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Record W4415648297 · doi:10.1136/bmjopen-2025-104487

Examining the impact of the first wave of COVID-19 on equitable access to emergency care across Alberta demographic groups: a retrospective observational study

2025· article· en· W4415648297 on OpenAlexafffundabout
Patrick McLane, Mandi Gray, Cheryl Barnabé, Katherine Rittenbach, Lea Bill, Brian R. Holroyd, Eddy Lang, Antonia Stang, Jake Hayward, Rita K. Henderson, Greta G. Cummings, Rhonda J. Rosychuk

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsInstitute on GovernanceUniversity of CalgaryTrent UniversityAlberta HealthGovernment of AlbertaUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsObservational studyEmergency departmentRetrospective cohort studyEpidemiologyHealth services researchPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: During the first wave of the COVID-19 pandemic, there was a notable decline in emergency department (ED) usage in many jurisdictions. This study assessed changes in ED use during this period and explored how the pandemic may have aggravated existing healthcare access inequities. OBJECTIVES: Our primary objective was to assess pandemic-related changes to ED visits and emergency hospitalisations for distinct demographic groups. DESIGN: We conducted a retrospective observational study using population-based provincial administrative data. SETTING: We analysed data from all the 109 EDs and urgent care centres in Alberta, Canada, during the first wave of the COVID-19 pandemic (15 March 2020 to 30 June 2020), and during the corresponding (control) period 1 year earlier. We conducted subgroup analyses by age, First Nations status, sex, location and material deprivation. We repeated all analyses for pre-selected life-threatening emergency diagnoses. POPULATIONS: We examined outcomes for a priori subgroups, including female and 'other' sex patients, paediatric patients (age 0-17 years), seniors (age 65 years and older), patients living in remote areas (greater than 200 km from an urban centre), First Nations members and patients living in materially deprived postal codes falling into the two most deprived Pampalon Index quintiles. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were number of ED visits, number of ED visits with admission to hospital and number of ED visits resulting in patient death in the ED. A secondary outcome was change in ED use for life-threatening diagnoses (eg, cardiac conditions and hepatic disease). RESULTS: ED visits in the COVID-19 period decreased by 34% (Poisson means test p <0.001) and hospitalisations decreased by 15% (p <0.001) compared with 2019. Multivariable models showed an average decrease of 79.9 (p <0.001) ED visits, and 7.7 (p <0.001) fewer average admissions per facility for the COVID-19 period (vs 2019) in our 'baseline' group (non-First Nations, male, adult and metropolitan residents who were not materially deprived). First Nations patients, seniors and remote residents experienced smaller declines in ED visits compared with the baseline group. Females, seniors and children experienced larger reductions in emergency admissions, while First Nations patients had smaller reductions. CONCLUSION: Reductions in critical emergency care and emergency hospital admissions were unequally distributed across demographic groups during the COVID-19 period. Study methods could be used to monitor and support equitable access to emergency care among distinct populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.371
GPT teacher head0.554
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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