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在Spectrum中使用TIME模型来估计结核病-艾滋病病毒的发病率和死亡率。

Using the TIME model in Spectrum to estimate tuberculosis-HIV incidence and mortality.

作者信息

Pretorius Carel, Glaziou Philippe, Dodd Peter J, White Richard, Houben Rein

机构信息

aFutures Institute, Glastonbury, Connecticut, 06033, USA bGlobal TB Programme, World Health Organization, 20 avenue Appia, 1211 Geneva 27, Switzerland cHealth Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK dTB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, UK. *Philippe Glaziou, Peter J. Dodd and Richard White contributed equally to the writing of this article.

出版信息

AIDS. 2014 Nov;28 Suppl 4(4):S477-87. doi: 10.1097/QAD.0000000000000484.

Abstract

OBJECTIVES

Reliable estimates of the joint burden of HIV and tuberculosis epidemics are crucial to planning strategic global and national tuberculosis responses. Prior to the Global Tuberculosis Report 2013, the Global Tuberculosis Programme (GTB) released estimates for tuberculosis-HIV incidence at the global level only. Neither GTB nor United Nations Programme on HIV/AIDS (UNAIDS) published country specific estimates for tuberculosis-HIV mortality. We used a regression approach that combined all available data from GTB and UNAIDS in order to estimate tuberculosis-HIV incidence and mortality at country level.

METHODS

A regression method was devised to relate CD4 dynamics (based on national Spectrum files) to an increased relative risk (RR) of tuberculosis disease. The objective function is based on least squares and incorporates all available country-level estimates of tuberculosis-HIV incidence. Global regression parameters, obtained from averaging results over countries with population survey estimates for tuberculosis-HIV burden, were applied to countries with no survey tuberculosis-HIV incidence estimates.

RESULTS

The method produced results that are in reasonably close agreement with existing GTB estimates for global tuberculosis-HIV burden. It estimated that tuberculosis-HIV accounts for 12.6% of global tuberculosis incidence, 21.3% of all tuberculosis deaths, and 20% of all HIV deaths as estimated by the Spectrum AIDS Impact Module (AIM). Regional estimates show the highest absolute incidence burden in East and Southeast Asia, and the highest per capita burden in sub-Saharan Africa, where between 12.5% (Central sub-Saharan Africa) and 60.6% (Southern sub-Saharan Africa) of all tuberculosis disease occurs in people living with HIV (PLWH). Tuberculosis mortality follows a similar pattern, except that a disproportionate percentage of global tuberculosis deaths (12.1%) relative to global incidence (8.7%) occurred in Southern sub-Saharan Africa.

CONCLUSION

The disaggregation of tuberculosis incidence using a regression method on RR of tuberculosis disease and all available data on HIV burden (from UNAIDS) and tuberculosis-HIV testing (survey, sentinel and routine surveillance data) produces results that closely match GTB estimates for 2011. The tuberculosis-HIV incidence and mortality results were published in the Global Tuberculosis Report 2013. Several limitations of and potential improvements to the process are suggested.

摘要

目标

可靠估计艾滋病毒和结核病流行的联合负担对于规划全球和国家结核病应对战略至关重要。在《2013年全球结核病报告》发布之前,全球结核病规划署(GTB)仅公布了全球层面的结核-艾滋病毒发病率估计数。GTB和联合国艾滋病毒/艾滋病规划署(UNAIDS)均未发布各国结核-艾滋病毒死亡率的具体估计数。我们采用一种回归方法,将GTB和UNAIDS的所有可用数据结合起来,以估计各国结核-艾滋病毒的发病率和死亡率。

方法

设计了一种回归方法,将CD4动态变化(基于各国的Spectrum文件)与结核病发病相对风险(RR)增加相关联。目标函数基于最小二乘法,并纳入了所有可用国家层面的结核-艾滋病毒发病率估计数。通过对有结核-艾滋病毒负担人群调查估计数的国家的结果进行平均获得全球回归参数,并应用于没有结核-艾滋病毒发病率调查估计数的国家。

结果

该方法得出的结果与GTB对全球结核-艾滋病毒负担的现有估计数相当接近。据Spectrum艾滋病影响模块(AIM)估计,该方法估计结核-艾滋病毒占全球结核病发病率的12.6%、所有结核病死亡的21.3%以及所有艾滋病毒死亡的20%。区域估计数显示,东亚和东南亚的绝对发病负担最高,撒哈拉以南非洲的人均负担最高,在那里,所有结核病患者中,12.5%(撒哈拉以南非洲中部)至60.6%(撒哈拉以南非洲南部)为艾滋病毒感染者(PLWH)。结核病死亡率也呈现类似模式,只是相对于全球发病率(8.7%)而言,全球结核病死亡中不成比例的百分比(12.1%)发生在撒哈拉以南非洲南部。

结论

使用结核病发病RR的回归方法以及关于艾滋病毒负担(来自UNAIDS)和结核-艾滋病毒检测(调查、哨点和常规监测数据)的所有可用数据对结核病发病率进行分解,得出的结果与GTB对2011年的估计数非常接近。结核-艾滋病毒发病率和死亡率结果已发表在《2013年全球结核病报告》中。文中还提出了该过程的几个局限性和潜在改进建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb65/4247264/4769351f6dd0/aids-28-s477-g001.jpg

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