Evaluating the quality of HIV epidemiologic evidence for populations in the absence of a reliable sampling frame: a modified quality assessment tool.
Evaluating the quality of HIV epidemiologic evidence for populations in the absence of a reliable sampling frame: a modified quality assessment tool.
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DOI：
10.1016/j.annepidem.2021.07.009
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发表时间：
2022-01
影响因子：
5.6
通讯作者：
Baral S
中科院分区：
文献类型：
--
作者：
Rao A;Schwartz S;Viswasam N;Rucinski K;Van Wickle K;Sabin K;Wheeler T;Zhao J;Baral S
艾滋病毒高危关键人群的抽样框架很少，如性工作者、男男性行为者、吸毒者和变性人。没有可靠的抽样框架，大多数数据收集依赖于非概率抽样方法，包括基于网络的方法(例如，受访者驱动的抽样)和基于地点的方法(例如，时间-地点抽样)。这些研究的执行和报告质量变化很大，因此范围很广的估计常常很难比较。在这里，开发了一种改进的质量评估工具--通过非概率抽样产生的数据的全球艾滋病毒质量评估工具(GHQAT)，以评估使用非概率方法产生的艾滋病毒流行病学证据的质量。GHQAT评估三个主要领域：研究设计、研究实施和特定指标标准(流行率、发病率、艾滋病毒持续护理和人口规模估计)。研究设计领域主要集中在目标和研究人群的规范上。研究实施领域与抽样实施有关。每一具体指标部分都包含与该具体指标有关的项目。对与艾滋病毒和关键人群有关的流行病学数据进行了更大规模的系统性审查，生成了50项研究的随机子集，并由两名独立审查者使用GHQAT进行了审查。通过计算分配给研究设计、研究实施和每个指标特定标准的分数的组内相关系数来评估评分者之间的可靠性。一致性被归类为差(0.00-0.50)、可(0.51-0.70)和好(0.71-1.00)。描述了每个部分的好、中、差分数的分布情况。总体而言，两位独立审查员之间的一致意见是好的(ICC>；0.7)。对于评估HIV持续护理的部分，一致性最好(ICC=0.96)。对于HIV发病率，观察到了完全一致的结果，但这可能是因为回顾了该评估发病率的少数研究(n=3)。在所回顾的研究中，2%(n=1)的研究设计得分为“差”，而50%(n=25)的研究实施得分为“差”。越来越多的人认识到，满足关键人群的艾滋病毒预防和治疗需求是整个艾滋病毒流行环境中艾滋病毒防治工作的核心，尽管描述具体需求的数据仍然变化无常，在最令人羞辱的环境中信息量最少。在这里，我们提出了一种有效的工具，通过可靠地综合关键人群可用的非概率流行病学信息的质量，来指导艾滋病毒预防和治疗计划以及流行病学数据收集。这一工具可能有助于阐明研究人员如何不仅改进其研究的实施，而且改进其研究的报告。
Sampling frames rarely exist for key populations at highest risk for HIV, such as sex workers, men who have sex with men, people who use drugs, and transgender populations. Without reliable sampling frames, most data collection relies on non-probability sampling approaches including network-based methods (e.g. respondent driven sampling) and venue-based methods (e.g. time-location sampling). Quality of implementation and reporting of these studies is highly variable, making wide-ranging estimates often difficult to compare. Here, a modified quality assessment tool, Global.HIV Quality Assessment Tool for Data Generated through Non-Probability Sampling (GHQAT), was developed to evaluate the quality of HIV epidemiologic evidence generated using non-probability methods. The GHQAT assesses three main domains: study design, study implementation, and indicator-specific criteria(prevalence, incidence, HIV continuum of care, and population size estimates). The study design domain focuses primarily on the specification of the target and study populations. The study implementation domain is concerned with sampling implementation. Each indicator-specific section contains items relevant to that specific indicator. A random subset of 50 studies from a larger systematic review on epidemiologic data related to HIV and key populations was generated and reviewed using the GHQAT by two independent reviewers. Inter-rater reliability was assessed by calculating intraclass correlation coefficients for the scores assigned to study design, study implementation and each of the indicator-specific criteria. Agreement was categorized as poor(0.00–0.50), fair(0.51–0.70), and good(0.71–1.00). The distribution of good, fair, and poor scores for each section was described. Overall, agreement between the two independent reviewers was good(ICC>0.7). Agreement was best for the section evaluating the HIV continuum of care(ICC=0.96). For HIV incidence, perfect agreement was observed, but this is likely due to the small number of studies reviewed that assessed incidence(n=3). Of the studies reviewed, 2% (n=1) received a score of “poor” for study design, while 50% (n=25) received a score of “poor” for study implementation. Addressing HIV prevention and treatment needs of key populations is increasingly understood to be central to HIV responses across HIV epidemic settings, though data characterizing specific needs remains highly variable with the least amount of information in the most stigmatizing settings. Here, we present an efficient tool to guide HIV prevention and treatment programs as well as epidemiological data collection by reliably synthesizing the quality of available non-probability based epidemiologic information for key populations. This tool may help shed light on how researchers may improve not only the implementation of, but also the reporting on their studies.
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