Amsterdam Institute for International Development; VU University Amsterdam, Faculty of Economics; Tinbergen Institute, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands,
Demography. 2014 Jun;51(3):1131-57. doi: 10.1007/s13524-014-0290-0.
In 2007, UNAIDS corrected estimates of global HIV prevalence downward from 40 million to 33 million based on a methodological shift from sentinel surveillance to population-based surveys. Since then, population-based surveys are considered the gold standard for estimating HIV prevalence. However, prevalence rates based on representative surveys may be biased because of nonresponse. This article investigates one potential source of nonresponse bias: refusal to participate in the HIV test. We use the identity of randomly assigned interviewers to identify the participation effect and estimate HIV prevalence rates corrected for unobservable characteristics with a Heckman selection model. The analysis is based on a survey of 1,992 individuals in urban Namibia, which included an HIV test. We find that the bias resulting from refusal is not significant for the overall sample. However, a detailed analysis using kernel density estimates shows that the bias is substantial for the younger and the poorer population. Nonparticipants in these subsamples are estimated to be three times more likely to be HIV-positive than participants. The difference is particularly pronounced for women. Prevalence rates that ignore this selection effect may be seriously biased for specific target groups, leading to misallocation of resources for prevention and treatment.
2007 年，艾滋病署根据从哨点监测到基于人群的调查的方法转变，将全球艾滋病毒流行率估计数从 4000 万向下修正为 3300 万。从那时起，基于人群的调查被认为是估计艾滋病毒流行率的黄金标准。然而，基于代表性调查的流行率可能存在偏差，因为存在无回应。本文研究了一种潜在的无回应偏差来源：拒绝参加艾滋病毒检测。我们使用随机分配的访谈者的身份来识别参与效应，并使用 Heckman 选择模型估计未观察到特征的艾滋病毒流行率。该分析基于对纳米比亚城市 1992 人的调查，其中包括艾滋病毒检测。我们发现，拒绝造成的偏差对总体样本来说并不显著。然而，使用核密度估计的详细分析表明，对于年轻和贫困人群来说，这种偏差是相当大的。这些子样本中的非参与者被估计比参与者感染艾滋病毒的可能性高三倍。对于女性来说，这种差异尤其明显。忽略这种选择效应的流行率可能会对特定目标群体产生严重的偏差，导致预防和治疗资源的错误分配。
