1086-P: Rural Residence Is Associated with a Lower Likelihood of Dipeptidyl Peptidase 4 Inhibitor Use for Treatment Intensification
Bibliographic record
Abstract
Introduction: Several drug therapy management strategies exist when treatment intensification is required in type 2 diabetes, however, an understudied factor influencing drug therapy choice is an individual’s location of residence along the rural-urban continuum. The objective of our study was to explore the association between location of residence (rural, urban, metropolitan) and the use of dipeptidyl peptidase 4 inhibitors (DPP-4i) for first treatment intensification of type 2 diabetes. Methods: We conducted a retrospective cohort study from 2008 to 2019 using administrative data from Alberta, Canada. Cohort entry was established when an individual became a new metformin user and at this time, location of residence was defined using postal codes. Individuals were followed until a dispensation for treatment intensification occurred (classified as DPP-4i or non-DPP-4i-based therapy). A multivariable logistic regression analysis was performed to determine the association between location of residence and likelihood of DPP-4i dispensation, adjusting for clinically relevant confounders. Results: Of 66,064 new metformin users experiencing treatment intensification, 15,467 (23%) were intensified with a DPP-4i. At the beginning of the observation period, proportion of DPP-4i dispensations were similar (7% metropolitan, 6% urban, 5% rural). However, over time a maximum 10% difference was noted between rural and metropolitan/urban (32% metropolitan, 27% urban, 22% rural). After adjusting for potential confounders, we determined that rural-dwellers are 36% less likely to have a DPP-4i dispensed, compared to metropolitan (aOR:0.64;95%CI:0.61-0.67) and over time, uptake in rural areas is slower. Conclusion: Our study sheds light on the impact of location of residence on drug therapy management in type 2 diabetes. The differential management experienced by rural-dwellers demonstrates a substantial gap in health equality across our jurisdiction. Disclosure D.K. Nagy: None. L. Bresee: None. D. Eurich: None. S.H. Simpson: Research Support; Merck & Co., Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".