Cumulative Case Rate Research Articles

Spatiotemporal Ecologic Analysis of COVID-19 Vaccination Coverage and Outcomes, Oklahoma, USA, February 2020-December 2021.

Data on COVID-19 cases, deaths, hospitalizations, and vaccinations in Oklahoma, USA, have not been systematically described. The relationship between vaccination and COVID-19-related outcomes over time has not been investigated. We graphically described data collected during February 2020-December 2021 and conducted spatiotemporal modeling of monthly increases in COVID-19 cumulative death and hospitalization rates, adjusting for cumulative case rate, to explore the relationship. A 1 percentage point increase (absolute change) in the cumulative vaccination rate was associated with a 6.3% (95% CI 1.4%-10.9%) relative decrease in death outcome during April-June 2021, and a 1.9% (95% CI 1.1%-2.6%) relative decrease in death outcome and 1.1% (95% CI 0.5%-1.7%) relative decrease in hospitalization outcome during July-December 2021; the effect on hospitalizations was driven largely by data from urban counties. Our findings from Oklahoma suggest that increasing cumulative vaccination rates might reduce the increase in cumulative death and hospitalization rates from COVID-19.

Elimination, Containment, and Mitigation of COVID-19: A Personal Story about International Travels.

The author and his wife report their unique experience of international travels during the COVID-19 pandemic, and discuss issues encountered in various countries. Narrated discussion of issues encountered during the COVID-19 pandemic. "Zero-COVID" versus "Living with COVID-19" strategies are compared. Children have unique issues with COVID-19 pandemic. Evaluation of efficacy of the approaches in pandemic with time should consider the main types of interventions (e.g., border management/quarantine; physical distancing; mask use; case isolation, testing and contact tracing; vaccination). The key metrics that can be used to compare the impacts of different strategies between the cities (e.g., cumulative case rate, cumulative mortality rate, case fatality risk, stringency index, economic performance) should be identified. Research in these approaches can help manage future pandemics in coronaviruses and emerging infections. The UK started with very loose mitigation ('herd immunity') and then switched to a suppression approach in 2021, followed by "living with the virus" approach. Whereas HK has been targeting towards elimination throughout. In between, countries like Singapore, Australia and New Zealand have shifted from zero-COVID strategy to living with the virus. It is easier to have effective social control measures in Hong Kong because it has clear borders and an authoritarian government, but it did not have a clear exit policy when Omicron spread.

Lifting Statewide Mask Mandates and COVID-19 Cases: A Synthetic Control Study.

Importance:As states reopened their economies state and local officials made decisions on policies and restrictions that had an impact on the evolution of the pandemic and the health of the citizens. Some states made the decision to lift mask mandates starting spring 2021. Data-driven methods help evaluate the appropriateness and consequences of such decisions.Objective:To investigate the association of lifting the mask mandate with changes in the cumulative coronavirus case rate.Design:Synthetic control study design on lifting mask mandate in the state of Iowa implemented on February 7, 2021.Setting:Daily state-level data from the COVID-19 Community Profile Report published by the US Department of Health & Human Services, COVIDcast dataset of the Delphi Research Group, and Google Community Mobility Reports.Exposures and Outcome:Mask mandate policy lift at the state level. State-day observations of the cumulative case rate measured as the cumulative number of new cases per 100,000 people in the previous 7 days.Results:The cumulative case rate in Iowa increased by 20%–30% within 3 weeks of lifting the mask mandate as compared with a synthetic control unit. This association appeared to be related to people, in fact, reducing their mask-wearing habits.Conclusions:Lifting the mask mandate in Iowa was associated with an increase in new COVID-19 cases. Caution should be applied when making this type of policy decision before having achieved a more stable control of the pandemic.

Place, Race, and Case: Examining Racialized Economic Segregation and COVID-19 in Louisiana.

Early COVID-19 pandemic data suggested racial/ethnic minority and low-income earning people bore the greatest burden of infection. Structural racism, the reinforcement of racial and ethnic discrimination via policy, provides a framework for understanding disparities in health outcomes like COVID-19 infection. Residential racial and economic segregation is one indicator of structural racism. Little attention has been paid to the relationship of infection to relative overall concentrations of risk (i.e., segregation of the most privileged from the most disadvantaged). We used ordinary least squares and geographically weighted regression models to evaluate the relationship between racial and economic segregation, measured by the Index of Concentration at the Extremes, and COVID-19 cases in Louisiana. We found a significant global association between racial segregation and cumulative COVID-19 case rate in Louisiana and variation across the state during the study period. The northwest and central regions exhibited a strong negative relationship indicating greater risk in areas with high concentrations of Black residents. On the other hand, the southeastern part of the state exhibited more neutral or positive relationships indicating greater risk in areas with high concentrations of White residents. Our findings that the relationship between racial segregation and COVID-19 cases varied within a state further support evidence that social and political determinants, not biological, drive racial disparities. Small area measures and measures of polarization provide localized information better suited to tailoring public health policy according to the dynamics of communities at the census tract level, which may lead to better health outcomes.

Understanding contributors to racial and ethnic inequities in COVID-19 incidence and mortality rates.

Racial inequities in Coronavirus 2019 (COVID-19) have been reported over the course of the pandemic, with Black, Hispanic/Latinx, and Native American individuals suffering higher case rates and more fatalities than their White counterparts. We used a unique statewide dataset of confirmed COVID-19 cases across Missouri, linked with historical statewide hospital data. We examined differences by race and ethnicity in raw population-based case and mortality rates. We used patient-level regression analyses to calculate the odds of mortality based on race and ethnicity, controlling for comorbidities and other risk factors. As of September 10, 2020 there were 73,635 confirmed COVID-19 cases in the State of Missouri. Among the 64,526 case records (87.7% of all cases) that merged with prior demographic and health care utilization data, 12,946 (20.1%) were Non-Hispanic (NH) Black, 44,550 (69.0%) were NH White, 3,822 (5.9%) were NH Other/Unknown race, and 3,208 (5.0%) were Hispanic. Raw cumulative case rates for NH Black individuals were 1,713 per 100,000 population, compared with 2,095 for NH Other/Unknown, 903 for NH White, and 1,218 for Hispanic. Cumulative COVID-19-related death rates for NH Black individuals were 58.3 per 100,000 population, compared with 38.9 for NH Other/Unknown, 19.4 for NH White, and 14.8 for Hispanic. In a model that included insurance source, history of a social determinant billing code in the patient's claims, census block travel change, population density, Area Deprivation Index, and clinical comorbidities, NH Black race (OR 1.75, 1.51-2.04, p<0.001) and NH Other/Unknown race (OR 1.83, 1.36-2.46, p<0.001) remained strongly associated with mortality. In Missouri, COVID-19 case rates and mortality rates were markedly higher among NH Black and NH Other/Unknown race than among NH White residents, even after accounting for social and clinical risk, population density, and travel patterns during COVID-19.

Status of water, sanitation, and hygiene and standard precautions in healthcare facilities and its relevance to COVID-19 in Afghanistan.

BackgroundTo protect the health and safety of healthcare workers (HCWs), it is essential to ensure the provision of sustainable water, sanitation, and hygiene (WASH) services and standard precautions in healthcare facilities (HCF). The objectives of this short communication were 1) to assess the availability of WASH services and standard precautions in HCFs in seven provinces in Afghanistan before the COVID-19 pandemic, and 2) to elucidate the relevance of these patterns with the number of reported HCW infections from COVID-19 in the mentioned provinces.MethodsWe analyzed secondary data from the 2018-19 Afghanistan Service Provision Assessment survey, which included 142 public and private HCFs in seven major provinces in Afghanistan. Data on COVID-19 cases were obtained from the Afghanistan Ministry of Public Health Data Warehouse. Weighted prevalence of WASH services and standard precautions were calculated using frequencies and percentages. ArcGIS maps were used to visualize the distribution of COVID-19 cases, and scatter plots were created to visualize the relevance of WASH services and standard precautions to COVID-19 cases in provinces.ResultsOf the 142 facilities surveyed, about 97% had improved water sources, and over 94% had improved toilet for clients. Overall, HCFs had limited availability of hygiene services and standard precautions, which was lower in private than public facilities. More than half of the facilities had safe final disposal and appropriate storage of sharps and medical waste. Of the seven provinces, Herat province had the highest cumulative COVID-19 case rate among HCWs per 100,000 population and reported lower availability of WASH services and standard precautions in HCFs compared to other provinces.ConclusionOur findings show disparities in the availability of WASH services and standard precautions in public and private facilities. Private facilities had a lower availability of hygiene services and standard precautions than public facilities. Provinces with higher availability of WASH services and standard precautions in HCFs had a lower cumulative COVID-19 case rate among HCWs per 100,000 population. Pre-pandemic preparation of adequate WASH services and standard precautions in HCFs could be potentially important in combating infectious disease emergence.Supplementary informationThe online version contains supplementary material available at https://doi.org/10.1265/ehpm.21-00272.

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

SummaryBackgroundPopulation 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.MethodsPublicly 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.FindingsThe 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.InterpretationThe indices presented offer an efficacious way of identifying geographical disparities in COVID-19 risk, thus helping focus resources according to need.FundingFunder: Integrated Covid Hub North EastAward numbern/aGrant recipientFiona Matthews

Comparing the COVID-19 Responses in Cuba and the United States.

The purpose of this analytic essay is to contrast the COVID-19 responses in Cuba and the United States, and to understand the differences in outcomes between the 2 nations. With fundamental differences in health systems structure and organization, as well as in political philosophy and culture, it is not surprising that there are major differences in outcomes. The more coordinated, comprehensive response to COVID-19 in Cuba has resulted in significantly better outcomes compared with the United States. Through July 15, 2021, the US cumulative case rate is more than 4 times higher than Cuba's, while the death rate and excess death rate are both approximately 12 times higher in the United States. In addition to the large differences in cumulative case and death rates between United States and Cuba, the COVID-19 pandemic has unmasked serious underlying health inequities in the United States. The vaccine rollout presents its own set of challenges for both countries, and future studies can examine the comparative successes to identify effective strategies for distribution and administration. (Am J Public Health. 2021;111(12):2186-2193. https://doi.org/10.2105/AJPH.2021.306526).

Dimensions of uncertainty: a spatiotemporal review of five COVID-19 datasets

ABSTRACT COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen’s kappa) and agreement across all datasets (Fleiss’ kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.

Comparison of all-cause mortality in England with Europe and the USA: January 2020 to February 2021

BackgroundAssessing mortality during the COVID-19 pandemic is vital for informing public health strategies and policy decision making. All-cause excess mortality provides an objective measure of the impact of the pandemic including both the direct and indirect effects. Our study considers the burden of mortality in the UK, Europe and the USA. We examine variation between countries, by age and sex. We explore the extent to which this variation is associated with COVID-19 case rates and other population characteristics.MethodsThe study is a secondary analysis of routine administrative population and mortality data. Weekly death occurrences and population estimates were obtained from Eurostat and national statistical agencies. Contextual information on COVID-19 case rates, population-level risk factors and healthcare were obtained from various open-source databases. Weekly age-standardised mortality rates (ASMRs) were calculated and presented relative to a baseline average from the preceding 5-year period. Relative cumulative (rc) ASMRs were then calculated to provide a comparable assessment of excess mortality at a point in time.ResultsPreliminary results show that, by end of the analysis period, England had an overall rcASMR of 10.09%. Higher excess mortality was identified for some countries (eg USA 14.58%) and lower - even below average mortality - for others (eg Norway -6.8%). Under 65 rcASMR showed substantial variation between countries. Cumulative COVID-19 case rates showed a moderate effect size (R2 = 0.51) when used to explain the proportion of variation observed between rcASMRs. Other population factors showed a smaller effect.ConclusionsThe burden of mortality experienced between countries and populations over the COVID-19 pandemic period has shown significant variation. Factors which may have contributed to the position of some countries should be further explored in order to inform ongoing management of Covid-19 and future pandemic events.Key messages Significant variation in all-cause excess mortality has been identified across the COVID-19 pandemic period between nations and particularly in younger age groups.COVID-19 case rates are associated with relative cumulative all-cause excess mortality among the nations assessed.

Comment on “Economic Impacts of SARS/MERS/COVID‐19 in Asian Countries”

Tanaka (2022) is a clearly written survey of the impact of the COVID-19 pandemic on Asian economies. It covers the single sector SIR (susceptible, infected, recovered) epidemiological macro-model and discusses results from this class of models for Asian countries. Then, relaxing the model to allow heterogeneity across sectors and workers, the paper examines the impact of the pandemic in Asia. The paper concludes by briefly addressing the policy responses of Asian countries. At this writing, there have been an estimated 212 million COVID-19 cases worldwide (though this is a vast undercount) and more than 4 million deaths (again an undercount of the true excess death associated with the pandemic). The pandemic's severity demands greater attention to those policy issues. East Asia appears to have been uniquely adroit at implementing nonpharmaceutical interventions (NPIs). Targeted forms of interventions have mitigated the pandemic without incurring large economic costs. These would include mass masking, widespread testing, extensive contact tracing, systematic quarantines of individuals likely to be infected, border and movement restrictions, and timely, clear, and consistent public health communication. The execution of some of these measures in East Asia has been overly intrusive and subject to abuse, so implementation should be done with the maintenance of civil liberties in mind (Chorzempa & Huang, 2021). In assessing policy effectiveness, the control of borders should not be underestimated: among the few robust cross-country correlates with effective pandemic response has been being an island. (It is no accident that Hawaii has the lowest cumulative case rate among US states.) In this context, South Korea is for all intents and purposes an island. Other robust correlates include previous experience with pandemics such as SARS or MERS, and having a female leader, though this result may be due to outperformance by New Zealand, and possibly, Germany. That said, vaccination is key: the attainment of herd immunity will facilitate the return to normal economic and social life. As of this writing, approximately 2 billion people or about 25% of the world's population have been fully vaccinated. Further progress will be complicated by the apparent need to give booster shots. Thus far, Asia has been relatively strong on NPIs, but relatively weak on vaccine rollout. There are two core issues: first, the social benefit of vaccination exceeds its private benefit at the level of the individual, and the global benefit exceeds the national benefit at the level of the nation-state. Consequently, there is a prima facie case for the subsidization of production and consumption at the national and global levels. A related issue is distributional: we face an emerging “vaccine famine.” In famines, people do not die because there is not enough food to go around in a physical sense; they die because they are too poor to purchase adequate supplies. “Vaccine nationalism” in which a limited number of producer countries impose export controls, or pre-emptively purchase huge stocks for their citizenry, will leave vast swaths of the world's population, including in Asia, exposed. Unvaccinated populations can act as incubators for the development of variants of concern. Bown and Bollyky (2021) propose a COVID-19 Vaccine an Investment Trade Agreement (CVITA) to leverage COVAX; subsidize investment in vaccine production and the related supply chain; eschew export restrictions; create a transparent monitoring system; and create an authority to act as contractor and ombudsman. Tanaka documents the uneven incidence of the pandemic burden across households; similar inequities are arising in vaccine distribution within countries, particularly in countries characterized by high levels of inequality and ethnic or other social tensions. This could prove particularly challenging in parts of Southeast Asia. A related issue is vaccine hesitancy. This phenomenon has been particularly vexing in countries with populist leaders such as Donald Trump, Jair Bolsonaro or Rodrigo Duterte. Attainment of herd immunity will determine the length of the pandemic and the magnitude of scarring. As Tanaka observes, duration also is relevant to the preferability of providing support to firms (as has been emphasized by the European Union) or support to individual households and workers (as has been emphasized in the USA). The former preserves the enterprise value of firms at the risk of creating zombies; the latter is advisable if considerable cross-sectoral redeployment of resources is necessary but may incur the cost of firm shutdown needlessly if the shock is temporary. The risk is that without an effective global pandemic response, we could find ourselves in a world of recurrent outbreaks, inevitably prompting the imposition of cross-border impediments to the movement of goods and people, with adverse implications for long-run productivity, economic growth, and poverty alleviation.

Identifying US County-level characteristics associated with high COVID-19 burden

BackgroundIdentifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems.MethodsSynthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends.ResultsRural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates.ConclusionsThese county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.

The Impact of Preparedness in Defying COVID-19 Pandemic Expectations in the Lower Mekong Region: A Case Study.

ABSTRACTDire COVID-19 expectations in the Lower Mekong Region (LMR) can be understood as Cambodia, the Lao PDR, Myanmar, Thailand, and Vietnam have stared down a succession of emerging infectious disease (EID) threats from neighboring China. Predictions that the LMR would be overwhelmed by a coming COVID-19 tsunami were felt well before the spread of the COVID-19 pandemic had been declared. And yet, the LMR, excepting Myanmar, has proved surprisingly resilient in keeping COVID-19 contained to mostly sporadic cases. Cumulative case rates (per one million population) for the LMR, including or excluding Myanmar, from January 1 to October 31 2020, are 1,184 and 237, respectively. More telling are the cumulative rates of COVID-19–attributable deaths for the same period of time, 28 per million with and six without Myanmar. Graphics demonstrate a flattening of pandemic curves in the LMR, minus Myanmar, after managing temporally and spatially isolated spikes in case counts, with negligible follow-on community spread. The comparable success of the LMR in averting pandemic disaster can likely be attributed to years of preparedness investments, triggered by avian influenza A (H5N1). Capacity building initiatives applied to COVID-19 containment included virological (influenza-driven) surveillance, laboratory diagnostics, field epidemiology training, and vaccine preparation. The notable achievement of the LMR in averting COVID-19 disaster through to October 31, 2020 can likely be credited to these preparedness measures.

Evaluating the Impact of Remdesivir on Covid-19 Patient Outcomes: An Inpatient County Hospital Analysis

Background We performed a retrospective chart review on COVID-19 patients treated at Arrowhead Regional Medical Center (ARMC) in Colton, California from January through October 2020. Two outcomes were measured: time to recovery and mortality outcome. Models were subsequently generated to investigate the role of remdesivir on these patient outcomes. Methods This study compares data on the Top 20 MSAs with the highest cumulative COVID-19 case rate at two timepoints (October 13, 2020 and January 1, 2021). The means of CDC’s Social Vulnerability Index (SVI) variables for the highest risk MSAs were compared with the Welch Two Sample t-test to the means of the SVI variables for the rest of the U.S. Results While initial research indicated that remdesivir decreased time to recovery, we found no such appreciable decrease in our study population. Our data corroborates existing studies on the lack of remdesivir’s impact on mortality. When controlling for days from symptom onset to administration of this medication, these outcomes do not change. Conclusion Our data demonstrate that remdesivir does not significantly alter time to recovery or mortality in COVID-19 patients. While the National Institute of Health (NIH) has sanctioned remdesivir’s use for certain COVID-19 populations, we do not observe any appreciable effects of this implementation at our county hospital. Increasing sample size and investigating inclusion criteria may help elucidate this observed lack of effect.

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

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. 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 24/06/2021) was determined using Spearman correlation. The indices were split into quintiles, and MSOAs in with the highest vulnerability and vaccine hesitancy quintiles were mapped.Findings: The SEVI showed a 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 and outperformed an existing MSOA-level vulnerability index. The VHI was significantly negatively correlated with vaccine coverage in the validation data at the time of writing (r -0·43 95% CI -0·46 to -0·41). London had the largest number and proportion of MSOAs in quintile 5 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 Information: Grant from Integrated Covid Hub North East.Declaration of Interests: DS and CW are part-funded by a Department of Health and Social Care grant to the Newcastle upon Tyne Hospitals NHS Foundation Trust for the Integrated COVID Hub North East for the purposes of local pandemic response, paid via the University of Newcastle (the primary employer of all authors). Grant awarded to FM.

Identification of “Hot Spots” of COVID-19 in the United States (January 2020-January 2021)

Objective To examine the sociodemographic factors associated with US metropolitan or micropolitan statistical areas (MSAs) most impacted by the COVID-19 pandemic and how the sociodemographic profiles of these highest-risk MSAs have changed across two timepoints during the pandemic. Methods This study compares data on the Top 20 MSAs with the highest cumulative COVID-19 case rate at two timepoints (October 13, 2020 and January 1, 2021). The means of CDC’s Social Vulnerability Index (SVI) variables for the highest risk MSAs were compared with the Welch Two Sample t-test to the means of the SVI variables for the rest of the U.S. Results The Top 20 MSAs on October 13, 2020 (Time 1) tended to be located around the US-Mexico border and the South, and the Top 20 MSAs on January 1, 2021 (Time 2) tended to located in the Southwest and Midwest regions. At Time 1, more vulnerable MSAs were disproportionately affected. Conclusion Our findings highlight the excessively high rates of COVID-19 and how it is important for clinicians and public health practitioners to consider these “hot spots” when planning interventions.

Comparison of All-Cause Mortality in England With European Countries and the USA: January 2020 Through to February 2021: A Secondary Analysis of Routine Administrative Data

Background: Assessments of mortality continue to inform public health strategies and policy making during the COVID-19 pandemic. All-cause excess mortality is a robust measure of the impact of the pandemic. Our study assesses the relative burden of excess mortality in England during the pandemic compared with 28 European countries and the USA. Methods: Weekly deaths and population estimates were obtained from Eurostat or directly from national statistical and public health agencies. Contextual information on COVID-19 case rates and population-level risk factors were obtained from open-source databases. Weekly directly age-standardised mortality rates (ASMRs) during week 1 2020 to week 7 2021 were calculated and summed cumulatively. These were compared with weekly sums of mean ASMRs observed between 2015 and 2019 and presented relative to the mean cumulative 2015-2019 ASMR averaged over an equivalent period. Findings: By February 2021, England had an overall relative excess mortality of 10·05%. This was the fifth highest out of 29 countries, the USA (14·82%) being the highest. Five countries showed below average mortality, with Norway the lowest (-6·76%). The same statistic for deaths under 65 years showed substantial international variation, with England having the second highest excess of 11·46%. Cumulative COVID-19 case rates explained about half of the international variation in relative excess mortality. Interpretation: England has experienced comparably high all-cause excess mortality during the COVID-19 pandemic. Factors contributing to England’s relative position should be further explored, particularly the association of health inequalities and wider determinants of health on the mortality risk of younger age groups. Funding Information: This study was carried out as part of the PHE’s work during the pandemic and therefore funded by PHE. Declaration of Interests: All authors declare no conflicts of interest. Ethics Approval Statement: This study was carried out as part of the PHE responsibility to manage the COVID-19 pandemic. PHE has legal permission, provided by Regulation 3 of The Health Service (Control of Patient Information) Regulations 2002 to process confidential patient information (http://www.legislation.gov.uk/uksi/2002/1438/regulation/3/made) under Sections 3(i) (a) to (c), 3(i)(d) (i) and (ii) and 3(3) as part of its outbreak response activities. As such this work falls outside the remit for ethical review. The study was subject to an internal review by the PHE Research Ethics and Governance Group and was found to be fully compliant with all regulatory requirements. As a full ethical review is not a requirement for this type of study and as no ethical or regulatory issues had been identified the study was approved.

The Impact of Vaccinations on COVID-19 Case Rates at the State Level

This study uses a stepwise regression model to measure the efficacy of vaccination in reducing COVID-19 case rates through 8/10/21. In order to hold other covariants constant, variables like density, poverty, and governmental stringency were also included in the regression tests. The statistical results rigorously show that higher vaccination rates led to significantly lower COVID-19 case rates at the state level. A simulation is presented that estimates the cumulative COVID-19 case rate had vaccinations not been available. With respect to the other variables tested, density was significant in positively affecting case rates in 2021 after not being significant in the last half of 2020. Poverty rates were significant during all periods tested in the study. Surprisingly, governmental stringency as measured by the Oxford Stringency Index was not found significant in reducing COVID-19 case rates in 2021. Finally, no significant evidence of herd immunity was found in 2021.

The Effects of the First National Lockdown in England on Geographical Inequalities in the Evolution of COVID-19 Case Rates: An Ecological Study

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.Funding Information: This work was supported by a grant from The Health Foundation (Ref: 2211473).Declaration of Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Ethics Approval Statement: This study was approved by the Newcastle University Ethics Committee (Ref: 7543/2020).

The Use of Penalized Regression Analysis to Identify County-Level Demographic and Socioeconomic Variables Predictive of Increased COVID-19 Cumulative Case Rates in the State of Georgia.

Systemic inequity concerning the social determinants of health has been known to affect morbidity and mortality for decades. Significant attention has focused on the individual-level demographic and co-morbid factors associated with rates and mortality of COVID-19. However, less attention has been given to the county-level social determinants of health that are the main drivers of health inequities. To identify the degree to which social determinants of health predict COVID-19 cumulative case rates at the county-level in Georgia, we performed a sequential, cross-sectional ecologic analysis using a diverse set of socioeconomic and demographic variables. Lasso regression was used to identify variables from collinear groups. Twelve variables correlated to cumulative case rates (for cases reported by 1 August 2020) with an adjusted r squared of 0.4525. As time progressed in the pandemic, correlation of demographic and socioeconomic factors to cumulative case rates increased, as did number of variables selected. Findings indicate the social determinants of health and demographic factors continue to predict case rates of COVID-19 at the county-level as the pandemic evolves. This research contributes to the growing body of evidence that health disparities continue to widen, disproportionality affecting vulnerable populations.