Factors associated with low coverage in mass drug administration for schistosomiasis in mobile populations in Mali: a cross-sectional study
Notice bibliographique
Résumé
BACKGROUND: Neglected tropical diseases (NTDs) affect over a billion people globally. From 2020 to 2021, when this study was conducted, Mali remained endemic for multiple NTDs, including schistosomiasis and trachoma. At the time, significant efforts were underway to scale up control and elimination programs, although challenges persisted, particularly in reaching mobile populations such as nomads, migrants, and internally displaced persons (IDPs), with mass drug administration (MDA). These groups were often missed during campaigns, contributing to gaps in coverage and sustained transmission in certain areas. This study was designed to investigate the factors contributing to non-participation in schistosomiasis MDA among mobile populations in Mali, to inform strategies for more equitable and effective delivery. METHODS: A cross-sectional study was conducted among adults (18 + years) in two Malian health districts targeting nomads, migrants, and IDPs from March to July 2020. A multi-stage cluster sampling approach was used to select participants. Mobility was defined as temporary or permanent movement for livelihood (e.g., herding, mining) or due to displacement. Structured, interviewer-administered questionnaires were used, after development by the study team and pre-tested in a similar population. Questions focused on barriers to MDA access, mobility patterns, awareness about MDA, and logistical challenges. The main outcome was self-reported participation in the last MDA (i.e., taking praziquantel). Data were analyzed using descriptive statistics and multivariable mixed-effects logistic regression models. RESULTS: A total of 1067 participants were included in the study. All groups had MDA coverage rates below the recomended 75% threshold for schistosomiasis elimination. Only 40.8% of IDPs and 3.62% of migrants participated in the last MDA. The most reported reason for non-participation was a lack of information (64.5%). Lower income and occupations such as mining were significantly associated with non-participation (p < 0.001). Mixed-effects logistic regression showed that males were nearly three times more likely to miss MDA than females (aOR = 2.89, 95% CI = 1.65-5.06). Participants facing accessibility barriers (e.g., long distances, physical limitations) were also more likely to miss MDA (aOR = 2.60, 95% CI = 1.45-4.66). Nomads and transhumants were more likely to miss MDA compared to IDPs (aOR = 3.16, 95% CI = 1.05-9.47). CONCLUSION: These findings reveal notable disparities in MDA participation, influenced by mobility patterns, information access, and trust in health programs. Addressing these barriers requires context-specific approaches, such as improved communication, tailored MDA delivery, and greater community engagement. Strengthening these efforts is essential for equitable NTDs control and ensuring mobile populations are not left behind in schistosomiasis elimination efforts.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».