Analyzing impact of the provincial COVID-19 health coverage directives on healthcare access and outcomes for patients without public insurance: Secondary data analysis of hospital data
Bibliographic record
Abstract
Previous research has found that emergency room data can reveal patterns of delayed care, such that people without health insurance arrive at emergency rooms sicker, are less likely to be admitted, and are more likely to die (Hynie et al., 2016). This study will use a similar data analytic strategy to explore healthcare access and outcomes. Data collected by the Canadian Institutes for Health Information from hospitals in the three provinces will be requested in order to explore 1) the main presenting complaints and illness severity of emergency room visits by insurance status, age, and gender and 2) patterns of hospital admissions by insurance status, age, and gender. In an effort to understand how COVID directives regarding healthcare coverage impacted both health access and health outcomes, these patterns will be compared between the two years prior to the COVID pandemic (April, 2018 to March, 2020) and the two first years of the COVID pandemic (April 2020 to March 2022) for the three provinces. Specifically, we predict that patients without health insurance will show different patterns of healthcare access across the 4 years in the three provinces as a function of the implementation of the healthcare directives Controlling for age and gender, we predict that: All provinces will show those without insurance arrive in ER with more serious triage, more injuries, more mental health problems and more pregnancy related issues than those with insurance. They will wait longer to be triaged and treated, have fewer and less intensive interventions for comparable diagnoses and severity, and have shorter inpatient stays. Prior to 2020, in all 3 provinces, those without insurance will be more likely to leave the ER without treatment, and be less likely to be admitted. Consistent with previous research, we expect that in all three provinces, those with ambulatory care sensitive conditions (ACSCs) will be sicker on arrival to ER than those without regardless of insurance status EXCEPT that for children (under 18) uninsured with ACSCs will have more serious health status In all provinces, those without insurance will be sicker at the point of admission to the hospital than those with insurance In 2020 and 2021, in Ontario only, the differences between insured and uninsured patients will decrease in terms of likelihood of leaving ER without treatment, dying in ER, being admitted to hospital, type and intensity of intervention and length of stay. Exploratory questions will include: whether there are changes in Ontario post 2020 in severity on arrival, length of wait times, severity of those presenting with ACSC, and whether there are greater differences between those with and without insurance by region (urban versus rural).
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".