Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates.
Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates.
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DOI：
10.1186/s12963-016-0106-0
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发表时间：
2016
影响因子：
3.3
通讯作者：
Tatem AJ
中科院分区：
文献类型：
--
作者：
Zu Erbach-Schoenberg E;Alegana VA;Sorichetta A;Linard C;Lourenço C;Ruktanonchai NW;Graupe B;Bird TJ;Pezzulo C;Wesolowski A;Tatem AJ
可靠的健康指标对于准确评估疾病负担和规划干预措施至关重要。许多健康指标是通过被动监测系统测量的，并依赖于对发病因素的准确估计，以将病例数转化为发病率指标。这些分母估计值通常来自全国人口普查，并使用大面积增长率来估计年度变化。通常情况下，它们不考虑任何季节性波动，因此假设分母人口是静态的。最近的许多研究通过对移动的电话数据记录和一系列其他来源的定量分析，强调了人口的动态性质，并强调了季节性变化。在本研究中，我们使用移动的电话数据来捕捉短期人口流动的模式并绘制人口密度的动态图。我们展示了如何使用移动的电话数据来测量健康区人口数量的季节性变化，这些人口数量被用作计算区级疾病发病率的指标。以纳米比亚的疟疾病例报告为例，我们使用3.5年的电话数据来调查季节性流动对疟疾发病率估计造成的疟疾发病率波动的时空影响。我们表明，即使在人口稀少的国家，人口中心之间的距离很大，如纳米比亚，人口是高度动态的全年。我们强调季节性流动如何影响疟疾发病率估计，与使用静态人口地图创建的估计值相比，差异高达30%。这些差异表现出明显的空间模式，与使用静态人口相比，纳米比亚北部高流行区的发病率可能被高估，而低风险地区的发病率则被低估。这里的结果强调了健康指标，依赖于静态估计的人口普查可能会有很大的不同，一旦考虑到流动性和季节性变化。关于纳米比亚的疟疾情况，结果表明，纳米比亚实际上可能比以前认为的更接近消灭疟疾。更广泛地说，结果突出了人口的动态。除了影响发病率估计外，人口密度的这些变化也会影响医疗资源的分配。对季节性流动的认识有可能改善干预措施的影响，如疫苗接种运动或分发蚊帐等商品。本文的在线版本（doi：10.1186/s12963-016-0106-0）包含补充材料，可供授权用户使用。
Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities. We show how mobile phone data can be used to measure seasonal changes in health district population numbers, which are used as denominators for calculating district-level disease incidence. Using the example of malaria case reporting in Namibia we use 3.5 years of phone data to investigate the spatial and temporal effects of fluctuations in denominators caused by seasonal mobility on malaria incidence estimates. We show that even in a sparsely populated country with large distances between population centers, such as Namibia, populations are highly dynamic throughout the year. We highlight how seasonal mobility affects malaria incidence estimates, leading to differences of up to 30 % compared to estimates created using static population maps. These differences exhibit clear spatial patterns, with likely overestimation of incidence in the high-prevalence zones in the north of Namibia and underestimation in lower-risk areas when compared to using static populations. The results here highlight how health metrics that rely on static estimates of denominators from censuses may differ substantially once mobility and seasonal variations are taken into account. With respect to the setting of malaria in Namibia, the results indicate that Namibia may actually be closer to malaria elimination than previously thought. More broadly, the results highlight how dynamic populations are. In addition to affecting incidence estimates, these changes in population density will also have an impact on allocation of medical resources. Awareness of seasonal movements has the potential to improve the impact of interventions, such as vaccination campaigns or distributions of commodities like bed nets. The online version of this article (doi:10.1186/s12963-016-0106-0) contains supplementary material, which is available to authorized users.
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DOI：
10.1126/science.1210554
发表时间：
2011-12-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bharti N;Tatem AJ;Ferrari MJ;Grais RF;Djibo A;Grenfell BT
通讯作者：
Grenfell BT
影响因子：
4.6
作者：
Le Menach, Arnaud;Tatem, Andrew J.;Cohen, Justin M.;Hay, Simon I.;Randell, Heather;Patil, Anand P.;Smith, David L.
通讯作者：
Smith, David L.
DOI：
10.1073/pnas.1203882109
发表时间：
2012-07-17
影响因子：
11.1
作者：
Lu, Xin;Bengtsson, Linus;Holme, Petter
通讯作者：
Holme, Petter
影响因子：
4.6
作者：
de Montjoye, Yves-Alexandre;Hidalgo, Cesar A.;Verleysen, Michel;Blondel, Vincent D.
通讯作者：
Blondel, Vincent D.
影响因子：
3
作者：
Chisha, Zunda;Larsen, David A.;Winters, Anna M.
通讯作者：
Winters, Anna M.