Strategies to Increase Response Rate and Reduce Nonresponse Bias in Population Health Research: Analysis of a Series of Randomized Controlled Experiments during a Large COVID-19 Study.

IF 3.5 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Christina J Atchison, Nicholas Gilby, Galini Pantelidou, Sam Clemens, Kevin Pickering, Marc Chadeau-Hyam, Deborah Ashby, Wendy S Barclay, Graham S Cooke, Ara Darzi, Steven Riley, Christl A Donnelly, Helen Ward, Paul Elliott
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引用次数: 0

Abstract

Background: High response rates are needed in population-based studies, as nonresponse reduces effective sample size and bias affects accuracy and decreases the generalizability of the study findings.

Objective: We tested different strategies to improve response rate and reduce nonresponse bias in a national population-based COVID-19 surveillance program in England, United Kingdom.

Methods: Over 19 rounds, a random sample of individuals aged 5 years and older from the general population in England were invited by mail to complete a web-based questionnaire and return a swab for SARS-CoV-2 testing. We carried out several nested randomized controlled experiments to measure the impact on response rates of different interventions, including (1) variations in invitation and reminder letters and SMS text messages and (2) the offer of a conditional monetary incentive to return a swab, reporting absolute changes in response and relative response rate (95% CIs).

Results: Monetary incentives increased the response rate (completed swabs returned as a proportion of the number of individuals invited) across all age groups, sex at birth, and area deprivation with the biggest increase among the lowest responders, namely teenagers and young adults and those living in more deprived areas. With no monetary incentive, the response rate was 3.4% in participants aged 18-22 years, increasing to 8.1% with a £10 (US $12.5) incentive, 11.9% with £20 (US $25.0), and 18.2% with £30 (US $37.5) (relative response rate 2.4 [95% CI 2.0-2.9], 3.5 [95% CI 3.0-4.2], and 5.4 [95% CI 4.4-6.7], respectively). Nonmonetary strategies had a modest, if any, impact on response rate. The largest effect was observed for sending an additional swab reminder (SMS text message or email). For example, those receiving an additional SMS text message were more likely to return a completed swab compared to those receiving the standard email-SMS approach, 73.3% versus 70.2%: percentage difference 3.1% (95% CI 2.2%-4.0%).

Conclusions: Conditional monetary incentives improved response rates to a web-based survey, which required the return of a swab test, particularly for younger age groups. Used in a selective way, incentives may be an effective strategy for improving sample response and representativeness in population-based studies.

人群健康研究中提高应答率和减少无应答偏倚的策略:一项大型COVID-19研究中的一系列随机对照实验分析
背景:基于人群的研究需要高应答率,因为无应答会减少有效样本量,偏倚会影响准确性并降低研究结果的普遍性。目的:我们在英国英格兰的一项基于全国人群的COVID-19监测项目中测试了提高反应率和减少非反应偏倚的不同策略。方法:在19轮的时间里,通过邮件邀请英格兰普通人群中随机抽取5岁及以上的个体,填写一份基于网络的问卷,并返回拭子进行SARS-CoV-2检测。我们进行了几个嵌套随机对照实验,以衡量不同干预措施对回复率的影响,包括(1)邀请和提醒信件和短信的变化;(2)提供有条件的金钱激励以返回棉签,报告了响应和相对响应率的绝对变化(95% ci)。结果:金钱激励提高了所有年龄组、出生性别和地区贫困的回复率(已完成的棉签回复率占受邀人数的比例),其中最低应答者的增幅最大,即青少年和年轻人以及生活在更贫困地区的人。在没有金钱激励的情况下,18-22岁参与者的反应率为3.4%,在10英镑(12.5美元)激励下增加到8.1%,20英镑(25.0美元)增加到11.9%,30英镑(37.5美元)增加到18.2%(相对反应率分别为2.4 [95% CI 2.0-2.9], 3.5 [95% CI 3.0-4.2]和5.4 [95% CI 4.4-6.7])。非货币策略对回应率的影响不大,如果有的话。观察到,发送额外的棉签提醒(短信或电子邮件)效果最大。例如,那些收到额外短信的人比那些收到标准电子邮件短信的人更有可能返回完整的棉签,73.3%对70.2%:百分比差异3.1% (95% CI 2.2%-4.0%)。结论:有条件的金钱激励提高了网络调查的回复率,该调查需要返回棉签测试,特别是对于年轻年龄组。在以人群为基础的研究中,以选择性的方式使用激励措施可能是改善样本反应和代表性的有效策略。
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来源期刊
CiteScore
13.70
自引率
2.40%
发文量
136
审稿时长
12 weeks
期刊介绍: JMIR Public Health & Surveillance (JPHS) is a renowned scholarly journal indexed on PubMed. It follows a rigorous peer-review process and covers a wide range of disciplines. The journal distinguishes itself by its unique focus on the intersection of technology and innovation in the field of public health. JPHS delves into diverse topics such as public health informatics, surveillance systems, rapid reports, participatory epidemiology, infodemiology, infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media and social media campaigns, health communication, and emerging population health analysis systems and tools.
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