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Objectives: This study aimed to compare differences in mortality risk factors between admission and follow-up incorporated models. Methods: A retrospective cohort study of 524 patients with confirmed COVID-19 infection admitted to a tertiary medical center in São Paulo, Brazil from 13 March to 30 April 2020. Data were collected on admission, and the third, eighth and fourteenth days of hospitalization. The hazard ratio (HR) was calculated and 28-day in-hospital mortality risk factors were compared between admission and follow-up models using a time-dependent Cox regression model. Results: Of 524 patients, 50.4% needed mechanical ventilation. The 28-day mortality rate was 32.8%. Compared with follow-up, admission models under-estimated the mortality HR for peripheral oxygen saturation 100 bpm (1.19 versus 2.04), respiratory rate >24/min (1.01 versus 1.82) and mechanical ventilation (1.92 versus 12.93). Low oxygen saturation, higher oxygen support and more biomarkers–including lactate dehydrogenase, C-reactive protein, neutrophil-lymphocyte ratio, and urea remained associated with mortality after adjustment for clinical factors at follow-up compared with only urea and oxygen support at admission. Conclusions: The inclusion of follow-up measurements changed mortality hazards of clinical signs and biomarkers. Low oxygen saturation, higher oxygen support, lactate dehydrogenase, C-reactive protein, neutrophil-lymphocyte ratio, and urea could help with prognosis of patients during follow-up.

Introdução/Objetivos: Entre os pacientes admitidos em Unidades de Terapia Intensiva (UTI) por COVID-19, as taxas de letalidade reportadas têm sido amplamente variáveis. Em uma meta-análise recente que descreve 57.420 pacientes adultos com COVID-19 que receberam ventilação mecânica invasiva, a letalidade foi estimada em 45% (IC95% 39-52%), variando de 36% (IC95% 24-48%) na Europa, até 52% (IC95% 18-95%) no Oriente Médio. Em outro estudo em países africanos, nos 40,1% dos pacientes que necessitaram de ventilação mecânica, a taxa de letalidade foi de 78,9%. No Brasil, a letalidade foi de 80% nos pacientes que receberam ventilação mecânica em estudo conduzido com dados das 250.000 primeiras internações. Em nosso estudo, procuramos avaliar associação entre preditores sociodemográficos e os desfechos de casos graves admitidos em unidades de terapia intensiva na região da Baixada Santista/SP. Métodos: Foi desenhado estudo de coorte retrospectiva, incluídos dados disponíveis publicamente da base secundária nacional SIVEP/OPENDATASUS. Foram incluídos apenas moradores dos nove municípios da região com confirmação da COVID-19 por critérios da vigilância epidemiológica, notificados entre 26/02/2020 e 27/09/2021. Através de modelo multivariado com ajuste para idade, sexo e suporte ventilatório utilizado, foi avaliado o efeito do município de internação com relação à letalidade em UTI. Resultados: A mediana de idade dos pacientes convalescentes foi de 55 anos (IIQ 43-63), e 68 anos (IIQ 58-78) à dos que faleceram. Dos pacientes admitidos em UTI, 69,1% tiveram como desfecho o óbito; Dentre os 1783 pacientes que necessitaram de ventilação mecânica, 83,6% faleceram. Internações em unidades de Cubatão(OR 2,19, IC95%1,35-3,54, p = 0.001), Guarujá (OR 2,79, IC95%1,84-4,20, p < 0.001), Itanhaém (OR 2,93, IC95% 1,52-5,80, p = 0,002), Praia Grande (OR 14,27, IC95%7,99-26,35 p < 0.001), Santos (OR 1,83, IC95% 1,23-2,70, p = 0,003), e São Vicente (OR 6,75, IC95% 2,95-17,06, p < 0.001) estiveram associadas ao desfecho óbito. Conclusões: É urgente avaliar o efeito de fatores de risco modificáveis para letalidade em pacientes submetidos à internações em UTI, como por exemplo a incidência de infecções relacionadas à assistência à saúde (IRAS). Para além de características individuais do hospedeiro e do vírus, tais fatores podem explicar a grande variabilidade nos desfechos de pacientes com COVID-19. Merecem maior atenção as cidades de Praia Grande, São Vicente, Itanhaém e Guarujá.

ABSTRACT Dengue fever is re-emerging worldwide, however the reasons of this new emergence are not fully understood. Our goal was to report the incidence of dengue in one of the most populous States of Brazil, and to assess the high-risk areas using a spatial and spatio-temporal annual models including geoclimatic, demographic and socioeconomic characteristics. An ecological study with both, a spatial and a temporal component was carried out in Sao Paulo State, Southeastern Brazil, between January 1st, 2007 and December 31st, 2019. Crude and Bayesian empirical rates of dengue cases following by Standardized Incidence Ratios (SIR) were calculated considering the municipalities as the analytical units and using the Integrated Nested Laplace Approximation in a Bayesian context. A total of 2,027,142 cases of dengue were reported during the studied period. The spatial model allocated the municipalities in four groups according to the SIR values: (I) SIR2.0 identified the municipalities with higher risk for dengue outbreaks. “Hot spots” are shown in the thematic maps. Significant correlations between SIR and two climate variables, two demographic variables and one socioeconomical variable were found. No significant correlations were found in the spatio-temporal model. The incidence of dengue exhibited an inconstant and unpredictable variation every year. The highest rates of dengue are concentrated in geographical clusters with lower surface pressure, rainfall and altitude, but also in municipalities with higher degree of urbanization and better socioeconomic conditions. Nevertheless, annual consolidated variations in climatic features do not influence in the epidemic yearly pattern of dengue in southeastern Brazil.