Antimicrobial consumption and drug utilization patterns among COVID-19 and non-COVID-19 patients.

OBJECTIVES: To understand differences in antimicrobial use between COVID-19 and non-COVID-19 patients. To compare two metrics commonly used for antimicrobial use: Defined Daily Dose (DDD) and Days of Therapy (DOT). To analyse the order in which antimicrobials were prescribed to COVID-19 patients using process mining techniques. METHODS: We analysed data regarding all ICU admissions from 1 January 2018 to 14 September 2020, in 17 Brazilian hospitals. Our main outcome was the antimicrobial use estimated by the DDD and DOT (Days of Therapy). We compared clinical characteristics and antimicrobial consumption between COVID-19 and non-COVID-19 patients. We used process mining to evaluate the order in which the antimicrobial schemes were prescribed to each COVID-19 patient. RESULTS: We analysed 68 405 patients admitted before the pandemic, 12 319 non-COVID-19 patients and 3240 COVID-19 patients. Comparing those admitted during the pandemic, the COVID-19 patients required advanced respiratory support more often (42% versus 12%). They also had longer ICU length of stay (6 versus 3 days), higher ICU mortality (18% versus 5.4%) and greater use of antimicrobials (70% versus 39%). Most of the COVID-19 treatments started with penicillins with ß-lactamase inhibitors (30%), third-generation cephalosporins (22%), or macrolides in combination with penicillins (19%). CONCLUSIONS: Antimicrobial prescription increased in Brazilian ICUs during the COVID-19 pandemic, especially during the first months of the epidemic. We identified greater use of broad-spectrum antimicrobials by COVID-19 patients. Overall, the DDD metric overestimated antimicrobial use compared with the DOT metric.

Vaccine effectiveness of ChAdOx1 nCoV-19 against COVID-19 in a socially vulnerable community in Rio de Janeiro, Brazil: a test-negative design study.

OBJECTIVES: To estimate vaccine effectiveness after the first and second dose of ChAdOx1 nCoV-19 against symptomatic COVID-19 and infection in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating. METHODS: We conducted a test-negative study in the community Complexo da Maré, the largest group of slums (n = 16) in Rio de Janeiro, Brazil, from January 17, 2021 to November 27, 2021. We selected RT-qPCR positive and negative tests from a broad community testing program. The primary outcome was symptomatic COVID-19 (positive RT-qPCR test with at least one symptom) and the secondary outcome was infection (any positive RT-qPCR test). Vaccine effectiveness was estimated as 1 - OR, which was obtained from adjusted logistic regression models. RESULTS: We included 10 077 RT-qPCR tests (6,394, 64% from symptomatic and 3,683, 36% from asymptomatic individuals). The mean age was 40 (SD: 14) years, and the median time between vaccination and RT-qPCR testing among vaccinated was 41 (25-75 percentile: 21-62) days for the first dose and 36 (25-75 percentile: 17-59) days for the second dose. Adjusted vaccine effectiveness against symptomatic COVID-19 was 31.6% (95% CI, 12.0-46.8) 21 days after the first dose and 65.1% (95% CI, 40.9-79.4) 14 days after the second dose. Adjusted vaccine effectiveness against COVID-19 infection was 31.0% (95% CI, 12.7-45.5) 21 days after the first dose and 59.0% (95% CI, 33.1-74.8) 14 days after the second dose. DISCUSSION: ChAdOx1 nCoV-19 was effective in reducing symptomatic COVID-19 in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating.

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.

BACKGROUND: Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems. METHODS AND FINDINGS: We use nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. The dataset contains hospitalized patients who tested positive for COVID-19 and had either recovered or were deceased between March 1 and June 30, 2020. A total of 113,214 patients with 50,387 deceased, were included. Both interpretable (sparse versions of Logistic Regression and Support Vector Machines) and state-of-the-art non-interpretable (Gradient Boosted Decision Trees and Random Forest) classification methods are employed. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Variables highly predictive of mortality included geographic location of the hospital (OR = 2.2 for Northeast region, OR = 2.1 for North region); renal (OR = 2.0) and liver (OR = 1.7) chronic disease; immunosuppression (OR = 1.7); obesity (OR = 1.7); neurological (OR = 1.6), cardiovascular (OR = 1.5), and hematologic (OR = 1.2) disease; diabetes (OR = 1.4); chronic pneumopathy (OR = 1.4); immunosuppression (OR = 1.3); respiratory symptoms, ranging from respiratory discomfort (OR = 1.4) and dyspnea (OR = 1.3) to oxygen saturation less than 95% (OR = 1.7); hospitalization in a public hospital (OR = 1.2); and self-reported patient illiteracy (OR = 1.1). Validation accuracies (AUC) for predicting mortality and ventilation need reach 79% and 70%, respectively, when using only pre-admission variables. Models that use post-admission disease progression information reach accuracies (AUC) of 86% and 87% for predicting mortality and ventilation use, respectively. CONCLUSIONS: The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic.