To compare differences in recruitment and attrition between placebo control randomised trials of surgery, and trials of the same surgical interventions and conditions that used non-operative (non-placebo) controls.

Meta-epidemiological study.

Randomised controlled trials were identified from an electronic search of MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials from their inception date to 21 November 2018.

Placebo control trials evaluating efficacy of any surgical intervention and non-operative control trials of the same surgical intervention were included in this study. 25 730 records were retrieved from our systemic search, identifying 61 placebo control and 38 non-operative control trials for inclusion in analysis.

Primary outcome measures were recruitment and attrition. These were assessed in terms of recruitment rate (number of participants enrolled, as a proportion of those eligible) and overall attrition rate (composite of dropout, loss to follow-up and cross-overs, expressed as proportion of total sample size). Secondary outcome measures included participant cross-over rate, dropout and loss to follow-up.

Unadjusted pooled recruitment and attrition rates were similar between placebo and non-operative control trials. Study characteristics were not significantly different apart from time to primary timepoint which was shorter in studies with placebo controls (365 vs 274 days, p=0.006). After adjusting for covariates (follow-up duration and number of timepoints), the attrition rate of placebo control trials was almost twice as high compared with non-operative controlled-trials (incident rate ratio (IRR) (95% CI) 1.8 (1.1 to 3.0), p=0.032). The incorporation of one additional follow-up timepoint (regardless of follow-up duration) was associated with reduced attrition in placebo control surgical trials (IRR (95% CI) 0.64 (0.52 to 0.79), p

Placebo control trials of surgery have similar recruitment issues but higher attrition compared with non-operative (non-placebo) control trials. Study design should incorporate strategies such as increased timepoints for given follow-up duration to mitigate losses to follow-up and dropout.

CRD42019117364.

To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches.

Cross-sectional analysis of survey data.

Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021.

270 000 adults aged 18–85+ years who participated in the Global Mind Project.

We used 120+ demographic and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor.

Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within ±15% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18–24 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18–24 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45–54 years.

Social determinants of traumas, adversities and lifestyle can account for 60%–90% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

5241 patients were included in the analysis. For ICU days 2–7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4–7 for all states.

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a

Strengthening of emergency care systems, including prehospital systems, can reduce death and disability. We aimed to identify perspectives on barriers and facilitators relating to the development and implementation of a prehospital emergency care system assessment tool (PEC-SET) from prehospital providers representing several South and Southeast (SE) Asian countries.

We conducted a qualitative study using focus group discussions (FGD) informed by the Consolidated Framework for Implementation Research (CFIR). FGDs were conducted in English, audioconferencing/videoconferencing was recorded, transcribed verbatim and coded using an inductive and deductive approach. Participants suggested specific elements to be measured within three main ‘pillars’ of disease conditions proposed by the research team of the tool being developed (cardiovascular, trauma and perinatal emergencies).

We explored the perspectives of medical directors in six low-income and middle-income countries (LMICs) in South and SE Asia.

A total of 16 participants were interviewed (1 Vietnam, 4 Philippines, 4 Thailand, 5 Malaysia, 1 Indonesia and 1 Pakistan) as a part of 4 focus groups.

Themes identified within the four CFIR constructs included: (1) Intervention characteristics: importance of developing an contextually specific tool, need for generalisability, trialling in one geographical area or with one pillar before expanding; (2) Inner setting: data transfer barriers, workforce shortages; (3) Outer setting: underdevelopment of EMS nationally; need for further EMS system development prior to implementing a tool and (4) Individual characteristics: lack of buy-in by prehospital personnel. Elements proposed by participants included both process and outcome measures.

Through the CFIR framework, we identified several themes which can provide a basis for codeveloping a PEC-SET for LMICs with local stakeholders. This work may inform development of quality improvement tools in LMIC PEC systems.

Urinary incontinence (UI) is a highly prevalent condition affecting nearly 25% of women, especially in older age groups.1 It is characterised by involuntary leakage of urine and can have a significant impact on quality of life, leading to psychological distress and placing a financial burden...