Adverse childhood experiences and adult dental care utilization in the United States: Variation by race and ethnicity
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) are associated with negative health outcomes, including poorer oral health. While research has shown that ACEs are associated with lower dental care utilization, most studies focus on childhood and adolescence, with limited attention to their long-term impact in adulthood or differences across racial and ethnic groups.. This study examines the relationship between ACE exposure and past-year dental care use in adulthood and assesses racial/ethnic differences in this association. Using data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS), we analyzed a sample of 88,728 adults from 21 states and the District of Columbia. The primary outcome was any past-year dental care use. ACEs were summed into a composite score. Multivariable logistic regression models assessed the relationship between ACEs and past-year dental care use, with multiplicative interaction terms used to examine racial/ethnic differences in the observed association. Overall, 63.2% of adults reported any past-year dental care use. Higher ACE exposure was associated with lower odds of dental care use (adjusted odds ratio [aOR] = 0.95, 95% CI: 0.93-0.97, p < .001). The negative association between ACEs and dental care use was strongest for non-Hispanic White respondents, whereas the relationship between ACEs and dental care was attenuated for non-Hispanic Black and Hispanic respondents. These findings expand knowledge on the association of ACEs with dental care use in adulthood and how this relationship may vary across the population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".