艾滋病规划署的传播方式模型误导了非洲普遍流行的艾滋病毒预防工作

D. Gisselquist
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摘要

联合国艾滋病规划署(UNAIDS)鼓励各国政府使用传播模式(MOT)模型来估计各种风险的感染人数,从而指导艾滋病毒预防工作。模型中一个简单的设计错误——忽略了配偶之间频繁的血清一致性——极大地夸大了配偶感染的估计。该模型在乌干达和斯威士兰的应用说明了这一误差的影响。在乌干达,使用基于夫妻血清不一致的调查数据将配偶之间的估计年传播从60,948例减少到30,000例;有了这一变化,修订后的MOT模型——考虑到来自所有风险的感染——估计只需51%的发病率就能解释乌干达的流行病轨迹。在斯威士兰,使用夫妻血清不一致的数据将每年配偶间的艾滋病毒传播从9166例减少到3900例,修订后的模型估计仅需要47%的感染就可以解释斯威士兰的流行轨迹。这一误差对其他非洲国家的MOT估计也有类似的影响。MOT模型中的一些设计错误是可以修正的。然而,即使经过修改的设计,模型对不可靠数据(风险事件的数量)和参数(每个事件的传输)的依赖也会导致不可靠的估计。为了指导艾滋病毒预防工作,可以从偶发感染风险的前瞻性研究和追踪感染的调查中获得关于传播方式的更可靠信息。
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UNAIDS’ Modes of Transmission Model Misinforms HIV Prevention Efforts in Africa's Generalized Epidemics
The Joint United Nations Programme on AIDS (UNAIDS) encourages governments to use the Modes of Transmission (MOT) model to estimate numbers of infections from various risks and thereby to guide HIV prevention efforts. A simple design error in the model – ignoring frequent sero-concordance among spouses – hugely inflates estimates of infections from spouses. Applications of the model to Uganda and Swaziland illustrate the impact of this error. For Uganda, using survey-based data on sero-discordance in couples cuts estimated annual spouse-to-spouse transmission from 60,948 to 30,000; with this change, the revised MOT model – considering infections from all risks – estimates only 51% of incidence needed to explain Uganda’s epidemic trajectory. For Swaziland, using data on sero-discordance in couples cuts annual spouse-to-spouse HIV transmission from 9,166 to 3,900, and the revised model estimates only 47% of infections needed to explain Swaziland’s epidemic trajectory. This error has similar impacts on MOT estimates for other African countries. Several design errors in the MOT model can be fixed. However, even with a revised design, the model’s dependence on unreliable data (numbers of risk events) and parameters (transmission per event) leads to unreliable estimates. To guide HIV prevention efforts, more reliable information about modes of transmission is and can be available from prospective studies of risks for incident infections and investigations that trace infections.
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