PDF(Pubmed)
Abstract：
UNASSIGNED: There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high.
UNASSIGNED: The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.
UNASSIGNED: We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.
UNASSIGNED: Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.
UNASSIGNED: Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.
Main findings: For humanitarian organizations it is imperative to document the methodological limitations of cluster surveys and discuss the utility.Added knowledge: This paper adds new knowledge on cluster surveys for highly clustered data such us in Ebola virus disease.Global health impact of policy and action: We provided empirical estimates and discuss design improvements to inform future study.
UNASSIGNED: The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.
UNASSIGNED: We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.
UNASSIGNED: Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.
UNASSIGNED: Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.
Main findings: For humanitarian organizations it is imperative to document the methodological limitations of cluster surveys and discuss the utility.Added knowledge: This paper adds new knowledge on cluster surveys for highly clustered data such us in Ebola virus disease.Global health impact of policy and action: We provided empirical estimates and discuss design improvements to inform future study.
摘要：
■对于高度聚集的数据，例如埃博拉病毒病（EVD），缺乏有关死亡率的设计效果（DEFF）的经验数据，以及缺乏对DEFF较高时从整群抽样研究估计的死亡率的方法局限性和操作实用性的文献。
■本文的目标是报告EVD死亡率和DEFF估计值，并讨论当数据高度聚集时，例如在EVD爆发期间，群集调查的方法局限性。
■我们分析了在2014-2016年博区EVD爆发结束时进行的两次独立的基于人群的调查的结果，塞拉利昂，在城市和农村地区。在每个领域,选择了14个家庭的35个集群，其概率与人口规模成正比。我们收集了发病率的信息，召回期间的死亡率和家庭构成变化(2014年5月至2015年4月).费率是针对所有原因计算的，所有年龄，5岁以下和EVD特异性死亡率，分别,按地区和总体。粗死亡率和调整后的死亡率使用泊松回归进行估计，调查样本权重的核算和聚类设计。
■总共980个家庭和6,522个人参与了这两项调查。共报告64人死亡,其中20人归因于EVD。粗死亡率和EVD特异性死亡率分别为0.35/10000人天(95CI:0.23-0.52)和0.12/10000人天(95CI:0.05-0.32),分别。EVD死亡率的DEFF为5.53，非EVD死亡率为，是1.53。农村地区EVD特异性死亡率的DEFF为6.18，城市地区为0.58。非EVD特异性死亡率的DEFF在农村地区为1.87，在城市地区为0.44。
■我们的研究结果表明了高度的聚类；这导致了不精确的死亡率估计，在评估疾病的影响时，其效用有限。我们提供了DEFF估计，可以为未来的群集调查提供信息，并讨论设计改进，以减轻高度群集数据调查的局限性。
主要发现：对于人道主义组织，必须记录集群调查的方法局限性并讨论其效用。增加的知识：本文增加了有关高度聚集的数据的群集调查的新知识，例如我们在埃博拉病毒病中的数据。政策和行动对全球健康的影响：我们提供了经验估计并讨论了设计改进，以指导未来的研究。
■本文的目标是报告EVD死亡率和DEFF估计值，并讨论当数据高度聚集时，例如在EVD爆发期间，群集调查的方法局限性。
■我们分析了在2014-2016年博区EVD爆发结束时进行的两次独立的基于人群的调查的结果，塞拉利昂，在城市和农村地区。在每个领域,选择了14个家庭的35个集群，其概率与人口规模成正比。我们收集了发病率的信息，召回期间的死亡率和家庭构成变化(2014年5月至2015年4月).费率是针对所有原因计算的，所有年龄，5岁以下和EVD特异性死亡率，分别,按地区和总体。粗死亡率和调整后的死亡率使用泊松回归进行估计，调查样本权重的核算和聚类设计。
■总共980个家庭和6,522个人参与了这两项调查。共报告64人死亡,其中20人归因于EVD。粗死亡率和EVD特异性死亡率分别为0.35/10000人天(95CI:0.23-0.52)和0.12/10000人天(95CI:0.05-0.32),分别。EVD死亡率的DEFF为5.53，非EVD死亡率为，是1.53。农村地区EVD特异性死亡率的DEFF为6.18，城市地区为0.58。非EVD特异性死亡率的DEFF在农村地区为1.87，在城市地区为0.44。
■我们的研究结果表明了高度的聚类；这导致了不精确的死亡率估计，在评估疾病的影响时，其效用有限。我们提供了DEFF估计，可以为未来的群集调查提供信息，并讨论设计改进，以减轻高度群集数据调查的局限性。
主要发现：对于人道主义组织，必须记录集群调查的方法局限性并讨论其效用。增加的知识：本文增加了有关高度聚集的数据的群集调查的新知识，例如我们在埃博拉病毒病中的数据。政策和行动对全球健康的影响：我们提供了经验估计并讨论了设计改进，以指导未来的研究。
