Adekanmbi et al (2011) analyzed the 2008 NDHS data set at national level, and included 28, 647 children, and applied an analysis that separated individual from community level indicators. At individual level, the factors that increased the odds of childhood stunting were male gender, age 11-23 months, multiple birth, low birth weight, low maternal education and BMI, poor maternal health seeking behaviour, poor household wealth and short birth interval (being born within 24 months of the previous child). The odds of being stunted also increased with the duration of breastfeeding beyond six months  of age, suggesting that the complementary foods are poor; there was no association between being stunted with sanitation  or access to safe water (only weak associations were found in earlier analyses).  Community level factors having significant association with child hood stunting were’ child residing in community with high illiteracy rate and residence in the NW and NE regions.

Omilola (2010) also used 2003 data to assess patterns of child and maternal nutrition inequalities, and confirms some findings from other data:  that this data set showed that child stunting is a NW States, rural, mother with no or little education, male child, fewer health centre visits and child aged 18-26 months and public well drinking characterized phenomenon.  Maternal malnutrition is of a rural, NW states, low years of education, and (not given in other data sets) age 15-19 years character. It is logical to propose at least that child stunting is therefore strongly linked to intergenerational factors.   There are considerable inequalities between levels of child nutrition: rural inequality in child nutrition is greater than urban inequality; the NW (and NE) has the highest level of inequalities.

Ojiako et al conducted a study in five villages in Kaduna and Kano states collecting household data and anthropometric measurements. There was random sampling of 511 preschool children age 0-59 months, and to determine and quantify the relationship between nutritional status of children and the explanatory variables, a two-limit tobit regression analysis was applied. The prevalence of moderate or severe nutritional problems were 61% using height for age, 40% using weight for age and 17% using weight for height z scores. In terms of determinants of nutritional status, seven of 47% of the included variables were significant in explaining children’s nutritional status. Those with the expected positive signs and significance at the 1% level were soybean consumption, mother’s educational level and child’s height.  However, the child’s mother’s age and child’s own age were both significant at the 1% level but with a negative sign, as was mother’s potion among women married to the male household head, but at the 5% level of significance. Dependency ratio showed the expected negative sign and significant at the 5% level.