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Factors associated with attrition in a diverse birth cohort study in Detroit, Michigan
Published online by Cambridge University Press: 18 February 2025
Abstract
Long-term birth cohorts are essential for studying health and disease over the life course. The retention of participants remains a challenge in study design. Previous research works on attrition are limited in length of follow-up time and lack of racial/ethnic diversity. Using data from the Wayne County Health, Environment, Allergy, and Asthma Longitudinal Study (WHEALS; United States cohort born between 2004 and 2007, n = 1258), we first performed longitudinal latent class analyses to identify patterns of participation spanning the prenatal period and six follow-up timepoints: 1, 6, 12, and 24 months; 3–6 years; and 10–12 years. Data collection included a combination of in-person visits, home visits, home specimen kits, and staff-administered questionnaires. We examined associations between baseline factors and participation class using multinomial logistic regression modeling, and with conditional inference modeling to identify variables most strongly associated with class. We identified four participation classes: high early participation with gradual loss-to-follow-up, sporadic participation, consistently high participation, and consistently low participation. Multiple baseline characteristics were associated with participation class. The “consistently high participation” class was disproportionately composed of participants who were older, were of higher education, had private insurance, had suburban residence, and were with higher income. Conditional inference trees identified maternal education, insurance, and income as most strongly associated with participation class. Through latent class modeling, we show that participants who were lost to follow-up fell into distinct groupings of participation. In the future, preparatory communications with those who are at the highest risk of study discontinuation may improve long-term retention.
Keywords
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- Original Article
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- © The Author(s), 2025. Published by Cambridge University Press in association with The International Society for Developmental Origins of Health and Disease (DOHaD)
References
Richmond, RC, Al-Amin, A, Smith, GD, Relton, CL. Approaches for drawing causal inferences from epidemiological birth cohorts: a review. Early Hum Dev. 2014; 90(11), 769–780.CrossRefGoogle ScholarPubMed
Rothman, KJ, Greenland, S, Lash, TL. Modern epidemiology. 3rd edn, 2008. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia.Google Scholar
Oken, E, Bastain, TM, Bornkamp, N, et al. When a birth cohort grows up: challenges and opportunities in longitudinal developmental origins of health and disease (DOHaD) research. J Dev Orig Health Dis. 2023; 14(2), 175–181.CrossRefGoogle Scholar
Ji, Y, Hong, X, Wang, G, et al. A prospective birth cohort study on early childhood lead levels and attention deficit hyperactivity disorder: new insight on sex differences. J Pediatr. 2018; 199, 124–131.e128.CrossRefGoogle ScholarPubMed
Bousquet, J, Gern, JE, Martinez, FD, et al. Birth cohorts in asthma and allergic diseases: report of a NIAID/NHLBI/MeDALL joint workshop. J Allergy Clin Immunol. 2014; 133(6), 1535–1546.CrossRefGoogle ScholarPubMed
Kaseva, N, Vääräsmäki, M, Matinolli, HM, et al. Pre-pregnancy overweight or obesity and gestational diabetes as predictors of body composition in offspring twenty years later: evidence from two birth cohort studies. Int J Obes (Lond). 2018; 42(4), 872–879.CrossRefGoogle ScholarPubMed
Surén, P, Roth, C, Bresnahan, M, et al. Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children. JAMA. 2013; 309(6), 570–577.CrossRefGoogle ScholarPubMed
Farhan, AJ, Kothalawala, DM, Kurukulaaratchy, RJ, et al. Prediction of adult asthma risk in early childhood using novel adult asthma predictive risk scores. Allergy. 2023; 78(11), 2969–2979.CrossRefGoogle ScholarPubMed
Fewtrell, MS, Domellof, M, Hojsak, I, et al. Attrition in long-term nutrition research studies: a commentary by the european society for pediatric gastroenterology, hepatology, and nutrition early nutrition research working group. J Pediatr Gastroenterol Nutr. 2016; 62(1), 180–182.CrossRefGoogle Scholar
Young, AF, Powers, JR, Bell, SL. Attrition in longitudinal studies: who do you lose? Aust N Z J Public Health. 2006; 30(4), 353–361.CrossRefGoogle Scholar
Kristman, V, Manno, M, Cote, P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol. 2004; 19(8), 751–760.CrossRefGoogle ScholarPubMed
Lemas, DJ, Wright, L, Flood-Grady, E, et al. Perspectives of pregnant and breastfeeding women on longitudinal clinical studies that require non-invasive biospecimen collection - a qualitative study. BMC Pregnancy Childbirth. 2021; 21(1), 67.CrossRefGoogle ScholarPubMed
Goldstein, E, Bakhireva, LN, Nervik, K, et al. Recruitment and retention of pregnant women in prospective birth cohort studies: a scoping review and content analysis of the literature. Neurotoxicol Teratol. 2021; 85, 106974.CrossRefGoogle ScholarPubMed
Cameron, CM, Osborne, JM, Spinks, AB, Davey, TM, Sipe, N, McClure, RJ. Impact of participant attrition on child injury outcome estimates: a longitudinal birth cohort study in Australia. BMJ Open. 2017; 7(6), e015584.CrossRefGoogle ScholarPubMed
Zook, PM, Jordan, C, Adams, B, et al. Retention strategies and predictors of attrition in an urban pediatric asthma study. Clin Trials. 2010; 7(4), 400–410.CrossRefGoogle Scholar
Launes, J, Hokkanen, L, Laasonen, M, et al. Attrition in a 30-year follow-up of a perinatal birth risk cohort: factors change with age. PeerJ. 2014; 2, e480.CrossRefGoogle Scholar
Qiu, X, Lu, JH, He, JR, et al. The born in Guangzhou cohort study (BIGCS). Eur J Epidemiol. 2017; 32(4), 337–346.CrossRefGoogle ScholarPubMed
Vejrup, K, Magnus, P, Magnus, M. Lost to follow-up in the norwegian mother, father and child cohort study. Paediatr Perinat Epidemiol. 2022; 36(2), 300–309.CrossRefGoogle ScholarPubMed
Teixeira, R, Queiroga, AC, Freitas, AI, et al. Completeness of retention data and determinants of attrition in birth cohorts of very preterm infants: a systematic review. Front Pediatr. 2021; 9, 529733.CrossRefGoogle ScholarPubMed
Abshire, M, Dinglas, VD, Cajita, MI, Eakin, MN, Needham, DM, Himmelfarb, CD. Participant retention practices in longitudinal clinical research studies with high retention rates. BMC Med Res Methodol. 2017; 17(1), 30.CrossRefGoogle ScholarPubMed
Canova, C, Cantarutti, A. Population-based birth cohort studies in epidemiology. Int J Environ Res Public Health. 2020; 17(15), 5276.CrossRefGoogle ScholarPubMed
Murray, AL, Xie, T. Engaging adolescents in contemporary longitudinal health research: strategies for promoting participation and retention. J Adolesc Health. 2024; 74(1), 9–17.CrossRefGoogle ScholarPubMed
Robinson, KA, Dinglas, VD, Sukrithan, V, et al. Updated systematic review identifies substantial number of retention strategies: using more strategies retains more study participants. J Clin Epidemiol. 2015; 68(12), 1481–1487.CrossRefGoogle ScholarPubMed
Teague, S, Youssef, GJ, Macdonald, JA, et al. Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis. BMC Med Res Methodol. 2018; 18(1), 151.CrossRefGoogle ScholarPubMed
Wegienka, G, Havstad, S, Joseph, CL, et al. Racial disparities in allergic outcomes in African Americans emerge as early as age 2 years. Clin Exp Allergy. 2012; 42(6), 909–917.CrossRefGoogle Scholar
Havstad, S, Johnson, CC, Kim, H, et al. Atopic phenotypes identified with latent class analyses at age 2 years. J Allergy Clin Immunol. 2014; 134(3), 722–727.e2.CrossRefGoogle ScholarPubMed
Cassidy-Bushrow, AE, Sitarik, AR, Johnson-Hooper, TM, et al. Prenatal pet keeping and caregiver-reported attention deficit hyperactivity disorder through preadolescence in a United States birth cohort. BMC Pediatr. 2019; 19(1), 390.CrossRefGoogle Scholar
Havstad, S, Wegienka, G, Zoratti, EM, et al. Effect of prenatal indoor pet exposure on the trajectory of total igE levels in early childhood. J Allergy Clin Immunol. 2011; 128(4), 880–885.e4.CrossRefGoogle ScholarPubMed
Gern, JE, Jackson, DJ, Lemanske, RF Jr., et al. The children’s respiratory and environmental workgroup (CREW) birth cohort consortium: design, methods, and study population. Respir Res. 2019; 20(1), 115.CrossRefGoogle ScholarPubMed
Zanobetti, A, Ryan, PH, Coull, B, et al. Childhood asthma incidence, early and persistent wheeze, and neighborhood socioeconomic factors in the ECHO/CREW consortium. JAMA Pediatr. 2022; 176(8), 759–767.CrossRefGoogle ScholarPubMed
Ownby, DR, Johnson, CC. Does exposure to dogs and cats in the first year of life influence the development of allergic sensitization? Curr Opin Allergy Clin Immunol. 2003; 3(6), 517–522.CrossRefGoogle ScholarPubMed
Ownby, DR, Johnson, CC, Peterson, EL. Exposure to dogs and cats in the first year of life and risk of allergic sensitization at 6 to 7 years of age. JAMA. 2002; 288(8), 963–972.CrossRefGoogle ScholarPubMed
Hesselmar, B, Aberg, N, Aberg, B, Eriksson, B, Bjorksten, B. Does early exposure to cat or dog protect against later allergy development? Clin Exp Allergy. 1999; 29(5), 611–617.CrossRefGoogle ScholarPubMed
Almqvist, C, Egmar, AC, Hedlin, G, et al. Direct and indirect exposure to pets - risk of sensitization and asthma at 4 years in a birth cohort. Clin Exp Allergy. 2003; 33(9), 1190–1197.CrossRefGoogle ScholarPubMed
Nicholas, CE, Wegienka, GR, Havstad, SL, Zoratti, EM, Ownby, DR, Johnson, CC. Dog allergen levels in homes with hypoallergenic compared with nonhypoallergenic dogs. Am J Rhinol Allergy. 2011; 25(4), 252–256.CrossRefGoogle ScholarPubMed
Panzer, AR, Sitarik, AR, Fadrosh, D, et al. The impact of prenatal dog keeping on infant gut microbiota development. Clin Exp Allergy. 2023; 53(8), 833–845.CrossRefGoogle ScholarPubMed
Aichbhaumik, N, Zoratti, EM, Strickler, R, et al. Prenatal exposure to household pets influences fetal immunoglobulin E production. Clin Exp Allergy. 2008; 38(11), 1787–1794.CrossRefGoogle ScholarPubMed
Sinha, P, Calfee, CS, Delucchi, KL. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit Care Med. 2021; 49(1), e63–e79.CrossRefGoogle ScholarPubMed
Nylund-Gibson, K, Garber, AC, Carter, DB, et al. Ten frequently asked questions about latent transition analysis. Psychol Methods. 2023; 28(2), 284–300.CrossRefGoogle ScholarPubMed
Venkatasubramaniam, A, Wolfson, J, Mitchell, N, Barnes, T, JaKa, M, French, S. Decision trees in epidemiological research. Emerg Themes Epidemiol. 2017; 14(1), 11.CrossRefGoogle ScholarPubMed
Hothorn, T, Hornik, K, Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006; 15(3), 651–674.CrossRefGoogle Scholar
Jang, M, Vorderstrasse, A. Socioeconomic status and racial or ethnic differences in participation: web-based survey. JMIR Res Protoc. 2019; 8(4), e11865.CrossRefGoogle ScholarPubMed
Zivadinovic, N, Abrahamsen, R, Pesonen, M, et al. Loss to 5-year follow-up in the population-based telemark study: risk factors and potential for bias. BMJ Open. 2023; 13(3), e064311.CrossRefGoogle ScholarPubMed
Guzek, LM, Fadel, WF, Golomb, MR. A pilot study of reasons and risk factors for “No-shows” in a pediatric neurology clinic. J Child Neurol. 2015; 30(10), 1295–1299.CrossRefGoogle Scholar
Wolf, ER, Hochheimer, CJ, Sabo, RT, et al. Gaps in well-child care attendance among primary care clinics serving low-income families. Pediatrics. 2018; 142(5), e20174019.CrossRefGoogle ScholarPubMed
Bohnhoff, JC, Taormina, JM, Ferrante, L, Wolfson, D, Ray, KN. Unscheduled referrals and unattended appointments after pediatric subspecialty referral. Pediatrics. 2019; 144(6), e20190545.CrossRefGoogle ScholarPubMed
Gustavson, K, von Soest, T, Karevold, E, Roysamb, E. Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a monte carlo simulation study. BMC Public Health. 2012; 12(1), 918.CrossRefGoogle Scholar
Murray, SA, Kendall, M, Carduff, E, et al. Use of serial qualitative interviews to understand patients’ evolving experiences and needs. BMJ. 2009; 339(sep28 1), b3702–b3702.CrossRefGoogle ScholarPubMed
Graziotti, AL, Hammond, J, Messinger, DS, et al. Maintaining participation and momentum in longitudinal research involving high-risk families. J Nurs Scholarsh. 2012; 44(2), 120–126.CrossRefGoogle ScholarPubMed
Ely, B, Coleman, C. Recruitment and retention of children in longitudinal research. J Spec Pediatr Nurs. 2007; 12(3), 199–202.CrossRefGoogle ScholarPubMed
Ojikutu, BO, Bogart, LM, Dong, L. Empowerment, and structural change: lessons we should be learning from COVID-19. Am J Public Health. 2022; 112(3), 401–404.CrossRefGoogle ScholarPubMed
Urquhart et al. supplementary material
Urquhart et al. supplementary material
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