Predicting complete loss to follow-up after a health-education program: number of absences and face-to-face contact with a researcher

• Published in: BMC medical research methodology 2011-10, Vol.11 (1), p.145-145, Article 145
• Main Authors: Park, M J, Yamazaki, Yoshihiko, Yonekura, Yuki, Yukawa, Keiko, Ishikawa, Hirono, Kiuchi, Takahiro, Green, Joseph
• Format: Article
• Language: English
• Subjects: Administrative support; Adolescent; Adult; Aged; Aged, 80 and over; Bias; Chronic Disease - epidemiology; Chronic diseases; Chronic illnesses; Communication; Education; Female; Health education; Humans; Japan - epidemiology; Logistic Models; Longitudinal Studies; Lost to Follow-Up; Male; Marital status; Medical research; Middle Aged; Multivariate Analysis; Patient Education as Topic - trends; Prevention; Questionnaires; Rheumatic diseases; ROC Curve; Self Care; Studies; Young Adult
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/1471-2288-11-145

Research on health-education programs requires longitudinal data. Loss to follow-up can lead to imprecision and bias, and complete loss to follow-up is particularly damaging. If that loss is predictable, then efforts to prevent it can be focused on those program participants who are at the highest risk. We identified predictors of complete loss to follow-up in a longitudinal cohort study. Data were collected over 1 year in a study of adults with chronic illnesses who were in a program to learn self-management skills. Following baseline measurements, the program had one group-discussion session each week for six weeks. Follow-up questionnaires were sent 3, 6, and 12 months after the baseline measurement. A person was classified as completely lost to follow-up if none of those three follow-up questionnaires had been returned by two months after the last one was sent.We tested two hypotheses: that complete loss to follow-up was directly associated with the number of absences from the program sessions, and that it was less common among people who had had face-to-face contact with one of the researchers. We also tested predictors of data loss identified previously and examined associations with specific diagnoses.Using the unpaired t-test, the U test, Fisher's exact test, and logistic regression, we identified good predictors of complete loss to follow-up. The prevalence of complete loss to follow-up was 12.2% (50/409). Complete loss to follow-up was directly related to the number of absences (odds ratio; 95% confidence interval: 1.78; 1.49-2.12), and it was inversely related to age (0.97; 0.95-0.99). Complete loss to follow-up was less common among people who had met one of the researchers (0.51; 0.28-0.95) and among those with connective tissue disease (0.29; 0.09-0.98). For the multivariate logistic model the area under the ROC curve was 0.77. Complete loss to follow-up after this health-education program can be predicted to some extent from data that are easy to collect (age, number of absences, and diagnosis). Also, face-to-face contact with a researcher deserves further study as a way of increasing participation in follow-up, and health-education programs should include it.