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

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.

Figure 1

Directed acyclic graphs illustrating possible mechanisms through which loss to follow-up may operate in studies of socioeconomic inequalities. These directed acyclic graphs represent a series of possible ways through which loss to follow-up could be associated with different variables of interest when studying socioeconomic inequalities. The mechanisms have different consequences for whether estimates of inequalities are biased, and different possible analysis solutions to address bias when it is present. SEP indicates socioeconomic position, which is assumed to be completely measured; Y, the outcome of interest, which has some missing data; C, other variables that may be measured or unmeasured; R, response, that is, continued participation in the cohort study such that R = 1 if the individual contributed data on the outcome Y and R = 0 if they did not. The box around R indicates that analysis is restricted to participants not lost to follow-up, that is, R = 1.

Figure 2

Mediation of the association between maternal education and birth weight: one possible mechanism through which loss to follow-up could affect estimates of inequalities. Coefficients are regression coefficients (robust standard errors) from path analysis. Maternal education is a rank variable, that is, the proportion of individuals in the sample with a lower level of maternal education, and is treated as a continuous variable such that 0 is the lowest maternal education (on the latent continuous scale that this method assumes is underlying the categorical variable) and 1 is the highest maternal education. Maternal smoking in pregnancy is coded as 0 for none and 1 for any. Attendance at 15-year clinic is coded 0 for did not attend and 1 for did attend. Birth weight is standardized to have a mean of zero and variance of 1. Each arrow in Figure 2 represents a linear regression analyses; we mapped the binary indicators (maternal smoking in pregnancy and attendance at the 15-year clinic) to a standardized normal distribution, and as such the coefficients for these variables represent mean differences between the category coded 1 and the category coded 0—for example, for the association between maternal smoking during pregnancy and attendance at the 15-year clinic, a coefficient of −0.133 is interpreted as follows: the proportion of participants who attended the 15-year clinic was 13.3% lower among those whose mothers smoked during pregnancy compared with those whose mothers did not smoke during pregnancy. All coefficients had P values ≤ 0.01.