Presents data 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.
The National Study of Mental Health and Wellbeing Follow up is a longitudinal survey designed to provide a longitudinal perspective on whether people use health services for their mental health and the impact, or otherwise, of the use of services on mental health.
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, refer to Comparison between 2021-22, 2020-21, 2007 and 1997 below and 2020-2022 Methodology information.
TableBuilder - an online tool for creating tables and graphs. This product is available for NSMHW 2020- 2022.
Basic microdata – approved users can download and analyse unit record data in their own environment. This product is available for the 1997 and 2007 NSMHW. It is not available for the 2020-21, 2021-22, or 2020-2022 NSMHW.
Detailed microdata - approved users can access a remote desktop environment in DataLab for in-depth and interactive data analysis using a range of statistical software packages. This product is available for NSMHW reference periods: 2007, 2020-21, 2021-22, and 2020-2022. Detailed microdata is also available for the NSMHW and Follow up Study for 2020-2023.
A comparison of key features of each microdata product type can be found in Types of Microdata.
For more information and instructions on how to access microdata, refer to the relevant product link below:
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 Data Item List is available in the Data downloads section. This includes the output categories for each data item and any special codes used.
The 2020-21 and 2021-22 collections have also been pooled to create a 2020-2022 dataset.
The NSMHW and Follow up Study 2020-2023 dataset contains data items from the NSMHW 2020-2022 as well as Follow up Study data items. 15, 893 people completed the NSMHW and of these, 3, 079 went on to complete the Follow up Study. As a result, there are a number of missing responses for records that did not complete the Follow up Study. More information is available in the National Study of Mental Health and Wellbeing: Follow up Study methodology.
The following table shows the levels available in the microdata products 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:
NSMHW and Follow up Study 2020-2023 has not been weighted, and as such, weights have not been included on the Detailed microdata files. Unlike the NSMHW, the Follow up Study was not designed to provide nationally representative estimates for the Australian population.
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.
For the NSMHW TableBuilder, the default summation is the Person weight, and without changing the summation option, all tables will automatically count the number of people in the table.
Generally, you should use the weight which corresponds to the level of analysis you are undertaking. For example, use the Household weight for items on the household level or use the Person weight for items on the person level.
For geography data items, the value of the data item is the same for all people in the household. Geography data items are included on the household level but may be used in tables with data items on the person level using Person weights.
Data items for 2020-2022 include:
The Data Item Lists contain a full list of available data items and categories for each separate product for the 2020-2022, 2021-22, 2020-21 and 2007 collections.
The Follow up Study content comprised a reduced set of questions from the NSMHW, with some additional content. As was done for the NSMHW, Pharmaceutical Benefits Scheme (PBS) mental health-related medications data was sourced from the Pharmaceutical Benefits Scheme (PBS) instead of being collected directly from survey respondents.
More detailed information about the content collected in both the NSMHW and the Follow up Study is available in the National Study of Mental Health and Wellbeing Methodology. Refer to the Data Item Lists for full details of the content collected for the Follow up Study.
Every record on each level of the file is uniquely identified. Refer to the 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'). When creating ranges for such continuous items for use in the TableBuilder, these special codes will NOT be included in these ranges. 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. The total shown only represents 'valid responses' of that continuous data item rather than all responses (including special codes).
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
Relative standard errors will be automatically included in any downloaded table. For an explanation of Relative standard error, see the How the data is processed section of the National Study of Mental Health and Wellbeing methodology.
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 |
|
Refer to the National Study of Mental Health and Wellbeing: Summary and the 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.
Refer to the methodology section of the National Study of Mental Health and Wellbeing: Follow up Study article for further information about the Follow up collection.
Refer to the National Survey of Mental Health and Wellbeing, 2007, National Survey of Mental Health and Wellbeing, Methodology, 2007 and the National Survey of Mental Health and Wellbeing, Users’ Guide, 2007 for further information about the 2007 collection.
Refer to the Mental Health and Wellbeing: Profile of Adults in Australia, 1997 and the National Survey of Mental Health and Wellbeing of Adults: Users' Guide, 1997 for further information about the 1997 collection. Refer to the Information Paper: Mental Health and Wellbeing of Adults, Australia, Confidentialised Unit Record File, 1997 for the basic microdata data item list.
The Variations of Sex Characteristics data item has been corrected on the 2021-22 (Cohort 2) and 2020-2022 (Pooled) NSMHW datasets, and the NSMHW Follow up Study dataset in the DataLab.
The 2020-2022 (Pooled) NSMHW dataset is now available in TableBuilder. A Data Item List has been made available.
The NSMHW Follow up Study dataset is now available in the DataLab. A Data Item List has been made available. Information pertaining to the Follow up Study is available in the methodology section of the National Study of Mental Health and Wellbeing: Follow up Study article.
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.