Mental and physical health morbidity among people in prisons: an umbrella review
Notice bibliographique
Résumé
BACKGROUND: People who experience incarceration are characterised by poor health profiles. Clarification of the disease burden in the prison population can inform service and policy development. We aimed to synthesise and assess the evidence regarding the epidemiology of mental and physical health conditions among people in prisons worldwide. METHODS: In this umbrella review, five bibliographic databases (Web of Science, PubMed, PsycINFO, Embase, and Global Health) were systematically searched from inception to identify meta-analyses published up to Oct 31, 2023, which examined the prevalence or incidence of mental and physical health conditions in general prison populations. We excluded meta-analyses that examined health conditions in selected or clinical prison populations. Prevalence data were extracted from published reports and study authors were contacted for additional information. Estimates were synthesised and stratified by sex, age, and country income level. The robustness of the findings was assessed in terms of heterogeneity, excess significance bias, small-study effects, and review quality. The study protocol was pre-registered with PROSPERO, CRD42023404827. FINDINGS: Our search of the literature yielded 1909 records eligible for screening. 1736 articles were excluded and 173 full-text reports were examined for eligibility. 144 articles were then excluded due to not meeting inclusion criteria, which resulted in 29 meta-analyses eligible for inclusion. 12 of these were further excluded because they examined the same health condition. We included data from 17 meta-analyses published between 2002 and 2023. In adult men and women combined, the 6-month prevalence was 11·4% (95% CI 9·9-12·8) for major depression, 9·8% (6·8-13·2) for post-traumatic stress disorder, and 3·7% (3·2-4·1) for psychotic illness. On arrival to prison, 23·8% (95% CI 21·0-26·7) of people met diagnostic criteria for alcohol use disorder and 38·9% (31·5-46·2) for drug use disorder. Half of those with major depression or psychotic illness had a comorbid substance use disorder. Infectious diseases were also common; 17·7% (95% CI 15·0-20·7) of people were antibody-positive for hepatitis C virus, with lower estimates (ranging between 2·6% and 5·2%) found for hepatitis B virus, HIV, and tuberculosis. Meta-regression analyses indicated significant differences in prevalence by sex and country income level, albeit not consistent across health conditions. The burden of non-communicable chronic diseases was only examined in adults aged 50 years and older. Overall, the quality of the evidence was limited by high heterogeneity and small-study effects. INTERPRETATION: People in prisons have a specific pattern of morbidity that represents an opportunity for public health to address. In particular, integrating prison health within the national public health system, adequately resourcing primary care and mental health services, and improving linkage with post-release health services could affect public health and safety. Population-based longitudinal studies are needed to clarify the extent to which incarceration affects health. FUNDING: Research Foundation-Flanders, Wellcome Trust, National Institutes of Health.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,028 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».