Impacts of the first wave of the COVID-19 pandemic on leisure and transportation physical activity among healthcare workers.

Introduction: The COVID-19 pandemic may lead to reduced physical activity (PA) in health care workers (HCWs). Objective: To evaluate leisure and transport-related PA in HCW of a COVID-19-dedicated hospital during the first wave of the COVID-19 pandemic. Methods: This is a cross-sectional study with a sample of 1,527 HCWs. Socioeconomic aspects, occupational characteristics, and engagement in leisure and transport-related PA were investigated through an online survey administered in August of 2020. Results: More than 80 % HCWs performed < 150 min/week of leisure-related PA, and 85 % performed ≤ 30 min/day transport-related PA. Being male was associated with more PA (OR: 1.93; 95 % CI:1.40-2.66) and transport-related PA; working in nursing, physical therapy, and cleaning/housekeeping services was associated with low PA (OR: 0.70; 95 % CI:0.51-0.95). Physicians and administrative staff were less active in transport-related PA. Conclusions: HCWs working in a COVID-19 hospital had low levels of PA in the domains of leisure and transportation.

Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning.

Background: The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person's blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes. Methods: We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil. Results: We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses. Conclusions: We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios-both during the pandemic and after.

No association between vitamin D status and COVID-19 infection in São Paulo, Brazil.

In recent years the immunomodulatory actions of vitamin D, a steroid hormone, have been extensively studied. In 2020, due to the COVID-19 pandemic, the question arose as to 25(OH)D status would be related to susceptibility to SARS-CoV-2 infection, since several studies pointed out a higher prevalence and severity of the disease in populations with low levels of 25(OH)D. Thus, we investigated the 25(OH)D levels in adults "Detected" positive for SARS CoV-2 by RT-PCR (reverse transcriptase polymerase chain reaction) test, and in negative controls, "not Detected", using the Fleury Group's examination database, in Sao Paulo, Brazil. Of a total of 14.692 people with recent assessments of 25(OH)D and RT-PCR tests for COVID-19, 2.345 were positive and 11.585 were negative for the infection. The groups did not differ in the percentage of men and women, or in the age distribution. There were no differences in the distribution of 25(OH)D between the two groups (p = 0.08); mean 25(OH)D of 28.8 ± 21.4 ng/mL and 29.6 ± 18.1 ng/mL, respectively. In the specific population studied, clinical, environmental, socioeconomic and cultural factors should have greater relevance than 25(OH)D in determining the susceptibility to COVID-19.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seroprevalence and Risk Factors Among Oligo/Asymptomatic Healthcare Workers: Estimating the Impact of Community Transmission.

We evaluated the seroprevalence of SARS-CoV-2 and risk factors among 4987 oligo/asymptomatic healthcare workers; seroprevalence was 14% and factors associated with SARS-CoV-2 infection were lower educational level (aOR, 1.93; 95% CI, 1.03-3.60), using public transport to work (aOR, 1.65; 95% CI, 1.07-2.62), and working in cleaning or security (aOR, 2.05; 95% CI, 1.04-4.03).