Cohort participant retention is a crucial element and may depend on several factors. Based on data from a multicentre cohort of European children, the effect of baseline participation on attrition and the association with and the impact of single determinants in relation to the extent of attrition were investigated. Data was available for 16,225 children from the IDEFICS baseline survey (2007/2008). Attrition was defined as nonparticipation in the first follow-up examination (2009/2010). Determinants of attrition were analysed by logistic regression. The statistical significance level was set at to account for the large sample size. The strongest associations were seen for baseline item non-response, especially when information on migration background (odds ratio (OR) = 1.55; 99% confidence interval (CI): 1.04, 2.31), single parenthood (OR = 1.37; 99% CI: 1.12, 1.67), or well-being (OR = 1.46; 99% CI: 1.19, 1.79) was lacking. Drop-out proportion rose with the number of missing items. Overweight, low education, single parenthood and low well-being scores were independent determinants of attrition. Baseline participation, and the individual determinant effects seemed unrelated to the variation of the extent of attrition between study centres. A high level of item nonresponse as well as overweight and disadvantageous sociodemographic conditions were identified as main attrition determinants, suggesting the consideration of these aspects in conduct and analysis of cohort studies in childhood obesity research. 1. Introduction In longitudinal studies, participant adherence is a crucial element of study organisation that requires considerable effort and time. However, attrition is an inevitable problem in almost every epidemiological cohort study, which leads to a loss of power and potentially introduces selection bias when drop-out is related to the exposures or outcomes of interest. The most commonly reported types of attrition are noncontact or refusal [1]. In fact, the motivation of study participants to remain involved in the study activities may decline over time and will depend on several factors. To apply appropriate analysis strategies and to allow for a correct interpretation of the results from longitudinal studies, knowledge on the determinants of attrition is important. Additionally, information about factors affecting attrition is crucial to improve retention of cohort participants. One aspect that has been reported to affect attrition is baseline response and concomitant recruitment efforts [2]. With regard to longitudinal studies in children
References
[1]
F. Lamers, A. W. Hoogendoorn, J. H. Smit, D. R. van, F. G. Zitman, W. A. Nolen, et al., “Sociodemographic and psychiatric determinants of attrition in the Netherlands study of depression and anxiety (NESDA),” Comprehensive psychiatry, vol. 53, no. 1, pp. 63–70, 2012.
[2]
R. Haring, D. Alte, H. V？lzke et al., “Extended recruitment efforts minimize attrition but not necessarily bias,” Journal of Clinical Epidemiology, vol. 62, no. 3, pp. 252–260, 2009.
[3]
M. Vinther-Larsen, M. Riegels, M. H. Rod, M. Schi？tz, T. Curtis, and M. Gr？nbaek, “The danish youth cohort: characteristics of participants and non-participants and determinants of attrition,” Scandinavian Journal of Public Health, vol. 38, no. 6, pp. 648–656, 2010.
[4]
P. M. Zook, C. Jordan, B. Adams et al., “Retention strategies and predictors of attrition in an urban pediatric asthma study,” Clinical Trials, vol. 7, no. 4, pp. 400–410, 2010.
[5]
J. P. Rey-López, G. Vicente-Rodríguez, M. Biosca, and L. A. Moreno, “Sedentary behaviour and obesity development in children and adolescents,” Nutrition, Metabolism and Cardiovascular Diseases, vol. 18, no. 3, pp. 242–251, 2008.
[6]
S. Hense, H. Pohlabeln, S. De Henauw et al., “Sleep duration and overweight in European children: is the association modified by geographic region?” Sleep, vol. 34, no. 7, pp. 885–890, 2011.
[7]
L. Johnson, A. P. Mander, L. R. Jones, P. M. Emmett, and S. A. Jebb, “A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children,” International Journal of Obesity, vol. 32, no. 4, pp. 586–593, 2008.
[8]
T. A. McCaffrey, K. L. Rennie, M. A. Kerr et al., “Energy density of the diet and change in body fatness from childhood to adolescence; is there a relation?” American Journal of Clinical Nutrition, vol. 87, no. 5, pp. 1230–1237, 2008.
[9]
J. Williams, M. Wake, K. Hesketh, E. Maher, and E. Waters, “Health-related quality of life of overweight and obese children,” Journal of the American Medical Association, vol. 293, no. 1, pp. 70–76, 2005.
[10]
W. Ahrens, K. Bammann, A. Siani, K. Buchecker, H. S. de, L. Iacoviello, et al., “The IDEFICS cohort: design, characteristics and participation in the baseline survey,” International Journal of Obesity, vol. 35, supplement 1, pp. S3–S15, 2011.
[11]
S. de Henauw, V. Verbstel, S. Marild, G. Barba, K. Bammann, G. Eiben, et al., “The IDEFICS community-oriented intervention programme: a new model for childhood obesity prevention in Europe?” International Journal of Obesity, vol. 35, supplement 1, pp. S16–S23, 2011.
[12]
K. Bammann and W. Ahrens, Measurement Tools for a Health Survey on Nutrition, Physical Activity and Lifestyle in Children: the European IDEFICS Study, Springer, New York, NY, USA, 2013.
[13]
T. J. Cole, M. C. Bellizzi, K. M. Flegal, and W. H. Dietz, “Establishing a standard definition for child overweight and obesity worldwide: international survey,” British Medical Journal, vol. 320, no. 7244, pp. 1240–1243, 2000.
[14]
M. Bullinger, A. L. Brütt, M. Erhart, and U. Ravens-Sieberer, “Psychometric properties of the KINDL-R questionnaire: results of the BELLA study,” European Child and Adolescent Psychiatry, vol. 17, no. 1, pp. 125–132, 2008.
[15]
U. Ravens-Sieberer and M. Bullinger, Measuring Health Related Quality of Life in Children and Adolescents, 2000.
[16]
S. Bel-Serrat, T. Mouratidou, V. Pala, I. Huybrechts, C. Bornhorst, J. M. Fernandez-Alvira, et al., “Relative validity of the children's eating habits questionnaire-food frequency section among young European children: the IDEFICS study,” Public Health Nutrition, pp. 1–11, 2013.
[17]
A. Lanfer, A. Hebestreit, W. Ahrens et al., “Reproducibility of food consumption frequencies derived from the children's eating habits questionnaire used in the IDEFICS study,” International Journal of Obesity, vol. 35, supplement 1, pp. S61–S68, 2011.
[18]
S. Hense, G. Barba, H. Pohlabeln et al., “Factors that influence weekday sleep duration in European children,” Sleep, vol. 34, no. 5, pp. 633–639, 2011.
[19]
C. L. Booker, S. Harding, and M. Benzeval, “A systematic review of the effect of retention methods in population-based cohort studies,” BMC Public Health, vol. 11, article 249, 2011.
[20]
S. A. Fr？jd, R. Kaltiala-Heino, and M. J. Marttunen, “Does problem behaviour affect attrition from a cohort study on adolescent mental health?” European Journal of Public Health, vol. 21, no. 3, pp. 306–310, 2011.
[21]
I. Moroshko, L. Brennan, and P. O'Brien, “Predictors of dropout in weight loss interventions: a systematic review of the literature,” Obesity Reviews, vol. 12, pp. 912–934, 2011.
[22]
D. Wolke, A. Waylen, M. Samara et al., “Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders,” British Journal of Psychiatry, vol. 195, no. 3, pp. 249–256, 2009.
[23]
J. S. Goodman and T. C. Blum, “Assessing the non-random sampling effects of subject attrition in longitudinal research,” Journal of Management, vol. 22, no. 4, pp. 627–652, 1996.
[24]
L. Marcellus, “Are we missing anything? Pursuing research on attrition,” Canadian Journal of Nursing Research, vol. 36, no. 3, pp. 82–98, 2004.
[25]
S. Dhuper, S. Buddhe, and S. Patel, “Managing cardiovascular risk in overweight children and adolescents,” Paediatr Drugs. In press.
[26]
A. D. Liese, R. B. D'Agostino Jr, R. F. Hamman, et al., “The burden of diabetes mellitus among US youth: prevalence estimates from the SEARCH for diabetes in youth study,” Pediatrics, vol. 118, no. 4, pp. 1510–1518, 2006.
