Time courses of COVID-19 infection and local variation in socioeconomic and health disparities in England
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Abstract
Objective
To identify factors associated with local variation in the time course of COVID-19 case burden in England.
Methods
We analyzed laboratory-confirmed COVID-19 case data for 150 upper tier local authorities, from the period from January 30 to May 6, 2020, as reported by Public Health England. Using methods suitable for time-series data, we identified clusters of local authorities with distinct trajectories of daily cases, after adjusting for population size. We then tested for differences in sociodemographic, economic, and health disparity factors between these clusters.
Results
Two clusters of local authorities were identified: a higher case trajectory that rose faster over time to reach higher peak infection levels, and a lower case trajectory cluster that emerged more slowly, and had a lower peak. The higher case trajectory cluster (79 local authorities) had higher population density (p<0.001), higher proportion of Black and Asian residents (p=0.03; p=0.02), higher multiple deprivation scores (p<0.001), a lower proportions of older adults (p=0.005), and higher preventable mortality rates (p=0.03). Local authorities with higher proportions of Black residents were more likely to belong to the high case trajectory cluster, even after adjusting for population density, deprivation, proportion of older adults and preventable mortality (p=0.04).
Conclusion
Areas belonging to the trajectory with significantly higher COVID-19 case burden were more deprived, and had higher proportions of ethnic minority residents. A higher proportion of Black residents in regions belonging to the high trajectory cluster was not fully explained by differences in population density, deprivation, and other overall health disparities between the clusters.
What is already known on this subject?
Emerging evidence suggests that the burden of COVID-19 infection is falling unequally across England, with provisional data suggesting higher overall infection and mortality rates for Black, Asian, and mixed race/ethnicity individuals.
What does this study add?
We found that regions with greater socioeconomic deprivation and poorer population health measures showed a faster rise in COVID-19 cases, and reached higher peak case levels. Areas with a higher proportion of Black residents were more likely to show this kind of time course, even after adjusting for multiple co-occurring factors, including population density. This finding merits further investigation in terms of the intersecting vulnerability factors Black and other minority ethnic individuals face in England (e.g. proportion of people working in service and caring roles, and the role of structural discrimination), and has implications for the ongoing allocation of public health resources, in order to better mitigate such inequalities.
Article activity feed
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SciScore for 10.1101/2020.05.29.20116921: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study had some limitations. There may be some local differences in availability and provision of COVID-19 testing, although this is not expected to be a significant limitation, as there is standardized government guidance …
SciScore for 10.1101/2020.05.29.20116921: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study had some limitations. There may be some local differences in availability and provision of COVID-19 testing, although this is not expected to be a significant limitation, as there is standardized government guidance for members of the public who suspect they have COVID-19, to contact NHS 111 or visit https://nhs.net, rather than contacting their GP. Further, there may be outflow and inflow of residents to areas which were not their primary residence prior to and during the lockdown period, which could slightly alter the data profile of local authorities. Our ecological study design serves as an important complement to analyses of individual-level patient data, to help understand how structural inequities at the population level affect COVID-19 burden. Here, we also adjusted for multiple potential confounders in our models to better identify associations between proportions of ethnicity groups in a local authority, or deprivation of the local authority, and COVID-19 case burden. Our findings imply that structural inequities related to race/ethnicity, socioeconomic deprivation, local health disparities and environmental factors may be “root causes” of disparities in COVID-19 infection in England. The interaction between these factors is likely to multifaceted and complex [26], and may evolve over time. The identified clusters of COVID-19 caseload trajectory and their varied associations with contextual factors may also have important implications for future resource ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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