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. ...

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...

In January 2001 the Pew Environmental Health Commission called for the creation of a coordinated public health system to prevent disease in the United States by tracking and combating environmental health threats. In response, the Centers for Disease Control and Prevention initiated the Environmental Public Health Tracking (EPHT) Program to integrate three distinct components of hazard monitoring and exposure and health effects surveillance into a cohesive tracking network. Uniform and acceptable data standards, easily understood case definitions, and improved communication between health and environmental agencies are just a few of the challenges that must be addressed for this network to be effective. The nascent EPHT program is attempting to respond to these challenges by drawing on a wide range of expertise from federal agencies, state health and environmental agencies, nongovernmental organizations, and the program’s academic Centers of Excellence. In this mini-monograph, we present innovative strategies and methods that are being applied to the broad scope of important and complex environmental public health problems by developing EPHT programs. The data resulting from this program can be used to identify areas and populations most likely to be affected by environmental contamination and to provide important information on the health and environmental status of communities. EPHT will develop valuable data on possible associations between the environment and the risk of noninfectious health effects. These data can be used to reduce the burden of adverse health effects on the American public.

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 m (PM 2.5 ) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM 2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM 2.5 from the EPA database for the year 2003 as well as PM 2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM 2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM 2.5 not only provides a more complete daily representation of PM 2.5 than either dataset alone would allow, but it also reduces the errors in the PM 2.5 -estimated surfaces. The results of this study also show that IMPLICATIONSThe described method of estimating concentrations of PM 2.5 by merging NASA MODIS remote sensing data with surface observations provides a more complete daily representation of PM 2.5 than either dataset alone, and it reduces the errors in the PM 2.5 -estimated surfaces with respect to observations. These new data products have the potential to serve as a tool for environmental public health surveillance to monitor trends and as an early warning system for prevention of human exposure to potential hazards. Such continuous spatial surfaces of environmental hazards as those developed in this study would enable linking environmental hazards to health outcomes on the grid-aggregated level as well as the individual level at the geographic locations of patients' residences.

A retrospective cohort mortality study was conducted in a population of workers employed at a facility with the primary task of production of nuclear fuels and other materials. Data for hourly and salaried employees were analyzed separately by time period of first employment and length of employment. The hourly (N = 6,687 with 728 deaths) and salaried (N = 2,745 with 294 deaths) employees had a mortality experience comparable to that of the United States and, in fact, exhibited significant fewer deaths in many categories of diseases that are traditionally associated with the healthy worker effect. Specifically, fewer deaths were noted in the categories of all causes, all cancers, cancer of the digestive organs, lung cancer, brain cancer (hourly workers only), diabetes, all diseases of the circulatory system, all respiratory diseases, all digestive system diseases, all diseases of the genitourinary system (hourly only), and all external causes of death. A statistically significant, and as yet unexplained increase in leukemia mortality (6 observed vs. 2.18 expected) appeared among a subset of the hourly employees, first hired before 1955, and employed between 5-15 years.

Public health surveillance is the foundation of effective public health practice. Public health surveillance is defined as the ongoing systematic collection, analysis, and interpretation of data, closely integrated with the dissemination of these data to the public health practitioners, clinicians, and policy makers responsible for preventing and controlling disease and injury.1 Ideally, surveillance systems should support timely, efficient, flexible, scalable, and interoperable data acquisition, analysis, and dissemination. However, many current systems rely on disease-specific approaches that inhibit efficiency and interoperability (eg, manual data entry and data recoding that place a substantial burden on data partners) and use slow, inefficient, out-of-date technologies that no longer meet user needs for data management, analysis, visualization, and dissemination.2-4 Advances in information technology, data science, analytic methods, and information sharing provide an opportunity to substantially enhance surveillance. As a global leader in public health surveillance, the Centers for Disease Control and Prevention (CDC) is working with public health partners to transform and modernize CDC's surveillance systems and approaches. Here, we describe recent enhancements in surveillance data analysis and visualization, information sharing, and dissemination at CDC and identify the challenges ahead.

No abstract