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Kathleen Falster, Mikaela Jorgensen, Mark Hanly, Emily Banks, Marni Brownell, Sandra Eades, Rhonda Craven, Sharon Goldfeld, Deborah Randall, Louisa Jorm, Data Resource Profile: Seeding Success: a cross-sectoral data resource for early childhood health and development research in Australian Aboriginal and non-Aboriginal children, International Journal of Epidemiology, Volume 46, Issue 5, October 2017, Pages 1365–1366j, https://doi.org/10.1093/ije/dyx051
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Data Resource Basics
Background
In common with other indigenous populations worldwide,1 Aboriginal and Torres Strait Islander Australians experience worse health, development and later life outcomes than non-Indigenous Australians across most routinely reported population measures throughout the life course.2–5 These inequalities are founded in a history of colonization and discriminatory legislation, policy and practices.6 Despite the Australian Government’s commitment in 2008 to ‘Closing the Gap’ in outcomes between Indigenous and non-Indigenous people, progress towards the majority of targets has been largely disappointing to date.7
It is well recognized that the social conditions of children and their families are an important determinant of their health and development, and the early childhood years represent an opportunity for interventions that may improve health and development trajectories.8,9 Accordingly, a number of longitudinal cohort studies have been established in recent years to better understand the child, family and community factors that promote positive and negative health and development outcomes in Aboriginal and Torres Strait Islander children.10–13 Large-scale, population-level epidemiological research is also required to identify policy-sensitive targets for intervention, and to assess their impact. To conduct this research, longitudinal data resources with large-scale population coverage are needed.
Purpose and scope of the Seeding Success data resource
The Seeding Success data resource was established to conduct child health and health equity research in Australia’s most populous state, New South Wales (NSW), with a focus on Aboriginal and Torres Strait Islander children and scope to investigate multiple dimensions of disadvantage. It is a unique resource comprising individual-level administrative data with comprehensive population coverage, routinely collected by several sectors (including health, education and community services), that have been linked together to construct the health and development trajectories of a large, population-based cohort of children from birth to school age. More details about the rationale, aims and methodological plan are available elsewhere.14 In brief, the Seeding Success data resource capitalizes on recently available data from the Australian Early Development Census (AEDC; formerly the Australian Early Development Index15), a population-level measure of child development at school entry, linked to other administrative data sources to enable investigation of: (i) early life characteristics that promote positive and negative early childhood development; (ii) how early childhood outcomes vary geographically and how features of local communities contribute to this variation; and (iii) the relation of child outcomes to programme and service delivery.
The Seeding Success data resource setting: New South Wales, Australia
Australia is divided into five state and two territory jurisdictions (Figure 1). The state of NSW is home to almost one-third of Australia’s population (7.2 million of 22.3 million people) and nearly one-third of Australia’s Aboriginal and Torres Strait Islander population (209 000 of 670 000 people).16 Aboriginal and Torres Strait Islander people comprise 2.9% of the total NSW population; of these, 95% identify as Aboriginal, 3% as Torres Strait Islander and 2% as Aboriginal and Torres Strait Islander.16 Because Aboriginal people are the original inhabitants of NSW17 and account for 95% of the NSW Indigenous population, the term ‘Aboriginal’ will be used throughout this paper.18 The majority of the NSW population lives in major cities (74%), followed by regional (26%) and remote (< 1%) areas. In contrast, 44% of Aboriginal people in NSW live in major cities, 51% in regional areas and 5% in remote areas.19
The Seeding Success data resource population
The Seeding Success data resource includes a population-based cohort of children who were born in NSW and started school in 2009 (N = 79 432) or 2012 (N = 86 846) (Table 1). Births were identified from the NSW Register of Births, Deaths and Marriages and the NSW Perinatal Data Collection; birth dates ranged from 1 January 2002 to 31 December 2008. School starters were identified from the 2009 and 2012 AEDC, which has high population coverage in NSW (99.1% in 2009 and 97.3% in 2012).2,20
. | AEDC collection year . | Source data information . | |||||
---|---|---|---|---|---|---|---|
. | 2009a . | 2012 . | . | . | |||
Data source . | n . | % . | n . | % . | Years of data linked . | Key measurements . | |
Children’s linked data | |||||||
Australian Early Development Census | 79432 | 100.0 | 86846 | 100.0 | 2009, 2010,a 2012 | Developmental outcomes in first year of school; demographic details; preschool attendance; health and development needs | |
Birth record in NSW (total) | 79432 | 100.0 | 86846 | 100.0 | – | – | |
Perinatal Data Collection (PDC)b | 78742 | 99.1 | 85821 | 98.8 | 01/2002–12/2008 | Items relating to pregnancy, labour, baby’s condition, mother’s previous pregnancies | |
Registry of Births, Deaths and Marriages birth registrations | 78713 | 99.1 | 85758 | 98.7 | 01/2002–12/2008 | Date of birth; demographic items for mother, father and baby at child’s birth | |
Register of Congenital Conditionsc | 423 | 0.5 | 1352 | 1.6 | 01/2004–01/2008 | Details of congenital conditions detected during pregnancy, birth, first year of life | |
Admitted Patients Data Collectiond | 78665 | 99.0 | 86141 | 99.2 | 01/2002–12/2012 | Hospital admissions and separations; diagnoses; procedures | |
Emergency Department Data Collectiond | 51163 | 64.4 | 65840 | 75.8 | 01/2005–12/2012 | Mode of arrival and separation; triage category; diagnoses; procedures | |
Mental Health Ambulatory Data Collectiond | 982 | 1.2 | 1100 | 1.3 | 04/2003–12/2012 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |
Key Information Directory Systemd | 10939 | 13.8 | 13527 | 15.6 | 12/2002–12/2012 | Items relating to contacts with community services, including child protection, out-of-home-care, Brighter Futures programme | |
Public School Enrolmentse | 50732 | 63.9 | 56934 | 65.6 | 2009, 2012 | Mother’s and father’s language, occupation, schooling and tertiary education | |
Total cohort children with linked data for parents (before cohort child’s birth) and siblings | |||||||
Total children with mothers who have linked dataf | 78265 | 98.5 | 85325 | 98.2 | – | ||
Total children with second parentsg who have linked dataf | 14107 | 17.8 | 24771 | 28.5 | – | ||
Total children who have siblingsh within the cohort | 13411 | 16.9 | 13378 | 15.4 | – | ||
Total children with mothers with a linked PDC record before cohort child’s birth | 01/1994–12/2008 | Mother’s age; mother’s date of birth; and the child’s date of birth were obtained for children born to the mother of cohort child, before the birth of cohort child, to calculate mother’s age at first birthi | |||||
40690 | 51.2 | 45147 | 52.0 | ||||
Admitted Patients Data Collectionj | 01/2001–12/2008 | Hospital admissions and separations; diagnoses; procedures | |||||
Mothers | 78133 | 98.4 | 85269 | 98.2 | |||
Second parentsg | 13626 | 17.2 | 24072 | 27.7 | |||
Mental Health Ambulatory Data Collectionj | 01/2001–12/2008 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |||||
Mothers | 2107 | 2.7 | 4047 | 4.7 | |||
Second parentsg | 963 | 1.2 | 2112 | 2.4 |
. | AEDC collection year . | Source data information . | |||||
---|---|---|---|---|---|---|---|
. | 2009a . | 2012 . | . | . | |||
Data source . | n . | % . | n . | % . | Years of data linked . | Key measurements . | |
Children’s linked data | |||||||
Australian Early Development Census | 79432 | 100.0 | 86846 | 100.0 | 2009, 2010,a 2012 | Developmental outcomes in first year of school; demographic details; preschool attendance; health and development needs | |
Birth record in NSW (total) | 79432 | 100.0 | 86846 | 100.0 | – | – | |
Perinatal Data Collection (PDC)b | 78742 | 99.1 | 85821 | 98.8 | 01/2002–12/2008 | Items relating to pregnancy, labour, baby’s condition, mother’s previous pregnancies | |
Registry of Births, Deaths and Marriages birth registrations | 78713 | 99.1 | 85758 | 98.7 | 01/2002–12/2008 | Date of birth; demographic items for mother, father and baby at child’s birth | |
Register of Congenital Conditionsc | 423 | 0.5 | 1352 | 1.6 | 01/2004–01/2008 | Details of congenital conditions detected during pregnancy, birth, first year of life | |
Admitted Patients Data Collectiond | 78665 | 99.0 | 86141 | 99.2 | 01/2002–12/2012 | Hospital admissions and separations; diagnoses; procedures | |
Emergency Department Data Collectiond | 51163 | 64.4 | 65840 | 75.8 | 01/2005–12/2012 | Mode of arrival and separation; triage category; diagnoses; procedures | |
Mental Health Ambulatory Data Collectiond | 982 | 1.2 | 1100 | 1.3 | 04/2003–12/2012 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |
Key Information Directory Systemd | 10939 | 13.8 | 13527 | 15.6 | 12/2002–12/2012 | Items relating to contacts with community services, including child protection, out-of-home-care, Brighter Futures programme | |
Public School Enrolmentse | 50732 | 63.9 | 56934 | 65.6 | 2009, 2012 | Mother’s and father’s language, occupation, schooling and tertiary education | |
Total cohort children with linked data for parents (before cohort child’s birth) and siblings | |||||||
Total children with mothers who have linked dataf | 78265 | 98.5 | 85325 | 98.2 | – | ||
Total children with second parentsg who have linked dataf | 14107 | 17.8 | 24771 | 28.5 | – | ||
Total children who have siblingsh within the cohort | 13411 | 16.9 | 13378 | 15.4 | – | ||
Total children with mothers with a linked PDC record before cohort child’s birth | 01/1994–12/2008 | Mother’s age; mother’s date of birth; and the child’s date of birth were obtained for children born to the mother of cohort child, before the birth of cohort child, to calculate mother’s age at first birthi | |||||
40690 | 51.2 | 45147 | 52.0 | ||||
Admitted Patients Data Collectionj | 01/2001–12/2008 | Hospital admissions and separations; diagnoses; procedures | |||||
Mothers | 78133 | 98.4 | 85269 | 98.2 | |||
Second parentsg | 13626 | 17.2 | 24072 | 27.7 | |||
Mental Health Ambulatory Data Collectionj | 01/2001–12/2008 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |||||
Mothers | 2107 | 2.7 | 4047 | 4.7 | |||
Second parentsg | 963 | 1.2 | 2112 | 2.4 |
aIncludes 1352 children from the 2010 Australian Early Development Census (AEDC) top-up collection.
bPDC records contain information on both mothers and babies.
cBecause there are only ever 5 years of RoCC data available for linkage, fewer children in the 2009 AEDC collection year linked to the RoCC.
dChildren may have one or more linked records from this data source.
eOnly available for children enrolled in a NSW public school.
fParent must have linked data in the PDC, APDC or MH-AMB and child must have RBDM birth record.
gThe second parent reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
hOther children born to the same mother, identified via linkage to the RBDM, who had a 2009 or 2012 AEDC record.
iFor children born 2002–08, the full PDC record is available.
jParents may have one or more linked records from this data source, for the 5 years preceding the child’s birth (where available).
. | AEDC collection year . | Source data information . | |||||
---|---|---|---|---|---|---|---|
. | 2009a . | 2012 . | . | . | |||
Data source . | n . | % . | n . | % . | Years of data linked . | Key measurements . | |
Children’s linked data | |||||||
Australian Early Development Census | 79432 | 100.0 | 86846 | 100.0 | 2009, 2010,a 2012 | Developmental outcomes in first year of school; demographic details; preschool attendance; health and development needs | |
Birth record in NSW (total) | 79432 | 100.0 | 86846 | 100.0 | – | – | |
Perinatal Data Collection (PDC)b | 78742 | 99.1 | 85821 | 98.8 | 01/2002–12/2008 | Items relating to pregnancy, labour, baby’s condition, mother’s previous pregnancies | |
Registry of Births, Deaths and Marriages birth registrations | 78713 | 99.1 | 85758 | 98.7 | 01/2002–12/2008 | Date of birth; demographic items for mother, father and baby at child’s birth | |
Register of Congenital Conditionsc | 423 | 0.5 | 1352 | 1.6 | 01/2004–01/2008 | Details of congenital conditions detected during pregnancy, birth, first year of life | |
Admitted Patients Data Collectiond | 78665 | 99.0 | 86141 | 99.2 | 01/2002–12/2012 | Hospital admissions and separations; diagnoses; procedures | |
Emergency Department Data Collectiond | 51163 | 64.4 | 65840 | 75.8 | 01/2005–12/2012 | Mode of arrival and separation; triage category; diagnoses; procedures | |
Mental Health Ambulatory Data Collectiond | 982 | 1.2 | 1100 | 1.3 | 04/2003–12/2012 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |
Key Information Directory Systemd | 10939 | 13.8 | 13527 | 15.6 | 12/2002–12/2012 | Items relating to contacts with community services, including child protection, out-of-home-care, Brighter Futures programme | |
Public School Enrolmentse | 50732 | 63.9 | 56934 | 65.6 | 2009, 2012 | Mother’s and father’s language, occupation, schooling and tertiary education | |
Total cohort children with linked data for parents (before cohort child’s birth) and siblings | |||||||
Total children with mothers who have linked dataf | 78265 | 98.5 | 85325 | 98.2 | – | ||
Total children with second parentsg who have linked dataf | 14107 | 17.8 | 24771 | 28.5 | – | ||
Total children who have siblingsh within the cohort | 13411 | 16.9 | 13378 | 15.4 | – | ||
Total children with mothers with a linked PDC record before cohort child’s birth | 01/1994–12/2008 | Mother’s age; mother’s date of birth; and the child’s date of birth were obtained for children born to the mother of cohort child, before the birth of cohort child, to calculate mother’s age at first birthi | |||||
40690 | 51.2 | 45147 | 52.0 | ||||
Admitted Patients Data Collectionj | 01/2001–12/2008 | Hospital admissions and separations; diagnoses; procedures | |||||
Mothers | 78133 | 98.4 | 85269 | 98.2 | |||
Second parentsg | 13626 | 17.2 | 24072 | 27.7 | |||
Mental Health Ambulatory Data Collectionj | 01/2001–12/2008 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |||||
Mothers | 2107 | 2.7 | 4047 | 4.7 | |||
Second parentsg | 963 | 1.2 | 2112 | 2.4 |
. | AEDC collection year . | Source data information . | |||||
---|---|---|---|---|---|---|---|
. | 2009a . | 2012 . | . | . | |||
Data source . | n . | % . | n . | % . | Years of data linked . | Key measurements . | |
Children’s linked data | |||||||
Australian Early Development Census | 79432 | 100.0 | 86846 | 100.0 | 2009, 2010,a 2012 | Developmental outcomes in first year of school; demographic details; preschool attendance; health and development needs | |
Birth record in NSW (total) | 79432 | 100.0 | 86846 | 100.0 | – | – | |
Perinatal Data Collection (PDC)b | 78742 | 99.1 | 85821 | 98.8 | 01/2002–12/2008 | Items relating to pregnancy, labour, baby’s condition, mother’s previous pregnancies | |
Registry of Births, Deaths and Marriages birth registrations | 78713 | 99.1 | 85758 | 98.7 | 01/2002–12/2008 | Date of birth; demographic items for mother, father and baby at child’s birth | |
Register of Congenital Conditionsc | 423 | 0.5 | 1352 | 1.6 | 01/2004–01/2008 | Details of congenital conditions detected during pregnancy, birth, first year of life | |
Admitted Patients Data Collectiond | 78665 | 99.0 | 86141 | 99.2 | 01/2002–12/2012 | Hospital admissions and separations; diagnoses; procedures | |
Emergency Department Data Collectiond | 51163 | 64.4 | 65840 | 75.8 | 01/2005–12/2012 | Mode of arrival and separation; triage category; diagnoses; procedures | |
Mental Health Ambulatory Data Collectiond | 982 | 1.2 | 1100 | 1.3 | 04/2003–12/2012 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |
Key Information Directory Systemd | 10939 | 13.8 | 13527 | 15.6 | 12/2002–12/2012 | Items relating to contacts with community services, including child protection, out-of-home-care, Brighter Futures programme | |
Public School Enrolmentse | 50732 | 63.9 | 56934 | 65.6 | 2009, 2012 | Mother’s and father’s language, occupation, schooling and tertiary education | |
Total cohort children with linked data for parents (before cohort child’s birth) and siblings | |||||||
Total children with mothers who have linked dataf | 78265 | 98.5 | 85325 | 98.2 | – | ||
Total children with second parentsg who have linked dataf | 14107 | 17.8 | 24771 | 28.5 | – | ||
Total children who have siblingsh within the cohort | 13411 | 16.9 | 13378 | 15.4 | – | ||
Total children with mothers with a linked PDC record before cohort child’s birth | 01/1994–12/2008 | Mother’s age; mother’s date of birth; and the child’s date of birth were obtained for children born to the mother of cohort child, before the birth of cohort child, to calculate mother’s age at first birthi | |||||
40690 | 51.2 | 45147 | 52.0 | ||||
Admitted Patients Data Collectionj | 01/2001–12/2008 | Hospital admissions and separations; diagnoses; procedures | |||||
Mothers | 78133 | 98.4 | 85269 | 98.2 | |||
Second parentsg | 13626 | 17.2 | 24072 | 27.7 | |||
Mental Health Ambulatory Data Collectionj | 01/2001–12/2008 | Contacts with mental health day programmes, psychiatric outpatients, outreach services; diagnoses | |||||
Mothers | 2107 | 2.7 | 4047 | 4.7 | |||
Second parentsg | 963 | 1.2 | 2112 | 2.4 |
aIncludes 1352 children from the 2010 Australian Early Development Census (AEDC) top-up collection.
bPDC records contain information on both mothers and babies.
cBecause there are only ever 5 years of RoCC data available for linkage, fewer children in the 2009 AEDC collection year linked to the RoCC.
dChildren may have one or more linked records from this data source.
eOnly available for children enrolled in a NSW public school.
fParent must have linked data in the PDC, APDC or MH-AMB and child must have RBDM birth record.
gThe second parent reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
hOther children born to the same mother, identified via linkage to the RBDM, who had a 2009 or 2012 AEDC record.
iFor children born 2002–08, the full PDC record is available.
jParents may have one or more linked records from this data source, for the 5 years preceding the child’s birth (where available).
In total, data are available for 166 278 children. Data on children’s parents are also available. For 163 590 children, data are available for their mother and for 38 878 children, data are also available for their ‘second’ parent, of whom 99.7% are fathers (Table 1). Of the total 166 278 children in the cohort, 154 935 children (93.2%) were born and started school in NSW, and 11 343 children (6.8%) were born in NSW and started school in another jurisdiction (Figure 2).
Data Collected
Data sources and measures
Data were provided by national and state government agencies. Table 1 summarises the individual-level measures available in the Seeding Success data resource for children and their parents, from the administrative data sources described below. Figure 3 illustrates the time coverage of the measures available from each data source. For children in the data resource, data were obtained from all sources. Where data were obtained for the parents, this is indicated below.
The NSW Perinatal Data Collection (PDC) includes records for all children born at ≥ 20 weeks of gestation or weighing ≥ 400 g in NSW public or private hospitals, as well as planned home births. It includes demographic variables and information on maternal health, the pregnancy, labour, birth, and perinatal outcomes. For the mothers of the children in the resource, the month and year of birth from each of her previous birth records in the PDC were obtained to enable calculation of the mother’s age at first motherhood.
The NSW Registry of Births, Deaths and Marriages compiles birth registrations for NSW. Birth registrations include month and year of birth and Aboriginal status for mothers and other parents (mostly fathers).
The NSW Register of Congenital Conditions includes records of congenital conditions identified during pregnancy, at birth or during the first year of life, as well as the date of diagnosis. Because identifiers are removed from the register after 5 years, data were only available for linkage for pregnancy outcomes recorded in the period 2004–08.
The NSW Admitted Patients Data Collection includes records of all public and private hospital separations (discharges, transfers and deaths) in NSW since 1 July 2001. It includes patient demographics, and diagnoses and procedures coded according to the Australian Modification of the International Statistical Classification of Diseases and Related Problems, 10th revision (ICD-10-AM).21 For parents of children in the resource, hospitalization records for the 5 years preceding the child’s birth were obtained, where available.
The NSW Emergency Department Data Collection includes records of all presentations to metropolitan, and the majority of regional, emergency departments in NSW since 1 January 2005. It includes patient demographics, mode of arrival, triage category, mode of separation, diagnoses, and procedures coded according to ICD-10-AM21, ICD-9-CM22, or Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT). or Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT®).
The NSW Mental Health Ambulatory Data Collection (MH-AMB) includes the assessment, treatment, rehabilitation or care of non-admitted patients since January 2000, although there was significant undercounting of contacts until 2005/6. The MH-AMB data collection includes patient demographics, diagnosis codes coded according to the ICD-10-AM,21 and other characteristics of the service provided for each ‘contact’ between a clinician and a patient. For parents of children in the resource, MH-AMB records for the 5 years preceding the child’s birth were obtained, where available.
The Key Information Directory System (KiDS) includes records of all child protection contacts with the NSW Department of Family and Community Services (FACS) since 2003, including information about whether a child has: (i) been assessed by a caseworker as being at actual harm/risk of harm; (ii) had a legal decision made in relation to them; (iii) been placed in out-of-home care; or (iv) been referred to and participated in a FACS early intervention programme.
The Australian Early Development Census is a population measure of child development that has been collected nationwide every 3 years since 2009 for children enrolled in their first year of formal full-time school.15 In Australia, the school year starts in late January/early February and the majority of children start school at the age of 5 years. The AEDC is a teacher-completed checklist, collected between May and August, and includes items about the child’s development on five domains: (i) physical health and well-being; (ii) social competence; (iii) emotional maturity; (iv) language and cognitive skills; and (v) communication skills and general knowledge. At the time when data were linked for Seeding Success, AEDC data for 2009 and 2012 were available.
NSW Public School Enrolment data include demographic information about the child and his or her family, including parent education and occupation, for children enrolled in NSW Public Schools (i.e. government-funded schools). In NSW in 2009 and 2012, 70% of children in their first year of school were enrolled in a public school, 20% in a Catholic school and 10% in an independent school.23
For children in the data resource, approval has also been obtained to link to: (i) Medicare Benefits Schedule data, which consist of records for claims for medical and diagnostic services; and (ii) Centrelink income assistance data, consisting of records of receipt of Australian Government payments for families with low incomes. Details of these linkages have been described elsewhere;14 they have not yet commenced.
Where standard units of geography for areas of residence are available in the resource (e.g. PDC birth or AEDC record), publicly available area-level information, such as geographical remoteness24 or socioeconomic indices,25 have been attached to child and parent records.
Data linkage
The NSW Centre for Health Record Linkage [http://www.cherel.org.au/] linked the individual-level data from the sources described above to create the Seeding Success data resource. The Australian Institute of Health and Welfare (AIHW) will undertake the planned linkages of Medicare and Centrelink data.
The majority of data sources currently in the resource, with the exception of the KiDS and the school enrolment data, are routinely linked within the Centre for Health Record Linkage Master Linkage Key, which includes a set of regularly updated links within and between core population data sources in NSW. Custodians of each data source provide the Centre for Health Record Linkage with an encrypted source record number and demographic details (including full name, address, date of birth, sex) for each record in the source data; in the case of data sources not in the Master Linkage Key, this is done on an ad hoc basis. Records for each individual are then linked within and between data sources using probabilistic methods based on demographic details.26
For each individual identified in the linkage process for this resource, a project person-specific number (PPN) was created; this PPN was then assigned to all records that belonged to that individual in each data source. Following linkage, the PPN and associated source record numbers for each data source were returned to the relevant data custodians. The data custodians then extracted and supplied the approved variables and relevant PPNs to the study investigators. In this way, the separation principle was applied, such that no one working with the data had access to both identifying information and the content data.27
Measurement of Aboriginality using multiple, linked population data sources
Because Aboriginal people are known to be under-enumerated in administrative data,28–32 and use of multiple sources of linked data has been shown to enhance Aboriginal enumeration in population studies,33–36 we derived the child’s Aboriginality from multiple data sources available for children at birth and school age. The Aboriginality of the child may be derived from the Aboriginal status recorded for the mother, second parent (mostly fathers) or child, depending on the source data, or a combination from multiple data sources. Table 2 summarises the number of children recorded as Aboriginal in the birth and school age source data, and the enhanced enumeration of Aboriginal children when Aboriginality is derived from multiple linked data sources. Of the child cohort, 10 430 children (6.3%) were classified as Aboriginal using the ‘ever identified’ identification algorithm (i.e. the child or their parents were recorded as Aboriginal on any of the available birth records, or the AEDC) (Table 2), which was applied for the descriptive statistics presented in Table 3.
aThe second parent, reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
bDenominator varies based on combinations of datasets used and amount of missing data in source data.
aThe second parent, reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
bDenominator varies based on combinations of datasets used and amount of missing data in source data.
. | Aboriginala, n (%) . | Non-Aboriginal, n (%) . | Total . |
---|---|---|---|
Total n | 10430 (100.0) | 155848 (100.0) | 166278 (100.0) |
Child’s sex | |||
Male | 5313 (50.9) | 80385 (51.6) | 85698 (51.5) |
Female | 5117 (49.1) | 75463 (48.4) | 80580 (48.5) |
Age of mother at child’s birthb | |||
Median years (interquartile range) | 25 (21–30) | 30 (26–34) | 30 (26–34) |
Missing | 1 (0.0) | 4 (0.0) | 5 (0.0) |
Age of second parentc at child’s birthb | |||
Median years (interquartile range) | 28 (23–33) | 33 (29–37) | 32 (29–37) |
Missing | 1570 (15.1) | 5242 (3.4) | 6812 (4.1) |
Mother’s country of birth | |||
Australia | 9814 (94.1) | 112049 (71.9) | 121863 (73.3) |
Other country | 564 (5.4) | 42931 (27.5) | 43495 (26.2) |
Missing | 52 (0.5) | 868 (0.6) | 920 (0.6) |
Mother’s marital status at child’s birthd | |||
Married/de facto | 4909 (47.1) | 129989 (83.4) | 134898 (81.1) |
Single/widowed/divorced/separated | 4742 (45.5) | 21830 (14.0) | 26572 (16.0) |
Missing/unknown | 779 (7.5) | 4029 (2.6) | 4808 (2.9) |
Private health insurance at child’s birthd | |||
Yes | 880 (8.4) | 59146 (38.0) | 60026 (36.1) |
No | 9430 (90.4) | 95016 (61.0) | 104446 (62.8) |
Missing | 120 (1.2) | 1686 (1.1) | 1806 (1.1) |
Mother’s highest level of schooling | |||
Year 12 or equivalent | 1964 (18.8) | 59559 (38.2) | 61523 (37.0) |
Year 11 or equivalent | 791 (7.6) | 6549 (4.2) | 7340 (4.4) |
Year 10 or equivalent | 2447 (23.5) | 20744 (13.3) | 23191 (13.9) |
Year 9 or equivalent or below | 1185 (11.4) | 4934 (3.2) | 6119 (3.7) |
Missing/not applicable | 4043 (38.8) | 64062 (41.1) | 68105 (41.0) |
Second parent’sc highest level of schooling | |||
Year 12 or equivalent | 1305 (12.5) | 48653 (31.2) | 49958 (30.0) |
Year 11 or equivalent | 505 (4.8) | 5559 (3.6) | 6064 (3.6) |
Year 10 or equivalent | 2069 (19.8) | 24378 (15.6) | 26447 (15.9) |
Year 9 or equivalent or below | 1099 (10.5) | 5650 (3.6) | 6749 (4.1) |
Missing/not applicable | 5452 (52.3) | 71608 (45.9) | 77060 (46.3) |
Highest occupation level of either parente | |||
Manager/professional | 596 (5.7) | 24527 (15.7) | 25123 (15.1) |
Business manager/associate professional | 758 (7.3) | 22781 (14.6) | 23539 (14.2) |
Trade/clerk/services | 1454 (13.9) | 23524 (15.1) | 24978 (15.0) |
Driver/hospitality/labourer | 1820 (17.4) | 15256 (9.8) | 17076 (10.3) |
Not in paid work in past 12 months | 2020 (19.4) | 7297 (4.7) | 9317 (5.6) |
Missing/not applicable | 3782 (36.3) | 62463 (40.1) | 66245 (39.8) |
Geographical remotenessf | |||
Major city | 3948 (37.9) | 102112 (65.5) | 106060 (63.8) |
Inner regional | 3498 (33.5) | 37705 (24.2) | 41203 (24.8) |
Outer regional | 2116 (20.3) | 12128 (7.8) | 14244 (8.6) |
Remote/very remote | 631 (6.0) | 1052 (0.7) | 1683 (1.0) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Area socioeconomic advantage and disadvantagef | |||
First quintile (most disadvantaged) | 2179 (20.9) | 12720 (8.2) | 14899 (9.0) |
Second quintile | 1921 (18.4) | 16110 (10.3) | 18031 (10.8) |
Third quintile | 4211 (40.4) | 52641 (33.8) | 56852 (34.2) |
Fourth quintile | 1261 (12.1) | 32413 (20.8) | 33674 (20.3) |
Fifth quintile (most advantaged) | 621 (6.0) | 39113 (25.1) | 39734 (23.9) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Age at AEDC collection | |||
< 5 years 1 month | 1828 (17.5) | 27459 (17.6) | 29287 (17.6) |
5 years 1 month to 5 years 3 months | 1837 (17.6) | 26702 (17.1) | 28539 (17.2) |
5 years 4 months to 5 years 6 months | 2234 (21.4) | 34999 (22.5) | 37233 (22.4) |
5 years 7 months to 5 years 9 months | 2456 (23.5) | 37911 (24.3) | 40367 (24.3) |
5 years 10 months to 6 years 0 months | 913 (8.8) | 13130 (8.4) | 14043 (8.4) |
> 6 years 0 months | 1162 (11.1) | 15647 (10.0) | 16809 (10.1) |
Child is repeating school yearg | |||
No | 9815 (94.1) | 149789 (96.1) | 159604 (96.0) |
Yes | 427 (4.1) | 3657 (2.3) | 4084 (2.5) |
Missing | 188 (1.8) | 2402 (1.5) | 2590 (1.6) |
Child has health and development needs | |||
No needs identified | 6638 (63.6) | 125342 (80.4) | 131980 (79.4) |
Special needs statush | 772 (7.4) | 6983 (4.5) | 7755 (4.7) |
Additional needsi | 2388 (22.9) | 16685 (10.7) | 19073 (11.5) |
Child requires further assessment | 632 (6.1) | 6838 (4.4) | 7470 (4.5) |
. | Aboriginala, n (%) . | Non-Aboriginal, n (%) . | Total . |
---|---|---|---|
Total n | 10430 (100.0) | 155848 (100.0) | 166278 (100.0) |
Child’s sex | |||
Male | 5313 (50.9) | 80385 (51.6) | 85698 (51.5) |
Female | 5117 (49.1) | 75463 (48.4) | 80580 (48.5) |
Age of mother at child’s birthb | |||
Median years (interquartile range) | 25 (21–30) | 30 (26–34) | 30 (26–34) |
Missing | 1 (0.0) | 4 (0.0) | 5 (0.0) |
Age of second parentc at child’s birthb | |||
Median years (interquartile range) | 28 (23–33) | 33 (29–37) | 32 (29–37) |
Missing | 1570 (15.1) | 5242 (3.4) | 6812 (4.1) |
Mother’s country of birth | |||
Australia | 9814 (94.1) | 112049 (71.9) | 121863 (73.3) |
Other country | 564 (5.4) | 42931 (27.5) | 43495 (26.2) |
Missing | 52 (0.5) | 868 (0.6) | 920 (0.6) |
Mother’s marital status at child’s birthd | |||
Married/de facto | 4909 (47.1) | 129989 (83.4) | 134898 (81.1) |
Single/widowed/divorced/separated | 4742 (45.5) | 21830 (14.0) | 26572 (16.0) |
Missing/unknown | 779 (7.5) | 4029 (2.6) | 4808 (2.9) |
Private health insurance at child’s birthd | |||
Yes | 880 (8.4) | 59146 (38.0) | 60026 (36.1) |
No | 9430 (90.4) | 95016 (61.0) | 104446 (62.8) |
Missing | 120 (1.2) | 1686 (1.1) | 1806 (1.1) |
Mother’s highest level of schooling | |||
Year 12 or equivalent | 1964 (18.8) | 59559 (38.2) | 61523 (37.0) |
Year 11 or equivalent | 791 (7.6) | 6549 (4.2) | 7340 (4.4) |
Year 10 or equivalent | 2447 (23.5) | 20744 (13.3) | 23191 (13.9) |
Year 9 or equivalent or below | 1185 (11.4) | 4934 (3.2) | 6119 (3.7) |
Missing/not applicable | 4043 (38.8) | 64062 (41.1) | 68105 (41.0) |
Second parent’sc highest level of schooling | |||
Year 12 or equivalent | 1305 (12.5) | 48653 (31.2) | 49958 (30.0) |
Year 11 or equivalent | 505 (4.8) | 5559 (3.6) | 6064 (3.6) |
Year 10 or equivalent | 2069 (19.8) | 24378 (15.6) | 26447 (15.9) |
Year 9 or equivalent or below | 1099 (10.5) | 5650 (3.6) | 6749 (4.1) |
Missing/not applicable | 5452 (52.3) | 71608 (45.9) | 77060 (46.3) |
Highest occupation level of either parente | |||
Manager/professional | 596 (5.7) | 24527 (15.7) | 25123 (15.1) |
Business manager/associate professional | 758 (7.3) | 22781 (14.6) | 23539 (14.2) |
Trade/clerk/services | 1454 (13.9) | 23524 (15.1) | 24978 (15.0) |
Driver/hospitality/labourer | 1820 (17.4) | 15256 (9.8) | 17076 (10.3) |
Not in paid work in past 12 months | 2020 (19.4) | 7297 (4.7) | 9317 (5.6) |
Missing/not applicable | 3782 (36.3) | 62463 (40.1) | 66245 (39.8) |
Geographical remotenessf | |||
Major city | 3948 (37.9) | 102112 (65.5) | 106060 (63.8) |
Inner regional | 3498 (33.5) | 37705 (24.2) | 41203 (24.8) |
Outer regional | 2116 (20.3) | 12128 (7.8) | 14244 (8.6) |
Remote/very remote | 631 (6.0) | 1052 (0.7) | 1683 (1.0) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Area socioeconomic advantage and disadvantagef | |||
First quintile (most disadvantaged) | 2179 (20.9) | 12720 (8.2) | 14899 (9.0) |
Second quintile | 1921 (18.4) | 16110 (10.3) | 18031 (10.8) |
Third quintile | 4211 (40.4) | 52641 (33.8) | 56852 (34.2) |
Fourth quintile | 1261 (12.1) | 32413 (20.8) | 33674 (20.3) |
Fifth quintile (most advantaged) | 621 (6.0) | 39113 (25.1) | 39734 (23.9) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Age at AEDC collection | |||
< 5 years 1 month | 1828 (17.5) | 27459 (17.6) | 29287 (17.6) |
5 years 1 month to 5 years 3 months | 1837 (17.6) | 26702 (17.1) | 28539 (17.2) |
5 years 4 months to 5 years 6 months | 2234 (21.4) | 34999 (22.5) | 37233 (22.4) |
5 years 7 months to 5 years 9 months | 2456 (23.5) | 37911 (24.3) | 40367 (24.3) |
5 years 10 months to 6 years 0 months | 913 (8.8) | 13130 (8.4) | 14043 (8.4) |
> 6 years 0 months | 1162 (11.1) | 15647 (10.0) | 16809 (10.1) |
Child is repeating school yearg | |||
No | 9815 (94.1) | 149789 (96.1) | 159604 (96.0) |
Yes | 427 (4.1) | 3657 (2.3) | 4084 (2.5) |
Missing | 188 (1.8) | 2402 (1.5) | 2590 (1.6) |
Child has health and development needs | |||
No needs identified | 6638 (63.6) | 125342 (80.4) | 131980 (79.4) |
Special needs statush | 772 (7.4) | 6983 (4.5) | 7755 (4.7) |
Additional needsi | 2388 (22.9) | 16685 (10.7) | 19073 (11.5) |
Child requires further assessment | 632 (6.1) | 6838 (4.4) | 7470 (4.5) |
aDefined as child or parent identified as Aboriginal on any of PDC, RBDM or APDC birth records, or AEDC school record.
bAge derived from date of birth on RBDM birth record or PDC record where RBDM date of birth missing.
cThe second parent reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
dBased on APDC birth record.
eBased on highest ranking occupation of either parent from the school enrolment record.
fBased on mother’s statistical local area of residence from PDC birth record.
gChild is repeating first year of school at the time of the AEDC.
hChildren medically diagnosed as having high needs requiring special assistance due to chronic medical, physical or intellectually disabling conditions.
iChildren not classified as special needs status who have medically diagnosed or parent-reported physical, visual, hearing, speech, learning, emotional or behavioural problems.
. | Aboriginala, n (%) . | Non-Aboriginal, n (%) . | Total . |
---|---|---|---|
Total n | 10430 (100.0) | 155848 (100.0) | 166278 (100.0) |
Child’s sex | |||
Male | 5313 (50.9) | 80385 (51.6) | 85698 (51.5) |
Female | 5117 (49.1) | 75463 (48.4) | 80580 (48.5) |
Age of mother at child’s birthb | |||
Median years (interquartile range) | 25 (21–30) | 30 (26–34) | 30 (26–34) |
Missing | 1 (0.0) | 4 (0.0) | 5 (0.0) |
Age of second parentc at child’s birthb | |||
Median years (interquartile range) | 28 (23–33) | 33 (29–37) | 32 (29–37) |
Missing | 1570 (15.1) | 5242 (3.4) | 6812 (4.1) |
Mother’s country of birth | |||
Australia | 9814 (94.1) | 112049 (71.9) | 121863 (73.3) |
Other country | 564 (5.4) | 42931 (27.5) | 43495 (26.2) |
Missing | 52 (0.5) | 868 (0.6) | 920 (0.6) |
Mother’s marital status at child’s birthd | |||
Married/de facto | 4909 (47.1) | 129989 (83.4) | 134898 (81.1) |
Single/widowed/divorced/separated | 4742 (45.5) | 21830 (14.0) | 26572 (16.0) |
Missing/unknown | 779 (7.5) | 4029 (2.6) | 4808 (2.9) |
Private health insurance at child’s birthd | |||
Yes | 880 (8.4) | 59146 (38.0) | 60026 (36.1) |
No | 9430 (90.4) | 95016 (61.0) | 104446 (62.8) |
Missing | 120 (1.2) | 1686 (1.1) | 1806 (1.1) |
Mother’s highest level of schooling | |||
Year 12 or equivalent | 1964 (18.8) | 59559 (38.2) | 61523 (37.0) |
Year 11 or equivalent | 791 (7.6) | 6549 (4.2) | 7340 (4.4) |
Year 10 or equivalent | 2447 (23.5) | 20744 (13.3) | 23191 (13.9) |
Year 9 or equivalent or below | 1185 (11.4) | 4934 (3.2) | 6119 (3.7) |
Missing/not applicable | 4043 (38.8) | 64062 (41.1) | 68105 (41.0) |
Second parent’sc highest level of schooling | |||
Year 12 or equivalent | 1305 (12.5) | 48653 (31.2) | 49958 (30.0) |
Year 11 or equivalent | 505 (4.8) | 5559 (3.6) | 6064 (3.6) |
Year 10 or equivalent | 2069 (19.8) | 24378 (15.6) | 26447 (15.9) |
Year 9 or equivalent or below | 1099 (10.5) | 5650 (3.6) | 6749 (4.1) |
Missing/not applicable | 5452 (52.3) | 71608 (45.9) | 77060 (46.3) |
Highest occupation level of either parente | |||
Manager/professional | 596 (5.7) | 24527 (15.7) | 25123 (15.1) |
Business manager/associate professional | 758 (7.3) | 22781 (14.6) | 23539 (14.2) |
Trade/clerk/services | 1454 (13.9) | 23524 (15.1) | 24978 (15.0) |
Driver/hospitality/labourer | 1820 (17.4) | 15256 (9.8) | 17076 (10.3) |
Not in paid work in past 12 months | 2020 (19.4) | 7297 (4.7) | 9317 (5.6) |
Missing/not applicable | 3782 (36.3) | 62463 (40.1) | 66245 (39.8) |
Geographical remotenessf | |||
Major city | 3948 (37.9) | 102112 (65.5) | 106060 (63.8) |
Inner regional | 3498 (33.5) | 37705 (24.2) | 41203 (24.8) |
Outer regional | 2116 (20.3) | 12128 (7.8) | 14244 (8.6) |
Remote/very remote | 631 (6.0) | 1052 (0.7) | 1683 (1.0) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Area socioeconomic advantage and disadvantagef | |||
First quintile (most disadvantaged) | 2179 (20.9) | 12720 (8.2) | 14899 (9.0) |
Second quintile | 1921 (18.4) | 16110 (10.3) | 18031 (10.8) |
Third quintile | 4211 (40.4) | 52641 (33.8) | 56852 (34.2) |
Fourth quintile | 1261 (12.1) | 32413 (20.8) | 33674 (20.3) |
Fifth quintile (most advantaged) | 621 (6.0) | 39113 (25.1) | 39734 (23.9) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Age at AEDC collection | |||
< 5 years 1 month | 1828 (17.5) | 27459 (17.6) | 29287 (17.6) |
5 years 1 month to 5 years 3 months | 1837 (17.6) | 26702 (17.1) | 28539 (17.2) |
5 years 4 months to 5 years 6 months | 2234 (21.4) | 34999 (22.5) | 37233 (22.4) |
5 years 7 months to 5 years 9 months | 2456 (23.5) | 37911 (24.3) | 40367 (24.3) |
5 years 10 months to 6 years 0 months | 913 (8.8) | 13130 (8.4) | 14043 (8.4) |
> 6 years 0 months | 1162 (11.1) | 15647 (10.0) | 16809 (10.1) |
Child is repeating school yearg | |||
No | 9815 (94.1) | 149789 (96.1) | 159604 (96.0) |
Yes | 427 (4.1) | 3657 (2.3) | 4084 (2.5) |
Missing | 188 (1.8) | 2402 (1.5) | 2590 (1.6) |
Child has health and development needs | |||
No needs identified | 6638 (63.6) | 125342 (80.4) | 131980 (79.4) |
Special needs statush | 772 (7.4) | 6983 (4.5) | 7755 (4.7) |
Additional needsi | 2388 (22.9) | 16685 (10.7) | 19073 (11.5) |
Child requires further assessment | 632 (6.1) | 6838 (4.4) | 7470 (4.5) |
. | Aboriginala, n (%) . | Non-Aboriginal, n (%) . | Total . |
---|---|---|---|
Total n | 10430 (100.0) | 155848 (100.0) | 166278 (100.0) |
Child’s sex | |||
Male | 5313 (50.9) | 80385 (51.6) | 85698 (51.5) |
Female | 5117 (49.1) | 75463 (48.4) | 80580 (48.5) |
Age of mother at child’s birthb | |||
Median years (interquartile range) | 25 (21–30) | 30 (26–34) | 30 (26–34) |
Missing | 1 (0.0) | 4 (0.0) | 5 (0.0) |
Age of second parentc at child’s birthb | |||
Median years (interquartile range) | 28 (23–33) | 33 (29–37) | 32 (29–37) |
Missing | 1570 (15.1) | 5242 (3.4) | 6812 (4.1) |
Mother’s country of birth | |||
Australia | 9814 (94.1) | 112049 (71.9) | 121863 (73.3) |
Other country | 564 (5.4) | 42931 (27.5) | 43495 (26.2) |
Missing | 52 (0.5) | 868 (0.6) | 920 (0.6) |
Mother’s marital status at child’s birthd | |||
Married/de facto | 4909 (47.1) | 129989 (83.4) | 134898 (81.1) |
Single/widowed/divorced/separated | 4742 (45.5) | 21830 (14.0) | 26572 (16.0) |
Missing/unknown | 779 (7.5) | 4029 (2.6) | 4808 (2.9) |
Private health insurance at child’s birthd | |||
Yes | 880 (8.4) | 59146 (38.0) | 60026 (36.1) |
No | 9430 (90.4) | 95016 (61.0) | 104446 (62.8) |
Missing | 120 (1.2) | 1686 (1.1) | 1806 (1.1) |
Mother’s highest level of schooling | |||
Year 12 or equivalent | 1964 (18.8) | 59559 (38.2) | 61523 (37.0) |
Year 11 or equivalent | 791 (7.6) | 6549 (4.2) | 7340 (4.4) |
Year 10 or equivalent | 2447 (23.5) | 20744 (13.3) | 23191 (13.9) |
Year 9 or equivalent or below | 1185 (11.4) | 4934 (3.2) | 6119 (3.7) |
Missing/not applicable | 4043 (38.8) | 64062 (41.1) | 68105 (41.0) |
Second parent’sc highest level of schooling | |||
Year 12 or equivalent | 1305 (12.5) | 48653 (31.2) | 49958 (30.0) |
Year 11 or equivalent | 505 (4.8) | 5559 (3.6) | 6064 (3.6) |
Year 10 or equivalent | 2069 (19.8) | 24378 (15.6) | 26447 (15.9) |
Year 9 or equivalent or below | 1099 (10.5) | 5650 (3.6) | 6749 (4.1) |
Missing/not applicable | 5452 (52.3) | 71608 (45.9) | 77060 (46.3) |
Highest occupation level of either parente | |||
Manager/professional | 596 (5.7) | 24527 (15.7) | 25123 (15.1) |
Business manager/associate professional | 758 (7.3) | 22781 (14.6) | 23539 (14.2) |
Trade/clerk/services | 1454 (13.9) | 23524 (15.1) | 24978 (15.0) |
Driver/hospitality/labourer | 1820 (17.4) | 15256 (9.8) | 17076 (10.3) |
Not in paid work in past 12 months | 2020 (19.4) | 7297 (4.7) | 9317 (5.6) |
Missing/not applicable | 3782 (36.3) | 62463 (40.1) | 66245 (39.8) |
Geographical remotenessf | |||
Major city | 3948 (37.9) | 102112 (65.5) | 106060 (63.8) |
Inner regional | 3498 (33.5) | 37705 (24.2) | 41203 (24.8) |
Outer regional | 2116 (20.3) | 12128 (7.8) | 14244 (8.6) |
Remote/very remote | 631 (6.0) | 1052 (0.7) | 1683 (1.0) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Area socioeconomic advantage and disadvantagef | |||
First quintile (most disadvantaged) | 2179 (20.9) | 12720 (8.2) | 14899 (9.0) |
Second quintile | 1921 (18.4) | 16110 (10.3) | 18031 (10.8) |
Third quintile | 4211 (40.4) | 52641 (33.8) | 56852 (34.2) |
Fourth quintile | 1261 (12.1) | 32413 (20.8) | 33674 (20.3) |
Fifth quintile (most advantaged) | 621 (6.0) | 39113 (25.1) | 39734 (23.9) |
Missing | 237 (2.3) | 2851 (1.8) | 3088 (1.9) |
Age at AEDC collection | |||
< 5 years 1 month | 1828 (17.5) | 27459 (17.6) | 29287 (17.6) |
5 years 1 month to 5 years 3 months | 1837 (17.6) | 26702 (17.1) | 28539 (17.2) |
5 years 4 months to 5 years 6 months | 2234 (21.4) | 34999 (22.5) | 37233 (22.4) |
5 years 7 months to 5 years 9 months | 2456 (23.5) | 37911 (24.3) | 40367 (24.3) |
5 years 10 months to 6 years 0 months | 913 (8.8) | 13130 (8.4) | 14043 (8.4) |
> 6 years 0 months | 1162 (11.1) | 15647 (10.0) | 16809 (10.1) |
Child is repeating school yearg | |||
No | 9815 (94.1) | 149789 (96.1) | 159604 (96.0) |
Yes | 427 (4.1) | 3657 (2.3) | 4084 (2.5) |
Missing | 188 (1.8) | 2402 (1.5) | 2590 (1.6) |
Child has health and development needs | |||
No needs identified | 6638 (63.6) | 125342 (80.4) | 131980 (79.4) |
Special needs statush | 772 (7.4) | 6983 (4.5) | 7755 (4.7) |
Additional needsi | 2388 (22.9) | 16685 (10.7) | 19073 (11.5) |
Child requires further assessment | 632 (6.1) | 6838 (4.4) | 7470 (4.5) |
aDefined as child or parent identified as Aboriginal on any of PDC, RBDM or APDC birth records, or AEDC school record.
bAge derived from date of birth on RBDM birth record or PDC record where RBDM date of birth missing.
cThe second parent reported on the RBDM birth registration, includes 63 228 (99.73%) males, 156 (0.25%) females and 16 (0.3%) with missing data for sex.
dBased on APDC birth record.
eBased on highest ranking occupation of either parent from the school enrolment record.
fBased on mother’s statistical local area of residence from PDC birth record.
gChild is repeating first year of school at the time of the AEDC.
hChildren medically diagnosed as having high needs requiring special assistance due to chronic medical, physical or intellectually disabling conditions.
iChildren not classified as special needs status who have medically diagnosed or parent-reported physical, visual, hearing, speech, learning, emotional or behavioural problems.
Table 3 presents select characteristics for Aboriginal and non-Aboriginal children in the cohort and their parents, available from one or more of the linked data sources. Aboriginal children were more likely to be born to mothers who were unmarried, did not have private health insurance, lived in more socioeconomically disadvantaged areas and were younger than mothers of non-Aboriginal children. Although the majority of mothers of Aboriginal children lived in major cities or inner regional areas at the time of their child’s birth, mothers of Aboriginal children were more likely to live in outer regional and remote/very remote areas compared with mothers of non-Aboriginal children (20.3% versus 7.8%, and 6.0% versus 0.7%, respectively). Based on teacher report on the AEDC, 4.1% of Aboriginal children were repeating their first year of school compared with 2.3% of non-Aboriginal children in the cohort, and medically diagnosed special needs were more common among Aboriginal children compared with non-Aboriginal children (7.4% versus 4.5%).
Ethical approval and study governance
The study has received ethical approval from the NSW Population and Health Services Research Ethics Committee (AU RED Reference: HREC/14/CIPHS/23, Cancer Institute NSW reference: 2014/04/523), the NSW Aboriginal Health and Medical Research Council Ethics Committee (1031/14), the Australian Institute of Health and Welfare Ethics Committee (EO2015/2/141) and the Australian National University Human Research Ethics Committee (2014/384). A reference group consisting of Aboriginal community organizations, service providers and their representatives, has been established to provide the study investigators with guidance on the study aims and priorities and interpretation of the findings, to advise on community engagement and to connect with relevant groups and organizations to facilitate translation of findings into policy and practice.
Data Resource Use
The Seeding Success data resource is currently being analysed to investigate the relationships between child development outcomes and maternal age at childbirth, gestational age at birth, preschool attendance and school starting age (relative to NSW school enrolment policy), in Aboriginal and non-Aboriginal children. The resource is also being used to examine injury-related emergency department visits and hospitalizations during early childhood, and child development at age 5 years, for children in contact with the child protection system in NSW, including children whose families participated in an early intervention programme (Brighter Futures) that aims to prevent families from entering or escalating within the child protection system. Children whose families were exposed to the early intervention programme have been identified in the data resource, as well as a propensity score-matched comparison group of unexposed children who were similar on available measured characteristics. All analyses will be written up for publication in peer-reviewed scientific journals and/or reports to relevant policy agencies.
Strengths and Weaknesses
A significant strength of this resource is the use of population data with high coverage and large numbers of children. As such, statistical power has been optimized and selection bias minimized. Moreover, these data will enable the experience of Aboriginal children, a small and vulnerable population group, to be made visible. The use of state-wide, linked population data will also enable exploration of geographical variation in outcomes across metropolitan, regional and remote locations. The linkage of data about programme participation to this resource is another strength, and will enable the application of quasi-experimental methodologies to assess the impact of programme participation on early childhood outcomes.
Some key strengths and limitations of the main outcome measure in the resource–child development, measured by the AEDC–should also be acknowledged. A major strength is the availability of the AEDC for an almost complete population of school starters in census years. Although teachers complete the AEDC checklist about individual children, it is not designed as a diagnostic tool. Rather, the AEDC is a holistic population measure of child development that can be used to identify groups of children, or communities, that may benefit from prevention and early intervention to improve child outcomes, including school readiness.15 A limitation of the AEDC as an outcome measure includes bias that might be introduced via teacher completion of the checklist, which could be affected by the characteristics of the teacher, the child or the school, or a combination of factors. Another challenge is the suitability of the instrument for use with culturally and linguistically diverse groups of children. However, the AEDC and its predecessor, the Canadian Early Development Instrument,37 have been the subject of numerous studies that suggest acceptable measures of validity and reliability, and similar psychometric properties of the instrument in different countries.37–42 In Australia, there have also been studies examining the appropriate use and interpretation of the AEDC with Indigenous and culturally and linguistically diverse communities.43–45
An important limitation of this resource is that Aboriginal people are less likely to have a birth registration,30 are known to be under-enumerated in administrative datasets,28,29,31,32 and that the recording of Aboriginal status has changed over time in some data sources.28,32 However, use of multiple linked data sources has been shown to enhance enumeration of Aboriginal people,33–36 which is an option available in this resource. Another limitation is that emergency department and congenital conditions data were not available for the entire study period for all children with an AEDC record in 2009 or 2012. Additionally, emergency department presentations in regional and remote areas were not complete; in 2012, it was estimated that 88% of all presentations to public hospital emergency departments in NSW were captured.46 Service use (e.g. hospitalizations) may be underestimated for children who live in areas close to the state border because these children may access some services in the neighbouring jurisdiction. It is also not possible to ascertain from the data currently in the resource whether, or when, cohort children lived outside NSW for any period between birth and school age. Additional linkage to national Medicare data may provide information about child mobility between jurisdictions during the study period.
Data Resource Access
The Seeding Success investigators welcome researchers to make contact regarding collaborative project proposals and applying for data resource access. There are a few conditions of data access that must be met. First, the proposed project needs to relate to the overall programme of research. The investigators will review project proposals and determine whether specific scientific questions can be addressed using the data resource and lie within the scope of data custodian and ethical approvals.14 Second, the data resource is currently stored within the Secure Unified Research Environment (SURE), which is a remote-access computing environment accessible over encrypted Internet and Australian Academic and Research Network connections.47 Because of the highly confidential nature of the data, all data preparation and analysis must be conducted within the SURE virtual project workspace, where security controls are in place to protect the privacy and confidentiality of the data. Before researchers can obtain access to the data, researchers who will directly access the data must complete the online SURE training module and activate a SURE user account including payment of user fee, and the names of collaborating researchers must be added to the study protocol for ethical approval.
Funding
This work was supported by an Australian National Health and Medical Research Council (NHMRC) Project Grant (#1061713). K.F. was supported by an NHMRC Early Career Fellowship (#1016475) and an NHMRC capacity building grant (#573122). E.B. was supported by an NHMRC Senior Research Fellowship (#1042717). S.E. was supported by an NHMRC Career Development Fellowship (#1013418). M.B. was supported by the Manitoba Center for Health Policy Population-Based Child Health Research Award. S.G. was supported by an NHMRC Career Development Fellowship (#1082922).
Conflict of interest: None declared.
Acknowledgements
The authors would like to thank the Australian Government Department of Education, the NSW Ministry of Health, the NSW Register of Births, Deaths and Marriages, the NSW Department of Education and the NSW Department of Family and Community Services for allowing access to the data. They also thank the Australian Institute of Health and Welfare Data Integration Services Centre, Medicare Australia and the Australian Government Departments of Health, Human Services and Social Services for negotiating data access and linkage processes for the stage 2 linkage. The authors thank the NSW Centre for Health Record Linkage for conducting the linkage of data sources detailed in this paper. The authors acknowledge comments on the draft manuscript from Dr Emily Klineberg (NSW Health), in addition to the named collaborators. The Seeding Success investigator team comprises Louisa Jorm, Kathleen Falster, Sandra Eades, John Lynch, Emily Banks, Marni Brownell, Rhonda Craven, Kristjana Einarsdóttir, Deborah Randall, Sharon Goldfeld, Alastair Leyland, Elizabeth Best and Marilyn Chilvers.
Author Contributions
K.F. and L.J. had overall responsibility for the concept of this study, with scientific input from the chief investigators. M.J. prepared the first draft of this paper, with input from K.F., M.H. and L.J. K.F. wrote subsequent drafts of the paper, with input from all authors. M.H. and M.J. prepared and analysed the data. All authors approved the final draft.
References