Farm Animal and Veterinary Public Health, Faculty of Veterinary Science, The University of Sydney, Sydney, New South Wales, Australia; Marie Bashir Institute for Infectious Diseases and Biosecurity/School of Public Health, The University of Sydney, Sydney, New South Wales, Australia; Bureau of Infectious Diseases, Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, Massachusetts, United States of America.
Massachusetts Department of Public Health, Boston, Massachusetts, United States of America.
PLoS One. 2014 Apr 16;9(4):e93744. doi: 10.1371/journal.pone.0093744. eCollection 2014.
We outline a framework for evaluating food- and water-borne surveillance systems using hospitalization records, and demonstrate the approach using data on salmonellosis, campylobacteriosis and giardiasis in persons aged ≥65 years in Massachusetts. For each infection, and for each reporting jurisdiction, we generated smoothed standardized morbidity ratios (SMR) and surveillance to hospitalization ratios (SHR) by comparing observed surveillance counts with expected values or the number of hospitalized cases, respectively. We examined the spatial distribution of SHR and related this to the mean for the entire state. Through this approach municipalities that deviated from the typical experience were identified and suspected of under-reporting. Regression analysis revealed that SHR was a significant predictor of SMR, after adjusting for population age-structure. This confirms that the spatial "signal" depicted by surveillance is in part influenced by inconsistent testing and reporting practices since municipalities that reported fewer cases relative to the number of hospitalizations had a lower relative risk (as estimated by SMR). Periodic assessment of SHR has potential in assessing the performance of surveillance systems.
我们概述了一个使用住院记录评估食源性和水源性疾病监测系统的框架,并使用马萨诸塞州≥65 岁人群的沙门氏菌病、弯曲菌病和贾第虫病的数据演示了该方法。对于每种感染和每个报告管辖区,我们通过将观察到的监测计数与预期值或住院病例数进行比较,分别生成了平滑标准化发病比 (SMR) 和监测到住院的比值 (SHR)。我们检查了 SHR 的空间分布,并将其与全州平均值进行了比较。通过这种方法,我们确定了偏离典型经验的城市,并怀疑其存在漏报情况。回归分析表明,在调整人口年龄结构后,SHR 是 SMR 的一个显著预测因子。这证实了监测所描绘的空间“信号”部分受到不一致的检测和报告实践的影响,因为与住院病例数相比报告较少病例的城市的相对风险较低(如 SMR 估计的那样)。定期评估 SHR 有可能评估监测系统的性能。