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.

Measuring the impact of donations at the Bottom of the Pyramid (BoP) amid the COVID-19 pandemic.

The governments' isolation measures to contain the transmission of COVID-19 imposed a dilemma for the people at the bottom of the pyramid. Since these people have very unreliable sources of income, a dilemma arises: they must either work under risky conditions or refrain from work and suffer from income cuts. Emergency donations of food and cleaning supplies in a pandemic context might be overlooked by government and civil society actors. This paper aims to model the effects of donations on mitigating the negative effects of COVID-19 on vulnerable communities. Applying the system dynamics method, we simulated the behaviour of the pandemic in Rio de Janeiro (Brazil) communities and the impacts that donations of food and cleaning supplies have in these settings. We administered surveys to the beneficiaries and local organisations responsible for the final distribution of donations to gather information from the field operations. The results show that increasing access to cleaning supplies in communities through donations can significantly reduce coronavirus transmission, particularly in high-density and low-resource areas, such as slums in urban settings. In addition, we also show that food donations can increase the vulnerable population's ability to afford necessities, alleviating the stress caused by the pandemic on this portion of the population. Therefore, this work helps decision-makers (such as government and non-governmental organisations) understand the impacts of donations on controlling outbreaks, especially under COVID-19 conditions, in a low-resource environment and, thus, aid these hard-to-reach populations in a pandemic setting.

Using data envelopment analysis to perform benchmarking in intensive care units.

BACKGROUND: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. METHODS: We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. RESULTS: We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79-1.21] and SRU was 1.15 [IQR: 0.95-1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18-1.88] vs. 1.7 [IQR: 1.36-2.00]) and nursing workload (168 hours [IQR: 168-291] vs 396 hours [IQR: 336-672]) but higher nurses per bed ratio (2.02 [1.16-2.48] vs. 1.71 [1.43-2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the "most efficient" quadrant. CONCLUSION: Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.