Association Between Social Vulnerability and a County’s Risk for Becoming a COVID-19 Hotspot — United States, June 1–July 25, 2020

Dasgupta, Sharoda; Bowen, Virginia; Leidner, Andrew J.; Fletcher, Kelly; Musial, Trieste; Rose, Charles E.; Cha, Amy; Kang, Gloria; Dirlikov, Emilio; Pevzner, Eric; Rose, Dale A.; Ritchey, Matthew D.; Villanueva, Julie; Philip, Celeste; Liburd, Leandris; Oster, Alexandra M.

doi:10.15585/mmwr.mm6942a3

Cited by 166 publications

(161 citation statements)

References 6 publications

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“…Previous studies have shown differences in mask-wearing behavior and other mitigation behaviors by gender, age, ethnicity, and urbanicity 21 , 22 , 23 , 24 . Further, inherent differences in structural and social factors of counties could have influenced both statewide mitigation policies and individual behavior, and thus, may also have contributed to differences in observed associations by urbanicity [ 14 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, although metropolitan counties were strongly affected by the pandemic during the spring and much of the summer of 2020, COVID-19 incidence began rapidly increasing in non-metropolitan counties, eventually exceeding that of metropolitan areas starting in August; this trajectory continued through much of the fall and winter [ 1 , 12 ], [13] . In addition, because social vulnerabilities may be higher among less urban areas [14] , examining associations between community mitigation policies and COVID-19 incidence by urbanicity might be important in informing public health action, particularly during periods of high COVID-19 incidence.…”

Section: Introductionmentioning

confidence: 99%

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Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates — United States, June 1–September 30, 2020

Dasgupta¹,

Kassem²,

Sunshine³

et al. 2021

Annals of Epidemiology

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Background and Objective : Community mitigation strategies could help reduce COVID-19 incidence. In a national county-level analysis, we examined the probability of being identified as a county with rapidly increasing COVID-19 incidence (rapid riser identification) during the summer of 2020 by implementation of mitigation policies prior to the summer, overall and by urbanicity. Methods : We analyzed county-level data on rapid riser identification during June 1–September 30, 2020 and statewide closures and statewide mask mandates starting March 19 (obtained from state government websites). Poisson regression models with robust standard error estimation were used to examine differences in the probability of rapid riser identification by implementation of mitigation policies ( P -value<.05); associations were adjusted for county population size. Results : Counties in states that closed for 0–59 days were more likely to become a rapid riser county than those that closed for >59 days, particularly in nonmetropolitan areas. The probability of becoming a rapid riser county was 43% lower among counties that had statewide mask mandates at reopening (adjusted prevalence ratio [aPR] = 0.57; 95% confidence intervals [CI] = 0.51–0.63); when stratified by urbanicity, associations were more pronounced in nonmetropolitan areas. Conclusions : These results underscore the potential value of community mitigation strategies in limiting the COVID-19 spread, especially in nonmetropolitan areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates — United States, June 1–September 30, 2020

Dasgupta¹,

Kassem²,

Sunshine³

et al. 2021

Annals of Epidemiology

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“…A recent systematic review of 50 studies have showed that people from ethnic minority background in the UK and other countries, particularly Black and South Asian groups, have been disproportionately affected by the Coronavirus (COVID-19) pandemic compared to people of White ethnic background [1] While several studies have investigated whether adjusting for sociodemographic and economic factors and medical history reduces the estimated difference in risk of mortality and hospitalisation [2,3,4], the reasons for the differences in the risk of experiencing harms from COVID-19 are still being explored during the course of the pandemic. Factors including structural racism [5,6], social vulnerability [7,8] social and material deprivation, [9] have widely been suggested as potential mechanisms for these reported inequalities.…”

Section: Introductionmentioning

confidence: 99%

Ethnic differences in COVID-19 mortality during the first two waves of the Coronavirus Pandemic: a nationwide cohort study of 29 million adults in England

Nafilyan

Islam

Mathur

et al. 2021

Preprint

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Background: Ethnic minorities have experienced disproportionate COVID-19 mortality rates in the UK and many other countries. We compared the differences in the risk of COVID-19 related death between ethnic groups in the first and second waves the of COVID-19 pandemic in England. We also investigated whether the factors explaining differences in COVID-19 death between ethnic groups changed between the two waves. Methods: Using data from the Office for National Statistics Public Health Data Asset on individuals aged 30-100 years living in private households, we conducted an observational cohort study to examine differences in the risk of death involving COVID-19 between ethnic groups in the first wave (from 24th January 2020 until 31st August 2020) and second wave (from 1st September to 28th December 2020). We estimated age-standardised mortality rates (ASMR) in the two waves stratified by ethnic groups and sex. We also estimated hazard ratios (HRs) for ethnic-minority groups compared with the White British population, adjusted for geographical factors, socio-demographic characteristics, and pre-pandemic health conditions. Results: The study population included over 28.9 million individuals aged 30-100 years living in private households. In the first wave, all ethnic minority groups had a higher risk of COVID-19 related death compared to the White British population. In the second wave, the risk of COVID-19 death remained elevated for people from Pakistani (ASMR: 339.9 [95% CI: 303.7 - 376.2] and 166.8 [141.7 - 191.9] deaths per 100,000 population in men and women) and Bangladeshi (318.7 [247.4 - 390.1] and 127.1 [91.1 - 171.3] in men and women)background but not for people from Black ethnic groups. Adjustment for geographical factors explained a large proportion of the differences in COVID-19 mortality in the first wave but not in the second wave. Despite an attenuation of the elevated risk of COVID-19 mortality after adjusting for sociodemographic characteristics and health status, the risk was substantially higher in people from Bangladeshi and Pakistani background in both the first and the second waves. Conclusion: Between the first and second waves of the pandemic, the reduction in the difference in COVID-19 mortality between people from Black ethnic background and people from the White British group shows that ethnic inequalities in COVID-19 mortality can be addressed. The continued higher rate of mortality in people from Bangladeshi and Pakistani background is alarming and requires focused public health campaign and policy changes.

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“…Eleven analytic studies provided estimates of an effect measure for risk factors for laboratory-confirmed infections. [59,61, 62, 63, 64, 65, 66, 67, 68, 69, 70] Five of the eleven were conducted using a sample of the general population. These examined the frequency or particular demographics, [61] neighborhood deprivation/social vulnerability levels, [63,68] relationship to a confirmed case, [59] and recent social activities, including restaurant dining.…”

Section: Resultsmentioning

confidence: 99%

“…[59,61, 62, 63, 64, 65, 66, 67, 68, 69, 70] Five of the eleven were conducted using a sample of the general population. These examined the frequency or particular demographics, [61] neighborhood deprivation/social vulnerability levels, [63,68] relationship to a confirmed case, [59] and recent social activities, including restaurant dining. [66] Other investigations examined various factors related to confirmed infection in sub-populations, including face coverings and distancing among occupants of a military ship, [62] ethnic composition of employees of industrial facilities, [64] shelter residence status among people experiencing homelessness, [69] screening strategies and staffing levels in skilled nursing facilities.…”

Section: Resultsmentioning

confidence: 99%

The Missing Science: Epidemiological data gaps for COVID-19 policy in the United States

Bhatia

I²,

Baral

2021

Preprint

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We report on a scoping study of COVID-19 epidemiological data available for COVID-19 policy and management decisions for U.S. settings. We synthesize current US Centers for Disease and Control and Prevention (CDC) estimates for parameter of infectious transmission, infection severity, and disease burden, and summarize epidemiologic contributions to these parameters published by CDC-affiliated investigators through Oct 30, 2020. Authoritative estimates of most infectious transmission and infection severity parameters exist but rely primarily on data from studies conducted in non-U.S. populations. Estimates of secondary infection risks for household, workplace, school, or other community settings are lacking and estimates of the clinical fraction remain uncertain. The CDC reports multiple disease incidence and prevalence measures at national and state geographies, including some measures disaggregated by age group, race/ethnicity and sex; however, nationally uniform disease burden measures are not available at the sub-state level or for sub-populations defined by exposure setting, limiting opportunities for targeted interventions. CDC-affiliated investigators authored 133 quantitative studies on COVID-19 through Oct 30, 2020; however only 34 employed analytic methods. The remainder were descriptive. Of the 34 analytic studies, eleven reported on risk factors for infection, seven reported on risk factors for severe disease, three on symptomatic infections, three reported secondary infection risks, and four reported on indirect pandemic effects. Gaps remain in the epidemiological data required for an efficient and equitable public health policy response to COVID-19. The existence of these gaps one year after the onset of the COVID-19 pandemic underscores the need for standardizing data collection and research priorities and protocols in the context of a rapidly emerging infectious disease epidemics.

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Association Between Social Vulnerability and a County’s Risk for Becoming a COVID-19 Hotspot — United States, June 1–July 25, 2020

Cited by 166 publications

References 6 publications

Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates — United States, June 1–September 30, 2020

Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates — United States, June 1–September 30, 2020

Ethnic differences in COVID-19 mortality during the first two waves of the Coronavirus Pandemic: a nationwide cohort study of 29 million adults in England

The Missing Science: Epidemiological data gaps for COVID-19 policy in the United States

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