Evidence map›Paper›PMID 38485179›Full record

ArticleBMJ open2024

Cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area.

Zahra Movahedi Nia, Cheryl Prescod, Michelle Westin, Patricia Perkins, Mary Goitom, Kesha Fevrier, Sylvia Bawa, Jude Kong

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Zahra Movahedi NiaAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.ORCID 0000-0002-5528-638X
Cheryl PrescodBlack Creek Community Health Centre, Toronto, Ontario, Canada.
Michelle WestinBlack Creek Community Health Centre, Toronto, Ontario, Canada.
Patricia PerkinsResilience Research Atlantic Alliance on Sustainability, Supporting Recovery and Renewal (REASURE2) Network, Toronto, Ontario, Canada.
Mary GoitomResilience Research Atlantic Alliance on Sustainability, Supporting Recovery and Renewal (REASURE2) Network, Toronto, Ontario, Canada.
Kesha FevrierResilience Research Atlantic Alliance on Sustainability, Supporting Recovery and Renewal (REASURE2) Network, Toronto, Ontario, Canada.
Sylvia BawaResilience Research Atlantic Alliance on Sustainability, Supporting Recovery and Renewal (REASURE2) Network, Toronto, Ontario, Canada.
Jude KongAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada jdkong@yorku.ca.
York University · CARegent Park Community Health Centre · CAQueen's University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe COVID-19 pandemic disrupted healthcare services, leading to the cancellation of non-urgent tests, screenings and procedures, a shift towards remote consultations, stalled childhood immunisations and clinic closures which had detrimental effects across the healthcare system. This study investigates the impact of the COVID-19 pandemic on clinical admissions and healthcare quality in the Peel, York and Toronto regions within the Greater Toronto Area (GTA).

designIn a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including preventive and primary care (PPC), the emergency department (ED), alternative level of care (ALC) and imaging, procedures and surgeries is investigated. Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical subregions that require special attention to recover. The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage. Statistical analyses (Mann-Whitney U test, analysis of variance, Dunn's test) is used to evaluate sector-specific changes and inter-relationships.

settingThis work uses primary data which were collected by the Black Creek Community Health Centre. The study population was from three regions of GTA, namely, the city of Toronto, York and Peel. For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared with before that.

resultsAll sectors experienced a significant decline in patient volume during the pandemic. ALC admissions surged in some areas, while IPS patients faced delays. Surgery waitlists increased by an average of 9.75%, and completed IPS procedures decreased in several subregions.

conclusionsThe COVID-19 pandemic had a universally negative impact on healthcare sectors across various subregions. Identification of the hardest-hit subregions in each sector can assist health officials in crafting recovery policies.

Indexed as

COVID-19PandemicsChildCross-Sectional StudiesHumansResearch DesignSample SizeCOVID-19health servicesophthalmologypaediatricssurgery

Identifiers

PMID38485179
PMCPMC10941105
OpenAlexW4392814494

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.