blood pressure was collected in the National Health Survey using two methods. These were:

  • a question on whether respondents had ever been told by a doctor or nurse they had any circulatory conditions (including hypertension or high blood pressure), and
  • for adults aged 18 years and over, the taking of blood pressure measurements. A person was defined as having high blood pressure if their systolic/diastolic blood pressure was equal to or greater than 140/90 mmHg. Numbers of people with measured high blood pressure do not include people who have high blood pressure but are managing their condition through the use of blood pressure medications.
     

In the National Health Survey 2017-18, the term 'Hypertension' refers specifically to respondents who had ever been told by a doctor or nurse that they had hypertension or high blood pressure, and does not relate to the voluntary blood pressure measurement.

Tables in NHS publications previous to 2014-15 referred to hypertension as 'hypertensive disease'.

ICD-10

ICD-10 refers to the tenth revision of the International Classification of Diseases and Health Related Problems. The classification of long-term conditions most commonly used in output from the 2017-18 NHS was developed for use in this survey based on the ICD-10. See Appendix 2: Classification of Health Conditions for the content of the classifications.

Index of Relative Socio-Economic Disadvantage

This is one of four Socio-Economic Indexes for Areas (SEIFA) compiled by ABS following each Census of Population and Housing. The indexes are compiled from various characteristics of persons resident in particular areas: the Index of Relative Socio-Economic Disadvantage summarises attributes such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations. A lower Index of Relative Socio-Economic Disadvantage quintile (e.g. the first quintile) indicates relatively greater disadvantage and a lack of advantage in general. A higher Index of Relative Socio-Economic Disadvantage (e.g. the fifth quintile) indicates a relative lack of disadvantage and greater advantage in general. For further information about the indexes, see Census of Population and Housing: SEIFA, Australia, 2016.

Ischaemic heart disease

A disease of the blood vessels supplying the heart muscle.

Kidney disease

A subset of symptoms including: problems or complaints about the kidneys, renal pain and renal colic (kidney stones).

Long sightedness

Long sightedness (or hyperopia/hypermetropia) is a common condition of the eye where the light that comes into the eye focuses behind the retina, causing the image of a close object to be out of focus, but that of a distant object to be in focus. Glasses, contact lenses and laser techniques are used to correct long sightedness.

Long-term health condition

A medical condition (illness, injury or disability) which has lasted at least six months, or which the respondent expects to last for six months or more. Some reported conditions were assumed to be long-term, including asthma, arthritis, cancer, osteoporosis, diabetes, sight problems, rheumatic heart disease, heart attack, angina, heart failure and stroke. Diabetes, rheumatic heart disease, heart attack, angina, heart failure and stroke were also assumed to be current.

Margin of Error (MoE)

Margin of Error describes the distance from the population value that the sample estimate is likely to be within, and is specified at a given level of confidence. Confidence levels typically used are 90%, 95% and 99%. For example, at the 95% confidence level the MoE indicates that there are about 19 chances in 20 that the estimate will differ by less than the specified MoE from the population value (the figure obtained if all dwellings had been enumerated). For further information see Technical Note and Data Quality (see future release of the user guide).

Mental and behavioural conditions

Includes organic mental problems, alcohol and drug problems, mood (affective) disorders such as depression, anxiety related problems and other mental and behavioural problems.

Metric cup

Selected sugar sweetened and diet drink consumption was collected using the metric cup measurement. A metric cup is 250 millilitres in Australia, Canada, New Zealand and the United Kingdom.

Moderate exercise

Exercise for fitness, recreation, or sport which caused a moderate increase in heart rate or breathing.

National Nutrition and Physical Activity Survey (NNPAS)

The 2011-12 National Nutrition and Physical Activity Survey focused on collecting information on detailed dietary behaviour and food avoidance (including 24-hour dietary recall).

Neoplasm

A neoplasm is a new growth of abnormal tissue (a tumour). Tumours can be either benign (non-cancerous) or malignant (cancer). Cancer refers to several diseases and can affect most types of cells in various parts of the body.

Not in the labour force

Persons who are not employed or unemployed as defined, including persons who:

  • Are retired;
  • No longer work;
  • Do not intend to work in the future;
  • Are permanently unable to work; or
  • Have never worked and never intend to work.
     

Osteoporosis

A condition that thins and weakens bone mineral density, generally caused by loss of calcium, which leads to increased risk of fracture.

Physical activity

Refers to exercise only. The 2014 Physical Activity Guidelines are based on Australia’s Physical Activity and Sedentary Behaviour Guidelines. In the 2017-18 data cubes, 'any physical activity' refers to exercise and workplace activity. See also exercise and workplace activity.

Prevalence

The number of cases, of a particular characteristic (e.g. a specific long-term condition such as cancer), that are present in a population at one point in time. This differs from incidence, which refers to the number of new cases of a particular characteristic occurring within a certain period.

Proxy

A proxy is a person who answers the survey questions when the person selected for the interview is incapable of answering for themselves. Reasons the selected person may not be able to answer for themselves include illness/injury or language difficulties. A proxy also answers on behalf of a child under 15 years of age; or for a child aged 15-17 years when parental consent is not given to interview them personally. For further information see the Personal and Proxy Interviews section of Data Collection (see future release of the user guide).

Psychological distress

Derived from the Kessler Psychological Distress Scale (K10). This is a scale of non-specific psychological distress based on 10 questions about negative emotional states in the past 30 days. The K10 is scored from 10 to 50, with higher scores indicating a higher level of distress; low scores indicate a low level of distress. In this publication, scores are grouped as follows:

  • Low levels of distress (10-15);
  • Moderate levels of distress (16-21);
  • High levels of distress (22-29); and
  • Very high levels of distress (30-50).
     

Data was collected from respondents aged 18 years and over.

Self-assessed health status

A person's general assessment of their own health against a five point scale from excellent through to poor. Data was collected from respondents aged 15 years and over.

Significance testing

To determine whether a difference between two survey estimates is a real difference in the populations to which the estimates relate, or merely the product of different sampling variability, the statistical significance of the difference can be tested. This is particularly useful for interpreting apparent changes in estimates over time. The test is done by calculating the standard error of the difference between two estimates and then dividing the actual difference by the standard error of the difference. If the result is greater than 1.96, there are 19 chances in 20 that there is a real difference in the populations to which the estimates relate. For further information see Data Quality (see future release of the user guide).

Smoker status

Refers to the frequency of smoking of tobacco, including manufactured (packet) cigarettes, roll-your-own cigarettes, cigars and pipes, but excluding chewing tobacco, electronic cigarettes (and similar) and smoking of non-tobacco products. Categorised as:

  • Current daily smoker - a respondent who reported at the time of interview that they regularly smoked one or more cigarettes, cigars or pipes per day;
  • Current smoker - Other - a respondent who reported at the time of interview that they smoked cigarettes, cigars or pipes, less frequently than daily;
  • Ex-smoker - a respondent who reported that they did not currently smoke, but had regularly smoked daily, or had smoked at least 100 cigarettes, or smoked pipes, cigars, etc at least 20 times in their lifetime; and
  • Never smoked - a respondent who reported they had never regularly smoked daily, and had smoked less than 100 cigarettes in their lifetime and had smoked pipes, cigars, etc less than 20 times.
     

Data was collected from respondents aged 15 years and over.

Socio-Economic Indexes for Areas (SEIFAs)

Four Indexes compiled by the ABS following each population Census. Each index summarises different aspects of the socio-economic condition of areas. The Index of Disadvantage is the SEIFA index most frequently used in health analysis.

The Indexes available for use with 2017-18 NHS data are those compiled from the 2016 Census of Population and Housing. For further information about the indexes, see Census of Population and Housing: SEIFA, Australia, 2016.

Standard drink

A standard drink of alcohol in Australia is defined as containing 12.5 mLs of alcohol. See Alcohol Guidelines: Reducing the Health Risks for more information.

Stratification

Stratification involves dividing a population or dataset into like groups and can be used in sampling or statistical analysis.

Sugar sweetened drinks

In the National Health Survey: First Results, 2017-18 (cat. no. 4364.0.55.001), sugar sweetened drinks include soft drinks, cordials, sports drinks or energy drinks. This includes soft drinks in ready to drink alcoholic beverages. Excludes fruit juice, flavoured milk, 'sugar free' drinks, or coffee/hot tea. This was reported on usual consumption per day/week.

Note the inclusions and collection methodology are slightly different to the definition of 'Sugar sweetened beverages', previously published in the Australian Health Survey: Nutrition First Results - Foods and nutrients, 2011-12 (4364.0.55.007). 'Sugar sweetened beverages' also include fruit and vegetable drinks that contain added sugar. Data is based on 24-hour dietary recall information.

Systolic blood pressure

Measures the pressure in the arteries as the heart pumps blood during each beat. It is the higher number of the blood pressure reading.

Unemployed

Persons aged 15 years and over who were not employed and actively looking for work in the four weeks prior to the survey, and were available to start work in the week prior to the survey.

Usual daily intake of fruit

Refers to the number of serves of fruit (excluding drinks and beverages) usually consumed each day, as reported by the respondent. A serve is approximately 150 grams of fresh fruit or 50 grams of dried fruit. Adequate daily fruit intake refers to whether the respondent met the minimum number of serves as recommended in the NHMRC 2013 Australian Dietary Guidelines. Juices were excluded.

Usual daily intake of vegetables

Refers to the number of serves of vegetables (excluding drinks and beverages) usually consumed each day, as reported by the respondent. A serve is approximately half a cup of cooked vegetables (including legumes) or one cup of salad vegetables - equivalent to approximately 75 grams. Adequate daily vegetable intake refers to whether the respondent met the minimum number of serves as recommended in the NHMRC 2013 Australian Dietary Guidelines. Tomatoes were included as vegetables while juices were excluded.

2013 NHMRC Australian dietary guidelines

Recommended
serves per day
Age group (years)
2-34-89-1112-1314-1819-5051-7070 years
and over
 Fruit
Males11.5222222
Females11.5222222
 Vegetables
Males2.54.555.55.565.5(a)5
Females2.54.5555555
a. Rounded up to 6 serves in published data.
 

Vigorous exercise

Exercise for fitness, recreation or sport which caused a large increase in heart rate or breathing.

Waist circumference

Waist circumference is associated with an increased risk of metabolic complications associated with obesity. The World Health Organization (WHO) guidelines for Caucasian men and women are as follows:

Waist measurement guidelines, adults

 MenWomen
Not at riskWaist circumference less than 94 cmWaist circumference less than 80 cm
Increased riskWaist circumference more than or equal to 94 cmWaist circumference more than or equal to 80 cm
Greatly increased riskWaist circumference more than or equal to 102 cmWaist circumference more than or equal to 88 cm

Data presented in the waist circumference chapter on people at 'Increased risk' of developing chronic disease includes people at 'Greatly increased risk', while Table 8 presents these categories separately.

Workplace physical activity

Physical activity undertaken in the workplace which consists of two domains; moderate and vigorous workplace activity, which was undertaken on a typical work day. This information was collected from persons aged 15 and over who worked in a workplace in the last week in a job, business, unpaid internship, cadetship or farm including a family business without pay.

Abbreviations

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The following symbols and abbreviations are used in this publication:

. .not applicable
ABS Australian Bureau of Statistics 
ADFAustralian Defence Force
AHSAustralian Health Survey
ASGCAustralian Standard Geographical Classification
ASGSAustralian Standard Geography Standard
BMIBody Mass Index
cmcentimetre
COPD Chronic Obstructive Pulmonary Disease 
DVADepartment of Veterans' Affairs
HSLhigh sugar level in blood and/or urine
ICDInternational Classification of Diseases
K10Kessler Psychological Distress Scale
Kg Kilogram 
kJ kilojoules 
mmetre
mLmillilitre
MmHg millimetre of mercury 
mmol/Lmilimoles per litre
MoEmargin of error
nanot available
NHMRCNational Health and Medical Research Council
NHSNational Health Survey
NNPASNational Nutrition and Physical Activity Survey
npnot available for publication but included in totals where applicable, unless otherwise indicated
RSErelative standard error
SEstandard error
SEIFA Socio-Economic Indexes for Areas 
TIA Transient ischaemic attack 
SIHSurvey of Income and Housing
WHOWorld Health Organisation