International comparison of hospitalizations and emergency department visits related to mental health conditions across high‐income countries before and during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
OBJECTIVE: To explore variation in rates of acute care utilization for mental health conditions, including hospitalizations and emergency department (ED) visits, across high-income countries before and during the COVID-19 pandemic. DATA SOURCES AND STUDY SETTING: Administrative patient-level data between 2017 and 2020 of eight high-income countries: Canada, England, Finland, France, New Zealand, Spain, Switzerland, and the United States (US). STUDY DESIGN: Multi-country retrospective observational study using a federated data approach that evaluated age-sex standardized rates of hospitalizations and ED visits for mental health conditions. PRINCIPAL FINDINGS: There was significant variation in rates of acute mental health care utilization across countries. Among the subset of four countries with both hospitalization and ED data, the US had the highest pre-COVID-19 combined average annual acute care rate of 1613 episodes/100,000 people (95% CI: 1428, 1797). Finland had the lowest rate of 776 (686, 866). When examining hospitalization rates only, France had the highest rate of inpatient hospitalizations of 988/100,000 (95% CI 858, 1118) while Spain had the lowest at 87/100,000 (95% CI 76, 99). For ED rates for mental health conditions, the US had the highest rate of 958/100,000 (95% CI 861, 1055) while France had the lowest rate with 241/100,000 (95% CI 216, 265). Notable shifts coinciding with the onset of the COVID-19 pandemic were observed including a substitution of care setting in the US from ED to inpatient care, and overall declines in acute care utilization in Canada and France. CONCLUSION: The study underscores the importance of understanding and addressing variation in acute care utilization for mental health conditions, including the differential effect of COVID-19, across different health care systems. Further research is needed to elucidate the extent to which factors such as workforce capacity, access barriers, financial incentives, COVID-19 preparedness, and community-based care may contribute to these variations. WHAT IS KNOWN ON THIS TOPIC: Approximately one billion people globally live with a mental health condition, with significant consequences for individuals and societies. Rates of mental health diagnoses vary across high-income countries, with substantial differences in access to effective care. The COVID-19 pandemic has exacerbated mental health challenges globally, with varying impacts across countries. WHAT THIS STUDY ADDS: This study provides a comprehensive international comparison of hospitalization and emergency department visit rates for mental health conditions across eight high-income countries. It highlights significant variations in acute care utilization patterns, particularly in countries that are more likely to care for people with mental health conditions in emergency departments rather than inpatient facilities The study identifies temporal and cross-country differences in acute care management of mental health conditions coinciding with the onset of the COVID-19 pandemic.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".