The under-reporting of Aboriginal and Torres Strait Islander people on routinely collected health datasets has important implications for understanding the health of this population. By pooling available information on individuals’ Aboriginal or Torres Strait Islander status from probabilistically linked datasets, methods have been developed to adjust for this under-reporting.

To explore different algorithms that enhance reporting of Aboriginal status in birth data to define a cohort of Aboriginal women, examine any differences between women recorded as Aboriginal and those assigned enhanced Aboriginal status, and assess the effects of using different reported populations to estimate within-group comparisons for Aboriginal people.

Three algorithms, with different levels of inclusiveness, were used to establish different study populations all of which aimed to include all singleton babies born to Aboriginal or Torres Strait Islander women residing in New South Wales, Australia between 2010 and 2014 and thanced reporting of indigenous peoples.

This work provides evidence that estimates of within-group relative risks are reliable regardless of the assumptions made for establishing the study population through the enhanced reporting of indigenous peoples.

In the ongoing debate on optimum methods for identification of Indigenous people within linked administrative data, few studies have examined the impacts of method on population counts and outcomes in family-based linkage studies of Aboriginal children.

To quantify differences between three algorithms in ascertaining Aboriginal and Torres Strait Islander children in linked administrative data.

Linked administrative health data for children born in Western Australia (WA) from 2000-2013, were used to examine the cohorts identified by three methods A) the Indigenous Status Flag (ISF, derived by the WA Data Linkage Branch using a multistage-median approach) for the children alone; B) the ISF of the children, their parents and grandparents; and C) Indigenous status of the child, mother or father on either of the child’s perinatal records (Midwives or birth registration), to determine differing characteristics of each cohort.

Method B established a larger cohort (33,489) than Method C (33,306) and Method A (27,279), with all methods identifying a core group of 26,790 children (80-98%). Compared with children identified by Method A, additional children identified by Methods B or C, were from less-disadvantaged and more urban areas, and had better perinatal outcomes (e.g. selleck kinase inhibitor lower proportions of small-for-gestational age, 10% vs 16%). Differences in demographics and health outcomes between Methods C and B were minimal.

Demographic and perinatal health characteristics differ by Aboriginal identification method. Using perinatal records or the ISF of parents and grandparents (in addition to the ISF of the child) appear to be more inclusive methods for identifying young Indigenous children in administrative datasets.

Aboriginal health, identification, data linkage, Indigenous, child, methodology.

Aboriginal health, identification, data linkage, Indigenous, child, methodology.

The Administrative Data Research Northern Ireland (ADR NI), is a partnership between academia and the local statistics agency to advance the access to and use of administrative data in Northern Ireland. These goals are currently being advanced by undertaking a series of demonstrator Strategic Impact Projects developed to provide input to departmental areas of research interest and the current draft Programme for Government.

ADR NI does not currently operate as a data repository but will negotiate access to and link subsets of administrative data from other departments and agencies as required for specific and approved projects. It is, however, anticipated that this model will broaden with the creation and retention of large linked datasets that could be used to address questions across a range of policy areas. At present accredited researchers can access the anonymised data only from within the safe setting situated in Belfast, although consideration is being given to using the Office for National Statistns throughout the UK and throughout the world. We look forward to greater access to and use of administrative data both within Northern Ireland and across the UK.

Over the last decade we have demonstrated to data owners and the different publics that it is safe and acceptable to link administrative data for public benefit. The evolution and maturation of the ADR NI progresses apace and we continue to learn from developments in our sister organisations throughout the UK and throughout the world. We look forward to greater access to and use of administrative data both within Northern Ireland and across the UK.

The Western Cape Provincial Health Data Centre (PHDC) consolidates person-level clinical data across government services, leveraging sustained investments in patient registration systems, a unique identifier, and maturation of administrative and clinical digital health systems.

The PHDC supports clinical care directly through tools for clinicians which integrate patient data or identify patients in need of interventions, and indirectly through supporting operational and epidemiological analyses.

The PHDC is housed entirely within government. Data are processed from a range of source systems, usually daily, through distinct harmonisation and curation, beneficiation, and reporting processes. Linkage is predominantly through the unique identifier which doubles as a pervasive folder number, augmented by other identifiers. Further data processing includes triangulation of multiple data sources for enumerating health conditions, with assignment of certainty levels for each enumeration. Outputs include patientlth data in the Western Cape has created new opportunities for supporting patient care, while improving the governance around access to and release of sensitive patient data.

The single consolidated environment for person-level health data in the Western Cape has created new opportunities for supporting patient care, while improving the governance around access to and release of sensitive patient data.