Tiffany Amanda, Moundekeno Faya Pascal, Traoré Alexis, Haile Melat, Sterk Esther, Guilavogui Timothé, Serafini Micaela, Genton Blaise, Grais Rebecca F
Epicentre, Geneva, Switzerland.
Médecins sans Frontières, Guéckédou, Guinea.
Am J Trop Med Hyg. 2016 Dec 7;95(6):1389-1397. doi: 10.4269/ajtmh.16-0376. Epub 2016 Oct 3.
Multiple community-based approaches can aid in quantifying mortality in the absence of reliable health facility data. Community-based sentinel site surveillance that was used to document mortality and the systems utility for outbreak detection was evaluated. We retrospectively analyzed data from 46 sentinel sites in three sous-préfectures with a reinforced malaria control program and one sous-préfecture without (Koundou) in Guinea. Deaths were recorded by key informants and classified as due to malaria or another cause. Malaria deaths were those reported as due to malaria or fever in the 3 days before death with no other known cause. Suspect Ebola virus disease (sEVD) deaths were those due to select symptoms in the EVD case definition. Deaths were aggregated by sous-préfecture and analyzed by a 6-month period. A total of 43,000 individuals were monitored by the surveillance system; 1,242 deaths were reported from July 2011-June 2014, of which 55.2% (N = 686) were reported as due to malaria. Malaria-attributable proportional mortality decreased by 26.5% (95% confidence interval [CI] = 13.9-33.1, P < 0.001) in the program area and by 6.6% (95% CI = -17.3-30.5, P = 0.589) in Koundou. Sixty-eight deaths were classified as sEVD and increased by 6.1% (95% CI = 1.3-10.8, P = 0.021). Seventeen sEVD deaths were reported from November 2013 to March 2014 including the first two laboratory-confirmed EVD deaths. Community surveillance can capture information on mortality in areas where data collection is weak, but determining causes of death remains challenging. It can also be useful for outbreak detection if timeliness of data collection and reporting facilitate real-time data analysis.
在缺乏可靠的医疗机构数据的情况下,多种基于社区的方法有助于对死亡率进行量化。对用于记录死亡率的基于社区的哨点监测以及疫情检测的系统效用进行了评估。我们回顾性分析了几内亚三个实施强化疟疾控制项目的副专区和一个未实施该项目的副专区(孔杜)中46个哨点的数据。由关键信息提供者记录死亡情况,并将其分类为疟疾或其他原因导致的死亡。疟疾死亡是指在死亡前3天内报告为因疟疾或发烧且无其他已知原因的死亡。疑似埃博拉病毒病(sEVD)死亡是指符合埃博拉病毒病病例定义中特定症状的死亡。按副专区汇总死亡情况,并按6个月的时间段进行分析。监测系统共监测了43000人;2011年7月至2014年6月报告了1242例死亡,其中55.2%(N = 686)报告为因疟疾死亡。项目地区疟疾归因比例死亡率下降了26.5%(95%置信区间[CI] = 13.9 - 33.1,P < 0.001),孔杜下降了6.6%(95% CI = -17.3 - 30.5,P = 0.589)。68例死亡被分类为sEVD,增加了6.1%(95% CI = 1.3 - 10.8,P = 0.021)。2013年11月至2014年3月报告了17例sEVD死亡,包括前两例实验室确诊的埃博拉病毒病死亡。社区监测可以在数据收集薄弱的地区获取死亡率信息,但确定死亡原因仍然具有挑战性。如果数据收集和报告的及时性有助于实时数据分析,那么它对疫情检测也可能有用。