Literature DB >> 23211345

Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities.

Laura D Howe1, Kate Tilling, Bruna Galobardes, Debbie A Lawlor.   

Abstract

BACKGROUND: Although cohort members tend to be healthy and affluent compared with the whole population, some studies indicate this does not bias certain exposure-outcome associations. It is less clear whether this holds when socioeconomic position (SEP) is the exposure of interest.
METHODS: As an illustrative example, we use data from the Avon Longitudinal Study of Parents and Children. We calculate estimates of maternal education inequalities in outcomes for which data are available on almost the whole cohort (birth weight and length, breastfeeding, preterm birth, maternal obesity, smoking during pregnancy, educational attainment). These are calculated for the full cohort (n~12,000) and in restricted subsamples defined by continued participation at age 10 years (n∼7,000) and age 15 years (n∼5,000).
RESULTS: Loss to follow-up was related both to SEP and outcomes. For each outcome, loss to follow-up was associated with underestimation of inequality, which increased as participation rates decreased (eg, mean birth-weight difference between highest and lowest SEP was 116 g [95% confidence interval = 78 to 153] in the full sample and 93 g [45 to 141] and 62 g [5 to 119] in those attending at ages 10 and 15 years, respectively).
CONCLUSIONS: Considerable attrition from cohort studies may result in biased estimates of socioeconomic inequalities, and the degree of bias may worsen as participation rates decrease. However, even with considerable attrition (>50%), qualitative conclusions about the direction and approximate magnitude of inequalities did not change among most of our examples. The appropriate analysis approaches to alleviate bias depend on the missingness mechanism.

Entities:  

Mesh:

Year:  2013        PMID: 23211345      PMCID: PMC5102324          DOI: 10.1097/EDE.0b013e31827623b1

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  30 in total

1.  Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.

Authors:  Ian R White; John B Carlin
Journal:  Stat Med       Date:  2010-12-10       Impact factor: 2.373

Review 2.  Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review.

Authors:  J J Reilly; J Kelly
Journal:  Int J Obes (Lond)       Date:  2010-10-26       Impact factor: 5.095

3.  Does low participation in cohort studies induce bias?

Authors:  Ellen Aagaard Nohr; Morten Frydenberg; Tine Brink Henriksen; Jorn Olsen
Journal:  Epidemiology       Date:  2006-07       Impact factor: 4.822

Review 4.  Measures of health inequalities: part 2.

Authors:  Enrique Regidor
Journal:  J Epidemiol Community Health       Date:  2004-11       Impact factor: 3.710

5.  Does survey non-response bias the association between occupational social class and health?

Authors:  Pekka Martikainen; Mikko Laaksonen; Kustaa Piha; Tea Lallukka
Journal:  Scand J Public Health       Date:  2007       Impact factor: 3.021

Review 6.  Objective measurement of physical activity and sedentary behaviour: review with new data.

Authors:  J J Reilly; V Penpraze; J Hislop; G Davies; S Grant; J Y Paton
Journal:  Arch Dis Child       Date:  2008-02-27       Impact factor: 3.791

7.  ALSPAC--the Avon Longitudinal Study of Parents and Children. I. Study methodology.

Authors:  J Golding; M Pembrey; R Jones
Journal:  Paediatr Perinat Epidemiol       Date:  2001-01       Impact factor: 3.980

8.  Survey non-response in the Netherlands: effects on prevalence estimates and associations.

Authors:  A Jeanne M Van Loon; Marja Tijhuis; H Susan J Picavet; Paul G Surtees; Johan Ormel
Journal:  Ann Epidemiol       Date:  2003-02       Impact factor: 3.797

9.  The effects of non-response in a prospective study of cancer: 15-year follow-up.

Authors:  L K Heilbrun; A Nomura; G N Stemmermann
Journal:  Int J Epidemiol       Date:  1991-06       Impact factor: 7.196

10.  Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders.

Authors:  Dieter Wolke; Andrea Waylen; Muthanna Samara; Colin Steer; Robert Goodman; Tamsin Ford; Koen Lamberts
Journal:  Br J Psychiatry       Date:  2009-09       Impact factor: 9.319

View more
  103 in total

1.  Effect of birth weight and weight change during the first 96 h of life on childhood body composition--path analysis.

Authors:  M J Fonseca; M Severo; S Correia; A C Santos
Journal:  Int J Obes (Lond)       Date:  2015-02-03       Impact factor: 5.095

2.  Can Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?: A Simulation Study.

Authors:  Elizabeth Rose Mayeda; Hailey R Banack; Kirsten Bibbins-Domingo; Adina Zeki Al Hazzouri; Jessica R Marden; Rachel A Whitmer; M Maria Glymour
Journal:  Epidemiology       Date:  2018-07       Impact factor: 4.822

3.  Bias from self selection and loss to follow-up in prospective cohort studies.

Authors:  Guido Biele; Kristin Gustavson; Nikolai Olavi Czajkowski; Roy Miodini Nilsen; Ted Reichborn-Kjennerud; Per Minor Magnus; Camilla Stoltenberg; Heidi Aase
Journal:  Eur J Epidemiol       Date:  2019-08-26       Impact factor: 8.082

4.  Studying the life course health consequences of childhood adversity: challenges and opportunities.

Authors:  Laura D Howe; Kate Tilling; Debbie A Lawlor
Journal:  Circulation       Date:  2015-04-09       Impact factor: 29.690

5.  Quantitative Bias Analysis for Collaborative Science.

Authors:  Jennifer Weuve; Sharon K Sagiv; Matthew P Fox
Journal:  Epidemiology       Date:  2018-09       Impact factor: 4.822

6.  KOOS pain as a marker for significant knee pain two and six years after primary ACL reconstruction: a Multicenter Orthopaedic Outcomes Network (MOON) prospective longitudinal cohort study.

Authors:  D Wasserstein; L J Huston; S Nwosu; C C Kaeding; R D Parker; R W Wright; J T Andrish; R G Marx; A Amendola; B R Wolf; E C McCarty; M Wolcott; W R Dunn; K P Spindler
Journal:  Osteoarthritis Cartilage       Date:  2015-06-11       Impact factor: 6.576

Review 7.  Case-finding for common mental disorders in primary care using routinely collected data: a systematic review.

Authors:  Harriet Larvin; Emily Peckham; Stephanie L Prady
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2019-07-12       Impact factor: 4.328

8.  Predicting Later Study Withdrawal in Participants Active in a Longitudinal Birth Cohort Study for 1 Year: The TEDDY Study.

Authors:  Suzanne Bennett Johnson; Kristian F Lynch; Judith Baxter; Barbro Lernmark; Roswith Roth; Tuula Simell; Laura Smith
Journal:  J Pediatr Psychol       Date:  2015-09-27

Review 9.  Cognitive Outcomes of Children Born Extremely or Very Preterm Since the 1990s and Associated Risk Factors: A Meta-analysis and Meta-regression.

Authors:  E Sabrina Twilhaar; Rebecca M Wade; Jorrit F de Kieviet; Johannes B van Goudoever; Ruurd M van Elburg; Jaap Oosterlaan
Journal:  JAMA Pediatr       Date:  2018-04-01       Impact factor: 16.193

10.  The Association Between Income and Incident Homebound Status Among Older Medicare Beneficiaries.

Authors:  Katherine A Ornstein; Melissa M Garrido; Evan Bollens-Lund; Jennifer M Reckrey; Mohammed Husain; Katelyn B Ferreira; Shelley H Liu; Claire K Ankuda; Amy S Kelley; Albert L Siu
Journal:  J Am Geriatr Soc       Date:  2020-08-10       Impact factor: 5.562

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.