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INTRODUCTION: Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners. METHODS AND ANALYSIS: Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression. ETHICS AND DISSEMINATION: Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool w

OBJECTIVE: To assess the association between maternal asthma and adverse perinatal outcomes in an Australian Indigenous population. METHODS: This prospective cohort study included all Indigenous mother and baby dyads for births from 2001 to 2013 in Western Australia (n = 25 484). Data were linked from Western Australia Births, Deaths, Midwives, Hospital, and Emergency Department collections. Maternal asthma was defined as a self-reported diagnosis at an antenatal visit or hospitalization or emergency visit for asthma during pregnancy or less than 3 years before pregnancy. Associations with birth, labor, and pregnancy outcomes were assessed using generalized estimating equations. Asthma exacerbation during pregnancy and stratification by remoteness was also assessed. RESULTS: Maternal asthma was associated with placental abruption (adjusted odds ratio [aOR], 1.59 [95% confidence interval (CI), 1.07-2.35]), threatened preterm labor (aOR, 1.58 [95% CI, 1.39-1.79]), and emergency cesarean sections (aOR, 1.27 [95% CI, 1.13-1.44]). These risks increased further with an asthma exacerbation during pregnancy or if the mother was from a remote area. No associations were found for low birth weight, preterm birth, small for gestational age, or perinatal mortality. CONCLUSION: Maternal asthma in Indigenous women is associated with an increased risk of emergency cesarean sections, placental abruption, and threatened preterm labor. These risks may be mitigated by improved management of asthma exacerbations during pregnancy.

Background Technology and web‐based approaches potentially provide scalable population‐based interventions to reduce modifiable risk factors for dementia such as physical inactivity, suboptimal nutrition and low cognitive activity. Our aim was to reduce cognitive decline with ageing using an online package of interventions delivered intensively for 12 months followed by monthly boosters for 24 months. The trial was completed in November 2021. Method Invitations were sent to people aged 55‐77 years from the 45 and Up study, a population‐based cohort study of one in ten people aged 45 years and older in New South Wales, Australia (n = 267,000). Participants were required to be eligible for at least two of four modules. The modules addressed physical inactivity and health risks associated with inactivity (Physical Activity), adherence to a Mediterranean‐type diet and health risks associated with poor nutrition (Nutrition), cognitive activity (Brain Training) and mental well‐being (Peace of Mind). All participants received modules based on their risks, with randomized allocation to active personalised coaching modules (intervention) or static information‐based modules (control). The primary outcome was change in an online combined multi‐domain cognitive score measured using COGSTATE and Cambridge Brain Sciences tests. Secondary outcomes included ANU‐ADRI risk score, specific cognitive domain scores and diagnoses of dementia. Result From 96,418 invitations, 14,064 (14%) consented; 12,281 (13%) were eligible. Of these, 6,236 (6%) completed all 10 baseline assessments and were enrolled in the trial. Nearly 70% or 4,365 participants provided follow‐up data. At final 36‐months’ follow‐up, 3,482 (55.8%) completed the primary outcome and 2594 (41.6%) had returned informant‐rated Amsterdam‐Instrumental Activities of Daily Living Scale questionnaires. Conclusion Online strategies to prevent cognitive decline in 55‐77 year olds are feasible and appear acceptable for more than hal

AIMS: The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). METHODS: This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC. RESULTS: The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively. CONCLUSION: When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates.Cite this article: Bone Joint J 2022;104-B(9):1060-1066.

Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidenc

OBJECTIVE: To assess the association between maternal asthma and adverse perinatal outcomes in an Australian Indigenous population. METHODS: This prospective cohort study included all Indigenous mother and baby dyads for births from 2001 to 2013 in Western Australia (n = 25 484). Data were linked from Western Australia Births, Deaths, Midwives, Hospital, and Emergency Department collections. Maternal asthma was defined as a self-reported diagnosis at an antenatal visit or hospitalization or emergency visit for asthma during pregnancy or less than 3 years before pregnancy. Associations with birth, labor, and pregnancy outcomes were assessed using generalized estimating equations. Asthma exacerbation during pregnancy and stratification by remoteness was also assessed. RESULTS: Maternal asthma was associated with placental abruption (adjusted odds ratio [aOR], 1.59 [95% confidence interval (CI), 1.07-2.35]), threatened preterm labor (aOR, 1.58 [95% CI, 1.39-1.79]), and emergency cesarean sections (aOR, 1.27 [95% CI, 1.13-1.44]). These risks increased further with an asthma exacerbation during pregnancy or if the mother was from a remote area. No associations were found for low birth weight, preterm birth, small for gestational age, or perinatal mortality. CONCLUSION: Maternal asthma in Indigenous women is associated with an increased risk of emergency cesarean sections, placental abruption, and threatened preterm labor. These risks may be mitigated by improved management of asthma exacerbations during pregnancy.

Background Technology and web‐based approaches potentially provide scalable population‐based interventions to reduce modifiable risk factors for dementia. Key issues in online interventions are recruitment and retention. To devise strategies to improve population reach We investigated which factors influence recruiting and maintaining participants in such an intervention, the in‐progress Maintain Your Brain trial. Method Invitations were sent to people aged 55‐77 years from the 45 and Up study, a population‐based cohort study of one in ten people aged 45 years and older in New South Wales, Australia (n = 267,000). For MYB, participants were required to be eligible for at least one of four modules to be enrolled (physical activity, nutrition, brain training and mental wellbeing). All participants received modules based on their risks and were randomly allocated to either personalised coaching (intervention) or static information (control). Associations between participant characteristics (listed Table 1) and likelihood of completing set assessment tasks was assessed at two key stages – end of baseline and end of 12‐month follow‐up using stepwise (forward) regression. Results Of 96,418 people invited, 12,281 (13%) participants started baseline and completed a mean of 6.2 (SD 4.3) of ten assessments. Of these, 6,236 (6%) were enrolled in the trial. At 12‐months participants completed a mean of 5.0 of 8 assessments (SD = 3.8). Completion rate of the primary outcome (two tasks) was 62% (3,869). In the final regression model for baseline (Table1), overall associations were weak even though statistically significant, with only years of education not entered in the final model. The follow‐up model included retirement status, gender, baseline dementia risk and baseline wellbeing. However, this model (df1 = 1, df2 =6231; R2 = .01) accounted for even less variation than baseline model (R2 = .04). Conclusion Overall, regression models of participant characteristics accounted fo

Background Australia has one of the highest rates of asthma worldwide. Indigenous children have a particularly high burden of risk determinants for asthma, yet little is known about the asthma risk profile in this population. Aim To identify and quantify potentially preventable risk factors for hospitalised asthma in Australian Aboriginal children (1-4 years of age). Methods Birth, hospital and emergency data for all Aboriginal children born 2003-2012 in Western Australia were linked (n=32 333). Asthma was identified from hospitalisation codes. ORs and population attributable fractions were calculated for maternal age at birth, remoteness, area-level disadvantage, prematurity, low birth weight, maternal smoking in pregnancy, mode of delivery, maternal trauma and hospitalisations for acute respiratory tract infection (ARTI) in the first year of life. Results There were 705 (2.7%) children hospitalised at least once for asthma. Risk factors associated with asthma included: being hospitalised for an ARTI (OR 4.06, 95% CI 3.44 to 4.78), area-level disadvantage (OR 1.58, 95% CI 1.28 to 1.94), being born at <33 weeks' gestation (OR 3.30, 95% CI 2.52 to 4.32) or birth weight <1500 g (OR 2.35, 95% CI 1.39 to 3.99). The proportion of asthma attributable to an ARTI was 31%, area-level disadvantage 18%, maternal smoking 5%, and low gestational age and birth weight were 3%-7%. We did not observe a higher risk of asthma in those children who were from remote areas. Conclusion Improving care for pregnant Aboriginal women as well as for Aboriginal infants with ARTI may help reduce the burden of asthma in the Indigenous population.

BackgroundBetween-hospital variation in rates of 30-day unplanned readmission after coronary artery bypass graft (CABG) surgery has significant clinical and policy implications, but little is known about the relative contributions of patient- and hospital-level factors to this variation and how these may differ by the cause of readmission.MethodsWe identified a patient cohort who underwent isolated CABG between 2002 and 2018 and survived for at least 30 days post-discharge using linked hospital morbidity and death records for New South Wales residents. Predictors of 30-day unplanned readmission (all cause; stratified by major cardiovascular disease [CVD] or other primary diagnosis) were assessed using multilevel logistic regression models. Proportional changes in variance were used to estimate how much between-hospital variation was explained by patient- and hospital-level factors.ResultsOf 51,868 CABG patients (mean age 66 years, 20% female), 14.3% had unplanned readmission within 30 days. Female sex, older age, obesity, emergency procedure, longer hospital stay, and various comorbidities were associated with all-cause readmission. Three-quarters of readmissions had primary diagnoses other than major CVD (including respiratory causes, non-specific cardiac symptoms, and infections) and they contributed to greater between-hospital variation. Observed patient-level factors explained 74% of between-hospital variation for readmissions with major CVD, while for other readmissions, they explained only 31% and further 42% was explained by hospital-level factors (public vs. private status and CABG volume).ConclusionsReadmissions with non-CVD diagnoses drive much of the between-hospital variation in readmission rates, suggesting there is scope for hospital-level intervention to reduce unplanned readmission after CABG.

Objective: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. Materials and Methods: Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. Results: Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%-16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%-94%. Discussion: ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. Conclusion: We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

Objective: To evaluate three birth-weight (BW) standards (Australian population-based, Fenton and INTERGROWTH-21st) and three estimated-fetal-weight (EFW) standards (Hadlock, INTERGROWTH-21st and WHO) for classifying small-for-gestational age (SGA) and large-for-gestational age (LGA) and predicting adverse perinatal outcomes in preterm and term babies. Methods: This was a nationwide population-based study conducted on a total of 2.4 million singleton births that occurred from 24 + 0 to 40 + 6 weeks' gestation between 2004 and 2013 in Australia. The performance of the growth charts was evaluated according to SGA and LGA classification, and relative risk (RR) and diagnostic accuracy based on the areas under the receiver-operating-characteristics curves (AUCs) for stillbirth, neonatal death, perinatal death, composite morbidity and a composite of perinatal death and morbidity outcomes. The analysis was stratified according to gestational age at delivery (< 37 + 0 vs ≥ 37 + 0 weeks). Results: Following exclusions, 2 392 782 singleton births were analyzed. There were significant differences in the SGA and LGA classification and risk of adverse outcomes between the six BW and EFW standards evaluated. For the term group, compared with the other standards, the INTERGROWTH-21st BW and EFW standards classified half the number of SGA (< 10th centile) babies (3–4% vs 7–11%) and twice the number of LGA (> 90th centile) babies (24–25% vs 8–15%), resulting in a smaller cohort of term SGA at higher risk of adverse outcome and a larger LGA cohort at lower risk of adverse outcome. For term SGA (< 3rd centile) babies, the RR of perinatal death using the two INTERGROWTH-21st standards was up to 1.5-fold higher than those of the other standards (including the WHO-EFW and Hadlock-EFW), while the INTERGROWTH-21st-EFW standard indicated a 12–26% reduced risk of perinatal death for LGA cases across centile thresholds. Conversely, for the preterm group, the WHO-EFW and Hadlock-EFW standards

Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidenc

To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy.

Background 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. Objective To quantify differences between three validated algorithms in ascertaining Aboriginal and Torres Strait Islander children in linked administrative data. Methods Linked administrative health data for children born in 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. Results Method B established a larger cohort (33,697) than Method C (33,510) and Method A (27,482), with all methods identifying a core group of 26,993 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. lower proportions of small-for-gestational age, 10% vs 16%). Differences in demographics and health outcomes between Methods C and B were minimal. Conclusions Demographic and perinatal health characteristics differ by Indigenous 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.

BACKGROUND: 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. OBJECTIVE: To quantify differences between three algorithms in ascertaining Aboriginal and Torres Strait Islander children in linked administrative data. METHODS: 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. RESULTS: 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. lower proportions of small-for-gestational age, 10% vs 16%). Differences in demographics and health outcomes between Methods C and B were minimal. CONCLUSIONS: 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. KEYWORDS: Aboriginal health, identification, data linkage, Indigenous, child, methodology.

IntroductionPatient comobidity at time of primary joint replacement (JR) impacts on outcomes including revision and mortality. Understanding changes in comorbidity profiles is important when assessing change in outcomes over time. Most arthroplasty registries have limited comorbidity information due to their minimum dataset. One approach to obtaining additional comorbidity data is linking registry data with national administrative data. Objectives and ApproachObjectives were to quantify pre-operative comorbidity profile of patients undergoing primary total hip replacement (THR) and total knee replacement (TKR) for osteoarthritis. Also, to examine temporal trends in individual comorbidities for THR and TKR patients. National pharmaceutical dispensing data were linked with THR and TKR arthroplasty patients. Medication dispensing histories in 12-months preceding JR (2003-2017) for 237,333 THR and 394,965 TKR patients, were mapped to 47 comorbidity classes using the Rx-Risk-V measure - a pharmacy-based measure of comorbidity. Comorbidity scores were calculated by summing comorbidity categories for individual patients. Trends in comorbidity scores/categories were described, with comorbidity information presented by PBS beneficiary category (concessional/general), stratified by age (<65/≥65 years). ResultsMedian (interquartile range) comorbidity scores were higher in concessional patients ≥65y, THR:5(3-6), TKR:5(3-7); <65y,TKR:5(3-6) but not THR:4(2-6). Comparative scores for general patients (both ages) were THR:4(2-6) and TKR:3(2-5). Trends in median comorbidity scores were consistent across study period, THR:4- 5(concessional)/2-3(general) and TKR:4-5(concessional)/4(general). Commonly identified comorbidities in younger concessional THR patients were pain, measured by opioid use (62.4%), inflammation/pain, measured by use of non-steroidal anti-inflammatories (62.2%), GORD (36.2%) and hypertension (36.1%). Individual comorbidities remained generally stable over t

IntroductionMonitoring of joint replacement (JR) data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has reduced revision rates and improved surgical practice. Outcome assessment post-arthroplasty is limited however, to revision (reoperation) surgery and mortality outcomes. The AOANJRR National Data Linkage project seeks to broaden the scope of outcomes investigation in Australia by linking registry and health administrative datasets. Objectives and ApproachUsing linked registry and administrative data, the project seeks to describe and quantify national/regional trends and variation in major complications (infection, dislocation, arthrofibrosis, chronic pain, venous thromboembolism, cardiac events), malignancy and health service utilisation (readmissions, emergency encounters and inpatient rehabilitation) following hip, knee and shoulder joint replacement surgery. Evidence will be generated on how these outcomes are associated with and vary according to patient, surgical, implant, hospital and pharmacological factors. As Australia lacks a national identifier, seven linkage agencies are probabilistically linking AOANJRR hip, knee and shoulder replacement data (1999-2017) with 20 datasets. Datasets include government-subsidised health services, procedural and prescription data. Hospital separations and emergency attendance data from Australia’s eight jurisdictions together with national cancer registry and rehabilitation service data are also planned for linkage. Linked data are maintained in a secure remote access computing environment. ResultsTo date, national Medicare Benefits Schedule, Pharmaceutical Benefits Scheme and the Australian Cancer Database data have been linked with >900,000 AOANJRR patients, representing 607.6 million health service records (1999-2018), 467.7 million prescriptions (2002-2018) and 184,000 cancer records, respectively. Remaining linked data will be available in mid-2020. So

IntroductionInfection is a major complication following joint replacement (JR) surgery. However, little data exist on baseline use of antibiotics following primary JR and how use changes with subsequent revision surgery. Objectives & ApproachOur study objectives were to describe community use of antibiotics before and after primary total hip replacement (THR) and change in use pre and post revision procedure. Registry data were linked with national medication dispensing data using probabilistic record linkage. Patients with THR for osteoarthritis in a private hospital between 1999 and 2017 were included. Three groups were analysed: patients with primary procedures revised for infection, revised for non-infection reasons and those not revised. Rate of antibiotic dispensing/month was calculated as number of patients dispensed at least one antibiotic in a given month divided by number of patients at-risk. ResultsThere were 102,577 patients included in the non-revised group, 3,156 revised for non-infection and 520 revised for infection. Prior to primary THR, baseline antibiotic dispensing rate was 9-11%/month in all groups. Post-primary rates were similar (10-11%) for non-revised and revised non-infection patients but higher (16-17%) for revised-infection patients. In 1, 6 and 12 months preceding revision for infection, antibiotic use was 55%, 27% and 22%, respectively. For patients revised for non-infection, antibiotic use was 21%, 14%, 13%, respectively. One-month following revision for infection, 82% of patients were dispensed antibiotics, remaining high (38%) at 6-months and 28% at 12-months. In the revision non-infection group, antibiotic use was 48% first month post-surgery, reducing rapidly to 15% at 6-months. Conclusion / ImplicationsNon-revision and revision non-infection patients had similar antibiotic dispensing before and after surgery. Revision infection patients however, maintained higher antibiotic dispensing post-primary, pre

BACKGROUND: Indigenous Australian children are twice as likely to score poorly on developmental outcomes at age 5 years than their non-Indigenous peers. Indigenous children are also more likely to be born to younger mothers. We aimed to quantify the relationship between maternal age at childbirth and early childhood development outcomes in Indigenous and non-Indigenous children. METHODS: In this population-based, retrospective cohort study, we used data from the Australian Early Development Census (AEDC) that were probabilistically linked by the New South Wales (NSW) Centre for Health Record Linkage to several NSW administrative datasets, including the Perinatal Data Collection, the Register of Births, Deaths and Marriages (for birth registrations), the Admitted Patient Data Collection, and public school enrolment records, as part of the Seeding Success study. The resulting data resource comprises a cohort of 166 278 children born in NSW whose first year of school was reported in a 2009 or 2012 AEDC record (which were the years of AEDC data available at the time of data linkage). The primary outcome was the aggregate outcome of developmental vulnerability (scores in the bottom decile, according to the 2009 benchmark, on one or more of the five AEDC domains, which include physical, social, emotional, language and cognitive, and communication development). This outcome was measured in singleton children without special needs recorded on the AEDC, in those with available developmental data. As a secondary outcome analysis, we also repeated the main analyses on the outcome of developmental vulnerability on the individual domains. We estimated the absolute risk of developmental vulnerability by maternal age in Indigenous and non-Indigenous populations, and we also estimated the risk difference and relative risk between Indigenous and non-Indigenous children by use of modified Poisson regression. FINDINGS: Of 166 278 children in the cohort, 107 666 (64·8%) children were e

Objective: To assess if rates of hospitalised injury in Australian Aboriginal children, and differences in these rates between Aboriginal and non-Aboriginal children, have changed over time. Methods: We used linked hospital data for New South Wales (NSW), Australia, to construct cohorts of children born in NSW hospitals between 2003–2007 and 2008–2012. We calculated rates of hospitalised injuries per 10,000 person years for Aboriginal and non-Aboriginal children for both cohorts, and compared these using rate differences and rate ratios. Results: Rates of unintentional injury hospitalisation were similar in Aboriginal children in both cohorts and Aboriginal children had 1.7 times higher rates of unintentional injury hospitalisation compared with non-Aboriginal children. Rate ratios between Aboriginal and non-Aboriginal children for leading injury mechanisms, burns, poisonings and transport were similar in both cohorts, with 2.5, 3.0 and 2.4 times higher rates in Aboriginal children in the 2008–2012 cohort, respectively. Conclusions and Implications for public health: Our findings suggest that current injury prevention measures have not been successful in reducing either rates of unintentional injury in Aboriginal children, or injury inequalities between Aboriginal and non-Aboriginal children. We recommend the implementation of targeted Aboriginal led injury prevention measures.