Decomposition Analysis of the Prevalence of Denture Use Between Rural and Urban Older Individuals With Edentulism in China: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Edentulism impacts the physical health and quality of life of older individuals. The prevalence, influencing factors, and differences in terms of edentulism in urban and rural areas of China are unclear. OBJECTIVE: This study investigated the denture-wearing conditions and causes affecting older patients with edentulism in urban and rural areas of China and analyzed the differences. METHODS: This cross-sectional study included the data of 5139 older individuals (age>65 years) with edentulism obtained from 23 Chinese provinces in 2018. Participants were divided into urban and rural groups based on their household registration. Factors influencing denture use in both groups were explored using binary logistic regression, while factors influencing differences in denture wearing in both groups were explored using the Fairlie decomposition model. RESULTS: Of the 5139 participants, 67.05% (808/1205) from urban areas and 51.12% (2011/3934) from rural areas wore dentures. In the urban group, participants with a higher level of education (1-6 years: odds ratio [OR] 2.093, 95% CI 1.488-2.945; ≥7 years: OR 2.187, 95% CI 1.565-3.055) or who exercised (OR 2.840, 95% CI 2.016-3.999) preferred wearing dentures, but individuals with BMI<18.5 kg/m2 (OR 0.558, 95% CI 0.408-0.762) or widowed (OR 0.618, 95% CI 0.414-0.921) did not. In the rural group, a higher level of education (1-6 years: OR 1.742, 95% CI 1.429-2.123; ≥7 years: OR 1.498, 95% CI 1.246-1.802), living alone (OR 1.372, 95% CI 1.313-1.663), exercise (OR 1.612, 95% CI 1.340-1.940), high economic status (OR 1.234, 95% CI 1.035-1.472), residence in the eastern area (OR 2.045, 95% CI 1.723-2.427), presence of chronic diseases (1 disease: OR 1.534, 95% CI 1.313-1.793; ≥2 diseases: OR 1.500, 95% CI 1.195-1.882) were positively associated and age≥80 years (OR 0.318, 95% CI 0.247-0.408), BMI<18.5 kg/m2 (OR 0.692, 95% CI 0.590-0.813), and widowed (OR 0.566, 95% CI 0.464-0.690) or other marital status (OR 0.600, 95% CI 0.392-0.918) were negatively associated with denture use. The Fairlie decomposition model revealed that the number of chronic diseases (16.34%), education level (11.94%), region of residence (11.00%), annual income (10.55%), exercise (6.81%), and age (-0.92%) were the main factors responsible for differences between urban and rural edentulism and could explain the difference in the denture-wearing rate (58.48%) between both groups. CONCLUSIONS: Older individuals with edentulism with a higher education level and who exercise are more willing to wear dentures, while those with BMI<18.5 kg/m2 show a decreased willingness to wear dentures in both urban and rural areas in China. Controlling the number of chronic diseases, improving the education level and annual income, cultivating good exercise habits, and bridging the gap between the economic status of the east and west can narrow the differences in denture wearing among urban and rural older individuals with edentulism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".