ORAL HEALTH STATUS AND ITS ASSOCIATED FACTORS AMONG TRIBALS OF SOUTH INDIA - A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Oral health is a critical but under-researched aspect of general health among tribal populations in South India. These communities face multiple structural and cultural barriers including poverty, geographical isolation, and reliance on traditional practices, leading to high disease burden and low dental service utilization. This systematic review evaluates the oral health status of South Indian tribal populations and identifies associated behavioral, systemic, and cultural determinants. The aim of the present review was to systematically assess the oral health status and its associated factors among tribal communities in South India using evidence from the past decade Materials and methods: This review followed PRISMA 2020 guidelines and the Cochrane Handbook. Searches were conducted in PubMed, Scopus, Web of Science, Google Scholar, Cochrane CENTRAL, and LILACS from 2014 to 2025. Studies included were original research focusing on dental caries, oral hygiene, periodontal status, oral healthrelated quality of life, or nutrition among South Indian tribals. Twenty-one cross-sectional and qualitative studies met the inclusion criteria. The Modified Newcastle Ottawa Scale was used for risk of bias assessment, and Egger’s test evaluated publication bias. Results:The included studies covered diverse tribes across Tamil Nadu, Kerala, Karnataka, Andhra Pradesh, and Telangana. DMFT scores ranged from moderate to alarmingly high (up to 18.32), with dental caries, poor hygiene, and periodontal disease being widespread. Children showed high caries prevalence (up to 89.3%), and elderly populations exhibited poor prosthetic status and compromised oral health-related quality of life. Behavioural determinants such as use of twigs, charcoal, and tobacco were common. Nutritional deficits and systemic health issues were correlated with poor oral outcomes. A few interventional studies demonstrated improved outcomes with culturally sensitive oral health education and access interventions. Conclusion: South Indian tribal populations experience severe oral health disparities shaped by systemic neglect, cultural practices, and inadequate access to care. There is a pressing need for integrated, community-engaged, and culturally competent public health interventions. The incorporation of oral health into tribal welfare policies, education of local health workers, and targeted research are crucial for addressing this longstanding inequity.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".