乌干达特定年龄组艾滋病毒流行率下降估计：采用含艾滋病毒队列成分预测模型的贝叶斯融合估计和概率人口预测。
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.
作者信息
Clark Samuel J, Thomas Jason R, Bao Le
机构信息
Department of Sociology, University of Washington, Seattle, WA, USA; Institute of Behavioral Science (IBS), University of Colorado at Boulder, CO, USA; MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
出版信息
Demogr Res. 2012 Dec 12;27. doi: 10.4054/DemRes.2012.27.26.
BACKGROUND
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.
OBJECTIVE
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
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.
背景
我们对非洲艾滋病毒流行的流行病学和人口统计学的许多了解都来自于拟合稀疏、缺乏代表性数据的模型。这些模型往往对年龄和其他重要维度进行平均，很少对不确定性进行量化，而且通常不会在流行病学和人口统计学方面保持一致性。
目的
这项工作对乌干达和坦桑尼亚截至20世纪90年代末的艾滋病毒流行历史进行了实证研究，重点关注特定性别和年龄的发病率，利用这些结果对十年后的艾滋病毒流行率进行概率预测，并将这些预测与后期的艾滋病毒流行率测量值进行比较，以描述在此期间流行率变化的性别和年龄模式。
方法
我们调整了一个受艾滋病毒影响人群的流行病学模型，以便可以使用贝叶斯融合IMIS估计方法和最大似然方法来估计其参数。使用该模型的贝叶斯版本，我们对感染艾滋病毒的人群进行概率预测。
结果
我们得出了20世纪90年代末乌干达和坦桑尼亚特定性别和年龄的艾滋病毒发病率估计值，对21世纪初乌干达和坦桑尼亚的艾滋病毒流行情况进行了概率预测，描述了21世纪初乌干达艾滋病毒流行率变化的性别和年龄模式，并比较了贝叶斯估计程序和最大似然估计程序的性能和结果。
结论
我们证明：（1）考虑性别和年龄对非洲的艾滋病毒流行情况进行建模是可行的；（2）贝叶斯估计方法有重要优势，包括对不确定性进行严格量化以及进行概率预测的能力；（3）21世纪初乌干达的艾滋病毒发病率在特定年龄组有重要变化。
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