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扩展贝叶斯反向计算以估计特定年龄和时间的艾滋病毒发病率。

Extending Bayesian back-calculation to estimate age and time specific HIV incidence.

作者信息

Brizzi Francesco, Birrell Paul J, Plummer Martyn T, Kirwan Peter, Brown Alison E, Delpech Valerie C, Gill O Noel, De Angelis Daniela

机构信息

Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK.

IARC, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France.

出版信息

Lifetime Data Anal. 2019 Oct;25(4):757-780. doi: 10.1007/s10985-019-09465-1. Epub 2019 Feb 27.

DOI:10.1007/s10985-019-09465-1
PMID:30811019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6776486/
Abstract

CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their inter-related contribution to the observed surveillance data. This paper, extends existing approaches to age-specific settings, permitting the joint estimation of age- and time-specific incidence and diagnosis rates and the derivation of other epidemiological quantities of interest. This allows the identification of specific age-groups at higher risk of infection, which is crucial in directing public health interventions. We investigate, through simulation studies, the suitability of various bivariate splines for the non-parametric modelling of the latent age- and time-specific incidence and illustrate our method on routinely collected data from the HIV epidemic among gay and bisexual men in England and Wales.

摘要

基于CD4的多状态反向计算方法是监测艾滋病流行情况的关键，通过梳理它们对观察到的监测数据的相互关联贡献，提供艾滋病发病率和诊断率的估计值。本文将现有方法扩展到特定年龄组的情况，允许联合估计特定年龄和时间的发病率和诊断率，并推导其他感兴趣的流行病学指标。这有助于识别感染风险较高的特定年龄组，这对于指导公共卫生干预措施至关重要。我们通过模拟研究，探讨了各种双变量样条对潜在的特定年龄和时间发病率进行非参数建模的适用性，并在英格兰和威尔士男同性恋和双性恋男性艾滋病流行的常规收集数据上说明了我们的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/6433486516f3/10985_2019_9465_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/577aaba81f7f/10985_2019_9465_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/1dc4d95852cb/10985_2019_9465_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/5bdb11d4638d/10985_2019_9465_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/b30643c271c5/10985_2019_9465_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/e42770155a49/10985_2019_9465_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/26c37196a180/10985_2019_9465_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/c3a7d417db67/10985_2019_9465_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/5ce41c47af1d/10985_2019_9465_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/6433486516f3/10985_2019_9465_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/577aaba81f7f/10985_2019_9465_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/1dc4d95852cb/10985_2019_9465_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/610beba921a8/10985_2019_9465_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/5bdb11d4638d/10985_2019_9465_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/b30643c271c5/10985_2019_9465_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/e42770155a49/10985_2019_9465_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/26c37196a180/10985_2019_9465_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/c3a7d417db67/10985_2019_9465_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/5ce41c47af1d/10985_2019_9465_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a626/6776486/6433486516f3/10985_2019_9465_Fig10_HTML.jpg

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