Dying young: Excess morbidity and mortality in individuals with severe mental illness and what we should be doing about it
Bibliographic record
Abstract
“We talk about people with mental illness, and people with diabetes, and smokers and the obese, and so on and so on. We’re talking about the same people – just with different labels.”– Health care professional [1, p. 6]Severe mental illness (SMI) most commonly refers to mental disorders with a psychotic component and significantly reduced functioning despite the presence of inherent differences in risk factors, etiologies, and treatments [1]. The most common disorders that fall under this term include schizophrenia and bipolar disorder [1]. Over a decade of research into the morbidity and mortality of individuals with SMI has consistently revealed mortality rates two to three times higher and a life expectancy of 25-30 years shorter compared to the general population [1-4]. Contrary to popular belief, the main causes of early death are not drug overdose or suicide, but rather, preventable illnesses such as cardiovascular disease, diabetes, and HIV/AIDS [1,3,5-7]. Incidence of other preventable conditions, such as obesity and respiratory disease, is also much higher among patients with SMI, and when present, is associated with a more severe course of mental illness and a reduced quality of life [3,8]. Such findings bring significant questions: what is the cause of this disparity in mortality/ morbidity? What can health care professionals do to help reduce this gap?A recent report by the Early Onset Illness and Mortality Working Group [1] outlines several factors that may contribute to poor physical health of people with SMI. Some factors, such as those related to the mental illness itself (e.g., cognitive impairment, a lack of communication skills, medication side-effects) and socioeconomic status (e.g., poverty, poor education) may be less amenable to modification, but should nevertheless be a target for action. Other contributing factors include behaviour and lifestyle (e.g., physical inactivity, obesity, tobacco smoking), and poor preventative medical care (e.g., disparity in quality of medical care), both of which are more easily modifiable with the assistance of medical care practitioners. Here we will summarize the factors responsible for poor physical health in SMI, specifically focusing on the mental illness itself, socioeconomic status, behaviour and lifestyle, health care system barriers, and insufficient preventative medical care. We will then propose future directions and ways in which medical students and current medical professionals can help reduce this gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".