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Variations in patterns of involuntary hospitalisation and in legal frameworks: an international comparative study
Open accessAbstract
BACKGROUND: Rising annual incidence of involuntary hospitalisation have been reported in England and some other higher-income countries, but the reasons for this increase are unclear. We aimed to describe the extent of variations in involuntary annual hospitalisation rates between countries, to compare trends over time, and to explore whether variations in legislation, demographics, economics, and health-care provision might be associated with variations in involuntary hospitalisation rates. METHODS: We compared annual incidence of involuntary hospitalisation between 2008 and 2017 (where available) for 22 countries across Europe, Australia, and New Zealand. We also obtained data on national legislation, demographic and economic factors (gross domestic product [GDP] per capita, prevalence of inequality and poverty, and the percentage of populations who are foreign born, members of ethnic minorities, or living in urban settings), and service characteristics (health-care spending and provision of psychiatric beds and mental health staff). Annual incidence data were obtained from government sources or published peer-reviewed literature. FINDINGS: The median rate of involuntary hospitalisation was 106·4 (IQR 58·5 to 150·9) per 100 000 people, with Austria having the highest (282 per 100 000 individuals) and Italy the lowest (14·5 per 100 000 individuals) most recently available rates. We observed no relationship between annual involuntary hospitalisation rates and any characteristics of the legal framework. Higher national rates of involuntary hospitalisation were associated with a larger number of beds (β coefficient 0·65, 95% CI 0·10 to 1·20, p=0·021), higher GDP per capita purchasing power parity (β coefficient 1·84, 0·30 to 3·38, p=0·019), health-care spending per capita (β coefficient 15·92, 3·34 to 28·49, p=0·013), the proportion of foreign-born individuals in the population (β coefficient 7·32, 0·44 to 14·19, p=0·037), and lower absolute poverty (β coefficient -11·5, -22·6 to -0·3, p=0·044). There was no evidence of an association between annual involuntary hospitalisation incidence and any other demographic, economic, or health-care indicator. INTERPRETATION: Variations between countries were large and for the most part unexplained. We found a higher annual incidence of involuntary hospitalisation to be associated with a lower rate of absolute poverty, with higher GDP and health-care spending per capita, a higher proportion of foreign-born individuals in a population, and larger numbers of inpatient beds, but limitations in ecological research must be noted, and the associations were weak. Other country-level demographic, economic, and health-care delivery indicators and characteristics of the legislative system appeared to be unrelated to annual involuntary hospitalisation rates. Understanding why involuntary hospitalisation rates vary so much could be advanced through a more fine-grained analysis of the relationships between involuntary hospitalisation and social context, clinical practice, and how legislation is implemented in practice. FUNDING: Commissioned by the Department of Health and funded by the National Institute of Health Research (NIHR) via the NIHR Mental Health Policy Research Unit.
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“The international literature shows wide disparities in the use of involuntary inpatient treatment between jurisdictions that do not appear explained by the relevant legal frameworks governing involuntary treatment (3).”
From The benefits and harms of inpatient involuntary psychiatric treatment: a scoping review (2025) · cites this paper as [3]
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Self-citationBartl, G., Stuart, R., Ahmed, N., Saunders, K., Loizou, S., Brady, G., Gray, H., Grundy, A. C., Jeynes, T., Nyikavaranda, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26739963
Self-citationBuswell, G., Haime, Z., Lloyd‐Evans, B., & Billings, J. (2021) · Figshare · doi:10.6084/m9.figshare.14432765
Self-citationSchölin, L., Tucker, Z., Chopra, A., Borschmann, R., & McKay, C. (2024) · Figshare · doi:10.6084/m9.figshare.24946349
Metadata
- Authors
- Sheridan Rains, L., Zenina, T., Dias, M. C., Jones, R., Jeffreys, S., Branthonne-Foster, S., Lloyd-Evans, B., & Johnson, S.
- Affiliations
- University College London (GB)
- King's College London (GB)
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 6
- Issue
- 5
- Pages
- 403-417
- ISSN
- 2215-0366 (ISSN-L)
- PMC
- PMC6475657
- OpenAlex
- W2925635931
- Cited by
- 230
- References
- 14
- Citations by year
- 2026: 27 · 2025: 37 · 2024: 47 · 2023: 30 · 2022: 33 · 2021: 30 · 2020: 16 · 2019: 10
- FWCI
- 25.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Lancet Psychiatry (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- UCL Discovery (University College London) (acceptedVersion)
- Funders
- National Institute for Health Research
- National Health Research Institutes
- UCLH Biomedical Research Centre
- Topics
- Social Sciences › Psychology › Clinical Psychology › Healthcare Decision-Making and Restraints
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Social Sciences › Psychology › Clinical Psychology › Historical Psychiatry and Medical Practices
- Keywords
- involuntary hospitalisation, psychiatric hospitalization, mental health law, cross-national comparison, mental health services, psychiatric hospital beds, health care spending, GDP per capita, poverty, foreign-born population, ethnic minorities, urbanization, cross-national variation, mental health policy, health care delivery, health care outcomes, mental health professionals
- MeSH terms
- Involuntary Treatment, Australia, England, Europe, Hospital Bed Capacity, Humans, New Zealand, Risk Factors, Incidence, Gross Domestic Product
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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