Technical information

  • We use administrative, census, and survey data mapped at different levels of geography to provide insights into child development across communities.

    Statistical Areas 2:

    The Statistical Areas 2 (SA2) presented in the Child Development Atlas are based on the Australian Statistical Geography Standard (ASGS) Edition 3 - Main Structure and Greater Capital City Statistical Areas, July 2021

    Statistical Areas Level 2 (SA2s) are medium-sized general-purpose areas built up from whole Statistical Areas Level 1 (SA1s).

    Their purpose is to represent a community that interacts together socially and economically. SA2s generally have a population between 3,000 and 25,000 with an average of about 10,000 people.

    SA2s in remote and regional areas generally have smaller populations than those in urban areas.

    SA2s are generally the smallest areas used for the release of ABS non-Census of Population and Housing statistics, including Estimated Resident Population and Health and Vitals data.

    Whole SA2s aggregate to form Statistical Areas Level 3 (SA3s). SA2s are also used to build Significant Urban Areas and to approximate Tourism Regions.

    See the ABS website for more information on SA2s.

    Statistical Areas 3:

    The Statistical Areas 3 (SA3) presented in the Child Development Atlas are based on the Australian Statistical Geography Standard (ASGS) Edition 3 - Main Structure and Greater Capital City Statistical Areas, July 2021

    Statistical Areas Level 3 (SA3s) are geographic areas built from whole Statistical Areas Level 2 (SA2s).

    SA3s are designed for the output of regional data, including 2021 Census of Population and Housing data.

    SA3s create a standard framework for the analysis of ABS data at the regional level through clustering groups of SA2s that have similar regional characteristics.

    In general, SA3s are designed to have populations between 30,000 and 130,000 people. However, the creation of meaningful regional areas takes priority over population criteria. As a result, there are some SA3s with populations above 130,000 or below 30,000. Whole SA3s aggregate to form Statistical Areas Level 4 (SA4s).

    See the ABS website for more information on SA3s.

    Local Government Areas:

    The Local Government Areas used in the Child Development Atlas are based on the Australian Statistical Geography Standard (ASGS) Edition 3 – Non ABS Structures, July 2021.

    LGAs are an ABS approximation of officially gazetted LGAs as defined by State and Local Government departments.

    LGAs cover incorporated areas of Australia, which are geographical areas that are the responsibility of incorporated local governing bodies.

    See the ABS website for more information on LGAs.

    Health Regions:

    Health Regions are defined by the Department of Health of Western Australia and consist of the state of Western Australia divided into 10 areas:

    • East Metro

    • Goldfields

    • Great Southern

    • Kimberley

    • Midwest

    • North Metro

    • Pilbara

    • South Metro

    • South West

    • Wheatbelt

    The boundary map outlining the Western Australian metropolitan health regions are aggregated from the Australian Bureau of Statistics Australian Statistical Geography Standard Statistical Area Level 2 geographical boundaries.

    Regional Development Commission Areas:

    The Regional Development Commission Areas dataset contains boundaries representing regions in Western Australia. These regions were established to support the Regional Development Commissions Act 1993, which defined their extents and established Regional Development Commissions to promote their economic development.

    This dataset was updated in April 2021 to reflect change of the Shire of Wiluna from the Mid-West to the Goldfields.

    The boundaries used in the Child Development Atlas contain an additional area called ‘Perth Metro’. This region comprises of the remaining Local Government Areas within the Perth Metropolitan area not already assigned to an RDC.

    More information on this dataset can be found on the Data WA website.

  • Our comprehensive maps cover indicators of learning, wellbeing, social, and developmental outcomes for children and young people in Western Australia.

    These include:

    • Data sources and measures

    • Rates

    • Age groupings

    • Standardised ratios

    • Association and Cause

    • Linkage Quality

    Please expand on each topic below to learn more

  • Population data:

    Where populations (denominators) cannot be ascertained from the data, estimates are obtained from ABS Censuses for computing population-level indicators, and in some instances, for numerators (e.g., births and deaths).

    The Australian Census is carried out every five years, with the most recent performed in 2016.

    Administrative data:

    Administrative data are routinely collected data that are captured whenever an individual comes into contact with a government agency or service. These data can be de-identified and made available for research purposes.

    There are some limitations that should be considered when interpreting indicators based on this data.

    Firstly, indicators derived from administrative data will be restricted to the individuals who made contact with the agency/service, and so may not be representative of the general population. For example, individuals presenting to hospitals for treatment could be those with more severe disease or come from more disadvantaged population groups who are more likely to access hospitals for treatment (e.g., due to issues of affordability and access to primary care).

    Reliance on administrative data also means that a section of the population who have a certain condition will not be captured (e.g., those treated in the community or in the home).

    Secondly, administrative data generally do not capture information about mediating or moderating mechanisms, such as social support networks, severity of illness, or cultural practices. Such variables can influence the incidence of a particular outcome and/or the likelihood of an individual making contact with a service.

    Many indicators derived from administrative data should therefore be viewed as underestimates of the true population incidence.

    Lastly, there are limitations associated with parent-level indicators. Parent-child links are obtained from the Western Australian Family Connections Genealogy System, which uses information from birth registrations to pair children with parents. As such, only biological family connections can be made – no information is included on divorce, adoptions, stepfamilies, grand-families, or other care arrangements.

    Family connections links can also not be made for individuals born outside of Western Australia, and for individuals who have missing paternity information on their birth registration record.

    Summary measures of population health:

    Measures of event or disease frequency represented by indicators used in the Child Development Atlas are summarised here.

  • Crude rates:

    All indicators included in the Child Development Atlas are at the population level, therefore the assumption is that everyone is at risk for the whole of the year (or years) of interest, as opposed to person-time at risk.

    Therefore, an event rate is calculated by dividing the total number of new cases of an event in a specified period (usually one year) by the average number of people in the population during the same period. This is then usually multiplied by 10,000 and presented as a rate per 10,000 people per year.

    Depending on the data source, population denominators are calculated as the average of the size of the population at the start and at the end of the period of interest or estimated from Census data.

    These basic rates are called ‘crude’ rates because they describe the overall incidence in a population without taking any other features of the population into account (e.g., age structures).

    Age-specific rates:

    A crude comparison may have little meaning if the groups that are being compared have very different age structures. A way to get around this problem is to calculate separate rates for different age groups (age-specific rates).

    The rate in a particular age group can then be compared between geographic areas. This process can be extended to calculate separate rates for other groups, for instance male and female (sex-specific rates), and for different racial or socioeconomic groups.

    Standardised rates:

    Comparisons between rates may become difficult if age-specific rates are presented for a large number of different age-groups.

    An alternative is to summarise or combine these age-specific rates using the process of direct standardisation.

    This involves calculating the overall incidence or mortality rate that would be expected in a ‘standard’ population (i.e. population with a hypothetical age structure) if it had the same age-specific rates as the study population.

    Direct standardisation requires:

    • The age-specific event rates in the study population and

    • The age distribution of the standard population

    When the population being studied are not known but the total number of events is known, then the indirect standardisation is commonly used.

    The indirect method is also often used for small populations where fluctuations in age-specific rates can affect the reliability of rates calculated using the direct method.

    There are many similarities as well as differences between the two methods. However, the two methods will yield comparable results in most cases. It could be argued that the choice of a standard population is more important than the choice of the direct or indirect method.

    The standard population used in the Child Development Atlas for purposes of indicator comparisons is the Western Australian population.

    Where the data allows, the direct method of age-standardisation is the method chosen for use in the Child Development Atlas because of its advantages over the indirect method when comparing Aboriginal and non-Aboriginal mortality rates, disease incidence and prevalence rates over time.

    Also note that indirect standardisation fixes the quantity of interest (i.e. age-specific rates) as the standard, and then compares the effect of differences in age-structure in two or more populations. For this reason, it is less useful as a public health comparator than direct standardisation.

    Formula¹:

    Direct method:

    SR= (SUM (rᵢ * Pᵢ))/SUM Pᵢ

    Indirect method:

    SR=(C/SUM(Rᵢ *pᵢ))*R

    Where:

    • SR is the age-standardised rate for the population being studied

    • rᵢ is the age-group specific rate for age group i in the population being studied

    • Pᵢ is the population of age group i in the standard population

    • C is the observed number of events* in the population being studied

    • SUM(Rᵢpᵢ) is the expected number of events in the population being studied

    • Rᵢ is the age-group specific rate for age group i in the standard population

    • pᵢ is the population for age group i in the population being studied

    • R is the crude rate in the standard population

    * 'Events' can include deaths, incident or prevalent cases of disease or other conditions, or health care utilisation occurrences.

    Prior moving averages:

    A prior moving average (PMA) is defined as the average of the span of series values preceding the current value.

    The span is the number of preceding series values used to compute the average.

    Click here to view the PMA Formula

    A 3-year prior moving average combines a sequence of 3 years of data prior to, and including, the selected year. Similarly, a 5 year prior moving average combines a sequence of 5 years of data prior to, and including, the selected year.

    Series based on prior moving averages are presented as overlapping sequences until the most recent year is included. Moving averages make it possible to combine more years of data to maximize sample size at each point while maintaining data confidentiality.

  • As there is little difference in the resulting rate ratios and rate differences using five or ten year age-groupings, we follow the usual convention of using five year age-groupings in the calculation of directly age-standardised rates. However, if the distribution of the data across age-groups requires collapsing of age-groups to overcome small numbers, then 10 year age-groupings may be used.

    Also, due to little or no difference in rate differences produced using 0-4 compared to using <1 and 1-4 age groups in the estimation of age-standardised rates, we follow the usual practice of using the 0-4 age group as the youngest age group in the calculation of age-standardised rates.

    This only applies to the calculation of age-standardised rates, and does not preclude presenting age-specific rates and distribution of events (e.g. deaths) for <1 and 1-4 age groups).

    If these age groups are not used, the actual age groups are detailed in notes accompanying the age standardised population rate information.

    Standardised rates are generally multiplied by 1,000 or 100,000 to avoid small decimal fractions. They are then called standardised rates per 1,000 or 100,000 population.

  • The indirect method is also used to calculate standardised mortality ratios (SMRs) and other standardised ratios, for example for health service utilisation and other events.

    These ratios express the overall experience of a comparison population in terms of the standard population by calculating the ratio of observed to expected deaths in the comparison population. This is calculated by dividing the observed number of deaths by the expected number.

    Sometimes the SMR is multiplied by 100 to express the ratio as a percentage, although this is not universally accepted. Not multiplying by 100 has the benefit of being able to say that the SMR was, for example, 2.3 times that expected rather than 130% higher.

  • All data presented in the Child Development Atlas are not based on specific information about individuals but relate to the number of events (or deaths) in a population relative to the size of that population (often an estimate from the ABS census).

    When comparing the strength of the relation between indicators, caution should therefore be made when trying to relate the occurrence of an event to potential causes.

    For example, there may be a statistical association between two indicators in the Child Development Atlas, which may lead to an assumption of a real association.

    However, there should be consideration of other possibilities that may be the cause of such associations, such as chance, bias or confounding. Three important ‘alternative explanations’ for associations are:

    • chance (random error)

    • bias (systematic error) and

    • confounding

    Random error:

    Random error is the divergence, by chance alone, of a measurement from the true value.

    There are three main sources of random error: biological variation (natural variation of measurement depending on an individual’s biology), measurement error (imprecision inherent in the measuring system being used), and sampling error (selection of sample from whole population).

    It is impossible to completely remove random error that has resulted from chance. Therefore, when examining an association between two indicators in the CDA, it is important to consider how likely it is to be a real effect, or whether it could have arisen by chance. Whilst associations between indicators should not be ignored, any interpretations of these relationships taken on its own should be cautious, and acknowledgement should be made of the possibility that it could just reflect the effect of chance.

    Bias:

    Many potential sources of bias have been identified in epidemiological studies, but all fall into two main areas: bias with respect to who gets into the study (selection bias) and bias with respect to the information we collect from, or on, these people about their exposures and their diseases (measurement, information or observation bias).

    Bias, also known as systematic error, is potentially more problematic than random error because it’s much harder to know what effect it might have on an outcome.

    The most common systematic errors with administrative data involve underreporting of activity for a specific population, inaccurate re-coding of spatial information, or differences in data entry protocols.⁴

    Confounding:

    Confounding is where an apparent relationship between an exposure and an outcome is really due, in whole or in part, to a third factor that is associated with both the exposure and the outcome of interest.

    Confounding is a mixing of effects because the effect of the exposure we might be interested in is mixed up with the effect of some other factor. Age, sex and socioeconomic status (SES) are common confounders.

  • The Data Linkage Branch of the WA Department of Health maintains the Data Linkage System.

    Please go to https://www.datalinkage-wa.org.au/dlb-services/ for insight into the data linkage process and the characteristics of many of the datasets that are used in the Child Development Atlas.

    This information will also help users of the Atlas to understand the variety of strategies and tools used to ensure that the linkage system contains the highest quality links.

    1. Australian Institute of Health and Welfare (AIHW). Age-standardised rates. AIHW (METeOR). Available from: http://meteor.aihw.gov.au/content/index.phtml/itemId/327276

    2. Australian Institute of Health and Welfare (AIHW). National Healthcare Agreement: PI 07–Infant and young child mortality rate, 2017, AIHW (METeOR). Available from: http://meteor.aihw.gov.au/content/index.phtml/itemId/630004

    3. Australian Institute of Health and Welfare (AIHW). 2011. Principles on the use of direct age-standardisation in administrative data collections: For measuring the gap between Indigenous and non-Indigenous Australians. Available from: https://www.aihw.gov.au/reports/indigenous-australians/principles-on-the-use-of-direct-age-standardisatio/contents/table-of-contents

    4. Ardal S, & Ennis S (2001). Data detectives: Uncovering systematic errors in administrative databases. In Proceedings: Symposium 2001, Achieving Data Quality in a Statistical Agency: A Methodological Perspective.

    5. Australian Bureau of Statistics (ABS). 2013. Statistical Language. Available from: http://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language

  • We encourage the download of Child Development Atlas maps, summary tables, and graphs for reproduction in your own documents.

    However, please note that all Child Development Atlas Content is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. Users must ensure that all use of Child Development Atlas content is done within the limits of this licence.

    The licence stipulates that Child Development Atlas content may not be used for commercial purposes, and that the original content may not be modified for redistribution.

    This licence also requires all users who reproduce Child Development Atlas content to appropriately credit the Child Development Atlas, but not in any way that suggests the University of Western Australia endorses the use.

    Example attribution:

    These data/maps are derived from the Western Australian Child Development Atlas, the University of Western Australia.

    Suggested citation:

    The University of Western Australia. Western Australian Child Development Atlas (online). At: https://childdevelopmentatlas.com.au/ Date accessed: [date].

    Please refer to the CDA Terms and Conditions of Use 202X