Treatment of Gestational Diabetes Mellitus Diagnosed Early in Pregnancy.

BACKGROUND: Whether treatment of gestational diabetes before 20 weeks' gestation improves maternal and infant health is unclear. METHODS: We randomly assigned, in a 1:1 ratio, women between 4 weeks' and 19 weeks 6 days' gestation who had a risk factor for hyperglycemia and a diagnosis of gestational diabetes (World Health Organization 2013 criteria) to receive immediate treatment for gestational diabetes or deferred or no treatment, depending on the results of a repeat oral glucose-tolerance test [OGTT] at 24 to 28 weeks' gestation (control). The trial included three primary outcomes: a composite of adverse neonatal outcomes (birth at <37 weeks' gestation, birth trauma, birth weight of ≥4500 g, respiratory distress, phototherapy, stillbirth or neonatal death, or shoulder dystocia), pregnancy-related hypertension (preeclampsia, eclampsia, or gestational hypertension), and neonatal lean body mass. RESULTS: A total of 802 women underwent randomization; 406 were assigned to the immediate-treatment group and 396 to the control group; follow-up data were available for 793 women (98.9%). An initial OGTT was performed at a mean (±SD) gestation of 15.6±2.5 weeks. An adverse neonatal outcome event occurred in 94 of 378 women (24.9%) in the immediate-treatment group and in 113 of 370 women (30.5%) in the control group (adjusted risk difference, -5.6 percentage points; 95% confidence interval [CI], -10.1 to -1.2). Pregnancy-related hypertension occurred in 40 of 378 women (10.6%) in the immediate-treatment group and in 37 of 372 women (9.9%) in the control group (adjusted risk difference, 0.7 percentage points; 95% CI, -1.6 to 2.9). The mean neonatal lean body mass was 2.86 kg in the immediate-treatment group and 2.91 kg in the control group (adjusted mean difference, -0.04 kg; 95% CI, -0.09 to 0.02). No between-group differences were observed with respect to serious adverse events associated with screening and treatment. CONCLUSIONS: Immediate treatment of gestational diabetes before 20 weeks' gestation led to a modestly lower incidence of a composite of adverse neonatal outcomes than no immediate treatment; no material differences were observed for pregnancy-related hypertension or neonatal lean body mass. (Funded by the National Health and Medical Research Council and others; TOBOGM Australian New Zealand Clinical Trials Registry number, ACTRN12616000924459.).

Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke.

BACKGROUND: In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit. MAIN TEXT: Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement. CONCLUSIONS: The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.

PURPOSE OF REVIEW: Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia. RECENT FINDINGS: From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.

Likelihood of primary cesarean section following induction of labor in singleton cephalic pregnancies at term, compared with expectant management: An Australian population-based, historical cohort study.

INTRODUCTION: There has been increased use of both induction of labor (IOL) and cesarean section for women with term pregnancies in many high-income countries, and a trend toward birth at earlier gestational ages. Existing evidence regarding the association between IOL and cesarean section for term pregnancies is mixed and conflicting, and little evidence is available on the differential effect at each week of gestation, stratified by parity. MATERIAL AND METHODS: To explore the association between IOL and primary cesarean section for singleton cephalic pregnancies at term, compared with two definitions of expectant management (first: at or beyond the week of gestation at birth following IOL; and secondary: only beyond the week of gestation at birth following IOL), we performed analyses of population-based historical cohort data on women who gave birth in one Australian state (Queensland), between July 1, 2012 and June 30, 2018. Women who gave birth before 37+0 or after 41+6 weeks of gestation, had stillbirths, no-labor, multiple births (twins or triplets), non-cephalic presentation at birth, a previous cesarean section, or missing data on included variables were excluded. Four sub-datasets were created for each week at birth (37-40). Unadjusted relative risk, adjusted relative risk using modified Poisson regression, and their 95% confidence intervals were calculated in each sub-dataset. Analyses were stratified by parity (nulliparas vs. parous women with a previous vaginal birth). Sensitivity analyses were conducted by limiting to women with low-risk pregnancies. RESULTS: A total of 239 094 women were included in the analysis, 36.7% of whom gave birth following IOL. The likelihood of primary cesarean section following IOL in a Queensland population-based cohort was significantly higher at 38 and 39 weeks, compared with expectant management up to 41+6 weeks, for both nulliparas and paras with singleton cephalic pregnancies, regardless of risk status of pregnancy and definition of expectant management. No significant difference was found for nulliparas at 37 and 40 weeks; and for paras at 40 weeks. CONCLUSIONS: Future studies are suggested to investigate further the association between IOL and other maternal and neonatal outcomes at each week of gestation in different maternal populations, before making any recommendation.

A longitudinal study of the impacts of a stay in a Prevention and Recovery Care service in Victoria, Australia.

BACKGROUND: Prevention and Recovery Care services are residential sub-acute services in Victoria, Australia, guided by a commitment to recovery-oriented practice. The evidence regarding the effectiveness of this service model is limited, largely relying on small, localised evaluations. This study involved a state-wide investigation into the personal recovery, perceived needs for care, well-being and quality-of-life outcomes experienced by Prevention and Recovery Care services' consumers. METHODS: A longitudinal cohort design examined the trajectory of self-reported personal recovery and other outcomes for consumers in 19 Victorian Prevention and Recovery Care services over 4 time points (T1 - 1 week after admission; T2 - within 1 week of discharge; T3 - 6 months after discharge; T4 - 12 months after discharge). T2-T4 time frames were extended by approximately 3 weeks due to recruitment challenges. The Questionnaire about the Process of Recovery was the primary outcome measure. RESULTS: At T1, 298 consumers were recruited. By T4, 114 remained in the study. Participants scored higher on the Questionnaire about the Process of Recovery at all three time points after T1. There were also sustained improvements on all secondary outcome measures. Improvements were then sustained at each subsequent post-intervention time point. Community inclusion and having needs for care met also improved. CONCLUSION: The findings provide a consistent picture of benefits for consumers using Prevention and Recovery Care services, with significant improvement in personal recovery, quality of life, mental health and well-being following an admission to a Prevention and Recovery Care service. Further attention needs to be given to how to sustain the gains made through a Prevention and Recovery Care service admission in the long term.

Identifying Longer-Term Health Events and Outcomes and Health Service Use of Low Birthweight CALD Infants in Australia.

INTRODUCTION: Approximately one-third of all births in Australia each year are by culturally and linguistically diverse (CALD) women. CALD women are at an increased risk of adverse pregnancy and birth outcomes including prematurity and low birthweight. Infants born weighing less than 2500 g are susceptible to increased risk of ill health and morbidities such as cognitive defects including cerebral palsy, and neuro-motor functioning. METHODS: An existing linked administrative dataset, Maternity 1000 was utilized for this study which has identified all children born in Queensland (QLD), Australia, between 1st July 2012 to 30th June 2018 from the QLD Perinatal Data Collection. This has then been linked to the QLD Hospital Admitted Patient Data Collection, QLD Hospital Non-Admitted Patient Data Collection, QLD Emergency Department Data Collection, and Medicare Benefits Schedule and Pharmaceutical Benefits Scheme Claims Records between 1 and 2012 to 30th June 2019. RESULTS: Culturally and linguistically diverse infants born with low birthweight had higher mean and standard deviation of all health events and outcomes; potentially preventable hospitalisations, hospital re-admissions, ED presentations without admissions, and development of chronic diseases compared to non-CALD infants born with low birthweight. DISCUSSION: Results from this study highlight the disparities in health service use and health events and outcomes associated with low birthweight infants, between both CALD and Australian born women. This study has responded to the knowledge gap of low birthweight on the Australian economy by identifying that there are significant inequalities in access to health services for CALD women in Australia, as well as increased health events and poor birth outcomes for these infants when compared to those of mothers born in Australia.

Models Predicting Postpartum Glucose Intolerance Among Women with a History of Gestational Diabetes Mellitus: a Systematic Review.

PURPOSE OF REVIEW: Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. The purpose of this review is to examine the methodological characteristics and quality of existing prognostic models predicting postpartum glucose intolerance following GDM. RECENT FINDINGS: A systematic review was conducted on relevant risk prediction models, resulting in 15 eligible publications from research groups in various countries. Our review found that traditional statistical models were more common than machine learning models, and only two were assessed to have a low risk of bias. Seven were internally validated, but none were externally validated. Model discrimination and calibration were done in 13 and four studies, respectively. Various predictors were identified, including body mass index, fasting glucose concentration during pregnancy, maternal age, family history of diabetes, biochemical variables, oral glucose tolerance test, use of insulin in pregnancy, postnatal fasting glucose level, genetic risk factors, hemoglobin A1c, and weight. The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM.

Pharmacoepidemiology and costs of medications dispensed during pregnancy: A retrospective population-based study.

OBJECTIVE: To describe the pharmacoepidemiology and costs associated with medications dispensed during pregnancy. DESIGN: Pharmacoepidemiological study and cost analysis. SETTING: Queensland, Australia. POPULATION: All women who gave birth in Queensland between January 2013 and June 2018. METHODS: We used a whole-of-population linked administrative dataset, Maternity1000, to describe medications approved for public subsidy that were dispensed to 255 408 pregnant women. We describe the volume of medications dispensed and their associated costs from a Government and patient perspective. MAIN OUTCOME MEASURES: Prevalence of medication use; proportion of total dispensings; total medication costs in AUD 2020/21 ($1AUD = $0.67USD/£0.55GBP in December 2022). RESULTS: During pregnancy, 61% (95% CI 60.96-61.29%) of women were dispensed at least one medication approved for public subsidy. The mean number of items dispensed per pregnancy increased from 2.14 (95% CI 2.11-2.17) in 2013 to 2.47 (95% CI 2.44-2.51) in 2017; an increase of 15%. Furthermore, mean Government cost per dispensing increased by 41% from $21.60 (95% CI $20.99-$22.20) in 2013 to $30.44 (95% CI $29.38-$31.49) in 2017. These factors influenced the 53% increase in total Government expenditure observed for medication use during pregnancy between 2013 and 2017 ($2,834,227 versus $4,324,377); a disproportionate rise compared with the 17% rise in women's total out-of-pocket expenses observed over the same timeframe ($1,880,961 versus $2,204,415). CONCLUSIONS: Prevalence of medication use in pregnancy is rising and is associated with disproportionate and rapidly escalating cost implications for the Government.

The value of maternity care in Queensland, 2012-18, based on an analysis of administrative data: a retrospective observational study.

OBJECTIVE: To quantify the value of maternity health care - the relationship of outcomes to costs - in Queensland during 2012-18. STUDY DESIGN: Retrospective observational study; analysis of Queensland Perinatal Data Collection data linked with the Queensland Health Admitted Patient, Non-Admitted Patient, and Emergency Data Collections, and with the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) databases. SETTING, PARTICIPANTS: All births in Queensland during 1 July 2012 - 30 June 2018. MAIN OUTCOME MEASURES: Maternity care costs per birth (reported in 2021-22 Australian dollars), both overall and by funder type (public hospital funders, MBS, PBS, private health insurers, out-of-pocket costs); value of care, defined as total cost per positive birth outcome (composite measure). RESULTS: The mean cost per birth (all funders) increased from $20 471 (standard deviation [SD], $17 513) during the second half of 2012 to $30 000 (SD, $22 323) during the first half of 2018; the annual total costs for all births increased from $1.31 billion to $1.84 billion, despite a slight decline in the total number of births. In a mixed effects linear analysis adjusted for demographic, clinical, and birth characteristics, the mean total cost per birth in the second half of 2018 was $9493 higher (99.9% confidence interval, $8930-10 056) than during the first half of 2012. The proportion of births that did not satisfy our criteria for a positive birth outcome increased from 27.1% (8404 births) during the second half of 2012 to 30.5% (9041 births) during the first half of 2018. CONCLUSION: The costs of maternity care have increased in Queensland, and many adverse birth outcomes have become more frequent. Broad clinical collaboration, effective prevention and treatment strategies, as well as maternal health services focused on all dimensions of value, are needed to ensure the quality and viability of maternity care in Australia.

Prevalence and determinants of mental health related to climate change in Australia.

AIMS: The climate emergency will likely prove this century's greatest threat to public health within which mental health effects need consideration. While studies consistently show the majority of Australians are very concerned about the impacts of climate change, there is limited evidence from nation-wide research linking climate change with mental health burden in sub-populations. This study aimed to understand the impact of climate change on mental health in the Australian population and identify populations who are most at risk of climate-related mental health burden. METHODS: A nation-wide Australian survey conducted between August and November 2020 of adults was approximately representative across sex, age, location, state and area disadvantage. Two-stage recruitment involved unrestricted self-selected community sample through mainstream and social media (N = 4428) and purposeful sampling using an online panel (N = 1055). RESULTS: Most Australians report having a direct experience of a climate change-related event. Young people are experiencing significant rates of eco-anxiety. One in four people with direct experience of a climate change-related event met post-traumatic stress disorder screening criteria. People who have not had a direct experience are showing symptoms of pre-trauma, particularly in younger age groups and women. There were 9.37% (503/5370) of respondents with responses indicating significant eco-anxiety, 15.68% (370/2359) with pre-traumatic stress and 25.60% (727/2840) with post-traumatic stress disorder. Multivariable regressions confirmed that younger people are more affected by eco-anxiety and post-traumatic stress disorder (pre- or post-trauma); women are more affected by post-traumatic stress disorder (pre- or post-trauma) and those from more disadvantaged regions are more affected by eco-anxiety. CONCLUSION: Australia is facing a potential mental health crisis. Individuals with and without direct experience of climate change are reporting significant mental health impacts, with younger age groups being disproportionately affected. There are key roles for clinicians and other health professionals in responding to and preventing climate-related mental health burden.

Mental health systems modelling for evidence-informed service reform in Australia.

Australia's Fifth National Mental Health Plan required governments to report, not only on the progress of changes to mental health service delivery, but to also plan for services that should be provided. Future population demand for treatment and care is challenging to predict and one solution involves modelling the uncertain demands on the system. Modelling can help decision-makers understand likely future changes in mental health service demand and more intelligently choose appropriate responses. It can also support greater scrutiny, accountability and transparency of these processes. Australia has an emerging national capacity for systems modelling in mental health which can enhance the next phase of mental health reform. This paper introduces concepts useful for understanding mental health modelling and identifies where modelling approaches can support health service planners to make evidence-informed decisions regarding planning and investment for the Australian population.

'It's Too nice': Adapting iSupport Lite for Ethnically Diverse Family Carers of a Person with Dementia.

OBJECTIVES: Resources to support dementia carers from ethnically diverse families are limited. We explored carers' and service providers' views on adapting the World Health Organization's iSupport Lite messages to meet their needs. METHODS: Six online workshops were conducted with ethnically diverse family carers and service providers (n = 21) from nine linguistic groups across Australia. Recruitment was via convenience and snowball sampling from existing networks. Data were analyzed using thematic analysis. RESULTS: Participants reported that iSupport Lite over-emphasized support from family and friends and made help-seeking sound "too easy". They wanted messages to dispel notions of carers as "superheroes", demonstrate that caring and help-seeking is stressful and time-consuming, and that poor decision-making and relationship breakdown does occur. Feedback was incorporated to co-produce a revised suite of resources. CONCLUSIONS: Beyond language translation, cultural adaptation using co-design provided participants the opportunity to develop more culturally relevant care resources that meet their needs. These resources will be evaluated for clinical and cost-effectiveness in future research. CLINICAL IMPLICATIONS: By design, multilingual resources for carers must incorporate cultural needs to communicate support messages. If this intervention is effective, it could help to reduce dementia care disparities in ethnically diverse populations in Australia and globally.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow.

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses.

COVID-19 research has relied heavily on convenience-based samples, which-though often necessary-are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study ( www.icarestudy.com ). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended.

Options for improving low birthweight and prematurity birth outcomes of indigenous and culturally and linguistically diverse infants: a systematic review of the literature using the social-ecological model.

BACKGROUND: Prematurity and low birthweight are more prevalent among Indigenous and Culturally and Linguistically Diverse infants. METHODS: To conduct a systematic review that used the social-ecological model to identify interventions for reducing low birthweight and prematurity among Indigenous or CALD infants. Scopus, PubMed, CINAHL, and Medline electronic databases were searched. Studies included those published in English between 2010 and 2021, conducted in high-income countries, and reported quantitative results from clinical trials, randomized controlled trials, case-control studies or cohort studies targeting a reduction in preterm birth or low birthweight among Indigenous or CALD infants. Studies were categorized according to the level of the social-ecological model they addressed. FINDINGS: Nine studies were identified that met the inclusion criteria. Six of these studies reported interventions targeting the organizational level of the social-ecological model. Three studies targeted the policy, community, and interpersonal levels, respectively. Seven studies presented statistically significant reductions in preterm birth or low birthweight among Indigenous or CALD infants. These interventions targeted the policy (n = 1), community (n = 1), interpersonal (n = 1) and organizational (n = 4) levels of the social-ecological model. INTERPRETATION: Few interventions across high-income countries target the improvement of low birthweight and prematurity birth outcomes among Indigenous or CALD infants. No level of the social-ecological model was found to be more effective than another for improving these outcomes.

Value in maternal care: Using the Learning Health System to facilitate action.

There is an increasing need to deliver high-value health care. Here, we discuss how value should be measured and implemented in maternity care through a Learning Health System. High-value maternity care will produce the highest level of benefit for women at a given cost. As pregnancy is not an illness state, and there is no cure or remission to be achieved, we believe that patient-reported outcomes should be an integral component of benefit quantification when measuring value. Furthermore, as care impacts more than just health outcomes-particularly in maternity care-there is also a need to consider patient-reported experiences as a part of defining the level of benefit. However, to move beyond traditional narrow and passive measurement of value, we need to partner with stakeholders to identify priorities for change, identify evidence for how to achieve this change, integrate measurement activities, and promote effective implementation, in a continuous, learning cycle-a Learning Health System. A robust Framework for implementing a Learning Health System has been developed, which could be applied in maternity care.

OPTIMISE: a pragmatic stepped wedge cluster randomised trial of an intervention to improve primary care for refugees in Australia.

OBJECTIVES: To examine whether primary care outreach facilitation improves the quality of care for general practice patients from refugee backgrounds. DESIGN: Pragmatic, cluster randomised controlled trial, with stepped wedge allocation to early or late intervention groups. SETTING, PARTICIPANTS: 31 general practices in three metropolitan areas of Sydney and Melbourne with high levels of refugee resettlement, November 2017 - August 2019. INTERVENTION: Trained facilitators made three visits to practices over six months, using structured action plans to help practice teams optimise routines of refugee care. MAJOR OUTCOME MEASURE: Change in proportion of patients from refugee backgrounds with documented health assessments (Medicare billing). Secondary outcomes were refugee status recording, interpreter use, and clinician-perceived difficulty in referring patients to appropriate dental, social, settlement, and mental health services. RESULTS: Our sample comprised 14 633 patients. The intervention was associated with an increase in the proportion of patients with Medicare-billed health assessments during the preceding six months, from 19.1% (95% CI, 18.6-19.5%) to 27.3% (95% CI, 26.7-27.9%; odds ratio, 1.88; 95% CI, 1.42-2.50). The impact of the intervention was greater in smaller practices, practices with larger proportions of patients from refugee backgrounds, recent training in refugee health care, or higher baseline provision of health assessments for such patients. There was no impact on refugee status recording, interpreter use increased modestly, and reported difficulties in refugee-specific referrals to social, settlement and dental services were reduced. CONCLUSIONS: Low intensity practice facilitation may improve some aspects of primary care for people from refugee backgrounds. Facilitators employed by local health services could support integrated approaches to enhancing the quality of primary care for this vulnerable population. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12618001970235 (retrospective).

Discrimination reported by older adults living with mental health conditions: types, contexts and association with healthcare barriers.

OBJECTIVE: Australian policy-making needs better information on the prevalence, context and types of discrimination reported by people living with mental health conditions and the association of exposure to discrimination with experiencing a barrier to accessing healthcare. METHODS: Secondary data analysis using the national representative General Social Survey 2014 to examine discrimination and healthcare barriers. Multivariable logistic regression was used to examine the association between discrimination and barriers to healthcare. RESULTS: Around 10% of older adults without mental health conditions reported an instance of discrimination in the last 12 months, compared to 22-25% of those with mental health conditions. Approximately 20% with mental health conditions attributed discrimination to their health conditions, along with other characteristics including age. Discrimination was reported in settings important to human capital (e.g., healthcare, workplace), but also in general social and public contexts. Everyday discrimination (OR = 2.11 p < 0.001), discrimination in healthcare (OR = 2.92 p < 0.001), and discrimination attributed to the person's health condition (OR = 1.99 p < 0.05) increased the odds of experiencing a barrier to care two-to-three-fold. For each type of discrimination reported (e.g., racism, ageism etc.), the odds of experiencing a barrier to care increased 1.3 times (OR = 1.29 p < 0.01). CONCLUSION: This new population-level evidence shows older adults with mental health conditions are experiencing discrimination at more than twofold compared to those without mental health conditions. Discrimination was associated with preventing or delaying healthcare access. These findings indicate that future strategies to promote mental healthcare in underserved groups of older people will need to be multidimensional and consideration given to address discrimination.

Learning health systems using data to drive healthcare improvement and impact: a systematic review.

BACKGROUND: The transition to electronic health records offers the potential for big data to drive the next frontier in healthcare improvement. Yet there are multiple barriers to harnessing the power of data. The Learning Health System (LHS) has emerged as a model to overcome these barriers, yet there remains limited evidence of impact on delivery or outcomes of healthcare. OBJECTIVE: To gather evidence on the effects of LHS data hubs or aligned models that use data to deliver healthcare improvement and impact. Any reported impact on the process, delivery or outcomes of healthcare was captured. METHODS: Systematic review from CINAHL, EMBASE, MEDLINE, Medline in-process and Web of Science PubMed databases, using learning health system, data hub, data-driven, ehealth, informatics, collaborations, partnerships, and translation terms. English-language, peer-reviewed literature published between January 2014 and Sept 2019 was captured, supplemented by a grey literature search. Eligibility criteria included studies of LHS data hubs that reported research translation leading to health impact. RESULTS: Overall, 1076 titles were identified, with 43 eligible studies, across 23 LHS environments. Most LHS environments were in the United States (n = 18) with others in Canada, UK, Sweden and Australia/NZ. Five (21.7%) produced medium-high level of evidence, which were peer-reviewed publications. CONCLUSIONS: LHS environments are producing impact across multiple continents and settings.

Socio-economic disadvantage and resource distribution for mental health care: a model proposal and example application for Victoria, Australia.

OBJECTIVE: A spreadsheet-based model for supporting equitable mental health resource distribution in Australia was developed, based on the Australian Health Survey (AHS) psychological distress findings associated with area socio-economic disadvantage (SED). An illustrative application is presented. METHOD: Stratum-specific psychological-distress rates for area SED quintiles are applied to local government areas, catchment areas and local health networks (LHNs). A case study applies the model to Victoria, including examining recommendations in the Royal Commission into Victoria's Mental Health Services (RCVMHS) 2019 interim report for increases to bed stock in two LHNs. RESULTS: Need-adjusted demand estimates considered as a ratio of raw population proportions for catchments range between 0.6 to 1.4 in Victoria. Applying the formula to the Royal Commission recommendations suggests the proposed distribution of beds is a reasonable correction for these two LHNs and indicates next expansion priorities for more equitable distribution to other LHNs. CONCLUSIONS: The spreadsheet, adaptable for other states and territories, could complement National Mental Health Services Planning Framework outputs and assist in evaluation, for instance, determining potential supply shortages in the tele-mental-health response to COVID-19. We outline research directions including consideration of the moral bases of value judgements and identification of other variables including their use in parameterisation and calibration.