Determining the Social Determinants of Health That Influence Self-Reported Dysphagia: A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: The prevalence of dysphagia has been increasing over the years, with some individuals at a greater risk. Social determinants of health (SDOH) can affect some individual's access to care and their health more than others. The objective of this study is to explore the role of SDOH on self-reported dysphagia in older adults (aged 65 years and older) living in the United States. METHOD: The 2022 National Health Interview Survey (NHIS) is a database that collects health information of over 35,000 individuals across the United States. A secondary cross-sectional data analysis determined the SDOH that influence self-reported dysphagia in older adults. Demographic data were represented as mean and standard deviation for continuous data and as frequency and percentage for categorical data. Two parallel analyses were performed, a stepwise logistic regression analysis to unweighted data and a manual backward elimination to data applying the NHIS sampling weights for both a statistically driven model and a theory-driven model. RESULTS: For stepwise logistic regression analysis, employment, race, food insecurity, and housing were found to influence self-reported dysphagia in the statistically driven model, while all but housing were significant in theory-driven model. For the manual backward elimination analysis, employment and race were significant in both models. Older adults who were unemployed due to health/disability, or retirement, reported sometimes worrying about food affordability, and those who rented a house/apartment were more likely to report swallowing difficulties. Older adults who identified as Black/African American or Asian were less likely to report swallowing difficulties. CONCLUSION: More research needs to be done to examine the role of SDOH on dysphagia. Identifying these SDOH can allow clinicians to advocate for vulnerable populations to have accessible access to dysphagia screening and care.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".