Accessibility of Ontario pharmacies offering COVID-19 vaccination by rurality, community material deprivation, and ethnic concentration: a repeated cross-sectional geospatial analysis
Bibliographic record
Abstract
BACKGROUND: Community pharmacies are largely recognized as geographically accessible; yet concerns arise regarding inequitable access to COVID-19 vaccination, especially during early vaccine availability. OBJECTIVES: This study aims to investigate the geographic accessibility of community pharmacies offering COVID-19 vaccination in Ontario's from April to December 2021 considering community-level rurality, material deprivation, and ethnic concentration. METHODS: Data from the Ontario Ministry of Health website COVID-19 vaccination pharmacies between April 27, 2021 and December 20, 2021, were analyzed. Pharmacy addresses were geocoded using Environics Analytics Business Data and the Postal Code Conversion File (PCCF+). Material deprivation and ethnic concentration at the Dissemination Area (DA) level were based on Public Health Ontario's marginalization data and organized into quintiles. Mean geographic accessibility was calculated for each quintile using the 2-Step Floating Catchment Area method using service areas of 1,000, 1,500, or 3,000 m for urban DAs and 10,000 m for rural DAs. Analysis of Variance (ANOVA) was used to compare mean geographic accessibility across eight selected dates reflecting vaccine eligibility and availability changes. RESULTS: Of 15,174 pharmacies identified, 92.9% were successfully linked to geographic coordinates. Three eras of vaccine availability were identified: [1] Intermediate; [2] Scarcity (May 2021); and [3] Abundance (November and December 2021). During vaccine shortages, more deprived and ethnically concentrated urban areas had greater geographic accessibility than less deprived areas, while rural areas had no access. For example, during vaccine scarcity, urban DAs in the highest ethnic concentration quintile had an accessibility score of 6.55 compared to 0.18 in the lowest quintile. During other periods, more deprived urban areas either showed higher geographic accessibility or no significant difference compared to less deprived areas; however, rural deprived areas generally had lower geographic accessibility than urban areas. CONCLUSIONS: During COVID-19 vaccine scarcity or abundance, deprived and ethnically concentrated urban areas had similar or higher access compared to less deprived areas. However, rural deprived areas experienced lower geographic accessibility. Access to pharmacies can be enhanced in rural deprived areas by incentivization and outreach. Further research examining whether this geographic accessibility variance influenced vaccine uptake and infection rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".