Sun protection behaviours methodology

Latest release

Reference period

Nov 2023 to Feb 2024

Released

3/09/2024

Next release Unknown

First release

Overview

Scope

People aged 15 and over who were usual residents of private dwellings.

Excludes:

Australian permanent defence force members and their dependants
non-Australian defence forces
residents of the Indigenous Community Strata.

Geography

Data available for:

Australia
Major cities
Inner regional
Outer regional/remote

Source

Multipurpose Household Survey

Collection method

Interviews were conducted by telephone with responses directly recorded in an electronic questionnaire.

Proxy interviews were permissible under certain circumstances.

Concepts, sources and methods

Not applicable to this release

History of changes

Not applicable to this release

Data collection

Overview

This publication contains results from the Sun protection behaviours survey, a topic collected throughout Australia on the Multipurpose Household Survey (MPHS) from November 2023 to February 2024, as part of the 2023-24 MPHS. The Sun protection behaviours survey was funded by the Cancer Councils of Australia. The MPHS, undertaken each financial year by the Australian Bureau of Statistics (ABS), is a supplement to the monthly Labour Force Survey (LFS) and is designed to collect statistics for a number of small, self-contained topics.

This is the first time the ABS has collected Sun protection data. The survey asked about respondents' sun protection behaviours, experience of sunburn and attempts at suntanning. Various reference periods were collected depending on the data item, for example, application of sunscreen in the last month, sunburnt in the last week, outdoor activity in the last week, suntan attempts in the last 12 months. Other demographic information about respondents, such as educational attainment, income details and labour force characteristics, is also available to analyse in conjunction with sun protection behaviour data.

Scope

The scope of the survey was restricted to people aged 15 years and over who were usual residents of private dwellings and excludes:

members of the Australian permanent defence forces
certain diplomatic personnel of overseas governments, customarily excluded from Census and estimated resident population counts
overseas residents in Australia (intending to stay less than 12 months)
members of non-Australian defence forces (and their dependants)
persons living in non-private dwellings such as hotels, university residences, boarding schools, hospitals, nursing homes, homes for people with disabilities, and prisons
persons resident in the Indigenous Community Strata (ICS).

The scope for the MPHS included households residing in urban, rural, remote and very remote parts of Australia, except the ICS.

Coverage

In the LFS, rules are applied which aim to ensure that each person in scope is associated with only one dwelling, and hence has only one chance of selection in the survey. See Labour Force, Australia for more detail.

Sample size

Information was collected from 8,595 fully responding persons. This includes 175 proxy interviews for people aged 15 to 17 years, where permission was not given by a parent or guardian for a personal interview, and 639 proxy interviews for people aged 18 years and over who were not capable of answering for themselves due to illness, injury or language reasons.

The LFS sample design includes very remote dwellings. But due to the small number in these strata, no very remote records were in the fully responding sample.

Collection method

The survey is one of a number of small, self-contained topics on the MPHS.

Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey and selected for the MPHS. 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.

In the MPHS, 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). If the randomly selected person was aged 18 years and over but was not capable of answering for themselves, due to illness, injury or language problems, the person responsible for them could be asked the questions on their behalf (proxy interview).

Although this survey was enumerated from November 2023 to February 2024 inclusive, the reference period for the data items "most recent date experienced sunburn" and "most recent date when outdoors for longer than 15 minutes during peak UV times" included October 2023. A small number of interviews were conducted at the start of March 2024 due to less days in the calendar month of February than other months.

Data were collected using Computer Assisted Telephone Interviewing (CATI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, with interviews conducted over the telephone.

Processing the data

Show all

Estimation methods

Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest.

Weighting

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.

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).

Benchmarks

The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks'. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the 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 persons/households which may occur due to either the random nature of sampling or non-response.

The survey was benchmarked to the Estimated Resident Population (ERP) living in private dwellings in each state and territory at December 2023. People living in Indigenous communities were excluded. These benchmarks are based on the 2021 Census.

Reliability of the estimates

The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey. This is known as sampling error.

Non-sampling error

Non-sampling error is caused by factors other than those related to sample selection. It is any factor that results in the data values not accurately reflecting the true value of the population.

It can occur at any stage throughout the survey process. Examples include:

selected people that do not respond (e.g. refusals, non-contact)
questions being misunderstood
responses being incorrectly recorded
errors in coding or processing the survey data.

Sampling error

Sampling error is the expected difference that can occur between the published estimates and the value that would have been produced if the whole population had been surveyed. Sampling error is the result of random variation and can be estimated using measures of variance in the data.

Standard error

One measure of sampling error is the standard error (SE). There are about two chances in three that an estimate will differ by less than one SE from the figure that would have been obtained if the whole population had been included. There are about 19 chances in 20 that an estimate will differ by less than two SEs.

Measures of error in this publication

This publication reports the relative standard error (RSE) for estimates of counts ('000) and the margin of error (MOE) for estimates of proportions (%).

Relative standard error

The relative standard error (RSE) is obtained by expressing the standard error as a percentage of the estimate.

\(RSE\%=\left(\frac{SE}{estimate}\right)\times 100\)

Only estimates with RSEs less than 25% are considered reliable for most purposes. Estimates with larger RSEs, between 25% and less than 50% have been included in the publication, but are flagged to indicate that they should be used with caution. Estimates with RSEs of 50% or more have also been flagged and are considered unreliable for most purposes. RSEs for these estimates are not published.

Margin of error

The margin of error (MOE) shows the largest possible distance (due to sampling error) that could exist between the estimate and what would have been produced had all people been included in the survey, at a given level of confidence. It is useful for understanding and comparing the accuracy of proportion estimates. Confidence levels can vary (e.g. typically 90%, 95% or 99%), but in this publication, all MOEs are provided for estimates at the 95% confidence level. At this level, there are 19 chances in 20 that the estimate will differ from the population value by less than the provided MOE.

The 95% confidence level MOE is obtained by multiplying the standard error by 1.96.

\(MOE=SE\times1.96\)

The RSE can also be used to directly calculate a 95% MOE by:

\(MOE=\frac{RSE\%\times estimate \times1.96}{100}\)

These can be converted to a 90% confidence level by multiplying the MOE by:

\(\frac{1.615}{1.96}\)

or to a 99% confidence level by multiplying the MOE by:

\(\frac{2.576}{1.96}\)

Depending on how the estimate is to be used, a MOE greater than 10% may be considered too large to inform decisions. For example, a proportion of 15% with a MOE of plus or minus 11% would mean the proportion could be anything from 4% to 26%. It is important to consider this range when using the estimates to make assertions about the population.

Confidence intervals

The estimate combined with the MOE defines a range, known as a confidence interval. This range is likely to include the true population value with a given level of confidence. A confidence interval is calculated by taking the estimate plus or minus the MOE of that estimate. It is important to consider this range when using the estimates to make assertions about the population or to inform decisions. Because MOEs in this publication are provided at the 95% confidence level, a 95% confidence interval can be calculated around the estimate, as follows:

\(95\%\space\text{Confidence Interval}=\space(\text{estimate}-MOE,\space\text{estimate}+MOE)\)

Calculating measures of error

Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. A formula to approximate the RSE of a proportion is given below. This formula is only valid when the numerator (x) is a subset of the denominator (y):

\(RSE\left(\frac{x}{y}\right)\approx \sqrt{[RSE\left(x\right)]^{2}-[RSE\left(y\right)]^{2}}\)

When calculating measures of error, it may be useful to convert RSE or MOE to SE. This allows the use of standard formulas involving the SE. The SE can be obtained from RSE or MOE using the following formulas:

\(SE\left(y\right)=\frac{RSE\left(y\right)\times y}{100}\)

\(SE=\frac{MOE}{1.96}\)

Calculating differences

The difference between two survey estimates (counts or percentages) can also be calculated from published estimates. Such an estimate is also subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:

\(SE\left(x-y\right)\approx \sqrt{[SE\left(x\right)]^{2}+[SE\left(y\right)]^{2}}\)

While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.

Significance testing

When comparing estimates between surveys or between populations within a survey, it is useful to determine whether apparent differences are 'real' differences or simply the product of differences between the survey samples.

One way to examine this is to determine whether the difference between the estimates is statistically significant. This is done by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below.

\(\frac{|x-y|}{SE\left(x-y\right)}\)

where:

\(SE\left(y\right)\approx \frac{RSE\left(y\right)\times y}{100}\)

If the value of this test statistic is greater than 1.96, there is good evidence of a statistically significant difference at 95% confidence levels between the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.

Data quality

Information recorded in this survey is '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 data in this publication.

Comparing the data

Comparability to monthly LFS Statistics

Since the survey is conducted as a supplement to the LFS, some data items collected in the LFS are also available in this publication. However, there are some important differences between the two surveys. The scope of the Sun protection behaviours and the LFS differ (refer to the Scope section above). Due to the differences between the samples, data from this survey and the LFS are weighted separately. Differences may therefore be found in the estimates for those data items collected in the LFS and published as part of the Sun protection behaviours survey.

Comparability with non-ABS surveys

Data of a similar nature has been previously captured in the National Sun Protection Survey, conducted by the Cancer Council in 2016-17. The 2016-17 survey collected data about sun protection behaviours and incidences of sunburn occurring on the previous weekend. Due to scope, methodological and conceptual differences, the two surveys are not directly comparable, and any data comparisons need to be made with caution.

Data release

Datacubes/spreadsheets

A datacube containing all tables for this publication in Excel spreadsheet format is available from the Data downloads section. The spreadsheets present tables of estimates and proportions, and their corresponding relative standard errors (RSEs) and margins of error (MOEs).

As well as the statistics included in this release, the ABS may be able to provide other relevant data on request. Subject to confidentiality and sampling variability constraints, tables can be tailored to individual requirements for a fee. A list of data items from this survey is available from the Data downloads section. For inquiries about these and related statistics, contact Client Services via the ABS website Contact Us page.

TableBuilder

TableBuilder microdata can be used by approved users to produce customised tables and analysis. The microdata is released through the TableBuilder product (see TableBuilder for more detail). Microdata products are designed to ensure the integrity of the data whilst maintaining the confidentiality of the respondents to the survey.

DataLab

Detailed microdata will be available in DataLab for approved users who want to undertake interactive (real time) complex analysis of microdata in the secure ABS environment. For more details, refer to the DataLab page.

Confidentiality

To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. 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.

Glossary

Show all

Index of relative socio-economic disadvantage

This is one of four Socio-Economic Indexes for Areas (SEIFAs) compiled by the ABS following each Census of Population and Housing, see Socio-Economic Indexes for Areas (SEIFA), Australia, 2016. This index summarises attributes such as low income, low educational attainment, unemployment, jobs in relatively unskilled occupations and dwellings without motor vehicles. The first or lowest quintile refers to the most disadvantaged areas, while the fifth or highest quintile refers to the least disadvantaged areas.

Location of most recent sunburn / Location when outside for longer than 15 minutes during peak UV times in the last week

The respondent reported the suburb or town in which they got sunburnt or were outside for longer than 15 minutes during peak UV times in the last week. Includes approximate locations when the respondent was unsure. If sunburn or the most recent exposure to sun for longer than 15 minutes during peak UV hours in the last week occurred in multiple locations on the same day, the respondent was prompted for an approximate location of where most of the sun exposure occurred.

Most days in the last month (with reference to sunscreen application)

Most days refers to 4 or more days a week.

Peak UV time

Sun protection behaviours was collected in November 2023 to February 2024 during the daylight saving period. For NSW, VIC, SA, TAS and ACT the peak UV time was between 11am and 3pm (Daylight Saving Time). For QLD, WA and NT this was between 10am and 2pm (no daylight saving in these states/territories).

Remoteness

The Australian Statistical Geography Standard (ASGS) was used to define remoteness. Remoteness Areas divide Australia into five classes of remoteness on the basis of a measure of relative access to services. The Remoteness Structure is described in detail in the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016.

Skin sensitivity

Respondent's interpretation of the effects on their skin of exposure to strong sunshine with no sun protection after 30 minutes. The following options were read out to respondent: 'just burn and not tan', 'burn first, tan afterwards', 'not burn at all', and 'nothing would happen'. The first option the respondent said 'yes' to was recorded as the response.

Statistical significance

Differences between population estimates are said to be statistically significant when it can be stated with 95% confidence that there is a real difference between the populations (see the Processing the data section for more information).

Sun Protection Factor (SPF)

The SPF indicates how effective the sunscreen is against sunburn. SPF is measured on human skin in a laboratory. Scientists measure how long it takes for intense ultraviolet radiation (specifically UVB) to burn skin with the sunscreen liberally applied compared to bare skin. For example, if skin protected with a sunscreen takes 300 seconds to burn, but bare skin burns in 10 seconds, the sunscreen’s SPF is 30 (300 divided by 10). See: About sunscreens | Therapeutic Goods Administration (TGA).

Sunburn

Includes any part of the skin that was red, sore or tender the following day, no matter how small or large.

Sunscreen (including broad spectrum sunscreen)

Includes any skincare products that have a SPF30 or higher.

Sunscreen application

Includes application of sunscreen to any body parts, including only the face.

Suntan attempt

Excludes spray tan or fake tan.

Sun protection behaviour

Includes SPF30 or higher sunscreen, broad brimmed hat or cap with a back flap, stayed in the shade, clothing or swimwear that covered three quarters or more of arms or legs, and sunglasses. The survey was unable to exclude where respondents implemented a sun protection behaviour but not for the primary purpose of sun protection.

Three or more sun protection behaviours

For a respondent to have implemented three or more sun protection behaviours at least one of the measures had to be applying sunscreen or wearing a broad brimmed hat or a cap with a back flap.

Abbreviation	Definition
ABS	Australian Bureau of Statistics
ANZSCO	Australian and New Zealand Standard Classification of Occupations
ANZSIC	Australian and New Zealand Standard Industrial Classification
ASCED	Australian Standard Classification of Education
ASGS	Australian Statistical Geography Standard
CAI	Computer Assisted Interview
CSA	Census and Statistics Act
ERP	Estimated Resident Population
GCCSA	Greater Capital City Statistical Areas
ICS	Indigenous Community Strata
LFS	Labour Force Survey
MOE	Measure of Error
MPHS	Multipurpose Household Survey
RSE	Relative Standard Error
SE	Standard Error
SEIFA	Socio-Economic Indexes for Areas
SPF	Sun Protection Factor
UV	Ultraviolet (light)

APA

Sun protection behaviours methodology

APA

Overview

Scope

Geography

Source

Collection method

Concepts, sources and methods

History of changes

Data collection

Overview

Scope

Coverage

Sample size

Collection method

Processing the data

Show all

Estimation methods

Weighting

Benchmarks

Reliability of the estimates

Non-sampling error

Sampling error

Standard error

Measures of error in this publication

Relative standard error

Margin of error

Confidence intervals

Calculating measures of error

Calculating differences

Significance testing

Data quality

Comparing the data

Comparability to monthly LFS Statistics

Comparability with non-ABS surveys

Data release

Datacubes/spreadsheets

TableBuilder

DataLab

Confidentiality

Glossary

Show all

Index of relative socio-economic disadvantage

Location of most recent sunburn / Location when outside for longer than 15 minutes during peak UV times in the last week

Most days in the last month (with reference to sunscreen application)

Peak UV time

Remoteness

Skin sensitivity

Statistical significance

Sun Protection Factor (SPF)

Sunburn

Sunscreen (including broad spectrum sunscreen)

Sunscreen application

Suntan attempt

Sun protection behaviour

Three or more sun protection behaviours

Abbreviations

Show all