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Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

Neighborhood level air pollution represents a long-standing issue for many communities that, until recently, has been difficult to address due to the cost of equipment and lack of related expertise. Changes in available technology and subsequent increases in community-based participatory research (CBPR) have drastically improved the ability to address this issue. However, much still needs to be learned as these types of studies are expected to increase in the future. To assist, we review the literature in an effort to improve understanding of the motivations, approaches, and outcomes of air monitoring studies that incorporate CBPR and citizen science (CS) principles. We found that the primary motivations for conducting community-based air monitoring were concerns for air pollution health risks, residing near potential pollution sources, urban sprawl, living in "unmonitored" areas, and a general quest for improved air quality knowledge. Studies were mainly conducted using community led partnerships. Fixed site monitoring was primarily used, while mobile, personal, school-based, and occupational sampling approaches were less frequent. Low-cost sensors can enable thorough neighborhood level characterization; however, keeping the community involved at every step, understanding the limitations and benefits of this type of monitoring, recognizing potential areas of debate, and addressing study challenges are vital for achieving harmony between expected and observed study outcomes. Future directions include assessing currently unregulated pollutants, establishing long-term neighborhood monitoring sites, performing saturation studies, evaluating interventions, and creating CS databases.

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