Income and wealth as correlates of socioeconomic disparity in dentist visits among adults aged 20 years and over in the United States, 2011–2014
Bibliographic record
Abstract
BACKGROUND: Most studies in the United States (US) have used income and education as socioeconomic indicators but there is limited information on other indicators, such as wealth. We aimed to assess how two socioeconomic status measures, income and wealth, compare as correlates of socioeconomic disparity in dentist visits among adults in the US. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 were used to calculate self-reported dental visit prevalence for adults aged 20 years and over living in the US. Prevalence ratios using Poisson regressions were conducted separately with income and wealth as independent variables. The dependent variable was not having a dentist visit in the past 12 months. Covariates included sociodemographic factors and untreated dental caries. Parsimonious models, including only statistically significant (p < 0.05) covariates, were derived. The Akaike Information Criterion (AIC) measured the relative statistical quality of the income and wealth models. Analyses were additionally stratified by race/ethnicity in response to statistically significant interactions. RESULTS: The prevalence of not having a dentist visit in the past 12 months among adults aged 20 years and over was 39%. Prevalence was highest in the poorest (58%) and lowest wealth (57%) groups. In the parsimonious models, adults in the poorest and lowest wealth groups were close to twice as likely to not have a dentist visit (RR 1.69; 95%CI: 1.51-1.90) and (RR 1.68; 95%CI: 1.52-1.85) respectively. In the income model the risk of not having a dentist visit were 16% higher in the age group 20-44 years compared with the 65+ year age group (RR 1.16; 95%CI: 1.04-1.30) but age was not statistically significant in the wealth model. The AIC scores were lower (better) for the income model. After stratifying by race/ethnicity, age remained a significant indicator for dentist visits for non-Hispanic whites, blacks, and Asians whereas age was not associated with dentist visits in the wealth model. CONCLUSIONS: Income and wealth are both indicators of socioeconomic disparities in dentist visits in the US, but both do not have the same impact in some populations in the US.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".