The association of demographic and socioeconomic factors with COVID-19 during pre- and post-vaccination periods: A cross-sectional study of Virginia.

Sociodemographic factors have been found to be associated with the transmission of coronavirus disease 2019 (COVID-19), yet most studies focused on the period before the proliferation of vaccination and obtained inconclusive results. In this cross-sectional study, the infections, deaths, incidence rates, case fatalities, and mortalities of Virginia's 133 jurisdictions during the pre-vaccination and post-vaccination periods were compared, and their associations with demographic and socioeconomic factors were studied. The cumulative infections and deaths and medians of incidence rates, case fatalities, and mortalities of COVID-19 in 133 Virginia jurisdictions were significantly higher during the post-vaccination period than during the pre-vaccination period. A variety of demographic and socioeconomic risk factors were significantly associated with COVID-19 prevalence in Virginia. Multiple linear regression analysis suggested that demographic and socioeconomic factors contributed up to 80% of the variation in the infections, deaths, and incidence rates and up to 53% of the variation in the case fatalities and mortalities of COVID-19 in Virginia. The demographic and socioeconomic determinants differed during the pre- and post-vaccination periods. The developed multiple linear regression models could be used to effectively characterize the impact of demographic and socioeconomic factors on the infections, deaths, and incidence rates of COVID-19 in Virginia.

The COVID-19 impacts on bikeshare systems in small rural communities: Case study of bikeshare riders in Montgomery County, VA.

The shared and micro-mobility industry (ride sharing and hailing, carpooling, bike and e-scooter shares) saw direct and almost immediate impacts from COVID-19 restrictions, orders and recommendations from local governments and authorities. However, the severity of that impact differed greatly depending on variables such as different government guidelines, operating policies, system resiliency, geography and user profiles. This study investigated the impacts of the pandemic regarding bike-share travel behavior in Montgomery County, VA. We used bike-usage dataset covering two small towns in Montgomery county, namely: Blacksburg and Christiansburg, including Virginia Tech campus. The dataset used covers the period of Jan 2019-Dec 2021 with more than 14,555 trips and 5,154 active users. Findings indicated that a bikeshare user's average trip distance and duration increased in 2020 (compared to 2019) from 2+ miles to 4+ and from half an hour to about an hour. While there was a slight drop in 2021, bikeshare users continued to travel farther distances and spend more time on the bikes than pre-COVID trips. When those averages were unpacked to compare weekday trips to weekend trips, a few interesting trip patterns were observed. Unsurprisingly, more trips still took place on the weekends (increasing from 2x as many trips to 4x as many trips than the weekday). These findings could help to better understand traveler's choices and behavior when encountering future pandemics.

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.

BACKGROUND: Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. OBJECTIVE: COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior-day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. METHODS: We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia's 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. RESULTS: The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days' counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. CONCLUSIONS: For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

Assessment of Seroprevalence of SARS-CoV-2 and Risk Factors Associated With COVID-19 Infection Among Outpatients in Virginia.

Importance: Data from seroepidemiologic surveys measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure in diverse communities and ascertaining risk factors associated with infection are important to guide future prevention strategies. Objective: To assess the prevalence of previous SARS-CoV-2 infection across Virginia and the risk factors associated with infection after the first wave of coronavirus disease 2019 (COVID-19) infections in the US. Design, Setting, and Participants: In this statewide cross-sectional surveillance study, 4675 adult outpatients presenting for health care not associated with COVID-19 in Virginia between June 1 and August 14, 2020, were recruited to participate in a questionnaire and receive venipuncture to assess SARS-CoV-2 serology. Eligibility was stratified to meet age, race, and ethnicity quotas that matched regional demographic profiles. Main Outcomes and Measures: The main outcome was SARS-CoV-2 seropositivity, as measured by the Abbott SARS-CoV-2 immunoglobulin G assay. Results: Among 4675 adult outpatients (mean [SD] age, 48.8 [16.9] years; 3119 women [66.7%]; 3098 White [66.3%] and 4279 non-Hispanic [91.5%] individuals) presenting for non-COVID-19-associated health care across Virginia, the weighted seroprevalence was 2.4% (95% CI, 1.8%-3.1%) and ranged from 0% to 20% by zip code. Seroprevalence was notably higher among participants who were Hispanic (10.2%; 95% CI, 6.1%-14.3%), residing in the northern region (4.4%; 95% CI, 2.8%-6.1%), aged 40 to 49 years (4.4%; 95% CI, 1.8%-7.1%), and uninsured (5.9%; 95% CI, 1.5%-10.3%). Higher seroprevalence was associated with Hispanic ethnicity (adjusted odds ratio [aOR], 3.56; 95% CI, 1.76-7.21), residence in a multifamily unit (aOR, 2.55; 95% CI, 1.25-5.22), and contact with an individual with confirmed COVID-19 infection (aOR, 4.33; 95% CI, 1.77-10.58). The sensitivity of serology results was 94% (95% CI, 70%-100%) among those who reported receiving a previous polymerase chain reaction test for COVID-19 infection. Among 101 participants with seropositive results, 67 individuals (66.3%) were estimated to have asymptomatic infection. These data suggested a total estimated COVID-19 burden that was 2.8-fold higher than that ascertained by PCR-positive case counts. Conclusions and Relevance: This large statewide serologic study estimated that 2.4% of adults in Virginia had exposure to SARS-CoV-2, which was 2.8-fold higher than confirmed case counts. Hispanic ethnicity, residence in a multifamily unit, and contact with an individual with confirmed COVID-19 infection were significant risk factors associated with exposure. Most infections were asymptomatic. As of August 2020, the population in Virginia remained largely immunologically naive to the virus.

Mitigating SARS-CoV-2 Transmission in Hispanic and Latino Communities-Prince William Health District, Virginia, June 2020.

OBJECTIVES: To identify factors contributing to disproportionate rates of COVID-19 among Hispanic or Latino persons in Prince William Health District, Virginia, and to identify measures to better engage Hispanic and Latino communities in COVID-19 mitigation. METHODS: Data collection proceeded via three methods in June 2020: a quantitative survey of Hispanic or Latino residents, key informant interviews with local leaders familiar with this population, and focus group discussions with Hispanic or Latino community members. RESULTS: Those who worked outside the home, lived in larger households, or lived with someone who had tested positive were more likely to report testing positive for SARS-CoV-2 (unadjusted odds ratios of 2.5, 1.2, and 12.9, respectively). Difficulty implementing COVID-19 prevention practices (reported by 46% of survey respondents), immigration-related fears (repeatedly identified in qualitative data), and limited awareness of local COVID-19 resources (57% of survey respondents spoke little or no English) were identified. Survey respondents also reported declines in their food security (25%) and mental health (25%). CONCLUSIONS: Specific attention to the needs of Hispanic or Latino communities could help reduce the burden of COVID-19. The investigation methods can also be used by other jurisdictions to evaluate the needs of and services provided to diverse underserved populations.

Outcomes of mechanically ventilated patients with COVID-19 associated respiratory failure.

PURPOSE: The outcomes of patients requiring invasive mechanical ventilation for COVID-19 remain poorly defined. We sought to determine clinical characteristics and outcomes of patients with COVID-19 managed with invasive mechanical ventilation in an appropriately resourced US health care system. METHODS: Outcomes of COVID-19 infected patients requiring mechanical ventilation treated within the Inova Health System between March 5, 2020 and April 26, 2020 were evaluated through an electronic medical record review. RESULTS: 1023 COVID-19 positive patients were admitted to the Inova Health System during the study period. Of these, 164 (16.0%) were managed with invasive mechanical ventilation. All patients were followed to definitive disposition. 70/164 patients (42.7%) had died and 94/164 (57.3%) were still alive. Deceased patients were older (median age of 66 vs. 55, p <0.0001) and had a higher initial d-dimer (2.22 vs. 1.31, p = 0.005) and peak ferritin levels (2998 vs. 2077, p = 0.016) compared to survivors. 84.3% of patients over 70 years old died in the hospital. Conversely, 67.4% of patients age 70 or younger survived to hospital discharge. Younger age, non-Caucasian race and treatment at a tertiary care center were all associated with survivor status. CONCLUSION: Mortality of patients with COVID-19 requiring invasive mechanical ventilation is high, with particularly daunting mortality seen in patients of advanced age, even in a well-resourced health care system. A substantial proportion of patients requiring invasive mechanical ventilation were not of advanced age, and this group had a reasonable chance for recovery.