Hospital Presentations in Long-Term Survivors of Stroke: Causes and Associated Factors in a Linked Data Study.

BACKGROUND AND PURPOSE: A comprehensive understanding of the long-term impact of stroke assists in health care planning. We aimed to determine changes in rates, causes, and associated factors for hospital presentations among long-term survivors of stroke. METHODS: Person-level data from the AuSCR (Australian Stroke Clinical Registry) during 2009 to 2013 were linked with state-based health department emergency department and hospital admission data. The study cohort included adults with first-ever stroke who survived the first 6 months after discharge from hospital. Annualized rates of hospital presentations (nonadmitted emergency department or admission)/person/year were calculated for 1 to 12 months prior, and 7 to 12 months (inclusive) after hospitalization. Multilevel, negative binomial regression was used to identify associated factors after adjustment for prestroke hospital presentations and stratification for perceived impairment status. Perceived impairments to health were defined according to the subscales and visual analog health status scores on the 5-Dimension European Quality of Life Scale. RESULTS: There were 7183 adults with acute stroke, 7-month survivors (median age 72 years; 56% male; 81% ischemic, and 42% with impairment at 90-180 days) from 39 hospitals included in this landmark analysis. Annualized presentations/person increased from 0.88 (95% CI, 0.86-0.91) to 1.25 (95% CI, 1.22-1.29) between the prestroke and poststroke periods, with greater rate increases in those with than without perceived impairment (55% versus 26%). Higher presentation rates were most strongly associated with older age (≥85 versus 65 years, incidence rate ratio, 1.52 [95% CI, 1.27-1.82]) and greater comorbidity score (incidence rate ratio, 1.06 [95% CI, 1.02-1.10]), whereas reduced rates were associated with greater social advantage (incidence rate ratio, 0.71 [95% CI, 0.60-0.84]). Poststroke hospital presentations (7-12 months) were most frequently related to recurrent cardiovascular and cerebrovascular events and sequelae of stroke. CONCLUSIONS: A large increase in annualized hospital presentation rates after stroke indicates the potential for improved community management and support for this vulnerable patient group.

Factors Associated With 90-Day Readmission After Stroke or Transient Ischemic Attack: Linked Data From the Australian Stroke Clinical Registry.

Background and Purpose- Readmissions after stroke are common and appear to be associated with comorbidities or disability-related characteristics. In this study, we aimed to determine the patient and health-system level factors associated with all-cause and unplanned hospital readmission within 90 days after acute stroke or transient ischemic attack (TIA) in Australia. Methods- We used person-level linkages between data from the Australian Stroke Clinical Registry (2009-2013), hospital admissions data and national death registrations from 4 Australian states. Time to first readmission (all-cause or unplanned) for discharged patients was examined within 30, 90, and 365 days, using competing risks regression to account for deaths postdischarge. Covariates included age, stroke severity (ability to walk on admission), stroke type, admissions before stroke/TIA and the Charlson Comorbidity Index (derived from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, [Australian modified] coded hospital data in the preceding 5 years). Results- Among the 13 594 patients discharged following stroke/TIA (45% female; 65% ischemic stroke; 11% intracerebral hemorrhage; 4% undetermined stroke; and 20% TIA), 25% had an all-cause readmission and 15% had an unplanned readmission within 90 days. In multivariable analyses, the factors independently associated with a greater risk of unplanned readmission within 90 days were being female (subhazard ratio, 1.13 [95% CI, 1.03-1.24]), greater Charlson Comorbidity Index scores (subhazard ratio, 1.11 [95% CI, 1.09-1.12]) and having an admission ≤90 days before the index event (subhazard ratio, 1.85 [95% CI, 1.59-2.15]). Compared with being discharged to rehabilitation or aged care, those who were discharged directly home were more likely to have an unplanned readmission within 90 days (subhazard ratio, 1.44 [95% CI, 1.33-1.55]). These factors were similar for readmissions within 30 and 365 days. Conclusions- Apart from comorbidities and patient-level characteristics, readmissions after stroke/TIA were associated with discharge destination. Greater support for transition to home after stroke/TIA may be needed to reduce unplanned readmissions.

Maximising data value and avoiding data waste: a validation study in stroke research.

OBJECTIVES: To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR), the National Death Index (NDI), and state-managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables. DESIGN, SETTING, PARTICIPANTS: Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia. MAIN OUTCOME MEASURES: Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in-hospital death data for predicting NDI registrations. RESULTS: Data for 16 214 patients registered in the AuSCR during 2009-2013 were linked with one or more state datasets: 15 482 matches (95%) with hospital admissions data, and 12 902 matches (80%) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99% for sex, age, in-hospital death (each κ = 0.99), and Indigenous status (κ = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In-hospital death in AuSCR data had 98.7% sensitivity and 99.6% specificity for predicting in-hospital death in the NDI. CONCLUSION: We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow-up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed.

Factors influencing self-reported anxiety or depression following stroke or TIA using linked registry and hospital data.

PURPOSE: Approximately 30-50% of survivors experience problems with anxiety or depression post-stroke. It is important to understand the factors associated with post-stroke anxiety or depression to identify effective interventions. METHODS: Patient-level data from the Australian Stroke Clinical Registry (years 2009-2013), from participating hospitals in Queensland (n = 23), were linked with Queensland Hospital Emergency and Admission datasets. Self-reported anxiety or depression was assessed using the EQ-5D-3L, obtained at 90-180 days post-stroke. Multivariable multilevel logistic regression, with manual stepwise elimination of variables, was used to investigate the association between self-reported anxiety or depression, patient factors and acute stroke processes of care. Comorbidities, including prior mental health problems (e.g. anxiety, depression and dementia) coded in previous hospital admissions or emergency presentations using ICD-10 diagnosis codes, were identified from 5 years prior to stroke event. RESULTS: 2853 patients were included (median age 74; 45% female; 72% stroke; 24% transient ischaemic attack). Nearly half (47%) reported some level of anxiety or depression post-stroke. The factors most strongly associated with anxiety or depression were a prior diagnosis of anxiety or depression [Adjusted Odds Ratio (aOR) 2.37, 95% confidence interval (95% CI) 1.66-3.39; p < 0.001], dementia (aOR 1.91, 95% CI 1.24-2.93; p = 0.003), being at home with support (aOR 1.41, 95% CI 1.12-1.69; p = < 0.001), and low socioeconomic advantage compared to high (aOR 1.59, 95% CI 1.21-2.10; p = 0.001). Acute stroke processes of care were not independently associated with anxiety or depression. CONCLUSIONS: Identification of those with prior mental health problems for early intervention and support may help reduce the prevalence of post-stroke anxiety or depression.

Should diagnosis codes from emergency department data be used for case selection for emergency department key performance indicators?

OBJECTIVE: The aim of the present study was to assess the suitability of emergency department (ED) discharge diagnosis for identifying patient cohorts included in the definitions of key performance indicators (KPIs) that are used to evaluate ED performance. METHODS: Hospital inpatient episodes of care with a principal diagnosis that corresponded to an ED-defined KPI were extracted from the Queensland Hospital Admitted Patient Data Collection (QHAPDC) for the year 2010-2011. The data were then linked to the corresponding ED patient record and the diagnoses applied in the two settings were compared. RESULTS: The asthma and injury cohorts produced favourable results with respect to matching the QHAPDC principal diagnosis with the ED discharge diagnosis. The results were generally modest when the QHAPDC principal diagnosis was upper respiratory tract infection, poisoning and toxic effects or a mental health diagnosis, and were quite poor for influenza. CONCLUSIONS: There is substantial variation in the capture of patient cohorts using discharge diagnosis as recorded on Queensland Hospital Emergency Department data. WHAT IS KNOWN ABOUT THE TOPIC? There are several existing KPIs that are defined according to the diagnosis recorded on ED data collections. However, there have been concerns over the quality of ED diagnosis in Queensland and other jurisdictions, and the value of these data in identifying patient cohorts for the purpose of assessing ED performance remains uncertain. WHAT DOES THIS PAPER ADD? This paper identifies diagnosis codes that are suitable for use in capturing the patient cohorts that are used to evaluate ED performance, as well as those codes that may be of limited value. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS? The limitations of diagnosis codes within ED data should be understood by those seeking to use these data items for healthcare planning and management or for research into healthcare quality and outcomes.

Pre-pregnancy BMI: costs associated with maternal underweight and obesity in Queensland.

BACKGROUND: The economic costs of maternal obesity and underweight have not been described. We aim to assess the effect of maternal underweight and obesity on hospital utilisation and hospital costs. METHODS: Data from the Queensland Perinatal Data Collection and Queensland Hospital Admitted Patient Data Collection were analysed for 2008. The sample included 37,912 Queensland resident mothers with a singleton pregnancy who gave birth in a public facility. Outcome measures were hospital length of stay (LOS) and hospital costs accrued during the birth admission and during pre- and postnatal admissions within 90 days of the birth admission. RESULTS: There were 1,581 (4.2%) underweight, 17,175 (45.3%) normal weight, 10,155 (26.8%) overweight and 9,001 (23.7%) obese women. Maternal obesity was associated with significantly longer stays although effect sizes were modest (≤0.5 days) and specific to women who delivered vaginally. LOS was significantly higher among babies born to underweight mothers when compared to those born to normal weight women. Maternal obesity was associated with a total increase of $5 million in mothers' hospital costs when compared to those amongst normal weight women; the corresponding figure for underweight mothers was $385,734. The total hospital costs for babies born to underweight women were $1.6 million higher than those born to mothers in the normal weight category. Maternal obesity was not associated with an increase in babies' hospital costs. CONCLUSIONS: Maternal obesity contributed to an increase in mothers' hospital LOS and hospitalisation costs. Maternal underweight contributed to an increase in babies' hospital costs.

Costs of acute hospitalisation for stroke and transient ischaemic attack in Australia.

BACKGROUND: Stroke is a high-cost condition. Detailed patient-level assessments of the costs of care received and outcomes achieved provide useful information for organisation and optimisation of the health system. OBJECTIVES: To describe the costs of hospital care for stroke and transient ischaemic attack (TIA) and investigate factors associated with costs. METHODS: Retrospective cohort study using data from the Australian Stroke Clinical Registry (AuSCR) collected between 2009 and 2013 linked to hospital administrative data and clinical costing data in Queensland. Clinical costing data include standardised assignment of costs from hospitals that contribute to the National Hospital Costing programme. Patient-level costs for each hospital admission were described according to the demographic, clinical and treatment characteristics of patients. Multivariable median regression with clustering by hospital was used to determine factors associated with greater costs. RESULTS: Among 22 hospitals, clinical costing data were available for 3909 of 5522 patient admissions in the AuSCR (71%). Compared to those without clinical costing data, patients with clinical costing data were more often aged <65 years (30% with cost data vs 24% without cost data, p < 0.001) and male (56% with cost data vs 49% without cost data, p < 0.001). Median cost of an acute episode was $7945 (interquartile range $4176 to $14970) and the median length of stay was 5 days (interquartile range 2 to 10 days). The most expensive cost buckets were related to medical (n = 3897, median cost $1577), nursing (n = 3908, median cost $2478) and critical care (n = 434, median cost $3064). Factors associated with greater total costs were a diagnosis of intracerebral haemorrhage, greater socioeconomic position, in-hospital stroke and prior history of stroke. CONCLUSION: Medical and nursing costs were incurred by most patients admitted with stroke or TIA, and were relatively more expensive on average than other cost buckets such as imaging and allied health. IMPLICATIONS: Scaling this data linkage to national data collections may provide valuable insights into activity-based funding at public hospitals. Regular report of these costs should be encouraged to optimise economic evaluations.

Investigating inequities in cardiovascular care and outcomes for Queensland Aboriginal and Torres Strait Islander people: protocol for a hospital-based retrospective cohort data linkage project.

INTRODUCTION: Cardiovascular disease (CVD) represents a significant burden of disease for Aboriginal and Torres Strait Islander people, a population that continues to experience a lower life expectancy than other Australians. The aim of the Better Cardiac Care Data Linkage project is to describe patient care pathways and to identify disparities in care and health outcomes between Aboriginal and Torres Strait Islander people and other Queensland residents diagnosed with CVD in the state of Queensland. METHODS: This is a population-based retrospective cohort study using linked regional, state and national health and administrative data collections to describe disparities in CVD healthcare in primary and secondary prevention settings and during hospitalisation. The CVD cohort will be identified from the Queensland Hospital Admitted Patient Data Collection for admissions that occurred between 1 July 2010 and 31 June 2016 and will include relevant International Classification of Disease codes for ischaemic heart disease, congestive heart failure, stroke, acute rheumatic fever and rheumatic heart disease. Person-level data will be linked by Data Linkage Queensland and the Australian Institute of Health and Welfare (AIHW) in accordance with ethical and public health approvals to describe the patient journey prior to, during and post the hospital admission. ANALYSIS: This project will focus largely on descriptive epidemiological measures and multivariate analysis of clinical care standards and outcomes for Aboriginal and Torres Strait Islander people compared with other Queenslanders, including identification of risk factors for suboptimal care and change over time. Variation in care pathways and patient outcomes will be compared by Indigenous status, sex, age group, remoteness of residence, year of index hospitalisation and socioeconomic status. Cox models for time-to-event data and mixed models or generalised estimating equations for longitudinal data will be used to measure change over time where temporal effects exist. ETHICS AND DISSEMINATION: Ethical approval has been granted by Human Research Ethics Committees of the Prince Charles Hospital (HREC/15/QPCH/289) and the AIHW (EO2016-1-233). The Northern Territory Department of Health and Menzies School of Health Research have also provided reciprocal ethical approval of the project (HREC 2019-3490). The deidentified results will be summarised in a report and shared with investigators, advisory groups, Queensland Health and key stakeholders. Findings will be disseminated through workshops, conferences and will be published in peer-reviewed journals.

Trends in neonatal resuscitation patterns in Queensland, Australia - A 10-year retrospective cohort study.

OBJECTIVES: To describe the frequency of neonatal resuscitation interventions implemented for newborn babies in the state of Queensland over a 10-year period and determine if these changes suggest adherence to changes in Australian guidelines. STUDY DESIGN: A population-based retrospective cohort study utilising the Queensland Perinatal Data Collection dataset. All liveborn babies ≥23 + 0 weeks + days gestation born between 1 July 2007 and 30 June 2017 were included except those for whom resuscitation was not attempted and those babies <25 + 0 weeks for whom it was unsuccessful. Trends in resuscitation were demonstrated using Loess regression. RESULTS: Of 618,589 eligible newborns,182,260 received any resuscitation manoeuvre (29.5%). The proportion receiving oxygen without assisted ventilation declined from 19.3% in 2007-08 to 5.6% in 2016-17. Upper airway suctioning also decreased. Assisted ventilation increased from 7.9% to 10.0% of all babies with the largest contribution from late preterm and term babies. The rate of endotracheal suctioning for meconium and the rate of narcotic antagonist use also declined. A greater proportion of babies received chest compressions (1.9-3.2 per 1000 babies) and adrenaline (epinephrine). Mortality decreased from 1.9 to 1.5 per 1000 babies in the cohort. CONCLUSION: Ten-year trends showed reduced use of oxygen or upper airway suctioning without assisted ventilation, reduced intubation to suction meconium, reduced use of narcotic antagonists and greater use of assisted ventilation suggesting appropriate practice change in response to Australian neonatal resuscitation guidelines. The increase in the use of chest compressions and adrenaline was unexpected and the reasons for it are unclear.

Chest infection within 30 days of acute stroke, associated factors, survival and the benefits of stroke unit care: Analysis using linked data from the Australian Stroke Clinical Registry.

INTRODUCTION: Chest infections following acute stroke contribute to increased morbidity and mortality. We aimed to investigate factors associated with chest infections that occur within 30 days of stroke, the impact on 90-day survival, and the role of stroke unit care. METHODS: Patient-level data from the Australian Stroke Clinical Registry (2010-13; 23 Queensland hospitals), were linked with Queensland hospital admission, emergency department (ED), and national death registry data. Acute chest infections were determined using ICD-10 codes from the stroke admission, hospital readmissions, ED contacts, and cause of death data. Patients aged ≥18 years without a prior stroke or chronic respiratory condition were included. Multilevel (hospital and patient) multivariable regression and survival analysis were used to identify associated factors and the influence on 90-day survival. RESULTS: Overall, 3149 patients (77% ischemic stroke, 47% female, median age 74 years) were included; 3.1% developed a chest infection within 30 days. Associated factors included: admission to intensive care (OR: 8.26, 95% CI: 4.07, 16.76); and urinary tract infection (OR: 3.09, 95% CI: 1.89, 5.04). Patients not treated in stroke units had a two-fold greater odds of chest infections (OR: 1.96, 95% CI: 1.25, 3.05). Chest infection afforded a greater hazard of death at 90 days (HR: 1.42, 95% CI 1.04, 1.93). This was reduced for those admitted to a stroke unit (HR: 1.31, 95% CI 0.99, 1.75). CONCLUSION: Results emphasize the need for active prevention and highlight the importance of stroke unit care in mitigating risk and improving survival in those with stroke-related chest infections.

Trends and determinants of caesarean sections births in Queensland, 1997-2006.

BACKGROUND: The determinants of Queensland's rising caesarean section (CS) rate remain poorly understood because of the historical absence of standard classification methods. AIMS: We applied the Robson Ten Group Classification System (RTGCS) to population-based data to identify the main contributors to Queensland's rising CS rate. METHOD: The RTGCS was applied retrospectively to the Queensland Perinatal Data Collection. CS rates were described for all ten RTGCS groups using data from 2006. Trends were evaluated using data for the years 1997-2006. Public and private sector patients were evaluated separately. RESULTS: In Queensland, in 2006, CS rates were 26.9 and 48.0% among public and private sector patients, respectively. Multiparous women with a previous caesarean birth (Group 5) made the greatest contribution to the CS rate in both sectors, followed by nulliparous women who had labour induced or were delivered by CS prior to the onset of labour (Group 2) and nulliparous women in spontaneous labour (Group 1). CS rates have risen in all RTGCS groups between 1997 and 2006. The trend was pronounced among multiparous women with a previous caesarean delivery (Group 5), among women with multiple pregnancies (Group 8) and among nulliparous women who had labour induced or were delivered by CS prior to the onset of labour (Group 2). CONCLUSIONS: The CS rate in Queensland in 2006 was higher than in any other Australian state. The increase in Queensland's CS rates can be attributed to both the rising number of primary caesarean births and the rising number of repeat caesareans.

Impact of advanced cardiac life support-skilled paramedics on survival from out-of-hospital cardiac arrest in a statewide emergency medical service.

BACKGROUND: Prehospital research has found little evidence in support of advanced cardiac life support (ACLS) for out-of-hospital cardiac arrest. However, these studies generally examine city-based emergency medical services (EMS) systems. The training and experience of ACLS-skilled paramedics differs internationally, and this may also contribute to negative findings. Additionally, the frequency of negative outcome in out-of-hospital cardiac arrest suggests that it is difficult to establish sufficient numbers to detect an effect. PURPOSE: To examine the effect of ACLS on cardiac arrest in Queensland, Australia. Queensland has a population of 3.8 million and an area of over 1.7 million km2, and is served by a statewide EMS system, which deploys resources using a two-tier model. Advanced treatments such as intubation and cardioactive drug administration are provided by extensively trained intensive care paramedics. METHODS: An observational, retrospective design was used to examine all cases of cardiac arrest attended by the Queensland Ambulance Service from January 2000 to December 2002. Logistic regression was used to examine the effect of the presence of an intensive care paramedic on survival to hospital discharge, adjusting for age, sex, initial rhythm, the presence of a witness and bystander cardiopulmonary resuscitation. RESULTS: The presence of an intensive care paramedic had a significant effect on survival (OR = 1.43, 95% CI = 1.02 to 1.99). CONCLUSIONS: Highly trained ACLS-skilled paramedics provide added survival benefit in EMS systems not optimised for early defibrillation. The reasons for this benefit are multifactorial, but may be the result of greater skill level and more informed use of the full range of prehospital interventions.

Trends in the annual prevalence of hospitalisation for remote indigenous communities in Queensland, 1997/98 to 2004/05.

OBJECTIVE: To describe trends in the annual prevalence of hospitalisation in remote Indigenous communities in Queensland, 1997/98 to 2004/05. METHODS: Descriptive analysis of computerised discharge abstracts that were linked using probabilistic matching to obtain person-based data. RESULTS: Over the eight years studied, the age-standardised annual prevalence of hospitalisation decreased by 4.5% per year (95% CI -4.8%- -4.1%). The decrease was largest at younger ages (e.g. 0-4 years: -6.0%; 70+ years: -1.9%). There were large decreases for infections (-6.1%; 95% CI -6.8%- -5.4%) and for injury (-7.3%; 95% CI -8.1%- -6.5%). However, there were increases for chronic diseases such as ischaemic heart disease (2.5%; 95% CI 0.2%-4.9%), diabetes (2.5%; 95% CI 0.5%--4.5%) and renal failure (6.8%, 95% CI 3.3%-10.4%). CONCLUSIONS: Indigenous health appears to be improving in the remote communities in Queensland, especially for infections and injury, but it appears that little progress has been made for chronic disease.

Addressing the challenges of cross-jurisdictional data linkage between a national clinical quality registry and government-held health data.

OBJECTIVE: To describe the challenges of obtaining state and nationally held data for linkage to a non-government national clinical registry. METHODS: We reviewed processes negotiated to achieve linkage between the Australian Stroke Clinical Registry (AuSCR), the National Death Index, and state held hospital data. Minutes from working group meetings, national workshop meetings, and documented communications with health department staff were reviewed and summarised. RESULTS: Time from first application to receipt of data was more than two years for most state data-sets. Several challenges were unique to linkages involving identifiable data from a non-government clinical registry. Concerns about consent, the re-identification of data, duality of data custodian roles and data ownership were raised. Requirements involved the development of data flow methods, separating roles and multiple governance and ethics approvals. Approval to link death data presented the fewest barriers. CONCLUSION: To our knowledge, this is the first time in Australia that person-level data from a clinical quality registry has been linked to hospital and mortality data across multiple Australian jurisdictions. Implications for Public Health: The administrative load of obtaining linked data makes projects such as this burdensome but not impossible. An improved national centralised strategy for data linkage in Australia is urgently needed.

Reducing perinatal mortality among Indigenous babies in Queensland: should the first priority be better primary health care or better access to hospital care during confinement?

BACKGROUND: The perinatal mortality rate among Indigenous Australians is still double that of the rest of the community. The aim of our study was to estimate the extent to which increased risk of low birthweight and preterm birth among Indigenous babies in Queensland account for their continuing mortality excess. If a large proportion of excess deaths can be explained by the unfavourable birthweight and gestational age distribution of Indigenous babies, then that would suggest that priority should be given to implementing primary health care interventions to reduce the risk of low birthweight and preterm birth (eg, interventions to reduce maternal smoking or genitourinary infections). Conversely, if only a small proportion is explained by birthweight and gestational age, then other strategies might need to be considered such as improving access to high-quality hospital care around the time of confinement. METHODOLOGY: Population-based, descriptive study of perinatal mortality rates among Indigenous and non-Indigenous babies, in Queensland, stratified by birthweight and gestational age. RESULTS: Indigenous babies are twice as likely to die as their non-Indigenous counterparts (rate ratio1998-2002: 2.01; 95%ci 1.77, 2.28). However, within separate strata of birth weight and gestational age, Indigenous and non-Indigenous rates are similar. The Mantel-Haenszel rate ratio adjusted for birth weight and gestational age was 1.13 (0.99, 1.28). This means that most of the excess mortality in Indigenous babies is largely due to their unfavourable birth weight and gestational-age distributions. If Indigenous babies had the same birth weight and gestational age distribution as their non-Indigenous counterparts, then the relative disparity would be reduced by 87% and 20 fewer Indigenous babies would die in Queensland each year. CONCLUSION: Our results suggest that Indigenous mothers at high risk of poor outcome (for example those Indigenous mothers in preterm labour) have good access to high quality medical care around the time of confinement. The main reason Indigenous babies have a high risk of death is because they are born too early and too small. Thus, to reduce the relative excess of deaths among Indigenous babies, priority should be given to primary health care initiatives aimed at reducing the prevalence of low birth weight and preterm birth.

Should we add clinical variables to administrative data?: The case of risk-adjusted case fatality rates after admission for acute myocardial infarction.

BACKGROUND: Previous studies have evaluated whether the addition of multiple laboratory and clinical factors to administrative data, or reabstraction of administrative data, improve the accuracy of risk adjustment. This study assessed if a more feasible strategy of adding 3 readily accessible clinical variables to hospital administrative data might improve the risk adjustment for interhospital comparisons. OBJECTIVES: We compared 3 alternative risk adjustment models for 30-day case-fatality rates (CFR) after admission for acute myocardial infarction (AMI): (1) administrative model (age, sex, and comorbidities); (2) clinical-augmented administrative model (administrative data plus 3 clinical variables: systolic blood pressure, heart rate, and ECG characteristics on admission); and (3) clinical-demographic model (3 clinical variables plus age and sex). DESIGN: Retrospective analysis of matched administrative and clinical datasets. SUBJECTS: A total of 1743 patients admitted to 21 hospitals in Queensland, Australia, with a principal diagnosis of AMI between January 1, 2003 and December 31, 2005. RESULTS: There was only fair agreement between the administrative model and the clinical-augmented administrative model (weighted kappa = 0.66). Only 68.7% of the risk-adjusted CFR were in the same decile of risk; 9.9% were 3 or more deciles apart. The clinical-augmented model reduced extrabinomial variation and slightly improved discrimination (c = 0.83 vs. 0.79, P = 0.01). In contrast, removing comorbidities from the clinical model did not alter performance greatly: similar discrimination (c = 0.80 vs. 0.83, P = 0.07), excellent agreement for predicted CFR (weighted kappa = 0.82), and no extrabinomial variation for either model. CONCLUSIONS: Addition of only 3 readily accessible clinical variables to administrative data improves the risk adjustment for interhospital comparisons of AMI case-fatality rates.