Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review
Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review
Numerous risk factors for mental disorders have been identified. However, we do not know how many disorders we could prevent and to what extent by modifying these risk factors. This study quantifies the Population Attributable Fraction (PAF) of potentially modifiable risk factors for mental disorders. We conducted a PRISMA 2020-compliant (Protocol: https://osf.io/hk2ag ) meta-umbrella systematic review (Web of Science/PubMed/Cochrane Central Register of Reviews/Ovid/PsycINFO, until 05/12/2021) of umbrella reviews reporting associations between potentially modifiable risk factors and ICD/DSM mental disorders, restricted to highly convincing (class I) and convincing (class II) evidence from prospective cohorts. The primary outcome was the global meta-analytical PAF, complemented by sensitivity analyses across different settings, the meta-analytical Generalised Impact Fraction (GIF), and study quality assessment (AMSTAR). Seven umbrella reviews (including 295 meta-analyses and 547 associations) identified 28 class I-II risk associations (23 risk factors; AMSTAR: 45.0% high-, 35.0% medium-, 20.0% low quality). The largest global PAFs not confounded by indication were 37.84% (95% CI = 26.77-48.40%) for childhood adversities and schizophrenia spectrum disorders, 24.76% (95% CI = 13.98-36.49%) for tobacco smoking and opioid use disorders, 17.88% (95% CI = not available) for job strain and depression, 14.60% (95% CI = 9.46-20.52%) for insufficient physical activity and Alzheimer's disease, 13.40% (95% CI = 7.75-20.15%) for childhood sexual abuse and depressive disorders, 12.37% (95% CI = 5.37-25.34%) for clinical high-risk state for psychosis and any non-organic psychotic disorders, 10.00% (95% CI = 5.62-15.95%) for three metabolic factors and depression, 9.73% (95% CI = 4.50-17.30%) for cannabis use and schizophrenia spectrum disorders, and 9.30% (95% CI = 7.36-11.38%) for maternal pre-pregnancy obesity and ADHD. The GIFs confirmed the preventive capacity for these factors. Addressing several potentially modifiable risk factors, particularly childhood adversities, can reduce the global population-level incidence of mental disorders.
Child, Female, Humans, Pregnancy, Incidence, Mental Disorders/epidemiology, Prospective Studies, Psychotic Disorders, Risk Factors, Meta-Analysis as Topic
3510-3519
Dragioti, Elena
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Radua, Joaquim
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Solmi, Marco
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Arango, Celso
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Oliver, Dominic
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Cortese, Samuele
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Jones, Peter B
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Il Shin, Jae
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Correll, Christoph U
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Fusar-Poli, Paolo
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21 November 2022
Dragioti, Elena
737161f7-ec35-4d20-80eb-5f382c0aa9b6
Radua, Joaquim
7443399e-6de8-48ac-a6cb-3b8663151462
Solmi, Marco
256504f2-483a-4b42-bfab-081c6a617867
Arango, Celso
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Oliver, Dominic
bf536e33-8e00-450a-8733-3dc02e7e8514
Cortese, Samuele
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Jones, Peter B
f8afa603-f19e-4afa-b997-27f6b842bffd
Il Shin, Jae
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Correll, Christoph U
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Fusar-Poli, Paolo
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Dragioti, Elena, Radua, Joaquim, Solmi, Marco, Arango, Celso, Oliver, Dominic, Cortese, Samuele, Jones, Peter B, Il Shin, Jae, Correll, Christoph U and Fusar-Poli, Paolo
(2022)
Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review.
Molecular Psychiatry, 27 (8), .
(doi:10.1038/s41380-022-01586-8).
Abstract
Numerous risk factors for mental disorders have been identified. However, we do not know how many disorders we could prevent and to what extent by modifying these risk factors. This study quantifies the Population Attributable Fraction (PAF) of potentially modifiable risk factors for mental disorders. We conducted a PRISMA 2020-compliant (Protocol: https://osf.io/hk2ag ) meta-umbrella systematic review (Web of Science/PubMed/Cochrane Central Register of Reviews/Ovid/PsycINFO, until 05/12/2021) of umbrella reviews reporting associations between potentially modifiable risk factors and ICD/DSM mental disorders, restricted to highly convincing (class I) and convincing (class II) evidence from prospective cohorts. The primary outcome was the global meta-analytical PAF, complemented by sensitivity analyses across different settings, the meta-analytical Generalised Impact Fraction (GIF), and study quality assessment (AMSTAR). Seven umbrella reviews (including 295 meta-analyses and 547 associations) identified 28 class I-II risk associations (23 risk factors; AMSTAR: 45.0% high-, 35.0% medium-, 20.0% low quality). The largest global PAFs not confounded by indication were 37.84% (95% CI = 26.77-48.40%) for childhood adversities and schizophrenia spectrum disorders, 24.76% (95% CI = 13.98-36.49%) for tobacco smoking and opioid use disorders, 17.88% (95% CI = not available) for job strain and depression, 14.60% (95% CI = 9.46-20.52%) for insufficient physical activity and Alzheimer's disease, 13.40% (95% CI = 7.75-20.15%) for childhood sexual abuse and depressive disorders, 12.37% (95% CI = 5.37-25.34%) for clinical high-risk state for psychosis and any non-organic psychotic disorders, 10.00% (95% CI = 5.62-15.95%) for three metabolic factors and depression, 9.73% (95% CI = 4.50-17.30%) for cannabis use and schizophrenia spectrum disorders, and 9.30% (95% CI = 7.36-11.38%) for maternal pre-pregnancy obesity and ADHD. The GIFs confirmed the preventive capacity for these factors. Addressing several potentially modifiable risk factors, particularly childhood adversities, can reduce the global population-level incidence of mental disorders.
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s41380-022-01586-8
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Accepted/In Press date: 13 April 2022
e-pub ahead of print date: 28 April 2022
Published date: 21 November 2022
Additional Information:
© 2022. The Author(s).
Keywords:
Child, Female, Humans, Pregnancy, Incidence, Mental Disorders/epidemiology, Prospective Studies, Psychotic Disorders, Risk Factors, Meta-Analysis as Topic
Identifiers
Local EPrints ID: 476056
URI: http://eprints.soton.ac.uk/id/eprint/476056
ISSN: 1359-4184
PURE UUID: 184827bc-d912-437f-b91a-e35afb32b159
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Date deposited: 04 Apr 2023 17:06
Last modified: 17 Mar 2024 03:37
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Contributors
Author:
Elena Dragioti
Author:
Joaquim Radua
Author:
Marco Solmi
Author:
Celso Arango
Author:
Dominic Oliver
Author:
Peter B Jones
Author:
Jae Il Shin
Author:
Christoph U Correll
Author:
Paolo Fusar-Poli
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