Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Add filters

Document Type
Year range
MMWR Morb Mortal Wkly Rep ; 71(10): 378-383, 2022 Mar 11.
Article in English | MEDLINE | ID: covidwho-1737448


On October 29, 2021, the Pfizer-BioNTech pediatric COVID-19 vaccine received Emergency Use Authorization for children aged 5-11 years in the United States.† For a successful immunization program, both access to and uptake of the vaccine are needed. Fifteen million doses were initially made available to pediatric providers to ensure the broadest possible access for the estimated 28 million eligible children aged 5-11 years, especially those in high social vulnerability index (SVI)§ communities. Initial supply was strategically distributed to maximize vaccination opportunities for U.S. children aged 5-11 years. COVID-19 vaccination coverage among persons aged 12-17 years has lagged (1), and vaccine confidence has been identified as a concern among parents and caregivers (2). Therefore, COVID-19 provider access and early vaccination coverage among children aged 5-11 years in high and low SVI communities were examined during November 1, 2021-January 18, 2022. As of November 29, 2021 (4 weeks after program launch), 38,732 providers were enrolled, and 92% of U.S. children aged 5-11 years lived within 5 miles of an active provider. As of January 18, 2022 (11 weeks after program launch), 39,786 providers had administered 13.3 million doses. First dose coverage at 4 weeks after launch was 15.0% (10.5% and 17.5% in high and low SVI areas, respectively; rate ratio [RR] = 0.68; 95% CI = 0.60-0.78), and at 11 weeks was 27.7% (21.2% and 29.0% in high and low SVI areas, respectively; RR = 0.76; 95% CI = 0.68-0.84). Overall series completion at 11 weeks after launch was 19.1% (13.7% and 21.7% in high and low SVI areas, respectively; RR = 0.67; 95% CI = 0.58-0.77). Pharmacies administered 46.4% of doses to this age group, including 48.7% of doses in high SVI areas and 44.4% in low SVI areas. Although COVID-19 vaccination coverage rates were low, particularly in high SVI areas, first dose coverage improved over time. Additional outreach is critical, especially in high SVI areas, to improve vaccine confidence and increase coverage rates among children aged 5-11 years.

COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Immunization Programs , Vaccination Coverage , Child , Child, Preschool , Humans , Pharmacies/statistics & numerical data , SARS-CoV-2/immunology , 34658
MMWR Morb Mortal Wkly Rep ; 70(32): 1075-1080, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1355296


Population-based analyses of COVID-19 data, by race and ethnicity can identify and monitor disparities in COVID-19 outcomes and vaccination coverage. CDC recommends that information about race and ethnicity be collected to identify disparities and ensure equitable access to protective measures such as vaccines; however, this information is often missing in COVID-19 data reported to CDC. Baseline data collection requirements of the Office of Management and Budget's Standards for the Classification of Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) include two ethnicity categories and a minimum of five race categories (1). Using available COVID-19 case and vaccination data, CDC compared the current method for grouping persons by race and ethnicity, which prioritizes ethnicity (in alignment with the policy directive), with two alternative methods (methods A and B) that used race information when ethnicity information was missing. Method A assumed non-Hispanic ethnicity when ethnicity data were unknown or missing and used the same population groupings (denominators) for rate calculations as the current method (Hispanic persons for the Hispanic group and race category and non-Hispanic persons for the different racial groups). Method B grouped persons into ethnicity and race categories that are not mutually exclusive, unlike the current method and method A. Denominators for rate calculations using method B were Hispanic persons for the Hispanic group and persons of Hispanic or non-Hispanic ethnicity for the different racial groups. Compared with the current method, the alternative methods resulted in higher counts of COVID-19 cases and fully vaccinated persons across race categories (American Indian or Alaska Native [AI/AN], Asian, Black or African American [Black], Native Hawaiian or Other Pacific Islander [NH/PI], and White persons). When method B was used, the largest relative increase in cases (58.5%) was among AI/AN persons and the largest relative increase in the number of those fully vaccinated persons was among NH/PI persons (51.6%). Compared with the current method, method A resulted in higher cumulative incidence and vaccination coverage rates for the five racial groups. Method B resulted in decreasing cumulative incidence rates for two groups (AI/AN and NH/PI persons) and decreasing cumulative vaccination coverage rates for AI/AN persons. The rate ratio for having a case of COVID-19 by racial and ethnic group compared with that for White persons varied by method but was <1 for Asian persons and >1 for other groups across all three methods. The likelihood of being fully vaccinated was highest among NH/PI persons across all three methods. This analysis demonstrates that alternative methods for analyzing race and ethnicity data when data are incomplete can lead to different conclusions about disparities. These methods have limitations, however, and warrant further examination of potential bias and consultation with experts to identify additional methods for analyzing and tracking disparities when race and ethnicity data are incomplete.

COVID-19/ethnology , Data Analysis , /statistics & numerical data , Bias , COVID-19/prevention & control , COVID-19/therapy , COVID-19 Vaccines/administration & dosage , Data Collection/standards , Health Status Disparities , Healthcare Disparities/ethnology , Humans , Treatment Outcome , United States/epidemiology , Vaccination Coverage/statistics & numerical data