**2. Data & methods**

546 Current Topics in Tropical Medicine

undernutrition, and they rely on statistical inference with various forms of regression models. Because of methodological restraints, it is difficult to detect nonlinear covariate effects adequately, for example, age, and it is impossible to recover small-scale, districtspecific spatial effects with common linear regression or correlation analysis. Recent research has therefore applied geoadditive regression models (Fahrmeir L 2001; Fahrmeir 2004) .They have been used in regression studies of risk factors for acute or chronic undernutrition (e.g., Kandala et al., 2001; Adebayo 2003; Khatab, 2007) and for morbidity.(Kandala 2001; Adebayo 2003; Kandala, Magadi et al. 2006; Kandala, et al. 2007; Khatab 2007) These models can account for nonlinear covariate effects and geographical

However, in all these studies regression analyses are carried out separately for certain types of undernutrition such as stunting, wasting or underweight, neglecting possible association among these response variables and without aiming at the detection of common latent risk factors. Because of common and overlapping risk factors, separate analyses may fail to give a comprehensive picture of the epidemiology for the malnutrition and the joint effects of

To asses the association between the the nutritional indicators, we applied the recently developed latent variable model. This model gives us the opportunity to study the association or interrelationship between the three types of malnutrition as indicators for nutritional status. The factor loadings describe the association between these indicators and their impact on the nutritional status of a child. Latent variable model permits modeling of

The objective of this study is to determine the associations between nutritional indicators among Nigerian children under 5 and also to examine the impact of socioeconomic and

Nutritional status is known to have various risk factors including geographical locations as a proxy of socioeconomic and environmental factors that affect the disease prevalence and

Spatial heterogeneity in these factors influences the nutritional status pattern. Consequently, efforts to reduce the burden of childhood undernutrition should include investigations into the influence of the associations between the different measurements of the malnutrition

Two approaches of latent variable models (joint model) analysis of malnutrition have emerged: the measurement model which accommodates and describes the effect of the latent variables and a set of observed covariates (e.g. child's sex, mother's educational attainment, working status, etc) on the nutritional indicators such as stunting, wasting and

The structural model is linking a set of observed covariates which have indirect effects (such

In the latent variables overall specific risks are estimated having adjusted for covariates, and in addition, the correlation of risk between measurements of the malnutrition can be

In this study, we considered the latent variable model to jointly analyse childhood stunting, wasting and underweight, with the objective of highlighting spatial patterns of these

variation while simultaneously controlling for other important risk factors.

covariate effects on the latent variables through a flexible geoadditive predictor

status of children and their distribution among the locations on child health.

as child and mother's age, etc), with the latent variables.

childhood malnutrition at population level.

public health factors on the nutritional status.

incidence.

underweight.

quantified.

indicators.

DHS collects information on household living conditions such as housing characteristics, on childhood morbidity, malnutrition and child health from mothers in reproductive ages (15- 49). There were 6029 children's records in the 2003 survey of Nigeria. Each record consists of information on childhood malnutrition and diseases and the list of covariates that could affect the health and nutritional status of children. In the following, we provide some more information about the nutritional indicators, which were used as response variables and information about the covariates considered in this study.

**Stunting**. Stunting is an indicator of linear growth retardation relatively uncommon in the first few months of life. However it becomes more common as children get older. Children with *height-for-age* z-scores below minus two standard deviations from the median of the reference population are considered short for their age or stunted.

**Wasting**. Wasting indicates body mass in relation to body length. Children whose *weight-forheight*'s z-scores are below minus two standard deviations (z-scores < 2 *SD* ) from the median of the reference population are considered wasted (i.e. too thin for their height) which implies that they are acutely undernourished otherwise they are not wasted.

**Underweight**. Underweight is a composite index of stunting and wasting. This means children may be underweight if they are either stunted or wasted, or both. In a similar manner to the two previous anthropometric incidences, children may be underweight when their z-score is below minus two standard deviations and they are severely or moderately so if their z-score is lower than two standard deviations. The included variables in Table 1 were considered in the analysis to study child nutritional status.
