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美国一个大都市地区的儿科初级保健网络作为基于社区的传染病监测系统的验证

Validation of a Pediatric Primary Care Network in a US Metropolitan Region as a Community-Based Infectious Disease Surveillance System.

作者信息

Feemster Kristen A, Li Yimei, Grundmeier Robert, Localio A Russell, Metlay Joshua P

机构信息

Division of Infectious Diseases, Department of Pediatrics, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

出版信息

Interdiscip Perspect Infect Dis. 2011;2011:219859. doi: 10.1155/2011/219859. Epub 2011 Dec 7.

Abstract

This cross-sectional study used Geographic Information System methods to compare sociodemographic and clinical characteristics of children enrolled and not enrolled in a primary care network to determine the suitability of the network to estimate population-based disease rates. We validated the network surveillance system by comparing invasive pneumococcal disease rates between network and nonnetwork children using population-based surveillance data. Among the study population of 130300 children, network children were more likely to be female, Black, non-Hispanic, younger, and receive Medicaid. These differences varied across neighborhoods, however, adjusting for neighborhood characteristics did not significantly change observed differences. Rates of invasive pneumococcal disease were not significantly different between network and non-network children. Significant demographic and clinical differences existed between network and non-network children and varied over small areas. Observed population rates of an infectious disease did not significantly differ suggesting that the network can potentially provide valid disease estimates for the community population.

摘要

这项横断面研究采用地理信息系统方法,比较了纳入和未纳入初级保健网络的儿童的社会人口学和临床特征,以确定该网络在估计基于人群的疾病发病率方面的适用性。我们通过使用基于人群的监测数据,比较网络内和网络外儿童的侵袭性肺炎球菌病发病率,对网络监测系统进行了验证。在130300名儿童的研究人群中,网络内儿童更有可能为女性、黑人、非西班牙裔、年龄较小且接受医疗补助。然而,这些差异因社区而异,调整社区特征后,观察到的差异没有显著变化。网络内和网络外儿童的侵袭性肺炎球菌病发病率没有显著差异。网络内和网络外儿童之间存在显著的人口统计学和临床差异,且在小区域内有所不同。观察到的传染病人群发病率没有显著差异,这表明该网络有可能为社区人群提供有效的疾病估计。

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