Dataset Information

Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study.

ABSTRACT:

Background

Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK.

Method

Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake and spatio-temporal correlation structure.

Findings

Comparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 297% to 335%. Similarly, prevalence increases by 10% from 037% to 041%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: in the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis.

Interpretation

At the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.

Fundings

EPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR.

SUBMITTER: Padellini T

PROVIDER: S-EPMC8597886 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study.

Padellini Tullia T Jersakova Radka R Diggle Peter J PJ Holmes Chris C King Ruairidh E RE Lehmann Brieuc C L BCL Mallon Ann-Marie AM Nicholson George G Richardson Sylvia S Blangiardo Marta M

medRxiv : the preprint server for health sciences 20211109

<h4>Background</h4>Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK.<h4>Method</h4>Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the populatio ...[more]

PMID: 34790983

Similar Datasets

Project description:The severe acute respiratory coronavirus 2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. The epidemic accelerated in Philadelphia, PA, in the spring of 2020, with the city experiencing a first peak of infections on 15 April, followed by a decline through midsummer. Here, we investigate spread of the epidemic in the first wave in Philadelphia using full-genome sequencing of 52 SARS-CoV-2 samples obtained from 27 hospitalized patients collected between 30 March and 17 July 2020. Sequences most commonly resembled lineages circulating at earlier times in New York, suggesting transmission primarily from this location, though a minority of Philadelphia genomes matched sequences from other sites, suggesting additional introductions. Multiple genomes showed even closer matches to other Philadelphia isolates, suggestive of ongoing transmission within Philadelphia. We found that all of our isolates contained the D614G substitution in the viral spike and belong to lineages variously designated B.1, Nextstrain clade 20A or 20C, and GISAID clade G or GH. There were no viral sequence polymorphisms detectably associated with disease outcome. For some patients, genome sequences were determined longitudinally or concurrently from multiple body sites. In both cases, some comparisons showed reproducible polymorphisms, suggesting initial seeding with multiple variants and/or accumulation of polymorphisms after infection. These results thus provide data on the sources of SARS-CoV-2 infection in Philadelphia and begin to explore the dynamics within hospitalized patients.IMPORTANCE Understanding how SARS-CoV-2 spreads globally and within infected individuals is critical to the development of mitigation strategies. We found that most lineages in Philadelphia had resembled sequences from New York, suggesting infection primarily but not exclusively from this location. Many genomes had even nearer neighbors within Philadelphia, indicating local spread. Multiple genome sequences were available for some subjects and in a subset of cases could be shown to differ between time points and body sites within an individual, indicating heterogeneous viral populations within individuals and raising questions on the mechanisms responsible. There was no evidence that different lineages were associated with different outcomes in patients, emphasizing the importance of individual-specific vulnerability.

Project description:BackgroundCommunity mobility data have been used to assess adherence to non-pharmaceutical interventions and its impact on SARS-CoV-2 transmission. We assessed the association between location-specific community mobility and the reproduction number (R) of SARS-CoV-2 across UK local authorities.MethodsIn this modelling study, we linked data on community mobility from Google with data on R from 330 UK local authorities, for the period June 1, 2020, to Feb 13, 2021. Six mobility metrics are available in the Google community mobility dataset: visits to retail and recreation places, visits to grocery and pharmacy stores, visits to transit stations, visits to parks, visits to workplaces, and length of stay in residential places. For each local authority, we modelled the weekly change in R (the R ratio) per a rescaled weekly percentage change in each location-specific mobility metric relative to a pre-pandemic baseline period (Jan 3-Feb 6, 2020), with results synthesised across local authorities using a random-effects meta-analysis.FindingsOn a weekly basis, increased visits to retail and recreation places were associated with a substantial increase in R (R ratio 1·053 [99·2% CI 1·041-1·065] per 15% weekly increase compared with baseline visits) as were increased visits to workplaces (R ratio 1·060 [1·046-1·074] per 10% increase compared with baseline visits). By comparison, increased visits to grocery and pharmacy stores were associated with a small but still statistically significant increase in R (R ratio 1·011 [1·005-1·017] per 5% weekly increase compared with baseline visits). Increased visits to parks were associated with a decreased R (R ratio 0·972 [0·965-0·980]), as were longer stays at residential areas (R ratio 0·952 [0·928-0·976]). Increased visits to transit stations were not associated with R nationally, but were associated with a substantial increase in R in cities. An increasing trend was observed for the first 6 weeks of 2021 in the effect of visits to retail and recreation places and workplaces on R.InterpretationIncreased visits to retail and recreation places, workplaces, and transit stations in cities are important drivers of increased SARS-CoV-2 transmission; the increasing trend in the effects of these drivers in the first 6 weeks of 2021 was possibly associated with the emerging alpha (B.1.1.7) variant. These findings provide important evidence for the management of current and future mobility restrictions.FundingWellcome Trust and Data-Driven Innovation initiative.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50-200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space.

Dataset Information

Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study.

Background

Method

Findings

Interpretation

Fundings

Publications

Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets