Patterns of outpatient antibiotic prescribing in older adults by social determinants of healthcare access: a population-based retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Strategies to improve antibiotic use may exacerbate health inequities if they do not consider existing barriers to healthcare access. We examined associations between social determinants of healthcare access (SDOH) and antibiotic prescribing and variations in these associations pre- and post-COVID-19 emergence. METHODS: We conducted a retrospective cohort study of community-dwelling adults aged ≥66 years in Ontario, Canada, between March 2018 and March 2020 (pre-pandemic period) and March 2020 and March 2022 (pandemic period). Multivariable Fine-Gray subdistribution hazard models were used to examine associations between three SDOH variables (neighbourhood-level income and proportion racialized, and individual-level recent immigration) and incident antibiotic prescriptions, accounting for mortality as a competing risk. We assessed for potential effect modification by the pandemic period. RESULTS: The pre-pandemic (n = 2 567 382) and pandemic (n = 2 744 337) cohorts were similar in average age (75 years). Antibiotic prescribing was slightly higher among residents in the highest income neighbourhoods in pre-pandemic (subdistribution hazard ratio [sHR], 1.03 [95% CI, 1.02-1.04], compared with lowest income) and pandemic (sHR, 1.02 [1.01-1.03]) periods. Prescribing was higher among recent immigrants (vs. long-term residents) in both periods, with a more pronounced difference observed during the pandemic (sHR, 1.21 [1.18-1.25]) than pre-pandemic (sHR, 1.12 [1.09-1.16]) period. Prescribing was lower among residents living in the most diverse neighbourhoods (vs. least diverse) in both periods, with a more pronounced difference during the pandemic (sHR, 0.81 [0.80-0.82]) than pre-pandemic (sHR, 0.92 [0.91-0.93]) period. DISCUSSION: SDOH variables are associated with antibiotic prescribing patterns over time among older outpatients, and the COVID-19 pandemic further modified some of these associations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".