Measures of the gap in living standards, life expectancy, education, health and employment between Indigenous and non-Indigenous Australians are primarily derived from administrative data sources. However, Indigenous identification in these data sources is affected by administrative practices, missing data, inconsistency, and error. As these factors have changed over time, assessing whether the gap between Indigenous and non-Indigenous Australians has changed over time, based on data unadjusted for these sources of error can potentially lead to misguided conclusions. Combining administrative data on the same individuals collected from different sources provides a method by which a more consistent derived Indigenous status can be applied across all records for an individual within a linked data environment. We used the Western Australian Data Linkage system to produce derived Indigenous statuses for individuals using a range of algorithms. We found that these algorithms reduced the amount of missing data and improved within-individual consistency. Based on these findings, we recommend our Multi-Stage Median algorithm be used as the standard indicator of Indigenous status for any reporting based on administrative datasets when multiple datasets are available for linkage, and that algorithmic approaches also be considered for improving the quality of other demographic variables from administrative data sources.

Background: The analysis aimed to assess the Indigenous status of an increasing number of deaths not coded with a useable Indigenous status from 1997 to 2002 and its impact on reported recent gains in Indigenous mortality. Methods: The Indigenous status of WA death records with a missing Indigenous status was determined based upon data linkage to three other data sources (Hospital Morbidity Database System, Mental Health Information System and Midwives Notification System). Results: Overall, the majority of un‐coded cases were assigned an Indigenous status, with 5.9% identified as Indigenous from the M1 series and 7.5% from the M2 series. The significant increase in Indigenous male LE of 5.4 years from 1997 to 2002 decreased to 4.0 and 3.6 years using the M1 and M2 series, respectively, but remained significant. For Indigenous females, the non‐significant increase in LE of 1.8 years from 1997 to 2002 decreased to 1.0 and 0.6 years. Furthermore, annual all‐cause mortality rates were higher than in the original data for both genders, but the significant decline for males remained. Conclusion: Through data linkage, the increasing proportion of deaths not coded with a useable Indigenous status was shown to impact on Indigenous mortality statistics in Western Australia leading to an overestimate of improvements in life expectancy. Greater attention needs to be given to better identification and recording of Indigenous identifiers if real improvements in health status are to be demonstrated. A system that captures an individual's Indigenous status once and is reflected in all health and administrative data systems needs consideration within Australia.

Background: In Australia, studies finding an association between area-level socioeconomic disadvantage and mortality are often based on aggregate-ecological designs which confound area-level and individuallevel sources of socioeconomic variation. Area-level socioeconomic differences in mortality therefore may be an artefact of varying population compositions and not the characteristics of areas as such. Objective: To examine the associations between area-level disadvantage and all-cause mortality before and after adjustment for within-area variation in individual-level socioeconomic position (SEP) using unlinked census and mortality-register data in a multilevel context. Setting, participants and design: The study covers the total Australian continent for the period 1998-2000 and is based on decedents aged 25-64 years (n = 43 257). The socioeconomic characteristics of statistical local areas (SLA, n = 1317) were measured using an index of relative socioeconomic disadvantage, and individual-level SEP was measured by occupation. Results: Living in a disadvantaged SLA was associated with higher all-cause mortality after adjustment for within-SLA variation in occupation. Death rates were highest for blue-collar workers and lowest among whitecollar employees. Cross-level interactions showed no convincing evidence that SLA disadvantage modified the extent of inequality in mortality between the occupation groups. Conclusions: Multilevel analysis can be used to examine area variation in mortality using unlinked census and mortality data, therefore making it less necessary to use aggregate-ecological designs. In Australia, area-level and individual-level socioeconomic factors make an independent contribution to the probability of premature mortality. Policies and interventions to improve population health and reduce mortality inequalities should focus on places as well as people.

BackgroundStatistical time series derived from administrative data sets form key indicators in measuring progress in addressing disadvantage in Aboriginal and Torres Strait Islander populations in Australia. However, inconsistencies in the reporting of Indigenous status can cause difficulties in producing reliable indicators. External data sources, such as survey data, provide a means of assessing the consistency of administrative data and may be used to adjust statistics based on administrative data sources.MethodsWe used record linkage between a large-scale survey (the Western Australian Aboriginal Child Health Survey), and two administrative data sources (the Western Australia (WA) Register of Births and the WA Midwives’ Notification System) to compare the degree of consistency in determining Indigenous status of children between the two sources. We then used a logistic regression model predicting probability of consistency between the two sources to estimate the probability of each record on the two administrative data sources being identified as being of Aboriginal and/or Torres Strait Islander origin in a survey. By summing these probabilities we produced model-adjusted time series of neonatal outcomes for Aboriginal and/or Torres Strait Islander births.ResultsCompared to survey data, information based only on the two administrative data sources identified substantially fewer Aboriginal and/or Torres Strait Islander births. However, these births were not randomly distributed. Births of children identified as being of Aboriginal and/or Torres Strait Islander origin in the survey only were more likely to be living in urban areas, in less disadvantaged areas, and to have only one parent who identifies as being of Aboriginal and/or Torres Strait Islander origin, particularly the father. They were also more likely to have better health and wellbeing outcomes. Applying an adjustment model based on the linked survey data increased the estimated number of Aboriginal and/or Torres Strait Islander births in WA by around 25%, however this increase was accompanied by lower overall proportions of low birth weight and low gestational age babies.ConclusionsRecord linkage of survey data to administrative data sets is useful to validate the quality of recording of demographic information in administrative data sources, and such information can be used to adjust for differential identification in administrative data.

There is a widening gap in minimal trauma hip fracture rates between indigenous and other Western Australians. This study demonstrates a need for public health review and management strategies to reduce falls and hip fracture in the indigenous community.