Underestimation of HIV prevalence in surveys when some people already know their status, and ways to reduce the bias
Underestimation of HIV prevalence in surveys when some people already know their status, and ways to reduce the bias
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DOI：
10.1097/qad.0b013e32835848ab
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发表时间：
2013-01-14
期刊：
影响因子：
3.8
通讯作者：
Glynn, Judith R.
中科院分区：
文献类型：
--
作者：
Floyd, Sian;Molesworth, Anna;Glynn, Judith R.
目的：量化一般人群调查中因先前 HIV 检测而导致的拒绝偏差及其对 HIV 患病率估计的影响。设计：2006 年至 2010 年期间，在马拉维北部的 33 000 名人口监测人群中进行了四次年度、横断面、挨家挨户的 HIV 血清调查。方法：分析了 HIV 状况的事先了解对后续调查中检测接受度的影响。然后使用十种调整方法估计艾滋病毒感染率，包括年龄标准化；缺失数据的多重插补；包含拒绝偏差的条件概率方程方法；使用先前和随后的艾滋病毒结果的纵向数据；包括自我报告的艾滋病毒状况；并包括相关的抗逆转录病毒治疗临床数据。结果：每次血清调查中 HIV 检测的接受率为 55-65%。到 2009/2010 年，79% 的男性和 85% 的女性至少接受过一次测试。已知的艾滋病毒阳性者更有可能缺席，并拒绝接受采访和检测。使用纵向数据并调整拒绝偏倚，2008/2009 年男性艾滋病毒感染率的最佳估计为 7%，女性为 9%。使用多重插补的估计值分别为 4.8% 和 6.4%。使用条件概率方法，使用本研究中观察到的艾滋病毒阳性与艾滋病毒阴性个体的拒绝风险比，可以得到良好的估计，但在使用之前唯一发表的对该比率的估计时，则不然，即使这也是来自马拉维。结论：随着知道自己艾滋病毒状况的人口比例增加，基于调查的患病率估计变得越来越有偏差。由于横截面数据的调整方法仍然难以捉摸，高覆盖率的数据来源（例如产前诊所监测）仍然很重要。 (c) 2013 年 Wolters Kluwer Health 竖条 Lippincott Williams & Wilkins 艾滋病 2013 年，27:233-242
Objective: To quantify refusal bias due to prior HIV testing, and its effect on HIV prevalence estimates, in general-population surveys.Design: Four annual, cross-sectional, house-to-house HIV serosurveys conducted during 2006-2010 within a demographic surveillance population of 33 000 in northern Malawi.Methods: The effect of prior knowledge of HIV status on test acceptance in subsequent surveys was analysed. HIV prevalence was then estimated using ten adjustment methods, including age-standardization; multiple imputation of missing data; a conditional probability equations approach incorporating refusal bias; using longitudinal data on previous and subsequent HIV results; including self-reported HIV status; and including linked antiretroviral therapy clinic data.Results: HIV test acceptance was 55-65% in each serosurvey. By 2009/2010 79% of men and 85% of women had tested at least once. Known HIV-positive individuals were more likely to be absent, and refuse interviewing and testing. Using longitudinal data, and adjusting for refusal bias, the best estimate of HIV prevalence was 7% in men and 9% in women in 2008/2009. Estimates using multiple imputations were 4.8 and 6.4%, respectively. Using the conditional probability approach gave good estimates using the refusal risk ratio of HIV-positive to HIV-negative individuals observed in this study, but not when using the only previously published estimate of this ratio, even though this was also from Malawi.Conclusion: As the proportion of the population who know their HIV-status increases, survey-based prevalence estimates become increasingly biased. As an adjustment method for cross-sectional data remains elusive, sources of data with high coverage, such as antenatal clinics surveillance, remain important. (c) 2013 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins AIDS 2013, 27:233-242