[Health insurance scheme: main contributor to inequalities in COVID-19 mortality in ColombiaTipo de cobertura de assistência à saúde: o principal fator contribuinte para as desigualdades em mortalidade por COVID-19 na Colômbia]. / Plan de seguro de salud: factor que más contribuye a las desigualdades en la mortalidad por COVID-19 en Colombia.

Objectives: To quantify socioeconomic inequalities in COVID-19 mortality in Colombia and to assess the extent to which type of health insurance, comorbidity burden, area of residence, and ethnicity account for such inequalities. Methods: We analyzed data from a retrospective cohort of COVID-19 cases. We estimated the relative and slope indices of inequality (RII and SII) using survival models for all participants and stratified them by age and gender. We calculated the percentage reduction in RII and SII after adjustment for potentially relevant factors. Results: We identified significant inequalities for the whole cohort and by subgroups (age and gender). Inequalities were higher among younger adults and gradually decreased with age, going from RII of 5.65 (95% confidence interval [CI] = 3.25, 9.82) in participants younger than 25 years to RII of 1.49 (95% CI = 1.41, 1.58) in those aged 65 years and older. Type of health insurance was the most important factor, accounting for 20% and 59% of the relative and absolute inequalities, respectively. Conclusions: Significant socioeconomic inequalities exist in COVID-19 mortality in Colombia. Health insurance appears to be the main contributor to those inequalities, posing challenges for the design of public health strategies.

Objetivos: Quantificar as desigualdades socioeconômicas na mortalidade por COVID-19 na Colômbia e avaliar até que ponto o tipo de cobertura de assistência à saúde, a carga de comorbidades, o local de residência e a etnia contribuíram para tais desigualdades. Métodos: Analisamos dados de uma coorte retrospectiva de casos de COVID-19. Calculamos os índices relativo e angular de desigualdade (RII e SII, respectivamente) utilizando modelos de sobrevivência em todos os participantes, estratificando-os por idade e gênero. Calculamos o percentual de redução no RII e no SII após ajuste para fatores possivelmente relevantes. Resultados: Identificamos desigualdades significativas na coorte como um todo e por subgrupos (idade e gênero). As desigualdades foram maiores para adultos mais jovens e decaíram gradualmente com a idade, indo de um RII de 5,65 (intervalo de confiança [IC] de 95% = 3,25; 9,82] nos participantes com idade inferior a 25 anos a um RII de 1,49 [IC 95% = 1,41; 1,58] nas pessoas com 65 anos ou mais. O tipo de cobertura de assistência à saúde foi o fator mais importante, representando 20% e 59% das desigualdades relativa e absoluta, respectivamente. Conclusões: Desigualdades socioeconômicas significativas afetaram a mortalidade por COVID-19 na Colômbia. O tipo de cobertura de saúde parece ser o principal fator contribuinte para essas desigualdades, impondo desafios à elaboração de estratégias de saúde pública.

COVID-19 Vaccine Acceptors, Refusers, and the Moveable Middle: A Qualitative Study from Central Texas.

COVID-19 has caused excessive morbidity and mortality worldwide. COVID-19 vaccines, including the two mRNA vaccines, were developed to help mitigate COVID-19 and to move society towards herd immunity. Despite the strong efficacy and effectiveness profile of these vaccines, there remains a degree of vaccine hesitancy among the population. To better understand hesitancy associated with COVID-19 vaccines and to delineate between those who are vaccine acceptors, vaccine refusers, and the moveable middle, we conducted a cross-sectional survey to understand respondents' decision to receive, or not, a COVID-19 vaccine at the onset of mRNA vaccine availability in Central Texas. A total of 737 individuals responded, with 685 responses classified to one of eight domains: A: End to the Pandemic (n = 48); B: Trust in Medical Community (n = 27); C: Illness-Focused Perceptions (n = 331); D: Social Motivation (n = 54); E: Vaccine-Focused Perceptions (n = 183); F: Knowledge Gap (n = 14); G: Underlying Health Concern (n = 9); and H: Undecided (n = 19). Vaccine acceptors (n = 535) were primarily represented in domains A-E, while vaccine refusers (n = 26) were primarily represented in domains C, E, G, and H. The moveable middle (n = 124) was primarily represented by domains C-H. These findings show clear delineations between vaccine acceptors, vaccine refusers, and the moveable middle across eight domains that can assist public health professionals in addressing vaccine hesitancy.

Using Health Insurance Network Provider Data and Public Data Sets to Identify SARS-CoV-2 Vaccinators in the USA.

Objective: Mass vaccination planning is occurring at all levels of government in advance of regulatory approval and manufacture of a SARS-CoV-2 vaccine for distribution sometime in 2021. We outline a methodology in which both health insurance provider network data and publicly available data sources can be used to identify and plan for SARS-CoV-2 vaccinator capacity at the county level. Methods: Sendero Health Plans, Inc. provider network data, Texas State Board of Pharmacy data, US Census Bureau data, and H1N1 monovalent vaccine data were utilized to identify providers with demonstrated capacity to vaccinate the population in Travis County, Texas to achieve an estimated SARS-CoV-2 herd immunity target of 67%. Results: Within the Sendero network, 2,356 non-pharmacy providers were identified with 788 (33.4%) practicing in primary care and 1,569 (66.6%) practicing as specialists. Of the total, 686 (29.1%) provided at least one immunization between January 1, 2019 and September 30, 2020. There are 300 pharmacies with active licenses in Travis County with 161 (53.7%) classified as community pharmacies. We estimate that 1,707,098 doses of a 2-dose SARS-CoV-2 vaccine series will need to be administered within Travis County, Texas to achieve the estimated 67% herd immunity threshold to disrupt person-to-person transmission of the SARS-CoV-2 virus based on 2020 census data. Conclusion: A community-based health insurance plan can use data from its provider network and public data sources to support the CDC call to action to identify SARS-CoV-2 vaccinators in the community, including physicians, nurse practitioners, physician assistants, and pharmacies in order to provide macro level estimates of SARS-CoV-2 administration and throughput.

Mobile Fotonovelas Within a Text Message Outreach: An Innovative Tool to Build Health Literacy and Influence Behaviors in Response to the COVID-19 Pandemic.

With all 50 US states reporting cases of coronavirus disease (COVID-19), people around the country are adapting and stepping up to the challenges of the pandemic; however, they are also frightened, anxious, and confused about what they can do to avoid exposure to the disease. Usual habits have been interrupted as a result of the crisis, and consumers are open to suggestions and strategies to help them change long-standing attitudes and behaviors. In response, a novel and innovative mobile communication capability was developed to present health messages in English and Spanish with links to fotonovelas (visual stories) that are accessible, easy to understand across literacy levels, and compelling to a diverse audience. While SMS text message outreach has been used to build health literacy and provide social support, few studies have explored the benefits of SMS text messaging combined with visual stories to influence health behaviors and build knowledge and self-efficacy. In particular, this approach can be used to provide vital information, resources, empathy, and support to the most vulnerable populations. This also allows providers and health plans to quickly reach out to their patients and members without any additional resource demands at a time when the health care system is severely overburdened.