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Patterns of Use of Mental Health Services and Prescription Medications methodology

Reference period
2011
Released
24/03/2016
Next release Unknown
First release

Explanatory notes

Introduction

1 The Mental Health Services-Census Data Integration project combined data from the 2011 Census of Population and Housing with a subset of data from the Medical Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS). De-identified transaction information from the MBS and PBS was transformed to person-level information. Probabilistic linkage techniques were used to combine this information with person-records from the Census to create the Mental Health Services-Census Integrated Dataset, 2011. 

Data

2 The data were produced using the following data sources:

  • 2011 Census of Population and Housing. The 2011 Census measured the number and key characteristics of people who were in Australia on Census night 9 August 2011. For information about the 2011 Census please refer to Census 2011 Reference and Information and Census Data Quality on the ABS website.
  • Medicare Benefits Schedule data. The Department of Human Services collects data on the activity of all persons making claims through the Medicare Benefits Scheme 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. The 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. The Mental Health Services-Census Integrated Dataset includes those MBS subsidised mental health-related services as defined in Appendix 1;
  • Pharmaceutical Benefits Scheme data. The Department of Human Services provides data on prescriptions funded through the Pharmaceutical Benefits Scheme to the Department of Health. The PBS lists all of the medicines available to be dispensed to patients at a Government-subsidised price. The Government is advised by the Pharmaceutical Benefits Advisory Committee (PBAC) regarding which drugs should be listed on the PBS Scheme. The Mental Health Services-Census Integrated Dataset includes those PBS subsidised mental health-related medications as defined in Appendix 2.

Scope

3 The scope of the data is restricted to persons who responded to the 2011 Census of Population and Housing and who accessed subsidised mental health-related items listed on the MBS or PBS datasets in 2011. Data excludes:

  • persons whose Census record indicated that they were an overseas visitor on Census night;
  • persons who were out of the country on Census night; and
  • persons who were not enumerated in the 2011 Census.


4 Data also excludes:

  • Persons who received services provided by hospital doctors to public patients in public hospitals, or services that qualify for a benefit under the Department of Veterans' Affairs National Treatment Account;
  • The Repatriation Pharmaceutical Benefits Scheme which is subsidised by the Department of Veterans’ Affairs;
  • Persons who were supplied medications or accessed services only through programs that do not use the Medicare processing system; for example, Aboriginal and Torres Strait Islander Health Programmes;
  • Persons accessing private prescription drugs, over the counter drugs, and drugs that cost less than the co-payment.


5 These exclusions are discussed further in the Data Quality section.

Linkage results

6 At the completion of the linkage process:

  • 1,072,284 person-records (69.6%) of the 1,540,836 person-records on the MBS dataset were linked to the 2011 Census;
  • 1,669,278 person-records (70.9%) of the 2,354,118 person-records on the PBS dataset were linked to the 2011 Census; and
  • 2,279,863 person-records (70.6%) of the 3,226,826 person-records on the combined MBS/PBS dataset were linked to the 2011 Census.
     

Methodology

Overview of data integration

7 Statistical data integration involves combining information from different administrative and/or statistical sources to provide new datasets for statistical and research purposes[1].

8 Data linking is a key part of statistical data integration and involves the technical process of combining records from different source datasets using variables that are shared between the sources. Data linkage is typically performed on records that represent individual persons, rather than aggregates. Two common methods used to link records are deterministic and probabilistic linkage. Deterministic linkage links person-records on exact matches using a unique identifier (such as a social security number or a created unique identifier such as a linkage key). Probabilistic linkage links person-records on close matches based on the relative likelihood that two records refer to the same person, using a number of linking variables (such as date of birth, sex, geographic area).

9 For further information on data integration see the National Statistical Service website – Data Integration.

Integration method

10 The Department of Health provided the ABS with de-identified MBS and de-identified PBS data extracts, while the Department of Human Services extracted and provided the associated de-identified demographic data extract on behalf of the Department of Health. This data was de-identified in that it did not include name, address, Medicare Number or Pharmaceutical Benefits number. ABS then transformed this administrative data from transaction-level to person-level.

11 Data from the 2011 Census, and the transformed MBS and PBS data, were brought together using probabilistic linkage. The variables used to link the MBS and PBS data to the Census were date of birth, sex and Mesh Block. The method involved linking without the use of name and address; this information was destroyed at the end of the 2011 Census processing cycle.

12 The process also placed importance on accuracy and uniqueness. Only records that matched exactly on the linkage variables and were unique matches were retained. In this linkage project, a unique match was defined as instances where a record on the MBS or PBS file had only one matching record on the Census, and that same Census record does not match to any other record on the MBS or PBS file.

13 Before records between datasets are compared, the contents of the linking variables of each dataset need to be as consistent as possible to facilitate comparison. This process is known as standardisation. The standardisation procedure for the Mental Health Services-Census Data Integration project included coding imputed and invalid values on the data to a common missing value. These variables included date of birth, age, sex, Mesh Block, Statistical Area Level 1 (SA1) and postcode.

14 The table below lists the variables used to link in each pass. Each record pair required exact matching of all variables used in the pass in order for a link to be created.

Linking variables used for each pass

 Pass 1Pass 2Pass 3Pass 4
Sex
Y
Y
Y
Y
Date of birth
Y
Y
Y
 
Age   
Y
Mesh Block
Y
  
Y
SA1 
Y
  
Postcode  
Y
 

Representativeness

15 The linkage rates that were achieved for the MBS and PBS datasets were in line with expected results, and were relatively consistent across most sub-populations - the exceptions were Northern Territory, Remote, Very Remote, and younger adults, which had lower linkage rates.

Linkage accuracy

16 False links can occur during the linkage process because, even when a record pair matches on all linking fields, the records may not actually belong to the same individual. While the methodology is designed to ensure that the majority of links are true some false links will be present within the dataset.

Unlinked records

17 There are three main reasons why records from the MBS and PBS datasets were not linked to a 2011 Census record:

  • records belonging to the same individual were present in the MBS or PBS dataset and the 2011 Census but these records failed to be linked because they contained missing or inconsistent information in one or more of the datasets.
  • there was no 2011 Census record corresponding to an MBS or PBS record because the person was not counted in the Census.
  • there were more than one Census records that agreed on the same linkage variables – only unique matches were retained.
     

Weighting

18 Some groups of records were more likely to link, or conversely less likely to link, than other groups of records. This resulted in over representation of some groups and under representation of others. Records are more difficult to link when a person has poorly reported, poorly coded, missing or non-applicable values for linking variables. The non-random distribution of links has the potential to cause bias. 

19 To compensate for differences in propensity to link, the data were weighted to represent the original MBS and PBS datasets.

20 Weighting is the process of adjusting a sample to infer results for the relevant population. To do this, a 'weight' is allocated to each sample unit - in this case, persons. The weight can be considered an indication of how many people in the relevant population are represented by each person in the sample.

21 For this project, estimates were created by weighting the linked records to represent the original MBS and PBS datasets, using: age group, sex, State/Territory, Remoteness Area, SEIFA, and broad groups for services and medications. For a relatively small number of records some of these variables were imputed for weighting purposes.

Data quality

22 All data collections are subject to sampling and non-sampling error. Non-sampling error may occur in any data collection. Possible sources of non-sampling error include errors in reporting or recording of information, occasional errors in coding and processing data, and errors introduced by the linkage process (discussed above).

23 A small number of geographies (State and Remoteness Area) were imputed, and a very small number of unusual records were removed prior to linkage.

MBS data

24 The Department of Human Services collects data on the activity of all persons making claims through the Medicare Benefits Scheme 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. The 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.

25 MBS data includes Medicare-subsidised mental health-related services provided by psychiatrists, general practitioners (GPs), psychologists and other allied health professionals—including mental health nurses, occupational therapists, some social workers, and Aboriginal health workers. These services are defined in the Medicare Benefits Schedule (See Appendix 1).

26 Medicare data covers services that are provided out-of-hospital (e.g. in doctors' consulting rooms) as well as in-hospital services provided to private patients whether they are treated in a private or public hospital. The figures do not include services provided to public patients in public hospitals or services that qualify for a benefit under the Department of Veterans Affairs National Treatment Account. The States and Territories are the custodians of public hospital data[2].

PBS data

27 The Department of Human Services provides data on prescriptions funded through the Pharmaceutical Benefits Scheme (PBS) to the Department of Health. The PBS lists all of the medicines available to be dispensed to patients at a Government-subsidised price. The Government is advised by the Pharmaceutical Benefits Advisory Committee (PBAC) regarding which drugs should be listed on the PBS Scheme.

28 PBS data include subsidised prescription medication from the following groups: Antipsychotics, Anxiolytics, Hypnotics and Sedatives, Antidepressants, and Psychostimulants, agents used for ADHD and nootropics (see Appendix 2).

29 The data refer only to prescriptions scripted by registered medical practitioners who are approved to work within the PBS and to paid services processed from claims presented by approved pharmacists who comply with certain conditions. They exclude adjustments made against pharmacists’ claims, any manually paid claims or any benefits paid as a result of retrospective entitlement or refund of patient contributions[3].

30 The PBS data exclude non-subsidised medications, such as private and over-the-counter medications. Under co-payment prescriptions (where the patient co-payment covers the total costs of the prescribed medication) data are available from mid-2012; and therefore not available for 2011[3].

31 Data does not include the Repatriation Pharmaceutical Benefits Scheme (RPBS) which is subsidised by the Department of Veterans’ Affairs[4].

32 Whilst data on medication dosage was obtained, it has not been considered in analyses in this publication.

Census of Population and Housing

33 The 2011 Census measured the number and key characteristics of people who were in Australia on Census night 9 August 2011. For information about the 2011 Census please refer to Census 2011 Reference and Information and Census Data Quality on the ABS website.

Geography

34 The Mesh Block information used in the linkage process may not be aligned between the MBS and PBS files, and the Census, for a range of reasons, including:

  • Differences arising because MBS and PBS mesh block are based on postal address whereas the Census mesh block was based on the usual residential address;
  • Persons may have changed their address but not updated their Medicare records.


35 Medicare claims data used in this dataset are based on the Mesh Block of the enrolment address of the patient. As clients may receive services in locations other than where they live, these data do not necessarily reflect the location in which services were received[5]. The data therefore reflects geographic information about the patient, rather than where they received each service – for example, the data does not show GP services by state, but rather the GP services provided to patients in each state.

Remoteness areas

36 People living in Remote and Very Remote areas of Australia are under-represented in the data. This may be for a number of reasons including:

  • GPs are less likely to charge Medicare in Remote areas[6].
  • Non-metropolitan hospitals are more likely to admit patients, and people in Remote areas are more likely to attend hospital accident and emergency departments for primary care medical consultations than people from Major Cities[6]. People accessing these hospital services may be public inpatients and therefore not in scope. States and Territories are the custodians for this data and it is not included in the dataset.
  • In 2010-11, despite there being more GPs in Remote areas, there were about half the GP services provided per person in Very Remote areas as in Major Cities [7].
  • The Aboriginal Health Services Program is funded by the PBS however person-level data is not in the PBS processing system. Data from Remote and Very Remote areas, and the data from the Northern Territory are most affected[3].
  • Section 100 of the National Health Act, 1953 allows for the Minister to make special arrangements for the supply of medications to people living in isolated areas. These medications do not appear in the PBS data.


37 The Census also undercounts the number of people living in some areas of Australia more than others. In 2011, the Northern Territory recorded the highest net undercount rate of all states and territories (6.9%) and showed the largest difference in the net undercount rate between its greater capital city and rest of state region (3.7% and 10.9% respectively)[8].

Rounding

38 Estimates presented in commentary in this publication have been rounded. Proportions are based on unrounded estimates. Calculations using rounded estimates may differ from those published.

Acknowledgement

39 The ABS acknowledges the continuing support provided by the National Mental Health Commission and the Department of Health for this project. The provision of data by the Department of Health and the Department of Human Services as well as funding from the National Mental Health Commission was essential to enable this important work to be undertaken. The enhancement of mental health statistics through data linkage by the ABS would not be possible without their cooperation and support. 

40 The ABS also acknowledges the importance of the information provided freely by individuals in the course of the 2011 Census. Census information provided by individuals to the ABS is treated in the strictest confidence as is required by the Census and Statistics Act (1905). MBS and PBS information provided by the Department of Health and the Department of Human Services to the ABS is treated in the strictest confidence as is required by the National Health Act (1953) and the Health Insurance Act (1973).

Endnotes

1. Australian Bureau of Statistics, 2006-2011, Outcomes from Vocational Education and Training in Schools, Experimental Estimates, Australia, (ABS cat. no. 4260.0, 2011), https://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/4260.0Explanatory%20Notes312006-2011?OpenDocument, last accessed 18/03/2016.

2. Department of Human Services, 2014, Medicare Item Reports, https://www.medicareaustralia.gov.au/statistics/mbs_item.shtml, last accessed 18/03/2016.

3. Australian Institute of Health and Welfare, 2014, 'Medicare-subsidised mental health-related prescriptions', https://www.aihw.gov.au/reports/mental-health-services/mental-health-services-in-australia/report-contents/mental-health-related-prescriptions/prescriptions, last accessed 18/03/2016.

4. Department of Veterans' Affairs, 2014 'Repatriation Pharmaceutical Benefits Scheme',http://www.dva.gov.au/about-dva/accountability-and-reporting/annual-reports/annual-reports-2012-13/department-veterans-12, last accessed 18/03/2016.

5. Productivity Commission, 2014, 'Data quality information — Mental health management, chapter 12', Report on Government Services 2014,
 http://www.pc.gov.au/research/ongoing/report-on-government-services/2014/health/download-the-volume/rogs-2014-volumee-health.pdf, last accessed 18/03/2016.

6. Australian Institute of Health and Welfare, 2005, 'Rural, regional and remote health Information framework and indicators', http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=6442459707, last accessed 18/03/2016.

7. Australian Institute of Health and Welfare, Jun 2014, Australia's health 2014, Chapter 5 'Health behaviours and other risks to health: Health in regional and remote areas',
  http://www.aihw.gov.au/publication-detail/?id=60129547205, last accessed 18/03/2016.

8 Australian Bureau of Statistics, 2012, 'Estimates of Undercount' Census of Population and Housing - Details of Undercount, 2011 (cat. no. 2940.0),
  https://www.abs.gov.au/ausstats/abs@.nsf/Products/2940.0~2011~Main+Features~Estimates+of+net+undercount?OpenDocument, last accessed 18/03/2016.

Appendix 1 - MBS items

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ProviderItem groupMBS Group & SubgroupMBS item numbers
PsychiatristsInitial consultation new patient(a)Group A8296, 297, 299
 Patient attendances—consulting roomGroup A8291(a), 293(a), 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 319
 Patient attendances—hospitalGroup A8320, 322, 324, 326, 328
 Patient attendances—other locationsGroup A8330, 332, 334, 336, 338
 Group psychotherapyGroup A8342, 344, 346
 Interview with non-patientGroup A8348, 350, 352
 TelepsychiatryGroup A8353, 355, 356, 357, 358, 359(b), 361(b), 364, 366, 367, 369, 370
 Case conferencing 855, 857, 858, 861, 864, 866
 Electroconvulsive therapy(c)Group T1, Subgroup 1314224
 Referred consultation for assessment, diagnosis and development of a treatment and management plan for autism or any other pervasive developmental disorder (PDD)(d)Group A8289
General PractitionersGP Mental Health Treatment Plan—accredittedGroup A20, Subgroup 12710(a)(f), 2715(g), 2717(g)
 GP Mental Health Treatment Plan—non-accreditted(a)Group A20, Subgroup 12700(g), 2701(g), 2702(g)
 GP Mental Health Treatment Plan—otherGroup A20, Subgroup 12712(a), 2713(a), 2719(g)(h)
 Focussed Psychological StrategiesGroup A20, Subgroup 22721, 2723, 2725, 2727
 Family Group TherapyGroup A6170, 171, 172
 Electroconvulsive therapy(i)Group T1020104
 3 Step Mental Health Process—GP(j)Group A18, Subgroup 42574, 2575, 2577, 2578
 3 Step Mental Health Process—other medical professional(j)Group A19, Subgroup 42704, 2705, 2707, 2708
Clinical PsychologistsPsychological Therapy Services(a)Group M680000, 80005, 80010, 80015, 80020
Other PsychologistsEnhanced Primary CareGroup M310968
 Focussed Psychological Strategies (Allied Mental Health)(a)Group M780100, 80105, 80110, 80115, 80120
 Assessment and treatment of PDD(c)Group A1082000, 82015
 Follow-up allied health service for Indigenous Australians(k)Group M1181355
Other Allied Health ProvidersEnhanced Primary Care—mental health workerGroup M310956
 Focussed Psychological Strategies (Allied Mental Health)—occupational therapist(a)Group M780125, 80130, 80135, 80140, 80145
 Focussed Psychological Strategies (Allied Mental Health)—social worker(a)Group M80150, 80155, 80160, 80165, 80170
 Follow-up allied health services for Indigenous Australians—mental health worker(k)Group M1181325
a. Item introduced 1 November 2006.
b. Item introduced 1 November 2007.
c. Item may include services provided by medical practitioners other than psychiatrists.
d. Item introduced 1 July 2008.
e. Item introduced 1 January 2010.
f. Item discontinued after 31 October 2011.
g. Item introduced 1 November 2011.
h. Item discontinued after 30 April 2012.
i. Item is for the initiation of anaesthesia for electroconvulsive therapy and includes services provided by medical practitioners other than GPs.
j. Item discontinued after 30 April 2007.
k. Item introduced 1 November 2008. Source: Australian Institute of Health and Welfare, 2014, 'Data Source' , Medicare-subsidised mental health-related services, viewed 13 August 2014, https://www.aihw.gov.au/reports/mental-health-services/mental-health-services-in-australia/report-contents/medicare-subsidised-mental-health-specific-services/data-source

 

Appendix 2 - PBS items

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CodeMedication groupsCodeMedication subgroup
N05Psycholeptics - A group of drugs that tranquillises (central nervous system depressants)
N05AAntipsychotics - drugs used to treat symptoms of psychosis (a severe mental disorder characterised by loss of contact with reality, delusions and hallucinations), common in conditions such as schizophrenia, mania and delusional disorderN05AAPhenothiazines with aliphatic side-chain
 N05ABPhenothiazines with piperazine structure
 N05ACPhenothiazines with piperidine structure
 N05ADButyrophenone derivatives
 N05AEIndole derivatives
 N05AFThioxanthene derivatives
 N05AHDiazepines, oxazepines, thiazepines and oxepines
 N05ALBenzamides
 N05AXOther antipsychotics
N05BAnxiolytics - drugs prescribed to treat symptoms of anxiety.N05BABenzodiazepine derivatives
N05CHypnotics and sedatives - hypnotic drugs are used to induce sleep and treat severe insomnia. Sedative drugs are prescribed to reduce excitability or anxiety.N05CDBenzodiazepine derivatives
N06Psychoanaleptics - A group of drugs that stimulates the mood (central nervous system stimulants)
N06AAntidepressants - drugs used to treat the symptoms of clinical depression.N06AANon-selective monoamine reuptake inhibitors
 N06ABSelective serotonin reuptake inhibitors
 N06AFMonoamine oxidase inhibitors, non-selective
 N06AGMonoamine oxidase A inhibitors
 N06AXOther antidepressants
N06BPsychostimulants, agents used for ADHD and nootropics - agents used for attention-deficit hyperactivity disorder and to improve impaired cognitive abilities (nootropics).N06BACentrally acting sympathomimetics
Source: Australian Institute of Health and Welfare, 2014, 'Medicare-subsidised mental health-related prescriptions', viewed 13 August 2014, https://www.aihw.gov.au/reports/mental-health-services/mental-health-services-in-australia/report-contents/expenditure-on-mental-health-related-services

 

Appendix 3 - logistic regression modelling

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Logistic regression is widely used in many fields, including the medical and social sciences, as a statistical method for modelling categorical outcomes. Binary logistic regression is used for modelling dichotomous outcomes (e.g. modelling 1 and 0, where 1 = a change in status and 0 = no change in status). Multinomial logistic regression is applicable to situations in which the modelled outcome has multiple categories (e.g. modelling treatment 'types of use' groups).

Outputs of the logistic regression model are usually presented in terms of the odds of the event or the probability that the event will occur. For each characteristic in the model a comparison group is selected.

To model the probability of a person being in a particular 'Type of use' group in the 'Types of use and transitions between treatments' section of this publication, the following variables were used in the multinomial logistic regression model: sex, age group, marital status, country of birth, level of highest educational attainment, labour force status, state, remoteness, socio-economic status (Index of Relative Socio-economic Disadvantage quintiles), household composition, household income and special dwelling indicator. For each variable the group with the highest frequency was the comparison or reference group.

It should be noted that the effects detected in the analysis are person rather than population effects. For example, if the conclusion is that females use multiple services more often than males, this simply means that a female with certain socio-demographic characteristics would use multiple services with a higher probability than a male with the same socio-demographic characteristics.

The 'Transitions between treatments' analysis in the 'Types of use and transitions between treatments' section of this publication examines the transitions of people between services and medications and explains the influences of certain socio-demographic factors on the probabilities of these transactions occurring. Binary logistic regression was used to model the probability of changing status from service to medication and vice versa (modelling 1 and 0, where 1 = a transition from a service to medication or vice versa, and 0 = no transition occurred), while controlling for the effects of socio-demographic characteristics.

In addition, the history of treatments of a person, such as:

  • the number of services/medications in a sequence before the current transition;
  • the number of services in a history of treatment until the current transition; and
  • the number of medications in a history of treatment until the current transition
     

were also included in the model.

The number of services/medications in a sequence before the current transition counts all the events (services or medications) from the last transition up to the current event. The number of services/medications in a history of treatment until the current event counts all services/medications accessed from 1st January 2011 up to but excluding the current event. The table below illustrates the calculation of these variables.

Example of calculating history of treatment variables

 Sequence of treatments
Current event in a history of treatment*
s
s
m
m
m
s
s
s
m
m
m
m
Previous event in a history of treatment*
s
s
m
m
m
s
s
s
m
m
m
Number of services/medications in a sequence before the current transition
1
2
1
1
3
1
2
3
1
2
3
Number of services in a history of treatment until the current event
1
2
2
2
2
3
4
5
5
5
5
Number of medications in a history of treatment until the current event
0
0
1
2
3
3
3
3
4
5
6
* s = service; m = medication

 

Two separate models were developed: one for transition from service to medication, and another for transition from medication to service.

The models included not only the main effects of the person’s socio-demographic characteristics but also the interactions between these characteristics and the history of treatment of the person. These are called the interaction terms. Data downloads Table 7 shows the calculated odds for the main effects and interactions for which significant effect was detected. Note that the interactions between socio-demographic characteristics can also be estimated, but in the interest of not complicating the analysis these were not included. The reason for adding the interaction terms with the treatment history is that this allows for treatment history to have a different impact depending on individual characteristics.

Note that any interpretation of the main effects without taking into account the odds of the interaction terms is inappropriate. The effects can be estimated for subgroups by multiplying the odds ratio for the main effect and the odds ratios for the corresponding interactions; however the analysis of probabilities rather than odds ratios is a simpler approach (as illustrated by the graphs in the 'Types of use and transitions between treatments' section).