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Enregistrement W4395462521 · doi:10.1101/2024.04.23.24306238

Mass incarceration as a driver of the tuberculosis epidemic in Latin America and projected impacts of policy alternatives: A mathematical modeling study

2024· preprint· en· W4395462521 sur OpenAlexaff
Yiran E. Liu, Yasmine Mabene, Sergio Camelo, Zulma Vanessa Rueda, Daniele Maria Pelissari, Fernanda Dockhorn Costa Johansen, Moisés A. Huamán, Tatiana Avalos-Cruz, Valentina A. Alarcón, Lawrence Michael Ladutke, Marcelo Bergman, Ted Cohen, Jeremy D. Goldhaber‐Fiebert, Júlio Croda, Jason R. Andrews

Notice bibliographique

RevuemedRxiv · 2024
Typepreprint
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesNational Institutes of Health
Mots-clésTuberculosisPopulationLatin AmericansDemographyCounterfactual thinkingIncidence (geometry)MedicinePrisonMass incarcerationEnvironmental healthTransmission (telecommunications)GeographyPolitical sciencePsychologySociology

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Background Tuberculosis incidence is increasing in Latin America, where the incarcerated population has nearly quadrupled since 1990. The full impact of incarceration on the tuberculosis epidemic, accounting for effects beyond prisons, has never been quantified. Methods We calibrated dynamic compartmental transmission models to historical and contemporary data from Argentina, Brazil, Colombia, El Salvador, Mexico, and Peru, which comprise approximately 80% of the region’s incarcerated population and tuberculosis burden. Using historical counterfactual scenarios, we estimated the transmission population attributable fraction (tPAF) for incarceration and the excess population-level burden attributable to increasing incarceration prevalence since 1990. We additionally projected the impact of alternative incarceration policies on future population tuberculosis incidence. Findings Population tuberculosis incidence in 2019 was 29.4% (95% UI, 23.9-36.8) higher than expected without the rise in incarceration since 1990, corresponding to 34,393 (95% UI, 28,295-42,579) excess incident cases across countries. The incarceration tPAF in 2019 was 27.2% (95% UI, 20.9-35.8), exceeding estimates for other risk factors like HIV, alcohol use disorder, and undernutrition. Compared to a scenario where incarceration rates remain stable at current levels, a gradual 50% reduction in prison admissions and duration of incarceration by 2034 would reduce population tuberculosis incidence by over 10% in all countries except Mexico. Interpretation The historical rise in incarceration in Latin America has resulted in a large excess tuberculosis burden that has been under-recognized to-date. International health agencies, ministries of justice, and national tuberculosis programs should collaborate to address this health crisis with comprehensive strategies, including decarceration. Funding National Institutes of Health Research in context Evidence before this study We searched PubMed for studies on tuberculosis in prisons in Latin America, using the search terms (“tuberculosis”) AND (“prisons” OR “incarceration”) AND (“Latin America” OR “Argentina” OR “Brazil” OR “Colombia” OR “El Salvador” OR “Mexico” OR “Peru”), published in any language. Previous studies have identified a high risk of tuberculosis in prisons in Latin America, finding that notifications in prisons are increasing and account for a growing proportion of all cases in the region. Other national or sub-national studies have found elevated tuberculosis risk among formerly incarcerated individuals and transmission chains spanning prisons and communities. However, the full contribution of incarceration to the broader tuberculosis epidemic in Latin America—accounting for historical incarceration trends, under-detection in prisons, and “spillover” effects into communities—has never been quantified. Furthermore, previous studies have evaluated biomedical interventions in prisons; the regional impact of alternative incarceration policies on future population tuberculosis incidence is unknown. Added value of this study Here we quantify the full contribution of incarceration to the tuberculosis epidemic in Latin America. Our model captures the dynamic nature of incarceration, incorporating historical and contemporary data sources to account for varying prison turnover rates and mechanisms underlying historical incarceration growth. By modeling the population with incarceration history, we estimate the true size of the ever-exposed population, which across the six countries is over 11 times the size of the population within prison at any one time. We identify the settings where excess cases occur and compare our results to crude estimates based on notifications in prisons. We show, across six countries with diverse carceral contexts and tuberculosis epidemiology, that incarceration is a leading driver on par with other major tuberculosis risk factors, a role that has been under-recognized to date. Finally, we demonstrate the potential impact of alternative incarceration policies in reducing future tuberculosis burden in carceral settings and the general population. Implications of all the available evidence To date the true impact of incarceration on the tuberculosis epidemic across the region has been underestimated due to a narrow focus on disease occurring during incarceration. In light of the substantial excess tuberculosis burden attributable to incarceration, interventions targeting incarceration can have outsized effects on the broader tuberculosis epidemic in Latin America— much greater than previously appreciated. These interventions should include not only strategies to reduce tuberculosis risk among currently and formerly incarcerated individuals, but also efforts to end mass incarceration.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,067
Score d'incertitude au seuil0,134

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,384
Écart entre enseignants0,339 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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