Details

Autor(en) / Beteiligte

• Howe, Laura D.
• Tilling, Kate
• Galobardes, Bruna
• Lawlor, Debbie A.

Titel

Loss to Follow-up in Cohort Studies: Bias in Estimates of Socioeconomic Inequalities

Ist Teil von

• Epidemiology (Cambridge, Mass.), 2013-01, Vol.24 (1), p.1-9

Ort / Verlag

Philadelphia, PA: Lippincott Williams & Wilkins

Erscheinungsjahr

2013

Quelle

MEDLINE

Beschreibungen/Notizen

• 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, breast-feeding, 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.