Intersecting health burdens: oral health, non-communicable disease screening, and women's health in rural Tanzania
Bibliographic record
Abstract
Introduction: This cross-sectional study explored the intersection of oral health, other non-communicable diseases (NCDs), and women's health in rural Tanzania, using community-based screenings to identify syndemic patterns of vulnerability and inform integrated care strategies. Methods: A total of 224 adult women were recruited during outreach events in three Rorya District villages of Burere, Nyambogo, and Roche in July 2023. Clinical oral examinations were conducted alongside biomarker analysis using the PerioMonitor™, as well as survey-based assessments, including the Oral Health-Related Quality of Life (OHRQoL) scale and the Hologic Global Women's Health Index (HGWI). A subsample of 45 participants underwent additional screening for blood pressure (BP) and blood glucose levels. Results: Only 18.2% of participants reported having received prior BP screening. The mean DMFT score was 5.16, and 40% of the sample showed elevated periodontal inflammation. The average OHRQoL score was 11.15, indicating substantial functional and psychosocial impacts. Among those screened further, 49% were hypertensive, 2% were hyperglycemic, and 18% were hypoglycemic, most without a prior diagnosis. Conclusions: Community-based screening proved both feasible and impactful, uncovering overlapping burdens of untreated oral disease, metabolic dysregulation, and unmet preventive care. These findings reflect the structural and clinical dimensions of oral health inequity and align with syndemic theory, underscoring the need for integrated, gender-responsive, and culturally grounded interventions. They also offer a foundation for scalable, sustainable models of care in low-resource settings.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".