RESUMO
BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 - 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 - 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results.
Assuntos
Bioterrorismo , Doenças Transmissíveis/diagnóstico , Serviço Hospitalar de Emergência/estatística & dados numéricos , Vigilância de Evento Sentinela , Doenças Transmissíveis/epidemiologia , Simulação por Computador , Surtos de Doenças/prevenção & controle , Gastroenteropatias/diagnóstico , Gastroenteropatias/epidemiologia , Humanos , Doenças do Sistema Nervoso/diagnóstico , Doenças do Sistema Nervoso/epidemiologia , New Mexico/epidemiologia , Infecções Respiratórias/diagnóstico , Infecções Respiratórias/epidemiologia , Estações do Ano , TempoAssuntos
Sistemas de Gerenciamento de Base de Dados/normas , Surtos de Doenças/estatística & dados numéricos , Armazenamento e Recuperação da Informação/métodos , Armazenamento e Recuperação da Informação/normas , Sistemas Computadorizados de Registros Médicos/normas , Vigilância da População/métodos , Medição de Risco/métodos , Medição de Risco/normas , Bioterrorismo/prevenção & controle , Redes de Comunicação de Computadores/normas , Surtos de Doenças/prevenção & controle , New Mexico/epidemiologia , Gestão de Riscos/métodos , SíndromeRESUMO
Public health authorities need a surveillance system that is sensitive enough to detect a disease outbreak early to enable a proper response. In order to meet this challenge we have deployed a pilot component-based system in Albuquerque, NM as part of the National Biodefense Initiative (BDI). B-SAFER gathers routinely collected data from healthcare institutions to monitor disease events in the community. We describe initial results from the deployment of the system for the past 6 months