4839.0 - Patient Experiences in Australia: Summary of Findings, 2017-18 Quality Declaration 
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 13/11/2018   
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1 This publication contains results from the Patient Experience Survey, a topic on the Multipurpose Household Survey (MPHS) conducted throughout Australia from July 2017 to June 2018. The MPHS, undertaken each financial year by the Australian Bureau of Statistics (ABS), is a supplement to the monthly Labour Force Survey (LFS) and is designed to collect statistics for a number of small, self-contained topics. In 2017-18, the topics were:

    • Patient Experiences in Australia
    • Attendance at Selected Cultural Venues and Events
    • Participation in Selected Cultural Activities
    • Crime Victimisation
    • Income (Personal and Household).

2 The Patient Experience Survey collected information from people about their experiences with selected aspects of the health system in the 12 months before their interview, including access and barriers to a range of health care services. Information on labour force characteristics, education, income and other demographics was also collected.


3 The scope of the Patient Experience Survey was restricted to people aged 15 years and over who were usual residents of private dwellings and excludes:
    • members of the Australian permanent defence forces
    • certain diplomatic personnel of overseas governments, customarily excluded from Census and estimated resident population counts
    • overseas residents in Australia
    • members of non-Australian defence forces (and their dependants)
    • persons living in non-private dwellings such as hotels, university residences, boarding schools, hospitals, nursing homes, homes for people with disabilities, and prisons
    • persons resident in the Indigenous Community Strata (ICS).

4 The scope for MPHS included households residing in urban, rural, remote and very remote parts of Australia, except the ICS.

5 In the LFS, rules are applied which aim to ensure that each person in coverage is associated with only one dwelling, and hence has only one chance of selection in the survey. See Labour Force, Australia (cat. no. 6202.0) for more detail.


Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey. These dwellings were selected for the MPHS. In these dwellings, after the LFS had been fully completed for each person in scope and coverage, a usual resident aged 15 years or over was selected at random (based on a computer algorithm) and asked the additional MPHS questions in a personal interview. The publication Labour Force, Australia (cat. no. 6202.0) contains information about survey and sample design, scope, coverage and population benchmarks relevant to the monthly LFS, and consequently the MPHS. This publication also contains definitions of demographic and labour force characteristics, and information about telephone interviewing.

7 In the MPHS, if the randomly selected person was aged 15 to 17 years, permission was sought from a parent or guardian before conducting the interview. If permission was not given, the parent or guardian was asked the questions on behalf of the 15 to 17 year old (proxy interview).

Data were collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, with interviews conducted either face-to-face or over the telephone. The majority of interviews were conducted over the telephone.


After taking into account sample loss, the response rate for the Patient Experience Survey was 71.1%. In total, information was collected from 28,243 fully responding persons. This includes 464 proxy interviews for people aged 15 to 17 years, where permission was not given by a parent or guardian for a personal interview.



Weighting is the process of adjusting results from a sample survey to infer results for the total 'in-scope' population. To do this, a 'weight' is allocated to each enumerated person. The weight is a value which indicates the number of persons in the population represented by the sample person.

The first step in calculating weights for each unit is to assign an initial weight, which is 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 people).


12 The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks'. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the population rather than the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular categories of persons/households which may occur due to either the random nature of sampling or non-response.

The survey was benchmarked to the Estimated Resident Population (ERP) living in private dwellings in each state and territory at December 2017. People living in Indigenous communities were excluded. These benchmarks are based on the 2016 Census.

14 While Labour Force Survey benchmarks are revised every 5 years, to take into account the outcome of the 5-yearly rebasing of the ERP following the latest Census, the supplementary surveys and Multipurpose Household Surveys (from which the statistics in this publication are taken) are not. Small differences will therefore exist between the civilian population aged 15 years and over reflected in the Labour Force Survey and other labour household surveys estimates, as well as over time.


Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest.


16 To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. 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. The introduction of perturbation in publications ensures that these statistics are consistent with statistics released via services such as TableBuilder.

17 Perturbation has been applied since 2013–14. Data from previous cycles (2009 to 2012–13) have not been perturbed.


All sample surveys are subject to error which can be broadly categorised as either sampling error or non-sampling error. For more information refer to the Technical Note.

19 Sampling error is the difference between the published estimate, derived from a sample of dwellings, and the value that would have been produced if all dwellings in scope of the survey had been included.

Non-sampling error may occur in any collection, whether it is based on a sample or a full count of the population such as a census. Sources of non-sampling error include: non-response; errors in reporting by respondents or recording of answers by interviewers; and errors in coding and processing data. Every effort was made to reduce the non-sampling error by: careful design and testing of the questionnaire; training and supervision of interviewers; follow-up of respondents; and extensive editing and quality control procedures at all stages of data processing.


21 Information recorded in this survey is 'as reported' by respondents, and may differ from that which might be obtained from other sources or via other methodologies. This factor should be considered when interpreting the estimates in this publication.

Information was collected on respondents' perception of their health status and experiences with services. Perceptions are influenced by a number of factors and can change quickly. Care should therefore be taken when analysing or interpreting the data.

The definition of 'need' (in questions where respondents were asked whether they needed to use a particular health service) was left to the respondents' interpretation.

For some questions which called for personal opinions, such as self-assessed health or whether waiting times were felt to be unacceptable, responses from proxy interviews were not collected.

25 A small proportion of respondents were resident in areas with no Socio-economic Indexes for Areas (SEIFA) scores allocated. For the purposes of the Patient Experience Survey, these records have had a SEIFA decile imputed, based on the deciles of the surrounding areas. For information on SEIFA, see the Socio-economic Indexes for Areas (SEIFA) section below.


Comparability of Time Series

26 The ABS seeks to maximise consistency and comparability over time by minimising changes to surveys. Sound survey practice, however, requires ongoing development to maintain and improve the integrity of the data. When comparing data from different cycles of the survey, users are advised to consult the questionnaires (available from the Downloads tab), check whether question wording or sequencing has changed, and consider whether this may have had an impact on the way questions were answered by respondents.

27 The following data items were collected in 2016-17 but not in 2017-18:
    • Whether any medical care treatment or tests caused harm or harmful side-effects in last 12 months
    • Where had medication, medical care treatment or test causing most recent harm/harmful side-effect (eg. home, GP, hospital)
    • Whether had received information that most recent harm or harmful side-effect could happen
    • Whether received an explanation about most recent harm or harmful side-effect
    • How well explanation about most recent harm or harmful side-effect was understood
    • Whether saw a health professional about the harm or harmful side-effect
    • Satisfaction with way situation was handled by health professional regarding most recent harm or harmful side-effect

28 The following data items were collected in 2017–18 but not in 2016–17:
    • Whether has private health insurance cover
    • Type of private health insurance cover

29 All data items shown in time series tables are comparable between the survey cycles presented.

Comparability with other ABS surveys

Caution should be taken when comparing across ABS surveys and with administrative by-product data that address the access and use of health services. Estimates from the Patient Experience Survey may differ from those obtained from other surveys (such as the National Aboriginal and Torres Strait Islander Health Survey, National Aboriginal and Torres Strait Islander Social Survey, National Health Survey, General Social Survey and Survey of Disability, Ageing and Carers) due to differences in survey mode, methodology and questionnaire design.

Comparability to monthly LFS Statistics

31 Since the Patient Experience Survey is conducted as a supplement to the LFS, data items collected in the LFS are also available in this publication. However, there are some important differences between the two surveys. The LFS had a response rate of over 90% compared to the MPHS response rate of 71.1%. The scope of the Patient Experience Survey and the LFS (refer to the Scope and Coverage section above) also differ. Due to the differences between the samples, data from the Patient Experience Survey and the LFS are weighted separately. Differences may therefore be found in the estimates for those data items collected in the LFS and published as part of the Patient Experience Survey.



32 Australian geographic data are classified according to the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011 (cat. no. 1270.0.55.001). Remoteness areas are classified according to the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2011 (cat. no. 1270.0.55.005)

Country of birth

Country of birth data are classified according to the Standard Australian Classification of Countries (SACC), Second Edition (cat. no. 1269.0).


Industry data are classified according to the Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (Revision 2.0) (cat. no. 1292.0).


Occupation data are classified according to the Australian and New Zealand Standard Classifications of Occupations, 2013, Version 1.2 (cat. no. 1220.0).


Education data are classified according to the Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0). The ASCED is a national standard classification which can be applied to all sectors of the Australian education system including schools, vocational education and training and higher education. The ASCED comprises two classifications: Level of Education and Field of Education.

Socio-economic Indexes for Areas (SEIFA)

The 2017–18 survey uses the 2011 Socio-economic Indexes for Areas (SEIFA).

38 SEIFA is a suite of four summary measures that have been created from 2011 Census information. Each index summarises a different aspect of the socio-economic conditions of people living in an area. The indexes provide more general measures of socio-economic status than is given by measures such as income or unemployment alone.

For each index, every geographic area in Australia is given a SEIFA number which shows how disadvantaged that area is compared with other areas in Australia.

The index used in the Patient Experience publication is the Index of Relative Socio-economic Disadvantage, derived from Census variables related to disadvantage such as low income, low educational attainment, unemployment, jobs in relatively unskilled occupations and dwellings without motor vehicles.

SEIFA uses a broad definition of relative socio-economic disadvantage in terms of people's access to material and social resources, and their ability to participate in society. While SEIFA represents an average of all people living in an area, it does not represent the individual situation of each person. Larger areas are more likely to have greater diversity of people and households.

For more detail, see the following:


43 Data Cubes containing all tables for this publication in Excel spreadsheet format are available from the Downloads tab. The spreadsheets present tables of estimates and proportions, and their corresponding relative standard errors (RSEs) and/or Margins of Error (MoEs).

44 Special tabulations of the data 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 (including state and territory level data), tailored to individual requirements. These are provided in electronic form. A list of data items from the 2017-18 Patient Experience Survey is available from the Downloads tab. All enquiries should be made to the National Information and Referral Service on 1300 135 070, or email client.services@abs.gov.au

45 For further information about these and related statistics, contact the National Information and Referral Service on 1300 135 070, or email client.services@abs.gov.au. The ABS Privacy Policy outlines how the ABS will handle any personal information that you provide to us.


ABS surveys draw extensively on information provided by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated and 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.


The Patient Experience Survey is conducted annually, with the next survey occurring in 2018-19.