Do changes in primary care service use over time differ by neighbourhood income? Population-based longitudinal study in British Columbia, Canada
Bibliographic record
Abstract
Background Strong primary care systems have been associated with improved health equity. Primary care system reforms in Canada may have had equity implications, but these have not been evaluated. We sought to determine if changes in primary care service use between 1999/2000 and 2017/2018 differ by neighbourhood income in British Columbia. Methods We used linked administrative databases to track annual primary care visits, continuity of care, emergency department (ED) visits, specialist referrals, and prescriptions dispensed over time. We use generalized estimating equations to examine differences in the magnitude of change by neighbourhood income quintile, adjusting for age, sex/gender, and comorbidity, and stratified by urban/rural location of residence. We also compared the characteristics of physicians providing care to people living in low- and high-income neighbourhoods at two points in time. Results Between 1999/2000 and 2017/8 the average number of primary care visits per person, specialist referrals, and continuity of care fell in both urban and rural settings, while ED visits and prescriptions dispensed increased. Over this period in urban settings, primary care visits, continuity, and specialist referrals fell more rapidly in low vs. high income neighbourhoods (relative change in primary care visits: Incidence Rate Ratio (IRR) 0.881, 95% CI: 0.872, 0.890; continuity: partial regression coefficient -0.92, 95% CI: -1.18, -0.66; specialist referrals: IRR 0.711, 95%CI: 0.696, 0.726), while ED visits increased more rapidly (IRR 1.06, 95% CI: 1.03, 1.09). The percentage of physicians who provide the majority of visits to patients in neighbourhoods in the lower two income quintiles declined from 30.6% to 26.3%. Conclusion Results raise concerns that equity in access to primary care has deteriorated in BC. Reforms to primary care that fail to attend to the multidimensional needs of low-income communities may entrench existing inequities. Policies that tailor patterns of funding and allocation of resources in accordance with population needs, and that align accountability measures with equity objectives are needed as part of further reform efforts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".