Fieldworker effects on substance use reporting in a rural South African setting

Aims: Fieldworkers capturing reports of sensitive behaviors, such as substance use, may influence survey responses and represent an important factor in response validity. We explored the effects and interaction of fieldworker and respondent characteristics (age and gender) in substance (tobacco and alcohol) use reporting. We aim to further the literature on conditional social attribution effects on substance use reporting in the context of South Africa, where accurate estimates of modifiable risk factors are critical for medical and public health practitioners and policy-makers in efforts to reduce chronic disease burden and mortality.Design: We modeled substance use reporting using binary logistic regression. We also tested if fieldworker effects remained, allowing for correlation in reporting for respondents with the same fieldworker using multi-level logistic regression.Setting: Agincourt Health and Socio-Demographic Surveillance System site, rural South Africa.Participants: We used data from a 2010–2011 study on HIV and cardiometabolic risk, ages 15+ (N = 4,684).Measures: Lifetime and current alcohol and tobacco use.Findings: Respondents reported higher lifetime smoking use to older fieldworkers. Male respondents reported higher lifetime alcohol use to older fieldworkers. No fieldworker effects were significant on reports of current smoking. An older, male fieldworker increased the probability of reports of current alcohol use. Adjusting for intra-fieldworker correlation explained many of the observed fieldworker effects.Conclusions: Our results highlight the importance of adjusting for interviewer characteristics to improve the accuracy of chronic disease risk factor estimates and validity of inferred associations. We recommend that surveys collecting information that may be subject to response bias routinely include anonymized fieldworker identifiers and demographic information. Analysts can then use these additional fieldworker data as a tool in evaluating probable bias in respondent reporting.