Oral Health Status and Practices, and Anthropometric Measurements of Preschool Children: Protocol for a Multi-African Country Survey
Bibliographic record
Abstract
BACKGROUND: Oral diseases are among the most prevalent conditions with significant impact on the growth and development of young children. Data are required to plan effectively for the management of early childhood caries (ECC) and other oral diseases in this age. There are currently very few African countries with updated and nationally representative data on ECC prevalence, and risk indicators and regional data on ECC and other oral diseases are scarce. OBJECTIVE: We aim to determine the oral health status and practices, dietary intake, and anthropometric measurements of preschool children in several African countries. METHODS: A cross-sectional study will be conducted in several African countries using a standardized questionnaire and clinical examination for data collection from healthy preschool children in kindergartens and primary health care facilities. The clinical examination will assess ECC using the decayed, missing due to caries, and filled teeth (dmft) index according to the World Health Organization (WHO) criteria, dental erosion (using the Basic Erosive Wear Examination Index), deciduous molar hypomineralization (using the European Association of Paediatric Dentistry criteria), dental fluorosis (using Dean's Index), oral hygiene status (using the Oral Hygiene Index Simplified), and oral mucosal lesions. Oral hygiene habits and dental visits will be assessed using the WHO child questionnaire, and dietary intake will be assessed using the Food and Agriculture Organization method. Anthropometric measurements will be obtained following the International Society for the Advancement of Kinanthropometry standard protocol, and the children's nutritional status will be assessed following the WHO child growth standards. To train and calibrate examiners, educational resources and electronic forms will be used to reach interexaminer and intraexaminer reliability with κ>0.6. Descriptive analysis will determine the prevalence of clinical conditions by age and sex. Bivariate analysis and multivariable regression will assess associations between the clinical conditions and sociodemographic factors, and oral health behaviors. RESULTS: Data collection will begin after approvals and ethical clearance are obtained. The first stage will include 3 countries, namely Egypt, Nigeria, and South Africa, and collaborators from other African countries will join afterward. CONCLUSIONS: This study will lay down the foundations for using validated tools to collect data on the oral health of young children in Africa, allowing researchers from different countries across Africa to collect standardized data on ECC and other oral conditions. This will facilitate comparisons and analysis of risk factors that might be unique to the African continent. The results will provide baseline data on the prevalence of oral diseases and enable planning to address the treatment needs of young African children and design programs to prevent oral diseases in the African continent. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33552.
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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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".