There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Background
Significant disparities in COVID-19 morbidity and mortality exist for Native American
(NA) people, the majority of whom live in urban areas. COVID-19 vaccination is a key
strategy for mitigating these disparities; however, vaccination disparities affect
NA communities. The current study investigated COVID-19 vaccine decision-making before
widespread vaccine rollout occurred, among urban NA communities. We aimed to understand
vaccine decision-making factors to develop recommendations about COVID-19 vaccine
outreach.
Methods
We conducted three in-depth virtual focus groups with 17 NA adults living in an urban
community (Los Angeles County) between December 2020 and January 2021. Participants
were recruited through NA community-based organizations and community stakeholders.
Reflexive thematic analysis was conducted using Atlas.ti.
Findings
Participants in this study identified two overarching themes with implications for
health vaccination campaigns. First, participants described a need for tailored information
and outreach, including NA vaccine outreach that addresses misconceptions about vaccine
development to calm fears of experimentation and support communication of vaccine
evidence specific to NA people. Second, participants suggested strategies to improve
public health resources in the urban NA community, such as the need for unified, proactive
communication across trusted NA entities, navigation support to improve vaccine accessibility,
and adequately resourcing health partnerships with and among trusted NA community
agencies for improved reach.
Conclusion
In this qualitative study, we found that urban NA participants reported several factors
that affected their vaccine decision-making, including a lack of tailored information
for their communities. Our findings also underscore the need to work with tribes,
tribal leadership, and urban NA serving organizations to coordinate vaccine communication
and distribution to urban communities where the majority of NAs now reside. Further,
these findings have implications for COVID-19 vaccine outreach among urban NA communities
and demonstrate the need for clear and tailored engagement about the COVID-19 vaccine.
Standards for reporting exist for many types of quantitative research, but currently none exist for the broad spectrum of qualitative research. The purpose of the present study was to formulate and define standards for reporting qualitative research while preserving the requisite flexibility to accommodate various paradigms, approaches, and methods.
Background Following the emergency use authorisation of the Pfizer–BioNTech mRNA COVID-19 vaccine BNT162b2 (international non-proprietary name tozinameran) in Israel, the Ministry of Health (MoH) launched a campaign to immunise the 6·5 million residents of Israel aged 16 years and older. We estimated the real-world effectiveness of two doses of BNT162b2 against a range of SARS-CoV-2 outcomes and to evaluate the nationwide public-health impact following the widespread introduction of the vaccine. Methods We used national surveillance data from the first 4 months of the nationwide vaccination campaign to ascertain incident cases of laboratory-confirmed SARS-CoV-2 infections and outcomes, as well as vaccine uptake in residents of Israel aged 16 years and older. Vaccine effectiveness against SARS-CoV-2 outcomes (asymptomatic infection, symptomatic infection, and COVID-19-related hospitalisation, severe or critical hospitalisation, and death) was calculated on the basis of incidence rates in fully vaccinated individuals (defined as those for whom 7 days had passed since receiving the second dose of vaccine) compared with rates in unvaccinated individuals (who had not received any doses of the vaccine), with use of a negative binomial regression model adjusted for age group (16–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥85 years), sex, and calendar week. The proportion of spike gene target failures on PCR test among a nationwide convenience-sample of SARS-CoV-2-positive specimens was used to estimate the prevelance of the B.1.1.7 variant. Findings During the analysis period (Jan 24 to April 3, 2021), there were 232 268 SARS-CoV-2 infections, 7694 COVID-19 hospitalisations, 4481 severe or critical COVID-19 hospitalisations, and 1113 COVID-19 deaths in people aged 16 years or older. By April 3, 2021, 4 714 932 (72·1%) of 6 538 911 people aged 16 years and older were fully vaccinated with two doses of BNT162b2. Adjusted estimates of vaccine effectiveness at 7 days or longer after the second dose were 95·3% (95% CI 94·9–95·7; incidence rate 91·5 per 100 000 person-days in unvaccinated vs 3·1 per 100 000 person-days in fully vaccinated individuals) against SARS-CoV-2 infection, 91·5% (90·7–92·2; 40·9 vs 1·8 per 100 000 person-days) against asymptomatic SARS-CoV-2 infection, 97·0% (96·7–97·2; 32·5 vs 0·8 per 100 000 person-days) against symptomatic COVID-19, 97·2% (96·8–97·5; 4·6 vs 0·3 per 100 000 person-days) against COVID-19-related hospitalisation, 97·5% (97·1–97·8; 2·7 vs 0·2 per 100 000 person-days) against severe or critical COVID-19-related hospitalisation, and 96·7% (96·0–97·3; 0·6 vs 0·1 per 100 000 person-days) against COVID-19-related death. In all age groups, as vaccine coverage increased, the incidence of SARS-CoV-2 outcomes declined. 8006 of 8472 samples tested showed a spike gene target failure, giving an estimated prevalence of the B.1.1.7 variant of 94·5% among SARS-CoV-2 infections. Interpretation Two doses of BNT162b2 are highly effective across all age groups (≥16 years, including older adults aged ≥85 years) in preventing symptomatic and asymptomatic SARS-CoV-2 infections and COVID-19-related hospitalisations, severe disease, and death, including those caused by the B.1.1.7 SARS-CoV-2 variant. There were marked and sustained declines in SARS-CoV-2 incidence corresponding to increasing vaccine coverage. These findings suggest that COVID-19 vaccination can help to control the pandemic. Funding None.
Although non-Hispanic American Indian and Alaska Native (AI/AN) persons account for 0.7% of the U.S. population,* a recent analysis reported that 1.3% of coronavirus disease 2019 (COVID-19) cases reported to CDC with known race and ethnicity were among AI/AN persons ( 1 ). To assess the impact of COVID-19 among the AI/AN population, reports of laboratory-confirmed COVID-19 cases during January 22 † –July 3, 2020 were analyzed. The analysis was limited to 23 states § with >70% complete race/ethnicity information and five or more laboratory-confirmed COVID-19 cases among both AI/AN persons (alone or in combination with other races and ethnicities) and non-Hispanic white (white) persons. Among 424,899 COVID-19 cases reported by these states, 340,059 (80%) had complete race/ethnicity information; among these 340,059 cases, 9,072 (2.7%) occurred among AI/AN persons, and 138,960 (40.9%) among white persons. Among 340,059 cases with complete patient race/ethnicity data, the cumulative incidence among AI/AN persons in these 23 states was 594 per 100,000 AI/AN population (95% confidence interval [CI] = 203–1,740), compared with 169 per 100,000 white population (95% CI = 137–209) (rate ratio [RR] = 3.5; 95% CI = 1.2–10.1). AI/AN persons with COVID-19 were younger (median age = 40 years; interquartile range [IQR] = 26–56 years) than were white persons (median age = 51 years; IQR = 32–67 years). More complete case report data and timely, culturally responsive, and evidence-based public health efforts that leverage the strengths of AI/AN communities are needed to decrease COVID-19 transmission and improve patient outcomes. Individual COVID-19 case reports submitted to CDC using the CDC COVID-19 case report form ¶ and through the National Notifiable Diseases Surveillance System** during January 22–July 3, 2020 were analyzed. Laboratory-confirmed †† and probable §§ COVID-19 cases are reported by state and local health jurisdictions based on reports submitted by health care providers and laboratories. Cases with missing report date were excluded. Probable cases (12,081) and cases among persons repatriated to the United States from Wuhan, China (two cases), and the Diamond Princess cruise ship (41 cases) ( 2 ) were also excluded. Analysis was limited to the 23 states with >70% complete race/ethnicity information and five or more laboratory-confirmed cases each among AI/AN and white persons. Arizona, which accounts for at least one third of all COVID-19 cases among AI/AN persons nationwide, was excluded from analysis because >30% of race/ethnicity data were missing. Because approximately 2.3 million of 5.2 million AI/AN persons identify with multiple races ( 3 ), AI/AN race/ethnicity was classified as either AI/AN alone or in combination with other races and ethnicities. White (non-Hispanic) was chosen as the comparator group to avoid comparing rates among AI/AN persons to other marginalized populations that experience similar health disparities. Whereas previous reports focused on COVID-19 incidence among black and Hispanic persons, the race/ethnicity categorization in this analysis maximized these data to allow for the calculation of more stable RR estimates. A generalized estimating equations Poisson regression model was used to calculate cumulative incidence (cumulative cases per 100,000 population), RRs, and 95% CIs for AI/AN and white race/ethnicity categories. Generalized estimating equations models, which perform well for estimating rates with correlated data, were used to account for nonindependence (i.e., clustering) by state ( 4 ). CDC’s National Center for Health Statistics (NCHS) postcensal bridged-race estimates were used as population denominators ( 5 ). Symptoms, underlying health conditions, hospitalizations, intensive care unit (ICU) admissions, and deaths were not analyzed because a large percentage of these data were missing. Analyses were conducted using SAS software (version 9.4; SAS Institute). Among the 1,613,949 laboratory-confirmed COVID-19 cases voluntarily reported to CDC during January 22–July 3, 2020, 424,899 (26.3%) were reported by the 23 included states. Among these cases, 340,059 (80.0%) had complete race/ethnicity data, including 9,072 (2.7%) among AI/AN persons and 138,960 (40.9%) among white persons. These cases represented 51% of 17,709 reported cases among AI/AN persons and 41% of 339,789 reported cases among whites in all U.S. states and territories. Among the 340,059 cases with complete race/ethnicity data, the cumulative incidence among AI/AN persons was 594 cases per 100,000 (95% CI = 203–1,740), 3.5 (95% CI = 1.2–10.1) times that among white persons (169 per 100,000; 95% CI = 137–209). The magnitude of this reported RR estimate is affected by the elevated RR in New Mexico (RR = 14.9). ¶¶ Median age among AI/AN and white patients was 40 years (IQR = 26–56 years) and 51 years (IQR = 32–67 years), respectively. AI/AN persons with COVID-19 tended to be younger than white persons with COVID-19: a higher proportion of AI/AN patients were aged 100.4°F [38°C] or subjective), cough, shortness of breath, wheezing, difficulty breathing, chills, rigors, myalgia, rhinorrhea, sore throat, chest pain, nausea or vomiting, abdominal pain, headache, fatigue, diarrhea (≥3 loose stools in a 24-hour period), or other symptom not otherwise specified on the form. ¶ Underlying health conditions were classified as “known” if any of the following conditions were reported as present or absent: diabetes mellitus, cardiovascular disease (including hypertension), severe obesity (body mass index ≥40 kg/m2), chronic renal disease, chronic liver disease, chronic lung disease, immunocompromising condition, autoimmune condition, neurologic condition (including neurodevelopmental, intellectual, physical, visual, or hearing impairment), psychologic/psychiatric condition, and other underlying medical condition not otherwise specified. ** Includes hospitalization with or without ICU admission. †† Hospitalization, ICU admission, and death status were considered known if the response was “yes” or “no” (not “missing” or “unknown”). Completeness of data on underlying health conditions (e.g., cardiovascular disease and diabetes), symptoms, hospitalization status, ICU admission, and death was lower for AI/AN patients than for white patients. Data on underlying health conditions were available for 762 (8.4%) AI/AN patients and 37,993 (27.3%) white patients, and symptom data were available for 998 (11.0%) AI/AN patients and 39,225 (28.2%) white patients. Whereas hospitalization status, ICU admission status, and vital status (i.e., outcome of death) were known for 78.9%, 26.7%, and 74.4%, respectively, of white COVID-19 patients, this information was available for approximately one third of those percentages of AI/AN patients (24.2%, 9.4%, and 22.5%, respectively). Because of the high prevalence of these missing data elements among AI/AN patients, analysis to identify overall prevalence, possible risk factors for COVID-19, and patient outcomes was not possible. Discussion In 23 states with sufficient COVID-19 patient race/ethnicity data, the overall COVID-19 incidence among AI/AN persons was 3.5 times that among white persons. Although this disparity is mostly influenced by the elevated RR in New Mexico, variability in the RR among states is reflected in the wide confidence interval (95% CI = 1.2, 10.1). Among 345,093 COVID-19 cases meeting the study inclusion criteria, 2.7% of cases occurred in AI/AN persons, more than twice the percentage of non-Hispanic AI/AN cases reported in CDC COVID-19 case surveillance data from all states (1.3%) ( 1 ). However, this analysis included AI/AN persons who identified as multiple races and ethnicities, which increased AI/AN case identification by 4%, from 8,691 to 9,072 cases in the 23 states. The higher proportion of AI/AN persons in this analysis is also the result of the more completely reported race/ethnicity data in these states. Historical trauma and persisting racial inequity have contributed to disparities in health and socioeconomic factors between AI/AN and white populations that have adversely affected AI/AN communities; these factors likely contribute to the observed elevated incidence of COVID-19 among the AI/AN population ( 6 ). The elevated incidence within this group might also reflect differences in reliance on shared transportation, limited access to running water, household size, and other factors that might facilitate COVID-19 community transmission ( 6 ). Although the elevated prevalence of underlying health conditions among AI/AN persons is well documented ( 7 , 8 ), in this analysis, data on underlying health conditions were unknown or missing for 91.6% of AI/AN patients compared with 72.7% of white patients, preventing examination of the association between underlying health conditions and COVID-19 incidence. The excessive absence of data among AI/AN persons represents an important gap in public health data for AI/AN persons and suggests a need for additional resources to support case investigation and reporting infrastructure in AI/AN communities. The findings in this report are subject to at least three limitations. First, data are presented as reported to CDC through a passive case surveillance system. Case data are voluntarily reported to CDC by states without active case finding. The high prevalence of missing data on symptoms, underlying health conditions, hospitalization, ICU admission, and death precluded the analysis of these characteristics and outcomes. Missing data likely reflect state, local, and tribal health jurisdictions’ ability to collect these data given their current case loads, incomplete reporting to CDC, or both. Second, this analysis represents an underestimate of the actual COVID-19 incidence among AI/AN persons for several reasons. Reporting of detailed case data to CDC by states is known to be incomplete; therefore, this analysis was restricted to 23 states with more complete reporting of race and ethnicity. As a result, the analysis included only one half of reported laboratory-confirmed COVID-19 cases among AI/AN persons nationwide, and the examined states represent approximately one third of the national AI/AN population.*** In addition, AI/AN persons are commonly misclassified as non-AI/AN races and ethnicities in epidemiologic and administrative data sets, leading to an underestimation of AI/AN morbidity and mortality ( 9 ). Finally, the NCHS bridged-race estimates used as population denominators are known to inflate the Hispanic AI/AN population in the United States, resulting in the underestimation of mortality rates among AI/AN populations that include Hispanic AI/AN persons ( 10 ). Despite these limitations, these findings suggest that the AI/AN population in the 23 examined states, particularly AI/AN persons aged <65 years, has been disproportionately affected by the COVID-19 pandemic, compared with the white population. More complete case information is needed to more effectively guide the public health response to COVID-19 among the AI/AN population. The collection of this information can be facilitated by more consistent, complete, and accurate collection and reporting by providers, reporting laboratories, and local, state, federal, and tribal public health practitioners, and ensuring the resources to do so. Race/ethnicity data should be collected following best practices for AI/AN data collection, including allowing for the reporting of multiple races and ethnicities and providing adequate training about asking about race and ethnicity in a culturally sensitive manner. §§§ Further, among federally recognized tribes, AI/AN race is a political status that confers access to health care services under treaty obligations of the U.S. government ¶¶¶ ; these findings highlight the important contribution of adequate health care and public health infrastructure resources to culturally responsive public health efforts intended to sustain the strengths of AI/AN communities. Summary What is already known about this topic? American Indian and Alaska Native (AI/AN) persons appear to be disproportionately affected by the COVID-19 pandemic; however, limited data are available to quantify the disparity in COVID-19 incidence, severity, and outcomes among AI/AN persons compared with those among other racial/ethnic groups. What is added by this report? In 23 states with adequate race/ethnicity data, the cumulative incidence of laboratory-confirmed COVID-19 among AI/AN persons was 3.5 times that among non-Hispanic white persons. A large percentage of missing data precluded analysis of some characteristics and outcomes. What are the implications for public health practice? Adequate health care and public health infrastructure resources are needed to support a culturally responsive public health effort that sustains the strengths of AI/AN communities. These resources would facilitate the collection and reporting of more complete case report data to support evidence-based public health efforts.
[a
]Department of Psychological Sciences, School of Social Sciences, Humanities and Arts,
University of California, Merced, Merced, CA, 95343
[b
]Division of General Internal Medicine and Health Services Research, Department of
Medicine, David Geffen School of Medicine, University of California, Los Angeles,
Los Angeles, CA 90095
[c
]Los Angeles County Department of Mental Health, Los Angeles, CA 90020
[d
]Olive View-UCLA Medical Center, Sylmar, CA 91342
[e
]Institute for Society & Genetics, College of Letters and Science, University of California,
Los Angeles, Los Angeles, CA 90095
[f
]Institute for Precision Health, Department of Medicine, David Geffen School of Medicine,
University of California, Los Angeles, Los Angeles, CA 90095
Author notes
[*
]Corresponding author at: Department of Psychological Sciences, School of Social Sciences,
Humanities, & Arts, University of California, Merced, 5200 N. Lake Road, Merced, CA,
95343, USA.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information
in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre
is hosted on Elsevier Connect, the company's public news and information website.
Elsevier hereby grants permission to make all its COVID-19-related research that is
available on the COVID-19 resource centre - including this research content - immediately
available in PubMed Central and other publicly funded repositories, such as the WHO
COVID database with rights for unrestricted research re-use and analyses in any form
or by any means with acknowledgement of the original source. These permissions are
granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Smart Citations