Poverty of brain: poverty of hypothesis testing

 

It should be easy to understand that people differ in ancestry, and that their genetics may influence their behaviour. However, some researchers regard that possibility as a distant cloud which should not disturb their picnic. They lay out their favourite hypotheses on a familiar rug, namely that the slings and arrows of outrageous fortune determine our lives, and then sit down to examine these notions to the exclusion of others. This is a pity, because finding the truth requires that all hypotheses should contend against each other.

Here is an example, from the “poverty shrinks your brain” school of thought. First of all, it is possible that poverty might shrink brains, particularly where poverty leads to starvation. Secondly, it is also possible that innate ability and character determine a major portion of achievement in life, such that poverty is not imposed from the outside, but created by inner failings perhaps associated with slightly smaller brains. Third, it could be a blend of both competing hypotheses.

Association of Child Poverty, Brain Development, and Academic Achievement. Nicole L. Hair; Jamie L. Hanson; Barbara L. Wolfe; Seth D. Pollak. JAMA Pediatr. Published online July 20, 2015. doi:10.1001/jamapediatrics.2015.1475

http://archpedi.jamanetwork.com/article.aspx?articleid=2381542

The authors have scanned 389 children aged 4 to 22. They say: Low-income students are now a majority of schoolchildren attending public schools in the United States. Data collected by the National Center for Education Statistics show that 51% of students across US public schools were from low-income families in 2013. If so, low income is the norm in the US, which suggests a sloppy criterion, and of doubtful status given that incomes of that magnitude are certainly well above the world standard. Few nations of the world can supply their citizens with so much food at so little cost.

The sample has an average Full Scale IQ of 112, which is almost one standard deviation above the mean, well above average in the US. Higher social classes refused to participate more often. Lower social classes were excluded almost twice more often. All this may have had some influence on the results.

In defence of their supposition that socio-economic status can boost IQ (and presumably brain size) the authors quote a very interesting study by Duyme (1999) which shows greater gains for borderline abused children when they are adopted age 4-6 into high SES families than low SES families, suggesting a compensatory effect of the better home environment ehrn measured in adolescence. It is possible the gains do not continue when leaving home, but it seems prima facie evidence of a successful intervention, and is an important finding. Incidentally, they describe the study in a misleading way. They say: In that study the IQs of more than 5000 children were assessed prior to adoption and again in adolescence. This is a sleight of hand, intended to make readers think that 5000 children were involved in the study of SES effects. That is not so, and the authors should issue a correction. This is what Duyme et al say: From 5,003 files of adopted children, 65 deprived children, defined as abused and/or neglected during infancy, were strictly selected with particular reference to two criteria: (i) They were adopted between 4 and 6 years of age, and (ii) they had an IQ <86 (mean = 77, SD = 6.3) before adoption. So, for 5000 read 65. Duyme et al. selected an extreme sub-sample for their analysis. There are only 22 low ability children, 24 middle ability and 19 higher ability.

A far better study on 286 adoptees, which also used MZ and DZ twins as controls, does not find any effect of adoption on intelligence.

http://drjamesthompson.blogspot.co.uk/2014/11/adopt-child-but-discard-illusion.html

The Duyme finding is hard to understand, other than it being based on initial testing when children were in a particularly bad state (not able to give of their actual best) and then on non-random allocation of small samples to adoptive families. It is not quite the compelling finding I had imagined.

Methodologically sound studies do not find an adoption effect on intelligence.

The authors give their results as follows:

Results Poverty is tied to structural differences in several areas of the brain associated with school readiness skills, with the largest influence observed among children from the poorest households. Regional gray matter volumes of children below 1.5 times the federal poverty level were 3 to 4 percentage points below the developmental norm (P < .05). A larger gap of 8 to 10 percentage points was observed for children below the federal poverty level (P < .05). These developmental differences had consequences for children’s academic achievement. On average, children from low-income households scored 4 to 7 points lower on standardized tests (P < .05). As much as 20% of the gap in test scores could be explained by maturational lags in the frontal and temporal lobes.

Comment: The phrase “poverty is tied to structural differences in several areas of the brain” is unwarranted. It is an inference, and subject to confounding by genetic effects.

Conclusions and Relevance The influence of poverty on children’s learning and achievement is mediated by structural brain development. To avoid long-term costs of impaired academic functioning, households below 150% of the federal poverty level should be targeted for additional resources aimed at remediating early childhood environments.

Comment: We cannot be sure from this study that poverty has a direct influence on learning and achievement, nor that it has a direct influence on brain development. It would be prudent to do further testing of the parents. Giving additional resources to US-classified-poor households may help them, but most of China is poorer and brighter, so a big effect is unlikely.

The authors doubt that genetics played a part in the results they obtained:

First, it is possible that reported differences across socioeconomic groups could have been caused by a third factor tied both to family poverty and smaller regional gray matter volumes, such as a genetic predisposition that might have led an individual to become poor. Our analyses mitigated concerns related to this competing explanation. We focused on regions of the brain known to undergo a protracted period of postnatal development (most likely to be influenced by environmental conditions), specifically, the brain’s gray matter tissue, which previous work suggests is likely affected by early environment and less heritable than other brain tissues. Second, the National Institutes of Health study was designed specifically to study typical development; therefore, children were screened based on factors thought to adversely affect brain development. However, such adversities are disproportionately represented among impoverished children, meaning that this study examined a sample of children who were likely doing better than most children living in poverty. Our analyses likely understated the full effects of poverty on children’s development. The strict exclusionary criteria were beneficial in that they allowed us to rule out a number of potentially confounding factors, particularly a child’s early or initial health status, as influencing reported associations with family income or socioeconomic status and mitigated the potential for adverse selection of sample families based on unobserved factors (eg, families who may volunteer out of concern for a child’s health or developmental progress). However, a true representative sample of children in poverty is likely to reveal even greater deficiencies than those reported in this relatively healthy sample of impoverished children, who, despite meeting the study’s inclusionary criteria, still evinced striking neurocognitive delays.

Comment: Of course, they do not know that the “striking neurocognitive delays” are delays. They may be striking differences of the sort also shown by their parents. I think that the author’s attempts to mitigate the genetic explanation by looking at gray matter tissue is oblique and uncertain. The better technique would have been to scan the brains of the parents and test their abilities.

As regards their second argument, about the exclusion of poorer children I was initially confused by it. The authors say: “children were screened based on factors thought to adversely affect brain development”. I think they mean “screened out” that is to say, excluded, and their methods section confirms a long list of exclusionary criteria: risky pregnancy, birth, and neonatal histories; physical/medical histories (eg, lead treatment or maternal medications during breastfeeding); family psychiatric history; and behavioral/psychiatric measures, including low IQ.

I agree that remaining differences are likely to be under-estimates of SES differences, but at the greater cost of slanting the study towards an unrepresentative brighter sample.

In sum, to do further work on these findings does not get round the confounding which lies at the heart of the method. At the very least testing the abilities of the parents would have illuminated inherited ability as exhibited by their children.