Search

Introduction: Mental health conditions, such as depression, anxiety, and psychological distress in the adult population significantly increased during the COVID-19 pandemic. However, the rates of prescribing psychotropic medications in adults during the COVID-19 period have not been well explored. Aim: The aim of this study was to examine the association between demographic characteristics and rates of prescribing psychotropic medications to general practice patients during 2018–2022. Methods: A total of 154 528 general practice patients aged 20 years and above were included in the study. A mixed effects negative binomial regression model was employed and incidence risk ratios (IRRs) with corresponding 95% confidence interval (CI) are presented to measure the association of demographic characteristics with rates of prescribing psychotropic medication. Results: Over half (56.2%) of study subjects were female and 41.4% were aged between 20 and 39 years. Males had lower prescribing rates of antidepressants (IRR = 0.95; 95% CI: 0.94, 0.97) and hypnotics and sedatives (IRR = 0.97; 95% CI: 0.96, 0.99) than females. People in the age group 60+ years (compared with age group 20–39 years) and those in low and middle socio-economic status (SES) categories (compared with high SES) had higher rates of prescribing psychotropic medication. Conclusion: Females, people aged 60+ years, and people with low and middle SES had higher prescribing rates of psychotropic medication. A consistent increase in prescribing rates over time, particularly during the COVID-19 pandemic, was observed. It is important that health care planners and policy makers monitor and account for population diversity and equity. What is already known: The use of psychotropic medications in Australia has increased over the last two decades, with general practitioners being the primary prescribers. The COVID-19 pandemic exacerbated mental health issues in adults and the older population, leading to increased concerns about mental health and wellbeing. What this study adds: This study estimates prescribing rates of psychotropic medication by patient demographics and found higher rates of prescribing among females, older people, and those with low to moderate SES. There was a consistent increase in prescribing rates between 2018 and 2022, particularly during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Objectives: To investigate associations between sociodemographic factors, pre‐existing chronic comorbidities, and general practitioner‐led diagnosis of long COVID. Design, setting, patients: We conducted a retrospective observational case–control study using de‐identified electronic general practice data, recorded between January 2020 and March 2023, from 869 general practice clinics across four primary health networks in Victoria and New South Wales. Main outcome measures: Sociodemographic factors and pre‐existing chronic comorbidities associated with general practitioner‐led diagnosis of long COVID. Results: A total of 1588 patients had a recorded general practitioner‐led long COVID diagnosis. Females exhibited a higher likelihood of general practitioner‐led long COVID diagnosis (adjusted odds ratio [aOR], 1.58; adjusted confidence interval [aCI], 1.35–1.85) compared with males. Patients aged 40–59 years had a higher likelihood of general practitioner‐led long COVID diagnosis (aOR, 1.68; aCI, 1.40–2.03) compared with patients aged 20–39 years. The diagnosis was more likely in patients of high socio‐economic status (aOR, 1.37; aCI, 1.05–1.79) compared with those of mid socio‐economic status. Mental health conditions (aOR, 2.69; aCI, 2.25–3.21), respiratory conditions (aOR, 2.25; aCI, 1.85–2.75), cancer (aOR, 1.64; aCI, 1.15–2.33) and musculoskeletal conditions (aOR, 1.50; aCI, 1.20–1.88) were all significantly associated with general practitioner‐led long COVID diagnosis. Conclusions: Female sex, middle age, high socio‐economic status and pre‐existing comorbidities, including mental health conditions, respiratory conditions, cancer and musculoskeletal conditions, were associated with general practitioner‐led long COVID diagnosis among general practice patients. These factors largely parallel the emerging international evidence on long COVID and highlight the patient characteristics that practitioners should be cognisant of when patients present with symptoms of long COVID. [ABSTRACT FROM AUTHOR]

Aims: The safety of continuing metformin during a hospital admission has not been robustly demonstrated. We evaluated the association of continuing metformin in hospital with the risk for a hospital‐acquired complication (HAC). Methods: This is a retrospective observational study of patients admitted to a medical or surgical ward. We considered those with diabetes who continued metformin (DM/MET group), those who discontinued metformin upon admission (DM/MET‐STOP), and those with diabetes not on metformin just prior to and during admission (DM/NoMET). We prepared propensity score‐matched (PSM) control groups from admitted patients without diabetes. The likelihood of a HAC was determined using a Kaplan–Meier survival analysis. A Cox proportional hazards model was employed to calculate the hazard ratio, adjusted for covariates. Results: Of the 4446 (14%) patients with diabetes, 3331 (10%) were prescribed metformin on admission, and it was continued in 2557 patients. HAC occurred in 5.5% of DM/MET group and 6.4% of the PSM control group. Continuation of metformin was associated with a lower likelihood of HAC, adjusted hazard ratio 0.85 (95% CI 0.69, 1.04), p = 0.117 compared to a PSM‐matched control group without diabetes. The DM/NoMET and DM/MET‐STOP groups had an increased risk for HAC, adjusted HR 1.77 (1.44, 2.18), p < 0.001 and 2.57 (2.10, 3.13), p < 0.001, as compared to their respective PSM control groups. Conclusion: An individualized assessment to continue metformin during hospital admission was associated with a reduced likelihood of HAC, with the caveat that there was limited matching to non‐diabetes controls. This finding warrants further exploration. [ABSTRACT FROM AUTHOR]

This article discusses the use of Bayesian methods and polynomial chaos in finding cardiac bidomain parameters for electrophysiological simulations of ventricular cardiac tissue. The article highlights the challenges in accurately determining the electrical properties of cardiac tissue and proposes a novel protocol that combines Bayesian inference techniques and polynomial chaos to retrieve these parameters from experimental measurements. The protocol is designed to account for outliers in the data and can incorporate multiple datasets obtained from different current application electrodes. The article presents results from simulated datasets and experimental measurements, demonstrating the protocol's ability to accurately infer certain parameters. However, further research is needed to improve the quality and consistency of experimental measurements for more accurate parameter retrieval. [Extracted from the article]

Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia. [ABSTRACT FROM AUTHOR]

Solving inverse problems is computationally expensive, if not infeasible, under specific scenarios. For example, many forward solutions are required when solving inverse problems using Bayesian techniques. In this work, a novel inference protocol is established, that can be used to infer the cardiac bidomain conductivities and the cardiac fibre rotation angle (bidomain parameters). This protocol uses a surrogate model, developed using generalised polynomial chaos techniques, to approximate cardiac potentials on a multi-electrode array. The resulting surrogate model is used in conjunction with Bayesian inference techniques to infer the bidomain parameters. A lower-order surrogate model (order three) can effectively characterise the influence of the extracellular conductivities and fibre rotation on the cardiac potentials; however, it is recommended that a higher-order surrogate model expansion of order seven be used to adequately characterise the influence of the intracellular conductivities as well. This seventh order surrogate model was successfully used to infer the extracellular conductivities and fibre rotation angle from a single set of synthetically generated noisy experimental potentials, while the intracellular conductivities were unable to be retrieved accurately under this scenario.