Trends and determinants of mental illness in humanitarian migrants resettled in Australia: Analysis of longitudinal data.

Given the frequent exposure of humanitarian migrants to traumatic or stressful circumstances, there exists a potential predisposition to mental illness. Our objective was to pinpoint the trends and determinants of mental illness among humanitarian migrants resettled in Australia. This study considered five waves of longitudinal data involving humanitarian migrants resettled in Australia. Post-traumatic stress disorder (PTSD) and psychological distress were assessed using PTSD-8 and Kessler-6 screening tools. Through a Generalised Linear Mixed model (GLMM), variables displaying a 95% CI that excluded the value of 1.0 for the odds ratio were identified as associated factors for both PTSD and elevated psychological distress. The selection of multivariable covariates was guided by causal loop diagrams and least absolute shrinkage and selection operators methods. At baseline, there were 2399 humanitarian migrants with 1881 retained and at the fifth yearly wave; the response rate was 78.4%. PTSD prevalence decreased from 33.3% (95% CI: 31.4-35.3) at baseline to 28.3% (95% CI: 26.2-30.5) at year 5. Elevated psychological distress persisted across all waves: 17.1% (95% CI: 15.5-18.6) at baseline and 17.1% (95% CI: 15.3-18.9) at year 5. Across the five waves, 34.0% of humanitarian migrants met screening criteria for mental illness, either PTSD or elevated psychological distress. In the multivariate model, factors associated with PTSD were loneliness (AOR 1.5, 95% CI: 1.3-1.8), discrimination (AOR 1.6: 1.2-2.1), temporary housing contract (AOR 3.7: 2.1-6.7), financial hardship (AOR 2.2:1.4-3.6) and chronic health conditions (AOR 1.3: 1.1-1.5), whereas the associated factors for elevated psychological distress were loneliness (AOR 1.8: 1.5-2.2), discrimination (AOR 1.7: 1.3-2.2) and short-term lease housing (AOR 1.6: 1.0-1.7). The prevalence, persistence and consequential burden of mental illness within this demographic underscore the urgent need for targeted social and healthcare policies. These policies should aim to mitigate modifiable risk factors, thereby alleviating the significant impact of mental health challenges on this population.

Diagnostic accuracy of oral glucose tolerance tests, fasting plasma glucose and haemoglobin A1c for type 2 diabetes in women with polycystic ovary syndrome: A systematic review and meta-analysis.

AIMS: To inform international guidelines, a systematic review and meta-analysis was conducted to assess the performance of diagnostic methods for type 2 diabetes in women with polycystic ovary syndrome (PCOS). METHODS: An updated systematic search was conducted on five databases from 2017 until October 2023 and combined with prior searches (from inception). Meta-analyses of diagnostic accuracy tests were conducted. RESULTS: Nine studies comprising 2628 women with PCOS were included. Against the oral glucose tolerance test, a haemoglobin A1C (HbA1c) ≥ 6.5% had a pooled sensitivity of 50.00% (95% confidence interval (CI): 35.53-64.47), specificity of 99.86% (95%CI: 99.49-99.98), and positive and negative predictive values of 92.59% (95%CI: 75.27-98.09) and 98.27% (95%CI: 97.73-98.68), respectively, with an accuracy of 98.17% (95%CI: 97.34-98.79). Fasting plasma glucose values ≥ 7.0 mmol/L had a pooled sensitivity of 58.14% (95%CI: 42.13-72.99), specificity of 92.59% (95%CI: 75.35-98.08), positive and negative predictive values of 92.59% (95%CI: 75.35-98.08) and 99.09% (95%CI: 98.71-99.36), respectively, and an accuracy of 99.00% (95%CI: 98.46-99.39) against the oral glucose tolerance test. CONCLUSIONS: To our knowledge, this is the first systematic review assessing the performance of diagnostic methods for type 2 diabetes in women with PCOS. We demonstrate that using a cut-off for HbA1c of ≥6.5% in this population may result in misdiagnosis of half of the women with type 2 diabetes. Our results directly informed the recommendations of the 2023 International PCOS Guideline, suggesting that the oral glucose tolerance test is the optimal method for screening and diagnosing type 2 diabetes in women with PCOS and is superior to fasting plasma glucose and HbA1c.

How the structural determinants of health inequities impact access to prescription medication for pregnant women in Australia: a narrative review.

Structural factors that contribute to health disparities (e.g., population-level policies, cultural norms) impact the distribution of resources in society and can affect medication accessibility; even in high-income countries like Australia. Industry practices and regulatory approaches (e.g., a conservative approach to testing medicines in pregnant women) influence the availability of safety and efficacy data necessary for the licencing and funding of prescription medications used during pregnancy. Consequently, pregnant women may be prescribed medications outside of regulatory or funder-approved indications, posing risks for both prescribers and pregnant women and potentially compromising equitable access to medications. This review examines the regulatory and legislative structural factors that contribute to health disparities and perpetuate the deeply ingrained social norm that we should be protecting pregnant women from clinical research rather than safeguarding them through such research. Addressing these challenges requires a renewed commitment to integrated, woman-centred maternal healthcare and strengthened collaboration across all sectors. Funding: Australian Government Research Training Program Stipend from the University of Technology Sydney, National Health and Medical Research Council (NHMRC) Fellowship, Channel 7 Children's Research Foundation Fellowship (CRF-210323).

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.

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.

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.

Assessing the fidelity of a behavioural intervention involving academic detailing in general practice: a sub-study of the 'Implementing work-related Mental health guidelines in general PRacticE' (IMPRovE) trial.

BACKGROUND: Assessing the fidelity of intervention components enables researchers to make informed judgements about the influence of those components on the observed outcome. The 'Implementing work-related Mental health guidelines in general PRacticE' (IMPRovE) trial is a hybrid III trial aiming to increase adherence to the 'Clinical Guidelines for the diagnosis and management of work-related mental health conditions in general practice'. IMPRovE is a multifaceted intervention, with one of the central components being academic detailing (AD). This study describes the fidelity to the protocol for the AD component of the IMPRovE intervention. METHOD: All AD sessions for the trial were audio-recorded and a sample of 22% were randomly selected for fidelity assessment. Fidelity was assessed using a tailored proforma based on the Modified Conceptual Framework for fidelity assessment, measuring duration, coverage, frequency and content. A descriptive analysis was used to quantify fidelity to the protocol and a content analysis was used to elucidate qualitative aspects of fidelity. RESULTS: A total of eight AD sessions were included in the fidelity assessment. The average fidelity score was 89.2%, ranging from 80 to 100% across the eight sessions. The sessions were on average 47 min long and addressed all of the ten chapters in the guideline. Of the guideline chapters, 9 were frequently discussed. The least frequently discussed chapter related to management of comorbid conditions. Most general practitioner (GP) participants used the AD sessions to discuss challenges with managing secondary mental conditions. In line with the protocol, opinion leaders who delivered the AD sessions largely offered evidence-based strategies aligning with the clinical guideline recommendations. CONCLUSIONS/IMPLICATIONS: The IMPRovE AD intervention component was delivered to high fidelity. The sessions adhered to the intended duration, coverage, frequency, and content allowing participating GPs to comprehend the implementation of the guideline in their own practice. This study also demonstrates that the Modified Conceptual Fidelity Framework with a mixed methods approach can support the assessment of implementation fidelity of a behavioural intervention in general practice. The findings enhance the trustworthiness of reported outcomes from IMPRovE and show that assessing fidelity is amenable for AD and should be incorporated in other studies using AD. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN 12620001163998, November 2020.

Improving the lives of ethnically diverse family carers and people living with dementia using digital media resources - Protocol for the Draw-Care randomised controlled trial.

Objectives: Ethnically diverse family carers of people living with dementia (hereafter carers and people with dementia) experience more psychological distress than other carers. To reduce this inequality, culturally adapted, multilingual, evidence-based practical assistance is needed. This paper details the Draw-Care study protocol including a randomised control trial (RCT) to test the effectiveness of a digital intervention comprising a multilingual website, virtual assistant, animated films, and information, on the lives of carers and people with dementia in Australia. Methods: The Draw-Care intervention will be evaluated in a 12-week active waitlist parallel design RCT with 194 carers from Arabic, Cantonese, Greek, Hindi, Italian, Mandarin, Spanish, Tamil, and Vietnamese-speaking language groups. Our intervention was based on the World Health Organization's (WHO) iSupport Lite online carer support messages and was co-designed with carers, people with dementia, service providers, and clinicians. Culturally adapted multilingual digital resources were created in nine languages and English. Results: In Phase I (2022), six co-design workshops with stakeholders and interviews with people with dementia informed the development of the intervention which will be trialled and evaluated in Phases II and III (2023 and 2024). Conclusions: Digital media content is a novel approach to providing cost-effective access to health care information. This study protocol details the three study phases including the RCT of a co-designed, culturally adapted, multilingual, digital intervention for carers and people with dementia to advance the evidence in dementia and digital healthcare research and help meet the needs of carers and people with dementia in Australia and globally.

'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.

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.

Background: Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively. Objective: This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions. Method: LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist. Results: Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting. Conclusion: To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus.

OBJECTIVE: The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability. STUDY DESIGN AND SETTING: The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores. RESULTS: In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing. CONCLUSION: We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model.

BACKGROUND: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. OBJECTIVE: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. METHODS: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. RESULTS: Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). CONCLUSIONS: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.

The MINDSET Study: Co-Designing Training for Interpreters in Dementia and Cognitive Assessments.

There is a growing demand for interpreter-mediated cognitive assessments for dementia. However, most interpreters lack specialist knowledge of dementia and cognitive assessment tools. This can negatively affect the way instructions and responses are conveyed between clinicians and patients, undermining clinicians' ability to accurately assess for cognitive impairment. This article reports on the co-design of an online dementia training package, MINDSET, which aims to address this gap. Two iterative online co-design workshops were conducted in October and November 2021, using a World Café approach. Sixteen clinicians, interpreters, and multilingual family carers of a person with dementia participated. Based on these workshops, training and assessment materials were developed and tested with 12 interpreters from April to June 2022. The training package comprises online modules: 1) Knowledge of Dementia and Australia's Aged Care System, 2) Briefings and Introductions, 3) Interpreting Skills, 4) Interpreting Ethics, and 5) Cross-cultural Communication. The codesign process highlighted divergent perspectives between clinicians and interpreters on an interpreter's role during a cognitive assessment, but it also facilitated negotiation and consensus building, which enriched the training content. The training is now developed and will be evaluated in a randomized control trial and subsequent implementation study.

Hospital length of stay prediction for general surgery and total knee arthroplasty admissions: Systematic review and meta-analysis of published prediction models.

Objective: Systematic review of length of stay (LOS) prediction models to assess the study methods (including prediction variables), study quality, and performance of predictive models (using area under receiver operating curve (AUROC)) for general surgery populations and total knee arthroplasty (TKA). Method: LOS prediction models published since 2010 were identified in five major research databases. The main outcomes were model performance metrics including AUROC, prediction variables, and level of validation. Risk of bias was assessed using the PROBAST checklist. Results: Five general surgery studies (15 models) and 10 TKA studies (24 models) were identified. All general surgery and 20 TKA models used statistical approaches; 4 TKA models used machine learning approaches. Risk scores, diagnosis, and procedure types were predominant predictors used. Risk of bias was ranked as moderate in 3/15 and high in 12/15 studies. Discrimination measures were reported in 14/15 and calibration measures in 3/15 studies, with only 4/39 externally validated models (3 general surgery and 1 TKA). Meta-analysis of externally validated models (3 general surgery) suggested the AUROC 95% prediction interval is excellent and ranges between 0.803 and 0.970. Conclusion: This is the first systematic review assessing quality of risk prediction models for prolonged LOS in general surgery and TKA groups. We showed that these risk prediction models were infrequently externally validated with poor study quality, typically related to poor reporting. Both machine learning and statistical modelling methods, plus the meta-analysis, showed acceptable to good predictive performance, which are encouraging. Moving forward, a focus on quality methods and external validation is needed before clinical application.

Key Components of Antenatal Lifestyle Interventions to Optimize Gestational Weight Gain: Secondary Analysis of a Systematic Review.

Importance: Randomized clinical trials have found that antenatal lifestyle interventions optimize gestational weight gain (GWG) and pregnancy outcomes. However, key components of successful interventions for implementation have not been systematically identified. Objective: To evaluate intervention components using the Template for Intervention Description and Replication (TIDieR) framework to inform implementation of antenatal lifestyle interventions in routine antenatal care. Data Sources: Included studies were drawn from a recently published systematic review on the efficacy of antenatal lifestyle interventions for optimizing GWG. The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Health Technology Assessment Database, MEDLINE, and Embase were searched from January 1990 to May 2020. Study Selection: Randomized clinical trials examining efficacy of antenatal lifestyle interventions in optimizing GWG were included. Data Extraction and Synthesis: Random effects meta-analyses were used to evaluate the association of intervention characteristics with efficacy of antenatal lifestyle interventions in optimizing GWG. The results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Data extraction was performed by 2 independent reviewers. Main Outcomes and Measures: The main outcome was mean GWG. Measures included characteristics of antenatal lifestyle interventions comprising domains related to theoretical framework, material, procedure, facilitator (allied health staff, medical staff, or researcher), delivery format (individual or group), mode, location, gestational age at commencement (<20 wk or ≥20 wk), number of sessions (low [1-5 sessions], moderate [6-20 sessions], and high [≥21 sessions]), duration (low [1-12 wk], moderate [13-20 wk], and high [≥21 wk]), tailoring, attrition, and adherence. For all mean differences (MDs), the reference group was the control group (ie, usual care). Results: Overall, 99 studies with 34 546 pregnant individuals were included with differential effective intervention components found according to intervention type. Broadly, interventions delivered by an allied health professional were associated with a greater decrease in GWG compared with those delivered by other facilitators (MD, -1.36 kg; 95% CI, -1.71 to -1.02 kg; P < .001). Compared with corresponding subgroups, dietary interventions with an individual delivery format (MD, -3.91 kg; 95% CI -5.82 to -2.01 kg; P = .002) and moderate number of sessions (MD, -4.35 kg; 95% CI -5.80 to -2.89 kg; P < .001) were associated with the greatest decrease in GWG. Physical activity and mixed behavioral interventions had attenuated associations with GWG. These interventions may benefit from an earlier commencement and a longer duration for more effective optimization of GWG. Conclusions and Relevance: These findings suggest that pragmatic research may be needed to test and evaluate effective intervention components to inform implementation of interventions in routine antenatal care for broad public health benefit.

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.

Impact of COVID-19 Restriction on Weight, Physical Activity, Diet and Psychological Distress on People with PCOS.

BACKGROUND: People with polycystic ovary syndrome (PCOS) have higher weight gain and psychological distress compared to those without PCOS. While COVID-19 restrictions led to population level adverse changes in lifestyle, weight gain and psychological distress, their impact on people with PCOS is unclear. The aim of this study was to investigate the impact the 2020 COVID-19 restrictions had on weight, physical activity, diet and psychological distress for Australians with PCOS. METHODS: Australian reproductive-aged women participated in an online survey with assessment of weight, physical activity, diet and psychological distress. Multivariable logistic and linear regression were used to examine associations between PCOS and residential location with health outcomes. RESULTS: On adjusted analysis, those with PCOS gained more weight (2.9%; 95% CI; 0.027-3.020; p = 0.046), were less likely to meet physical activity recommendations (OR 0.50; 95% CI; 0.32-0.79; p = 0.003) and had higher sugar-sweetened beverage intake (OR 1.74; 95% CI 1.10-2.75; p = 0.019) but no differences in psychological distress compared to women without PCOS. CONCLUSIONS: People with PCOS were more adversely affected by COVID-19 restrictions, which may worsen their clinical features and disease burden. Additional health care support may be necessary to assist people with PCOS to meet dietary and physical activity recommendations.

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.).

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.

Normative cut-offs for polycystic ovary syndrome diagnostic features in adolescents using cluster analysis.

OBJECTIVE: The diagnosis of polycystic ovary syndrome (PCOS) remains challenging with international guidelines prioritising accurate cut-offs for individual diagnostic features. These diagnostic cut-offs are currently based on arbitrary percentiles, often from poorly characterised cohorts, and are dependent on variable laboratory ranges defined by assay manufacturers, limiting diagnostic accuracy. Cluster analysis is the recommended approach for defining normative cut-offs within populations for clinical syndromes. Few PCOS adult studies have applied cluster analysis, with no studies in adolescents. We aimed to define normative cut-offs for individual PCOS diagnostic features in a community-based population of adolescents using cluster analysis. DESIGN: This analysis utilised data from the Menstruation in Teenagers Study, a subgroup of the Raine Study, which is a population based prospective cohort of 244 adolescents whose mean age at PCOS assessment was 15.2 years. METHODS: K-means cluster analysis and receiver operating characteristics curves were used to define normative cut-offs for modified Ferriman-Gallwey (mFG) score, free testosterone (free T), free androgen index (FAI), and menstrual cycle length. RESULTS: Normative cut-offs for mFG, free T, FAI, and menstrual cycle lengths were 1.0, 23.4 pmol/L, 3.6, and 29 days, respectively. These corresponded to the 65th, 71st, 70th, and 59th population percentiles, respectively. CONCLUSION: In this novel study, we define the normative diagnostic criteria cut-offs in this unselected adolescent population and show that these cut-offs correspond to lower percentiles than conventional cut-offs. These findings highlight the pertinent need to re-define PCOS diagnostic cut-offs in adolescents. Validation is required in larger, multi-ethnic, and well-characterised adolescent cohorts.