Vaccine coverage and effectiveness against laboratory-confirmed symptomatic and severe Covid-19 in indigenous people in Brazil: a cohort study.

BACKGROUND: Indigenous people have historically suffered devastating impacts from epidemics and continue to have lower access to healthcare and be especially vulnerable to respiratory infections. We estimated the coverage and effectiveness of Covid-19 vaccines against laboratory-confirmed Covid-19 cases among indigenous people in Brazil. METHODS: We linked nationwide Covid-19 vaccination data with flu-like surveillance records and studied a cohort of vaccinated indigenous people aged ≥ 5 years between 18th January 2021 and 1st March 2022. We considered individuals unexposed from the date they received the first dose of vaccine until the 13th day of vaccination, partially vaccinated from the 14th day after the first dose until the 13th day after receiving the second dose, and fully vaccinated onwards. We estimated the Covid-19 vaccination coverage and used Poisson regression to calculate the relative risks (RR) and vaccine effectiveness (VE) of CoronaVac, ChAdOx1, and BNT162b2 against Covid-19 laboratory-confirmed cases incidence, mortality, hospitalisation, and hospital-progression to Intensive Care Unit (ICU) or death. VE was estimated as (1-RR)*100, comparing unexposed to partially or fully vaccinated. RESULTS: By 1st March 2022, 48.7% (35.0-62.3) of eligible indigenous people vs. 74.8% (57.9-91.8) overall Brazilians had been fully vaccinated for Covid-19. Among fully vaccinated indigenous people, we found a lower risk of symptomatic cases (RR: 0.47, 95%CI: 0.40-0.56) and mortality (RR: 0.47, 95%CI: 0.14-1.56) after the 14th day of the second dose. VE for the three Covid-19 vaccines combined was 53% (95%CI:44-60%) for symptomatic cases, 53% (95%CI:-56-86%) for mortality and 41% (95%CI:-35-75%) for hospitalisation. In our sample, we found that vaccination did not reduce Covid-19 related hospitalisation. However, among hospitalised patients, we found a lower risk of progression to ICU (RR: 0.14, 95%CI: 0.02-0.81; VE: 87%, 95%CI:27-98%) and Covid-19 death (RR: 0.04, 95%CI:0.01-0.10; VE: 96%, 95%CI: 90-99%) after the 14th day of the second dose. CONCLUSIONS: Lower coverage but similar Covid-19 VE among indigenous people than overall Brazilians suggest the need to expand access, timely vaccination, and urgently offer booster doses to achieve a great level of protection among this group.

Text mining method to unravel long COVID's clinical condition in hospitalized patients.

Long COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.