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BACKGROUND: The Maintain Your Brain (MYB) trial aims to prevent cognitive decline and dementia through multidomain, web-based risk-reduction. To facilitate translation, it is important to understand drivers of participation. OBJECTIVE: To describe characteristics associated with participation in MYB. METHODS: This was an observational ancillary study of MYB, a randomized controlled trial nested within the 45 and Up Study in New South Wales, Australia. We linked 45 and Up Study survey and MYB participation data. The study cohort comprised 45 and Up Study participants, aged 55-77 years at 1 January 2018, who were invited to participate in MYB. 45 and Up Study participant characteristics and subsequent MYB consent and participation were examined. RESULTS: Of 98,836 invited, 13,882 (14%) consented to participate and 6,190 participated (6%). Adjusting for age and sex, a wide range of factors were related to participation. Higher educational attainment had the strongest relationship with increased MYB participation (university versus school non-completion; AdjOR = 5.15; 95% CI:4.70-5.64) and lower self-rated quality of life with reduced participation (Poor versus Excellent: AdjOR = 0.19; 95% CI:0.11-0.32). A family history of Alzheimer's disease was related to increased participation but most other dementia risk factors such as diabetes, obesity, stroke, high blood pressure, and current smoking were associated with reduced participation. CONCLUSION: Higher socio-economic status, particularly educational attainment, is strongly associated with engagement in online dementia prevention research. Increasing population awareness of dementia risk factors, and better understanding the participation barriers in at-risk groups, is necessary to ensure online interventions are optimally designed to promote maximum participation.

BACKGROUND: International evidence suggests patients receiving cardiac interventions experience differential outcomes by their insurance status. We investigated outcomes of in-hospital care according to insurance status among patients admitted in public hospitals with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI). METHODS: We conducted a cohort study within the Australian universal health care system with supplemental private insurance. Using linked hospital and mortality data, we included patients aged 18 + years admitted to New South Wales public hospitals with AMI and undergoing their first PCI from 2017-2020. We measured hospital-acquired complications (HACs), length of stay (LOS) and in-hospital mortality among propensity score-matched private and publicly funded patients. Matching was based on socio-demographic, clinical, admission and hospital-related factors. RESULTS: Of 18,237 inpatients, 30.0% were privately funded. In the propensity-matched cohort (n = 10,630), private patients had lower rates of in-hospital mortality than public patients (odds ratio: 0.59, 95% CI: 0.45-0.77; approximately 11 deaths avoided per 1,000 people undergoing PCI procedures). Mortality differences were mostly driven by STEMI patients and those from major cities. There were no significant differences in rates of HACs or average LOS in private, compared to public, patients. CONCLUSION: Our findings suggest patients undergoing PCI in Australian public hospitals with private health insurance experience lower in-hospital mortality compared with their publicly insured counterparts, but in-hospital complications are not related to patient health insurance status. Our findings are likely due to unmeasured confounding of broader patient selection, socioeconomic differences and pathways of care (e.g. access to emergency and ambulatory care; delays in treatment) that should be investigated to improve equity in health outcomes.

BACKGROUND: P2Y12 inhibitor therapy is recommended for 12 months in patients hospitalised for acute myocardial infarction (AMI) unless the bleeding risk is high. AIMS: To describe real-world use of P2Y12 inhibitor therapy following AMI hospitalisation. METHODS: We used population-level linked hospital data to identify all patients discharged from a public hospital with a primary diagnosis of AMI between July 2011 and June 2013 in New South Wales and Victoria, Australia. We used dispensing claims to examine dispensing of a P2Y12 inhibitor (clopidogrel, prasugrel or ticagrelor) within 30 days of discharge and multilevel models to identify predictors of post-discharge dispensing and persistence of therapy to 1 year. RESULTS: We identified 31 848 patients hospitalised for AMI, of whom 56.8% were dispensed a P2Y12 inhibitor within 30 days of discharge. The proportion of patients with post-discharge dispensing varied between hospitals (interquartile range: 25.0-56.5%), and significant between-hospital variation remained after adjusting for patient characteristics. Patient factors associated with the lowest likelihood of post-discharge dispensing were: having undergone coronary artery bypass grafting (odds ratio (OR): 0.17; 95% confidence intervals (CI): 0.15-0.20); having oral anticoagulants dispensed 180 days before or 30 days after discharge (OR: 0.39, 95% CI: 0.35-0.44); major bleeding (OR: 0.68, 95% CI: 0.61-0.76); or being aged ≥85 years (OR: 0.68, 95% CI: 0.62-0.75). A total of 26.8% of patients who were dispensed a P2Y12 inhibitor post-discharge discontinued therapy within 1 year. CONCLUSION: Post-hospitalisation use of P2Y12 inhibitor therapy in AMI patients is low and varies substantially by hospital of discharge. Our findings suggest strategies addressing both health system (hospital and physician) and patient factors are needed to close this evidence-practice gap.

Introduction Assisted reproductive technologies (ART), such as in-vitro fertilisation (IVF), have revolutionised the treatment of infertility, with an estimated 8 million babies born worldwide. However, the long-term health outcomes for women and their offspring remain an area of concern. Linking IVF treatment data to long-term health data is the most efficient method for assessing such outcomes. Objectives To describe the creation and performance of a bespoke population-based data linkage of an ART clinical quality registry to state-based and national administrative datasets. Methods The linked dataset was created by deterministically and probabilistically linking the Australia and New Zealand Assisted Reproduction Database (ANZARD) to New South Wales (NSW) and Australian Capital Territory (ACT) administrative datasets (performed by NSW Centre for Health Record Linkage (CHeReL)) and to national claims datasets (performed by Australian Institute of Health and Welfare (AIHW)). The CHeReL's Master Linkage Key (MLK) was used as a bridge between ANZARD's partially identifiable patient data (statistical linkage key) and NSW and ACT administrative datasets. CHeReL then provided personal identifiers to the AIHW to obtain national content data. The results of the linkage were reported, and concordance between births recorded in ANZARD and perinatal data collections (PDCs) was evaluated. Results Of the 62,833 women who had ART treatment in NSW or ACT, 60,419 could be linked to the CHeReL MLK (linkage rate: 96.2%). A reconciliation of ANZARD-recorded births among NSW residents found that 94.2% (95% CI: 93.9-94.4%) of births were also recorded in state/territorybased PDCs. A high concordance was found in plurality status and birth outcome (≥99% agreement rate, Cohen's kappa ranged: 0.78-0.98) between ANZARD and PDCs. Conclusion The data linkage resource demonstrates that high linkage rates can be achieved with partially identifiable data and that a population spine, such as th

Background: Readmission after coronary artery bypass graft (CABG) surgery is associated with adverse outcomes and significant healthcare costs, and 30-day readmission rate is considered as a key indicator of the quality of care. This study aims to: quantify rates of readmission within 30 days of CABG surgery; explore the causes of readmissions; and investigate how patient- and hospital-level factors influence readmission. Methods: We conducted systematic searches (until June 2020) of PubMed and Embase databases to retrieve observational studies that investigated readmission after CABG. Random effect meta-analysis was used to estimate rates and predictors of 30-day post-CABG readmission. Results: In total, 53 studies meeting inclusion criteria were identified, including 8,937,457 CABG patients. The pooled 30-day readmission rate was 12.9% (95% CI: 11.3–14.4%). The most frequently reported underlying causes of 30-day readmissions were infection and sepsis (range: 6.9–28.6%), cardiac arrythmia (4.5–26.7%), congestive heart failure (5.8–15.7%), respiratory complications (1–20%) and pleural effusion (0.4–22.5%). Individual factors including age (OR per 10-year increase 1.12 [95% CI: 1.04–1.20]), female sex (OR 1.29 [1.25–1.34]), non-White race (OR 1.15 [1.10–1.21]), not having private insurance (OR 1.39 [1.27–1.51]) and various comorbidities were strongly associated with 30-day readmission rates, whereas associations with hospital factors including hospital CABG volume, surgeon CABG volume, hospital size, hospital quality and teaching status were inconsistent. Conclusions: Nearly 1 in 8 CABG patients are readmitted within 30 days and the majority of these are readmitted for noncardiac causes. Readmission rates are strongly influenced by patients’ demographic and clinical characteristics, but not by broadly defined hospital characteristics.

Objective: To examine relationships between changing general practitioner after entering residential aged care and overall medicines prescribing (including polypharmacy) and that of psychotropic medicines in particular. Design: Retrospective data linkage study. Setting, participants: 45 and Up Study participants in New South Wales with dementia who were PBS concession card holders and entered permanent residential aged care during January 2010 ‒ June 2014 and were alive six months after entry. Main outcome measures: Inverse probability of treatment-weighted numbers of medicines dispensed to residents and proportions of residents dispensed antipsychotics, benzodiazepines, and antidepressants in the six months after residential care entry, by most frequent residential care GP category: usual (same as during two years preceding entry), known (another GP, but known to the resident), or new GP. Results: Of 2250 new residents with dementia (mean age, 84.1 years; SD, 7.0 years; 1236 women [55%]), 625 most frequently saw their usual GPs (28%), 645 saw known GPs (29%), and 980 saw new GPs (44%). The increase in mean number of dispensed medicines after residential care entry was larger for residents with new GPs (+1.6 medicines; 95% CI, 1.4‒1.9 medicines) than for those attended by their usual GPs (+0.7 medicines; 95% CI, 0.4‒1.1 medicines; adjusted rate ratio, 2.42; 95% CI, 1.59‒3.70). The odds of being dispensed antipsychotics (adjusted odds ratio [aOR], 1.59; 95% CI, 1.18‒2.12) or benzodiazepines (aOR, 1.69; 95% CI, 1.25‒2.30), but not antidepressants (aOR, 1.32; 95% CI, 0.98‒1.77), were also higher for the new GP group. Differences between the known and usual GP groups were not statistically significant. Conclusions: Increases in medicine use and rates of psychotropic dispensing were higher for people with dementia who changed GP when they entered residential care. Facilitating continuity of GP care for new residents and more structured transfer of GP care may prevent pot

OBJECTIVE: Compare long-term mortality, secondary intervention and secondary rupture following elective endovascular aneurysm repair (EVAR) and open surgical repair (OSR). BACKGROUND: EVAR has surpassed OSR as the most common procedure used to repair abdominal aortic aneurysm (AAA), but evidence regarding long-term outcomes is inconclusive. METHODS: We included patients in linked clinical registry and administrative data undergoing EVAR or OSR for intact AAA between January 2010 and June 2019. We used an inverse probability of treatment-weighted survival analysis to compare all-cause mortality, cause-specific mortality, secondary interventions and secondary rupture, and evaluate the impact of secondary interventions and secondary rupture on all-cause mortality. RESULTS: The study included 3460 EVAR and 427 OSR patients. Compared to OSR, the EVAR all-cause mortality rate was lower in the first 30 days (adjusted hazard ratio [HR]=0.24, 95% confidence interval (CI) 0.15-0.36), but higher between 1 and 4 years (HR=1.31, 95% CI 1.14-1.51) and after 4 years (HR=1.45, 95% CI 1.25-1.67). Secondary intervention rates were higher over the first 30 days (HR=2.20, 95% CI 1.09-4.44), but lower between 1 and 4 years (HR=0.60, 95% CI 0.49-0.75). Secondary aortic intervention rates were higher across the entire follow-up period (HR=2.45, 95% CI 2.01-2.99). Secondary rupture rates did not differ significantly (HR=1.15, 95% CI 0.78-1.70). All-cause mortality beyond 1 year remained significantly higher for EVAR after adjusting for any secondary interventions, or secondary rupture. CONCLUSIONS: EVAR has an early survival benefit compared to OSR. However, elevated long-term mortality and higher rates of secondary aortic interventions and subsequent aneurysm repair suggest that EVAR may be a less durable method of aortic aneurysm exclusion.

Importance: Low-value services have limited or no benefit to patients. Rates of low-value service in public hospitals may vary by patient insurance status, given that there may be different financial incentives for treatment of privately insured patients. Objective: To assess the variation in rates of 5 low-value services performed in Australian public hospitals according to patient funding status (ie, private or public). Design, Setting, and Participants: This retrospective cross-sectional study analyzed New South Wales public hospital data from January 2013 to June 2018. Patients included in the sample were over age 18 years and eligible to receive low-value services based on diagnoses and concomitant procedures. Data analysis was conducted from June to December 2020. Main Outcomes and Measures: Hospital-specific rates of low-value knee arthroscopic debridement, vertebroplasty for osteoporotic spinal fractures, hyperbaric oxygen therapy, oophorectomy with hysterectomy, and laparoscopic uterine nerve ablation for chronic pelvic pain were measured. For each measure, rates within each public hospital were compared by patient funding status descriptively and using multilevel models. Results: A total of 219862 inpatients were included in analysis from 58 public hospitals across the 5 measures. A total of 38365 (22904 [59.7%] women; 12448 [32.4%] aged 71-80 years) were eligible for knee arthroscopic debridement for osteoarthritis; 2520 (1924 [76.3%] women; 662 [26.3%] aged 71-80 years), vertebroplasty for osteoporotic spinal fractures; 162285 (82046 [50.6%] women; 28255 [17.4%] aged 61-70 years), hyperbaric oxygen therapy; 15916 (7126 [44.8%] aged 41-50 years), oophorectomy with hysterectomy; and 776 (327 [42.1%] aged 18-30 years), uterine nerve ablation for chronic pelvic pain. Overall rates of low-value services varied considerably between measures, with the lowest rate for hyperbaric oxygen therapy (0.3 procedures per 1000 inpatients [47 of 158220 eligible inpatients

IMPORTANCE: Low-value services have limited or no benefit to patients. Rates of low-value service in public hospitals may vary by patient insurance status, given that there may be different financial incentives for treatment of privately insured patients. OBJECTIVE: To assess the variation in rates of 5 low-value services performed in Australian public hospitals according to patient funding status (ie, private or public). DESIGN, SETTING, AND PARTICIPANTS: This retrospective cross-sectional study analyzed New South Wales public hospital data from January 2013 to June 2018. Patients included in the sample were over age 18 years and eligible to receive low-value services based on diagnoses and concomitant procedures. Data analysis was conducted from June to December 2020. MAIN OUTCOMES AND MEASURES: Hospital-specific rates of low-value knee arthroscopic debridement, vertebroplasty for osteoporotic spinal fractures, hyperbaric oxygen therapy, oophorectomy with hysterectomy, and laparoscopic uterine nerve ablation for chronic pelvic pain were measured. For each measure, rates within each public hospital were compared by patient funding status descriptively and using multilevel models. RESULTS: A total of 219 862 inpatients were included in analysis from 58 public hospitals across the 5 measures. A total of 38 365 (22 904 [59.7%] women; 12 448 [32.4%] aged 71-80 years) were eligible for knee arthroscopic debridement for osteoarthritis; 2520 (1924 [76.3%] women; 662 [26.3%] aged 71-80 years), vertebroplasty for osteoporotic spinal fractures; 162 285 (82 046 [50.6%] women; 28 255 [17.4%] aged 61-70 years), hyperbaric oxygen therapy; 15 916 (7126 [44.8%] aged 41-50 years), oophorectomy with hysterectomy; and 776 (327 [42.1%] aged 18-30 years), uterine nerve ablation for chronic pelvic pain. Overall rates of low-value services varied considerably between measures, with the lowest rate for hyperbaric oxygen therapy (0.3 procedures per 1000 inpatients [47 of 158 220 eligible i

Objectives To quantify age-stratified outcomes of bioprosthetic valve (BV) and mechanical valve (MV) surgical aortic valve replacement (AVR) in Australian patients. Design Retrospective cohort study using population-based linked hospital morbidity and mortality data. Setting Public and private hospitals. Participants Patients aged 18 years and over undergoing AVR from 2001 to 2013, stratified by age (18-64 years; 65+ years). Main outcome measures Age-standardized index AVR rates; rates and multivariable-adjusted (age, sex, Charlson Comorbidity Index) incidence rate ratios (IRRs) for reoperation, incident cardiovascular events (hospitalization or death for acute myocardial infarction (AMI), stroke, major hemorrhage or thromboembolism) and mortality (cardiovascular and all-cause). Results Our cohort comprised 13 377 patients, of whom 3464 (26%) were aged 18-64 years. Annual age-standardized AVR rates increased by 2.7% with BV implants increasing in both age groups. After 5 years of follow-up, patients implanted with BV had lower rates of stroke (IRR: 0.40, 95% CI 0.27 to 0.60) and hemorrhage (IRR: 0.36, 95% CI 0.26 to 0.50). Among patients 65+ years, those implanted with BV had lower rates of AMI, hemorrhage, and cardiovascular and all-cause mortality than those implanted with MV (IRR: 0.71, 95% CI 0.53 to 0.96; IRR: 0.77, 95% CI 0.62 to 0.95; IRR: 0.80, 95% CI 0.69 to 0.92 and IRR: 0.85, 95% CI 0.74 to 0.97, respectively). After 6-10 years of follow-up, reoperation rates among patients 18-64 years were markedly higher in those implanted with BV compared with MV (IRR: 5.48, 95% CI 2.38 to 12.62) and rates of AMI were lower among patients implanted with BV compared with MV (IRR: 0.49, 95% CI 0.26 to 0.94). Among patients 65+ years rates of cardiovascular and all-cause mortality remained significantly lower for patients implanted with BV compared with MV. Conclusions This study provides real-world evidence of AVR use and outcomes. Use of BV implants is increasing irresp

Parity is a potential confounder of the association between medically assisted reproduction (MAR) and health outcomes. This concept paper describes a population-based record linkage study design for selecting MAR-unexposed women matched to the parity of MAR-exposed women, at the time of the first exposure to MAR. Women exposed to MAR were identified from claims for government subsidies for relevant procedures and prescription medicines, linked to perinatal records. Women unexposed to MAR were identified from linked perinatal and death records, matched to exposed women by age, rurality, age of first child (if any) and parity at the date of first MAR. The availability of a longitudinal, whole-of-population dataset (“population spine”) based on enrolments in Australia's universal health insurance scheme was a critical design element. The example application examines cancer risk in women after exposure to MAR. Parity is a confounder in this setting because it is associated with MAR and hormone-sensitive cancers.

BACKGROUND: P2Y12 inhibitor therapy is recommended for 12 months in patients hospitalised for acute myocardial infarction (AMI) unless bleeding risk is high. AIMS: To describe real-world use of P2Y12 inhibitor therapy following AMI hospitalisation. METHODS: We used population-level linked hospital data to identify all patients discharged from a public hospital with a primary diagnosis of AMI between July 2011-June 2013 in New South Wales and Victoria, Australia. We used dispensing claims to examine dispensing of a P2Y12 inhibitor (clopidogrel, prasugrel or ticagrelor) within 30 days of discharge, and multilevel models to identify predictors of post-discharge dispensing and persistence of therapy to one year. RESULTS: We identified 31 848 patients hospitalised for AMI, of whom 56.8% were dispensed a P2Y12 inhibitor within 30 days of discharge. The proportion of patients with a post-discharge dispensing varied between hospitals (interquartile range: 25.0%-56.5%), and significant between-hospital variation remained after adjusting for patient characteristics. Patients factors associated with the lowest likelihood of post-discharge dispensing were having undergone coronary artery bypass grafting (OR:0.17, 95% CIs:0.15-0.20), having oral anticoagulants dispensed 180 days before or 30 days after discharge (OR:0.39, 95% CIs:0.35-0.44), major bleeding (OR:0.68, 95% CIs:0.61-0.76) or being aged ≥85 (OR:0.68, 95% CIs:0.62-0.75). 26.8% of patients who were dispensed a P2Y12 inhibitor post-discharge discontinued therapy within one year. CONCLUSION: Post-hospitalisation use of P2Y12 inhibitor therapy in AMI patients is low, and varies substantially by hospital of discharge. Our findings suggest strategies addressing both health system (hospital and physician) and patient factors are needed to close this evidence-practice gap. This article is protected by copyright. All rights reserved.

Background: Varenicline, bupropion and nicotine replacement therapy (NRT) are three effective pharmacotherapies for smoking cessation, but data about their safety in pregnancy are limited. We assessed the risk of adverse perinatal outcomes and major congenital anomalies associated with the use of these therapies in pregnancy in Australia. Methods: Perinatal data for 1,017,731 deliveries (2004 to 2012) in New South Wales and Western Australia were linked to pharmaceutical dispensing, hospital admission and death records. We identified 97,875 women who smoked during pregnancy; of those, 233, 330 and 1057 were exposed to bupropion, NRT and varenicline in pregnancy, respectively. Propensity scores were used to match exposed women to those who were unexposed to any smoking therapy (1:10 ratio). Propensity scores and gestational age at exposure were used to match varenicline-exposed to NRT-exposed women (1:1 ratio). Time-dependent Cox proportional hazards models estimated hazard ratios (HR) with 95% confidence intervals (95% CI) for any adverse perinatal event (a composite of 10 unfavourable maternal and neonatal outcomes) and any major congenital anomaly. Results: The risk of any adverse perinatal event was not significantly different between bupropion-exposed and unexposed women (39.2% versus 39.3%, HR 0.93, 95% CI 0.73-1.19) and between NRT-exposed and unexposed women (44.8% vs 46.3%, HR 1.02, 95% CI 0.84-1.23), but it was significantly lower in women exposed to varenicline (36.9% vs 40.1%, HR 0.86, 95% CI 0.77-0.97). Varenicline-exposed infants were less likely than unexposed infants to be born premature (6.5% vs 8.9%, HR 0.72, 95% CI 0.56-0.92), be small for gestational age (11.4% vs 15.4%, HR 0.68, 95% CI 0.56-0.83) and have severe neonatal complications (6.6% vs 8.2%, HR 0.74, 95% CI 0.57-0.96). Among infants exposed to varenicline in the first trimester, 2.9% had a major congenital anomaly (3.5% in unexposed infants, HR 0.91, 95% CI 0.72-1.15). Varenicline-exposed

© 2019 John Wiley & Sons, Ltd. Purpose: Records of antidepressant dispensings are often used as a surrogate measure of depression. However, as antidepressants are frequently prescribed for indications other than depression, this is likely to result in misclassification. This study aimed to develop a predictive algorithm that identifies patients using antidepressants for the treatment of depression. Methods: Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) claims data were linked to follow-up questionnaires (completed in 2012-2013) for participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and older. The sample composed participants who were dispensed an antidepressant in the 30 days prior to questionnaire completion (n = 3162). An algorithm based on patient characteristics, pharmaceutical dispensings, and claims for mental health services was built using group-lasso interaction network (glinternet), with self-reported receipt of treatment for depression as the outcome. The predictive performance of the algorithm was assessed via bootstrap resampling. Results: The algorithm composes 15 main effects and 11 interactions, with type of antidepressant dispensed and claims for mental health services the strongest predictors. The ability of the algorithm to discriminate between antidepressant users with and without depression was 0.73. At a predicted probability cut-off of 0.6, specificity was 93.8% and sensitivity was 23.6%. Conclusions: Using this algorithm with a high probability cut-off yields high specificity and facilitates the exclusion of individuals using antidepressants for indications other than depression, thereby mitigating the risk of confounding by indication when evaluating the outcomes of antidepressant use.

Background-—Oral anticoagulant (OAC) therapy reduces the risk of stroke in people with atrial fibrillation (AF), and is considered best practice; however, there is little Australian evidence around the uptake of OACs in this population. Methods and Results-—We used linked hospital admissions, pharmaceutical dispensing claims, medical services, and mortality data for people in Australia’s 2 most populous states (July 2010 to June 2015). Among OAC-naïıve people hospitalized with AF, we estimated initiation of OAC therapy within 30 days of discharge, and persistence with therapy in the first year. We analyzed both outcomes using multivariable Cox regression. In 71 184 people with AF (median age 78 years, 49% female), 22.7% initiated OAC therapy. Initiation was lowest in July to December 2011 (17.0%) and highest in July to December 2014 (30.1%) after subsidy of the direct OACs. In adjusted analyses, initiation was most likely in people with a CHA2DS2-VA score ≥7 (versus 0) (hazard ratio=6.25, 95% CI 5.08–7.69), and a history of venous thromboembolism (hazard ratio=2.65, 95% CI 2.49–2.83). Of the people who initiated OAC therapy, 39.9% discontinued within 1 year; a lower risk of discontinuation was associated with a CHA2DS2-VA score ≥7 (versus 0) (hazard ratio=0.22, 95% CI 0.14–0.35), or initiation on a direct OAC (versus warfarin) (hazard ratio=0.55, 95% CI 0.50–0.60). Conclusions-—We found that OAC therapy was severely underutilized in people hospitalized with AF, even among high-risk individuals. Reasons for this underuse, whether patient, prescriber, or hospital related, should be identified and addressed to reduce stroke-related morbidity and mortality in people with AF.

Objective Preventable hospitalisations are used internationally as a performance indicator for primary care, but the influence of other health system factors remains poorly understood. This study investigated between-hospital variation in rates of preventable hospitalisation. Setting Linked health survey and hospital admissions data for a cohort study of 266 826 people aged over 45 years in the state of New South Wales, Australia. Method Between-hospital variation in preventable hospitalisation was quantified using cross-classified multiple-membership multilevel Poisson models, adjusted for personal sociodemographic, health and area-level contextual characteristics. Variation was also explored for two conditions unlikely to be influenced by discretionary admission practice: emergency admissions for acute myocardial infarction (AMI) and hip fracture. Results We found significant between-hospital variation in adjusted rates of preventable hospitalisation, with hospitals varying on average 26% from the state mean. Patients served more by community and multipurpose facilities (smaller facilities primarily in rural areas) had higher rates of preventable hospitalisation. Community hospitals had the greatest between-hospital variation, and included the facilities with the highest rates of preventable hospitalisation. There was comparatively little between-hospital variation in rates of admission for AMI and hip fracture. Conclusions Geographic variation in preventable hospitalisation is determined in part by hospitals, reflecting different roles played by community and multipurpose facilities, compared with major and principal referral hospitals, within the community. Care should be taken when interpreting the indicator simply as a performance measure for primary care.

Objective: To demonstrate the use of multiple-membership multilevel models, which analytically structure patients in a weighted network of hospitals, for exploring between-hospital variation in preventable hospitalizations. Data Sources: Cohort of 267,014 people aged over 45 in NSW, Australia. Study Design: Patterns of patient flow were used to create weighted hospital service area networks (weighted-HSANs) to 79 large public hospitals of admission. Multiple-membership multilevel models on rates of preventable hospitalization, modeling participants structured within weighted-HSANs, were contrasted with models clustering on 72 hospital service areas (HSAs) that assigned participants to a discrete geographic region. Data Collection/Extraction Methods: Linked survey and hospital admission data. Principal Findings: Between-hospital variation in rates of preventable hospitalization was more than two times greater when modeled using weighted-HSANs rather than HSAs. Use of weighted-HSANs permitted identification of small hospitals with particularly high rates of admission and influenced performance ranking of hospitals, particularly those with a broadly distributed patient base. There was no significant association with hospital bed occupancy. Conclusion: Multiple-membership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. Weighted-HSANs have broad potential application in health services research and can be used across methods for creating patient catchments.

Objectives: To investigate the organisation and characteristics of general practice in Australia by applying novel network analysis methods to national Medicare claims data. Design: We analysed Medicare claims for general practitioner consultations during 1994-2014 for a random 10% sample of Australian residents, and applied hierarchical block modelling to identify provider practice communities (PPCs). Participants: About 1.7 million patients per year. Main outcome measures: Numbers and characteristics of PPCs (including numbers of providers, patients and claims), proportion of bulk-billed claims, continuity of care, patient loyalty, patient sharing. Results: The number of PPCs fluctuated during the 21-year period; there were 7747 PPCs in 2014. The proportion of larger PPCs (six or more providers) increased from 32% in 1994 to 43% in 2014, while that of sole provider PPCs declined from 50% to 39%. The median annual number of claims per PPC increased from 5000 (IQR, 40-19 940) in 1994 to 9980 (190-23 800) in 2014; the proportion of PPCs that bulk-billed all patients was lowest in 2004 (21%) and highest in 2014 (29%). Continuity of care and patient loyalty were stable; in 2014, 50% of patients saw the same provider and 78% saw a provider in the same PPC for at least 75% of consultations. Density of patient sharing in a PPC was correlated with patient loyalty to that PPC. Conclusions: During 1994-2014, Australian GP practice communities have generally increased in size, but continuity of care and patient loyalty have remained stable. Our novel approach to the analysis of routinely collected data allows continuous monitoring of the characteristics of Australian general practices and their influence on patient care.

Objective: This observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profles; (2) consistency in high-risk designation across models; and (3) persistence of high-risk status over time. Methods: Cross-sectional health survey data (2006-2009) for more than 260 000 Australian adults 45+ years were linked to longitudinal individual hospital, primary care, pharmacy and mortality data. Three risk models predicting acute emergency hospitalisations were explored, simulating conditions where data are accessed through primary care practice management systems, or through hospital-based electronic records, or through a hypothetical 'full' model using a wider array of linked data. High-risk patients were identified using different risk score thresholds. Models were reapplied monthly for 24 months to assess persistence in high-risk categorisation. Results: The three models displayed similar statistical performance. Three-quarters of patients in the high-risk quintile from the 'full' model were also identified using the primary care or hospital-based models, with the remaining patients differing according to age, frailty, multimorbidity, self-rated health, polypharmacy, prior hospitalisations and imminent mortality. The use of higher risk prediction thresholds resulted in lower levels of agreement in highrisk designation across models and greater morbidity and mortality in identified patient populations. Persistence of high-risk status varied across approaches according to updated information on utilisation history, with up to 25% of patients reassessed as lower risk within 1 year. Conclusion/implications: Small differences in risk predictors or risk thresholds resulted in comparatively large differences in who was classified as high risk and for how long. Pragmatic predictive risk modelling design decisions based on data availability or projected high-risk p

INTRODUCTION: This study examined the impact of antismoking activities targeting the general population and an advertising campaign targeting smoking during pregnancy on the prevalence of smoking during pregnancy in New South Wales (NSW), Australia. METHODS: Monthly prevalence of smoking during pregnancy was calculated using linked health records for all pregnancies resulting in a birth (800 619) in NSW from 2003 to 2011. Segmented regression of interrupted time series data assessed the effects of the extension of the ban on smoking in enclosed public places to include licensed premises (evaluated in combination with the mandating of graphic warnings on cigarette packs), television advertisements targeting smoking in the general population, print and online magazine advertisements targeting smoking during pregnancy and increased tobacco tax. Analyses were conducted for all pregnancies, and for the population stratified by maternal age, parity and socioeconomic status. Further analyses adjusted for the effect of the Baby Bonus maternity payment. RESULTS: Prevalence of smoking during pregnancy decreased from 2003 to 2011 overall (0.39% per month), and for all strata examined. For pregnancies overall, none of the evaluated initiatives was associated with a change in the trend of smoking during pregnancy. Significant changes associated with increased tobacco tax and the extension of the smoking ban (in combination with graphic warnings) were found in some strata. CONCLUSIONS: The declining prevalence of smoking during pregnancy between 2003 and 2011, while encouraging, does not appear to be directly related to general population antismoking activities or a pregnancy-specific campaign undertaken in this period.

Background: Data cleaning is an important quality assurance in data linkage research studies. This paper presents the data cleaning and preparation process for a large-scale cross-jurisdictional Australian study (the Smoking MUMS Study) to evaluate the utilisation and safety of smoking cessation pharmacotherapies during pregnancy. Methods: Perinatal records for all deliveries (2003-2012) in the States of New South Wales (NSW) and Western Australia were linked to State-based data collections including hospital separation, emergency department and death data (mothers and babies) and congenital defect notifications (babies in NSW) by State-based data linkage units. A national data linkage unit linked pharmaceutical dispensing data for the mothers. All linkages were probabilistic. Twenty two steps assessed the uniqueness of records and consistency of items within and across data sources, resolved discrepancies in the linkages between units, and identified women having records in both States. Results: State-based linkages yielded a cohort of 783,471 mothers and 1,232,440 babies. Likely false positive links relating to 3703 mothers were identified. Corrections of baby's date of birth and age, and parity were made for 43,578 records while 1996 records were flagged as duplicates. Checks for the uniqueness of the matches between State and national linkages detected 3404 ID clusters, suggestive of missed links in the State linkages, and identified 1986 women who had records in both States. Conclusions: Analysis of content data can identify inaccurate links that cannot be detected by data linkage units that have access to personal identifiers only. Perinatal researchers are encouraged to adopt the methods presented to ensure quality and consistency among studies using linked administrative data.

Background: Middle-aged and older patients are prominent users of telephone triage services for timely access to health information and appropriate referrals. Non-compliance with advice to seek appropriate care could potentially lead to poorer health outcomes among those patients. It is imperative to assess the extent to which middle-aged and older patients follow triage advice and how this varies according to their socio-demographic, lifestyle and health characteristics as well as features of the call. Methods: Records of calls to the Australian healthdirect helpline (July 2008-December 2011) were linked to baseline questionnaire data from the 45 and Up Study (participants age ≥ 45 years), records of emergency department (ED) presentations, hospital admissions, and medical consultation claims. Outcomes of the call included compliance with the advice "Attend ED immediately"; "See a doctor (immediately, within 4 hours, or within 24 hours)"; "Self-care"; and self-referral to ED or hospital within 24 h when given a self-care or low-urgency care advice. Multivariable logistic regression was used to investigate associations between call outcomes and patient and call characteristics. Results: This study included 8406 adults (age ≥ 45 years) who were subjects of 11,088 calls to the healthdirect helpline. Rates of compliance with the advices "Attend ED immediately", "See a doctor" and "Self-care" were 68.6%, 64.6% and 77.5% respectively, while self-referral to ED within 24 h followed 7.0% of calls. Compliance with the advice "Attend ED immediately" was higher among patients who had three or more positive lifestyle behaviours, called after-hours, or stated that their original intention was to attend ED, while it was lower among those who lived in rural and remote areas or reported high or very high levels of psychological distress. Compliance with the advice "See a doctor" was higher in patients who were aged ≥65 years, worked full-time, or lived in socio-economically advant

Objective: To explore patterns of health service use in the lead-up to, and following, admission for a 'preventable' hospitalisation. Setting: 266 950 participants in the 45 and Up Study, New South Wales (NSW) Australia Methods: Linked data on hospital admissions, general practitioner (GP) visits and other health events were used to create visual representations of health service use. For each participant, health events were plotted against time, with different events juxtaposed using different markers and panels of data. Various visualisations were explored by patient characteristics, and compared with a cohort of non-admitted participants matched on sociodemographic and health characteristics. Health events were displayed over calendar year and in the 90 days surrounding first preventable hospitalisation. Results: The visualisations revealed patterns of clustering of GP consultations in the lead-up to, and following, preventable hospitalisation, with 14% of patients having a consultation on the day of admission and 27% in the prior week. There was a clustering of deaths and other hospitalisations following discharge, particularly for patients with a long length of stay, suggesting patients may have been in a state of health deterioration. Specialist consultations were primarily clustered during the period of hospitalisation. Rates of all health events were higher in patients admitted for a preventable hospitalisation than the matched nonadmitted cohort. Conclusions: We did not find evidence of limited use of primary care services in the lead-up to a preventable hospitalisation, rather people with preventable hospitalisations tended to have high levels of engagement with multiple elements of the healthcare system. As such, preventable hospitalisations might be better used as a tool for identifying sicker patients for managed care programmes. Visualising longitudinal health data was found to be a powerful strategy for uncovering patterns of health service use, and s

Background: Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation-admissions, days and costs-associated with above-normal BMI. Methods: Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated. Results: There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%). Conclusions: The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their ann

Aims: Reducing preventable hospitalisation is a priority for health systems worldwide. This study sought to quantify the contribution of smoking to preventable hospitalisation in older adults and the potential benefits of smoking cessation. Methods: Self-reported smoking data for 267,010 Australian men and women aged 45+ years linked with administrative hospital data were analysed using Cox's models to estimate the effects on risk of hospitalisation for congestive heart failure (CHF), diabetes complications, chronic obstructive pulmonary disease (COPD) and angina. The impacts of smoking and quitting smoking were also quantified using risk advancement periods (RAP). Results: The cohort included 7% current smokers, 36% former smokers and 57% never smokers. During an average follow-up of 2.7 years, 4% of participants had at least one hospitalisation for any of the study conditions (0.8% for CHF, 1.7% for diabetes complications, 0.8% for COPD and 1.4% for angina). Compared to never smokers, the adjusted hazard ratio for hospitalisation for any of the conditions for current smokers was 1.89 (95% CI 1.75-2.03), and the RAP was 3.8 years. There were strong dose-response relationships between smoking duration, smoking intensity and cumulative smoking dose on hospitalisation risk. The excess risk of hospitalisation and RAP for COPD was reduced within 5 years of smoking cessation across all age groups, but risk reduction for other conditions was only observed after 15 years. Conclusion: Smoking is associated with increased risk of preventable hospitalisation for chronic conditions in older adults and smoking cessation, even at older ages, reduces this risk.

Background: Geographic rates of preventable hospitalization are used internationally as an indicator of accessibility and quality of primary care. Much research has correlated the indicator with the supply of primary care services, yet multiple other factors may influence these admissions. Objective: To quantify the relative contributions of the supply of general practitioners (GPs) and personal sociodemographic and health characteristics, to geographic variation in preventable hospitalization. Methods: Self-reported questionnaire data for 267,091 participants in the 45 and Up Study, Australia, were linked with administrative hospital data to identify preventable hospitalizations. Multilevel Poisson models, with participants clustered in their geographic area of residence, were used to explore factors that explain geographic variation in hospitalization. Results: GP supply, measured as full-time workload equivalents, was not a significant predictor of preventable hospitalization, and explained only a small amount (2.9%) of the geographic variation in hospitalization rates. Conversely, more than one-third (36.9%) of variation was driven by the sociodemographic composition, health, and behaviors of the population. These personal characteristics explained a greater amount of the variation for chronic conditions (37.5%) than acute (15.5%) or vaccine-preventable conditions (2.4%). Conclusions: Personal sociodemographic and health characteristics, rather than GP supply, are major drivers of preventable hospitalization. Their contribution varies according to condition, and if used for performance comparison purposes, geographic rates of preventable hospitalization should be reported according to individual condition or potential pathways for intervention.

Objective: To investigate changes in tobacco smoking in two consecutive pregnancies and factors associated with the change. Design: Population-based cohort study. Setting: New South Wales, Australia, 2000-10. Population: A total of 183 385 women having first and second singleton pregnancies. Methods: Descriptive and multivariable logistic regression analyses of perinatal data linked to hospital admission data. Main outcome measures: Proportion of women smoking during their first pregnancy who quit by their second, and of women not smoking in their first pregnancy who did smoke during their second. Results: Among 22 761 smokers in the first pregnancy, 33.5% had quit by their second. Among 160 624 non-smokers in their first pregnancy, 3.6% smoked during their second. Women who were aged ≥25 years, were married, born in a non-English speaking country, used private obstetric care, and lived in a socio-economically advantaged area were more likely to quit or less likely to start smoking in the second pregnancy. Smokers who had gestational hypertension (adjusted odds ratio [OR] 1.36, 95% confidence interval [95% CI] 1.23-1.51), a large-for-gestational-age infant (OR 1.66, 95% CI, 1.46-1.89), and a stillbirth (OR 1.44, 95% CI 1.06-1.94) were more likely to quit, whereas smokers whose infant was small-for-gestational-age (OR 0.65, 95% CI 0.60-0.70) or admitted to special care nursery (OR 0.87, 95% CI 0.81-0.94) were less likely to quit. Among non-smokers in the first pregnancy, the risk of smoking in the second pregnancy increased with late antenatal attendance (e.g. ≥26 weeks, OR 1.30, 95% CI 1.14-1.48), gestational diabetes (OR 1.25, 95% CI 1.07-1.45), preterm birth (e.g. spontaneous, OR 1.25, 95% CI 1.10-1.43), caesarean section (e.g. prelabour, OR 1.13, 95% CI 1.01-1.26), and infant small-for-gestational-age (OR 1.37, 95% CI 1.26-1.48) or required special care nursery (OR 1.14, 95% CI 1.06-1.23). Inter-pregnancy interval of ≥3 years was associated with either change in

Objective: Several studies have demonstrated the effects of health behaviours on risk of chronic diseases and mortality, but none have investigated their contribution to potentially preventable hospitalisation (PPH). We aimed to quantify the effects on risk of PPH of six health behaviours: smoking; alcohol consumption; physical activity; fruit and vegetables consumption; sitting time; and sleeping time. Design/Setting: Prospective observational study in New South Wales, Australia. Subjects: 267,006 men and women aged 45 years and over. Outcome Measures: PPH admissions and mortality during follow-up according to individual positive health behaviours (non-smoking, <14 alcoholic drinks per week, ≥2.5 hours of physical activity per week, ≥2 servings of fruit and 5 servings of vegetables per day, <8 hours sitting and >7 hours sleeping per day) and the total number of these behaviours. Results: During an average of 3 years follow-up, 20971 (8%) participants had at least one PPH admission. After adjusting for potential confounders, participants who reported all six positive health behaviours at baseline had 46% lower risk of PPH admission (95% CI 0.48-0.61), compared to those who reported having only one of these behaviours. Based on these risk estimates, approximately 29% of PPH admissions in Australians aged 45 years and over were attributable to not adhering to the six health behaviours. Estimates were similar for acute, chronic and vaccine-preventable categories of PPH admissions. Conclusions: Individual and combined positive health behaviours were associated with lower risk of PPH admission. These findings suggest that there is a significant opportunity to reduce PPH by promoting healthy behaviours. © 2014 Tran et al.

Objective: Birth records and hospital admission records are valuable for research on maternal smoking, but individually are known to under-estimate smokers. This study investigated the extent to which combining data from these records enhances the identification of pregnant smokers, and whether this affects research findings such as estimates of maternal smoking prevalence and risk of adverse pregnancy outcomes associated with smoking. Methods: A total of 846,039 birth records in New South Wales, Australia, (2001-2010) were linked to hospital admission records (delivery and antenatal). Algorithm 1 combined data from birth and delivery admission records, whereas algorithm 2 combined data from birth record, delivery and antenatal admission records. Associations between smoking and placental abruption, preterm birth, stillbirth, and low birthweight were assessed using multivariable logistic regression. Results: Algorithm 1 identified 127,612 smokers (smoking prevalence 15.1%), which was a 9.6% and 54.6% increase over the unenhanced identification from birth records alone (prevalence 13.8%), and delivery admission records alone (prevalence 9.8%), respectively. Algorithm 2 identified a further 2,408 smokers from antenatal admission records. The enhancement varied by maternal socio-demographic characteristics (age, marital status, country of birth, socioeconomic status); obstetric factors (multi-fetal pregnancy, diabetes, hypertension); and maternity hospital. Enhanced and unenhanced identification methods yielded similar odds ratios for placental abruption, preterm birth, stillbirth and low birthweight. Conclusions: Use of linked data improved the identification of pregnant smokers. Studies relying on a single data source should adjust for the under-ascertainment of smokers among certain obstetric populations. © 2014 The Authors.

Adjustment for the differing risk profiles of patients is essential to the use of administrative hospital data for epidemiological research. Smoking is an important factor to include in such adjustments, but the accuracy of the diagnostic codes denoting smoking in hospital records is unknown. The aims of this study were to measure the validity of current smoking and ever smoked status identified from diagnoses in hospital records using a range of algorithms, relative to self-reported smoking status; and to examine whether the misclassification of smoking identified through hospital data is differential or nondifferential with respect to common exposures and outcomes. Data from the baseline questionnaire of the 45 and Up Study, completed by 267,153 residents of New South Wales (NSW), Australia, aged 45 years and older, were linked to the NSW Admitted Patient Data Collection. Patients who had been admitted to hospital for an overnight stay between 1 July 2005 and the date of completion of the questionnaire (1 January 2006 to 2 March 2009) were included. Smokers were identified by applying a range of algorithms to hospital admission histories, and compared against self-reported smoking in the questionnaire ('gold standard'). Sensitivities for current smoking ranged from 59% to 84%, while specificities were 94% to 98%. Sensitivities for ever smoked ranged from 45% to 74% and specificities were 93% to 97%. For the majority of algorithms, sensitivities and/or specificities differed significantly according to principal diagnosis, number of comorbidities, socioeconomic status, residential remoteness, Indigenous status, 28 day readmission and 365 day mortality. The identification of smoking through diagnoses in hospital data results in differential misclassification. Risk adjustment based on smoking identified from these data will yield potentially misleading results. Systematic capture of information about smoking in hospital records using a mandatory item would increase th

Introduction: Approximately 14% of Australian women smoke during pregnancy. Although the risk of adverse outcomes is reduced by smoking cessation, less than 35% of Australian women quit smoking spontaneously during pregnancy. Evidence for the efficacy of bupropion, varenicline or nicotine replacement therapy as smoking cessation aids in the non-pregnant population suggest that pharmacotherapy for smoking cessation is worth exploring in women of childbearing age. Currently, little is known about the utilisation, effectiveness and safety of pharmacotherapies for smoking cessation during pregnancy; neither the extent to which they are used prior to pregnancy nor whether their use has changed in response to related policy reforms. The Smoking MUMS (Maternal Use of Medications and Safety) Study will explore these issues using linked person-level data for a population-based cohort of Australian mothers. Methods and analysis: The cohort will be assembled by linking administrative health records for all women who gave birth in New South Wales or Western Australia since 2003 and their children, including records relating to childbirth, use of pharmaceuticals, hospital admissions, emergency department presentations and deaths. These longitudinal linked data will be used to identify utilisation of smoking cessation pharmacotherapies during and between pregnancies and to explore the associated smoking cessation rates and maternal and child health outcomes. Subgroup and temporal analyses will identify potential differences between population groups including indigenous mothers and social security recipients and track changes associated with policy reforms that have made alternative smoking cessation pharmacotherapies available. Ethics and dissemination: Ethical approval has been obtained for this study. To enhance the translation of the project's findings into policy and practice, policy and clinical stakeholders will be engaged through a reference group and a policy forum will

BACKGROUND: Heart disease is a leading cause of the gap in burden of disease between Aboriginal and non-Aboriginal Australians. Our study investigated short- and long-term mortality after admission for Aboriginal and non-Aboriginal people admitted with acute myocardial infarction (AMI) to public hospitals in New South Wales, Australia, and examined the impact of the hospital of admission on outcomes. METHODS: Admission records were linked to mortality records for 60047 patients aged 25-84 years admitted with a diagnosis of AMI between July 2001 and December 2008. Multilevel logistic regression was used to estimate adjusted odds ratios (AOR) for 30- and 365-day all-cause mortality. RESULTS: Aboriginal patients admitted with an AMI were younger than non-Aboriginal patients, and more likely to be admitted to lower volume, remote hospitals without on-site angiography. Adjusting for age, sex, year and hospital, Aboriginal patients had a similar 30-day mortality risk to non-Aboriginal patients (AOR: 1.07; 95% CI 0.83-1.37) but a higher risk of dying within 365 days (AOR: 1.34; 95% CI 1.10-1.63). The latter difference did not persist after adjustment for comorbid conditions (AOR: 1.12; 95% CI 0.91-1.38). Patients admitted to more remote hospitals, those with lower patient volume and those without on-site angiography had increased risk of short and long-term mortality regardless of Aboriginal status. CONCLUSIONS: Improving access to larger hospitals and those with specialist cardiac facilities could improve outcomes following AMI for all patients. However, major efforts to boost primary and secondary prevention of AMI are required to reduce the mortality gap between Aboriginal and non-Aboriginal people.