How does vulnerability to COVID-19 vary between communities in England? Developing a Small Area Vulnerability Index (SAVI)

Abstract

BackgroundDuring the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality—including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities.MethodsWe performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index.ResultsFour vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively.ConclusionVulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.

Supporting pandemic disease preparedness: Development of a composite index of area vulnerability

“…Our use of confirmed COVID-19 case ratios to assess the predictive validity of the CPVI was a limitation, as case-ratio of infectious disease reflects testing access and uptake and hence contain bias. Arguably, a less biased approach would have been to use deaths and hospitalisations as these are not dependent on testing and uptake: however, there was insufficient data for these vital statistics available for this study ( Daras et al, 2021 ). Nevertheless, their use is recommended for future research data permitting.…”

Are we allowed to visit now? Concerns and issues surrounding vaccination and infection risks in UK care homes during COVID-19

“…Additionally, our sample was lacking ethnic minority representation, and mostly included family carers and staff from a White ethnic background. In light of increased susceptibility of people from minority ethnic backgrounds to the virus (Daras et al, 2021), future research needs to explore their views, as ethnicity may affect behaviour and attitudes towards infection control measures and visitation.…”

Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: A synthetic control study

“…Chronic disease was measured as the proportion of the population that had at least one admission to hospital with a diagnosis of cardiovascular disease, chronic respiratory disease, diabetes or chronic kidney disease recorded in their hospital record, which we found in previous research to be highly predictive of COVID-19 mortality. 5 To calculate this multiplier we estimated the increased risk of COVID-19 mortality and hospitalisation associated with chronic illness for each local authority compared to the average using Poisson regression models. The method outlined above was applied to 7 day moving averages of deaths and hospitalisation for each local authority. As hospital admissions and deaths occur sometime after initial infection, in order to estimate the true infection rate at a specific point in time we need to know the lag between initial case identification and both hospitalisation and death.…”

“…Chronic disease was measured as the proportion of the population that had at least one admission to hospital with a diagnosis of cardiovascular disease, chronic respiratory disease, diabetes or chronic kidney disease recorded in their hospital record, which we found in previous research to be highly predictive of COVID-19 mortality. 5 To calculate this multiplier we estimated the increased risk of COVID-19 mortality and hospitalisation associated with chronic illness for each local authority compared to the average using Poisson regression models.…”

“…Understanding the potential differences in effect of control measures by socioeconomic group is important because the impact of the pandemic has disproportionally affected more disadvantaged groups. 5 Interventions designed to control the pandemic may further exacerbate these inequalities if we do not evaluate their differential effects and take actions to mitigate any intervention-generated inequalities, or ideally prioritise approaches that reduce inequalities. 6 Despite these warnings, there has been little investigation of the differential impact of such control measures between different socioeconomic contexts.…”