Injury hospitalisations for children and young people: a 20-year review.

BACKGROUND: Childhood injuries can have significant lifelong consequences. Quantifying and understanding patterns in injury severity can inform targeted prevention strategies and policies. This study examines the characteristics of child injury hospitalisations in the Australian Capital Territory over 20 years. METHODS: This study is a retrospective analysis of Admitted Patient Care Collection data for persons aged 0-24 years who were hospitalised for an injury between July 2000 and June 2020. Injury severity was assessed using International Classification of Injury Severity Scores based on survival risk ratios. RESULTS: The age standardised rate for injury hospitalisations increased significantly from 10.2 per 1000 in 2000/2001 to 21.0 per 1000 in 2019/2020, representing an average annual per cent change of 3.6%. Almost two-thirds of injuries were for men, however the rate of injury hospitalisations increased more rapidly in women. The majority of injuries (81.8%) were classified as minor. The proportion of injuries classified as serious increased with age. For moderate and serious injuries, injuries to the head were the most common type of injury, while falls were the leading cause. Self-harm injuries emerged as a leading contributor to the increase in injuries in young people aged 13-24 years old. CONCLUSION: This study emphasises the concerning upward trend in injury hospitalisations among children and young people over the past two decades. Given Australia is yet to formalise a national injury prevention strategy, understanding the patterns and characteristics of injuries is vital to developing effective prevention interventions to reduce harm and improve child safety.

Adjusting for under-identification of Aboriginal and/or Torres Strait Islander births in time series produced from birth records: using record linkage of survey data and administrative data sources.

BACKGROUND: Statistical 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. METHODS: We 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. RESULTS: Compared 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. CONCLUSIONS: Record 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.

What is the impact of missing Indigenous status on mortality estimates? An assessment using record linkage in Western Australia.

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.

Do places affect the probability of death in Australia? A multilevel study of area-level disadvantage, individual-level socioeconomic position and all-cause mortality, 1998-2000.

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 individual-level 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 white-collar 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.