I’ve been doing a bit of reading to prepare for my next placement in paediatric community health.
In an intriguing paper, Ukoumunne and colleagues pushed the ELVS data through a statistical machine to discern discrete trajectories of language impairment and precocity. The best fit had five average paths for the children’s language between 1 and 4 years:
- Typical: 68.5% of children stayed pretty average
- Precocious (late): These children started typical, but were more likely to outpace their peers from 24 months.
- Impaired (early): These children were impaired up to 12 months, but were typical by 2-4 years.
- Impaired (late): Typical development to 2 years, with impairment from then to 4 years.
- Precocious (early): Likely precocity early on with a return to typicality at 4 years.
The authors are keen to stress that these paths are averages, and do not predict what individual children might achieve. To apply this analysis in a clinical context would be to commit the Ecological Fallacy, “where inferences about the nature of individuals are deduced from inference for the group to which those individuals belong“.
Suppose there is a suburb called “Exampletown” with 400 residents who each earn $60,000/annum, except for one who earns $50 million/annum (an extreme example, but good for making the point!). The average income for Exampletown is $184,850/annum (the median is obviously $60,000). You might read a list of suburbs ranked by average income. Say you meet someone from Exampletown – you might assume they are quite wealthy, because you know their suburb has an extremely high average income. But they aren’t! You have committed the ecological fallacy, trying to apply an inference that applies to a group to an individual member from that group.
Application to Language Delay
While the model above is seductive, it is also true that 6% of the typical group will be impaired at 4 years, and that 52% of the Impaired (late) are typical at 4 years. Because there are a lot of more in the typical group, this 6% actually represents 55% of the total number of impaired children at age 4.
People might say that the data shows that children are too variable to allow clinicians to make the call to intervene before 4 years. After all, they might improve! Or we could be taking away resources from children who will need them when their impairment manifests. It’s a tricky problem.
I have a small issue with people using the ELVS data to make clinical decisions (if they do). The study was not conducted to look at the efficacy or efficiency or early language intervention. Instead, it was an observational study: it cannot answer these questions.
It’s a rather unsatisfying conclusion, but I’ll be interested to report on what my next clinic does. All community health services suffer from chronic under-resourcing, so decisions need to be made about priorities. Are these decisions being made with reference to efficiency/efficacy data, or observational/epidemiological data?
Ukoumunne, O. C., Wake, M., Carlin, J., Bavin, E. L., Lum, J., Skeat, J., . . . Reilly, S. (2012). Profiles of language development in pre-school children: A longitudinal latent class analysis of data from the Early Language in Victoria Study. Child: Care, Health and Development, 38(3), 341-349. doi: 10.1111/j.1365-2214.2011.01234.x