Factors associated with low coverage in mass drug administration for schistosomiasis in mobile populations in Mali: a cross-sectional study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".