Urban–rural disparities in diabetes-related mortality in the USA 1999–2019
Bibliographic record
Abstract
Abstract Aims/hypothesis Our study aimed to examine the trends in diabetes-related mortality in urban and rural areas in the USA over the past two decades. Methods We examined the trends in diabetes-related mortality (as the underlying or a contributing cause of death) in urban and rural areas in the USA between 1999 and 2019, using the CDC WONDER Multiple Cause of Death database. We estimated the 20 year trends of the age-adjusted mortality rate (AAMR) per 100,000 population in urban vs rural counties. Results The AAMR of diabetes was higher in rural than urban areas across all subgroups. In urban areas, there was a significant decrease in the AAMR of diabetes as the underlying (−16.7%) and contributing (−13.5%) cause of death ( p trend <0.001), which was not observed in rural areas (+2.6%, +8.9%, respectively). AAMRs of diabetes decreased more significantly in female compared with male individuals, both in rural and urban areas. Among people younger than 55 years old, there was a temporal increase in diabetes-related AAMR (+13.8% to +65.2%). While the diabetes-related AAMRs of American Indian patients decreased in all areas (−19.8% to −40.5%, all p trend <0.001), diabetes-related AAMRs of Black and White patients decreased significantly in urban (−26.6% to −28.3% and −10.7% to −15.4%, respectively, all p trend <0.001) but not rural areas (−6.5% to +1.8%, +2.4% to +10.6%, respectively, p trend NS, NS, NS and <0.001). Conclusions/interpretation The temporal decrease in diabetes-related mortality in the USA has been observed only in urban areas, and mainly among female and older patients. A synchronised effort is needed to improve cardiovascular health indices and healthcare access in rural areas and to decrease diabetes-related mortality. Graphical abstract
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".