Resumo
Objetivo: Analisar as tendências dos tempos médios de internação pela COVID-19 na região Nordeste do Brasil entre 2020 e 2022. Métodos: Estudo ecológico de séries temporais, utilizando 248.330 hospitalizações notificadas no Sistema de Informação da Vigilância Epidemiológica da Gripe. Foram incluídos indivíduos ≥18 anos, com diagnóstico confirmado e até 90 dias de internação. Calculou-se o tempo médio de permanência (da internação até alta ou óbito) por semana epidemiológica em cada estado. As tendências temporais foram avaliadas pelo Programa Joinpoint. Resultados: Houve variações nos tempos médios entre estados e anos da pandemia. Sergipe apresentou o maior tempo médio de internação (19,1 dias) e o Rio Grande do Norte o menor (9,0 dias). Pacientes que evoluíram para alta tiveram internações mais curtas que aqueles com óbito. As tendências não foram homogêneas, sem padrão regional definido. Conclusão: As diferenças observadas sugerem disparidades na estrutura hospitalar e na organização dos serviços de saúde. Destaca-se a necessidade de políticas de reestruturação hospitalar e de protocolos de atendimento para enfrentar períodos epidêmicos ou não.
Referências
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.
