Crystal M. Y. Lee

Nov 22, 2024 | nature.com | Helen Brown |Sarah Gauci |Tiana Felmingham |Crystal M. Y. Lee |Sean M. Randall |George Mnatzaganian | +11 more

Coronary heart disease (CHD) is the leading cause of morbidity and mortality for people worldwide, yet differences in the likelihood of receiving optimal care occur depend on gender. This study therefore aimed to explore the healthcare experiences of men and women living with CHD. A systematic search of qualitative research was undertaken, following PRISMA guidelines. Forty-three studies were included for review, involving 1512 people (62% women, 38% men; 0% non-binary or gender diverse). Thematic synthesis of the data identified four themes: (1) assumptions about CHD; (2) gender assigned roles; (3) interactions with health care; and (4) return to ‘normal’ life. A multilevel approach across the entire ecosystem of healthcare is required to improve equity in care experienced by people living with CHD. This will involve challenging both the individuals’ knowledge of CHD and awareness of health professionals to entrenched gender bias in the health system that predominantly favours men.

Sep 4, 2024 | nature.com | Kevin chai |Daniel Rock |Peter M. McEvoy |Kim S. Betts |Suzanne Robinson |Kyran Graham-Schmidt | +2 more

Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. This research investigates whether community mental health treatment for anxiety disorder is associated with reliable changes in Kessler psychological distress scale (K10) scores and whether pre-treatment K10 scores and past health service interactions can accurately predict reliable change (improvement). The K10 assessment was administered to 46,938 public patients in a community setting within the Western Australia dataset in 2005–2022; of whom 3794 in 4067 episodes of care were reassessed at least twice for anxiety disorders, obsessive–compulsive disorder, or reaction to severe stress and adjustment disorders (ICD-10 codes F40–F43). Reliable change on the K10 was calculated and used with the post-treatment score as the outcome variables. Machine learning models were developed using features from a large health service administrative linked dataset that includes the pre-treatment K10 assessment as well as community mental health episodes of care, emergency department presentations, and inpatient admissions for prediction. The classification model achieved an area under the receiver operating characteristic curve of 0.76 as well as an F1 score, precision and recall of 0.69, and the regression model achieved an R2 of 0.37 with mean absolute error of 5.58 on the test dataset. While the prediction models achieved moderate performance, they also underscore the necessity for regular patient monitoring and the collection of more clinically relevant and contextual patient data to further improve prediction of treatment outcomes.