Mapping Disparities in Access to Spirometry
Bibliographic record
Abstract
Abstract RATIONALE: The underdiagnosis and misdiagnosis of chronic lung diseases, such as chronic obstructive pulmonary disease (COPD), lead to poorer health outcomes. Despite being frequently underdiagnosed, COPD was the second leading cause of hospitalizations in Canada in 2022-2023. Yet access to pulmonary function testing (PFT) labs in British Columbia (BC) remains inconsistent, primarily due to the province's vast geography and uneven population distribution. This disparity raises significant health equity concerns, particularly given that rural and remote parts of BC are home to a large proportion of the Indigenous population who continue to endure the effects of structural and environmental racism. Our aim was to quantify disparities in driving times, one metric of access, from the population centres of all census dissemination areas (average population of 400-700) in BC to the nearest PFT labs. METHODS: We collected addresses of publicly funded PFT labs using health authority websites. We used Google Maps Platform to estimate driving times from dissemination area population centroids within BC to the nearest PFT lab in BC or neighbouring Alberta and Yukon. We validated this approach using a 5-mile by 5-mile raster grid cell method in which we determined the driving time to the nearest PFT lab for each cell (Figure 1). We determined demographics by dissemination area from the 2021 Canadian census and further analyzed travel times by demographic attributes and deprivation indices. RESULTS: Travel time to the nearest PFT lab varied greatly across 7,848 census dissemination areas in BC. In 40% of these areas, driving time to the closest PFT lab was under 10 minutes. In 87% of the areas, it was under 30 minutes. Approximately 10% of the areas had a travel time of over an hour, while less than 1% required more than 5 hours of driving. Across the province, urban dissemination areas had significantly lower driving time to the closest PFT lab with an average driving time of 12.8 (95% CI 12.5-13.1) minutes compared to 75.4 (95% CI 70.6-80.2) minutes in rural dissemination areas. Subgroup analyses showed further disparities within rural areas based on income and other demographic variables. CONCLUSIONS: Travel time to reach PFT labs varies widely across BC, likely contributing to barriers in access and potentially exacerbating health disparities such as undiagnosed COPD among rural communities. Figure 1: Driving time to the closest PFT lab during the fall season for each 5-mile by 5-mile grid cell in BC
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".