Nekorchuk Dawn M, Gebrehiwot Teklehaimanot, Lake Mastewal, Awoke Worku, Mihretie Abere, Wimberly Michael C
Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK, USA.
Amhara Public Health Institute, Bahir Dar, Ethiopia.
BMC Public Health. 2021 Apr 24;21(1):788. doi: 10.1186/s12889-021-10850-5.
Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world's population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.
We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods.
All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80-100% CDC; 57-100% weekly statistical) and low to moderate alarm specificities (25-40% CDC; 16-61% weekly statistical). Farrington variants had a wide range of scores (20-100% sensitivities; 16-100% specificities) and could achieve various balances between sensitivity and specificity.
Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.
尽管在降低疟疾发病率方面取得了显著进展，但这种疾病仍然对世界上很大一部分人口构成公共卫生威胁。监测与早期检测算法相结合，可以成为一种有效的干预策略，为及时应对潜在疫情提供公共卫生响应信息。我们的主要目标是比较选定的事件检测方法在检测疟疾疫情方面的潜力。
我们使用了埃塞俄比亚阿姆哈拉地区确诊的恶性疟原虫（包括混合感染）病例每周计数的历史监测数据，该地区在病例数下降数年之后，2019年疟疾出现了复发。我们评估了三种早期检测2019年疟疾事件的方法：1）疾病预防控制中心（CDC）的早期异常报告系统（EARS），2）基于每周统计阈值的方法，包括世卫组织和卡伦方法，以及3）法林顿方法。
所有评估的方法都比简单的随机警报生成器表现更好。我们还发现，在检测到的事件百分比和真阳性警报百分比之间存在明显的权衡。CDC的EARS和每周统计阈值方法具有较高的事件敏感性（CDC为80 - 100%；每周统计为57 - 100%）和低到中等的警报特异性（CDC为25 - 40%；每周统计为16 - 61%）。法林顿变体的得分范围很广（敏感性为20 - 100%；特异性为16 - 100%），并且可以在敏感性和特异性之间实现各种平衡。
在测试的方法中，我们发现法林顿改进方法在最大化我们数据集中检测到的事件百分比和真阳性警报方面最有效（敏感性> 70%，特异性> 70%）。该方法使用统计模型来建立阈值，同时控制季节性和多年趋势，我们建议应更广泛地考虑将其和其他基于模型的方法用于疟疾早期检测。
