County-level COVID-19 Research Articles

Predicting asymptomatic severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection rates of inpatients: A time-series analysis.

Asymptomatic SARS-CoV-2 infections are often difficult to identify because widespread surveillance has not been the norm. Using time-series analyses, we examined whether COVID-19 rates at the county level could predict positivity rates among asymptomatic patients in a large health system. Asymptomatic positivity rates at the system level and county-level COVID-19 rates were not associated.

County-level association of COVID-19 mortality with 2020 United States presidential voting

ObjectiveThe objective of this study was to assess the association between United States county-level COVID-19 mortality and changes in presidential voting between 2016 and 2020. Study designThe study design is a county-level ecological study. MethodsWe analysed county-level population-weighted differences in partisan vote change, voter turnout and sociodemographic and health status characteristics across pre-election COVID-19 mortality quartiles. We estimated a population-weighted linear regression of the 2020–2016 Democratic vote change testing the significance of differences between quartiles of COVID-19 mortality, controlling for other county characteristics. ResultsThe overall change in the 2020–2016 Democratic vote was +2.9% but ranged from a +4.3% increase in the lowest mortality quartile counties to +0.9% in the highest mortality quartile counties. Change in turnout ranged from +9.1% in the lowest mortality counties to only +6.2% in highest mortality counties. In regression estimates, the highest mortality quartile was associated with a −1.26% change in the Democratic 2020–2016 vote compared with the lowest quartile (P < 0.001). ConclusionsHigher county-level COVID-19 mortality was associated with smaller increases in Democratic vote share in 2020 compared with 2016. Possible explanations to be explored in future research could include fear of in-person voting in heavily Democratic, high-mortality counties, fear of the economic effects of perceived Democratic support for tighter lockdowns and stay-at-home orders and general exhaustion that lowered political participation in hard-hit counties.

Assessing the Association Between Social Gatherings and COVID-19 Risk Using Birthdays

Many policies designed to stop the spread of COVID-19 address formal gatherings, such as workplaces and dining locations. Informal social gatherings are a potentially important mode of SARS-CoV-2 transmission, but studying their role in transmission is challenged by data and methodological limitations; birthdays offer an opportunity to empirically quantify the potential role of small social gatherings in COVID-19 spread. To assess the association between social gatherings and SARS-CoV-2 transmission by studying whether COVID-19 rates increase after birthdays in a household. This cross-sectional study used nationwide data from January 1 to November 8, 2020, from 2.9 million US households with private insurance to compare COVID-19 infections between households with and without a birthday in the preceding 2 weeks, stratified according to county-level COVID-19 prevalence in that week and adjusting for household size and both week- and county-specific differences. The study also compared how birthday-associated infection rates differed by type of birthday (eg, child vs adult birthday, or a milestone birthday such as a 50th birthday), county-level precipitation on the Saturday of each week (which could move gatherings indoors), political leanings in the county, and state shelter-in-place policies. Household-level COVID-19 infection. Among the 2.9 million households in the study, in the top decile of counties in COVID-19 prevalence, households with a birthday in the 2 weeks prior had 8.6 more diagnoses per 10 000 individuals (95% CI, 6.6-10.7 per 10 000 individuals) compared with households without a birthday in the 2 weeks prior, a relative increase of 31% above the county-level prevalence of 27.8 cases per 10 000 individuals, vs 0.9 more diagnoses per 10 000 individuals (95% CI, 0.6-1.3 per 10 000 individuals) in the fifth decile (P < .001 for interaction). Households in the tenth decile of COVID-19 prevalence had an increase in COVID-19 diagnoses of 15.8 per 10 000 persons (95% CI, 11.7-19.9 per 10 000 persons) after a child birthday, compared with an increase of 5.8 per 10 000 persons (95% CI, 3.7-7.9 per 10 000 persons) among households with an adult birthday (P < .001 in a test of interactions). No differences were found by milestone birthdays, county political leaning, precipitation, or shelter-in-place policies. This cross-sectional study suggests that birthdays, which likely correspond with social gatherings and celebrations, were associated with increased rates of diagnosed COVID-19 infection within households in counties with high COVID-19 prevalence.

Multilevel modeling of county-level excessive alcohol use, rurality, and COVID-19 case fatality rates in the US.

Reports of disparities in COVID-19 mortality rates are emerging in the public health literature as the pandemic continues to unfold. Alcohol misuse varies across the US and is related to poorer health and comorbidities that likely affect the severity of COVID-19 infection. High levels of pre-pandemic alcohol misuse in some counties may have set the stage for worse COVID-19 outcomes. Furthermore, this relationship may depend on how rural a county is, as access to healthcare in rural communities has lagged behind more urban areas. The objective of this study was to test for associations between county-level COVID-19 mortality, pre-pandemic county-level excessive drinking, and county rurality. We used national COVID-19 data from the New York Times to calculate county-level case fatality rates (n = 3,039 counties and county equivalents; October 1 -December 31, 2020) and other external county-level data sources for indicators of rurality and health. We used beta regression to model case fatality rates, adjusted for several county-level population characteristics. We included a multilevel component to our model and defined state as a random intercept. Our focal predictor was a single variable representing nine possible combinations of low/mid/high alcohol misuse and low/mid/high rurality. The median county-level COVID-19 case fatality rate was 1.57%. Compared to counties with low alcohol misuse and low rurality (referent), counties with high levels of alcohol and mid (β = -0.17, p = 0.008) or high levels of rurality (β = -0.24, p<0.001) demonstrated significantly lower case fatality rates. Our findings highlight the intersecting roles of county-level alcohol consumption, rurality, and COVID-19 mortality.

COVID-19 vaccination rates vary by community vulnerability: A county-level analysis

We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.

Low Insurance Coverage is Associated with Slower COVID-19 Vaccine Rollout and Housing Problems Are Most Strongly Related to 2021 Incidence Across the United States

Background: Equitable vaccination distribution is a priority for outcompeting the transmission of COVID-19. A recent CDC study found that US counties with high social vulnerability – according to metrics such as poverty, unemployment and low income – have significantly lower rates of vaccination compared to the national average. We build upon this analysis to assess the impact of demographic, socioeconomic, and environmental factors on county-level vaccination rates and COVID-19 incidence changes. Methods: Using data from 3,142 US counties (with over 328 million individuals), we computed Spearman rank-order correlations between the outcome variables (cumulative vaccination rate, change in COVID-19 incidence) and 68 demographic, environmental, and socioeconomic factors. We also performed analysis using multivariate linear regression to adjust for age as a potential confounding variable. Finally, we performed fold change analysis on counties stratified by vaccination rates to compute relative risks along with Fisher test p-value associated with these features. Findings: Our analysis reveals that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: -0.463). We separately assessed factors associated with COVID-19 incidence rates and found that the percent of a county’s population with severe housing problems and high housing costs were strongly correlated with COVID-19 incidence (Spearman correlations: 0.337, 0.314, p-values: <1e-34, <1e-50). Interpretation: This study shows that socioeconomic factors are correlated not just to COVID-19 vaccination rates but also to COVID-19 incidence fluctuations, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities. Funding: This work was supported by nference, inc. Declaration of Interest: EL, MC, GD, CP, and VS are employees of nference and have financial interests in the company.

Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States.

Long-term PM2.5 exposure might predispose populations to SARS-CoV-2 infection and intervention policies might interrupt SARS-CoV-2 transmission and reduce the risk of COVID-19. We conducted an ecologic study across the United States, using county-level COVID-19 incidence up to 12 September 2020, to represent the first two surges in the U.S., annual average of PM2.5 between 2000 and 2016 and state-level facemask mandates and stay home orders. We fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies. Stratified analyses by facemask policy and stay home policy were also performed. Each 1-µg/m3 increase in annual average concentration of PM2.5 exposure was associated with 7.56% (95% CI: 3.76%, 11.49%) increase in COVID-19 risk. Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. Our study added evidence that long-term PM2.5 exposure increased the risk of COVID-19 during each surge and cumulatively as of 12 September 2020, in the United States. Although both state-level implementation of facemask mandates and stay home orders were effective in preventing the spread of COVID-19, no clear effect modification was observed regarding long-term exposure to PM2.5 on the risk of COVID-19.

County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

BackgroundCOVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. ObjectivesWe evaluated whether greenness was related to COVID-19 incidence and mortality in the US. MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April–May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders. ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. DiscussionExposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.

Flattening the Latent Growth Curve? Explaining Within-Person Changes in Employee Well-Being during the COVID-19 Pandemic.

The COVID-19 pandemic represents one of the greatest global crises in modern history. In addition to recession and high unemployment, agencies such as the Centers for Disease Control and Prevention warn that stressors associated with a pandemic can cause increased strains, including difficulty concentrating, anxiety, and decreased mental health (CDC, 2020). Two general frameworks that explain these stressor-strain relationships over time include stress-reaction and adaptation models. Stress-reaction models suggest that stressors, such as heightened job demands due to the pandemic, accumulate over time and thus prolonged exposure to these stressors results in both immediate and long-term strain; conversely, adaptation models suggest that people adapt to stressors over time, such that strains produced by ongoing stressors tend to dissipate. After controlling for county-level COVID-19 cases, we found that (a) workers in general exhibited decreasing cognitive weariness and psychological symptoms over time, providing support for the adaptation model; (b) on-site workers experienced increasing physical fatigue over time, supporting the stress-reaction model among those workers; and (c) engaging in recovery behaviors was associated with improvements in cognitive weariness and psychological symptoms for all workers. We also found that our Time 1 outcomes were significantly different than pre-pandemic norms, such that our participants displayed lower initial levels of job-related burnout and higher initial levels of psychological symptoms than pre-pandemic norms. Furthermore, supplemental qualitative data support our quantitative findings for recovery behaviors. These findings have important implications for understanding workers’ responses to the pandemic and they can help inform organizational practice.

Hard Lessons: The Impact of Secondary Education on COVID-19 Cases and Deaths in the U.S.

Background: The association between education and health has been observed globally for a multitude of health measures. Variation in county-level COVID-19 burden in the United States (U.S.) provides an opportunity to identify community pandemic risk factors. We describe the influence of secondary education on county-level COVID-19 cases and deaths in the U.S.Methods: County-level environmental, social, behavioral, economic, political, regulatory, and health characteristics were obtained. Covariates used for modeling were selected from a candidate list through negative binomial stepwise forward selection with backwards elimination, as well as clinical judgement for prediction of COVID-19 cases and deaths. Counterfactual and bootstrap analyses estimated the impact of secondary education on COVID-19 mortality.Findings: High school dropout rate is one of the strongest predictors of COVID-19 cases (IRR 1.13, 95% CI 1.07; 1.18) and deaths (IRR 1.17, 95% CI 1.10; 1.26). Counterfactual analysis reducing the county-level high school dropout rate to the lowest in the nation resulted in a predicted 14.9% decrease in median county COVID-19 deaths per 100,000 (95% CI 6.98%; 22.82%). This hypothetical improvement in secondary education translated to approximately 57,680 preventable deaths throughout 2020 (95% CI: 30,654; 84,705).Interpretation: Adjusting for covariates, secondary education is among the strongest predictors of early county vulnerability to COVID-19 in the U.S. A reduction in high school dropout rate predicted a near 15% decrease in county COVID-19 mortality. Improving secondary education could strengthen the effectiveness of a community’s response to emerging threats such as a pandemic.Funding: None

Association Between Income Inequality and County-Level COVID-19 Cases and Deaths in the US

Socioeconomically marginalized communities have been disproportionately affected by the COVID-19 pandemic. Income inequality may be a risk factor for SARS-CoV-2 infection and death from COVID-19. To evaluate the association between county-level income inequality and COVID-19 cases and deaths from March 2020 through February 2021 in bimonthly time epochs. This ecological cohort study used longitudinal data on county-level COVID-19 cases and deaths from March 1, 2020, through February 28, 2021, in 3220 counties from all 50 states, Puerto Rico, and the District of Columbia. County-level daily COVID-19 case and death data from March 1, 2020, through February 28, 2021, were extracted from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University in Baltimore, Maryland. The Gini coefficient, a measure of unequal income distribution (presented as a value between 0 and 1, where 0 represents a perfectly equal geographical region where all income is equally shared and 1 represents a perfectly unequal society where all income is earned by 1 individual), and other county-level data were obtained primarily from the 2014 to 2018 American Community Survey 5-year estimates. Covariates included median proportions of poverty, age, race/ethnicity, crowding given by occupancy per room, urbanicity and rurality, educational level, number of physicians per 100 000 individuals, state, and mask use at the county level. As of February 28, 2021, on average, each county recorded a median of 8891 cases of COVID-19 per 100 000 individuals (interquartile range, 6935-10 666 cases per 100 000 individuals) and 156 deaths per 100 000 individuals (interquartile range, 94-228 deaths per 100 000 individuals). The median county-level Gini coefficient was 0.44 (interquartile range, 0.42-0.47). There was a positive correlation between Gini coefficients and county-level COVID-19 cases (Spearman ρ = 0.052; P < .001) and deaths (Spearman ρ = 0.134; P < .001) during the study period. This association varied over time; each 0.05-unit increase in Gini coefficient was associated with an adjusted relative risk of COVID-19 deaths: 1.25 (95% CI, 1.17-1.33) in March and April 2020, 1.20 (95% CI, 1.13-1.28) in May and June 2020, 1.46 (95% CI, 1.37-1.55) in July and August 2020, 1.04 (95% CI, 0.98-1.10) in September and October 2020, 0.76 (95% CI, 0.72-0.81) in November and December 2020, and 1.02 (95% CI, 0.96-1.07) in January and February 2021 (P < .001 for interaction). The adjusted association of the Gini coefficient with COVID-19 cases also reached a peak in July and August 2020 (relative risk, 1.28 [95% CI, 1.22-1.33]). This study suggests that income inequality within US counties was associated with more cases and deaths due to COVID-19 in the summer months of 2020. The COVID-19 pandemic has highlighted the vast disparities that exist in health outcomes owing to income inequality in the US. Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality.

694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

IntroductionCauses of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US.MethodsCounty-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros.ResultsMultivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates.ConclusionInsufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates.Support (if any)NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

Community Characteristics and COVID-19 Outcomes: A Study of 159 Counties in Georgia, United States.

The COVID-19 pandemic affects population groups differently, worsening existing social, economic, and health inequities. This study examined 159 counties within Georgia to identify community characteristics associated with county-level COVID-19 case, hospitalization, and death rates. Data from the 2020 County Health Rankings, the 2010 US Census, and the Georgia Department of Public Health COVID-19 Daily Status Report were linked using county Federal Information Processing Standard codes and evaluated through multivariable linear regression models. The percentages of children in poverty, severe housing problems, and people not proficient in the English language were significant predictors associated with increases in case, hospitalization, and death rates. Diabetic prevalence was significantly associated with increases in the hospitalization and death rates; in contrast, the percentages of people with excessive drinking and female were inversely associated with hospitalization and death rates. Other independent variables showing an association with death rate included the percentages of people reporting fair or poor health and American Indian/Alaska Native. Local authorities' proper allocation of resources and plans to address community social determinants of health are essential to mitigate disease transmission and reduce hospitalizations and deaths associated with COVID-19, especially among vulnerable groups.

Social Distancing Associations with COVID-19 Infection and Mortality Are Modified by Crowding and Socioeconomic Status

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has emerged as a worldwide public health emergency. Social distancing currently represents the primary public health approach to slowing disease transmission. However, to date, little evidence on the efficacy of these policies have been available, and nothing is known on the types of areas where these policies have been more or less effective in reducing incidence and mortality. Methods: We obtained county-level data on COVID-19 incidence and mortality, cell-phone based average social distancing (0-5, where higher numbers indicate more social distancing), and Census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution accounting for counties nested within states, we modeled associations between county-level social distancing and COVID-19 incidence and mortality. We used multiplicative interaction terms to determine if associations varied by demographics or socioeconomic status. Findings: In multivariable adjusted models, each unit increase in social distancing was associated with a 26% decrease (pInterpretation: County-level measures of social distancing lead to reductions in COVID-19 incidence and mortality, but was most effective in counties with lower percentages of black residents, higher median household incomes, and in counties with lower levels of household crowding. Funding: JEH and GA were supported by NIH/NIEHS P30 ES000002. TV was supported by NIH/NIDDK K01 DK125612. MDW was supported by CDC/NIOSH R01 OH011773 and the Brigham Research Institute Fund to Sustain Research Excellence.Declaration of Interests: Missing.

Laboratory correlates of SARS-CoV-2 seropositivity in a nationwide sample of patients on dialysis in the U.S.

Patients on dialysis are at high risk for death due to COVID-19, yet a significant proportion do survive as evidenced by presence of SARS-CoV-2 antibodies in 8% of patients in the U.S. in July 2020. It is unclear whether patients with seropositivity represent the subgroup with robust health status, who would be more likely to mount a durable antibody response. Using data from a July 2020 sample of 28,503 patients receiving dialysis, we evaluated the cross-sectional association of SARS-CoV-2 seropositivity with laboratory surrogates of patient health. In separate logistic regression models, we assessed the association of SARS-CoV-2 seropositivity with seven laboratory-based covariates (albumin, creatinine, hemoglobin, sodium, potassium, phosphate, and parathyroid hormone), across the entire range of the laboratory and in comparison to a referent value. Models accounted for age, sex, region, race and ethnicity, and county-level COVID-19 deaths per 100,000. Odds of seropositivity for albumin 3 and 3.5 g/dL were 2.1 (95% CI 1.9-2.3) and 1.3 (1.2-1.4) respectively, compared with 4 g/dL. Odds of seropositivity for serum creatinine 5 and 8 mg/dL were 1.8 (1.6-2.0) and 1.3 (1.2-1.4) respectively, compared with 12.5 mg/dL. Lower values of hemoglobin, sodium, potassium, phosphate, and parathyroid hormone were associated with higher odds of seropositivity. Laboratory values associated with poorer health status and higher risk for mortality were also associated with higher likelihood of SARS-CoV-2 antibodies in patients receiving dialysis.

COVID-19 morbidity and mortality in U.S. meatpacking counties

In this paper, we investigate the extent to which the presence of a large meatpacking (i.e., beef, pork, and broiler chicken) plant has affected county-level COVID-19 transmission dynamics. We find that—within 150 days after emergence of COVID-19 in a given county—the presence of a large beef packing facility increases per capita infection rates by 110%, relative to comparable counties without meatpacking plants. Large pork and chicken processing facilities increase transmission rates by 160% and 20%, respectively. While the presence of this type of industrial agricultural facility is shown to exacerbate initial disease transmission affecting large numbers of individuals in the community, over time daily case rates converge such that rates observed in meatpacking- and non-meatpacking counties become similar. In aggregate, results suggest that 334 thousand COVID-19 infections are attributable to meatpacking plants in the U.S. with associated mortality and morbidity costs totaling more than $11.2 billion.

County-Level COVID-19 Vaccination Coverage and Social Vulnerability - United States, December 14, 2020-March 1, 2021.

The U.S. COVID-19 vaccination program began in December 2020, and ensuring equitable COVID-19 vaccine access remains a national priority.* COVID-19 has disproportionately affected racial/ethnic minority groups and those who are economically and socially disadvantaged (1,2). Thus, achieving not just vaccine equality (i.e., similar allocation of vaccine supply proportional to its population across jurisdictions) but equity (i.e., preferential access and administra-tion to those who have been most affected by COVID-19 disease) is an important goal. The CDC social vulnerability index (SVI) uses 15 indicators grouped into four themes that comprise an overall SVI measure, resulting in 20 metrics, each of which has national and state-specific county rankings. The 20 metric-specific rankings were each divided into lowest to highest tertiles to categorize counties as low, moderate, or high social vulnerability counties. These tertiles were combined with vaccine administration data for 49,264,338 U.S. residents in 49 states and the District of Columbia (DC) who received at least one COVID-19 vaccine dose during December 14, 2020-March 1, 2021. Nationally, for the overall SVI measure, vaccination coverage was higher (15.8%) in low social vulnerability counties than in high social vulnerability counties (13.9%), with the largest coverage disparity in the socioeconomic status theme (2.5 percentage points higher coverage in low than in high vulnerability counties). Wide state variations in equity across SVI metrics were found. Whereas in the majority of states, vaccination coverage was higher in low vulnerability counties, some states had equitable coverage at the county level. CDC, state, and local jurisdictions should continue to monitor vaccination coverage by SVI metrics to focus public health interventions to achieve equitable coverage with COVID-19 vaccine.

Association of State-Issued Mask Mandates and Allowing On-Premises Restaurant Dining with County-Level COVID-19 Case and Death Growth Rates — United States, March 1–December 31, 2020

CDC recommends a combination of evidence-based strategies to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 (1). Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission (1). Starting in April, 39 states and the District of Columbia (DC) issued mask mandates in 2020. Reducing person-to-person interactions by avoiding nonessential shared spaces, such as restaurants, where interactions are typically unmasked and physical distancing (≥6 ft) is difficult to maintain, can also decrease transmission (2). In March and April 2020, 49 states and DC prohibited any on-premises dining at restaurants, but by mid-June, all states and DC had lifted these restrictions. To examine the association of state-issued mask mandates and allowing on-premises restaurant dining with COVID-19 cases and deaths during March 1-December 31, 2020, county-level data on mask mandates and restaurant reopenings were compared with county-level changes in COVID-19 case and death growth rates relative to the mandate implementation and reopening dates. Mask mandates were associated with decreases in daily COVID-19 case and death growth rates 1-20, 21-40, 41-60, 61-80, and 81-100 days after implementation. Allowing any on-premises dining at restaurants was associated with increases in daily COVID-19 case growth rates 41-60, 61-80, and 81-100 days after reopening, and increases in daily COVID-19 death growth rates 61-80 and 81-100 days after reopening. Implementing mask mandates was associated with reduced SARS-CoV-2 transmission, whereas reopening restaurants for on-premises dining was associated with increased transmission. Policies that require universal mask use and restrict any on-premises restaurant dining are important components of a comprehensive strategy to reduce exposure to and transmission of SARS-CoV-2 (1). Such efforts are increasingly important given the emergence of highly transmissible SARS-CoV-2 variants in the United States (3,4).

2020 trends in dental office visits during the COVID-19 pandemic

BackgroundCOVID-19 has created barriers to the delivery of health care services, including dental care. This study sought to quantify the change in dental visits in 2020 compared with 2019. MethodsThis retrospective, observational study examined the percentage change in weekly visits to dental offices by state (inclusive of the District of Columbia), nationally, and by county-level COVID-19 incidence using geographic information from the mobile applications of 45 million smartphones during 2019 and 2020. ResultsFrom March through August 2020, weekly visits to dental offices were 33% lower, on average, than in 2019. Weekly visits were 34% lower, on average, in counties with the highest COVID-19 rates. The greatest decline was observed during the week of April 12, 2020, when there were 66% fewer weekly visits to dental offices. The 5 states (inclusive of the District of Columbia) with the greatest declines in weekly visits from 2019 through 2020, ranging from declines of 38% through 53%, were California, Connecticut, District of Columbia, Massachusetts, and New Jersey. ConclusionsWeekly visits to US dental offices declined drastically during the early phases of the COVID-19 pandemic. Although rates of weekly visits rebounded substantially by June 2020, rates remain about 20% lower than the prior year as of August 2020. These findings highlight the economic challenges faced by dentists owing to the pandemic. Practical ImplicationsStates exhibited widespread variation in rates of declining visits during the pandemic, suggesting that dental practices may need to consider different approaches to reopening and encouraging patients to return depending on location.

COVID-19 Infection Rates and Propensity to Self-Respond in the 2020 U.S. Decennial Census

Survey methodologists have acknowledged that the social environment may influence survey and census participation—both at the societal level and at the community level. The 2020 COVID-19 pandemic affected daily life throughout the entire U.S., but to differing degrees depending upon the particular neighborhood or community. In this article, we use U.S. Centers for Disease Control county-level COVID-19 infection data coupled with U.S. 2020 Decennial Census response rate data to explore whether this societal level pandemic influenced participation in the census. We found that even when controlling for covariates predictive of COVID-19 infection rates (e.g., percent minority population, age 65+), infection rates were found to be significantly (negatively) associated with self-response.