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Soc Sci Med ; 311: 115319, 2022 Sep 05.
Article in English | MEDLINE | ID: covidwho-2008124


One of the most consistent and worrying features of the COVID-19 pandemic globally has been the disproportionate burden of the epidemic in the most deprived areas. Most of the literature so far though has focused on estimating the extent of these inequalities. There has been much less attention paid to exploring the main pathways underpinning them. In this study, we employ the syndemic pandemic theoretical framework and apply novel decomposition methods to investigate the proportion of the COVID-19 mortality gap by area-level deprivation in England during the first wave of the pandemic (January to July 2020) was accounted for by pre-existing inequalities in the compositional and contextual characteristics of place. We use a decomposition approach to explicitly quantify the independent contribution of four inequalities pathways (vulnerability, susceptibility, exposure and transmission) in explaining the more severe COVID-19 outcomes in the most deprived local authorities compared to the rest. We find that inequalities in transmission (73%) and in vulnerability (49%) factors explained the highest proportion of mortality by deprivation. Our results suggest that public health agencies need to develop short- and long-term strategies to alleviate these underlying inequalities in order to alleviate the more severe impacts on the most vulnerable communities.

Lancet Reg Health Eur ; 14: 100296, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1587065


BACKGROUND: Population characteristics can be used to infer vulnerability of communities to COVID-19, or to the likelihood of high levels of vaccine hesitancy. Communities harder hit by the virus, or at risk of being so, stand to benefit from greater resource allocation than their population size alone would suggest. This study reports a simple but efficacious method of ranking small areas of England by relative characteristics that are linked with COVID-19 vulnerability and vaccine hesitancy. METHODS: Publicly available data on a range of characteristics previously linked with either poor COVID-19 outcomes or vaccine hesitancy were collated for all Middle Super Output Areas of England (MSOA, n=6790, excluding Isles of Scilly), scaled and combined into two numeric indices. Multivariable linear regression was used to build a parsimonious model of vulnerability (static socio-ecological vulnerability index, SEVI) in 60% of MSOAs, and retained variables were used to construct two simple indices. Assuming a monotonic relationship between indices and outcomes, Spearman correlation coefficients were calculated between the SEVI and cumulative COVID-19 case rates at MSOA level in the remaining 40% of MSOAs over periods both during and out with national lockdowns. Similarly, a novel vaccine hesitancy index (VHI) was constructed using population characteristics aligned with factors identified by an Office for National Statistics (ONS) survey analysis. The relationship between the VHI and vaccine coverage in people aged 12+years (as of 2021-06-24) was determined using Spearman correlation. The indices were split into quintiles, and MSOAs within the highest vulnerability and vaccine hesitancy quintiles were mapped. FINDINGS: The SEVI showed a moderate to strong relationship with case rates in the validation dataset across the whole study period, and for every intervening period studied except early in the pandemic when testing was highly selective. The SEVI was more strongly correlated with case rates than any of its domains (rs 0·59 95% CI 0.57-0.62) and outperformed an existing MSOA-level vulnerability index. The VHI was significantly negatively correlated with COVID-19 vaccine coverage in the validation data at the time of writing (rs -0·43 95% CI -0·46 to -0·41). London had the largest number and proportion of MSOAs in quintile 5 (most vulnerable/hesitant) of SEVI and VHI concurrently. INTERPRETATION: The indices presented offer an efficacious way of identifying geographical disparities in COVID-19 risk, thus helping focus resources according to need. FUNDING: Funder: Integrated Covid Hub North East. AWARD NUMBER: n/a. GRANT RECIPIENT: Fiona Matthews.

PLoS One ; 16(11): e0259990, 2021.
Article in English | MEDLINE | ID: covidwho-1518365


BACKGROUND: COVID-19 vaccination in many countries, including England, has been prioritised primarily by age. However, people of the same age can have very different health statuses. Frailty is a commonly used metric of health and has been found to be more strongly associated with mortality than age among COVID-19 inpatients. METHODS: We compared the number of first vaccine doses administered across the 135 NHS Clinical Commissioning Groups (CCGs) of England to both the over 50 population and the estimated frail population in each area. Area-based frailty estimates were generated using the English Longitudinal Survey of Ageing (ELSA), a national survey of older people. We also compared the number of doses to the number of people with other risk factors associated with COVID-19: atrial fibrillation, chronic kidney disease, diabetes, learning disabilities, obesity and smoking status. RESULTS: We estimate that after 79 days of the vaccine program, across all Clinical Commissioning Group areas, the number of people who received a first vaccine per frail person ranged from 4.4 (95% CI 4.0-4.8) and 20.1 (95% CI 18.3-21.9). The prevalences of other risk factors were also poorly associated with the prevalence of vaccination across England. CONCLUSIONS: Vaccination with age-based priority created area-based inequities in the number of doses administered relative to the number of people who are frail or have other risk factors associated with COVID-19. As frailty has previously been found to be more strongly associated with mortality than age for COVID-19 inpatients, an age-based priority system may increase the risk of mortality in some areas during the vaccine roll-out period. Authorities planning COVID-19 vaccination programmes should consider the disadvantages of an age-based priority system.

COVID-19 Vaccines/immunology , Vaccination , COVID-19/epidemiology , COVID-19/immunology , Dose-Response Relationship, Immunologic , England/epidemiology , Geography , Humans , Prevalence , Risk Factors
SSRN; 2021.
Preprint in English | SSRN | ID: ppcovidwho-292071


Background: Socio-economic inequalities in COVID-19 case rates have been noted worldwide. Previous studies have compared case rates over set phases. There has been no analysis of how inequalities in cases changed overtime and were shaped by national mitigation strategies (e.g. lock downs). This paper provides the first analysis of the evolution of area-level inequalities in COVID-19 cases by deprivation levels in the first wave of the pandemic (January to July 2020) in England – with a focus on the effects of the first national lockdown (March – July 2020). Methods: Weekly case rates per Middle Super Output Area (MSOA, n=4412) in England from 2020-03-15 to 2020-07-04 were obtained, and characteristics of local epidemics were calculated, e.g. the highest case rate per area. Simple linear and logistic regression analyses were employed to assess the association of these metrics with index of multiple deprivation (IMD). Local authority-level (n=309) cases were used similarly in a sensitivity analysis, as these data were available daily and extended further back in time. The impact of lockdown was assessed by comparing the cumulative case rate in the most deprived 20% of MSOAs to the least deprived 20%, for the periods before the lockdown, and by the end of lockdown. Findings: Less deprived areas began recording COVID-19 cases earlier than more deprived areas and were more likely to have peaked by March 2020. More deprived areas’ case rates grew faster and peaked higher than less deprived areas. During the first national lockdown in the UK, the relative excess in case rates in the most deprived areas increased to 130% of that of the least deprived ones. Interpretation: The pattern of disease spread in England confirm the hypothesis that initial cases of a novel infectious disease are likely to occur in more affluent communities, but more deprived areas will overtake them once national mitigation strategies begin, and bear the brunt of the total case load. The strict first national lockdown served to increase case rate inequalities in England.