Tuesday 20 December 2016

The targeted cohort

If you're surprised by the latest results from the Dunedin cohort study, you haven't been paying attention:
We integrated multiple nationwide administrative databases and electronic medical records with the four-decade-long Dunedin birth cohort study to test child-to-adult prediction in a different way, using a population-segmentation approach. A segment comprising 22% of the cohort accounted for 36% of the cohort’s injury insurance claims; 40% of excess obese kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless child-rearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor brain health at three years of age, predicted this segment with large effect sizes. 
A relatively small group generates the preponderance of social cost. And it's G-loaded. A rough measure of child intelligence at age 3 predicted a lot of bad outcomes.

Some of those relationships eased back in multivariate analysis with childhood SES included. But that's a tricky thing. If income is increasing in IQ (albeit concavely), then childhood SES depends on parents' IQ, but parents' IQ is a predictor of the child's adult IQ independently of of childhood SES. Some of the effect of childhood measures of brain health on adult outcomes is then unduly attenuated by inclusion of childhood SES in the regressions as some of the IQ effect could be picked up as a measured SES effect. On the other side, a higher IQ kid born into a lower SES household with lower IQ parents would select into worse environments for cognitive development over time, following the Dickens-Flynn kind of model. You need twin studies or adoption studies to start teasing that out properly.

While a fifth of the Dunedin cohort was responsible for massive amounts of the cohort's crime, prescriptions, hospital stays, fatherless children and social welfare costs, another cohort had almost nil costs.

The paper is optimistic about the potential for interventions on the identified group to reduce long-term costs and improve outcomes. I agree that identifying the cohort for targeting is important, but I'm a bit more pessimistic about the chances of success.

They note the data is right-hand censored at age 38 years. I wonder how many children had accrued to people in each of the above-pictured cohorts by that age.

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