Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana (Mr Duszynski and Drs Fadel, Dixon, Yiannoutsos, Halverson, and Menachemi); and Regenstrief Institute, Inc, Indianapolis, Indiana (Drs Dixon and Menachemi).
J Public Health Manag Pract. 2022;28(4):E685-E691. doi: 10.1097/PHH.0000000000001508. Epub 2022 Feb 9.
Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation.
Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19-related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave.
Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants.
Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.
当研究中的参与者在可能扭曲研究结论的方面与合格的未参与者存在差异时，就会出现无应答偏差。本研究使用连续波分析，这是一种已建立但未充分利用的方法，来评估一项大规模 SARS-CoV-2 患病率研究中的无应答偏差。这种方法利用提醒来诱导个人参与。基于响应连续体理论，那些需要多次提醒才能参与的人比那些一收到邀请就参与研究的人更像未应答者，因此可以检查影响参与的因素。
印第安纳州人群 SARS-CoV-2 患病率研究的研究参与者根据需要多少个提醒来诱导参与，分为 3 组（例如，波）。然后使用独立变量来确定关键人口统计学特征以及其他假设影响研究参与的变量是否因波而异，使用卡方分析。具体来说，我们检查了种族、年龄、性别、教育程度、健康状况、烟草行为、COVID-19 相关症状、参与研究的原因以及 SARS-CoV-2 阳性率是否因波而异。
受访者包括 3658 人，其中第 1 波 1495 人（40.9%）、第 2 波 1246 人（34.1%）、第 3 波 917 人（25%），总参与率为 23.6%。在所有检查的变量中，各波之间没有差异，这表明与早期参与者相比，需要额外提醒的参与者具有相似的特征。
使用既定技术，我们在一个响应率相对较低的随机样本中没有发现无应答偏差的证据。假设增加一波参与者不太可能改变原始研究结论。连续波分析是一种有效的简单工具，可以让公共卫生研究人员评估并可能调整任何使用提醒来鼓励参与的流行病学调查中的无应答。
