Differences Among Older Adults in the Types of Dental Services Used in the United States
Bibliographic record
Abstract
The purpose of this article is to explore differences in the socioeconomic, demographic characteristics of older adults in the United States with respect to their use of different types of dental care services. The 2008 Health and Retirement Study (HRS) collected information about patterns of dental care use and oral health from individuals aged 55 years and older in the United States. We analyze these data and explore patterns of service use by key characteristics before modeling the relationship between service use type and those characteristics. The most commonly used service category was fillings, inlays, or bonding, reported by 43.6% of those with any utilization. Just over one third of those with any utilization reported a visit for a crown, implant, or prosthesis, and one quarter reported a gum treatment or tooth extraction. The strongest consistent predictors of use type are denture, dentate, and oral health status along with dental insurance coverage and wealth. Our results provide insights into the need for public policies to address inequalities in access to dental services among an older US population. Our findings show that lower income, less wealthy elderly with poor oral health are more likely to not use any dental services rather than using only preventive dental care, and that cost prevents most non-users who say they need dental care from going to the dentist. These results suggest a serious access problem and one that ultimately produces even worse oral health and expensive major procedures for this population in the future.
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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 | 0.003 |
| 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 | 0.001 |
| 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".