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# Patient Experiences in Australia: Summary of Findings methodology

Reference period
2018-19 financial year
Released
12/11/2019

## Explanatory notes

### Introduction

This publication contains results from the Patient Experience Survey, a topic on the Multipurpose Household Survey (MPHS) conducted throughout Australia from July 2018 to June 2019. 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.

The 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. Respondents were asked about their experiences with medical professionals, the frequency of their visits, waiting times, and barriers to accessing care, as well as their self-assessed health status, long term health conditions and private insurance. Data was also collected on aspects of communication between patients and health professionals. Labour force characteristics, education, income and other demographics was also collected.

### Scope and coverage

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).

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

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.

### Data collection

The Patient Experience Survey is one of a number of small, self-contained topics on the Multipurpose Household Survey (MPHS), conducted throughout Australia from July 2018 to June 2019. The MPHS is a supplement to the monthly LFS. In 2018–19, the MPHS topics were:

• Patient Experiences in Australia
• Crime Victimisation
• Barriers and Incentives to Labour Force Participation
• Retirement and Retirement Intentions
• Qualifications and Work
• Income (Personal, Partner's, Household).

Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey and selected for the MPHS. 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.

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.

### Sample size

After taking into account sample loss, the response rate for the Patient Experience Survey was 71.8%. In total, information was collected from 28,719 fully responding persons. This includes 477 proxy interviews for people aged 15 to 17 years, where permission was not given by a parent or guardian for a personal interview.

### Weighting

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).

### Benchmarks

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 2018. People living in Indigenous communities were excluded. These benchmarks are based on the 2016 Census.

While LFS 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 MPHS (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 LFS and other labour household surveys estimates, as well as over time.

### Estimation

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

### Confidentiality

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.

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

### Reliability of estimates

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.

### Data quality

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.

### Comparability of time series

When comparing data from different cycles of the survey, users are advised to consult the questionnaires (available from the Data downloads section), 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.

The data item 'Whether seen an other health professional for own health in the last 12 months' was collected in 2018–19, but not in 2017–18.

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

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 71.8%. The scope of the Patient Experience Survey and the LFS also differ (refer to these sections above). 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.

### Geography

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

### Country of birth

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

### Industry

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

### Education

Education data are classified according to the Australian Standard Classification of Education ASCED (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.

### Language

Language data are classified according to the Australian Standard Classification of Languages (ASCL) (cat. no. 1267.0).

### Socio-Economic Indexes for Areas (SEIFA)

This survey uses the 2016 Socio-economic Indexes for Areas (SEIFA).

SEIFA is a suite of four summary measures that have been created from 2016 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:

### Products and services

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

As well as the statistics included in this and related publications, the ABS may have other relevant data available on request. Subject to confidentiality and sampling variability constraints, tables can be tailored to individual requirements. A list of data items from this survey is available from the Data downloads section. All enquiries should be made to the National Information and Referral Service on 1300 135 070, or email client.services@abs.gov.au

### Acknowledgements

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.

### Privacy

The ABS Privacy Policy outlines how the ABS will handle any personal information that you provide to the ABS.

### Next survey

The next Patient Experience Survey will be collected from July 2019 to June 2020.

## Technical note - data quality

### Reliability of the estimates

The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey. This is known as sampling error.

### Non-sampling error

Non-sampling error may occur in any collection, whether it is based on a sample or a full count 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 is made to reduce non-sampling error by careful design and testing of questionnaires, training and supervision of interviewers, and extensive editing and quality control procedures at all stages of data processing. It is not possible to quantify the non-sampling error.

### Sampling error

One measure of sampling error is given by the standard error (SE), which indicates the extent to which an estimate might have varied by chance because only a sample of persons was included. There are about two chances in three (67%) that a sample estimate will differ by less than one SE from the number that would have been obtained if all persons had been surveyed, and about 19 chances in 20 (95%) that the difference will be less than two SEs.

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 error likely to have occurred due to sampling and therefore avoids the need to also refer to the size of the estimate.

$$\large\ R S E \%=\left(\frac{S E}{\text {estimate}}\right) \times 100$$

Only estimates (numbers or percentages) with RSEs less than 25% are considered sufficiently reliable for most analytical purposes. However, estimates with larger RSEs have been included. Estimates with an RSE in the range 25% to 50% should be used with caution while estimates with RSEs greater than 50% are considered too unreliable for general use. All cells in the Excel spreadsheets with RSEs greater than 25% have been annotated and footnoted.

Another measure of sampling error 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.

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

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

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:

$$\Large{\frac{2.576}{1.96}}$$

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.

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 to indicate they are usually considered unreliable for most purposes.

The Excel spreadsheets in the Data downloads section contain all the tables produced for this release and the calculated RSEs and/or MOEs for each of the estimates.

### Calculations of standard errors

Standard errors can be calculated using the estimates (counts or percentages) and the corresponding RSEs. See What is a Standard Error and Relative Standard Error, Reliability of estimates for Labour Force data for more details.

### Standard errors of proportions and estimates

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. A formula to approximate the RSE of a proportion is given below. This formula is only valid when x is a subset of y:

$$\ R S E\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}-[R S E(y)]^{2}}$$

### Comparisons of estimates

The difference between two survey estimates (counts or percentages) can also be calculated from published estimates. Such an estimate is also 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:

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

While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.

### Significance testing

A statistical significance test for a comparison between estimates can be performed to determine whether it is likely that there is a difference between the corresponding population characteristics. The standard error of the difference between two corresponding estimates (x and y) can be calculated using the formula shown above in the Comparison of estimates section. This standard error is then used to calculate the following test statistic:

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

where:

$$\large\ S E(y)=\frac{R S E(y) \times y}{100}$$

If the value of this test statistic is greater than 1.96 then there is evidence, with a 95% level of confidence, of a statistically significant difference in the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations with respect to that characteristic.

## Glossary

### Show all

#### After hours GP

After hours means before 8am or after 1pm on a Saturday, any time on a Sunday or Public Holiday, or before 8am or after 8pm on any other day.

#### At least once delayed seeing or did not see .... when needed - cost a reason

To be placed in this category, respondents must have stated that cost was one of the reasons they delayed going or did not go to see a health professional when needed.

#### At least once delayed seeing or did not see .... when needed - reasons other than cost

To be placed in this category, respondents must have stated that the main reason they delayed going or did not go to see a health professional when needed, included:

• Dislike or fear of the service
• Waiting time was too long
• Service was not available when required
• Was too busy, or
• Other reason (besides cost)

#### Coordination of health care

Coordination of health care has been defined as the deliberate organisation of patient care activities between two or more participants involved in a patient’s care to facilitate the appropriate delivery of health care services.

#### Dental professional

Includes dentists, dental hygienists and dental specialists such as periodontists, orthodontists, and oral and maxillofacial surgeons.

#### Full-time status of work

Full-time work is 35 hours or more per week. The number of hours can be calculated based on the number usually worked or the number actually worked during the week before interview.

#### General Practitioners (GPs)

GPs are doctors who have completed a basic medical degree and internship, then do additional medical training in general practice. This qualifies them to provide continuing care for everyone from babies to the elderly. They have broad knowledge and skills and are usually the first point of contact for health issues and referrals to specialists or other health professionals.

The formal acceptance by a hospital or other in-patient health care facility of a patient who is to be provided with a room and continuous nursing service. This includes respondents who have been to a hospital emergency department and have also been admitted to hospital.

#### Hospital emergency department visit

Any time a person went to an emergency department for their own health, whether it was within normal GP practising hours or after hours.

#### Imaging test

Imaging tests or diagnostic imaging include all tests that produce images or pictures of the inside of the body in order to diagnose diseases. Tests involve the use of radiant energy, including x-rays, sound waves, radio waves, and radioactive waves and particles that are recorded by photographic films or other types of detectors.

#### Index of relative socio-economic disadvantage

This is one of four Socio-Economic Indexes for Areas (SEIFAs) compiled by the ABS following each Census of Population and Housing. This index summarises attributes such as low income, low educational attainment, unemployment, jobs in relatively unskilled occupations and dwellings without motor vehicles. The first or lowest quintile refers to the most disadvantaged areas, while the fifth or highest quintile refers to the least disadvantaged areas.

#### Long term health condition

A condition that has lasted or is likely to last six months or more. Respondents were specifically asked whether they had any of the following conditions:

• arthritis or osteoporosis
• asthma
• cancer
• diabetes
• a heart or circulatory condition
• a mental health condition, including depression or anxiety
• a long term injury
• any other long term health condition.

If respondents sought clarification, interviewers were instructed to include:

• conditions currently controlled by medication
• cancer where the respondent reports having cancer without any explanation
• cancer where the respondent was undergoing treatment such as chemotherapy or radiotherapy
• cancer in partial remission
• mental illness where the respondent was not currently experiencing an episode.

and to exclude pregnancy, and cancer where the respondent received a false positive test result.

#### Medical specialist

Medical specialists play a crucial role in the management and treatment of health conditions where they have specialist knowledge and skills. If respondents sought clarification on the definition of medical specialist, interviewers were instructed to advise that medical specialists provide services which are covered, at least in part, by Medicare (e.g. dermatologists, cardiologists, neurologists and gynaecologists).

#### Need

In the data presented in this publication, populations are sometimes based on those who needed to use a service. In most cases, this population is a combination of those who used the service and those who didn't but said they needed to use the service. The definition of need was left to the respondents interpretation for this survey.

#### Other health professionals

Some people may receive health care from health professionals other than their General Practitioner (GP), dental professional or medical specialist for their physical and/or emotional or psychological health. Examples of selected other health professionals include:

• Audiologist or Audiometrist
• Chemist or Pharmacist for advice only
• Chiropractor
• Diabetes Educator
• Dietician or Nutritionist
• Occupational therapist
• Naturopath or Acupuncturist
• Osteopath
• Podiatrist or Chiropodist
• Physiotherapist or Hydrotherapist
• Psychologist or Accredited counsellor
• Social worker or Welfare officer
• Speech therapist or Speech pathologist
• Other

#### Part-time status of work

Part-time work is less than 35 hours per week. The number of hours can be calculated based on the amount usually worked or the number actually worked during the week before interview.

#### Pathology test

A laboratory test that includes analysis of specimens such as urine and blood in order to diagnose disease.

#### Personal income

Relates to gross income.

#### Private health insurance

Refers to voluntary coverage through a private insurer (e.g. Medibank Private, HCF and Bupa). Depending on the type of cover purchased, private health insurance provides cover against all or part of hospital theatre and accommodation costs in either a public or private hospital, medical costs in hospital and costs associated with a range of services not covered under Medicare, including ambulance services, private dental services, optical, chiropractic and physiotherapy.

#### Public dental care

Any public dental service that is partly or fully funded by the government, including public dental services provided at a private dental clinic.

#### Remoteness

The Australian Statistical Geography Standard (ASGS) is used by the ABS for the collection and dissemination of geographically classified statistics. Remoteness Areas divide Australia into five classes of remoteness on the basis of a measure of relative access to services.

#### Self-assessed health

A person's impression of their own health against a five point scale from excellent through to poor.

#### Statistical significance

Differences between population estimates are said to be statistically significant when it can be stated with 95% confidence that there is a real difference between the populations (see the Significance Testing section of the Technical Note for more information).

## Quality declaration - summary

### Institutional environment

For information on the institutional environment of the Australian Bureau of Statistics (ABS), including the legislative obligations of the ABS, financing and governance arrangements, and mechanisms for scrutiny of ABS operations, please see the ABS Institutional Environment.

### Relevance

This publication presents information from the 2018–19 Patient Experience Survey, which is the tenth in the series. It includes data from people aged 15 years and over that accessed health services in the last 12 months, as well as from those who did not, and enables analysis of health service information in relation to particular population groups. The type of information collected included their interactions with general practitioners, dental professionals, medical specialists, hospitals and other health professionals, as well as their use of pathology and imaging tests. For detailed information about the data items collected refer to the Data item list in the Data downloads section.

Data on patient experience is of value to both users of health services and those aiming to improve the health system. The data will assist in developing and improving policies on the provision of health care in Australia and measuring specific COAG requirements.

High quality health care leads to better health outcomes, and barriers to accessing health services may impede the best possible outcome. The availability of GPs, impact of varying levels of service and the coordination of health care are all important factors in ensuring an accessible, high quality health care system for all Australians.

The Patient Experience topic is collected as part of the Multipurpose Household Survey (MPHS). The MPHS is a supplement to the monthly Labour Force Survey (LFS) and is designed to collect annual statistics on a small number of self-contained topics. The scope of the LFS is restricted to persons aged 15 years and over and excludes members of the permanent defence forces; certain diplomatic personnel of overseas governments usually excluded from Census and estimated resident populations; overseas residents in Australia; and members of non-Australian defence forces (and their dependants). Refer to Labour Force, Australia (cat. no. 6202.0) for further information regarding the LFS. In addition, the 2018–19 MPHS excluded persons living in Indigenous communities and persons living in non-private dwellings such as hotels, university residences, students at boarding schools, patients in hospitals, inmates of prisons and residents of other institutions (e.g. retirement homes, homes for persons with disabilities).

### Timeliness

The MPHS is conducted annually with enumeration undertaken over the financial year. The Patient Experience topic has been collected each year as part of the MPHS since 2009. Generally, data are released approximately five months after the end of MPHS enumeration.

### Accuracy

The LFS, and consequently the MPHS, is primarily designed to provide estimates for the whole of Australia and, secondly, for each state and territory.

Two types of error are possible in an estimate based on a sample survey: non-sampling error and sampling error. Non-sampling error arises from inaccuracies in collecting, recording and processing the data. Every effort is made to minimise reporting error by the careful design of questionnaires, intensive training and supervision of interviewers, and efficient data processing procedures. Non-sampling error also arises because information cannot be obtained from all persons selected in the survey.

Sampling error occurs because a sample, rather than the entire population, is surveyed. One measure of the likely difference resulting from not including all dwellings in the survey 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 in the survey, and about 19 chances in 20 that the difference will be less than two SEs. Measures of the relative standard errors (RSE) of the estimates for this survey are included with this release.

Only estimates with RSEs less than 25% are considered sufficiently reliable for most purposes. Estimates with RSEs between 25% and 50% have been included and are annotated to indicate they are subject to high sample variability and should be used with caution. In addition, estimates with RSEs greater than 50% have also been included and annotated to indicate they are considered too unreliable for general use.

Another measure is the Margin of Error (MOE), which describes the distance from the population value of the estimate at a given confidence level, 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 MOEs in this publication are calculated at the 95% confidence level, and estimates of proportions with an MOE more than 10% are annotated to indicate they are subject to high sample variability. In addition, estimates with a corresponding standard 95% confidence interval that includes 0% or 100% are annotated to indicate they are usually considered unreliable for most purposes. For further information, please refer to the Technical Note.

### Coherence

The ABS seeks to maximise consistency and comparability over time by minimising changes to the survey. However, sound survey practice requires ongoing development to maintain and improve the integrity of the data. Due to changes in the questionnaire, certain data items from each iteration of the Patient Experience Survey are not comparable year to year. For changes between iterations of the survey please refer to the Data Comparability section of the Explanatory Notes. All data items shown in time series tables are comparable between the survey cycles presented.

Due to differences in collection methods and question wording, health data collected in the Patient Experience Survey may not be comparable with data from other ABS health surveys, such as the National Aboriginal and Torres Strait Islander Health Survey, National Aboriginal and Torres Strait Islander Social Survey, Australian Health Survey, National Health Survey, General Social Survey and the Survey of Disability, Ageing and Carers.

### Interpretability

This publication contains tables and a summary of findings to assist with the interpretation of the results of the survey. Detailed Methodology, a Technical Note on Reliability of Estimates and a Glossary are also included, providing information on the terminology, classifications and other technical aspects associated with these statistics.

### Accessibility

All tables and associated RSEs and MOEs are available in Excel spreadsheets, which can be accessed from the Data downloads section.

Additional tables may also be available on request. The Data downloads section also includes a document containing a complete list of the data items available. Note that detailed data can be subject to high RSEs and MOEs, which may be subject to confidentiality and sampling variability constraints.

Microdata from this survey will also be accessible in the DataLab environment, subject to the approval of the Australian Statistician. This will enable registered users to view and analyse unit record information using a wider range of statistical software to undertake complex analysis or modelling. For further details, refer to the Microdata Entry Page on the ABS website.

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.

## Abbreviations

### Show all

 ABS Australian Bureau of Statistics ANZSIC Australian and New Zealand Standard Industrial Classification ANZSCO Australian and New Zealand Standard Classification of Occupations ASCED Australian Standard Classification of Education ASGS Australian Statistical Geography Standard ASCL Australian Standard Classification of Languages CAI computer assisted interviewing CSA Census and Statistics Act ED emergency department ERP Estimated Resident Population GP general practitioner ICS Indigenous Community Strata LFS Labour Force Survey LTC long term health condition MOE margin of error MPHS Multipurpose Household Survey RSE relative standard error SACC Standard Australian Classification of Countries SE standard error SEIFA Socio-Economic Indexes for Areas