No authers have competing interests.
‡ These authors also contributed equally to this work.
The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated with illness and death, affecting hundreds of millions of pregnant women and young children. Sub-Saharan Africa is one of the regions in the world struggling with the burden of chronic malnutrition. The 2018 Zambia Demographic and Health Survey (ZDHS) report estimated that 35% of the children under five years of age are stunted. The objective of this study was to analyse the distribution, and associated factors of stunting in Zambia.
We analysed the relationships between socio-economic, and remote sensed characteristics and anthropometric outcomes in under five children, using Bayesian distributional regression. Georeferenced data was available for 25,852 children from two waves of the ZDHS, 31% observation were from the 2007 and 69% were from the 2013/14. We assessed the linear, non-linear and spatial effects of covariates on the height-for-age z-score.
Stunting decreased between 2007 and 2013/14 from a mean z-score of 1.59 (credible interval (CI): -1.63; -1.55) to -1.47 (CI: -1.49; -1.44). We found a strong non-linear relationship for the education of the mother and the wealth of the household on the height-for-age z-score. Moreover, increasing levels of maternal education above the eighth grade were associated with a reduced variation of stunting. Our study finds that remote sensed covariates alone explain little of the variation of the height-for-age z-score, which highlights the importance to collect socio-economic characteristics, and to control for socio-economic characteristics of the individual and the household.
While stunting still remains unacceptably high in Zambia with remarkable regional inequalities, the decline is lagging behind goal two of the SDGs. This emphasises the need for policies that help to reduce the share of chronic malnourished children within Zambia.
The burden of child malnutrition still remains a global challenge, with greater severity being faced by low-and middle-income countries [
Assessment of childhood malnutrition commonly relies on standard anthropometric measures for insufficient height-for-age (stunting) indicating chronic undernutrition, insufficient weight-for-height (wasting), indicating acute undernutrition; and insufficient weight-for-age (underweight), an indicator commonly used to asses, both, chronic, and acute undernutrition [
Anthropometric measurements are practical techniques for assessing children’s growth patterns during the first years of life. The measurements also provide useful insights into the nutrition and health situation of entire population groups. Anthropometric indicators are less accurate than clinical and biochemical techniques in assessing individual nutritional status. However in resources limited settings, the measurements are a useful screening tool to identify individuals at risk of undernutrition, who can later be referred to subsequent possible confirmatory investigation [
It is estimated that globally 52 million children under-five years of age are wasted, 17 million are severely wasted and 155 million are stunted. Around 45% of deaths among children under-five years of age, most of which occur in the sub-Saharan Africa are linked to undernutrition [
Global prevalence of stunting in children younger than five years declined during the past two decades, but still remain unacceptably high in South Asia and sub-Saharan Africa regions [
There are already fairly well documented perspectives on determinants of malnutrition. The treatise on these determinants mainly relies on the United Nations Children’s Fund (UNICEF) conceptual framework on malnutrition which has evolved over time as more knowledge and evidence on the causes, consequences and impacts of undernutrition is generated. The framework distinguishes between immediate, intermediate and underlying determinants of malnutrition [
The immediate causes of undernutrition include inadequate dietary intake and disease, while the underlying causes could include household food insecurity, inadequate care and feeding practices for children, unhealthy household and surrounding environments, and inaccessible and often inadequate health care. Basic causes of poor nutrition encompasses the societal structures and processes that neglect human rights and perpetuate poverty, constraints faced by populations to essential resources [
Several studies done within sub-Saharan Africa investigated determinants such as the mother’s level of education, income levels and these factors have been linked to malnutrition [
Previous studies elsewhere have observed that stunting tends to show regional variation [
The panel shows the average height-for-age z-score at district level for 2007 (left) and 2013/14 (right) of Zambia.
Much of the work done on the determinants of stunting in Zambia, have considered socio-economic characteristics and have assessed the linear effects of these determinants on the conditional mean [
The study by Kandala [
The following three reasons make our study novel compared to previous work [
We used data from the 2007 and 2013/14 ZDHS. The ZDHS is a national-wide survey which is representative at a sub-national level and contains information on trends in fertility, childhood mortality, use of family planning methods, and maternal and child health indicators including HIV and AIDS. For these population health indicators, data is collected for women aged 15–49, men aged 15–59 and children below five years of age [
The ZDHS provide besides information on the district a household pertains to, also information about the geolocation of the primary sampling unit a household belongs to, and from which the data was collected. The location of the primary sampling unit is the spatial information used in the empirical analysis. During data processing, GPS coordinates are displaced to ensure that respondent confidentiality is maintained. The displacement is randomly applied so that rural points contain a minimum of 0 and a maximum of 5 km of positional error. Urban points contain a minimum of 0 and a maximum of 2 km of error. A further 1% of the rural sample points are offset a minimum of 0 and a maximum of 10 km [
Demographic Health Surveys have documented weakness for estimation of individual anthropometric measurements. Potential threats to high data quality may occur across various research stages, from survey design to data analysis. There is also often a substantial amount of missing or implausible anthropometric data across surveys [
Furthermore, there is caution over the use of stunting as an individual classifier in epidemiologic research or its interpretation as a clinically meaningful health outcome. Stunting should be used as originally designed to be from its original use as a population level indicator of community well-being [
Despite the above highlighted limitations of DHS and anthropometric indicators, they remain useful national wide measurements that can be used to estimate child health. Moreover, in general anthropometric measures are a good indicator for planning as they can provide a lot of information to policy makers to answer, how, where and which type of intervention would be favourable in specific settings.
The effects of socio-economic factors, such as the education of the mother, household size, wealth of the household on the health status of children are well documented [
In our analysis we investigated the impact of different socio-economic factors, which impact on height-for age Z-score has been discussed in literature.
Covariate | Used data source | Effect on stunting found in literature | Reference |
---|---|---|---|
Asset Index | DHS | Household wealth inequality associated with childhood stunting | [ |
Age mother at birth | DHS | Increasing non-linearly | [ |
Age child | DHS | Decreasing non-linearly | [ |
Birth order | DHS | Being born forth or higher significantly more stunted | [ |
Breastfeeding duration | DHS | Breastfeeding interval ≤ associated with low level of stunting | [ |
Education mother | DHS | Stunting improves non-linearly with the educational level | [ |
Household size | DHS | Increases linearly | [ |
Mothers’ BMI | DHS | U-shape relationship with childhood stunting | [ |
Number of vaccinations | DHS | Lower levels of stunting when fully vaccinated | [ |
Drought severity index | See |
Not further specified | [ |
Malaria incidence | See |
No clear pattern | [ |
Population density | See |
Not further specified | [ |
We obtained remote sensed data on drought severity, malaria incidence, and population density. The description, and source to these data sets is provided in
Covariate | Description | Source | Reference |
---|---|---|---|
Drought index | scPDSI CRU4.03 | [ |
|
Malaria incidence | Plasmodium falciparum incidence | [ |
|
Population density | Number of people per km2 | [ |
For example, the malaria incidence data was obtained from the Malaria Atlas Project (MAP). The project collects malaria data on malaria cases reported by surveillance systems, nationally representative cross-sectional surveys of parasite rate, and satellite imagery capturing global environmental conditions that influence malaria transmission [
We assessed the relationships between socio-economic and remote sensed characteristics and anthropometric outcomes using the Bayesian Distributional Regression (BDR). BDR models all parameters of the response distribution based on structured additive predictors and allows to incorporate for example, non-linear effects of metric covariates, spatial effects, or varying effects. Applications of structured additive regression models to topics in Global Public Health are found in several publications [
Relying on Bayesian distributional regression requires to specify the distribution of the response variable. Assuming the response distribution to be Gaussian permits to model besides the conditional mean also the variance or standard deviation of the response variable. Graphical analysis using amongst others randomised quantile residuals [
The left-hand panel shows the histogram and kernel density estimates of the height-for-age z-score, the middle panel shows the histogram of the randomised quantile residuals together with the normal density estimates, and the right-hand panels depicts the QQ-plot of the randomised quantile residuals.
Assuming the response distribution of the height-for-age z-score to be Gaussian, both the mean
Here both parameters of the normal distribution the mean
The fit of the models are compared by relying on the Deviance Information Criterion (DIC) [
Model | DIC | WAIC |
---|---|---|
Model 1 | 95588.2 | 95550.7 |
Model 2 | 91027.1 | 91566.4 |
Model 3 | 91375.7 | 91884.0 |
Model 4 | 96263.1 | 96541.9 |
Model 6 | 91012.7 | 91543.3 |
Values of the DIC and the WAIC for different model specifications.
Model term | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
---|---|---|---|---|---|---|
yes (y) | y | y | y | y | y | |
n (n) | y | y | y | y | y | |
y | y | y | y | y | y | |
Asset index ) |
y | y | y | n | y | y |
Birth order, Age mother at birth ) |
y |
y |
y |
n | y |
y |
Age child, Breastfeeding duration ) |
y |
y |
y |
n | y |
y |
Education mother ) |
y | y | y | n | y | y |
Household size ) |
y | y | y | n | y | y |
BMI mother ) |
y | y | y | n | y | y |
Number of vaccinations ) |
y | y | y | n | y | y |
Drought severity index ) |
y | y | y | n | y | y |
Malaria incidence ) |
y | y | y | n | y | y |
Population density )) |
y | y | y | n | y | n |
Spatial, Time ) |
y | y | y | n | y | y |
Table of specified models indicating the differences between models and the included model terms and covariates. Note that after evaluating the sampling paths of the resulting Markov chains of the MCMC simulations, in
In the following Section we will discuss the results of Model 5, omitting insignificant terms, as Model 5 has both the lowest DIC and WAIC. Result of the included covariates are however, similar throughout all specifications.
2007 ZDHS | 2013/14 ZDHS | |||
---|---|---|---|---|
Mean (95% CI) | Mean (95% CI) | |||
Stunting | -1.59 (-1.63; -1.55) | 7,936 | -1.47 CI (-1.49; -1.44) | 17,916 |
Mean (95% CI), % | SD | Mean (95% CI), % | SD | |
Proportion of male children (= 1) | 49.62% | 50.08% | ||
Age children in months | 29.14 (28.76; 29.51) | 17.03 | 29.84 (29.58; 30.09) | 17.20 |
Breastfeeding duration in months | 16.22 (16.07; 16.38) | 7.04 | 15.69 (15.58; 15.79) | 7.11 |
Birth order within household | 2.99 (2.93; 3.04) | 2.38 | 2.88 (2.84; 2.91) | 2.37 |
Number of vaccinations | 5.64 (5.59; 5.69) | 2.40 | 7.45 (7.42; 7.49) | 2.17 |
Age mother at birth in years | 24.30 (24.15; 24.45) | 6.82 | 24.08 (23.98; 24.18) | 6.94 |
BMI mother | 22.35 (22.27; 22.43) | 3.42 | 22.57 (22.51; 22.62) | 3.75 |
Years of education mother | 7.18 (7.10; 7.26) | 3.53 | 7.81 (7.76; 7.87) | 3.64 |
Urban place of living (= 1) | 38.73% | 43.01% | ||
Size of the household | 6.27 (6.21; 6.32) | 2.49 | 6.60 (6.56; 6.64) | 2.77 |
Asset index deviation regional mean | 0.00 (-0.02; 0.02) | 0.88 | -0.01 (-0.02; 0.01) | 0.88 |
Malaria incidence | 0.26 (0.25; 0.26) | 0.11 | 0.20 (0.20; 0.20) | 0.12 |
Population density | 260.07 (241.79; 278.36) | 831.22 | 321.87 (305.78; 337.96) | 1098.58 |
Drought severity index | -0.59 (-0.60; -0.58) | 0.66 | 0.37 (0.36; 0.39) | 0.91 |
Descriptive statistics of categorical and continuous covariates. Note that the Drought severity index corresponds to the self-calibrating Palmer Drought Severity Index (scPDSI).
Data was available for 25,852 children from the two waves, 31% observation were from the 2007 and 69% were from the 2013/14 ZDHS. Levels of stunting decreased between 2007 and 13/14 from a mean z-scores of -1.59 CI(-1.63; -1.55) to -1.47 CI(-1.49; -1.44). The breastfeeding duration declined from 16.22 to 15.69. There was a notable increase in the number of received vaccinations by children from 5.6 to 7.5 vaccinations. There was a slight increase in the number of years the mother spent in school from 7.2 to 7.8. Malaria incidence rates (plasmodium falciparum incidence) declined from 26% to 20%. Night-time light increased from 2.75, to 3.72 (observed values were log transformed), a possible indication of increase in urbanisation. Night-time light was highly correlated (
High disparities in the height-for-age z-score have been observed at the district and provincial level within Zambia. There was a drift in the spatial pattern of malnutrition in the 2013/14 wave compared to the previous survey, indicating a general improvement. See also
We observed that in the 2007 wave, stunting was lowest in the Western and Muchinga province. In the Southern province generally, low values were also observed, except for the Sinazongwe district. For Eastern province, Nyimba, Katete, Petauke and Lundazi districts had high levels. In the Luapula province, high levels of stunting were observed in the districts of Milenge, Mwense, Kawambwa, Nchelenge and Chiengi. Stunting was severe in some parts of the Copperbelt province which is predominantly a mining region and the Northern province. Central province had moderate levels, except for Serenje district.
With respect to the linear effects,
Covariate | Posterior mean | 95% Credible interval | |
---|---|---|---|
Intercept | -1.70 | -1.84; -1.57 | |
Boys | -0.10 | -0.12; -0.08 | |
Urban | 0.04 | 0.00; 0.07 | |
Wave ZDHS 2007 | -0.07 | -0.13; -0.01 | |
Intercept | 0.23 | 0.17; 0.29 | |
Boys | -0.00 | -0.01; 0.01 | |
Urban | 0.01 | -0.01; 0.04 | |
Wave ZDHS 2007 | 0.05 | 0.01; 0.09 |
Results of linear covariates included in the Model 5.
The Figure depicts the mean effects on the mean
The bottom panel of
The Figure depicts the mean effects on the mean
Low values of the mothers BMI are negatively associated with the height-for-age z-score of the child, while for increasing values of the BMI also an increase in the posterior mean of the z/score can be observed. For values above 40 for the BMI of their mother the results are inconclusive indicated by the widening of the credible intervals. Low values of the BMI of the mother are associated with less variation compared to high values.
The Figure depicts the mean effects on the mean
Due to the high correlation of breastfeeding and age of the child, an interaction between these two variables can be presumed for which one has to account for.
The Figure depicts the mean effects on the mean
The Figure depicts the mean spatial effect of the mean
Using the two waves of the ZDHS, we modelled the height-for-age z-score by using socio-economic and remote sensed information. To analyse the whole distribution and not just focusing on the conditional mean, we used a Bayesian distributional regression approach accounting for heterogeneity as well.
Using Bayesian distributional regression, we assessed the relationship of socio-economic, and remote sensed covariates and stunting. Bayesian distributional regression, presents an advantage in terms of model flexibility allowing to incorporate, amongst others, non-linear effects and spatial effects. This however comes also with the drawback of data intensity and computational complexity.
Remote sensed techniques can be useful for future research on community health assessment as these techniques provide an advantage to take measurements quickly for remote and hard to reach areas. The data also enable to make meaningful analyses at sub-national levels which can improve targeting of interventions due to high levels of geographic specificity [
When relying on remote sensed information to asses anthropometric measures or biophysical developments, great caution should be taken with respect to data quality. Our study finds that remote sensed covariates alone explain little of the variation of the response, this emphasizes the need to control also for socio-economic characteristics. We find that the combination of remote sensed data and socio-economic characteristics explain more of the variation of the response, compared to solely focusing on one of the two sources of explanatory variables. In addition this also highlights the strong influence of socio-economic covariates or can be seen as an indicator of poor quality of the available remote sensed information.
Clear non-linear patterns emerged with respect to the years of education of the mother, and number of vaccinations. There was a clear non-linear tendency among children whose mothers had up to eight years of schooling having a low height-for-age z-score. For children of mothers with secondary or higher education the height-for-age z-score starts to improve strongly. This trend is consistent with what has been observed in others studies were odds of stunting were higher among children from mothers who had few years of education [
Moreover, considering the full distribution like we did shows that the variation is highest for low levels of education and decreases with increasing years of education. This study did not consider the association of paternal education and the child z-score, however in another study, it was found that it was Maternal education that had a positive impact on children’s nutritional status [
We observe differences in levels of malnutrition in various regions in Zambia. One consistent pattern is that of discrepancy between the rural areas which are worse off compared to urban areas and confirms socio-economic inequalities between rural and urban areas. This may suggest social and economic inequalities between such areas. This has already been documented in other studies [
The present study shows that stunting still remain high in Zambia with remarkable regional inequalities and the decline is gradual which is unacceptable. There is need therefore to address the socio-economic indicators if this status is to improve.
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The Figure depicts the sampling paths of the parameters in
(EPS)
The computational results presented have been achieved (in part) using the HPC infrastructure LEO of the University of Innsbruck.