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估算和预测 Spectrum 病例监测和生命登记工具中新的 HIV 诊断和发病率。

Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool.

机构信息

aCenter for Modeling and Analysis, Avenir Health, Glastonbury, Connecticut, USA bStrategic Information Department, UNAIDS, Geneva, Switzerland cDepartment of Infectious Disease Epidemiology, Imperial College London, London, UK.

出版信息

AIDS. 2019 Dec 15;33 Suppl 3(Suppl 3):S245-S253. doi: 10.1097/QAD.0000000000002324.

Abstract

OBJECTIVE

The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15-49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round.

METHODS

Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the 'r-logistic' model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4 at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds.

RESULTS

We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4 at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10-0.45] in 1990 to about 0.77 (IQR: 0.50-0.89) in 2017. The mean CD4 at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224-567) in 1990 and 373 cells/μl (IQR: 174-475) by 2017.

CONCLUSION

Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status.

摘要

目的

联合艾滋病规划署支持的 Spectrum 软件包被世界上大多数国家用于监测艾滋病毒流行情况。在 Spectrum 中,通过拟合血清流行率监测和调查数据或生成与病例监测和人口登记数据一致的曲线,来推算成年人(15-49 岁)的艾滋病毒发病率趋势,如新诊断感染或艾滋病相关死亡人数的历史趋势。本文描述了用于 2019 年估计轮次的病例监测和人口登记(CSAVR)工具的开发和应用。

方法

CSAVR 中的发病率可以直接使用单 logistic、双 logistic 或样条函数进行估计,也可以通过“r-logistic”模型间接估计,该模型使用 logistic 函数表示(对数转换后的)人均传播率。假设诊断的倾向是单调的,遵循伽马累积分布函数,并随时间的推移与感染后的死亡率成比例。模型参数是根据新报告的艾滋病毒病例的历史监测数据、艾滋病毒诊断时的平均 CD4 计数以及人口登记系统中艾滋病相关死亡的估计值综合得出的。贝叶斯校准用于确定最佳拟合的发病率趋势和不确定性范围。

结果

我们使用 CSAVR 估算了 31 个欧洲、拉丁美洲、中东和亚太国家的艾滋病毒发病率、新诊断数量、诊断时的平均 CD4 计数和未确诊比例。样条模型在大多数国家(45%)中似乎提供了最佳拟合,其次是 r-logistic(25%)、双 logistic(25%)和单 logistic 模型。知道自己艾滋病毒状况的艾滋病毒阳性者比例从 1990 年的约 0.31(四分位距:0.10-0.45)增加到 2017 年的约 0.77(四分位距:0.50-0.89)。诊断时的平均 CD4 计数似乎保持稳定,在 1990 年约为 410 个细胞/μl(四分位距:224-567),到 2017 年约为 373 个细胞/μl(四分位距:174-475)。

结论

许多中等收入和高收入国家有可靠的病例监测和人口登记数据,而艾滋病毒血清流行率监测和调查数据可能很少。在这些国家,CSAVR 提供了一种更简单、改进的方法来估计和预测艾滋病毒发病率和艾滋病毒感染状况知识的趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df5d/6919234/ee966f9a7195/aids-33-s245-g014.jpg

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