Prevalence, Extent, and Severity of Oral Health Impacts Among Adults in Rural Karnataka, India
Bibliographic record
Abstract
INTRODUCTION: Oral health affects quality of life. Many studies have investigated the factors associated with oral health-related quality of life (OHRQoL). Little is known about OHRQoL of adults living in rural and remote areas of India, where many have lower levels of education and limited availability of oral health care services. OBJECTIVES: To determine the prevalence, extent, and severity of OHRQoL impacts associated with psychosocial factors, functional dentition, and patterns of dental visits among rural Indian adults between the ages of 35 and 54 y. METHODS: A cross-sectional study was conducted with a multistage stratified sampling strategy targeting 35- to 54-year-olds. Interviews and oral examinations were performed to collect data on sociodemographic variables, Oral Health Impact Profile-14 (OHIP-14), patterns of dental visits, stress, tobacco and alcohol use, and dentition status. Univariate, bivariate, and multivariable analyses were done to determine the factors associated with prevalence, extent, and severity of OHIP-14 using SAS version 9.3. RESULTS: There were 873 participants. Prevalence, extent, and severity of OHIP-14 were 13.4%, 0.5 (0.4-0.7), and 11.8 (11.2-12.5), respectively. The OHIP-14 impacts reported were not severe and mostly affected physical functioning. Levels of education, income, and number of functional teeth (FT) were inversely associated and last dental visit within the previous year was positively associated with prevalence, extent, and severity of OHIP-14. The prevalence of 1 or more oral health impacts was nearly 13% among rural middle-aged adults in India. CONCLUSIONS: Low socioeconomic conditions, dental visits, and FT ≤19 were positively associated with prevalence, extent, and severity of oral health impacts. KNOWLEDGE TRANSFER STATEMENT: This article provides data regarding OHRQoL of people in rural areas of a developing country. The study was intended to determine the factors associated with OHRQoL in rural people who are less educated and living in areas with minimal oral health care facilities. The findings of this study could potentially facilitate further research and health promotional activities for rural people of developing countries.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".