4839.0 - Patient Experiences in Australia: Summary of Findings, 2015-16 Quality Declaration 
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 15/11/2016   
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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 2015 to June 2016. The MPHS, conducted each financial year by the Australian Bureau of Statistics (ABS) as a supplement to the monthly Labour Force Survey (LFS), is designed to collect statistics for a number of small, self-contained topics.

2 The Patient Experience Survey collected information from individuals about their experiences with selected aspects of the health system in the 12 months before their interview. 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 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.


ABS interviewers conducted personal interviews during the 2015–16 financial year for the monthly LFS. Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey. These dwellings that were rotated out of the survey were selected for 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 75.2%. In total, information was collected from 28,276 fully responding persons. This includes 433 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) in each state and territory at December 2015. Previously, March was used as the reference month for benchmarking. This is the first year that the reference month has been changed to December. This aligns MPHS with the weighting methodology generally adopted by other social surveys, whereby the middle month of the enumeration period is selected as the benchmark reference month. This will have a minor affect on the comparison of level estimates as there has only been 9 months of population growth accounted for between the 2014-15 publication (based on March 2015 benchmarks) and the 2015-16 publication (based on December 2015 benchmarks). There will be no affect on the analysis of proportions.

14 Every five years, the ERP series are revised to incorporate additional information available from the latest Census of Population and Housing (Census). The benchmarks used for the 2015–16 iteration are based on the 2011 Census.


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.

17 Perturbation has been applied to the 2013–14, 2014–15 and 2015-16 data. 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
    • non-sampling error.

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. For more information refer to the Technical Note.

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 2014-15 but not in 2015-16;
    • How often regular doctor or practice knew important information about medical history
    • How often received same-day answer to medical question or concern
    • How often regular doctor helped coordinate care for long term conditions

28 The following data items were collected in 2015–16 but not in 2014–15:
    • Whether has private health insurance
    • Type of private health insurance
    • Number of times visited a GP for own health in last 12 months
    • Number of times visited a GP after hours for own health in last 12 months
    • Number of times visited a medical specialist for own health in last 12 months
    • Number of times visited a dental professional for own health in last 12 months
    • Number of times been to hospital emergency department for own health in last 12 months
    • Number of times admitted to hospital for own health in last 12 months

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 in comparisons 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 Australian Health Survey, General Social Survey and Survey of Disability, Ageing and Carers) due to differences in survey mode, methodology and questionnaire design.


Remoteness areas are classified according to the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2011 (cat. no. 1270.0.55.005).

Socio-economic Indexes for Areas (SEIFA)

The 2015–16 survey uses the 2011 Socio-economic Indexes for Areas (SEIFA).

33 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:


    Data Cubes

    Data Cubes containing all tables in Excel spreadsheet format can be found on the ABS website (from the Downloads tab). The spreadsheets present tables of estimates and proportions, and their corresponding relative standard errors (RSEs).

    Customised data requests

    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 2015-16 Patient Experience Survey is available from the Downloads tab.

    40 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 2016–17.