BackgroundDespite most cases not requiring hospital care, there are limited community-based clinical data on COVID-19.MethodsThe Corona São Caetano programme is a primary care initiative providing care to all residents with COVID-19 in São Caetano do Sul, Brazil. It was designed to capture standardised clinical data on community COVID-19 cases. After triage of potentially severe cases, consecutive patients presenting to a multimedia screening platform between 13 April and 13 May 2020 were tested at home with SARS-CoV-2 reverse transcriptase (RT) PCR; positive patients were followed up for 14 days with phone calls every 2 days. RT-PCR-negative patients were offered additional SARS-CoV-2 serology testing to establish their infection status. We describe the clinical, virological and natural history features of this prospective population-based cohort.FindingsOf 2073 suspected COVID-19 cases, 1583 (76.4%) were tested by RT-PCR, of whom 444 (28.0%, 95% CI 25.9 to 30.3) were positive; 604/1136 (53%) RT-PCR-negative patients underwent serology, of whom 52 (8.6%) tested SARS-CoV-2 seropositive. The most common symptoms of confirmed COVID-19 were cough, fatigue, myalgia and headache; whereas self-reported fever (OR 3.0, 95% CI 2.4 to 3.9), anosmia (OR 3.3, 95% CI 2.6 to 4.4) and ageusia (OR 2.9, 95% CI 2.3 to 3.8) were most strongly associated with a positive COVID-19 diagnosis by RT-PCR or serology. RT-PCR cycle thresholds were lower in men, older patients, those with fever and arthralgia and closer to symptom onset. The rates of hospitalisation and death among 444 RT-PCR-positive cases were 6.7% and 0.7%, respectively, with older age and obesity more frequent in the hospitalised group.ConclusionCOVID-19 presents in a similar way to other mild community-acquired respiratory diseases, but the presence of fever, anosmia and ageusia can assist the specific diagnosis. Most patients recovered without requiring hospitalisation with a low fatality rate compared with other hospital-based studies.
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases in the world. Self-perceived health is considered a better predictor of mortality than objective measures of health status, and the context in which one lives influences this predictor. This study aimed to evaluate the prevalence and individual and contextual factors associated with poor self-rated health among CD patients from an endemic region in Brazil. It is a multilevel cross-sectional study. The individual data come from a cross-section of a cohort study named SaMi-Trop. Contextual data was collected from publicly accessible institutional information systems and platforms. The dependent variable was self-perceived health. The analysis was performed using multilevel binary logistic regression. The study included 1,513 patients with CD, where 335 (22.1%) had Poor self-rated health. This study revealed the influence of the organization/offer of the Brazilian public health service and of individual characteristics on the self-perceived health of patients with CD.
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML to predict death in two years of patients with CD. ML models were developed using different techniques and configurations. The techniques used were: Random Forests, Adaptive Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Networks. The adopted settings considered only interview variables, only complementary exam variables, and finally, both mixed. Data from a cohort study with CD patients called SaMi-Trop were analyzed. The predictor variables came from the baseline; and the outcome, which was death, came from the first follow-up. All models were evaluated in terms of Sensitivity, Specificity and G-mean. Among the 1694 individuals with CD considered, 134 (7.9%) died within two years of follow-up. Using only the predictor variables from the interview, the different techniques achieved a maximum G-mean of 0.64 in predicting death. Using only the variables from complementary exams, the G-mean was up to 0.77. In this configuration, the protagonism of NT-proBNP was evident, where it was possible to observe that an ML model using only this single variable reached G-mean of 0.76. The configuration that mixed interview variables and complementary exams achieved G-mean of 0.75. ML can be used as a useful tool with the potential to contribute to the management of patients with CD, by identifying patients with the highest probability of death. Trial Registration: This trial is registered with ClinicalTrials.gov, Trial ID: NCT02646943.
Background Chronic Chagas Cardiomyopathy (CCC) usually develops between 10 and 20 years after the first parasitic infection and is one of the leading causes of end-stage heart failure in Latin America. Despite the great inter-individual variability in CCC susceptibility (only 30% of infected individuals ever present CCC), there are no known predictors for disease development in those chronically infected. Methodology/Principal findings We describe a new susceptibility locus for CCC through a GWAS analysis in the SaMi-Trop cohort, a population-based study conducted in a Chagas endemic region from Brazil. This locus was also associated with CCC in the REDS II Study. The newly identified locus (rs34238187, OR 0.73, p-value 2.03 x 10−9) spans a haplotype of approximately 30Kb on chromosome 18 (chr18: 5028302–5057621) and is also associated with 80 different traits, most of them blood protein traits significantly enriched for immune-related biological pathways. Hi-C data show that the newly associated locus is able to interact with chromatin sites as far as 10Mb on chromosome 18 in a number of different cell types and tissues. Finally, we were able to confirm, at the tissue transcriptional level, the immune-associated blood protein signature using a multi-tissue differential gene expression and enrichment analysis. Conclusions/Significance We suggest that the newly identified locus impacts CCC risk among T cruzi infected individuals through the modulation of a downstream transcriptional and protein signature associated with host-parasite immune response. Functional characterization of the novel risk locus is warranted.
This study aimed to assess the prevalence of non-use of health services in the last year by people with Chagas disease (CD) in an endemic area in Brazil and the contextual and individual factors associated with this non-use. This is a multilevel study that considered contextual and individual data. Contextual data were collected from official publicly accessible databases of the Brazilian government, at the municipal level. The individual data came from the first follow-up of a Brazilian cohort that assessed patients with CD in 21 municipalities in endemic area for the disease. The sample consisted of 1,160 individuals with CD. The dependent variable “use of health services in the last year” was categorized as yes vs. no. The analysis was performed using Poisson regression with robust variance. The prevalence of non-use of health services in the last year was 23.5% (IC95%: 21.1–25.9). The contextual factor “larger population” (PR: 1.6; 95% CI = 1.2–2.0) and individual factors related to the lower severity of the disease as a functional class without limitations (PR: 1.6; 95% CI = 1.2–2.1) and unaltered N-terminal pro b-type natriuretic peptide levels (PR: 2.2; 95% CI = 1.3–3.6) increased the prevalence of non-use of the health service in the last year by people with CD. The results of this study showed that individual determinants are not isolated protagonists of the non-use of health services in the last year by people with CD, which reinforces the need for public policies that consider the contextual determinants of the use of health services by populations affected by the disease.
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