Chaining transitions in short longitudinal datasets to estimate the lifetime health benefits of childhood interventions

Publication type

Conference Paper


Understanding Society Scientific Conference 2015, 21-23 July 2015, University of Essex, Colchester, UK


Publication date

July 22, 2015


The benefits of childhood health interventions should be assessed in
terms of lifetime health consequences, but intervention studies often
consider only short-term changes. Previous studies have attempted to
match trial data to birth cohort datasets in order to proxy outcome
trajectories across the life course. However, cohort datasets are
inevitably dated and, even with the longest follow-up, measure
incomplete lifetimes. This study aims to describe and demonstrate a
method for estimating the full lifetime health returns to childhood
interventions via matching of data from intervention studies to short
longitudinal datasets collecting data on all ages. Using coarsened exact
matching, child-level data on outcomes and other characteristics from
an intervention study are matched to longitudinal data on children of
the same age. Lifetime trajectories are generated by chaining one-year
transitions from consecutive starting ages, using only the two most
recent waves of longitudinal data. The trajectories for each individual
ends when death occurs in a transition between waves. Confidence
intervals are generated using bootstrapping.
Data for 953 children from a randomised controlled trial of a
school-based social and emotional wellbeing intervention are linked to
790 children from a large longitudinal dataset, Understanding Society.
The mean 0.852-unit [CI:-1.46,-0.24] improvement in the Strength and
Difficulties Questionnaire score generated by the intervention is
associated with a mean reduction of 0.018 [CI:-0.294, 0.257] discounted
lifetime Quality-Adjusted-Life-Years. This study illustrates that
matching between intervention studies and consecutive one-year
transitions in longitudinal datasets offers a feasible method for
estimating lifetime outcomes using the most up-to-date information on
changes over the life course.





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