Excess Deaths Associated with Rheumatic Heart Disease, Australia, 2013-2017.

During 2013-2017, the mortality rate ratio for rheumatic heart disease among Indigenous versus non-Indigenous persons in Australia was 15.9, reflecting health inequity. Using excess mortality methods, we found that deaths associated with rheumatic heart disease among Indigenous Australians were probably substantially undercounted, affecting accuracy of calculations based solely on Australian Bureau of Statistics data.

Risk Factors Associated with Major Adverse Cardiovascular Events after Ischemic Stroke: A Linked Registry Study.

INTRODUCTION: Survivors of stroke are at risk of experiencing subsequent major adverse cardiovascular events (MACE). We aimed to determine the incidence of, and risk factors for, MACE after first-ever ischemic stroke, by age group (18-64 years vs. ≥65 years). METHODS: Observational cohort study using patient-level data from the Australian Stroke Clinical Registry (2009-2013), linked with hospital administrative data. We included adults with first-ever ischemic stroke who had no previous acute cardiovascular admissions and followed these patients for 2 years post-discharge, or until the first post-stroke MACE event. A Fine-Gray sub-distribution hazard model, accounting for the competing risk of non-cardiovascular death, was used to determine factors for incident post-stroke MACE. RESULTS: Among 5,994 patients with a first-ever ischemic stroke (median age 73 years, 45% female), 17% were admitted for MACE within 2 years (129 events per 1,000 person-years). The median time to first post-stroke MACE was 117 days (89 days if aged <65 years vs. 126 days if aged ≥65 years; p = 0.025). Among patients aged 18-64 years, receiving intravenous thrombolysis (sub-distribution hazard ratio [SHR] 0.51 [95% CI, 0.28-0.92]) or being discharged to inpatient rehabilitation (SHR 0.65 [95% CI, 0.46-0.92]) were associated with a reduced incidence of post-stroke MACE. In those aged ≥65 years, being unable to walk on admission (SHR 1.33 [95% CI 1.15-1.54]), and history of smoking (SHR 1.40 [95% CI 1.14-1.71]) or atrial fibrillation (SHR 1.31 [95% CI 1.14-1.51]) were associated with an increased incidence of post-stroke MACE. Acute management in a large hospital (>300 beds) for the initial stroke event was associated with reduced incidence of post-stroke MACE, irrespective of age group. CONCLUSIONS: MACE is common within 2 years of stroke, with most events occurring within the first year. We have identified important factors to consider when designing interventions to prevent MACE after stroke, particularly among those aged <65 years.

Determining the Optimal Methodology for Identifying Incident Stroke Deaths Using Administrative Datasets Within Australia.

BACKGROUND AND AIM: Quantifying stroke incidence and mortality is crucial for disease surveillance and health system planning. Administrative data offer a cost-effective alternative to "gold standard" population-based studies. However, the optimal methodology for establishing stroke deaths from administrative data remains unclear. We aimed to determine the optimal method for identifying stroke-related deaths in administrative datasets as the fatal component of stroke incidence, comparing counts derived using underlying and all causes of death (CoD). METHOD: Using whole-population multijurisdictional person-level linked data from hospital and death datasets from South Australia, the Northern Territory, and Western Australia, we identified first-ever stroke events between 2012 and 2015, using underlying CoD and all CoD to identify fatal stroke counts. We determined the 28-day case fatality for both counts and compared results with gold standard Australian population-based stroke incidence studies. RESULTS: The total number of incident stroke events was 16,150 using underlying CoD and 18,074 using all CoD. Case fatality was 24.7% and 32.7% using underlying and all CoD, respectively. Case fatality using underlying CoD was similar to that observed in four Australian "gold standard" population-based studies (20%-24%). CONCLUSIONS: Underlying CoD generates fatal incident stroke estimates more consistent with population-based studies than estimates based on stroke deaths identified from all-cause fields in death registers.

Sex- and Age-Specific Differences in Risk Profiles and Early Outcomes in Adults With Acute Coronary Syndromes.

BACKGROUND: Adults <55 years of age comprise a quarter of all acute coronary syndromes (ACS) hospitalisations. There is a paucity of data characterising this group, particularly sex differences. This study aimed to compare the clinical and risk profile of patients with ACS aged <55 years with older counterparts, and measure short-term outcomes by age and sex. METHOD: The study population comprised patients with ACS enrolled in the AUS-Global Registry of Acute Coronary Events (GRACE), Cooperative National Registry of Acute Coronary Syndrome Care (CONCORDANCE) and SNAPSHOT ACS registries. We compared clinical features and combinations of major modifiable risk factors (hypertension, smoking, dyslipidaemia, and diabetes) by sex and age group (20-54, 55-74, 75-94 years). All-cause mortality and major adverse events were identified in-hospital and at 6-months. RESULTS: There were 16,658 patients included (22.3% aged 20-54 years). Among them, 20-54 year olds had the highest proportion of ST-elevation myocardial infarction compared with sex-matched older age groups. Half of 20-54 year olds were current smokers, compared with a quarter of 55-74 year olds, and had the highest prevalence of no major modifiable risk factors (14.2% women, 12.7% men) and of single risk factors (27.6% women, 29.0% men), driven by smoking. Conversely, this age group had the highest proportion of all four modifiable risk factors (6.6% women, 4.7% men). Mortality at 6 months in 20-54 year olds was similar between men (2.3%) and women (1.7%), although lower than in older age groups. CONCLUSIONS: Younger adults with ACS are more likely to have either no risk factor, a single risk factor, or all four modifiable risk factors, than older patients. Targeted risk factor prevention and management is warranted in this age group.

Incidence of Stroke in the Aboriginal and Non-Aboriginal Populations of Australia: A Data Linkage Study.

BACKGROUND: Most estimates of stroke incidence among Aboriginal and Torres Strait Islander (hereinafter Aboriginal) Australians are confined to single regions and include small sample sizes. We aimed to measure and compare stroke incidence in Aboriginal and non-Aboriginal residents across central and western Australia. METHODS: Whole-population multijurisdictional person-linked data from hospital and death datasets were used to identify stroke admissions and stroke-related deaths (2001-2015) in Western Australia, South Australia, and the Northern Territory. Fatal (including out-of-hospital deaths) and nonfatal incident (first-ever) strokes in patients aged 20-84 years were identified during the 4-year study period (2012-2015), using a 10-year lookback period to exclude people with prior stroke. Incidence rates per 100 000 population/year were estimated for Aboriginal and non-Aboriginal populations, age-standardized to the World Health Organization World Standard population. RESULTS: In a population of 3 223 711 people (3.7% Aboriginal), 11 740 incident (first-ever) strokes (20.6% regional/remote location of residence; 15.6% fatal) were identified from 2012 to 2015, 675 (5.7%) in Aboriginal people (73.6% regional/remote; 17.0% fatal). Median age of Aboriginal cases (54.5 years; 50.1% female) was 16 years younger than non-Aboriginal cases (70.3 years; 44.1% female; P<0.001), with significantly greater prevalence of comorbidities. Age-standardized stroke incidence in Aboriginal people (192/100 000 [95% CI, 177-208]) was 2.9-fold greater than in non-Aboriginal people (66/100 000 [95% CI, 65-68]) aged 20-84 years; fatal incidence was 4.2-fold greater (38/100 000 [95% CI, 31-46] versus 9/100 000 [95% CI, 9-10]). Disparities were particularly apparent at younger ages (20-54 years), where age-standardized stroke incidence was 4.3-fold greater in Aboriginal people (90/100 000 [95% CI, 81-100]) than non-Aboriginal people (21/100 000 [95% CI, 20-22]). CONCLUSIONS: Stroke occurred more commonly, and at younger ages, in Aboriginal than non-Aboriginal populations. Greater prevalence of baseline comorbidities was present in the younger Aboriginal population. Improved primary prevention is required. To optimize stroke prevention, interventions should include culturally appropriate community-based health promotion and integrated support for nonmetropolitan health services.

Differing Definitions of First-Ever Stroke Influence Incidence Estimates More than Trends: A Study Using Linked Administrative Data.

INTRODUCTION: Researchers apply varying definitions when measuring stroke incidence using administrative data. We aimed to investigate the sensitivity of incidence estimates to varying definitions of stroke and lookback periods and to provide updated incidence rates and trends for Western Australia (WA). METHODS: We used linked state-wide hospital and death data from 1985 to 2017 to identify incident strokes from 2005 to 2017. A standard definition was applied which included strokes coded as the principal hospital diagnosis or the underlying cause of death, with a 10-year lookback used to clear prevalent cases. Alternative definitions were compared against the standard definition by percentage difference in case numbers. Age-standardised incidence rates were calculated, and age- and sex-adjusted Poisson regression models were used to estimate incidence trends. RESULTS: The standard definition with a 10-year lookback period captured 31,274 incident strokes. Capture increased by 19.3% when including secondary diagnoses, 4.1% when including nontraumatic subdural and extradural haemorrhage, and 8.1% when including associated causes of death. Excluding death records reduced capture by 11.1%. A 20-year lookback reduced over-ascertainment by 2.0%, and a 1-year lookback increased capture by 13.3%. Incidence declined 0.6% annually (95% confidence interval -0.9, -0.3). Annual reductions were similar for most definitions except when death records were excluded (-0.1%, CI: -0.4, 0.2) and with the shortest lookback periods (greatest annual reduction). CONCLUSION: Stroke incidence has declined in WA. Differing methods of identifying stroke influence estimates of incidence to a greater extent than estimates of trends. Reductions in stroke incidence over time are primarily driven by declines in fatal strokes.

The New South Wales Sudden Cardiac Arrest Registry: A Data Linkage Cohort Study.

BACKGROUND: Sudden cardiac arrest (SCA) in young people aged 1 to 50 years often occurs with no presenting symptoms or risk factors prompting screening for cardiovascular disease prior to their cardiac arrest. Approximately 3,000 young Australians suffer from sudden cardiac death (SCD) each year, making this a major public health issue. However, there is significant variation in the way incidence is estimated resulting in discrepancy across reporting which impacts our ability to understand and prevent these devastating events. We describe the New South Wales (NSW) Sudden Cardiac Arrest Registry: a retrospective, data linkage study which will identify all SCAs in the young in NSW from 2009 through to June 2022. OBJECTIVE: To determine the incidence, demographic characteristics and causes of SCA in young people. We will develop an NSW-based registry that will contribute to a greater understanding of SCA including risk factors and outcomes. METHODS: The cohort will include all people who experience a SCA in the NSW community aged between 1 to 50 years. Cases will be identified using the following three datasets: the Out of Hospital Cardiac Arrest Register housed at NSW Ambulance, the NSW Emergency Department Data Collection, and the National Coronial Information System. Data from eight datasets will be collected, anonymised and linked for the entire cohort. Analysis will be undertaken and reported using descriptive statistics. CONCLUSIONS: The NSW SCA registry will be an important resource for the improved understanding of SCA and inform the widespread impacts it has on individuals, their families and society.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature.

PURPOSE OF REVIEW: To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors. RECENT FINDINGS: Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

Validation of ICD-10-AM Coding for Myocardial Infarction Subtype in Hospitalisation Data.

BACKGROUND: International Classification of Disease (ICD) codes are central for identifying myocardial infarction (MI) in administrative hospitalisation data, however validation of MI subtype codes is limited. We measured the sensitivity and specificity of ICD-10-AM (Australian Modification) codes for ST-elevation MI (STEMI) and non-STEMI (NSTEMI). METHODS: A sample of MI admissions was obtained from a dataset containing all MI hospitalisations in Western Australia (WA) for 2003, 2008 and 2013. Clinical data were collected from hospital medical records (n=799 patients). Cases were classified by ICD-10-AM codes for STEMI, NSTEMI and unspecified MI, and compared to clinical classification from review of available electrocardiographs (ECGs) and cardiac biomarkers (n=660). Sensitivity and specificity for ICD-10-AM coding versus clinical classification was measured, stratified by calendar year of discharge. RESULTS: The majority of classifiable cases had MI recorded in the principal diagnosis field (STEMI n=293, 84.2%; NSTEMI n=202, 74.3%; unspecified MI n=20, 50.0%). Overall sensitivity of the ICD-10-AM STEMI code was 86.3% (95% CI 81.7-90.0%) and was higher when restricted to MI as a principal versus secondary diagnosis (88.8% vs 66.7%). Comparable values for NSTEMI were 66.7% (95% CI 61.5-71.6%), and 68.8% vs 61.4% respectively. Between 2003 and 2013, sensitivity for both MI subtypes increased: 80.2-89.5% for STEMI, and 51.2-73.8% for NSTEMI. Specificity was high for NSTEMI throughout (88.2% 95% CI 84.1-91.6%), although improving over time for STEMI (68.1-76.4%). CONCLUSIONS: The sensitivity and specificity of ICD-10-AM codes for MI subtypes in hospitalisation data are generally high, particularly for principal diagnosis cases. However, the temporal improvement in sensitivity in coding of MI subtypes, particularly NSTEMI, may necessitate modification to trend studies using administrative hospitalisation data.

Can the CHA2 DS2 -VA schema be used to decide on anticoagulant therapy in Aboriginal and other Australians with non-valvular atrial fibrillation?

The Australasian guidelines recommend use of the CHA2 DS2 -VA schema to stratify ischaemic stroke risk in patients with non-valvular atrial fibrillation (N-VAF) and determine risk thresholds for recommending oral anticoagulant (OAC) therapy. However, the CHA2 DS2 -VA score has not been validated in a representative Australian population cohort with N-VAF, including in Aboriginal people who are known to have a higher age-adjusted stroke risk than other Australians. In a retrospective data-linkage study of 49 114 patients aged 24-84 years with N-VAF, 40.0% women and 2.5% Aboriginal, we found that patients with a CHA2 DS2 -VA score >2 had high annual stroke rates (>2%) that would justify OAC therapy. This occurred regardless of Aboriginal status. Non-Aboriginal patients with a CHA2 DS2 -VA score of 0 had a mean annual stroke rate of 0.4%, and hence were not likely to benefit from antithrombotic therapy. However, Aboriginal patients with a zero CHA2 DS2 -VA score had a significantly higher annual stroke rate of 0.9%, and could potentially obtain net clinical benefit from anticoagulation, primarily with the safer non-vitamin K antagonist OAC. We conclude that clinicians can confidently use the CHA2 DS2 -VA score to make decisions regarding anticoagulation in accordance with stroke risk in patients with N-VAF, except in Aboriginal people in whom the risk score was unable to identify those at truly low risk of stroke.

A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health.

Cardiovascular diseases (CVD) are leading causes of death and morbidity in Australia and worldwide. Despite improvements in treatment, there remain large gaps in our understanding to prevent, treat and manage CVD events and associated morbidities. This article lays out a vision for enhancing CVD research in Australia through the development of a Big Data system, bringing together the multitude of rich administrative and health datasets available. The article describes the different types of Big Data available for CVD research in Australia and presents an overview of the potential benefits of a Big Data system for CVD research and some of the major challenges in establishing the system for Australia. The steps for progressing this vision are outlined.

Differences in stroke risk and cardiovascular mortality for Aboriginal and other Australian patients with atrial fibrillation.

OBJECTIVES: To assess the risks of stroke and cardiovascular mortality for Aboriginal and non-Aboriginal Australians with atrial fibrillation. DESIGN: Retrospective data linkage cohort study. SETTING, PARTICIPANTS: All people aged 20-84 years hospitalised with atrial fibrillation in Western Australia during 2000-2012. MAIN OUTCOME MEASURES: Stroke incidence rates and mortality after hospitalisation for atrial fibrillation, and 10-year risks of stroke and of cardiovascular and all-cause mortality. RESULTS: Among 55 482 index admissions with atrial fibrillation, 7.7% of 20-59-year-old patients and 1.3% of 60-84-year-old patients were Aboriginal Australians. A larger proportion of Aboriginal patients aged 20-59 years had CHA2 DS2 -VASc scores of 2 or more (59.8% v 21.8%). In 20-59-year-old Aboriginal patients, the incidence during follow-up (maximum, 10 years; median, 7.1 years) of stroke (incidence rate ratio [IRR], 3.2; 95% CI, 2.5-4.1) and fatal stroke (IRR, 5.7; 95% CI, 3.9-8.9) were markedly higher than for non-Aboriginal patients. Stroke incidence was higher for 60-84-year-old patients, but the difference between Aboriginal and non-Aboriginal patients was smaller (IRR, 1.6; 95% CI, 1.3-2.0). Cardiovascular mortality during follow-up was also higher for 20-59-year-old Aboriginal patients (IRR, 4.4; 95% CI, 4.3-5.9). The hazards of stroke (adjusted HR [aHR], 1.67; 95% CI, 1.22-2.28) and cardiovascular mortality (aHR, 1.47; 95% CI, 1.18-1.83) in younger Aboriginal patients remained significantly higher after multivariable adjustment; age/sex, principal diagnosis of atrial fibrillation, and CHA2 DS2 -VASc score were the most influential factors. CONCLUSION: Stroke risk and cardiovascular mortality are markedly higher for Aboriginal than non-Aboriginal patients with atrial fibrillation, particularly for patients under 60. Strategies for providing evidence-based therapies and cardiovascular prevention to Aboriginal people with atrial fibrillation must be improved.

Long-term Exposure to Low Air Pollutant Concentrations and the Relationship with All-Cause Mortality and Stroke in Older Men.

BACKGROUND: Long-term air pollution exposure has been associated with increased risk of mortality and stroke. Less is known about the risk at lower concentrations. The association of long-term exposure to PM2.5, PM2.5 absorbance, NO2, and NOx with all-cause mortality and stroke was investigated in a cohort of men aged ≥ 65 years who lived in metropolitan Perth, Western Australia. METHODS: Land use regression models were used to estimate long-term exposure to air pollutants at participant's home address (n = 11,627) over 16 years. Different metrics of exposure were assigned: baseline; year before the outcome event; and average exposure across follow-up period. The Mortality Register and Hospital Morbidity Data from the Western Australia Data Linkage System were used to ascertain mortality and stroke cases. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models, adjusting for age, smoking, education, and body mass index for all-cause mortality. For fatal and hospitalized stroke, the models included variables controlled for all-cause mortality plus hypertension. RESULTS: Fifty-four percent of all-participants died, 3% suffered a fatal stroke, and 14% were hospitalized stroke cases. PM2.5 absorbance increased the risk of all-cause mortality with adjusted HR of 1.12 (1.02-1.23) for baseline and average exposures, and 1.14 (1.02-1.24) for past-year exposure. There were no associations between PM2.5 absorbance, NO2, and NOx and stroke outcomes. However, PM2.5 was associated with reduced risks of fatal stroke. CONCLUSION: Long-term exposure to PM2.5 absorbance was associated with all-cause mortality among older men exposed to low concentrations; and exposure to PM2.5 was associated with reduced risk of fatal stroke.

Low positive predictive value of International Classification of Diseases, 10th Revision codes in relation to rheumatic heart disease: a challenge for global surveillance.

International Classification of Diseases, 10th Revision codes for rheumatic heart disease (RHD) include valvular heart disease of unspecified origin, limiting their usefulness for estimating RHD burden. A cross-disciplinary national consultation developed an algorithm to improve RHD identification in hospital data. The algorithm has been operationalised and piloted. The algorithm developed categorised 32% of RHD-coded patients as probable/possible RHD. We outline a series of research initiatives to improve identification of RHD in administrative data thereby contributing to monitoring the RHD burden globally.

Applying the international classification of diseases to perinatal mortality data, South Africa.

OBJECTIVE: To examine the feasibility of applying the International Classification of Diseases-perinatal mortality (ICD-PM) coding to an existing data set in the classification of perinatal deaths. METHODS: One author, a researcher with a non-clinical public health background, applied the ICD-PM coding system to South Africa's national perinatal mortality audit system, the Perinatal Problem Identification Program. The database for this study included all perinatal deaths (n = 26 810), defined as either stillbirths (of birth weight > 1000 g and after 28 weeks of gestation) or early neonatal deaths (age 0-7 days), that occurred between 1 October 2013 and 31 December 2016. A clinical obstetrician verified the coding. FINDINGS: The South African classification system does not include the timing of death; however, under the ICD-PM system, deaths could be classified as antepartum (n = 15 619; 58.2%), intrapartum (n = 3725; 14.0%) or neonatal (n = 7466; 27.8%). Further, the South African classification system linked a maternal condition to only 40.3% (10 802/26 810) of all perinatal deaths; this proportion increased to 68.9% (18 467/26 810) under the ICD-PM system. CONCLUSION: The main benefit of using the clinically relevant and user-friendly ICD-PM system was an enhanced understanding of the data, in terms of both timing of death and maternal conditions. We have also demonstrated that it is feasible to convert an existing perinatal mortality classification system to one which is globally comparable and can inform policy-makers internationally.

Temporal Trends in Sudden Cardiac Death From 1997 to 2010: A Data Linkage Study.

BACKGROUND: Community-wide trends data for sudden cardiac death (SCD) are scarce, unlike widely reported declines in cardiovascular disease (CVD) mortality. Using administrative data, we aimed to examine population-level trends in SCD, stratified by sex, age and prior CVD hospitalisation. METHODS: Person-linked mortality and hospital morbidity data were used to identify SCD and determine hospitalisation and comorbidity using a 10-year hospitalisation lookback period. Log-linear Poisson regression was used to calculate annual rate changes and rate ratios. RESULTS: In Western Australia, 7160 SCD cases were identified from 1997 to 2010 with males comprising 69%. Overall age-standardised SCD rates decreased by 17% in men and 31% in women from 1997-2001 to 2007-2010. The annual rate reduction was higher in women than men (-4.0%/year versus -2.3%/year; p=0.0039). Significant reductions were observed for 55-69 year-old and 70-84 year-old men and women but not for the 35-54 year-olds. The overall relative risk comparing men to women increased slightly from 2.4 in 1997 to 3.0 in 2010 (trend p=0.0039) but differed across age groups. The relative risk declined in 35-54 year-olds from 5.1 to 3.2 whereas it increased from 2.9 to 3.9 in 55-69 year-olds and 1.9 to 2.3 in 70-84 year-olds. Declining trends in SCD rates were observed in those with and without prior CVD and were similar to CVD mortality trends (-4.9%/year in men and -5.5%/year in women). CONCLUSIONS: Trends in rates of SCD fell in middle to older aged men and women, with and without CVD, and mirrored the fall in fatal CVD. Limited improvement in 35-54 year-olds requires further investigation.

Age-specific gender differences in long-term recurrence and mortality following incident myocardial infarction: a population-based study.

BACKGROUND: Higher mortality following myocardial infarction (MI) is reported in women compared with men with short-term follow-up. Our study aim was to compare long-term gender- and age-specific outcomes following incident MI. METHODS: 30-day survivors of incident MI from 2003-2009 were identified from linked administrative data in Western Australia. Outcomes identified were recurrent MI, and cardiovascular and all-cause mortality. Follow-up data was available until 30(th) June 2011. Unadjusted risk out to eight-years was estimated from Kaplan-Meier survival curves, and multivariate Cox regression models were used to estimate relative risk in women compared with men by age group. RESULTS: There were 12,420 30-day survivors of incident MI from 2003-2009 (males 71.2%). Women had higher levels of comorbidities across all age groups compared with men. Unadjusted event risks were higher in women than men overall, underpinned by higher risk of recurrent MI in 55-69 year-old women and of cardiovascular mortality across all age groups in women. Gender differences were generally attenuated after adjustment for demographic factors and comorbidities. CONCLUSIONS: This study highlights the elevated risk of cardiovascular events in women compared with men with long-term follow-up, and demonstrates the need for improved long-term secondary prevention in this patient group.

Sudden cardiac death rates in an Australian population: a data linkage study.

OBJECTIVE: The aim of the present study was to develop criteria to identify sudden cardiac death (SCD) and estimate population rates of SCD using administrative mortality and hospital morbidity records in Western Australia. METHODS: Four criteria were developed using place, death within 24 h, principal and secondary diagnoses, underlying and associated cause of death, and/or occurrence of a post mortem to identify SCD. Average crude, age-standardised and age-specific rates of SCD were estimated using population person-linked administrative data. RESULTS: In all, 9567 probable SCDs were identified between 1997 and 2010, with one-third aged ≥ 35 years having no prior admission for cardiovascular disease. SCD was more frequent in men (62.1%). The estimated average annual crude SCD rate for the period was 34.6 per 100 000 person-years with an average annual age-standardised rate of 37.8 per 100 000 person-years. Age-specific standardised rates were 1.1 per 100 000 person-years and 70.7 per 100 000 person-years in people aged 1-34 and ≥ 35 years, respectively. Ischaemic heart disease (IHD) was recorded as the underlying cause of death in approximately 80% of patients aged ≥ 35 years, followed by valvular heart disease and heart failure. IHD was the most common cause of death in those aged 1-34 years, followed by unspecified cardiomyopathy and dysrhythmias. CONCLUSIONS: Administrative morbidity and mortality data can be used to estimate rates of SCD and therefore provide a suitable methodology for monitoring SCD over time. The findings highlight the magnitude of SCD and its potential for public health prevention.

Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

BACKGROUND: Administrative data are a valuable source of estimates of diabetes prevalence for groups such as coronary heart disease (CHD) patients. The primary aim of this study was to measure concordance between medical records and linked administrative health data for recording diabetes in CHD patients, and to assess temporal differences in concordance. Secondary aims were to determine the optimal lookback period for identifying diabetes in this patient group, whether concordance differed for Indigenous people, and to identify predictors of false positives and negatives in administrative data. METHODS: A population representative sample of 3943 CHD patients hospitalized in Western Australia in 1998 and 2002-04 were selected, and designated according to the International Classification of Diseases (ICD) version in use at the time (ICD-9 and ICD-10 respectively). Crude prevalence and concordance were compared for the two samples. Concordance measures were estimated from administrative data comparing diabetes status recorded on the selected CHD admission ('index admission') and on any hospitalization in the previous 1, 2, 5, 10 or 15 years, against hospital medical records. Potential modifiers of agreement were determined using chi-square tests and multivariable logistic regression models. RESULTS: Identification of diabetes on the index CHD admission was underestimated more in the ICD-10 than ICD-9 sample (sensitivity 81.5% versus 91.1%, underestimation 15.1% versus 4.4% respectively). Sensitivity increased to 89.6% in the ICD-10 period using at least 10 years of hospitalization history. Sensitivity was higher and specificity lower in Indigenous patients, and followed a similar pattern of improving concordance with increasing lookback period. Characteristics associated with false negatives for diabetes on the index CHD hospital admission were elective admission, in-hospital death, principal diagnosis, and in the ICD-10 period only, fewer recorded comorbidities. CONCLUSIONS: The accuracy of identifying diabetes status in CHD patients is improved in linked administrative health data by using at least 10 years of hospitalization history. Use of this method would reduce bias when measuring temporal trends in diabetes prevalence in this patient group. Concordance measures are as reliable in Indigenous as non-Indigenous patients.

Disparities in equity and access for hospitalised atherothrombotic disease.

OBJECTIVE: This study of equity and access characterises admissions for coronary, cerebrovascular and peripheral arterial disease by hospital type (rural, tertiary and non-tertiary metropolitan) in a representative Australian population. METHODS: We conducted a descriptive analysis using data linkage of all residents aged 35-84 years hospitalised in Western Australia with a primary diagnosis for an atherothrombotic event in 2007. We compared sociodemographic and clinical features by atherothrombotic territory and hospital type. RESULTS: There were 11670 index admissions for atherothrombotic disease in 2007 of which 46% were in tertiary hospitals, 41% were in non-tertiary metropolitan hospitals and 13% were in rural hospitals. Coronary heart disease comprised 72% of admissions, followed by cerebrovascular disease (19%) and peripheral arterial disease (9%). Comparisons of socioeconomic disadvantage reveal that for those admitted to rural hospitals, more than one-third were in the most disadvantaged quintile, compared with one-fifth to any metropolitan hospital. CONCLUSIONS: Significant differences in demographic characteristics were evident between Western Australian tertiary and non-tertiary hospitals for patients hospitalised for atherothrombotic disease. Notably, the differences among tertiary, non-tertiary metropolitan and rural hospitals were related to socioeconomic disadvantage. This has implications for atherothrombotic healthcare provision and the generalisation of research findings from studies conducted exclusively in the tertiary metropolitan hospitals.