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This document was added 03/07/2007.
CHAPTER 3 AGREED PRIORITIES AND DIRECTIONS
The remaining three areas of priority that were identified and agreed upon were cross-cutting issues. These are:
During the consultations, stakeholders also identified specific data development needs within each priority area and these are presented in full in Appendix 2.
Consultations also identified three further priority directions for statistical development work in the field. These are:
Together, these priority areas (i.e. including key and cross-cutting issues and priority directions) form the framework of 13 priority areas for statistical data development in the field.
This chapter presents a summary of each of the 13 agreed priority areas for data development action. The order in which the priority areas are presented does not imply any ranking by importance.
AGREED PRIORITY AREAS FOR STATISTICAL DATA DEVELOPMENT
For each priority area, the section User perspective provides an overview of the priority area from a user perspective, identifying the statistical issues and/or priority data development needs (refer to Appendix 2 for more detailed information on the data development needs). The section following discusses selected data sources or activities relevant to the priority area.
The following priority areas are organised according to the key issues framework.
KEY ISSUE PRIORITIES
Improve data on childhood and maternal health 
There is a wide body of research supporting the view that the health of the mother (especially during pregnancy and the post natal period) and the health of the child, particularly in the early years of life, influence health and wellbeing patterns throughout the life cycle. Therefore, interventions in relation to maternal and early childhood health are an important way of improving the health of the population overall.
Childhood health is an important issue with numerous health and welfare policies aimed at improving both the health of children and mothers. Through consultation data users have identified a range of data needs that are not currently being met through existing sources (see below). These include: improving childhood injury data; developing data on a broader range of maternal health risk factors; updating and expanding data on children's nutrition; improving data on children's dental health; updating and expanding data on children's mental health and social/emotional development; and developing better measures of children's physical activity.
Selected data sources
A wide range of data sources relating to childhood and maternal health are available. Some of these include:
This issue focuses on those children who are in need of protection. This includes children who have been abused (physically, sexually or emotionally), neglected or otherwise harmed, and children who are at risk of such harm due to their parents not being able to provide adequate care and protection for them. Child abuse and neglect may be associated with multiple risk factors such as low socio-economic status, family disruption, domestic violence and substance abuse. Children in need of protection are of concern because of the profound negative effect abuse and neglect can have on their health and wellbeing, both in the short- and long-term.
Selected data sources
There are many stakeholders across the field with policy and program responsibilities for this sub-population of children. From a statistical data perspective, a large gap in the available information base has been identified by data users. Currently, data are only available on those incidents brought to the attention of authorities, be that abuse, neglect, homelessness or experience of crime. Data are needed on both reported and unreported cases of abuse and neglect. Data are also needed on the outcomes (e.g. education, health and employment) for children and young people who have experienced abuse and neglect. Improved data are also needed on children's and young people's experience of violence, both within and external to the family home.
Due to the difficulties in collecting data on abuse and neglect, the majority of data on the abuse and neglect of children are sourced from administrative collections. Reports on the incidence of different forms of abuse and neglect can be obtained from the annual AIHW collections: Child Protection Notifications, Investigations and Substantiations; Children under Care and Protection Orders; and Children in Out-of-Home Care. The Supported Accommodation Assistance Program (SAAP) National Data Collection includes information about people seeking and receiving accommodation support. The data provide information on children and young people experiencing homelessness as a result of various reasons including family conflict and/or domestic violence. The ABS' annual collections of crime and justice data show the level and nature of recorded crime (including assault and sexual assault) and records details regarding victims of recorded crime.
Improve data on children's learning and development 
Selected data sources
Data available to inform issues on children's learning and development are sourced from a range of administrative, survey and census collections. Information on children enrolled in school, both government and non-government, is available from state and territory administrative collections. Data on child care are collected by the ABS Child Care Survey and the FaCSIA's Child Care Census. The ABS Census of Population and Housing provides data on pre-school students, as does the DEST's National Indigenous Pre-school Census. Data on reading, writing and numeracy outcomes are collected annually for Year 3, 5 and 7 students throughout Australia and published as part of the National Report on Schooling. The Trends in International Mathematics and Science Study (TIMSS) collects data on mathematics and science assessments for students in Year 4 and Year 8. Administrative data on early childhood services are fragmented reflecting different roles and responsibilities across levels of government.
Improve data on youth educational attainment and participation 
Individuals can benefit from the opportunity early in life to acquire the knowledge, skills, values and understanding necessary for lifelong learning, employment and full participation in society. Education is essential to providing young people with skills and knowledge for future employment, as well as providing an environment for the development of social and life skills. In addition, higher levels of education, in particular literacy, have been associated with improved health and wellbeing.
As for children, governments have responsibilities for providing formal and informal education to young people and assist them in developing the skills and/or qualifications necessary to enter the labour market and make the transition to independent adulthood. Data users in this sector have highlighted the need for improvements to existing data sources to assist them in providing better and more targeted education services. These needs include:
Selected data sources
Relevant data sources for this key issue include:
Improve data about transitions to independent living 
Young people are in a transition phase from being dependent children to independent adults. This transition phase varies in duration and can involve numerous steps after leaving school (e.g. undertaking further education prior to entering full-time paid employment). Young people who have been unable to make this transition successfully can face significant barriers to gaining full-time employment and financial security in the longer-term. In addition, particular groups of young people have been identified as being at greatest risk of making unsuccessful transitions to independent living. For young people failure to make an effective transition can have a detrimental impact on the quality of their lives in both the short- and long-term, across a number of areas.
A range of agencies across both the government and non-government sectors have responsibilities in this area. Statistical information needs are currently served through a range of sources but users have identified deficiencies and gaps. The main areas of need which are not currently being met through these sources are:
Selected data sources
A number of data sources are available to inform this priority area. The ABS collections include the annual Survey of Education and Work, the monthly Labour Force Survey and the annual Job Search Experience Survey. The LSAY provide data on young people's experiences of leaving education, gaining employment and the transition to adulthood. The annual Graduate Destination Survey and the Graduate Outcomes Survey collect information on higher education and VTE graduates' experiences of entry into the labour force. The FaCSIA's Longitudinal Data Set is based on administrative data on income support from Centrelink and provides key demographic characteristics and a detailed income support history for a selected cohort of young persons.
Improve data on the social participation of youth 
Social participation is the active engagement and interaction of people within the community. Positive social participation provides opportunities for personal development, education and building of social networks, all of which are important for individual health and wellbeing. In addition, communities benefit from such participation through the provision of services and activities that might not otherwise occur, and the building of social networks between community members. Negative social participation has consequences for young people and it is important to understand the factors leading to it.
Data users require information to assist them to understand the social participation of youth, including the barriers to this participation, so as to develop appropriate services for those most in need. While current sources of data provide some information, more is required. In particular, data development is needed to improve measures of youth social participation and social cohesion, for both older and younger youth. Also data are needed to identify youth networks, including informal settings and connections to the community. Data are also sought on the barriers to social participation and negative social relationships and participation. In addition to specific data needs, further work needs to be undertaken to define many of the concepts used in this area before work on collecting appropriate data can begin.
Selected data sources
Although the concept of social participation is relatively new and as such methods of measurement are in their infancy and evolving, there are a number of relevant data sources available. The ABS General Social Survey collects data for those aged 18 years and over on social networks, cultural and recreational activities, social participation, personal safety and aspects of wellbeing. Other relevant ABS collections include the Survey of Voluntary Work (only 18 years and over), Time Use Survey, Crime and Safety Survey, and the Personal Safety Survey. The latter two providing data on barriers to social participation, such as fear of crime and personal safety issues. The Longitudinal Surveys of Australian Youth include data on young people’s participation in volunteer, leisure and non-formal learning activities. The HILDA Survey collects longitudinal information of a wide range of areas of social concern relevant to youth and social participation.
Improve data about risk behaviours for youth 
Youth is an important time for developing attitudes that will influence behaviours throughout life. As young people gain greater independence, they are exposed to new experiences, some of which may result in behaviours that lead to poor health or poor living conditions, in both the short and long-term. These behaviours can include: tobacco use; excessive alcohol use; illicit drug use; poor nutrition and diet; inadequate physical exercise; unprotected sex; dangerous driving; self-harm; and involvement in criminal activity.
Government departments and agencies with portfolio or service delivery responsibilities, as well as many non-government organisations in the areas of health, community services and crime and justice, are major users of statistical data with relevance to this issue. Their information requirements are many and diverse. While existing data sources are plentiful, a number of data development needs have been identified by users as important to address. These are:
Selected data sources
There are a large number of data sources to inform policy across this broad area of activity. The ABS ongoing National Health Survey provides data on injury, accidents, alcohol consumption, smoking, height and weight, as well as a number of other health related issues. Data on morbidity and mortality are available from jurisdictional administrative collections, in particular notifiable diseases, hospital separations and causes of death. Other important sources include the AIHW's Juvenile Justice National Minimum Data Set, the AIC's Juveniles in Detention collection, the ABS Prisoners in Australia collection (which presents administrative data from the annual prison census) and AIHW's National Drug Strategy Household Survey. The ongoing Australian Secondary Students Alcohol and Drug Survey provides national and state estimates for those aged 12-17 years for alcohol and drug use. Data on mental health are available from the 1998 Child and Adolescent Component of the National Survey of Mental Health and Wellbeing (conducted by the then Commonwealth Department of Health and Aged Care) and the ABS National Survey of Mental Health and Wellbeing of Adults (only 18 years and over). Both these surveys provide data on the prevalence of a range of major mental disorders.
There were three cross-cutting priority areas identified. Each of these areas span the statistical field in terms of relevance across all areas of social concern relating to children and youth and hence overlap the key issues.
Develop and promulgate the use of standards and concepts relating to children and youth 
Across the field, users identified the need for improved comparability of data across collections and jurisdictions. In particular, better comparability of administrative collections across jurisdictions would significantly improve data usability (e.g. abuse and neglect data, homeless data). The wider application of such data is hampered by a lack of comparability in terms of both data holdings (frequency, reference period, population) and meta-data (scope, definitions and standards). Data comparability could also be improved through the better coordination of survey methodologies, facilitating greater comparability between survey collections (e.g. comparability between the ABS General Social Survey and the ABS National Aboriginal and Torres Strait Islander Social Survey, relating to Indigenous and remoteness data).
Selected data activities
The development of statistical standards is an ongoing activity that many agencies in the field are involved in. For example, developing statistical standards and ensuring they are complied with is a function of the ABS, as noted in the ABS' legislation. The ABS' website contains information on classifications, concepts and standards many of which pertain to children and youth including: Information Paper: Measuring Learning in Australia: Dictionary of Standards for Education and Training Statistics (cat. no. 4232.0.55.001); Education Variables, 2002 (cat. no. 1246.0); Australian Standard Classification of Education, 2001 (cat. no. 1271.0); Australian Standard Classification of Drugs of Concern, 2000 (cat. no. 1248); and Family, Household and Income Unit Variables, 1995 (cat. no. 1286).
Other agencies in the field also undertake work to identify and promote standards. The AIHW produces a number of data dictionaries including the National Health Data Dictionary (cat. no. HWI 88) and the National Community Services Data Dictionary (cat. no. HWI 65).
In addition to the development of statistical standards, a number of projects are underway to promulgate the use of statistical standards. National minimum datasets (NMDS), developed for a particular collection area, are undertaken to incorporate agreed standards for the collection, processing and dissemination of data across different jurisdictions. A recent example is the NMDS relating to juvenile justice data. The Juvenile Justice NMDS is a collaborative effort between the AIHW and the Australasian Juvenile Justice Administrators to collect and report, from all states and territories, nationally consistent data on juvenile justice supervision.
Improve the range and quality of data on specific target populations of children and youth 
Data users have highlighted the need for statistical information to better understand particular target groups. The main target groups identified during the consultation were Indigenous; Culturally and Linguistically Diverse (CALD); children and youth with disabilities; and socio-economically disadvantaged children and youth. Children and young people in these populations are often the most in need of support and at whom policy is often directed. The specific data needs identified by stakeholders for these target populations are presented in Appendix 2.
Smaller populations and issues with identification present a number of difficulties in enumerating these groups, in particular through household surveys. Over-sampling or special methodologies are often required to produce accurate and reliable estimates, as well as the use of complex question modules to identify the specific groups (e.g. those with a disability or those from a CALD background). These enumeration issues often make collecting data on such groups time consuming and expensive.
For Indigenous populations, in particular, there is the issue of self-identification (or identification by a parent or guardian) across all collection types (census, surveys and administrative collections). In addition to issues of not identifying, there can also be incidences of individuals identifying differently in different collections. Both time and circumstances can affect responses to Indigenous identification. This presents issues of quality and comparability.
The identification of socio-economically disadvantaged children and youth is also problematic. There is no standard definition of this group, particularly in terms of the range of characteristics that need to be measured to identify them. Part of the reason for this is that the more characteristics used to identify them, the more questions are required adding to respondent burden and cost.
Selected data activities
Statistical standards, classifications and specially designed question modules have been used for some time to aid in the accurate identification of target populations. The ABS has adopted a standard Indigenous identification question and promulgates the use of this question in the census, surveys and administrative collections. Indigenous administrative data are being improved through the Indigenous Administrative Data Project. This project has supported and promoted the implementation of standard question wording in relevant collections, both ABS and non-ABS, in particular collections relating to births, deaths and hospital separations.
The ABS has also adopted standard question modules for the identification of persons with a disability across several collections. Data on those with a need for assistance, including those with a disability, will be improved through a new question module in the 2006 Census. This, combined with the introduction of mesh blocks, will provide for better identification of those with a need for assistance at small area levels. Mesh blocks are discussed in more detail under Priority Area 10.
The ABS has produced standards relating to CALD populations which are available from the ABS' website in Standards for Statistics on Cultural and Language Diversity, 1999(cat. no. 1289.0). The standards, which were endorsed by Council of Ministers of Immigration and Multicultural Affairs in April 1999, include recommended questions, classifications, coding structures and output categories for use in both interview-based and self-enumerated data collections.
The use of mesh blocks in the 2006 Census will facilitate dissemination and analysis of data on those children and youth from CALD backgrounds for a greater range of small areas.
Data on socio-economically disadvantaged populations have been of interest for some time. The ABS uses an index, called Socioeconomic Index for Areas (SEIFA), for identifying geographic areas of socio-economically advantaged/disadvantaged populations. Further information on SEIFA is available from the ABS' website in an information paper entitled Census of Population and Housing- Socioeconomic Index For Areas, Australia, 2001 (cat. no. 2039.0). The Longitudinal Surveys of Australian Youth collect extensive longitudinal data on young people's social and economic backgrounds and experiences.
Data on socio-economically disadvantaged children, young people and families will be improved through a project currently being undertaken by the National Centre for Social and Economic Modelling (NATSEM). The project, to develop a child-centred socio-economic index at Statistical Local Area level, will provide the basis for a number of research projects looking at social exclusion and the impacts of socio-economic status on life outcomes.
Improve the range and quality of small area data available on children and youth 
Most programs and support services for families, children and young people are delivered at the regional and/or community level. Consequently, data at these geographic levels are crucial to assess the need for such services (e.g. health, education, transport). Stakeholders identified a range of data needs at various geographic levels, in particular for the target populations discussed above. Data at the smaller geographic levels are required from both administrative and survey collections.
Collecting data for the smaller geographic levels (e.g. neighbourhood, catchment area) is difficult for a number of reasons. Household surveys do not often have sample sizes big enough to provide accurate and reliable estimates. Small area output from census, survey and administrative data collections may also be restricted due to confidentiality concerns.
Many of the issues surrounding production of small geographic area data are covered in the ABS' Information Development Plan for Rural and Regional Statistics (cat. no. 1362.0). The ABS has also recently established a National Centre for Rural and Regional Statistics to further work in this important area.
Selected data activities
The main source of small area data on children and youth is the ABS' Census of Population and Housing. From the 2006 Census, improvements in small area data will be achieved through the introduction of mesh blocks. Mesh blocks are micro-level spatial units (around 30-60 households) which can be aggregated to generate customised regions, enabling dissemination of Census data for non-standard geographic areas (e.g. school catchment areas).
Methods exist to create small area estimates based on survey estimates for larger regions using modelling techniques. These have been applied most recently to the results of the ABS' 2003 Survey of Disability, Ageing and Carers. The methodology allows production of estimates of the proportion of persons in private dwellings with disabilities, classified by some degree of severity, at the Local Government Area level. Such techniques might be feasible for producing certain indicators relating to children and youth.
OTHER DATA DEVELOPMENT PRIORITIES
In addition to identifying the specific data development needs for each priority area, users also noted a number of drivers for statistical data development across the field of children and youth statistics. These drivers or strategic directions have the capacity to facilitate wide-ranging improvements within the field, if adopted or implemented in appropriate statistical development activities.
Increase collaboration and coordination in statistical data development work
There is increasing recognition amongst stakeholders of the need to work collaboratively and in more coordinated ways when undertaking statistical data development work.
With so many government agencies, research organisations and collaborative groups involved in data collection (both survey and administrative), improved coordination was identified as crucial to support better relevancy, timeliness, dissemination and use of data. Improved coordination is also critical to reduce data duplication and support more efficient use of resources. Collaborative approaches are acknowledged as very important when seeking to improve the data available to the field. Collaborative approaches not only allow for improvements at the input stage of a data collection (e.g. the pooling of financial resources and data needs), but also provide a means for the sharing of data, knowledge and related analyses benefiting the field as a whole.
Better coordination of data collection activities will also support improvements in the comparability of collections. Many collections, in particular administrative, are produced for an agency's own purpose with little regard to possible wider application of the data. This approach has led to many data sources being incompatible with other collections, limiting their use in the field and also the information that can be gleaned from them. A much wider use of data can be achieved through the use of standards across a number of areas including survey methodology; collection scope; definitions and concepts used; question modules; and data edits.
In recent years, technological advances coupled with the increased recognition of the benefits of working collaboratively have led to greater coordination and collaboration in data collection and development activities. The increasing whole-of-government approaches to policy and program development have also given impetus to this change.
Following are examples of activities underway to improve coordination and collaboration in data development, collection and dissemination.
Improve the use of existing datasets on children and youth
There is widespread acknowledgement among the user community that there are a large number of relevant datasets, from both administrative and survey collections, that are under-utilised and almost certainly have the potential to meet many of the identified data development needs. Under-utilisation is occurring for a number of reasons: users may be unaware of the existence of these data or their potential value to them; access to the data may be restricted for confidentiality, privacy or ethical reasons; or access might be limited by a lack of appropriate technology to access the data. Given the potential of these collections, there is a need to promote and facilitate greater access, where applicable, to such collections and encourage better analysis of their data and promote research findings.
IDPs, such as this one, are one tool to support the identification and exposure of existing, but under-utilised data sets. These Plans, through extensive consultation with data providers, identify and document data collections and sources available for a particular subject matter area. There are a number of IDPs in existence including the National Community Services Information Plan (cat. no. AUS 14) and National Public Health Information Development Plan (cat. no. HWI 22), both available from the AIHW's website (www.aihw.gov.au). The ABS has also produced a number of IDPs, including the National Information Development Plan for Crime and Justice (cat. no. 4520.0) and the Information Paper: Regional Research in Australia - the Statistical Dimension: an Information Development Plan for Rural and Regional Statistics (cat. no. 1362.0).
Two national initiatives which will, in time, improve the use of existing data collections are the National Statistical Service and the NDN.
The National Statistical Service (NSS) is a cross-government initiative, led by the ABS, which seeks to improve statistical information produced by all levels of government, in terms of its quality, relevance and availability. This includes statistics generated as a by-product of the administrative processes of government, as well as the outputs of statistical collections conducted to support government activities (surveys and census collections). The key benefit of the NSS will be the availability of a larger range of relevant and high quality statistics to inform decision making. In addition, the NSS will support forums and networks, at national and jurisdictional levels, providing members the opportunity to discuss statistical issues and emerging requirements, as well as progress developments.
The NDN, as previously mentioned, will provide an on-line library of data holdings. The NDN will be a search facility allowing users to identify and access meta-data and/or data from a large number of data collections not previously available. Data holdings are held and controlled by their custodians allowing for data confidentiality to be retained. The NDN was developed in response to the concern that a wealth of data exists but is not identifiable or accessible by users.
Improve data collections that allow pathways to be identified
Across all the agreed priority areas, users have indicated a strong demand for longitudinal data on children and youth. Such data not only allow causal pathways and outcomes to be identified, but also enable the identification of at-risk populations. Alongside the need for an increase in the collection of longitudinal data is the need to promote the results of this research to support better policy and program development.
There are a number of well-established collections currently in the field that are providing valuable sources of longitudinal data. These include: the LSAC; the LSAY; the HILDA Survey; and the DoHA's Australian Longitudinal Study of Women's Health. Other longitudinal data collections, either planned or recently underway, include the LSIC and the FaCSIA's Youth in Focus survey.
Also planned is the establishment of the Statistical Longitudinal Census of Population and Housing Data Set starting with the 2006 Census. Using a five per cent sample, there will be the capacity to build a longitudinal dataset with samples matched from successive census collections, as well as the potential for other approved projects to statistically match this sample with ABS survey datasets.
There is also considerable interest, particularly from users in state and territory agencies, in linking data between administrative collections with a view to identifying causal pathways and outcomes. Following are two examples of data linkage initiatives.
The Data Linkage Unit, a collaborative unit between Western Australia's Department of Health, the University of Western Australia and the ICHR, was the first state based unit to achieve success in this area. The Data Linkage Unit has developed a core Data Linkage System which consists of links within, and between, the State's seven core population health datasets. Data spanning 35 years have been linked, connecting data about health events for individuals in Western Australia (including children and young people).
The AIHW has established a Community Services Integration and Linkage Unit to facilitate linkage of community services data collections. A number of data collections in the community services field now contain a common statistical linkage key consisting of a certain combination of letters from the person's name, their date of birth and sex. This allows records belonging to the same individual to be matched anonymously and combined where appropriate. The key is not a unique identifier so there is a small probability of error, which means that it can be used only for statistical purposes and not for the identification of particular individuals. It has been well-tested and used successfully for statistical analysis in an increasing number of data sets. Linked data sets can be very valuable for statistical analysis and policy development work. For example, linking child protection data, juvenile justice data and data from the SAAP could be very valuable in relation to identifying the outcomes of the abused and neglected children.