Adequacy of diabetes care for older U.S. rural adults: a cross-sectional population based study using 2009 BRFSS data
Bibliographic record
Abstract
BACKGROUND: In the U.S. diabetes prevalence estimates for adults ≥ 65 years exceed 20%. Rural communities have higher proportions of older individuals and health disparities associated with rural residency place rural communities at risk for a higher burden from diabetes. This study examined the adequacy of care received by older rural adults for their diabetes to determine if older rural adults differed in the receipt of adequate diabetes care when compared to their non-rural counterparts. METHODS: Cross-sectional data from the 2009 Behavioral Risk Factor Surveillance Survey were examined using bivariate and multivariate analytical techniques. RESULTS: Logistic regression analysis revealed that older rural adults with diabetes were more likely to receive less than adequate care when compared to their non-rural counterparts (OR = 1.465, 95% CI: 1.454-1.475). Older rural adults receiving less than adequate care for their diabetes were more likely to be: male, non-Caucasian, less educated, unmarried, economically poorer, inactive, a smoker. They were also more likely to: have deferred medical care because of cost, not have a personal health care provider, and not have had a routine medical check-up within the last 12 months. CONCLUSION: There are gaps between what is recommended for diabetes management and the management that older individuals receive. Older adults with diabetes living in rural communities are at greater risk for less than adequate care when compared to their non-rural counterparts. These results suggest the need to develop strategies to improve diabetes care for older adults with diabetes and to target those at highest risk.
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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 | 0.001 |
| 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".