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4 The coverage of the Patient Experience Survey was the same as the scope except that households in Indigenous communities were excluded for operational reasons.
5 In the LFS, coverage rules are applied which aim to ensure that each person 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.
6 ABS interviewers conducted personal interviews during the 2010-11 financial year for the monthly LFS. Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey and a sub-sample of these dwellings was selected for the MPHS.
7 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. 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).
8 Data was collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, usually during a telephone interview.
9 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.
10 The approximate response rate for persons asked to participate in the survey was 81.4%, which meant that 26,423 persons fully responded to the Patient Experience Survey. One person aged 15 years or over from each household was asked questions in relation to their own health. This included 464 proxy interviews for people aged 15 to 17 where permission was not given by a parent or guardian for a personal interview.
Weighting, benchmarks and estimation
11 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.
12 For the MPHS, 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).
13 The initial weights are then calibrated to align with independent estimates of the population, referred to as benchmarks. The population included in the benchmarks is the survey scope, for example, the estimated civilian population aged 15 years and over living in private dwellings in each State and Territory excluding persons out of scope. This calibration process ensures that the weighted data conform to the independently estimated distribution of the population described by the benchmarks rather than to the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular categories of persons which may occur due to either the random nature of sampling or non-response.
14 The survey was benchmarked to the estimated resident population (ERP) in each state and territory, excluding those living in very remote areas of Australia, at 31 March 2011.
15 Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest.
Reliability of Estimates
16 All sample surveys are subject to error which can be broadly categorised as either:
17 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.
18 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 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.
Interpretation of results
19 Information recorded in this survey is essentially '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.
20 Information was collected on respondents' perception of their health status. Perceptions are influenced by a number of factors and can change quickly. Care should therefore be taken when analysing or interpreting the data.
21 The definition of urgent medical care was left to respondents to determine, however, discretionary interviewer advice suggested, for example, that visiting a GP to get a medical certificate for work would probably not be considered urgent medical care. Care should be taken when analysing or interpreting this data.
22 Where questions called for personal opinions, such as self-assessed health or whether waiting times were felt to be inappropriate, responses from proxy interviews were not collected.
23 The number of people who saw a medical specialist are potentially undercounted by up to around 868,000 people. These people were not asked whether they had seen a medical specialist despite having received a referral from a GP in the 12 months prior to interview. Future iterations of the survey have corrected for this issue.
Comparability of Time Series
24 Due to changes in the questionnaire, the populations being asked some questions in the 2010-11 Patient Experience Survey may differ from the populations who were asked the same questions in 2009. All time series tables produced by the ABS ensure that the populations being compared between survey cycles are the same.
Comparability with other ABS surveys
25 The ABS produces statistics on the private hospital sector in Private Hospitals, Australia (cat. no. 4390.0). These can yield different results regarding the use of private hospitals by patients in Australia because of conceptual differences with the data collection. Caution should be taken in comparisons across ABS surveys and administrative by-product data that address the access and use of health services.
26 Country of birth data are classified according to the Standard Australian Classification of Countries (SACC), Second Edition, 2008 (cat. no. 1269.0).
27 Remoteness areas are classified according to the Statistical Geography: Volume 1 - Australian Standard Geographical Classification (ASGC), 2006 (cat. no. 1216.0).
28 Education data are classified according to the Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0).
Socio-economic Indexes for Areas (SEIFA)
29 Socio-economic Indexes for Areas (SEIFA) is a suite of four summary measures that have been created from 2006 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.
30 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.
31 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, and dwellings without motor vehicles.
32 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.
33 For more detail, see the following papers:
Products and services
34 Data cubes of all tables in Excel spreadsheet format can be found on the ABS website (from the download tab of cat. no. 4839.0). The spreadsheets present tables of estimates and proportions, and their corresponding relative standard errors (RSEs).
Customised data requests
35 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 tailored to individual requirements. These are provided in electronic form. All inquiries should be made to the National Information and Referral Service on 1300 135 070.
36 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.
37 The Patient Experience Survey is conducted annually, with the next survey occurring in 2011-12.
38 ABS publications which may also be of interest include:
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