Evidence map›Paper›PMID 37434746›Full record

ArticleEClinicalMedicine2023

Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform.

Ruth E Costello, John Tazare, Dominik Piehlmaier, Emily Herrett, Edward P K Parker, Bang Zheng, Kathryn E Mansfield, Alasdair D Henderson, Helena Carreira, Patrick Bidulka and 13 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Observational
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

23 authors at 7 institutions in 1 country.

Ruth E CostelloLondon School of Hygiene and Tropical Medicine, London, UK.
John TazareLondon School of Hygiene and Tropical Medicine, London, UK.
Dominik PiehlmaierBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Emily HerrettLondon School of Hygiene and Tropical Medicine, London, UK.
Edward P K ParkerLondon School of Hygiene and Tropical Medicine, London, UK.
Bang ZhengLondon School of Hygiene and Tropical Medicine, London, UK.
Kathryn E MansfieldLondon School of Hygiene and Tropical Medicine, London, UK.
Alasdair D HendersonLondon School of Hygiene and Tropical Medicine, London, UK.
Helena CarreiraLondon School of Hygiene and Tropical Medicine, London, UK.
Patrick BidulkaLondon School of Hygiene and Tropical Medicine, London, UK.
Angel Y S WongLondon School of Hygiene and Tropical Medicine, London, UK.
Charlotte Warren-GashLondon School of Hygiene and Tropical Medicine, London, UK.
Joseph F HayesDivision of Psychiatry, University College London, London, UK.
Jennifer K QuintFaculty of Medicine, National Heart & Lung Institute, Imperial College London, London, UK.
Brian MacKennaBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Amir MehrkarBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Rosalind M EggoLondon School of Hygiene and Tropical Medicine, London, UK.
Srinivasa Vittal KatikireddiMRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Laurie TomlinsonLondon School of Hygiene and Tropical Medicine, London, UK.
Sinéad M LanganLondon School of Hygiene and Tropical Medicine, London, UK.
Rohini MathurLondon School of Hygiene and Tropical Medicine, London, UK.
LH&W NCS (or CONVALESCENCE) Collaborative
OpenSAFELY collaborative
London School of Hygiene & Tropical Medicine · GBUniversity of Oxford · GBImperial College London · GBMRC/CSO Social and Public Health Sciences Unit · GBQueen Mary University of London · GBUniversity of Sussex · GBUniversity College London · GB

Funding

Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20058Medical Research Council MC_PC_20059Medical Research Council MC_UU_00022/2Medical Research Council MR/S004084/1Medical Research Council MR/W016729/1Medical Research Council MR/W021390/1Wellcome Trust
6 · The paper itself

Abstract

Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. Methods: In this population-based, observational cohort study we used primary care electronic health record data with linkage to hospital episode statistics data and mortality data within OpenSAFELY, a data analytics platform created, with approval of NHS England, to address urgent COVID-19 research questions. We included adults aged 18 years and over registered with a TPP practice between March 1, 2018, and April 30, 2022. We excluded those with missing age, sex, geographic region, or Index of Multiple Deprivation. We grouped ethnicity (exposure), into five categories: White, Asian, Black, Other, and Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (blood pressure and Hba1c measurements, chronic obstructive pulmonary disease and asthma annual reviews) before and after March 23, 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to diabetes, cardiovascular disease, respiratory disease, and mental health before and after March 23, 2020. Findings: Of 33,510,937 registered with a GP as of 1st January 2020, 19,064,019 were adults, alive and registered for at least 3 months, 3,010,751 met the exclusion criteria and 1,122,912 were missing ethnicity. This resulted in 14,930,356 adults with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to the White ethnic group (Pre-pandemic hazard ratio (HR): 0.50, 95% confidence interval (CI) 0.41, 0.60, Pandemic HR: 0.75, 95% CI: 0.65, 0.87). There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in those of White ethnicity (heart failure risk difference: 5.4). Relatively, ethnic differences narrowed for heart failure admission in those of Asian (Pre-pandemic HR 1.56, 95% CI 1.49, 1.64, Pandemic HR 1.24, 95% CI 1.19, 1.29) and Black ethnicity (Pre-pandemic HR 1.41, 95% CI: 1.30, 1.53, Pandemic HR: 1.16, 95% CI 1.09, 1.25) compared with White ethnicity. For other outcomes the pandemic had minimal impact on ethnic differences. Interpretation: Our study suggests that ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes. Funding: LSHTM COVID-19 Response Grant (DONAT15912).

Indexed as

Ethnic differencesHealthcare utilisationPandemic

Identifiers

PMID37434746
PMCPMC10331810
OpenAlexW4382602188

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.