Modeling Interviewer Effects in a Large National Health Study

Interviewers play a critical role in determining the quality of data collected in face-to-face surveys. Interviewers can have positive effects on recruiting sample members to participate, leading to higher response rates. Conversely, interviewers can have negative effects on the quality of measurement. The literature suggests that interviewers can bias answers when observable characteristics of the interviewer influence the respondent to answer questions a certain way. For example, the sex or race of interviewers may influence respondents’ answers about their own attitudes toward sex or race. However, it is more common for differences in interviewer behavior, such as how questions are asked or how answers are probed, to affect the variability of responses. These differences in interviewer characteristics and behaviors lead to answers that are clustered by the interviewer giving rise to a within interviewer correlation that inflates the estimated variability of survey statistics. The size of this increased variability or interviewer effect is often difficult to estimate in face-to-face surveys since standard estimation techniques assume interpenetrated designs that randomly assign interviewers to areas. Instead, multilevel models that control for respondent and area effects are often used to isolate interviewer effects from area effects in non-interpenetrated designs. This study uses multilevel models to model interviewer effects in the National Health Interview Survey (NHIS), a large national survey of approximately 35,000 households conducted annually. The NHIS is an entirely interviewer-administered survey conducted primarily face-to-face with some telephone follow-up. Using 2017 data, we begin by estimating multilevel models to compute estimates of interviewer variance across a variety of questions in the NHIS. The goal is to determine the extent to which interviewer variance is present in NHIS estimates. The analysis will include questions that vary by characteristics such as question sensitivity, question length, and response format. The models will include controls for Census demographics within areas to help separate interviewer effects from area effects. The next step in the analysis will attempt to understand the extent to which certain interviewer-level variables can explain the interviewer effects, including how much of the interviewer-level variance is explained by interviewer experience. We also include a measure of the interviewers’ cooperation rates to understand if differences in nonresponse error may explain some of the interviewer-level variance in key survey estimates. Finally, we will include interviewer-level variables such as average pace of the interview to understand how much of the variance may be explained by interviewer behavior. The overall goals of the paper are to 1) understand which questions on the NHIS are most vulnerable to interviewer effects, and 2) explain the relative impact of different potential causes of those effects

Health need and the use of alternative medicine among adults who do not use conventional medicine

<p>Abstract</p> <p>Background</p> <p>We hypothesize that a substantial portion of individuals who forgo conventional care in a given year turn to some form of alternative medicine. This study also examines whether individuals who use only alternative medicine will differ substantially in health and sociodemographic status from individuals using neither alternative medicine nor conventional care in a given year. To identify those factors that predict alternative medicine use in those not using conventional care, we employed the socio-behavioral model of healthcare utilization.</p> <p>Methods</p> <p>The current study is a cross-sectional regression analysis using data from the 2002 National Health Interview Survey. Data were collected in-person from 31,044 adults throughout the 50 states and the District of Columbia.</p> <p>Results</p> <p>19.3% of adults (38.3 million) did not use conventional care in a 12 month period, although 39.5% of these individuals (14.7 million) reported having one or more problems with their health. Of those not using conventional care, 24.8% (9.5 million) used alternative medicine. Users of alternative medicine had more health needs and were more likely to delay conventional care because of both cost and non-cost factors compared to those not using alternative medicine. While individual predisposing factors (gender, education) were positively associated with alternative medicine use, enabling factors (poverty status, insurance coverage) were not.</p> <p>Conclusions</p> <p>We found that a quarter of individuals who forgo conventional care in a given year turn towards alternative medicine. Our study suggests that the potential determinants of using only alternative medicine are multifactorial. Future research is needed to examine the decision process behind an individual's choice to use alternative medicine but not conventional medicine and the clinical outcomes of this choice.</p

Why it takes an 'ontological shock' to prompt increases in small firm resilience : sensemaking, emotions and flood risk

This article uses a sensemaking approach to understand small firms’ responses to the threat of external shocks. By analysing semi-structured interviews with owners of flooded small firms, we investigate how owners process flood experiences and explore why such experiences do not consistently lead to the resilient adaptation of premises. We, conclude that some of the explanation for low levels of adaptation relates to a desire to defend existing sensemaking structures and associated identities. Sensemaking structures are only revised if these structures are not critical to business identity, or if a flood constitutes an ‘ontological shock’ and renders untenable existing assumptions regarding long-term business continuity. This article has implications for adaptation to the growing risk of flooding, climate change and external shocks. Future research analysing external shocks would benefit from using a sensemaking approach and survey studies should include measurements of ‘ontological’ impact as well as material and financial damage. In addition, those designing information campaigns should take account of small firms’ resistance to information that threatens their existing sensemaking structures and social identities

Health behaviors and risk factors in those who use complementary and alternative medicine

<p>Abstract</p> <p>Background</p> <p>Surveys have generally found that individuals more likely to use complementary and alternative medicine are female, live in the western United States, are likely to have a health complaint, and have a higher socioeconomic status than do nonusers. What is not known is the extent to which those who use complementary and alternative medicine also engage in positive health behaviors, such as smoking cessation or increased physical activity and/or exhibit fewer health risk factors such as obesity. This has been identified as a key research question in a recent Institute of Medicine report. In the present study we sought to determine whether the use of complementary and alternative medicine is associated with health behaviors or risk factors known to impact on health status.</p> <p>Methods</p> <p>The current study is a cross-sectional regression analysis using data from the 2002 National Health Interview Survey. Data were collected in-person from 31,044 adults throughout the 50 states and the District of Columbia.</p> <p>Results</p> <p>After controlling for a range of other factors, we found that engaging in leisure-time physical activity, having consumed alcohol in one's life but not being a current heavy drinker, and being a former smoker are independently associated with the use of CAM. Obese individuals are slightly less likely to use CAM than individuals with a healthy body-mass index. No significant associations were observed between receipt of an influenza vaccine and CAM use.</p> <p>Conclusion</p> <p>Those engaging in positive health behaviors and exhibiting fewer health risk factors are more likely to use CAM than those who forgo positive health behaviors or exhibit more health risk factors. The fact that users of CAM tend to pursue generally healthy lifestyles suggests that they may be open to additional recommendations toward optimizing their health.</p

The social threats of COVID-19 for people with chronic pain.

In this review, we draw attention to the potential for social and systemic changes associated with attempts to contain the spread of COVID-19 to precipitate, maintain and exacerbate pain by increasing the social threats faced by individuals with chronic pain. We also suggest strategies for mitigating the social impact of COVID-19 on those living with chronic pain, for instance by learning from the resilience demonstrated by people in pain who have found ways to deal with social threat. Lastly, we suggest several time-critical, high-impact research questions for further investigation.K. Karos is a postdoctoral researcher supported by the Research Foundation, Flanders, Belgium (grant 1244820N). F. P. Kapos is a PhD candidate who is supported by the Patrick-Beresford Fellowship in Social Epidemiology and the P.E.O. International Peace Scholarship. H. Devan is a Postdoctoral Fellow supported by the Centre for Health, Activity and Rehabilitation Research (CHARR) Postdoctoral Fellowship at the School of Physiotherapy, University of Otago, New Zealand. This review was an initiative of the Social Aspects in Pain Special Interest Group (SocSIG) of the International Association of Pain (IASP)

Chapter 21: Modeling Interviewer Effects in the National Health Interview Survey. Appendix 21

Supplemental Table A21.1 Questions, Question Characteristics, and Intra-Interviewer Correlations (IIC) Table A21.2 Descriptive Statistics for Respondent and Case Characteristics Included in Multi-Level Models Table A21.3 Descriptive Statistics for County Measures Included in Multi-Level Models Table A21.4 Descriptive Statistics for Interviewer Characteristics Included in Multi-Level Models Table A21.5 Mock Dataset Structure Depicting Questions, Interviewer Groups, and IIC