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9 The scope excludes:
10 The sample frame for the Study was the Medicare Enrolment Database (MEDB). The sample was selected from this frame by the Department of Human Services (DHS) in accordance with a stratification and allocation specified by the ABS.
11 As people were in scope of the Study if they saw a GP at least once in the 12 months prior to selection (that is, from 24 November 2014 to 24 November 2015), there may have been people who saw a GP at least once in the 12 months prior to the enumeration period of the Survey of Health Care (that is, April 2016 to June 2016) who were not in scope as they did not visit a GP between 24 November 2014 and 24 November 2015. Similarly, there may have been people who visited a GP in the 12 months prior to selection but did not visit a GP in the 12 months prior to enumeration who were in scope.
12 The Study sample was designed to support estimates at the Primary Health Network (PHN) area level. A stratified random sample was used where the strata were based on the following variables:
13 People on the frame were assigned a PHN based on the postcode of their postal address as recorded on the MEDB. A correspondence between postal areas and PHNs was used to do this. As not every postcode is included in the ABS postal area classification, there were around 100,000 people who could not be allocated a PHN. At the sample design stage, these people were allocated to an unknown PHN category.
14 There were 8.8 million people in scope on the MEDB. A total sample of around 124,000 people was selected by sorting within stratum by number of GP visits and then applying a skip using a random start.
15 Also incorporated into the overall sample design was a requirement to oversample people who saw a GP more than 12 times such that the resulting sample consisted of approximately half people who saw a GP 12 or more times and half who saw a GP 1-11 times.
16 There were around 124,000 people selected for the Study.
17 From the people selected for the Study, 35,495 people responded to the Survey of Health Care, giving a response rate of 28.6%. In this survey it is not possible to distinguish between non-response and sample loss. For example, a person may have been selected to participate, but will not have received any survey materials due to an out-of-date address on the MEDB.
18 Of people who responded to the survey, around 18,100 provided consent for the release of their MBS information to the ABS. The effective MBS response rate from the 124,000 people initially selected for the Study is therefore 14.6%.
19 Of people who responded to the survey, around 16,700 provided consent for the release of their PBS information to the ABS. The effective PBS response rate from the 124,000 people initially selected for the Study is therefore 13.5%.
20 The table below contains response rates by the state or territory that the person was selected in. Persons selected in the unknown PHN category have unknown state for selection. In outputs from the Study, respondents are placed into the geographic regions (e.g. state, PHN) and SEIFA decile that correspond to their reported home postcode.
COORDINATION OF HEALTH CARE STUDY RESPONSE RATES
21 Survey of Health Care data and MBS, PBS and hospital consent information was collected by mail. In order to facilitate maximum response, a four stage mail-out approach was used. The four stages consisted of:
22 In each phase of the mail out, a cover letter from the DHS was included, explaining that the DHS had not provided the ABS with any personal details of the selected person.
23 People with low English proficiency, or who had a disability which prevented them from completing the survey on their own, were able to complete the survey over the phone. People with low English proficiency were offered the option of an interpreter from the Translation and Interpreting Service (TIS National) who could facilitate a phone call with the ABS and translate as an ABS officer provided information or collected the participant's data over the phone.
24 For consenting participants, de-identified MBS and PBS information was provided to the ABS by the DHS. The ABS provided a list of MBS and/or PBS consenting participants to the DHS who then extracted MBS and/or PBS data for these participants. The DHS then returned de-identified MBS and PBS data to the ABS.
WEIGHTING, BENCHMARKS AND ESTIMATION
25 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 people in the population represented by the sample person.
26 For information on calculation of weights for the Survey of Health Care component of the Study, see paragraphs 18-22 of the Explanatory Notes of Survey of Health Care, Australia, 2016 (cat. no. 4343.0). Weights for MBS and PBS data were calculated in a similar manner to the Survey of Health Care component, with additional benchmarks as specified in paragraph 29 below.
27 Weights are calibrated against population benchmarks such that estimates conform to the distribution of the MEDB 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 people/households which may occur due to either the random nature of sampling or non-response.
28 The Survey of Health Care includes weights benchmarked to counts of the in-scope population at November 2015 from the MEDB for PHNs (based on postal address postcode) by sex by 10 year age groups (to age 75 and over).
29 For MBS and PBS information integrated to the Survey of Health Care, additional benchmarks are included in calibration to improve estimates of the use of MBS services and PBS medicines. These benchmarks were the:
30 Estimates of counts of people are obtained by summing the weights of people with the characteristic of interest. For the Study the different responding/consenting sample groups (see paragraph 20 above) each weight up to the in scope population of 8.8 million people aged 45 years and over who had at least one GP visit in the 12 months between November 2014 and November 2015.
31 The Survey of Health Care weights sum the responding survey sample of 35,495 people to the 8.8 million in scope population.
32 The MBS weights sum the consenting MBS sample of 18,146 people to the 8.8 million in scope population.
33 The PBS weights sum the consenting PBS sample of 16,703 people to the 8.8 million in scope population.
34 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. The requirement means that the ABS must take care and make assurances that any statistical information about individual respondents cannot be derived from published data.
35 Perturbation is used in this publication to minimise the risk of identifying individuals in aggregate statistics. 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.
RELIABILITY OF ESTIMATES
36 All sample surveys are subject to error which can be broadly categorised as either:
37 Sampling error is the difference between the estimate derived from a sample of people, and the value that would have been produced if all people in scope of the survey had been included. For more information refer to the Technical Note.
38 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.
39 Margins of Error are provided for proportions to assist users in assessing the reliability of these data. The proportion combined with the MoE defines a range which is expected to include the true population value with a given level of confidence. This is known as the confidence interval. This range should be considered by users to inform decisions based on the proportion. Proportions with an MoE of greater than 10 percentage points are preceded by a hash (e.g. #40.1) to indicate the range in which the true population value is expected is relatively wide.
40 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; follow-up of respondents; and extensive editing and quality control procedures at all stages of data processing.
41 Non-response bias occurs where non-respondents may have different characteristics from those who did respond. While the Study is potentially affected by non-response bias, it is not possible to reliably quantify this. The magnitude of any bias depends on the rate of non-response and the extent of the differences in characteristics between those people who responded to the survey and those who did not. See Table 1.2 of the Explanatory Notes of Survey of Health Care, Australia, 2016 (cat. no. 4343.0) for an assessment of results from the Survey of Health Care component of the Study with other ABS collections.
Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data
42 The Department of Human Services collects data on the activity of all persons making claims through the Medicare Benefits Schedule and provides this information to the Department of Health. Information collected includes the type of service provided (MBS item number) and the benefit paid by Medicare for the service. Item numbers and benefits paid by Medicare are based on the Medicare Benefits Schedule which is a listing of the Medicare services subsidised by the Australian Government.
43 The Department of Human Services provides data on prescriptions funded through the Pharmaceutical Benefits Scheme/Repatriation Pharmaceutical Benefits Scheme to the Department of Health. The PBS/RPBS lists all of the medicines available to be dispensed to patients at a Government-subsidised price.
44 The scope of MBS and PBS data is restricted to persons who were in the in scope population (people aged 45 years and over who had at least one GP visit in the 12 months between November 2014 and November 2015) and accessed subsidised items listed on the MBS or PBS between 1 January 2014 and 30 July 2018, and excludes:
45 Geographic classifications were applied to the survey data based on the respondent’s reported home postcode, using correspondences between the geography of interest and ABS Postal Area geography.
46 Standard ABS Geographies were classified according to the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 (cat. no. 1270.0.55.001).
47 Remoteness areas are classified according to the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016 (cat. no. 1270.0.055.005).
48 Primary Health Networks (PHNs) are a classification developed by the Department of Health; see Primary Health Networks in the Glossary. The correspondence between PHN and ABS Postal Area geography was used to relate a person’s postcode as listed on the MEDB to a PHN.
49 Where a postcode crossed a PHN boundary the entire postcode was allocated to the PHN with largest proportion of people living in it. There was a slight exception where a postcode crossed a state boundary; in this case individuals were manually coded to the state they reported as their address.
50 In this publication MBS data is reported on groups based on Broad Type of Service groups (see Appendix 1 – MBS items).
PRODUCTS AND SERVICES
51 Data cubes containing 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) and margin of error (MoE).
Customised data requests
52 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.
54 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.
55 The Coordination of Health Care Study is funded by the AIHW, and jointly conducted by the ABS and AIHW. This publication was jointly prepared and released by the ABS and the AIHW.
56 The ABS and AIHW also acknowledge and thank the DHS for its assistance in the sample selection and postage process of the study.
57 The Study uses variations of questions sourced from other national and international non-ABS surveys (see Table 1.3 of the Explanatory Notes of Survey of Health Care, Australia, 2016, cat. no. 4343.0, for more information). The ABS and AIHW would like to acknowledge the following organisations:
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