Premature mortality of people with severe mental illness: a renewed focus for a new era

Breaking Barriers Transforming Primary Care to Serve the Physical Health Needs of Individuals With SMI in the NHS

This critical review paper examines the health inequalities faced by individuals with Severe Mental Illness (SMI) in the United Kingdom; highlighting the disproportionate burden of physical health conditions such as respiratory disorders, cardiac illnesses, diabetes and stroke amongst this population. These conditions contribute to a significantly higher rate of premature mortality in individuals with SMI, with two-thirds of these deaths deemed preventable. Despite the National Health Service (NHS) acknowledging the need to address these health inequalities, the mortality gap between those with and without SMI continues to widen. Additionally, there is limited engagement from service users in annual physical health checks, a concern that this paper addresses by identifying several barriers and providing recommendations to improve access and engagement in physical health checks. This review emphasises the focus on primary care systems as a critical point for addressing health disparities in individuals with SMI. Also, it highlights the need for primary care services to be more adaptive and integrated, playing a key role in managing the physical health of patients with SMI through regular health checks, flexible service delivery, and enhanced coordination with secondary care. Effectively supporting individuals with SMI requires tailored, integrated primary care interventions that address both psychological and physical health challenges, considering diverse demographic needs across the UK.

Conditional cash transfers and mortality in people hospitalised with psychiatric disorders: A cohort study of the Brazilian Bolsa Família Programme

BACKGROUND

Psychiatric patients experience lower life expectancy compared to the general population. Conditional cash transfer programmes (CCTPs) have shown promise in reducing mortality rates, but their impact on psychiatric patients has been unclear. This study tests the association between being a Brazilian Bolsa Família Programme (BFP) recipient and the risk of mortality among people previously hospitalised with any psychiatric disorders.

METHODS AND FINDINGS

This cohort study utilised Brazilian administrative datasets, linking social and health system data from the 100 Million Brazilian Cohort, a population-representative study. We followed individuals who applied for BFP following a single hospitalisation with a psychiatric disorder between 2008 and 2015. The outcome was mortality and specific causes, defined according to International Classification of Diseases 10th Revision (ICD-10). Cox proportional hazards models estimated the hazard ratio (HR) for overall mortality and competing risks models estimated the HR for specific causes of death, both associated with being a BFP recipient, adjusted for confounders, and weighted with a propensity score. We included 69,901 psychiatric patients aged between 10 and 120, with the majority being male (60.5%), and 26,556 (37.99%) received BFP following hospitalisation. BFP was associated with reduced overall mortality (HR 0.93, 95% CI 0.87,0.98, p 0.018) and mortality due to natural causes (HR 0.89, 95% CI 0.83, 0.96, p < 0.001). Reduction in suicide (HR 0.90, 95% CI 0.68, 1.21, p = 0.514) was observed, although it was not statistically significant. The BFP's effects on overall mortality were more pronounced in females and younger individuals. In addition, 4% of deaths could have been prevented if BFP had been present (population attributable risk (PAF) = 4%, 95% CI 0.06, 7.10).

CONCLUSIONS

BFP appears to reduce mortality rates among psychiatric patients. While not designed to address elevated mortality risk in this population, this study highlights the potential for poverty alleviation programmes to mitigate mortality rates in one of the highest-risk population subgroups.

Mental health nursing students and generic proficiencies: how educators can support assessment

Recent shifts in nursing education towards generic proficiencies pose challenges to the unique demands of mental health nursing education. Student mental health nurses at the authors' higher education institution (HEI) have provided service feedback that has expressed the difficulties they have encountered. This article examines the issues faced by students, assessors and educators in interpreting and applying the proficiency standards set by the Nursing and Midwifery Council. Addressing the complexities of mental health nursing education, as well as the barriers to learning and attainment, requires a collaborative effort and innovative solutions if HEIs are to successfully prepare future mental health nurses to meet the evolving demands of their role. The proposed solution is a 'Proficiency Directory', which provides structured resources for mental health nursing students to align learning experiences with assessment criteria, facilitating a more comprehensive and meaningful learning journey.

Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review

BACKGROUND

Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner.

OBJECTIVE

In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes.

METHODS

This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024.

RESULTS

Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions.

CONCLUSIONS

The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.

Mortality in Qatari individuals with mental illness: a retrospective cohort study

INTRODUCTION

There is substantial evidence that people with mental illness have higher mortality rates than the general population. However, most of the studies were from Western countries, and it is not clear whether this finding also applies to Arab countries like Qatar.

OBJECTIVES

We aimed to explore whether mortality in patients with mental illness in Qatar, is different from those without.

METHODS

We conducted a retrospective cohort study, including all Qatari nationals deceased in 2017 and 2018, using the list of registered deaths from Hamad Medical Corporation (HMC) Mortuary. We divided the cohort of deceased people into two groups: with and without mental illness. For each of the groups, we collected the age at death, the reported cause of death as well as sociodemographic and clinical data.

RESULTS

There were 602 registered deaths in 2017 and 589 deaths in 2018. The prevalence of mental illness was 20.4%. Compared to subjects without mental illness, subjects with mental illness surprisingly had higher age at death (median ± IQR = 76.5 ± 22.1 years vs. 62.7 ± 32.9 years; p < .001). This difference persisted even after we controlled for covariates. Individuals with mental illness were more likely to die of an infection (OR = 1.98[1.44;2.71]), or of chronic respiratory disease (OR = 3.53 [1.66;7.52]) but less likely to die because of accidental (OR = 0.21[0.09;0.49]) or congenital causes (OR = 0.18[0.04;0.77]).

CONCLUSION

Contrary to most previous studies, we did not find that mortality was higher in Qatari individuals with mental illness. Sociocultural factors, free and easy-to-access healthcare, and an enhanced role of mental health professionals in detecting medical comorbidities may explain this finding.

Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management

Populations with common physical diseases - such as cardiovascular diseases, cancer and neurodegenerative disorders - experience substantially higher rates of major depressive disorder (MDD) than the general population. On the other hand, people living with MDD have a greater risk for many physical diseases. This high level of comorbidity is associated with worse outcomes, reduced adherence to treatment, increased mortality, and greater health care utilization and costs. Comorbidity can also result in a range of clinical challenges, such as a more complicated therapeutic alliance, issues pertaining to adaptive health behaviors, drug-drug interactions and adverse events induced by medications used for physical and mental disorders. Potential explanations for the high prevalence of the above comorbidity involve shared genetic and biological pathways. These latter include inflammation, the gut microbiome, mitochondrial function and energy metabolism, hypothalamic-pituitary-adrenal axis dysregulation, and brain structure and function. Furthermore, MDD and physical diseases have in common several antecedents related to social factors (e.g., socioeconomic status), lifestyle variables (e.g., physical activity, diet, sleep), and stressful live events (e.g., childhood trauma). Pharmacotherapies and psychotherapies are effective treatments for comorbid MDD, and the introduction of lifestyle interventions as well as collaborative care models and digital technologies provide promising strategies for improving management. This paper aims to provide a detailed overview of the epidemiology of the comorbidity of MDD and specific physical diseases, including prevalence and bidirectional risk; of shared biological pathways potentially implicated in the pathogenesis of MDD and common physical diseases; of socio-environmental factors that serve as both shared risk and protective factors; and of management of MDD and physical diseases, including prevention and treatment. We conclude with future directions and emerging research related to optimal care of people with comorbid MDD and physical diseases.

Meeting the challenges of rising premature mortality in people with severe mental illness

People with severe mental illness are 4.5 times more likely to die prematurely than the general UK population. We review the drivers of poorer physical health across mental health conditions and propose some practical approaches to addressing this in the clinic.