Towards a suburban spatial epidemiology: differentiating geographical patterns of cancer incidence, patient access, and surgical treatment in Canada’s urban fringe
Bibliographic record
Abstract
Epidemiological studies have traditionally categorised study populations as urban or rural. However, a growing proportion of the global population resides in spaces that are neither dense urban cores nor rural/remote regions. These interstices are distinctly suburban, featuring a low density of services, poor walkability, and spatial isolation relative to their urban counterparts. Contrary to the dominant imaginary of the affluent ‘American Dream’, Canada’s suburbs are increasingly becoming home to socioeconomically deprived populations. Following from the well-established links between socioeconomic status and health geographies, this dissertation presents quantitative geographical evidence that the suburbs differ from their urban and rural counterparts, constituting a third, epidemiologically distinct space. The first substantive chapter provides an introductory tracing of the suburb’s socioeconomic history, laying the contextual foundation for a distinct categorisation. The following three chapters then draw upon this categorisation to differentiate spatial epidemiological patterns of cancer along both urban/suburban/rural and socioeconomic axes. The second chapter uses exploratory temporal mapping to document a recent emergence of oral cancer cases in British Columbia’s suburbs, geographically coincident with immigration from betel quid-chewing regions and an increase in local socioeconomic deprivation. The third chapter then explores head and neck cancer patients’ spatial access to cancer treatment centres across the province, highlighting significantly greater travel times among the most deprived suburban and rural populations. The fourth chapter evaluates whether these spatial and socioeconomic disparities reflect actual treatment rates, focussing on resection surgeries for five cancer types across Canada, excluding Québec. Resection rates were positively associated with socioeconomic deprivation in rural areas and inversely associated in urban areas, while the highest overall rates were observed in middle-SES suburban populations. Drawing upon these three cancer studies, this dissertation proposes a suburban spatial epidemiology, in which suburbs are differentiated from urban and rural spaces. I conclude by asserting that the suburbs’ unique placial contexts merit standalone attention in health research, calling for further examination of suburban spaces in epidemiological research.
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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) | 0.001 | 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".