The effectiveness, cost-effectiveness, budget impact, and return on investment of scaling up tuberculosis screening and preventive treatment in Brazil, Georgia, Kenya, and South Africa: a modelling study
Notice bibliographique
Résumé
BACKGROUND: Closing the tuberculosis diagnostic gap and scaling up tuberculosis preventive treatment (TPT) are two global priorities to end tuberculosis. We aimed to estimate the cost-effectiveness, budget impact, and societal return on investment of a comprehensive intervention to improve tuberculosis screening and prevention in Brazil, Georgia, Kenya, and South Africa-four distinct epidemiological settings. METHODS: In this modelling study, in partnership with national tuberculosis programmes we defined a set of interventions (the intervention package) related to tuberculosis screening and TPT in three priority populations: people with HIV, household contacts, and a country-defined high-risk population (people deprived of liberty [Brazil], people accessing care for injection drug use [Georgia], people in informal settlements in nine districts with a high prevalence of tuberculosis [Kenya], and people in the 22 subdistricts with the highest prevalence of tuberculosis [South Africa]). We developed transmission models calibrated to country-specific epidemiology and collated cost data for tuberculosis-related activities and patient costs in 2023 US dollars (US$). We compared the intervention package scaled up to reach all priority populations by 2030 to a status quo scenario based on projected tuberculosis epidemiology over a 27-year time horizon (Jan 1, 2024, to Dec 31, 2050); to delineate the impact of intervention components, we also evaluated the intervention package without TPT. Outcomes were health system and societal costs, number of tuberculosis episodes, tuberculosis deaths, and disability-adjusted life years (DALYs). We calculated the budget impact, health system cost per DALY averted, and societal return on the health system investment for each country. Outcomes were discounted at 3% per annum. FINDINGS: With the status quo scenario, by 2050, tuberculosis incidence is projected to be 41 per 100 000 population (95% uncertainty range 32-53) in Brazil, 45 per 100 000 population (36-60) in Georgia, 214 per 100 000 population (146-266) in Kenya, and 261 per 100 000 population (133-406) in South Africa. The percentage of all tuberculosis episodes prevented by implementing the intervention package in all priority populations is projected to be 15·0% (12·8-17·5) in Brazil, 14·3% (13·1-15·8) in Georgia, 21·3% (15·2-27·6) in Kenya, and 26·4% (21·1-31·8) in South Africa by 2050. If implemented without TPT (ie, tuberculosis disease screening alone), corresponding reductions were lower at 10·4% (8·6-12·2) in Brazil, 10·2% (9·5-11·2) in Georgia, 12·6% (9·5-15·9) in Kenya, and 16·8% (13·0-20·4) in South Africa. In 2030, the percentage of the national tuberculosis programme budget required for the intervention package was 62% in Brazil, 10% in Georgia, 67% in Kenya, and 44% South Africa. The incremental cost per DALY averted of the intervention package compared with the status quo in all priority populations is $386 in Brazil, $491 in Georgia, $53 in Kenya, and $160 in South Africa. The corresponding societal return per health system dollar invested is projected to be $51 in Brazil, $8 in Georgia, $27 in Kenya, and $54 in South Africa. INTERPRETATION: Scaling up tuberculosis screening and TPT requires substantial investment but is projected to be cost-effective compared with the status quo, to greatly reduce tuberculosis incidence, and to provide large returns on investment. FUNDING: World Health Organization.
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 | 0,005 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».