The development of an age-structured model for trachoma transmission dynamics, pathogenesis and control.
The development of an age-structured model for trachoma transmission dynamics, pathogenesis and control.
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DOI：
10.1371/journal.pntd.0000462
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发表时间：
2009-06-16
影响因子：
3.8
通讯作者：
Grassly NC
中科院分区：
文献类型：
--
作者：
Gambhir M;Basáñez MG;Burton MJ;Solomon AW;Bailey RL;Holland MJ;Blake IM;Donnelly CA;Jabr I;Mabey DC;Grassly NC
沙眼是世界范围内导致失明的主要传染性原因，是由于沙眼衣原体反复结膜感染造成的。控制干预措施对人群感染和活动性疾病水平的影响可以及时测量，但对严重眼部后遗症的影响需要长期监测。我们提出了沙眼传播和疾病的年龄结构数学模型，以预测干预措施对致盲沙眼患病率的影响。该模型基于多次再感染导致进行性结膜疤痕、倒睫、角膜混浊和失明的概念。它还包括沙眼自然史的各个方面，例如感染恢复率的增加以及衣原体负荷的减少以及随后的感染，这取决于（假定的）获得性免疫力，随着年龄的增长，这种免疫力可以更快地清除感染。通过将模型拟合到冈比亚和坦桑尼亚低流行、中流行和高流行社区的预控制感染流行率数据，使用最大似然估计参数。该模型再现了沙眼流行病学的主要特征：1）感染患病率的年龄分布，在很小的时候就达到峰值，在老年时下降； 2) 流行高峰发生转变，在感染力较高的环境中，年龄趋于年轻化； 3）感染率整体上升，感染力增强； 4) 随着年龄的增长，随之而来的严重后遗症（沙眼疤痕、倒睫）的患病率不断上升，以及在这些后遗症出现之前需要发生的感染数量的估计。我们提出了一个足够全面的框架来检查 SAFE 疾病策略 A（抗生素）部分的结果。考虑到导致这些模式的个体过程，讨论了该模型代表人群水平感染和疾病后遗症模式的适用性。沙眼是世界范围内导致失明的主要原因，是由于沙眼衣原体细菌反复结膜感染所致。控制干预措施对人群感染和活动性疾病水平的影响可以及时测量，但对严重眼部疾病结果的影响需要长期监测。我们提出了沙眼传播和疾病的数学模型，以预测干预措施对致盲沙眼的影响。该模型基于多次重复感染导致眼睛逐渐形成疤痕和潜在致盲疾病后遗症的概念。它包括沙眼自然史的各个方面，例如感染恢复率的增加以及随后感染中衣原体负荷的减少。该模型再现了沙眼流行病学的关键特征，例如感染患病率的年龄特征；在高传播环境中，患病率高峰向年轻年龄方向转变；严重后遗症（疤痕、倒睫）患病率的上升，以及在这些后遗症出现之前经历的感染人数的估计。该模型可用于检查各种感染和疾病控制策略的结果，并有助于针对不同的流行环境规划治疗干预措施。
Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma. The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear. We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns. Trachoma is the worldwide leading infectious cause of blindness and is due to repeated conjunctival infection with Chlamydia trachomatis bacteria. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular disease outcomes require long-term monitoring. We present a mathematical model of trachoma transmission and disease to predict the impact of interventions on blinding trachoma. The model is based on the concept of multiple re-infections leading to progressive scarring of the eye and the potentially blinding disease sequelae. It includes aspects of trachoma natural history such as an increasing rate of recovery from infection, and a decreasing chlamydial load with subsequent infections. The model reproduces key features of trachoma epidemiology such as the age-profile of infection prevalence; a shift in the prevalence peak toward younger ages in higher-transmission environments; and a rising profile of the prevalence of the severe sequelae (scarring, trichiasis), as well as estimates of the number of infections experienced before these sequelae appear. The model can be used to examine the outcomes of various control strategies on infection and disease and can help to plan treatment interventions for different endemic settings.