Do Participants With Different Patterns of Loss to Follow-Up Have Different Characteristics? A Multi-Wave Longitudinal Study

• Published in: Journal of epidemiology 2016-01, Vol.26 (1), p.45-49
• Main Authors: Saiepour, Nargess, Ware, Robert, Najman, Jake, Baker, Peter, Clavarino, Alexandra, Williams, Gail
• Format: Article
• Language: English
• Subjects: Design of experiments; Epidemiology; Others; Pregnancy; Research methodology; Short Communication
• ISSN: 0917-5040; 1349-9092
• DOI: 10.2188/jea.JE20150015

Background: To identify patterns of loss to follow-up and baseline predictors of each pattern. Methods: The Mater-University Study of Pregnancy collected baseline information for 7718 pregnant women who attended Mater Hospital in Brisbane, Australia, from 1981 through 1983. Follow-up data for 6753 eligible participants were collected at 6 months, 5 years, 14 years, 21 years, and 27 years after giving birth. Participants were partitioned into groups of 'Always Responders', 'Returners', 'Leavers', 'Intermittents', and 'Never Responders'. Multinomial logistic regression was used to simultaneously compare baseline characteristics of the last four groups with 'Always Responders'. Results: Being younger, less educated, having no partner, and living in rented housing were associated with being a 'Returner'. Not owning housing, receiving welfare benefits, and being younger, less educated, not married, a smoker, an Aboriginal/Islander, and born in a non-English-speaking country were associated with being a 'Leaver', an 'Intermittent', or a 'Never-responder'. Having higher mental health score and drinking before pregnancy were associated with being a 'Leaver' or an 'Intermittent'. Being unemployed and not physically active were associated with being a 'Leaver' or 'Never Responder'. The groups 'Leavers' and 'Never Responders' were the most different from the 'Always Responders'. The group that was most similar to 'Always Responders' was the 'Returners'. Conclusions: Patterns of loss to follow-up should be considered in the application of missing data techniques, where researchers make assumptions about the characteristics of those subjects who do not respond to assess the type of missing data. This information can be used to prevent individuals who are at high risk of dropping out of a study from doing so.