Sunday, February 15, 2015

Google Hangout Paper: Early Childhood Investments Substantially Boost Adult Health

The first of the resumed google hangout series will be on Friday 20th February at 2pm GMT and will finish at 3pm sharp. It will be on the recent science paper by Campbell, Conti, Heckman et al. To participate email me in advance and look up how to use google hangouts if you haven't used them before.
High-quality early childhood programs have been shown to have substantial benefits in reducing crime, raising earnings, and promoting education. Much less is known about their benefits for adult health. We report on the long-term health effects of one of the oldest and most heavily cited early childhood interventions with long-term follow-up evaluated by the method of randomization: the Carolina Abecedarian Project (ABC). Using recently collected biomedical data, we find that disadvantaged children randomly assigned to treatment have significantly lower prevalence of risk factors for cardiovascular and metabolic diseases in their mid-30s. The evidence is especially strong for males. The mean systolic blood pressure among the control males is 143 millimeters of mercury (mm Hg), whereas it is only 126 mm Hg among the treated. One in four males in the control group is affected by metabolic syndrome, whereas none in the treatment group are affected. To reach these conclusions, we address several statistical challenges. We use exact permutation tests to account for small sample sizes and conduct a parallel bootstrap confidence interval analysis to confirm the permutation analysis. We adjust inference to account for the multiple hypotheses tested and for nonrandom attrition. Our evidence shows the potential of early life interventions for preventing disease and promoting health.
Talking Points:

Discuss the intervention in terms of timing, intensity, sample size etc.,  What are the main advantages and limitations of the study design in general?

What do you think of the choice of bio-markers? What might be the role of medication in generating differences in the treatment and control group?

What potential mechanisms are driving the treatment effect?

Why is there such a gender difference in the treatment size?

How do you interpret the transformed dependent variable numbers?

What is the main advantage of the permutation techniques they are using in the paper?

To what extent might these results generalise to other contexts, treatments etc.,?

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