Cerqueira-Silva Thiago, Oliveira Juliane F, Oliveira Vinicius de Araújo, Florentino Pilar Tavares Veras, Sironi Alberto, Penna Gerson O, Ramos Pablo Ivan Pereira, Boaventura Viviane S, Barral-Netto Manoel, Marcilio Izabel
Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brasil.
Centro de Medicina Tropical, Universidade de Brasília, Brasília, Brasil.
Cad Saude Publica. 2024 Dec 20;40(11):e00010024. doi: 10.1590/0102-311XEN010024. eCollection 2024.
Syndromic surveillance using primary health care (PHC) data is a valuable tool for early outbreak detection, as demonstrated by the potential to identify COVID-19 outbreaks. However, the potential of such an early warning system in the post-COVID-19 era remains largely unexplored. We analyzed PHC encounter counter of respiratory complaints registered in the database of the Brazilian Unified National Health System from October 2022 to July 2023. We applied EARS (variations C1/C2/C3) and EVI to estimate the weekly thresholds. An alarm was determined when the number of encounters exceeded the week-specific threshold. We used data on hospitalization due to respiratory disease to classify as anomalies the weeks in which the number of cases surpassed predetermined thresholds. We compared EARS and EVI efficacy in anticipating anomalies. A total of 119 anomalies were identified across 116 immediate regions during the study period. The EARS-C2 presented the highest early alarm rate, with 81/119 (68%) early alarms, and C1 the lowest, with 71 (60%) early alarms. The lowest true positivity was the EARS-C1 118/1,354 (8.7%) and the highest was EARS-C3 99/856 (11.6%). Routinely collected PHC data can be successfully used to detect respiratory disease outbreaks in Brazil. Syndromic surveillance enhances timeliness in surveillance strategies, albeit with lower specificity. A combined approach with other strategies is essential to strengthen accuracy, offering a proactive and effective public health response against future outbreaks.
使用初级卫生保健（PHC）数据进行症状监测是早期发现疫情的宝贵工具，识别新冠疫情的潜力就证明了这一点。然而，这种预警系统在新冠疫情后时代的潜力在很大程度上仍未得到探索。我们分析了巴西统一国家卫生系统数据库中2022年10月至2023年7月登记的呼吸道疾病初级卫生保健就诊计数。我们应用EARS（C1/C2/C3变体）和EVI来估计每周阈值。当就诊次数超过特定周的阈值时确定发出警报。我们使用呼吸道疾病住院数据将病例数超过预定阈值的周分类为异常情况。我们比较了EARS和EVI在预测异常情况方面的效果。在研究期间，共在116个直属地区识别出119起异常情况。EARS-C2的早期警报率最高，有81/119（68%）起早期警报，C1最低，有71起（60%）早期警报。真阳性率最低的是EARS-C1，为118/1354（8.7%），最高的是EARS-C3，为99/856（11.6%）。常规收集的初级卫生保健数据可成功用于检测巴西的呼吸道疾病疫情。症状监测提高了监测策略的及时性，尽管特异性较低。与其他策略相结合的方法对于提高准确性至关重要，可为应对未来疫情提供积极有效的公共卫生应对措施。
