A review of the economic impact of mental illness
Bibliographic record
Abstract
Objective To examine the impact and cost associated with mental illness. Methods A rapid review of the literature from Australia, New Zealand, UK and Canada was undertaken. The review included literature pertaining to the cost-of-illness and impact of mental illness as well as any modelling studies. Included studies were categorised according to impact on education, labour force engagement, earlier retirement or welfare dependency. The well-accepted Drummond 10-point economic appraisal checklist was used to assess the quality of the studies. Results A total of 45 methodologically diverse studies were included. The studies highlight the significant burden mental illness places on all facets of society, including individuals, families, workplaces and the wider economy. Mental illness results in a greater chance of leaving school early, a lower probability of gaining full-time employment and a reduced quality of life. Research from Canada suggests that the total economic costs associated with mental illness will increase six-fold over the next 30 years with costs likely to exceed A$2.8 trillion (based on 2015 Australian dollars). Conclusions Mental illness is associated with a high economic burden. Further research is required to develop a better understanding of the trajectory and burden of mental illness so that resources can be directed towards cost-effective interventions. What is known about the topic? Although mental illness continues to be one of the leading contributors to the burden of disease, there is limited information on the economic impact that mental illness imposes on individuals, families, workplaces and the wider economy. What does this paper add? This review provides a summary of the economic impact and cost of mental illness. The included literature highlights the significant burden mental illness places on individuals, families, workplaces, society and the economy in general. The review identified several areas for improvement. For example, only limited information is available on the impact of attention deficit hyperactivity disorder, anxiety, cognitive function, conduct disorder, eating disorder and psychological distress. There was also a dearth of evidence on the intangible elements of pain and suffering of people and their families with depressive disorders. More research is required to better understand the full extent of the impact of mental illness and strategies that may be implemented to minimise this harm. What are the implications for practitioners? Knowing the current and future impact of mental illness highlights the imperative to develop an effective policy response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".