County-Level Longitudinal Clustering of COVID-19 Mortality to Incidence Ratio in the United States

Vahabi, Nasim; Salehi, Masoud; Duarte, Julio Enrique; Mollalo, Abolfazl; Michailidis, George

doi:10.21203/rs.3.rs-40632/v1

Cited by 5 publications

(6 citation statements)

References 64 publications

(85 reference statements)

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“…Variables in the symbol column correspond to Equation 1 . INDEX DOMAIN INDICATOR SOURCE LEVEL SYMBOL NOTES REFERENCES SEVI 1 Jobseekers Allowance Rate NOMIS 1 MSOA v 1 6 , 11 , 12 1 Rank of Index of Multiple Deprivation (IMD) Gov.uk 2 LSOA v 2 Averaged over LSOAs within MSOA 6 , 12 , 13 , 14 , 15 1 Long term unemployment, per 100 population of working age† PHE Fingertips 6 MSOA v 3 6 , 11 , 12 , 16 2 Proportion population over 65 ONS 4 MSOA v 4 17 , 18 2 Ethnicity (% non-white population) * NOMIS 1 MSOA v 5 5 , 19 , 20 2 Household overcrowding* Gov.uk 2 LSOA v 6 Averaged over LSOAs within each MSOA 2 , 6 , 21 …”

Section: Methodsmentioning

confidence: 99%

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

Welsh

Sinclair

Matthews

2022

The Lancet Regional Health - Europe

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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 ( r s 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 ( r s -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

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Section: Methodsmentioning

confidence: 99%

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

Welsh

Sinclair

Matthews

2022

The Lancet Regional Health - Europe

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“…We assume zero international migration, and do not include marriage rates. Second, we do not consider within-country heterogeneity or clustering of excess mortality within groups or families, which means that our estimates are likely a lower bound for excess bereavement [13]. Third, we assume demographic stability before 1950 in our simulations: this is a necessary assumption since reliable historical demographic data is not available for all countries studied.…”

Section: Methodsmentioning

confidence: 99%

Estimates from 11 countries show the significant impact of COVID-19 excess mortality on the incidence of family bereavement

Snyder¹,

Alburez-Gutiérrez²,

Williams³

et al. 2022

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“…Other works have used clusters to analyze the Covid-19 impact on counties across the United States [9][10][11]. The authors clusterized counties with similar Covid-19 fatality levels and concluded that heart disease and cancer are the risk factors that most increase the Covid-19 fatality rate [9]. Combining unsupervised and supervised learning, a study showed that the risk of infection and death by Covid-19 in US counties are positively associated with the poverty rate, percentage of people without health insurance, percentage of non-white people, smoker percentage, ICU bed rate, and population density [10].…”

Section: Sociodemographic Factors Associated Withmentioning

confidence: 99%

“…In the literature, some studies identified patterns of the Covid-19 impact in different countries [4][5][6][7][8]. Other works have used clusters to analyze the Covid-19 impact on counties across the United States [9][10][11]. The authors clusterized counties with similar Covid-19 fatality levels and concluded that heart disease and cancer are the risk factors that most increase the Covid-19 fatality rate [9].…”

Section: Sociodemographic Factors Associated Withmentioning

confidence: 99%

Sociodemographic Factors Associated with Covid-19 Mortality in Brazilian Municipalities: an approach supported by clustering

Lima

Silva

Meira

et al. 2022

Preprint

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The Covid-19 pandemic affected Brazil with severity. Brazil is a large country characterized by significant socioeconomic inequalities among its regions. This study aimed to check the sociodemographic factors associated with the Covid-19 mortality rate in Brazilian municipalities. This paper is an ecological study that analyzed data of the first three death waves, with Brazilian municipalities as a unit of analysis. Municipalities were clusterized based on their vulnerabilities in the face of Covid-19. The results show that, from the second death wave in the country, the municipalities with lower social vulnerability were more likely to have a higher mortality level than the other municipalities. The results also show that, among the attributes analyzed, the urban population percentage was the one that most strongly contributed to a municipality being more likely to have a higher level of mortality. Finally, this work demonstrated that using municipality clustering contributes to a more thoughtful and accurate analysis of correlations in ecological studies.

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County-Level Longitudinal Clustering of COVID-19 Mortality to Incidence Ratio in the United States

Cited by 5 publications

References 64 publications

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

Estimates from 11 countries show the significant impact of COVID-19 excess mortality on the incidence of family bereavement

Sociodemographic Factors Associated with Covid-19 Mortality in Brazilian Municipalities: an approach supported by clustering

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