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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».