HIV incidence in 3 years of follow-up of a Zimbabwe cohort - 1998-2000 to 2001-03: contributions of proximate and underlying determinants to transmission
HIV incidence in 3 years of follow-up of a Zimbabwe cohort - 1998-2000 to 2001-03: contributions of proximate and underlying determinants to transmission
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DOI：
10.1093/ije/dym255
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发表时间：
2008-02-01
影响因子：
7.7
通讯作者：
Gregson, Simon
中科院分区：
文献类型：
--
作者：
Lopman, Ben;Nyamukapa, Constance;Gregson, Simon
背景近年来，津巴布韦的艾滋病毒流行率开始下降，这与性风险行为的减少有关。在这里，我们分析了艾滋病毒的发病率在此期间的下降的决定因素，估计人口水平的影响，确定的风险factors.Methods一个基于人口的队列1672艾滋病毒阴性的成年男性和2465艾滋病毒阴性的成年女性之间招募1998年和2000年。每个人都在3年后进行了随访。社会，行为和人口变量的影响和相互关系进行了研究，使用一个近似的决定因素框架。结果HIV感染率男性为19.9例/1000人年，女性为15.7例/1000人年（95 CI为13.018.9），男性为19.9例/1000人年（95 CI为16.324.2），女性为15.7例/1000人年（95 CI为13.018.9）。多个性伴侣、有一个身体不适的伴侣和报告患有另一种性传播疾病是风险因素，这些风险因素分别反映了直接决定因素框架的主要方面：个人行为、伙伴关系特征和传播概率。如果直接决定因素完全捕获艾滋病毒感染的风险，潜在的因素将不会影响一个完全参数化的模型。然而，一些潜在的社会和人口的决定因素仍然是重要的回归模型后，包括直接的决定因素。对于男女，有多个性伴侣作出了重大CRN，但对于妇女，没有行为解释超过10的新infections.Conclusions的直接决定因素并没有解释大多数的新感染在人口水平。这可能是因为我们无法衡量某些风险，但识别风险因素的假设是，那些获得感染的人与其他没有获得感染的人有某种不同。这表明，在这种广泛流行的情况下，感染者和未感染者之间的个体易于识别的特征几乎没有差异。
Background In recent years, HIV prevalence has begun to decline in Zimbabwe, which has been associated with reductions in sexual risk behaviour. Here, we analyse the determinants of HIV incidence in this period of decline and estimate the population-level impact of identified risk factors.Methods A population-based cohort of 1672 HIV-negative adult males and 2465 HIV-negative adult females was recruited between 1998 and 2000. Each individual was then followed-up 3 years later. The influence and inter-relationship of social, behavioural and demographic variables were examined using a proximate determinants framework. To explore the population-level influence of a variable, methods were developed for estimating a risk factors contribution to the reproductive number (CRN).Results HIV incidence was 19.9 [95 confidence interval (CI) 16.324.2] per 1000 person years in men and 15.7 (95 CI 13.018.9) in women. Multiple sexual partners, having an unwell partner, and reporting another sexually transmitted disease were risk factors that captured the main aspects of the proximate determinants framework: individual behaviour, partnership characteristics and the probability of transmission, respectively. If the proximate determinants fully captured risk of HIV infection, underlying factors would not influence a fully parameterized model. However, a number of underlying social and demographic determinants remained important in regression models after including the proximate determinants. For both sexes, having multiple sexual partners made a substantial CRN, but, for women, no behaviour explained more than 10 of new infections.Conclusions The proximate determinants did not explain the majority of new infections at the population level. This may be because we have been unable to measure some risks, but identifying risk factors assumes that those acquiring infections are somehow different from others who do not acquire infections. That they are not suggests that in this generalized epidemic there is little difference in readily identifiable characteristics of the individual between those who acquire infection and those who do not.