Examining Primary Care Performance by Population Segments in Three Canadian Provinces: are there Healthcare Disparities?
Bibliographic record
Abstract
Context: Information in primary care can be more actionable and guide planning if there is some population disaggregation based on differences in expected needs for care. Additionally, few studies have incorporated vulnerability into population segments, likely because of the complexity and evolving understanding of this construct, and because of the limits of routinely available data to measure it. Objective: To identify population segments, stratified by expected need for care, across three Canadian provinces (British Columbia-BC, Ontario-ON, Nova Scotia-NS) and report on variation in comparable primary care administrative data metrics by socioeconomic status (SES). Study Design and Analysis: Cross sectional study. We created four segments of the population: low need, multiple morbidities, medically complex, and frail using patient characteristics, physician and hospital billings, prescription medicines data, and emergency department visits within each province. We used descriptive statistics and rates to examine primary care performance where administrative data can be accurate. Dataset: Separate provincial administrative data (2013-2016): patient characteristics, physician billings, hospital billings and emergency department visits. Population Studied: adults (> 18 years) living in BC, ON, NS. Exposure: low/high SES within each segment. Outcome measures: % of pts (aged 65+) diagnosed with diabetes with Metformin as first hypoglycemic and emergency room visits for all. We also examined osteoporosis screening for those aged 65+ in BC and ON. Results: There were >1 million adults in each province who were eligible for health insurance during the study period. Regardless of SES status, NS had the highest emergency room visits for those in the multiple morbidity and medically complex segments compared to BC and ON. Compared to those in the high SES category within each segment, those in the low SES had a higher number of emergency room visits, those aged 65+ did not have Metformin as their first hypoglycemic if diagnosed with diabetes and were less likely to be screened for osteoporosis. More people in the low SES multiple morbidity, medically complex and frail segments are more likely to have 4 or more chronic conditions in all provinces. Conclusions: Four distinct population segments have potential utility for primary care performance measurement and reporting. Within each segment those with lower SES experienced health care disparities.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".