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.

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.

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.

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).