The impact of health inequity on spatial variation of COVID-19 transmission in England

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Considerable spatial heterogeneity has been observed in COVID-19 transmission across administrative areas of England throughout the pandemic. This study investigates what drives these differences. We constructed a probabilistic case count model for 306 administrative areas of England across 95 weeks, fit using a Bayesian evidence synthesis framework. We incorporate the impact of acquired immunity, of spatial exportation of cases, and 16 spatially-varying socio-economic, socio-demographic, health, and mobility variables. Model comparison assesses the relative contributions of these respective mechanisms. We find that spatially-varying and time-varying differences in week-to-week transmission were definitively associated with differences in: time spent at home, variant-of-concern proportion, and adult social care funding. However, model comparison demonstrates that the impact of these terms is negligible compared to the role of spatial exportation between administrative areas. While these results confirm the impact of some, but not all, static measures of spatially-varying inequity in England, our work corroborates the finding that observed differences in disease transmission during the pandemic were predominantly driven by underlying epidemiological factors rather than aggregated metrics of demography and health inequity between areas. Further work is required to assess how health inequity more broadly contributes to these epidemiological factors.

Author summary

During the COVID-19 pandemic, different geographic areas of England saw different patterns in the number of confirmed cases over time. This study investigated whether demographic differences between these areas (such as the amount of deprivation, the age and ethnicity of the populations, or differences in where people spent their time) were linked to these differences in disease transmission. We also considered whether this was associated with the number of cases in neighbouring areas as well. Using a mathematical model fit to multiple data streams, we discovered that a statistically significant link between some demographic variables (time spent at home, COVID-19 variant, and the amount of adult social care funding) and week-to-week transmission exists, but this relationship is very small, and the influence of cases in neighbouring areas was far more impactful in explaining differences in transmission between areas over time.

Related collections

Most cited references 25

Record: found
Abstract: found
Article: found

Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform

Rohini Mathur, Christopher T. Rentsch, Caroline Morton … (2021)

Background COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. Methods We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. Findings Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07–1·09]), Black group (1·08 [1·06–1·09]), and mixed ethnicity group (1·04 [1·02–1·05]) and was decreased in the other ethnicity group (0·77 [0·76–0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94–2·04]), Black group (1·69 [1·62–1·77]), mixed ethnicity group (1·49 [1·39–1·59]), and other ethnicity group (1·20 [1·14–1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41–1·55], Black group 1·78 [1·67–1·90], mixed ethnicity group 1·63 [1·45–1·83], other ethnicity group 1·54 [1·41–1·69]), COVID-19-related ICU admission (2·18 [1·92–2·48], 3·12 [2·65–3·67], 2·96 [2·26–3·87], 3·18 [2·58–3·93]), and death (1·26 [1·15–1·37], 1·51 [1·31–1·71], 1·41 [1·11–1·81], 1·22 [1·00–1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories. Interpretation Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination. Funding Medical Research Council.

0 comments Cited 214 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence

Moritz Kraemer, Verity Hill, Christopher Ruis … (2021)

The B.1.1.7 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused fast-spreading outbreaks globally. Intrinsically, this variant has greater transmissibility than its predecessors, but this capacity has been amplified in some circumstances to tragic effect by a combination of human behavior and local immunity. What are the extrinsic factors that help or hinder the rapid dissemination of variants? Kraemer et al . explored the invasion dynamics of B.1.1.7. in fine detail, from its location of origin in Kent, UK, to its heterogenous spread around the country. A combination of mobile phone and virus data including more than 17,000 genomes shows how distinct phases of dispersal were related to intensity of mobility and the timing of lockdowns. As the local outbreaks grew, importation from the London source area became less important. Had B.1.1.7. emerged at a slightly different time of year, its impact might have been different. —CA Disentangling the factors that contribute to the rapid spread of virus variants is essential for understanding their epidemiological consequences. Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7’s increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.

0 comments Cited 91 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

Victoria McGowan, Clare Bambra (2022)

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

0 comments Cited 61 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Thomas Rawson:

ORCID: https://orcid.org/0000-0001-8182-4279

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Wes Hinsley: Role: Data curationRole: ResourcesRole: SoftwareRole: Writing – review & editing

Raphael Sonabend: Role: MethodologyRole: SoftwareRole: Writing – review & editing

Elizaveta Semenova: Role: MethodologyRole: SoftwareRole: Writing – review & editing

Anne Cori: Role: SupervisionRole: Writing – review & editing

Neil M Ferguson: Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – review & editing

Samuel V. Scarpino: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Comput Biol

Journal ID (iso-abbrev): PLoS Comput Biol

Journal ID (publisher-id): plos

Title: PLOS Computational Biology

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-734X

ISSN (Electronic): 1553-7358

Publication date (Electronic): 28 May 2024

Publication date Collection: May 2024

Volume: 20

Issue: 5

Electronic Location Identifier: e1012141

Affiliations

[1 ] MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom

[2 ] Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

[3 ] National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, United Kingdom

Northeastern University, UNITED STATES

Author notes

I have read the journal’s policy and the authors of this manuscript have the following competing interests: AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. All other authors declare no competing interests.

* E-mail: t.rawson@ 123456imperial.ac.uk

Author information

Thomas Rawson https://orcid.org/0000-0001-8182-4279

Article

Publisher ID: PCOMPBIOL-D-23-01964

DOI: 10.1371/journal.pcbi.1012141

PMC ID: 11161116

PubMed ID: 38805483

SO-VID: 82ba467f-0f89-44f1-8213-692b3ae4e7fe

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 5 December 2023

Date accepted : 7 May 2024

Page count

Figures: 4, Tables: 1, Pages: 15

Funding

Funded by: Community Jameel

Award Recipient :

ORCID: https://orcid.org/0000-0001-8182-4279

Thomas Rawson

Funded by: funder-id http://dx.doi.org/10.13039/100027426, Schmidt Futures;

Award ID: Grant [G-22-64476]

Award Recipient : Elizaveta Semenova

TR acknowledges funding by Community Jameel and from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), funded by the UK Medical Research Council (MRC). This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking. ES acknowledges support in part by the AI2050 program at Schmidt Futures (Grant [G-22-64476]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Custom metadata

PLOS Publication Stage vor-update-to-uncorrected-proof

Publication Update 2024-06-07

Data Availability The source code and data used to produce the results and analyses presented in this manuscript are available from our GitHub repository: https://github.com/thomrawson/Rawson-spatial-covid.

The impact of health inequity on spatial variation of COVID-19 transmission in England

Read this article at

Abstract

Author summary

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 25

Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform

Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

Author and article information

Contributors

Journal

Affiliations

Author notes

Author information

Article

History

Page count

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 67

Most referenced authors 3,156