Evaluation of Selection Bias in an Internet-based Study of Pregnancy Planners. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Hatch, Elizabeth E.; Hahn, Kristen A.; Wise, Lauren A.; Mikkelsen, Ellen M.; Kumar, Ramya; Fox, Matthew P.; Brooks, Daniel R.; Riis, Anders H.; Sorensen, Henrik Toft; Rothman, Kenneth J.
• Is Part Of: Epidemiology. Volume 27:Issue 1(2016:Jan.)
• Publication Date: 2016-01
• Subjects: Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000400
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : Selection bias is a potential concern in all epidemiologic studies, but it is usually difficult to assess. Recently, concerns have been raised that internet-based prospective cohort studies may be particularly prone to selection bias. Although use of the internet is efficient and facilitates recruitment of subjects that are otherwise difficult to enroll, any compromise in internal validity would be of great concern. Few studies have evaluated selection bias in internet-based prospective cohort studies. Using data from the Danish Medical Birth Registry from 2008 to 2012, we compared six well-known perinatal associations (e.g., smoking and birth weight) in an internet-based preconception cohort (Snart Gravid n = 4, 801) with the total population of singleton live births in the registry (n = 239, 791). We used log-binomial models to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for each association. We found that most results in both populations were very similar. For example, maternal obesity was associated with an increased risk of delivering a macrosomic infant in Snart Gravid (RR = 1.5; 95% CI: 1.2, 1.7) and the total population (RR = 1.5; 95% CI: 1.45, 1.53), and maternal smoking of >10 cigarettes per day was associated with a higher risk of low birth weight (RR = 2.7; 95% CI: 1.2, 5.9 vs. RR = 2.9; 95% CI: 2.6, 3.1) in Snart Gravid and the total population, respectively. We cannot be certain that our results would apply to other associations or different populations. Nevertheless, our results suggest that recruitment of reproductive aged women via the internet may be no more prone to selection bias than traditional methods of recruitment.