Palabras clave
Hospitalización
SRAG
COVID-19
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Resumen
Objetivo: Analizar las tendencias de los tiempos medios de hospitalización por COVID-19 en la región Nordeste de Brasil entre 2020 y 2022. Métodos: Estudio ecológico de series temporales basado en 248.330 hospitalizaciones notificadas en el Sistema de Información de la Vigilancia Epidemiológica de la Gripe. Se incluyeron adultos ≥18 años con diagnóstico confirmado y hasta 90 días de hospitalización. Se calculó la media de días de internación (desde el ingreso hasta el alta o el óbito) por semana epidemiológica en cada estado. Las tendencias temporales se evaluaron mediante el Programa de Regresión Joinpoint. Resultados: Se observaron variaciones en los tiempos medios entre los estados y a lo largo de los años de la pandemia. Sergipe presentó la mayor media de internación (19,1 días), mientras que Rio Grande do Norte la menor (9,0 días). Los pacientes dados de alta tuvieron estancias más cortas que aquellos que fallecieron. Las tendencias no fueron homogéneas y no se identificó un patrón regional consistente. Conclusión: Las diferencias observadas sugieren disparidades en la infraestructura hospitalaria y en la organización de los servicios de salud. Se destaca la necesidad de políticas de reestructuración hospitalaria y protocolos de atención para enfrentar periodos epidémicos o no epidémicos.
Referencias
ANDERSON RM, et al. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet, 2020; 395(10228): 931-4.
BAEK H, et al. Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PLoS One, 2018;13(4): e0195901.
BAQUI P, et al. Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. Lancet Global Health, 2020; 8(8): e1018-e1026.
BARROS EO, et al. Perfil epidemiológico de pacientes acometidos por COVID-19 internados em hospital de referência no Pará. Revista Eletrônica Acervo Saúde, 2022; 14(6): e10825.
BARRETO ML, et al. What is urgent and necessary to inform policies to deal with the COVID-19 pandemic in Brazil? Revista Brasileira de Epidemiologia, 2020; 23: e200032.
BERRA TZ, et al. The COVID-19 pandemic in Brazil: space-time approach of vaccine coverage varied among municipalities. BMC Infectious Diseases, 2024.
BOING AF, et al. The Brazilian vaccine divide: how some municipalities were left behind in the Covid-19 vaccine coverage. PLOS Global Public Health, 2023; 3(11).
BRASIL. Ministério da Saúde. Sistema de Vigilância Epidemiológica da Gripe - SIVEP-Gripe. Casos e óbitos por COVID-19 no Brasil de 2020 a 2022. Boletim Epidemiológico.
CASTRO MC. et al. Brazil’s unified health system: the first 30 years and prospects for the future. The Lancet, 2021; 394(10195): 345–356.
CASTRO NUNES M, et al. Síndrome da COVID longa: uma revisão integrativa. Research, Society and Development, 2022; 11(13): e572111335990.
CAVALCANTE JR, et al. COVID-19 in Brazil: evolution of the epidemic up until epidemiological week 20 of 2020. Epidemiol Serv Saude, 2020; 29(4): e2020376.
ESCOSTEGUY CC, et al. Perfil clínico-epidemiológico de casos suspeitos de COVID-19 internados no Hospital Federal dos Servidores do Estado, Rio de Janeiro. Rev Bras Epidemiol., 2021; 30: e2020750.
FENG CX, LI L. Modeling zero inflation and overdispersion in the length of hospital stay for patients with ischaemic heart disease. In: CHEN DG, et al. Advanced statistical methods in data science. Singapore: Springer; 2016; 49-71.
GRASSELLI G, et al. COVID-19 Lombardy ICU Network. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA, 2020; 323(16): 1574-81.
HALLAL PC, et al. SARS-CoV-2 antibody prevalence in Brazil: results from two successive nationwide serological household surveys. The Lancet Global Health, 2021; 9(7): e1005–e1014.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). Censo demográfico 2022: características dos domicílios: resultados do universo. Rio de Janeiro: IBGE; 2024.
JIN L, et al. A new non-parametric stationarity test of time series in the time domain. J R Stat Soc Series B Stat Methodol., 2015; 77(5): 893–922.
KERR L, et al. COVID-19 no Nordeste brasileiro: sucessos e limitações nas respostas dos governos dos estados. Cien Saúde Colet., 2020; 25: 4099-120.
KIM HJ, et al. Permutation tests for joinpoint regression with applications to cancer rates. Statistics in Medicine, 2001; 20(4): 655-65.
KRISHNAN K, et al. Hospital length of stay and risk factors for severe COVID-19 in India and South Africa: a comparative cohort analysis. BMJ Open, 2022; 12(5): e055763.
LORING JT, et al. Replication data for: Spatial heterogeneity can lead to substantial local variations in the timing and severity of COVID-19. Sci Data, 2020; 7(1): 158.
MATEEN BA, et al. Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis. BMJ Open, 2021; 11(1): e042945.
MARTINS-FILHO PR, et al. Dynamics of hospitalizations and in-hospital deaths from COVID-19 in northeast Brazil: a retrospective analysis based on the circulation of SARS-CoV-2 variants and vaccination coverage. Epidemiol Health, 2022; 44: e2022036.
MOREIRA MRC, et al. Picos epidemiológicos da COVID-19 no Ceará, Brasil: estudo comparativo. Saúde e Desenvolvimento Humano, 2022; 10(2).
RACHE B, et al. Necessidades de infraestrutura do SUS em preparo à COVID-19: leitos de UTI, respiradores e ocupação hospitalar. São Paulo: Instituto de Estudos para Políticas de Saúde; 2020; 1-5.
REES EM, et al. COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Med., 2020; 18: 270.
RÖMMELE C, et al. Bettenkapazitätssteuerung in Zeiten der COVID-19-Pandemie: eine simulationsbasierte Prognose der Normal- und Intensivstationsbetten anhand der deskriptiven JVK
ROSENBAUM L. Facing Covid-19 in Italy – Ethics, logistics, and therapeutics on the epidemic's front line. N Engl J Med., 2020; 382(20): 1873-5.
SANTOS TBS, et al. Hospital contingency in coping with COVID-19 in Brazil: governmental problems and alternatives. Cien Saude Colet., 2021; 26(4): 1407–18.
SATO RC. Disease management with ARIMA model in time series. Einstein (São Paulo), 2013; 11(1): 128–31.
SHAABAN AN, et al. Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model. BMC Health Serv Res., 2021; 21(1): 372.
SILVA EN, et al. Fluxo de internação por Covid-19 nas regiões de saúde do Brasil. Saúde em Debate, 2021; 45(131): 1111-25.
SORIANO JB, et al. WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet In Dis., 2022; 22(4): e102-e107.
SPITERI G, et al. First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020. Eurosurveillance, 2020; 25(9): 2000178.
STONE K, et al. A systematic review of the prediction of hospital length of stay: towards a unified framework. PLOS Digit Health, 2022; 1(4): e0000017.
TENFORDE MW, et al. Association between mRNA vaccination and COVID-19 hospitalization and disease severity. JAMA, 2021; 326(20): 2043–2054.