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Background: Aboriginal language interpreters are under-utilised in healthcare in northern Australia. Self-discharge from hospital is an adverse outcome occurring at high rates among Aboriginal people, with poor communication thought to be a contributor. We previously reported increased Aboriginal interpreter uptake and decreased rates of self-discharge during implementation of a 12-month hospital-based intervention. Interrupted time-series analysis showed sudden increase and up-trending improvement in interpreter use, and a corresponding decrease in self-discharge rates, during a 12-month intervention period (April 2018—March 2019) compared with a 24-month baseline period (April 2016 –March 2018). This paper aims to investigate reasons for these outcomes and explore a potential causal association between study activities and outcomes. Methods: The study was implemented at the tertiary referral hospital in northern Australia. We used the Template for Intervention Description and Replication (TIDieR) as a framework to describe intervention components according to what, how, where, when, how much, tailoring, modifications and reach. Components of the study intervention were: employment of an Aboriginal Interpreter Coordinator, 'Working with Interpreters' training for healthcare providers, and championing of interpreter use by doctors. We evaluated the relative importance of intervention components according to TIDieR descriptors in relation to outcomes. Activities independent of the study that may have affected study findings were reviewed. The relationship between proportion of hospital separations among Aboriginal people ending in self-discharge and numbers of Aboriginal interpreter bookings made during April 2016-March 2019 was tested using linear regression. 'Working with Interpreters' training sessions were undertaken at a regional hospital as well as the tertiary hospital. Training evaluation comprised an anonymous online survey before the training, immediately after and then at six to eight months. Survey data from the sites were pooled for analysis. Results: Employment of the Aboriginal Interpreter Coordinator was deemed the most important component of the intervention, based on reach compared to the other components, and timing of the changes in outcomes in relation to the employment period of the coordinator. There was an inverse association between interpreter bookings and self-discharge rate among Aboriginal inpatients throughout the baseline and intervention period (p = 0.02). This association, the timing of changes and assessment of intercurrent activities at the hospital indicated that the study intervention was likely to be casually related to the measured outcomes. Conclusions: Communication in healthcare can be improved through targeted strategies, with associated improvements in patient outcomes. Health services with high interpreter needs would benefit from employing an interpreter coordinator. [ABSTRACT FROM AUTHOR]

This paper estimates the influence of inadequate access to healthcare services on the rate of Emergency Room (ER) hospital visits in Australia. We take micro-data on different types of healthcare shortfalls from the 2012 Australian Survey of Disability, Aging and Carers, and employ Propensity Score Matching (PSM) techniques to identify their effects on ER visits. We find that shortfalls in access to various medical services increases ER visits for individuals with mental and physical conditions by about the same degree. Conversely, inadequate community care services significantly predict ER visits for individuals with physical conditions, but not for persons with mental conditions. The lack of predictive power for inadequate community care for persons with mental health problems is surprising, as “acopia” is thought to be a significant driver of crises that require emergency treatment. We discuss some of the mechanisms that may underpin this finding and address the policy implications of our results. Lastly a number of robustness checks and diagnostics tests are presented which confirm that our modelling assumptions are not violated and that our results are insensitive to the choice of matching algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Colorectal cancer (CRC) care costs the Australian healthcare system more than any other cancer. We estimated costs and days in hospital for CRC cases, stratified by site (colon/rectal cancer) and disease stage, to inform detailed analyses of CRC-related healthcare. Methods: Incident CRC patients were identified using the Australian 45 and Up Study cohort linked with cancer registry records. We analysed linked hospital admission records, emergency department records, and reimbursement records for government-subsidised medical services and prescription medicines. Cases' health system costs (2020 Australian dollars) and hospital days were compared with those for cancer-free controls (matched by age, sex, geography, smoking) to estimate excess resources by phase of care, analysed by sociodemographic, health, and disease characteristics. Results: 1200 colon and 546 rectal cancer cases were diagnosed 2006–2013, and followed up to June 2016. Eighty-nine percent of cases had surgery, chemotherapy or radiotherapy, and excess costs were predominantly for hospitalisations. Initial phase (12 months post-diagnosis) mean excess health system costs were $50,434 for colon and $60,877 for rectal cancer cases, with means of 16 and 18.5 excess hospital days, respectively. The annual continuing mean excess costs were $6,779 (colon) and $8,336 (rectal), with a mean of 2 excess hospital days each. Resources utilised (costs and days) in these phases increased with more advanced disease, comorbidities, and younger age. Mean excess costs in the year before death were $74,952 (colon) and $67,733 (rectal), with means of 34 and 30 excess hospital days, respectively–resources utilised were similar across all characteristics, apart from lower costs for cases aged ≥75 at diagnosis. Conclusions: Health system costs and hospital utilisation for CRC care are greater for people with more advanced disease. These findings provide a benchmark, and will help inform future cost-effectiveness analyses of potential approaches to CRC screening and treatment. [ABSTRACT FROM AUTHOR]

Background: Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment. Methods: We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates. Results: The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7). Conclusions: Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance. [ABSTRACT FROM AUTHOR]

Background: This study aims to examine the costs associated with a Hospital in the Nursing Home (HiNH) program in Queensland Australia directed at patients from residential aged care facilities (RACFs) with emergency care needs. Methods: A cost analysis was undertaken comparing the costs under the HiNH program and the current practice, in parallel with a pre-post controlled study design. The study was conducted in two Queensland public hospitals: the Royal Brisbane and Women’s Hospital (intervention hospital) and the Logan Hospital (control hospital). Main outcome measures were the associated incremental costs or savings concerning the HiNH program provision and the acute hospital care utilisation over one year after intervention. Results: The initial deterministic analysis calculated the total induced mean costs associated with providing the HiNH program over one year as AU$488,116, and the total induced savings relating to acute hospital care service utilisation of AU$8,659,788. The total net costs to the health service providers were thus calculated at -AU$8,171,671 per annum. Results from the probabilistic sensitivity analysis (based on 10,000 simulations) showed the mean and median annual net costs associated with the HiNH program implementation were -AU$8,444,512 and–AU$8,202,676, and a standard deviation of 2,955,346. There was 95% certainty that the values of net costs would fall within the range from -AU$15,018,055 to -AU$3,358,820. Conclusions: The costs relating to implementing the HiNH program appear to be much less than the savings in terms of associated decreases in acute hospital service utilisation. The HiNH service model is likely to have the cost-saving potential while improving the emergency care provision for RACF residents. [ABSTRACT FROM AUTHOR]

Background: Acute gastroenteritis illness is a common illness that causes considerable morbidity, but current estimates of the cost to the Australian healthcare system are unknown. Objective: To estimate the current healthcare utilisation and direct public healthcare system costs attributable to acute gastroenteritis illness in Australia. Methods: This is an incidence-based cost-of-illness study focused on quantifying direct health care costs using a bottom-up approach. Data on general practitioner consultations, prescribed medications, diagnostic tests, specialist consultations, emergency department visits and hospital admissions were collected from national reports. Results: Using 2016 prices, the estimated annual direct per capita cost of acute gastroenteritis illness was AUD$14.87 (USD$10.71), equating to AUD$20.27 (USD$14.59) per case. The estimated overall economic burden in Australia was AUD$359 million (USD$258 million; AUD$1.5 million per 100,000 people). The major contributors to this cost were hospital admissions (57.1%), emergency department visits (17.7%), and general practitioner consultations (14.0%). Children under five years of age have the highest per capita rates of acute gastroenteritis illness; however, service utilisation rates vary by age group and both young children and older adults accounted for a substantial proportion of the overall economic burden attributable to acute gastroenteritis illness. Conclusions: Although chronic diseases comprise a large cost burden on the healthcare system, acute illnesses, including acute gastroenteritis illness, also impose substantial direct healthcare system costs. Providing data on current cost estimates is useful for prioritizing public health interventions, with our findings suggesting that it would be ideal if targeted interventions to reduce hospitalisation rates among young children and older adults were available. [ABSTRACT FROM AUTHOR]

Background: Estimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources. Methods: Baseline survey data for 90,352 participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and over—were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (κ).The characteristics of people classified as multimorbid were compared using logistic regression modelling. Results: Agreement was found to be highest for diabetes in both hospital and claims data (κ = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data. Conclusions: Different individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions. [ABSTRACT FROM AUTHOR]

Background: Indigenous infants (infants aged under 12 months) have the highest hospital admission and emergency department presentation risks in Australia. However, there have been no recent reports comparing hospital utilisation between Indigenous and non-Indigenous infants. Methods: Our primary objective was to use a large prospective population-based linked dataset to assess the risk of all-cause hospital admission and emergency department presentation in Indigenous compared to non-Indigenous infants in Western Australia (WA). Secondary objectives were to assess the effect of socio-economic status (Index of Relative Socio-Economic Disadvantage [IRSD]) on hospital utilisation and to understand the causes of hospital utilisation. Findings: There were 3,382 (5.4%) Indigenous and 59,583 (94.6%) non-Indigenous live births in WA from 1 January 2010 to 31 December 2011. Indigenous infants had a greater risk of hospital admission (adjusted odds ratio [aOR] 1.90, 95% confidence interval [95% CI] 1.77–2.04, p = <0.001) and emergency department presentation (aOR 2.15, 95% CI 1.98–2.33, p = <0.001) compared to non-Indigenous infants. Fifty nine percent (59.0%) of admissions in Indigenous children were classified as preventable compared to 31.2% of admissions in non-Indigenous infants (aOR 2.12, 95% CI 1.88–2.39). The risk of hospital admission in the most disadvantaged (IRSD 1) infants in the total cohort (35.7%) was similar to the risk in the least disadvantaged (IRSD 5) infants (30.6%) (aOR 1.04, 95% CI 0.96–1.13, p = 0.356). Interpretation: WA Indigenous infants have much higher hospital utilisation than non Indigenous infants. WA health services should prioritise Indigenous infants regardless of their socio economic status or where they live. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) remains a leading cause of death and disability. The National Institute for Health and Clinical Excellence (NICE) guidelines recommend transfer of severe TBI cases to neurosurgical centres, irrespective of the need for neurosurgery. This observational study investigated the risk-adjusted mortality of isolated TBI admissions in England/Wales, and Victoria, Australia, and the impact of neurosurgical centre management on outcomes. Methods: Isolated TBI admissions (>15 years, July 2005-June 2006) were extracted from the hospital discharge datasets for both jurisdictions. Severe isolated TBI (AIS severity >3) admissions were provided by the Trauma Audit and Research Network (TARN) and Victorian State Trauma Registry (VSTR) for England/Wales, and Victoria, respectively. Multivariable logistic regression was used to compare risk-adjusted mortality between jurisdictions. Findings: Mortality was 12% (749/6256) in England/Wales and 9% (91/1048) in Victoria for isolated TBI admissions. Adjusted odds of death in England/Wales were higher compared to Victoria overall (OR 2.0, 95% CI: 1.6, 2.5), and for cases <65 years (OR 2.36, 95% CI: 1.51, 3.69). For severe TBI, mortality was 23% (133/575) for TARN and 20% (68/346) for VSTR, with 72% of TARN and 86% of VSTR cases managed at a neurosurgical centre. The adjusted mortality odds for severe TBI cases in TARN were higher compared to the VSTR (OR 1.45, 95% CI: 0.96, 2.19), but particularly for cases <65 years (OR 2.04, 95% CI: 1.07, 3.90). Neurosurgical centre management modified the effect overall (OR 1.12, 95% CI: 0.73, 1.74) and for cases <65 years (OR 1.53, 95% CI: 0.77, 3.03). Conclusion: The risk-adjusted odds of mortality for all isolated TBI admissions, and severe TBI cases, were higher in England/ Wales when compared to Victoria. The lower percentage of cases managed at neurosurgical centres in England and Wales was an explanatory factor, supporting the changes made to the NICE guidelines. [ABSTRACT FROM AUTHOR]

Background: The first wave of pandemic influenza A(H1N1)2009 (pH1N1) reached New South Wales (NSW), Australia in May 2009, and led to high rates of influenza-related hospital admission of infants and young to middle-aged adults, but no increase in influenza-related or all-cause mortality. Methodology/Principal Findings: To assess the population rate of pH1N1 infection in NSW residents, pH1N1-specific haemagglutination inhibition (HI) antibody prevalence was measured in specimens collected opportunistically before (2007-2008; 474 specimens) and after (August-September 2009; 1247 specimens) the 2009 winter, and before the introduction of the pH1N1 monovalent vaccine. Age- and geographically-weighted population changes in seroprevalence were calculated. HI antibodies against four recent seasonal influenza A viruses were measured to assess cross-reactions. Pre- and post-pandemic pH1N1 seroprevalences were 12.8%, and 28.4%, respectively, with an estimated overall infection rate of 15.6%. pH1N1 antibody prevalence increased significantly - 20.6% overall - in people born since 1944 (26.9% in those born between 1975 and 1997) but not in those born in or before 1944. People born before 1925 had a significantly higher pH1N1 seroprevalence than any other age-group, and against any seasonal influenza A virus. Sydney residents had a significantly greater change in prevalence of antibodies against pH1N1 than other NSW residents (19.3% vs 9.6%). Conclusions/Significance: Based on increases in the pH1N1 antibody prevalence before and after the first pandemic wave, 16% of NSW residents were infected by pH1N1 in 2009; the highest infection rates (27%) were among adolescents and young adults. Past exposure to the antigenically similar influenza A/H1N1(1918) is the likely basis for a very high prevalence (49%) of prepandemic cross-reacting pH1N1 antibody and sparing from pH1N1 infection among people over 85 years. Unless pre-season vaccine uptake is high, there are likely to be at least moderate rates including some life-threatening cases of pH1N1 infection among young people during subsequent winters. [ABSTRACT FROM AUTHOR]

Background: Bacterial pneumonia is a well described complication of influenza. In recent years, community-onset methicillin-resistant Staphylococcus aureus (cMRSA) infection has emerged as a contributor to morbidity and mortality in patients with influenza. Since the emergence and rapid dissemination of pandemic A(H1N1)2009 influenzavirus in April 2009, initial descriptions of the clinical features of patients hospitalized with pneumonia have contained few details of patients with bacterial co-infection. Methodology/Principal Findings: Patients with community-acquired pneumonia (CAP) caused by co-infection with pandemic A(H1N1)2009 influenzavirus and cMRSA were prospectively identified at two tertiary hospitals in one Australian city during July to September 2009, the period of intense influenza activity in our region. Detailed characterization of the cMRSA isolates was performed. 252 patients with pandemic A(H1N1)2009 influenzavirus infection were admitted at the two sites during the period of study. Three cases of CAP due to pandemic A(H1N1)2009/cMRSA co-infection were identified. The clinical features of these patients were typical of those with S. aureus co-infection or sequential infection following influenza. The 3 patients received appropriate empiric therapy for influenza, but inappropriate empiric therapy for cMRSA infection; all 3 survived. In addition, 2 fatal cases of CAP caused by pandemic A(H1N1)2009/cMRSA co-infection were identified on post- mortem examination. The cMRSA infections were caused by three different cMRSA clones, only one of which contained genes for Panton-Valentine Leukocidin (PVL). Conclusions/Significance: Clinicians managing patients with pandemic A(H1N1)2009 influenzavirus infection should be alert to the possibility of co-infection or sequential infection with virulent, antimicrobial-resistant bacterial pathogens such as cMRSA. PVL toxin is not necessary for the development of cMRSA pneumonia in the setting of pandemic A( H1N1) 2009 influenzavirus co-infection. [ABSTRACT FROM AUTHOR]