Refusal bias in HIV prevalence estimates from nationally representative seroprevalence surveys.
Refusal bias in HIV prevalence estimates from nationally representative seroprevalence surveys.
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DOI：
10.1097/qad.0b013e3283269e13
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发表时间：
2009-03-13
期刊：
影响因子：
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通讯作者：
Eaton J
中科院分区：
文献类型：
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作者：
Reniers G;Eaton J
评估事先了解自己的艾滋病毒状况与在基于人口的调查中拒绝艾滋病毒检测的可能性之间的关系，并探讨其在艾滋病毒流行率估计中产生偏差的可能性。使用纵向调查数据从马拉维，我们估计艾滋病毒阳性状态的先验知识和随后拒绝艾滋病毒检测之间的关系。我们使用该参数来开发一个启发式模型的拒绝偏见，适用于六个人口与健康调查，其中拒绝艾滋病毒状态没有观察到。该模型只调整拒绝偏见的条件下完成的采访。从理论上讲，艾滋病毒流行率、先前的检测率和拒绝艾滋病毒检测是高度相关的。马拉维的数据进一步表明，在知道自己状况的个人中，艾滋病毒阳性者拒绝检测的可能性是艾滋病毒阴性者的4.62倍（95%置信区间，2.60-8.21）。根据这一参数和人口与健康调查的其他输入，我们的模型预测了国家艾滋病毒流行率估计值的向下偏差，范围从塞内加尔的1.5%（95%置信区间，0.7-2.9）到马拉维的13.3%（95%置信区间，7.2-19.6）。就绝对值而言，塞内加尔艾滋病毒流行率估计数的偏差可以忽略不计，但马拉维为1.6个百分点（95%置信区间，0.8-2.3）。城市人口中的向下偏差更为严重。由于男性的拒绝率较高，血清阳性率调查也往往高估了女性与男性的感染比率。事先了解艾滋病毒状况有助于决定是否参加血清阳性率调查。知情的拒绝可能会对艾滋病毒流行率和感染性别比例的估计产生偏差。
To assess the relationship between prior knowledge of one's HIV status and the likelihood to refuse HIV testing in populations-based surveys and explore its potential for producing bias in HIV prevalence estimates. Using longitudinal survey data from Malawi, we estimate the relationship between prior knowledge of HIV-positive status and subsequent refusal of an HIV test. We use that parameter to develop a heuristic model of refusal bias that is applied to six Demographic and Health Surveys, in which refusal by HIV status is not observed. The model only adjusts for refusal bias conditional on a completed interview. Ecologically, HIV prevalence, prior testing rates and refusal for HIV testing are highly correlated. Malawian data further suggest that amongst individuals who know their status, HIV-positive individuals are 4.62 (95% confidence interval, 2.60–8.21) times more likely to refuse testing than HIV-negative ones. On the basis of that parameter and other inputs from the Demographic and Health Surveys, our model predicts downward bias in national HIV prevalence estimates ranging from 1.5% (95% confidence interval, 0.7–2.9) for Senegal to 13.3% (95% confidence interval, 7.2–19.6) for Malawi. In absolute terms, bias in HIV prevalence estimates is negligible for Senegal but 1.6 (95% confidence interval, 0.8–2.3) percentage points for Malawi. Downward bias is more severe in urban populations. Because refusal rates are higher in men, seroprevalence surveys also tend to overestimate the female-to-male ratio of infections. Prior knowledge of HIV status informs decisions to participate in seroprevalence surveys. Informed refusals may produce bias in estimates of HIV prevalence and the sex ratio of infections.