Historical Perspective and Risk of Multiple Neglected Tropical Diseases in Coastal Tanzania: Compositional and Contextual Determinants of Disease Risk
Bibliographic record
Abstract
BACKGROUND: In the past decade, research on neglected tropical diseases (NTDs) has intensified in response to the need to enhance community participation in health delivery, establish monitoring and surveillance systems, and integrate existing disease-specific treatment programs to control overlapping NTD burdens and detrimental effects. In this paper, we evaluated the geographical distribution of NTDs in coastal Tanzania. METHODS AND FINDINGS: We also assessed the collective (compositional and contextual) factors that currently determine risks to multiple NTDs using a cross sectional survey of 1253 individuals in coastal Tanzania. The results show that the effect size in decreasing order of magnitude for non-binary predictors of NTD risks is as follows: NTD comorbidities > poverty > educational attainment > self-reported household quality of life > ethnicity. The multivariate analysis explained 95% of the variance in the relationship between NTD risks and the theoretically-relevant covariates. Compositional (biosocial and sociocultural) factors explained more variance at the neighbourhood level than at the regional level, whereas contextual factors, such as access to health services and household quality, in districts explained a large proportion of variance at the regional level but individually had modest statistical significance, demonstrating the complex interactions between compositional and contextual factors in generating NTD risks. CONCLUSIONS: NTD risks were inequitably distributed over geographic space, which has several important policy implications. First, it suggests that localities of high burden of NTDs are likely to diminish within statistical averages at higher (regional or national) levels. Second, it indicates that curative or preventive interventions will become more efficient provided they can be focused on the localities, particularly as populations in these localities are likely to be burdened by several NTDs simultaneously, further increasing the imperative of multi-disease interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".