Institute for Global Health, University College London, London, UK.
Department of Global Health, University of Washington, Seattle, WA, USA.
BMC Med Res Methodol. 2019 Mar 15;19(1):60. doi: 10.1186/s12874-019-0703-2.
Interviewers can substantially affect self-reported data. This may be due to random variation in interviewers' ability to put respondents at ease or in how they frame questions. It may also be due to systematic differences such as social distance between interviewer and respondent (e.g., by age, gender, ethnicity) or different perceptions of what interviewers consider socially desirable responses. Exploration of such variation is limited, especially in stigmatized populations.
We analyzed data from a randomized controlled trial of HIV self-testing amongst 965 female sex workers (FSWs) in Zambian towns. In the trial, 16 interviewers were randomly assigned to respondents. We used hierarchical regression models to examine how interviewers may both affect responses on more and less sensitive topics, and confound associations between key risk factors and HIV self-test use.
Model variance (ICC) at the interviewer level was over 15% for most topics. ICC was lower for socio-demographic and cognitively simple questions, and highest for sexual behaviour, substance use, violence and psychosocial wellbeing questions. Respondents reported significantly lower socioeconomic status and more sex-work related violence to female interviewers. Not accounting for interviewer identity in regressions predicting HIV self-test behaviour led to coefficients moving from non-significant to significant.
We found substantial interviewer-level effects for prevalence and associational outcomes among Zambian FSWs, particularly for sensitive questions. Our findings highlight the importance of careful training and response monitoring to minimize inter-interviewer variation, of considering social distance when selecting interviewers and of evaluating whether interviewers are driving key findings in self-reported data.
clinicaltrials.gov NCT02827240 . Registered 11 July 2016.
访员会对自我报告的数据产生重大影响。这可能是由于访员让受访者感到轻松的能力或他们提出问题的方式存在随机差异，也可能是由于受访者与访员之间的社会距离（例如，年龄、性别、种族）或访员对什么是社会期望的反应的不同看法等系统性差异所致。这种差异的探索受到限制，尤其是在污名化人群中。
我们分析了赞比亚城镇中针对 965 名女性性工作者（FSWs）进行的 HIV 自我检测的随机对照试验数据。在该试验中，16 名访员被随机分配给受访者。我们使用层次回归模型来检验访员如何影响更敏感和不太敏感的话题的回答，以及混淆关键风险因素与 HIV 自我检测使用之间的关联。
对于大多数话题，访员层面的模型方差（ICC）超过 15%。社会人口统计学和认知简单问题的 ICC 较低，而性行为、药物使用、暴力和心理社会健康问题的 ICC 较高。受访者向女性访员报告的社会经济地位较低，性工作相关暴力事件较多。在回归预测 HIV 自我检测行为时，如果不考虑访员身份，系数会从无显著意义变为显著。
我们发现，在赞比亚 FSWs 中，访员层面的影响对流行率和关联结果都很大，特别是对于敏感问题。我们的研究结果强调了精心培训和响应监测的重要性，以尽量减少访员之间的差异，在选择访员时考虑社会距离，并评估访员是否在推动自我报告数据中的关键发现。
clinicaltrials.gov NCT02827240。注册于 2016 年 7 月 11 日。
