Study evaluates community-level influences on COVID-19 incidence in England and their variations over time

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In a recent article posted to the medRxiv* preprint server, researchers assessed community-level impacts on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence in England.

Study: Understanding community level influences on COVID-19 prevalence in England: New insights from comparison over time and space. Image Credit: MARCIO DELGADO/Shutterstock

Background

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc on communities worldwide, putting enormous strain on global healthcare systems. Understanding and tracking the primary factors that impact SARS-CoV-2 prevalence is critical for informing policymaking and designing effective non-pharmaceutical intervention (NPI) packages.

Analysis of community-level COVID-19 incidence imparts deep insights into the correlations between demographic and socioeconomic profiles, land use, and movement patterns. This includes spatial sorting and self-selection by inhabitants, where residents pick their residential area according to their social structure or preferences, inequality, and travel attitudes.

Community-level SARS-CoV-2 infection analysis also aids in determining the impact of policy initiatives on various areas and communities, taking into account possible differences in COVID-19 vaccination rates, health inequities, mobility, and behavioral responses. Furthermore, community-level data can lead to more thorough individual and epidemiological models by customizing and guiding policy issues for additional research.

About the study

In the present study, the scientists assessed the community-level determinants of the SARS-CoV-2 infection occurrence in England and how they changed with time between October 4, 2020, and December 5, 2021. The team specifically focused on the effect of working in so-called high-risk sectors like warehouses and care homes and COVID-19 incidence.

The scientists compiled a wide range of static (demographic and socioeconomic profiles and land use properties) and dynamic (COVID-19 cases, real-time SARS-CoV-2 vaccination uptake, and mobility indicators) details for small area statistical spaces (Lower Layer Super Output Areas (LSOA)) in England. Telecommunications firms, national travel surveys, test and trace data, mid-year projections, and censuses were among the sources used to compile the data.

The authors used a combination of different machine learning and statistical approaches to address methodological constraints, notably accounting for strongly interconnected factors. The team opted for a two-stage modeling approach: 1) latent cluster analysis (LCA) to categorize the nation into diverse travel and land use patterns and 2) multivariate linear regression to assess impacts at each diverse travel cluster. 

The researchers then divided the data into time segments based on policy changes and evolvement in the COVID-19 pandemic course, like the emergence of a novel SARS-CoV-2 variant. The team analyzed more homogenous behavior and uniform distribution of SARS-CoV-2 infection risks by comparing and segmenting impacts over time and space, which increased the capacity to establish causal inferences and better explain variances between communities and time.

Results and discussions

The study results after adjusting for real-time mobility, socioeconomic and demographic profiles, and COVID-19 vaccination demonstrated that areas with a high proportion of inhabitants working in warehouses and care homes, and to a minor extent in the textile and ready meals sectors were more susceptible to SARS-CoV-2 infection across all time points and travel clusters. Similar impacts in all residential area clusters point to a possible link with workplace COVID-19 risk and restrictions, which should be investigated further in a more extensive, individual-level workplace COVID-19 outbreak investigation.

The current data highlight the significant role of regional variability in impacts on COVID-19 prevalence after adjusting for vaccination and mobility rate. For example, in the most recent timeframe, i.e., between July to December 2021, spanning the period when COVID-19 lockdown restrictions were lifted in England, regions with a higher proportion of small families and few children were at a lower risk of SARS-CoV-2 infection in smaller and medium rural and urban areas. Nevertheless, this was not a substantial risk factor for COVID-19 in inner and central London and metropolitan areas. 

Similar trends were also seen in areas with a higher proportion of people who can work from home. While working from home has been shown to reduce the risk of COVID-19 in smaller and less populated cities, this was not the case in metropolitan core inhabitants, who may be more active in a wide range of activities outside of work.

Finally, regions where inhabitants were more reliant on public transportation for commuting, have been linked to a higher risk of SARS-CoV-2 infection among all travel clusters, except for rural settlements. Public transportation use has been a major COVID-19 risk factor in both small and large cities. However, the likelihood was decreased in the fifth tranche of time (February to April 2021), when COVID-19 vaccination began to act, and the SARS-CoV-2 Delta variant had not yet become prevalent.

Conclusions

According to the authors, the present dataset was the most detailed dataset assembled in the United Kingdom (UK) for SARS-CoV-2 infection community-level analysis.

The study findings imply that the SARS-CoV-2 infection risk effects vary significantly across space, with some being highly constant and durable across time. In particular, the assessment of industrial sectors revealed that communities of employees in warehouses and care homes, and to a lesser degree, the ready-meal and textile industries, had a greater risk of SARS-CoV-2 infection across all geographical clusters, and during the whole period represented in this investigation.

The present data revealed the critical role workplace risk plays in the COVID-19 outbreak threat after accounting for the features of the residential region of the employees (including demographic and socioeconomic profile and land use characteristics), movement patterns, and vaccination rate.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Source: News Medical


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