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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
  • , Emily Herrett
  • , Edward P.K. Parker
  • , Bang Zheng
  • , Kathryn E. Mansfield
  • , Alasdair D. Henderson
  • , Helena Carreira
  • , Patrick Bidulka
  • , Angel Y.S. Wong
  • , Charlotte Warren-Gash
  • , Rosalind M. Eggo
  • , Laurie Tomlinson
  • , Sinéad M. Langan
  • , Rohini Mathur
  • , Dominik Piehlmaier
  • , Brian MacKenna
  • , Amir Mehrkar
  • , Dominik Piehlmaier
  • , Joseph F. Hayes
  • Jennifer K. Quint, Srinivasa Vittal Katikireddi, Rohini Mathur, The OpenSAFELY collaborative, LH&W NCS (or CONVALESCENCE) Collaborative
*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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).

Original languageEnglish
Article number102077
Number of pages12
JournalEClinicalMedicine
Volume61
Early online date29 Jun 2023
DOIs
Publication statusPublished - Jul 2023

Data Availability Statement

All data were linked, stored and analysed securely within the OpenSAFELY platform https://opensafely.org/. All code is shared openly for review and re-use under MIT open license (https://github.com/opensafely/covid-collateral-research).

Funding

This work was funded by the LSHTM COVID-19 Response Grant (reference: DONAT15912 ). This research was supported by the National Core Studies, which is funded by UK Research and Innovation , the NIHR , and the Health and Safety Executive (grant ref MC_PC_20059 ). In addition, the OpenSAFELY Platform is supported by grants from the Wellcome Trust ( 222097/Z/20/Z ); MRC ( MR/V015757/1 , MC_PC-20059 , MR/W016729/1 ); NIHR ( NIHR135559 , COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157). SVK acknowledges funding from a NRS Senior Clinical Fellowship ( SCAF/15/02 ), the Medical Research Council ( MC_UU_00022/2 ) and the Scottish Government Chief Scientist Office ( SPHSU17 ). DP was supported by a Medical Research Council fellowship ( MR/W02148X/1 ), as was EPKP ( MR/W021420/1 ). EH was funded by an NIHR post-doctoral fellowship ( PDF-2016-09-029 ). SML was supported by a Wellcome Trust Senior Research Fellowship in Clinical Science ( 205039/Z/16/Z ). SML was also supported by Health Data Research UK (Grant number: LOND1 ), which is funded by the UK Medical Research Council , Engineering and Physical Sciences Research Council , Economic and Social Research Council , Department of Health and Social Care , Chief Scientist Office of the Scottish Government Health and Social Care Directorates , Health and Social Care Research and Development Division ( Welsh Government ), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust . CWG is supported by a Wellcome Career Development award ( 225868/Z/22/Z ). RME is supported by grants from HDR UK and MRC . AM acknowledges support from the Bennett Foundation , Wellcome Trust , NIHR Oxford Biomedical Research Centre , NIHR Applied Research Collaboration Oxford and Thames Valley, Mohn-Westlake Foundation . The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funders. We are very grateful for all the support received from the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate. We have a publicly available website https://opensafely.org/ where we invite individuals to contact us regarding this study or the broader OpenSAFELY project.

FundersFunder number
Chief Scientist Office of the Scottish Government205039/Z/16/Z, SPHSU17, MR/W02148X/1, LOND1, MR/W021420/1, SCAF/15/02
Wellcome Trust225868/Z/22/Z, 222097/Z/20/Z, 205039/Z/16/Z
Medical Research CouncilMR/V015757/1, MR/W016729/1, NIHR135559, MC_PC_20059, MC_UU_00022/2, COV-LT2-0073, MR/W02148X/1, MR/W021420/1
Health Data Research UKHDRUK2021.000, 2021.0157, LOND1
London School of Hygiene & Tropical MedicineDONAT15912
National Institute for Health and Care ResearchNIHR135559, COV-LT2-0073, PDF-2016-09-029

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Ethnic differences
    • Healthcare utilisation
    • Pandemic

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