Estimates of age-specific reductions in HIV prevalence in Uganda: Bayesian melding estimation and probabilistic population forecast with an HIV-enabled cohort component projection model.
Estimates of age-specific reductions in HIV prevalence in Uganda: Bayesian melding estimation and probabilistic population forecast with an HIV-enabled cohort component projection model.
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DOI：
10.4054/demres.2012.27.26
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发表时间：
2012-12-12
影响因子：
2.1
通讯作者：
Bao L
中科院分区：
文献类型：
--
作者：
Clark SJ;Thomas JR;Bao L
我们对非洲艾滋病毒流行病的流行病学和人口统计学的大部分知识都来自于适合稀疏、非代表性数据的模型。这些指标通常是年龄和其他重要方面的平均值，很少量化不确定性，而且通常不会对人口的流行病学和人口统计学造成一致性。这项工作进行了实证调查的艾滋病毒流行病在乌干达和坦桑尼亚的历史，通过20世纪90年代末，侧重于性别-年龄-特定的发病率，使用这些结果产生的概率预测艾滋病毒的流行率十年后，并比较这些措施的艾滋病毒流行率在以后的时间来描述的性别-年龄模式的变化在干预期间。我们采用了一个受艾滋病影响的人口的统计模型，使其参数可以估计使用贝叶斯融合与IMIS估计方法和最大似然方法。使用贝叶斯版本的模型，我们产生的概率预测的人口与艾滋病毒。我们在20世纪90年代末在乌干达和坦桑尼亚的性别-年龄-特定的艾滋病毒发病率的估计，在21世纪初在乌干达和坦桑尼亚的艾滋病毒流行的概率预测，描述在乌干达艾滋病毒流行的变化的性别-年龄模式在21世纪初，并比较贝叶斯和最大似然估计程序的性能和结果。我们证明：（1）考虑到性别和年龄，可以对非洲的艾滋病毒流行病进行建模，（2）贝叶斯估计方法具有重要优势，包括严格量化不确定性和进行概率预测的能力，以及（3）在21世纪初，乌干达的艾滋病毒发病率发生了重要的年龄变化。
Much of our knowledge of the epidemiology and demography of HIV epidemics in Africa is derived from models fit to sparse, non-representative data. These often average over age and other important dimensions, rarely quantify uncertainty, and typically do not impose consistency on the epidemiology and the demography of the population. This work conducts an empirical investigation of the history of the HIV epidemic in Uganda and Tanzania through the late 1990s, focusing on sex-age-specific incidence, uses those results to produce probabilistic forecasts of HIV prevalence ten years later, and compares those to measures of HIV prevalence at the later time to describe the sex-age pattern of changes in prevalence over the intervening period. We adapt an epidemographic model of a population affected by HIV so that its parameters can be estimated using both the Bayesian melding with IMIS estimation method and maximum likelihood methods. Using the Bayesian version of the model we produce probabilistic forecasts of the population with HIV. We produce estimates of sex-age-specific HIV incidence in Uganda and Tanzania in the late 1990s, produce probabilistic forecasts of the HIV epidemics in Uganda and Tanzania during the early 2000s, describe the sex-age pattern of changes in HIV prevalence in Uganda during the early 2000s, and compare the performance and results of the Bayesian and maximum likelihood estimation procedures. We demonstrate that: (1) it is possible to model HIV epidemics in Africa taking account of sex and age, (2) there are important advantages to the Bayesian estimation method, including rigorous quantification of uncertainty and the ability to make probabilistic forecasts, and (3) that there were important age-specific changes in HIV incidence in Uganda during the early 2000s.