Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC

Abstract

Background: Longitudinal studies are crucial for identifying potential risk factors for infection with, and consequences of, COVID-19, but relationships can be biased if they are associated with invitation and response to data collection. We describe factors relating to questionnaire invitation and response in COVID-19 questionnaire data collection in a multigenerational birth cohort (the Avon Longitudinal Study of Parents and Children, ALSPAC). Methods: We analysed online questionnaires completed between the beginning of the pandemic and easing of the first UK lockdown by participants with valid email addresses who had not actively disengaged from the study. We assessed associations of pre-pandemic sociodemographic, behavioural, anthropometric and health-related factors with: i) being sent a questionnaire; ii) returning a questionnaire; and iii) item response (for specific questions). Analyses were conducted in three cohorts: the index children born in the early 1990s (now young adults; 41 variables assessed), their mothers (35 variables) and the mothers’ partners (27 variables). Results: Of 14,849 young adults, 41% were sent a questionnaire, of whom 57% returned one. Item response was >95%. In this cohort, 78% of factors were associated with being sent a questionnaire, 56% with returning one, and, as an example of item response, 20% with keyworker status response. For instance, children from mothers educated to degree-level had greater odds of being sent a questionnaire (OR=5.59; 95% CI=4.87-6.41), returning one (OR=1.60; 95% CI=1.31-1.95), and responding to items (e.g., keyworker status OR=1.65; 95% CI=0.88-3.04), relative to children from mothers with fewer qualifications. Invitation and response rates and associations were similar in all cohorts. Conclusions: These results highlight the importance of considering potential biases due to non-response when using longitudinal studies in COVID-19 research and interpreting results. We recommend researchers report response rates and factors associated with invitation and response in all COVID-19 observational research studies, which can inform sensitivity analyses.

Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors in two UK longitudinal studies

“…This may cause collider bias and affect findings as outlined elsewhere ( Griffith et al, 2020 ; Munafò et al, 2018 ). For example, indicators of poorer health have been associated with lower response to COVID-19 questionnaires in ALSPAC ( Fernández-Sanlés et al, 2021 ), which may bias the observed results. Acknowledging the potential effects of biases, the replication of multiple associations with lower antibody levels across compositionally varied TwinsUK and ALSPAC cohorts and across multiple rounds of vaccination support the robustness of our findings.…”

Effects of portable pedal machines at work on lipoprotein subfraction profile in sedentary workers – the REMOVE study

“…However, this study is very novel as it is the first study assessing the effects of active workstation on lipoprotein subfraction profile in healthy sedentary workers. The limited small sample size was partly due to recruitment difficulties linked with the Covid-19 pandemic when the study was conducted, which may be another source of bias [61,62]. Nevertheless, the study was a randomized controlled trial, precluding recruitment bias as well as the bias related with external factors.…”

A framework for assessing selection and misclassification bias in mendelian randomisation studies: an illustrative example between body mass index and covid-19

“…When the exposure and outcome (or a cause of these) influence the probability of being selected into the analytical sample, collider bias can be introduced 16. In the context of covid-19, selection bias can arise from differential response to covid-19 study questionnaires, differential risk of being exposed to SARS-Cov-2 infection, and differences in who receives a SARS-CoV-2 test or who is admitted to hospital 1415. For example, if a person never receives a SARS-CoV-2 test, their covid-19 status remains unknown, and they would not be selected into any analysis that depends on having a test (table 1).…”

“… 16 In the context of covid-19, selection bias can arise from differential response to covid-19 study questionnaires, differential risk of being exposed to SARS-Cov-2 infection, and differences in who receives a SARS-CoV-2 test or who is admitted to hospital. 14 15 For example, if a person never receives a SARS-CoV-2 test, their covid-19 status remains unknown, and they would not be selected into any analysis that depends on having a test ( table 1 ). Genome wide association studies (GWAS) are studies where genetic variants are tested for an association with a phenotype (ie, a trait or disease outcome, such as body mass index (BMI)).…”

“…We estimated the genetic correlation between covid-19 SNPs and SNPs associated with potential predictors of testing (smoking, education, and income), 53 determined a priori from estimates in previous studies 14 15 54 and available GWAS summary data. We found evidence of genetic correlation between most covid-19 outcomes and BMI, smoking, education, and income in three of the covid-19 GWAS (but not for C1 and D1 GWAS) ( table 2 ).…”