Disease burden and government spending on mental, neurological, and substance use disorders, and self-harm: cross-sectional, ecological study of health system response in the Americas
Bibliographic record
Abstract
BACKGROUND: Disorders affecting mental health are highly prevalent, can be disabling, and are associated with substantial premature mortality. Yet national health system responses are frequently under-resourced, inefficient, and ineffective, leading to an imbalance between disease burden and health expenditures. We estimated the disease burden in the Americas caused by disorders affecting mental health. This measure was adjusted to include mental, neurological, and behavioural disorders that are frequently not included in estimates of mental health burden. We propose a framework for assessing the imbalance between disease burden and health expenditures. METHODS: In this cross-sectional, ecological study, we extracted disaggregated disease burden data from the Global Health Data Exchange to produce country-level estimates for the proportion of total disease burden attributable to mental disorders, neurological disorders, substance use disorders, and self-harm (MNSS) in the Americas. We collated data from the WHO Assessment Instrument for Mental Health Systems and the WHO Mental Health Atlas on country-level mental health spending as a proportion of total government health expenditures, and of psychiatric hospital spending as a proportion of mental health expenditures. We used a metric capturing the imbalance between disease burden and mental health expenditures, and modelled the association between this imbalance and real (ie, adjusted for purchasing power parity) gross domestic product (GDP). FINDINGS: Data were collected from July 1, 2016, to March 1, 2017. MNSS comprised 19% of total disability-adjusted life-years in the Americas in 2015. Median spending on mental health was 2·4% (IQR 1·3-4·1) of government health spending, and median allocation to psychiatric hospitals was 80% (52-92). This spending represented an imbalance in the ratio between disease burden and efficiently allocated spending, ranging from 3:1 in Canada and the USA to 435:1 in Haiti, with a median of 32:1 (12-170). Mental health expenditure as a proportion of government health spending was positively associated with real GDP (β=0·68 [95% CI 0·24-1·13], p=0·0036), while the proportion allocated to psychiatric hospitals (β=-0·5 [-0·79 to -0·22], p=0·0012) and the imbalance in efficiently allocated spending (β=-1·38 [-1·97 to -0·78], p=0·0001) were both inversely associated with real GDP. All estimated coefficients were significantly different from zero at the 0·005 level. INTERPRETATION: A striking imbalance exists between government spending on mental health and the related disease burden in the Americas, which disproportionately affects low-income countries and is likely to result in undertreatment, increased avoidable disability and mortality, decreased national economic output, and increased household-level health spending. FUNDING: Weatherhead Center for International Affairs, Harvard University.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".