EQ-5D-5L Population Norms and Quality-Adjusted Life Expectancy by Sociodemographic Characteristics and Modifiable Risk Factors for Adults in Queensland, Australia.

OBJECTIVES: Over half of Australia's disease burden is due to morbidity, predominantly chronic conditions. Health-related quality of life instruments provide measures of morbidity and health status across different dimensions with EQ-5D being one of the most widely used. This study reports EQ-5D-5L general population norms for Queensland, Australia using the recently published Australian value set. METHODS: Population survey results from cross-sectional computer-assisted telephone interviews for Queensland adults in 2022 and 2023 were analyzed. EQ-5D-5L, as well as modifiable risk factors and sociodemographic data were collected. Using the recently published final Australian EQ-5D-5L value set, mean utility scores were calculated for Queensland, as well as by sociodemographic characteristics, including remoteness and socioeconomic area-based measures, and modifiable risk factors, such as smoking and body mass index. Results were combined with life tables to estimate quality-adjusted life expectancy (QALE) for subgroups with different lifestyles. RESULTS: The EQ-5D utility score for the Queensland adult population was 0.916. Smoking daily, being obese or older in age, or living in the most disadvantaged socioeconomic area were associated with lower mean scores. QALE was 6.1 and 7.9 years shorter than the life expectancy for Queensland males and females, respectively, but generally, those who reported having healthier lifestyles had higher mean utility scores and thus longer QALE. CONCLUSIONS: In addition to reporting Queensland EQ-5D-5L general population norms, these results demonstrate potential QALE gains in people following healthier lifestyles. The results support investment in prevention and may motivate further studies in this important area.

Protocol for a process evaluation of a stepped wedge randomised controlled trial to reduce unnecessary hospitalisations of older people from residential aged care: the EDDIE+ study.

INTRODUCTION: The Early Detection of Deterioration in Elderly residents (EDDIE+) programme is a theory-informed, multi-component intervention aimed at upskilling and empowering nursing and personal care staff to identify and manage early signs of deterioration in residents of aged care facilities. The intervention aims to reduce unnecessary hospital admissions from residential aged care (RAC) homes. Alongside a stepped wedge randomised controlled trial, an embedded process evaluation will be conducted to assess the fidelity, acceptability, mechanisms of action and contextual barriers and enablers of the EDDIE+ intervention. METHODS AND ANALYSIS: Twelve RAC homes in Queensland, Australia are participating in the study. A comprehensive mixed-methods process evaluation, informed by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, will assess intervention fidelity, contextual barriers and enablers, mechanisms of action, and the acceptability of the programme from various stakeholder perspectives. Quantitative data will be collected prospectively from project documentation, including baseline context mapping of participating sites, activity tracking and regular check-in communication sheets. Qualitative data will be collected postintervention via semi-structured interviews with a range of stakeholder groups. The i-PARIHS constructs of innovation, recipients, context and facilitation will be applied to frame the analysis of quantitative and qualitative data. ETHICS AND DISSEMINATION: Ethical approval for this study has been granted by the Bolton Clarke Human Research Ethics Committee (approval number: 170031) with administrative ethical approval granted by the Queensland University of Technology University Human Research Ethics Committee (2000000618). Full ethical approval includes a waiver of consent for access to residents' demographic, clinical and health services de-identified data. A separate health services data linkage based on RAC home addresses will be sought through a Public Health Act application. Study findings will be disseminated through multiple channels, including journal publications, conference presentations and interactive webinars with a stakeholder network. TRIAL REGISTRATION NUMBER: Australia New Zealand Clinical Trial Registry (ACTRN12620000507987).

Factors associated with non-beneficial treatments in end of life hospital admissions: a multicentre retrospective cohort study in Australia.

OBJECTIVE: To quantitatively assess the factors associated with non-beneficial treatments (NBTs) in hospital admissions at the end of life. DESIGN: Retrospective multicentre cohort study. SETTING: Three large, metropolitan tertiary hospitals in Australia. PARTICIPANTS: 831 adult patients who died as inpatients following admission to the study hospitals over a 6-month period in 2012. MAIN OUTCOME MEASURES: Odds ratios (ORs) of NBT derived from logistic regression models. RESULTS: Overall, 103 (12.4%) admissions involved NBTs. Admissions that involved conflict within a patient's family (OR 8.9, 95% CI 4.1 to 18.9) or conflict within the medical team (OR 6.5, 95% CI 2.4 to 17.8) had the strongest associations with NBTs in the all subsets regression model. A positive association was observed in older patients, with each 10-year increment in age increasing the likelihood of NBT by approximately 50% (OR 1.5, 95% CI 1.2 to 1.9). There was also a statistically significant hospital effect. CONCLUSIONS: This paper presents the first statistical modelling results to assess the factors associated with NBT in hospital, beyond an intensive care setting. Our findings highlight potential areas for intervention to reduce the likelihood of NBTs.

Review of methods and study designs of evaluations related to clinical pathways.

Objective The HealthPathways program is an online information portal that helps clinicians provide consistent and integrated patient care within a local health system through localised pathways for diagnosis, treatment and management of various health conditions. These pathways are consistent with the definition of clinical pathways. Evaluations of HealthPathways programs have thus far focused primarily on website utilisation and clinical users' experience and satisfaction, with limited evidence on changes to patient outcomes. This lack motivated a literature review of the effects of clinical pathways on patient and economic outcomes to inform a subsequent HealthPathways evaluation. Methods A systematic review was performed to summarise the analytical methods, study designs and results of studies evaluating clinical pathways with an economic outcome component published between 1 January 2000 and 31 August 2017 in four academic literature databases. Results Fifty-five relevant articles were identified for inclusion in this review. The practical pre-post study design with retrospective baseline data extraction and prospective intervention data collection was most commonly used in the evaluations identified. Straightforward statistical methods for comparing outcomes, such as the t-test or χ2 test, were frequently used. Only four of the 55 articles performed a cost-effectiveness analysis. Clinical pathways were generally associated with improved patient outcomes and positive economic outcomes in hospital settings. Conclusions Clinical pathways evaluations commonly use pragmatic study designs, straightforward statistical tests and cost-consequence analyses. More HealthPathways program evaluations focused on patient and economic outcomes, clinical pathway evaluations in a primary care setting and cost-effectiveness analyses of clinical pathways are needed. What is known about the topic? HealthPathways is a web-based program that originated from Canterbury, New Zealand, and has seen uptake elsewhere in New Zealand, Australia and the UK. The HealthPathways program aims to assist the provision of consistent and integrated health services through dedicated, localised pathways for various health conditions specific to the health region. Evaluations of HealthPathways program focused on patient and economic outcomes have been limited. What does this paper add? This review synthesises the academic literature of clinical pathways evaluations in order to inform a subsequent HealthPathways evaluation. The focus of the synthesis was on the analytical methods and study designs used in the previous evaluations. The previous clinical pathway evaluations have been pragmatic in nature with relatively straightforward study designs and analysis. What are the implications for practitioners? There is a need for more economic and patient outcome evaluations for HealthPathways programs. More sophisticated statistical analyses and economic evaluations could add value to these evaluations, where appropriate and taking into consideration the data limitations.

Impact of a new mandatory reporting law on reporting and identification of child sexual abuse: A seven year time trend analysis.

Child sexual abuse is widespread and difficult to detect. To enhance case identification, many societies have enacted mandatory reporting laws requiring designated professionals, most often police, teachers, doctors and nurses, to report suspected cases to government child welfare agencies. Little research has explored the effects of introducing a reporting law on the number of reports made, and the outcomes of those reports. This study explored the impact of a new legislative mandatory reporting duty for child sexual abuse in the State of Western Australia over seven years. We analyzed data about numbers and outcomes of reports by mandated reporters, for periods before the law (2006-2008) and after the law (2009-2012). Results indicate that the number of reports by mandated reporters of suspected child sexual abuse increased by a factor of 3.7, from an annual mean of 662 in the three year pre-law period to 2448 in the four year post-law period. The increase in the first two post-law years was contextually and statistically significant. Report numbers stabilized in 2010-2012, at one report per 210 children. The number of investigated reports increased threefold, from an annual mean of 451 in the pre-law period to 1363 in the post-law period. Significant decline in the proportion of mandated reports that were investigated in the first two post-law years suggested the new level of reporting and investigative need exceeded what was anticipated. However, a subsequent significant increase restored the pre-law proportion, suggesting systemic adaptive capacity. The number of substantiated investigations doubled, from an annual mean of 160 in the pre-law period to 327 in the post-law period, indicating twice as many sexually abused children were being identified.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets.

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1-28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.