Patterns of Outpatient Antibiotic Prescribing in Older Adults by Social Determinants of Health Before and During COVID-19
Bibliographic record
Abstract
Background: Identifying patterns and predictors of antibiotic prescribing can begin to inform interventions aimed at improving antibiotic use and slowing the emergence of antimicrobial resistance. As it stands, patterns of prescribing among older adults by social determinants of health (SDOH) remain poorly described. We sought to examine associations between SDOH variables and antibiotic prescribing among older community-dwelling adults over time and identify variations in these associations before and during the COVID-19 pandemic. Methods: We conducted a retrospective, population-based cohort study of community-dwelling older adults (≥66 years of age) in Ontario between March 2018-March 2020 (pre-pandemic period) and March 2020-March 2022 (pandemic period). We used multivariable Fine-Gray subdistribution hazard models to evaluate associations between SDOH variables (neighbourhood income, neighbourhood proportion of racially minoritized groups, and immigration status) and incident antibiotic prescriptions (overall and for respiratory infections), accounting for mortality as a competing risk. We used interaction terms and stratification to assess for potential effect modification by the COVID-19 pandemic period. Results: After exclusions, 2,567,382 outpatients were identified in the pre-pandemic period, and 2,744,337 in the pandemic period. In both study periods, antibiotic prescribing was higher among residents in highest income neighbourhoods (vs. lowest) overall (subdistribution hazard ratio [sHR] 1.03, 95% CI 1.02-1.04 and sHR 1.02, 95% CI 1.01-1.03, respectively), with a similar pattern for prescriptions for respiratory infections (sHR 1.06, 95% CI 1.05-1.07 and sHR 1.05, 1.04-1.06, respectively). Antibiotic prescribing was higher among recent immigrants (vs. long-term residents) in both periods, with a more pronounced increase during the pandemic than pre-pandemic period in overall prescriptions (sHR 1.21, 95% CI 1.18-1.25 vs. sHR 1.12, 95% CI 1.09-1.16, p=0.049) and in prescriptions for respiratory infections (sHR 1.27, 95% CI 1.23-1.32 vs. sHR 1.15, 95% CI 1.11-1.18, p<0.001). Overall antibiotic prescribing was lower among residents in neighbourhoods with the highest proportion racially minoritized (vs. lowest) in both periods, with a more pronounced decrease during the pandemic than pre-pandemic period (sHR 0.81, 95% CI 0.80-0.82 vs. sHR 0.92, 95% CI 0.91-0.93, p<0.001); similarly, there was a more pronounced decrease in prescriptions for respiratory infections during the pandemic than prepandemic period (sHR 0.93, 95% CI 0.92-0.94 vs. sHR 1.07, 95% CI 1.05-1.08, p<0.001). Conclusion: In this cohort of older outpatients, SDOH variables were associated with outpatient antibiotic prescribing during the two years before and the first two years of the COVID-19 pandemic, with some of these associations being modified during the COVID-19 pandemic period. These findings suggest that patient sociodemographic characteristics are important for identifying populations who could be at risk of disproportionate antibiotic use in the outpatient setting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".