Notification of malaria cases in the Brazilian Amazon Basin from 2010 to 2020: an analysis of the reporting times.

BACKGROUND: As controlling malaria transmission remains a public-health challenge in the Brazilian Amazon basin, the National Surveillance System for Malaria (SIVEP-MALARIA) has registered malaria notifications for over fifteen years helping in the decision-making on control and elimination. As a surveillance database, the system is prone to reporting delays, and knowledge about reporting patterns is essential in decisions. METHODS: This study contains an analysis of temporal and state trends of reporting times in a total of 1,580,617 individual malaria reports from January 2010 to December 2020, applying procedures for statistical distribution fitting. A nowcasting technique was applied to show an estimation of number of cases using a statistical model of reporting delays. RESULTS: Reporting delays increased over time for the states of Amazonas, Rondônia, Roraima, and Pará. Amapá has maintained a similar reporting delay pattern, while Acre decreased reporting delay between 2010 and 2020. Predictions were more accurate in states with lower reporting delays. The temporal evolution of reporting delays only showed a decrease in malaria reports in Acre from 2010 to 2020. CONCLUSION: Malaria notifications may take days or weeks to enter the national surveillance database. The reporting times are likely to impact incidence estimation over periods when data is incomplete, whilst the impact of delays becomes smaller for retrospective analysis. Short-term assessments for the estimation of malaria incidence from the malaria control programme must deal with reporting delays.

The spatio-temporal distribution of acute encephalitis syndrome and its association with climate and landcover in Vietnam.

BACKGROUND: Acute encephalitis syndrome (AES) differs in its spatio-temporal distribution in Vietnam with the highest incidence seen during the summer months in the northern provinces. AES has multiple aetiologies, and the cause remains unknown in many cases. While vector-borne disease such as Japanese encephalitis and dengue virus and non-vector-borne diseases such as influenza and enterovirus show evidence of seasonality, associations with climate variables and the spatio-temporal distribution in Vietnam differs between these. The aim of this study was therefore to understand the spatio-temporal distribution of, and risk factors for AES in Vietnam to help hypothesise the aetiology. METHODS: The number of monthly cases per province for AES, meningitis and diseases including dengue fever; influenza-like-illness (ILI); hand, foot, and mouth disease (HFMD); and Streptococcus suis were obtained from the General Department for Preventive Medicine (GDPM) from 1998-2016. Covariates including climate, normalized difference vegetation index (NDVI), elevation, the number of pigs, socio-demographics, JEV vaccination coverage and the number of hospitals were also collected. Spatio-temporal multivariable mixed-effects negative binomial Bayesian models with an outcome of the number of cases of AES, a combination of the covariates and harmonic terms to determine the magnitude of seasonality were developed. RESULTS: The national monthly incidence of AES declined by 63.3% over the study period. However, incidence increased in some provinces, particularly in the Northwest region. In northern Vietnam, the incidence peaked in the summer months in contrast to the southern provinces where incidence remained relatively constant throughout the year. The incidence of meningitis, ILI and S. suis infection; temperature, relative humidity with no lag, NDVI at a lag of one month, and the number of pigs per 100,000 population were positively associated with the number of cases of AES in all models in which these covariates were included. CONCLUSIONS: The positive correlation of AES with temperature and humidity suggest that a number of cases may be due to vector-borne diseases, suggesting a need to focus on vaccination campaigns. However, further surveillance and research are recommended to investigate other possible aetiologies such as S. suis or Orientia tsutsugamushi.

Effects of Tranexamic Acid in Patients with Subarachnoid Hemorrhage in Brazil: A Prospective Observational Study with Propensity Score Analysis.

BACKGROUND: Rebleeding from a ruptured aneurysm increases the risk of unfavorable outcomes after subarachnoid hemorrhage (SAH) and is prevented by early aneurysm occlusion. The role of antifibrinolytics before aneurysm obliteration remains controversial. We investigated the effects of tranexamic acid on long-term functional outcomes of patients with aneurysmal SAH (aSAH). METHODS: This was a single-center, prospective, observational study conducted in a high-volume tertiary hospital in a middle-income country from December 2016 to February 2020. We included all consecutive patients with aSAH who either received or did not receive tranexamic acid (TXA) treatment. Multivariate logistic regression analysis using propensity score was used to evaluate the association of TXA use with long-term functional outcomes, measured by the modified Rankin Scale (mRS) at 6 months. RESULTS: A total of 230 patients with aSAH were analyzed. The median (interquartile range) age was 55 (46-63) years, 72% were women, 75% presented with good clinical grade (World Federation of Neurological Surgeons grade 1-3), and 83% had a Fisher scale of 3 or 4. Around 80% of patients were admitted up to 72 h from ictus. The aneurysm occlusion method was surgical clipping in 80% of the patients. A total of 129 patients (56%) received TXA. In multivariable logistic regression using inverse probability treatment weighting, the long-term rate of unfavorable outcomes (modified Rankin scale 4-6) was the same in the TXA and non-TXA groups (61 [48%] in TXA group vs. 33 [33%] in non-TXA group; odds ratio [OR] 1.39, 95% confidence interval [CI] 0.67-2.92; p = 0.377). The TXA group had higher in-hospital mortality (33 vs. 11% in non-TXA group; OR 4.13, 95% CI 1.55-12.53, p = 0.007). There were no differences between the groups concerning intensive care unit length of stay (16 ± 11.22 days in TXA group vs. 14 ± 9.24 days in non-TXA group; p = 0.2) or hospital (23 ± 13.35 days in TXA group vs. 22 ± 13.36 days in non-TXA group; p = 0.9). There was no difference in the rates of rebleeding (7.8% in TXA group vs. 8.9% in non-TXA group; p = 0.31) or delayed cerebral ischemia (27% in TXA group vs. 19% in non-TXA group; p = 0.14). For the propensity-matched analysis, 128 individuals were selected (64 in TXA group and 64 in non-TXA group), and the rates of unfavorable outcomes at 6 months were also similar between groups (45% in TXA group and 36% in non-TXA group; OR 1.22, 95% CI 0.51-2.89; p = 0.655). CONCLUSIONS: Our findings in a cohort with delayed aneurysm treatment reinforce previous data that TXA use before aneurysm occlusion does not improve functional outcomes in aSAH.

On multifactorial drivers for malaria rebound in Brazil: a spatio-temporal analysis.

BACKGROUND: Malaria incidence in Brazil reversed its decreasing trend when cases from recent years, as recent as 2015, exhibited an increase in the Brazilian Amazon basin, the area with the highest transmission of Plasmodium vivax and Plasmodium falciparum. In fact, an increase of more than 20% in the years 2016 and 2017 revealed possible vulnerabilities in the national malaria-control programme. METHODS: Factors potentially associated with this reversal, including migration, economic activities, and deforestation, were studied. Past incidences of malaria cases due to P. vivax and P. falciparum were analysed with a spatio-temporal Bayesian model using more than 5 million individual records of malaria cases from January of 2003 to December of 2018 in the Brazilian Amazon to establish the municipalities with unexpected increases in cases. RESULTS: Plasmodium vivax incidence surpassed the past trends in Amazonas (AM), Amapá (AP), Acre (AC), Pará (PA), Roraima (RR), and Rondônia (RO), implying a rebound of these states between 2015 and 2018. On the other hand, P. falciparum also surpassed the past trends in AM, AC, AP, and RR with less severity than P. vivax incidence. Outdoor activities, agricultural activities, accumulated deforestation, and travelling might explain the rebound in malaria cases in RR, AM, PA, and RO, mainly in P. vivax cases. These variables, however, did not explain the rebound of either P. vivax and P. falciparum cases in AC and AP states or P. falciparum cases in RR and RO states. CONCLUSION: The Amazon basin has experienced an unexpected increase in malaria cases, mainly in P. vivax cases, in some regions of the states of Amazonas, Acre, Pará, Amapá, Roraima, and Rondônia from 2015 to 2018 and agricultural activities, outdoor activities, travelling activities, and accumulated deforestation appear linked to this rebound of cases in particular regions with different impact. This shows the multifactorial effects and the heterogeneity of the Amazon basin, boosting the necessity of focusing the malaria control programme on particular social, economic, and environmental conditions.

Systemic Severity and Organ Dysfunction in Subarachnoid Hemorrhage: A Large Retrospective Multicenter Cohort Study.

BACKGROUND AND PURPOSE: Acute physiologic derangements and multiple organ dysfunction are common after subarachnoid hemorrhage. We aimed to evaluate the simplified acute physiology score 3 (SAPS-3) and the sequential organ failure assessment (SOFA) scores for the prediction of in-hospital mortality in a large multicenter cohort of SAH patients. METHODS: This was a retrospective analysis of prospectively collected data from 45 ICUs in Brazil, during 2014 and 2015. Patients admitted with non-traumatic subarachnoid hemorrhage (SAH) were included. Clinical and outcome data were retrieved from an electronic ICU quality registry. SAPS-3 and SOFA scores, without the neurological components (i.e., nSAPS-3 and nSOFA, respectively) were recorded, as well as the World Federation of Neurological Surgeons (WFNS) scale. We used multilevel logistic regression analysis to identify factors associated with in-hospital mortality. We evaluated performance using the area under the receiver operating characteristic curve (AUROC), as well as calibration belts and precision-recall plots. RESULTS: The study included 997 patients, from which 426 (43%) had poor clinical grade (WFNS 4 or 5) and in-hospital mortality was 34%. Median nSAPS-3 and nSOFA score at admission were 46 (IQR: 38-55) and 2 (0-5), respectively. Non-survivors were older, had higher nSAPS-3 and nSOFA, and more often poor grade. After adjustment for age, poor grade and withdrawal of life sustaining therapies, multivariable analysis identified nSAPS-3 and nSOFA score as independent clinical predictors of in-hospital mortality. The AUROC curve that included nSAPS-3 and nSOFA scores significantly improved the already good discrimination and calibration of age and WFNS to predict in-hospital mortality (AUROC: 0.89 for the full final model vs. 0.85 for age and WFNS; P < 0.0001). CONCLUSIONS: nSAPS-3 and nSOFA scores were independently associated with in-hospital mortality after SAH. The addition of these scores improved early prediction of hospital mortality in our cohort and should be integrated to other specific prognostic indices in the early assessment of SAH.

Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication.

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

The cost-effectiveness of controlling dengue in Indonesia using wMel Wolbachia released at scale: a modelling study.

BACKGROUND: Release of virus-blocking Wolbachia-infected mosquitoes is an emerging disease control strategy that aims to control dengue and other arboviral infections. Early entomological data and modelling analyses have suggested promising outcomes, and wMel Wolbachia releases are now ongoing or planned in 12 countries. To help inform government, donor, or philanthropist decisions on scale-up beyond single city releases, we assessed this technology's cost-effectiveness under alternative programmatic options. METHODS: Using costing data from existing Wolbachia releases, previous dynamic model-based estimates of Wolbachia effectiveness, and a spatially explicit model of release and surveillance requirements, we predicted the costs and effectiveness of the ongoing programme in Yogyakarta City and three new hypothetical programmes in Yogyakarta Special Autonomous Region, Jakarta, and Bali. RESULTS: We predicted Wolbachia to be a highly cost-effective intervention when deployed in high-density urban areas with gross cost-effectiveness below $1500 per DALY averted. When offsets from the health system and societal perspective were included, such programmes even became cost saving over 10-year time horizons with favourable benefit-cost ratios of 1.35 to 3.40. Sequencing Wolbachia releases over 10 years could reduce programme costs by approximately 38% compared to simultaneous releases everywhere, but also delays the benefits. Even if unexpected challenges occurred during deployment, such as emergence of resistance in the medium-term or low effective coverage, Wolbachia would remain a cost-saving intervention. CONCLUSIONS: Wolbachia releases in high-density urban areas are expected to be highly cost-effective and could potentially be the first cost-saving intervention for dengue. Sites with strong public health infrastructure, fiscal capacity, and community support should be prioritised.

Measuring the contribution of human mobility to malaria persistence.

BACKGROUND: To achieve malaria elimination, it is important to determine the role of human mobility in parasite transmission maintenance. The Alto Juruá basin (Brazil) exhibits one of the largest vivax and falciparum malaria prevalence in the Amazon. The goal of this study was to estimate the contribution of human commutes to malaria persistence in this region, using data from an origin-destination survey. METHODS: Data from an origin-destination survey were used to describe the intensity and motivation for commutations between rural and urban areas in two Alto Juruá basin (Brazil) municipalities, Mâncio Lima and Rodrigues Alves. The relative time-person spent in each locality per household was estimated. A logistic model was developed to estimate the effect of commuting on the probability of contracting malaria for a certain residence zone inhabitant commuting to another zone. RESULTS: The main results suggest that the assessed population is not very mobile. A total of [Formula: see text] households reported spending over [Formula: see text] of their annual person-hour in areas within the same residence zone. Study and work were the most prevalent commuting motivations, calculated at [Formula: see text] and [Formula: see text] respectively. Spending person-hours in urban Rodrigues Alves conferred relative protection to urban Mâncio Lima residents. The opposite effect was observed for those spending time in rural areas of both municipalities. CONCLUSION: Residence area is a stronger determinant for contracting malaria than commuting zones in the Alto Juruá region. As these municipalities are a hotspot for Plasmodium transmission, understanding the main local human fluxes is essential for planning control strategies, since the probability of contracting malaria is dependent on the transmission intensity of both the origin and the displacement area. The natural conditions for the circulation of certain pathogens, such as Plasmodium spp., combined with the Amazon human mobility pattern indicate the need for disease control perspective changes. Therefore, intersectoral public policies should become the basis for health mitigation actions.

A modelling approach for correcting reporting delays in disease surveillance data.

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.

Years worked at night and body mass index among registered nurses from eighteen public hospitals in Rio de Janeiro, Brazil.

BACKGROUND: Employees working night shifts are at a greater risk of being overweight or obese. Few studies on obesity and weight gain analyze the years of exposure to night work. The aim of this study was to determine the relationship between the years of exposure to night work and body mass index (BMI) among registered nurses. METHODS: A cross-sectional analysis was performed in 18 largest public hospitals in Rio de Janeiro, Brazil. A total of 2,372 registered nurses (2,100 women) completed a comprehensive questionnaire concerning sociodemographic, professional, lifestyle, and health behavioral data. Current and past exposures to night shifts as well as BMI values were measured as continuous variables. A gamma regression model was used with an identity link function to establish the association. RESULTS: The association between years of exposure to night work and BMI was statistically significant for both women and men after adjusting for all covariates [ß = 0.036; CI95% = 0.009-0.063) and ß = 0.071 (CI95% = 0.012-0.129), respectively]. The effect of night work was greater among men than women. For example, for those women who have worked at night for 20 years the estimated average BMI was 25.6 kg/m2 [range, 25.0-26.2]. In relation to men, after 20 years of exposure to night work the estimated average BMI was 26.9 kg/m2 [range, 25.6-28.1]. CONCLUSIONS: These findings suggest that night shift exposure is related to BMI increases. Obesity prevention strategies should incorporate improvements in work environments, such as the provision of proper meals to night workers, in addition to educational programs on the health effects of night work.

Persistent high mortality rates for Diabetes Mellitus and Hypertension after excluding deaths associated with COVID-19 in Brazil, 2020-2022.

INTRODUCTION: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a significant public health challenge globally, with Brazil being no exception. Excess mortality during this period reached alarming levels. Cardiovascular diseases (CVD), Systemic Hypertension (HTN), and Diabetes Mellitus (DM) were associated with increased mortality. However, the specific impact of DM and HTN on mortality during the pandemic remains poorly understood. METHODS: This study analyzed mortality data from Brazil's mortality system, covering the period from 2015 to 2022. Data included all causes of death as listed on death certificates, categorized by International Classification of Diseases 10th edition (ICD-10) codes. Population data were obtained from the Brazilian Census. Mortality ratios (MRs) were calculated by comparing death rates in 2020, 2021, and 2022 to the average rates from 2015 to 2019. Adjusted MRs were calculated using Poisson models. RESULTS: Between 2015 and 2022, Brazil recorded a total of 11,423,288 deaths. Death rates remained relatively stable until 2019 but experienced a sharp increase in 2020 and 2021. In 2022, although a decrease was observed, it did not return to pre-pandemic levels. This trend persisted even when analyzing records mentioning DM, HTN, or CVD. Excluding death certificates mentioning COVID-19 codes, the trends still showed increases from 2020 through 2022, though less pronounced. CONCLUSION: This study highlights the persistent high mortality rates for DM and HTN in Brazil during the years 2020-2022, even after excluding deaths associated with COVID-19. These findings emphasize the need for continued attention to managing and preventing DM and HTN as part of public health strategies, both during and beyond the COVID-19 pandemic. There are complex interactions between these conditions and the pandemic's impact on mortality rates.

The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients.

BACKGROUND: During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs. RESEARCH QUESTION: Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19? STUDY DESIGN AND METHODS: This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission. RESULTS: Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge. INTERPRETATION: ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.

Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review.

BACKGROUND: Hospital-Acquired Infections (HAI) represent a public health priority in most countries worldwide. Our main objective was to systematically review the quality of the predictive modeling literature regarding multidrug-resistant gram-negative bacteria in Intensive Care Units (ICUs). METHODS: We conducted and reported a Systematic Literature Review according to the recommendations of the PRISMA statement. We analysed the quality of the articles in terms of adherence to the TRIPOD checklist. RESULTS: The initial search identified 1935 papers and 15 final articles were included in the review. Most studies analysed used traditional prediction models (logistic regression), and only three developed machine-learning techniques. We noted poor adherence to the main methodological issues recommended in the TRIPOD checklist to develop prediction models, such as handling missing data (20% adherence), model-building procedures (20% adherence), assessing model performance (47% adherence), and reporting performance measures (33% adherence). CONCLUSIONS: Our review found few studies that use efficient alternatives to predict the acquisition of multidrug-resistant gram-negative bacteria in ICUs. Furthermore, we noted a lack of strategies for dealing with missing data, feature selection, and imbalanced datasets, a common problem in HAI studies.

Impact of the first year of COVID-19 vaccination strategy in Brazil: an ecological study.

OBJECTIVES: No consensus exists about the best COVID-19 vaccination strategy to be adopted by low-income and middle-income countries. Brazil adopted an age-based calendar strategy to reduce mortality and the burden on the healthcare system. This study evaluates the impact of the vaccination campaign in Brazil on the progression of the reported COVID-19 deaths. METHODS: This ecological study analyses the dynamic of vaccination coverage and COVID-19 deaths in hospitalised adults (≥20 years) during the first year of the COVID-19 vaccination roll-out (January to December 2021) using nationwide data (DATASUS). We stratified the adult population into 20-49, 50-59, 60-69 and 70+ years. The dynamic effect of the vaccination campaign on mortality rates was estimated by applying a negative binomial regression. The prevented and possible preventable deaths (observed deaths higher than expected) and potential years of life lost (PYLL) for each age group were obtained in a counterfactual analysis. RESULTS: During the first year of COVID-19 vaccination, 266 153 517 doses were administered, achieving 91% first-dose coverage. A total of 380 594 deaths were reported, 154 091 (40%) in 70+ years and 136 804 (36%) from 50-59 or 20-49 years. The mortality rates of 70+ decreased by 52% (rate ratio [95% CI]: 0.48 [0.43-0.53]) in 6 months, whereas rates for 20-49 were still increasing due to low coverage (52%). The vaccination roll-out strategy prevented 59 618 deaths, 53 088 (89%) from those aged 70+ years. However, the strategy did not prevent 54 797 deaths, 85% from those under 60 years, being 26 344 (45%) only in 20-49, corresponding to 1 589 271 PYLL, being 1 080 104 PYLL (68%) from those aged 20-49 years. CONCLUSION: The adopted aged-based calendar vaccination strategy initially reduced mortality in the oldest but did not prevent the deaths of the youngest as effectively as compared with the older age group. Countries with a high burden, limited vaccine supply and young populations should consider other factors beyond the age to prioritise who should be vaccinated first.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19.

PURPOSE: To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. MATERIALS AND METHODS: Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed. RESULTS: Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]). CONCLUSIONS: An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency.

PURPOSE: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency. METHODS: A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF. RESULTS: The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: -0.13 [-0.24, -0.01] and -0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect. CONCLUSION: In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.

Persistent carbapenem resistance in mechanically ventilated ICU patients: A before-and-after analysis of the COVID-19 surge.

OBJECTIVE: How did the antimicrobial resistance profile of critically ill patients evolve before, during, and after COVID-19 surge periods? METHODS: We retrospectively analysed all critically ill mechanically ventilated adult patients admitted to eight Brazilian hospitals from January 1st, 2018, to April 30th, 2023. We stratified the patients into three periods based on their admission date: pre-surge (Jan 01/2018-Mar 01/2020), surge (Mar 01/2020 - Oct 01/2021), and post-surge (after Oct 01/2021). We compared the proportion of positive cultures, prevalence of pathogens, and resistance rates across periods using the rate ratios (RR) and their 95% confidence intervals (95% CI). RESULTS: We analysed 9,780 ICU patients: 3,718 were in the pre-surge, 3,815 in the surge, and 2,247 in the post-surge period. Patients in the surge period were younger (median: 70 vs. 74 pre-surge vs. 75 post-surge) and presented a higher duration of invasive mechanical ventilation (median 7 vs. 5 days). The utilisation of blood and respiratory cultures increased throughout periods (56.9 pre-surge vs. 69.4 surge vs. 70.4 patients/1,000 patient days post-surge). The isolation of carbapenem-resistant gram-negative bacteria increased during the surge (RR [95% CI]: 1.8 [1.5-2.2], compared to pre-surge), decreased in post-surge (RR [95% CI]: 0.72 [0.6-0.9], and remained higher than pre-surge (RR [95% CI]: 1.3 [1.0-1.6]). Resistance rates for Pseudomonas aeruginosa reduced from 32% in pre- to 23% post-surge, whereas Klebsiella pneumoniae doubled during the surge, 26% to 52%, and remained higher than pre-surge. CONCLUSION: Carbapenem resistance increased during the surge period. Although it decreased post-surge, it remained higher than the rates observed before the pandemic.

Prevalence, risk factors, and impact of long COVID in a socially vulnerable community in Brazil: a prospective cohort study.

Background: Long COVID is an emerging global public health issue. Socially vulnerable communities in low- and-middle-income countries were severely impacted by the pandemic and are underrepresented in research. This prospective study aimed to determine the prevalence of long COVID, its impact on health, and associated risk factors in one such community in Rio de Janeiro, Brazil. Methods: A total of 710 individuals aged 18 and older, with confirmed SARS-CoV-2 infection at least three months prior, were enrolled between November 25, 2021, and May 5, 2022. Participants were assessed via telephone or in person using a standardized questionnaire to evaluate their perception of recovery, symptoms, quality of life, and functional status. Findings: Twenty percent of participants did not feel fully recovered, 22% experienced new or persistent symptoms, 26% had worsened functional status, 18% had increased dyspnoea, and 32% reported a worse quality of life. Persistent symptoms included headache, cough, fatigue, muscle pain, and shortness of breath. Dyspnoea during the acute phase was the strongest independent predictor of worsening outcomes. Females and individuals with comorbidities were more likely to report worse recovery, functioning, dyspnoea, and quality of life. Interpretation: Our findings reveal a high burden of severe and persistent physical and mental health sequelae in a socially vulnerable community following COVID-19. Funding: UK Foreign, Commonwealth and Development Office and Wellcome Trust Grant (222048/Z/20/Z), Fundação Oswaldo Cruz (FIOCRUZ), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), and the Centers for Disease Control and Prevention (CDC).

Joint spatial modeling of the risks of co-circulating mosquito-borne diseases in Ceará, Brazil.

Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.