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 administration 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.COVID-19 vaccine administration data are reported to CDC by multiple entities via immunization information systems (IIS), the Vaccine Administration Management System, or direct data submission. † Vaccination coverage was defined as the number of residents who

On May 18, 2021, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).Approximately 60 million persons in the United States live in rural counties, representing almost one fifth (19.3%) of the population.* In September 2020, COVID-19 incidence (cases per 100,000 population) in rural counties surpassed that in urban counties (1). Rural communities often have a higher proportion of residents who lack health insurance, live with comorbidities or disabilities, are aged ≥65 years, and have limited access to health care facilities with intensive care capabilities, which places these residents at increased risk for COVID-19-associated morbidity and mortality (2,3). To better understand COVID-19 vaccination disparities across the urban-rural continuum, CDC analyzed county-level vaccine administration data among adults aged ≥18 years who received their first dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine, or a single dose of the Janssen COVID-19 vaccine (Johnson & Johnson) during December 14, 2020-April 10, 2021 in 50 U.S. jurisdictions (49 states and the District of Columbia [DC]). Adult COVID-19 vaccination coverage was lower in rural counties (38.9%) than in urban counties (45.7%) overall and among adults aged 18-64 years (29.1% rural, 37.7% urban), those aged ≥65 years (67.6% rural, 76.1% urban), women (41.7% rural, 48.4% urban), and men (35.3% rural, 41.9% urban). Vaccination coverage varied among jurisdictions: 36 jurisdictions had higher coverage in urban counties, five had higher coverage in rural counties, and five had similar coverage (i.e., within 1%) in urban and rural counties; in four jurisdictions with no rural counties, the urban-rural comparison could not be assessed. A larger proportion of persons in the most rural counties (14.6%) traveled for vaccination to nonadjacent counties (i.e., farther from their county of residence) compared with persons in the most urban counties (10.3%). As availability of COVID-19 vaccines expands, public health practitioners should continue collaborating with health care providers, pharmacies, employers, faith leaders, and other community partners to identify and address barriers to COVID-19 vaccination in rural areas (2).Data on COVID-19 vaccine doses administered in the United States are reported to CDC by jurisdictions, pharmacies, and

Disparities in vaccination coverage by social vulnerability, defined as social and structural factors associated with adverse health outcomes, were noted during the first 2.5 months of the U.S. COVID-19 vaccination campaign, which began during mid-December 2020 (1). As vaccine eligibility and availability continue to expand, assuring equitable coverage for disproportionately affected communities remains a priority. CDC examined COVID-19 vaccine administration and 2018 CDC social vulnerability index (SVI) data to ascertain whether inequities in COVID-19 vaccination coverage with respect to county-level SVI have persisted, overall and by urbanicity. Vaccination coverage was defined as the number of persons aged ≥18 years (adults) who had received ≥1 dose of any Food and Drug Administration (FDA)-authorized COVID-19 vaccine divided by the total adult population in a specified SVI category. † SVI was examined overall and by its four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation). Counties were categorized into SVI quartiles, in which quartile 1 (Q1) represented the lowest level of vulnerability and quartile 4 (Q4), the highest. Trends in vaccination coverage were assessed by SVI quartile and urbanicity, which was categorized as large central metropolitan, large fringe metropolitan (areas surrounding large cities, e.g., suburban), medium and small metropolitan, and nonmetropolitan counties. § During December 14, 2020-May 1, 2021, disparities in vaccination coverage by SVI increased, especially in large † Vaccination coverage was calculated by summing the number of vaccinated adults in each SVI category and dividing by the total adult population in the specified SVI category. Population denominators were obtained from the U.S. Census Bureau. § Urbanicity was defined on the basis of the 2013 National Center for Health Statistics urban-rural classification scheme. For this analysis, categories included large central metropolitan counties, large fringe metropolitan counties, medium and small metropolitan counties, and nonmetropolitan counties. Large central metropolitan counties are counties in metropolitan statistical areas (MSAs) with ≥1 million population; large fringe metropolitan counties are counties in MSAs with ≥1 million population that did not qualify as large central metropolitan counties; medium metropolitan counties are counties in MSAs with populations of 250,000-999,999; small metropolitan counties are counties in MSAs with populations <250,000; nonmetropolitan counties are all micropolitan and noncore counties. https://www.cdc.gov/nchs/data_access/urban_rural.htm fringe metropolitan (e.g., suburban) and nonmetropolitan counties. By May 1, 2021, vaccination coverage was lower among adults living in counties with the highest overall SVI; differences were most pronounced in large fringe metropolitan (Q4 coverage = 45.0% versus Q1 coverage = 61.7%) and nonmetropolitan (Q4 = 40.6% versus Q1 = 52.9%) counties. ...

Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural

Coverage was evaluated by selected community-level characteristics matched to vaccine recipients' county of residence. § § § County-level rankings of social vulnerability from the 2018 CDC Social Vulnerability Index (SVI), which is used to identify community needs during emergencies, were categorized into quartiles based on distribution among all U.S. counties. ¶ ¶ ¶ County-level data on Social Determinants of Health**** obtained from the American Community Survey † † † † were dichotomized based on the median of all U.S. counties. § § § § County-level urbanicity was based on the 2013 National Center for Health Statistics urban-rural classification scheme. ¶ ¶ ¶ ¶ Generalized estimating equation models with binomial regression and an identity link were used to † † † Periods are based on eligibility and other process factors (e.g., phase of vaccine rollout, eligible population, supply, and programs and policy enacted) important in framing the specific needs and constraints at that time. Period 1 represented when most states opened eligibility to health care workers, residents in long-term care facilities, and older adults while there was a highly constrained supply, which overlapped phase 1a, and a portion of phase 1b (https://www.cdc.gov/mmwr/volumes/69/wr/ mm695152e2.htm). Period 2 represented when states were expanding eligibility inconsistently, and supply was becoming more available, which overlapped with phases 1b and 1c. Period 3 represented when all states expanded eligibility to all adults while supply was steady and increased, which overlapped with phases 1c and 2. § § § The following jurisdictions were excluded from all county-level analyses (National Center for Health Statistics urban-rural, SVI, and Social Determinants of Health) due to lack of county-level vaccination data: all counties in Hawaii and eight counties in California for which total population was <20,000. Among all first doses analyzed during December 14, 2020-May 22, 2021, 5.9% were missing county data and were therefore excluded from models. ¶ ¶ ¶ Fifteen elements categorized into four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation) are included in SVI (https:// www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/ SVI2018Documentation-H.pdf ). Overall SVI includes all 15 indicators as a composite measure (https://www.atsdr.cdc.gov/placeandhealth/svi/ fact_sheet/fact_sheet.html). One county in New Mexico was excluded because SVI ranking could not be calculated (https://www.atsdr.cdc.gov/ placeandhealth/svi/index.html). **** Measures of Social Determinants of Health from the American Community Survey: percentage of the total population 1) unemployed, 2) uninsured, 3) that earned an income below the federal poverty level, 4) without a computer (e.g., desktop or laptop computer [excludes mobile phones]), 5) with a computer but without Internet access, and 6) identifying as a racial/ethnic group other than non-Hispanic White (https://healt...