Presents microdata from the National Study of Mental Health and Wellbeing for key mental health statistics including prevalence of mental disorders.
The National Study of Mental Health and Wellbeing (NSMHW) is comprised of a survey that is collected on an irregular basis and is designed to provide a range of information about the mental health of Australians. It provides information on the prevalence of selected lifetime and 12-month mental disorders, by the major disorder groups:
It also provides information on the level of impairment, and health services used for mental health problems.
This information can be cross classified by selected demographic and socioeconomic characteristics.
This product provides information about the 2021-22, 2020-21 and 2007 NSMHW collections. It includes details about data files, Data Item Lists, and information about the survey methodology. A link to microdata for the 1997 release is also provided.
Due to changes in survey content and the application of diagnostic criteria for mental disorders, some data are not comparable between collections. For more information, see Comparison between 2021-22, 2020-21, 2007 and 1997 below and 2020-2022 Methodology information.
The following microdata products are available from this survey:
To apply for access, see Microdata Entry Page.
Before you apply for access, read Responsible Use of ABS Microdata, User Guide.
Estimates from the 2021-22, 2020-21, and 2007 NSMHW are available at two levels contained in separate data files: Household and Selected Person.
A complete list of data items can be accessed from the Data Item List in the Data downloads section. This contains details for each data item including the output categories and any special codes used.
The 2020-21 and 2021-22 collections have also been pooled to create a 2020-2022 dataset.
The following table shows the levels available in the microdata product and the information contained on those levels:
| Level name | Information contained on level |
|---|---|
| Household | Geographic classifications, household size and structure, dwelling characteristics and household income details. |
| Selected Person | Demographic and socioeconomic characteristics of survey respondents, as well as health, mental health and related information provided by respondents. |
The following table shows the hierarchical file structure and the relationship between each level:
| Level 1 | Level 2 | Relationship type |
|---|---|---|
| Household | One record per in scope household | |
| Selected Persons | One selected person record per household |
| Levels | Record Counts (Unweighted) | Weighted Counts |
|---|---|---|
| Household | 15,893 | 9,897,838 |
| Person (Selected persons) | 15,893 | 19,828,348 |
| Levels | Record Counts (Unweighted) | Weighted Counts |
|---|---|---|
| Household | 10,339 | 9,897,838 |
| Person (Selected persons) | 10,339 | 19,828,348 |
| Levels | Record Counts (Unweighted) | Weighted Counts |
|---|---|---|
| Household | 5,554 | 9,787,651 |
| Person (Selected persons) | 5,554 | 19,644,025 |
| Levels | Record Counts (Unweighted) | Weighted Counts |
|---|---|---|
| Household | 8,841 | 8,159,637 |
| Person (Selected persons) | 8,841 | 16,015,345 |
For NSMHW, there are two weight variables on the file:
The NSMHW is a sample survey, so to produce estimates for the in-scope population you must use weight fields in your calculations. When analysing a Household level item, you will need to use the household weight. When analysing a Selected Person level item, you will need to use the person weight.
Weights have been created for each sample. It is important to use the weights created for the sample you are using. In particular, the weights for the pooled 2020-2022 sample should not be used with the individual 2020-21 or 2021-22 samples. Estimates from the individual 2020-21 and 2021-22 samples will not match estimates derived from the pooled 2020-2022 sample. Sample counts and unperturbed weighted estimates are included in the National Study of Mental Health and Wellbeing methodology for 2020-2022, 2021-22 and 2020-21 to assist you in validating your estimates.
Data items for 2020-2022 include:
The Data Item Lists contain a full list of available data items and categories for the 2020-2022, 2021-22, 2020-21 and 2007 collections.
Every record on each level of the file is uniquely identified. See Data Item Lists for details on which ID equates to which level.
Each household has a unique random identifier, ABSHID. This identifier appears on the household level and is repeated on the selected person level. The combination of identifiers uniquely identifies a record at a particular level as shown below.
The Household record identifier, ABSHID, assists with linking people from the same household, and with household characteristics such as geography (located on the household level) to the Person records.
Several questions in the survey allowed respondents to provide one or more responses. Each response category for these multi-response data items is treated as a separate data item. In the detailed microdata, these data items share the same identifier (SAS name) prefix but are each separately suffixed with a letter - A for the first response, B for the second response, C for the third response and so on. Where there are more than 26 categories, the next suffix after Z is 0, then 1, 2, etc.
For example, the multi-response data item 'Long-term Health Condition' has thirteen response categories (including 'No long-term health conditions'). There are thirteen data items named LTHCONDA, LTHCONDB, LTHCONDC...LTHCONDM. Each data item in the series will have either a positive response code or a null response code, with the exception of the first item in the series, LTHCONDA. LTHCONDA has three potential response codes: the positive response code 10 - 'Arthritis', the code 0 - null response, as well as the additional response codes, code 98 - 'Not known' and code 99 – ‘Refused’. The remaining items LTHCONDB--M have just the two response codes each. The data item list identifies all multi-response items and lists the corresponding codes with the corresponding response categories.
Note that the sum of individual multi-response categories will be greater than the population applicable to a particular data item as respondents can select more than one response.
Some continuous data items are allocated special codes for certain responses (e.g., 9997 = 'Not applicable'). Any special codes for continuous (summation) data items are listed in the Data Item List and will be found in the categorical version of the continuous item.
As the survey was conducted on a sample of private households in Australia, it is important to take account of the method of sample selection when deriving estimates from the detailed microdata. This is important because a person's chance of selection in the survey varied depending on the state or territory in which the person lived. If these chances of selection are not accounted for by use of appropriate weights, the results could be biased.
Each person or household record has a main weight (FINWTP or FINWTH). This weight indicates how many population units are represented by the sample unit. When producing estimates of sub-populations from the detailed microdata, it is essential that they are calculated by adding the weights of persons or households in each category and not just by counting the sample number in each category. If each person or household’s weight were to be ignored when analysing the data to draw inferences about the population, then no account would be taken of a person or household's chance of selection or of different response rates across population groups, with the result that the estimates produced could be biased. The application of weights ensures that estimates will conform to an independently estimated distribution of the population by age, by sex, etc. rather than to the distributions within the sample itself.
It is also important to calculate a measure of sampling error for each estimate. Sampling error occurs because only part of the population is surveyed to represent the whole population. Sampling error should be considered when interpreting estimates as this gives an indication of accuracy and reflects the importance that can be placed on interpretations using the estimate. Measures of sampling error include standard error (SE), relative standard error (RSE) and margin of errors (MoE). These measures of sampling error can be estimated using the replicate weights. The replicate weight variables provided on the microdata are labelled WPMXX (person) and WHMXX (household), where XX represents the number of the given replicate group. The exact number of replicates will vary depending on the survey but will generally be 30, 60 or 200 replicate groups. As an example, for survey microdata with 60 replicate groups, you will find 60 person replicate weight variables labelled WPM01 to WPM60.
ABS household surveys employ complex sample designs and weighting which require special methods for estimating the variance of survey statistics. Variance estimators for a simple random sample are not appropriate for this survey microdata.
A class of techniques called 'replication methods' provide a general process for estimating variance for the types of complex sample designs and weighting procedures employed in ABS household surveys. The ABS uses a method called the Group Jackknife Replication Method.
A basic idea behind the replication approach is to split the sample into G replicate groups. One replicate group is then dropped from the file and a new set of weights is produced for the remaining sample. This is repeated for all G replicate groups to provide G sets of replicate weights. For each set of replicate weights, the statistic of interest is recalculated and the variance of the full sample statistic is estimated using the variability among the replicate statistics.
The statistics calculated from these replicates are called replicate estimates. Replicate weights provided on the microdata file enable variance of survey statistics, such as means and medians, to be calculated relatively simply (Further technical explanation can be found in Section 4 of Research Paper: Weighting and Standard Error Estimation for ABS Household Surveys (Methodology Advisory Committee).
To calculate the standard error of any statistic derived from the survey data, the method is as follows:
\(SE(y) = \sqrt{\frac{G-1}{G} \sum_{g=1}^G (y_{(g)} - y)^2}\)
[Equation 1]
From the replicate variance you can then derive the following measures of sampling error: relative standard error (RSE), or margin of error (MOE) of the estimate.
\(\text{Relative Standard Error (RSE)} = \frac{\text{SE}}{\text{Estimate}} \)
[Equation 2]
\(\text{Margin of Error (MoE)} = 1.96 \times \text{SE} \)
[Equation 3]
Suppose you are calculating the mean value of earnings, y, in a sample. Using the main weight produces an estimate of $500.
You have 5 sets of Group Jackknife replicate weights and using these weights (instead of the main weight) you calculate 5 replicate estimates of $510, $490, $505, $503, $498 respectively.
To calculate the standard error of the estimate you will substitute the following inputs to equation [1]
G = 5
y = 500
g = 1, y(g) = 510
g = 2, y(g) = 490
…
\(SE(y) = \sqrt{\frac{5-1}{5} \sum_{g=1}^5 (y_{(g)}- 500)^2} \)
\(SE(y) = \sqrt{\frac{4}{5}((510-500)^2+(490-500)^2+(505-500)^2+(503-500)^2+(498-500)^2)} \)
\(SE(y)= \sqrt{\frac{4}{5} \times 238}\)
\(SE(y)= 13.8\)
To calculate the RSE you divide the SE by the estimate of y ($500) and multiply by 100 to get a %
To calculate the margin of error you multiply the SE by 1.96
Data from the 2021-22 and 2020-21 surveys has been released as the National Study of Mental Health and Wellbeing. The ABS also conducted this survey in 2007 and 1997. Data from the 2007 survey was released as the National Survey of Mental Health and Wellbeing and data from the 1997 survey was released as the National Survey of Mental Health and Wellbeing of Adults.
| MicrodataDownload | DataLab |
National Survey of Mental Health and Wellbeing, 2007 | Basic microdata | Detailed microdata |
Mental Health and Wellbeing of Adults, 1997 |
|
See National Study of Mental Health and Wellbeing: Summary and National Study of Mental Health and Wellbeing, Methodology, 2020-2022 for further information about the pooled 2020-2022 dataset as well as the individual 2020-21 and 2021-22 collections.
See National Survey of Mental Health and Wellbeing, 2007, National Survey of Mental Health and Wellbeing, Methodology, 2007 and National Survey of Mental Health and Wellbeing, Users’ Guide, 2007 for further information about the 2007 collection.
See Mental Health and Wellbeing: Profile of Adults in Australia, 1997 and the National Survey of Mental Health and Wellbeing of Adults: Users' Guide, 1997 (cat. no. 4327.0) for further information about the 1997 collection.
PBS mental health medications data has been added to the 2020-21 (Cohort 1), 2021-22 (Cohort 2), and 2020-2022 (Pooled) datasets which are now available in the DataLab. Information pertaining to the PBS mental health medications data has been added to the Methodology Section of the 2020-2022 National Study of Mental Health and Wellbeing Summary Publication, and the Data Item Lists for these datasets have been updated.
A non-response adjustment has been applied to the 2020-21 (Cohort 1) dataset which is now available in the DataLab. This adjustment had already been applied to the 2021-22 (Cohort 2) and 2020-2022 datasets. Information pertaining to the Cohort 1 dataset has been added to the Methodology Section of the 2020-2022 National Study of Mental Health and Wellbeing Summary Publication.
In addition, the Data Item List for 2020-21 has been updated.