ABSTRACT
Objectives: investigating the impact of the pandemic on breast cancer screening in the Unified Health System, in addition to comparing the data obtained from other countries. Method(s): a quantitative cross-sectional observational study was carried out, with references from the Cancer Information System - SISCAN on the number of mammograms performed from 2014 to 2022 by women in Brazil. Result(s): data regarding mammography in the high-risk population showed a drop of 38, 39% from 2019 to 2020. While in screening mammography, the decline was slightly more significant, at 39.18% in the same period. Regarding diagnostic mammography, the reduction was 33.15%, and in target population mammography, the peak was in 2019 with 2.721.075. On the other hand, the performance of mammography in patients already treated had a smaller decrease of 9.35%. Conclusion(s): there was a significant reduction in the number of mammograms performed in 2019 and 2020, which might lead to a late diagnosis of the disease and a worse prognosis.Copyright © 2023 Faculdade de Medicina de Ribeirao Preto - U.S.P.. All rights reserved.
ABSTRACT
A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.
ABSTRACT
Introduction: Current literature on 1-minute sit-to-stand (1-STS) role in COVID-19 focuses on its ability to predict need for hospitalization or home discharge, but not Long COVID diagnosis where gaps in knowledge are recognized in NICE Long COVID guidelines. Aim(s): Assess 1-STS role in Long COVID definition. Method(s): Prospective ongoing cohort of post COVID-19 patients referred to body plethysmography at a university tertiary hospital. Result(s): Thirty-two patients were analyzed (53.1% male, mean 54 years-old), of whom 15 (46.9%) fulfilled Long COVID criteria. Of these, the most common symptoms were insomnia and/or excessive fatigue (n=8, 53.3%) and dyspnea (n=4, 26.7%). Long COVID patients had higher body mass index (29.7+/-6.0 vs 26.2+/-3.3Kg/m2;p=0.043). Patients with and without Long COVID were similar regarding age (p=0.827), hospitalization due to COVID-19 (p=0.811) or smoking history (p=0.234). Parameters of the 1-STS most associated with Long COVID were lower heart rate (HR) at 30' (89.1+/-14.0 in Long COVID vs 105.9+/-14.3bpm for no Long COVID criteria;p=0.002) and at 60' (99.3+/-24.0 vs 120.8+/-13.9bpm;p=0.004), as well as lower SpO at 60' (94.5+/-4.4 vs 97.1+/-1.6%;p=0.046). The parameter with the highest predictive power for Long COVID was HR at 60'(AUC=0.808;p=0.003), and when <80bpm revealed 90% sensitivity and 99% specificity in this study population. Decline of SpO during 1-STS was tendentially greater in those with Long COVID, yet without significance (-2.7+/-4.4 vs -0.8+/-1.4%;p=0.093). Conclusion(s): Lower final SpO and HR on 1-STS, as well as 30' HR, were associated with the occurrence of Long COVID. Final HR<80 bpm in a post COVID-19 setting might be the best 1-STS predictor of Long COVID.
ABSTRACT
Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.
ABSTRACT
Background: With the continuous spreading of SARS-CoV-2 globally, the probability for interactions between humans who are infected and wildlife tends to grow intensely, as well as the likelihood of viral spillover from humans to biodiversity. This aspect is of great concern for wildlife conservation and human health, because the list of highly susceptible animal groups that have contracted SARS-CoV-2 (bats, mustelids, and primates) is large and, once infected, these groups can act as vectors and reservoirs, becoming a substrate for viral mutations and recombinations and boosting the risk of new strains emerging, which can return to humans as new diseases. Little is known about the inducing factors facilitating coronavirus spillover from one species to another, but it can be argued that interface zones between wild fauna and humans, which are narrow edges between anthropic (cities, roads, parks, ecotourism sites, and agricultural frontiers) and sylvatic habitat, are zones of increased interaction between humans and wild animals, and thus have a higher probability of viral spillover events than other areas. In a similar context, the habitat compression by forest fragmentation also brings species and infected beings closer, reducing their home ranges and intensifying the risk of spillover among wild populations. Therefore, on the basis of the premise for zoonosis—the greater human–animal interaction, the greater risk of viral spillover—we aimed to identify the most and least susceptible areas to viral spillover in Brazil. Methods: We developed an approach combining ecological modelling (Biomod2: modelling habitat suitability for 158 bat and 49 primate species) and geographical information systems (by using demographic indicators, roads, and related variables) to map the most and least susceptible areas to spillover in Brazil. This map indicates priority areas for serological surveillance of fauna for monitoring the spillover and circulation of SARS-CoV-2 strains and variants in Brazilian biodiversity. Findings: Among our most relevant preliminary results, we found that forested areas surrounding the São Paulo Metropolitan Area are among the most susceptible areas for spillover. This resulted from the combination of high contaminated human density and high density of non-human primates interacting with humans in these transitional areas. Interpretation: Because of the high resolution of the results, the map can be useful for action planning and decision making in conservation and health, since susceptible areas denote not only a greater risk of virus jumping from humans to animals, but also of coronaviruses returning from fauna to humans in new viral strains. Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP;2019/12988-7 and 2018/14389-0).
ABSTRACT
INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 has infected more than 9,834,513 Brazilians up to February 2021. Knowledge of risk factors of coronavirus disease among Brazilians remains scarce, especially in the adult population. This study verified the risk factors for intensive care unit admission and mortality for coronavirus disease among 20-59-year-old Brazilians. METHODS: A Brazilian database on respiratory illness was analyzed on October 9, 2020, to gather data on age, sex, ethnicity, education, housing area, and comorbidities (cardiovascular disease, diabetes, and obesity). Multivariate logistic regression analysis was performed to identify the risk factors for coronavirus disease. RESULTS: Overall, 1,048,575 persons were tested for coronavirus disease;among them, 43,662 were admitted to the intensive care unit, and 34,704 patients died. Male sex (odds ratio=1.235 and 1.193), obesity (odds ratio=1.941 and 1.889), living in rural areas (odds ratio=0.855 and 1.337), and peri-urban areas (odds ratio=1.253 and 1.577) were predictors of intensive care unit admission and mortality, respectively. Cardiovascular disease (odds ratio=1.552) was a risk factor for intensive care unit admission. Indigenous people had reduced chances (odds ratio=0.724) for intensive care unit admission, and black, mixed, East Asian, and indigenous ethnicity (odds ratio=1.756, 1.564, 1.679, and 1.613, respectively) were risk factors for mortality. CONCLUSIONS: Risk factors for intensive care unit admission and mortality among adult Brazilians were higher in men, obese individuals, and non-urban areas. Obesity was the strongest risk factor for intensive care unit admission and mortality.