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Artigo em Chinês | WPRIM (Pacífico Ocidental) | ID: wpr-246182

RESUMO

<p><b>OBJECTIVE</b>From May 2009-January 2010, a total of 3768 biosamples were tested for influenza A (H1N1) infection at Zhengzhou center for disease control and prevention, China. 1452 cases were laboratory confirmed H1N1 infection and 2316 were considered suspected victims. To evaluate the current protocol of influenza A (H1N1) based on the epidemic situations of Zhengzhou, relationships among features were explored and whether additional clinical characteristics should be part of H1N1 diagnosis protocols were determined.</p><p><b>METHODS</b>Both clinical and epidemiologic findings as well as statistical analyses were described in this article. Test for independence between features related to the disease diagnosis has been proposed. Furthermore, logistic regression was carried out to measure the association among features and latent class analysis was performed to identify additional crucial features in laboratory confirmed H1N1 by building various latent models with different combinatorial features.</p><p><b>RESULTS</b>The mean generation time for H1N1 was estimated as 3.59 +/- 1.41 days (range = 2.01-7.26). The estimated infection rate was 0.258 +/- 0.088 3, and reproduction number was 1.94 (95% CI = 1.12-3.18). Our results revealed that the six features, including molecular detections using three separate primer/probe sets, gender, age and temperature, are all associated with clinical diagnosis of H1N1, and that three separate primer/probe sets for laboratory confirmed H1N1, age and temperature are associated with each other.</p><p><b>CONCLUSION</b>Additional clinical features applied into the H1N1 diagnosis with current three primers/probe sets can increase the diagnostic efficiency.</p>


Assuntos
Adolescente , Adulto , Idoso , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Distribuição por Idade , China , Epidemiologia , Diagnóstico Diferencial , Vírus da Influenza A Subtipo H1N1 , Influenza Humana , Diagnóstico , Epidemiologia , Modelos Logísticos
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