Prevalence and Associated Factors of Past-Year Dental Visit Among US Veterans 2012–2022: Findings from the Behavioral Risk Factor Surveillance System
Bibliographic record
Abstract
This study uses cross-sectional data from the Behavioral Risk Factor Surveillance System from 2012 to 2022 to examine the prevalence and factors associated with past-year dental visits among US veterans. We dichotomized past-year dental visits into a dichotomous outcome (yes/no) and conducted descriptive and multivariable logistic regression analyses. The analyses, presenting results in adjusted odds ratios (AOR) and 95% confidence intervals (CIs), included 276,368 participants. Significant findings indicated that veterans aged ≥ 30 years had 23–48% less odds of having a past year dental visit than veterans aged 18–29. Factors positively associated with dental visits included higher educational attainment and annual household income. Conversely, unemployed veterans and those without health insurance were less likely to have visited a dentist in the past year, with AORs of 0.80 (95% CI: 0.72–0.90) and 0.49 (95% CI: 0.45–0.54), respectively. Veterans with comorbidities also showed lower odds of dental visits. Although some factors align with those influencing dental care in the general population, veterans face unique barriers such as limited access to Veterans Affairs-provided dental services and distinct health needs that underscore the necessity of targeted oral health programs to address these specific challenges, improve outcomes, and reduce disparities in this community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".