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The Impact of Household Food Security on Child Nutritional Status in Zambia

In Zambia, about 70% of the population are dependent on agriculture for their livelihood yet there still exists the lack of adequate nutrition among Zambian Children (Mofya-Mukuka and Kuhlgatz, 2015:1). Children may experience chronic hunger and energy deficiency because they do not consume the right type of food or do not have access to the correct quantity and quality of food. Therefore, household food security is an important issue in Zambia. In addition to household food security, community characteristics such as sanitation and household characteristics maternal care and education also play a role in childhood nutritional status (Fukunda-Parr and Taylor, 2015:7).


INTRODUCTION
In Zambia, about 70% of the population are dependent on agriculture for their livelihood yet there still exists the lack of adequate nutrition among Zambian Children (Mofya-Mukuka and Kuhlgatz, 2015:1). Children may experience chronic hunger and energy deficiency because they do not consume the right type of food or do not have access to the correct quantity and quality of food. Therefore, household food security is an important issue in Zambia. In addition to household food security, community characteristics such as sanitation and household characteristics maternal care and education also play a role in childhood nutritional status (Fukunda-Parr and Taylor, 2015:7).
To determine nutritional status of children, anthropometric measures are often used. These include insufficient height-for-age (stunting) an indication of chronic malnutrition, insufficient weight-for-height (wasting), an indication of acute malnutrition; and insufficient weight-forage, an indication of chronic and/or acute undernutrition (Adebayo, 2003:228).
This research is conducted from a health perspective that focuses on food security from an availability perspective. Section 2 provides a descriptive study of child malnutrition in Zambia. According to the CSO et al.
(2015:161), 40% of Zambian children under the age of five are stunted, 15% are underweight and 6% are wasted. Mothers with no education have a higher percentage of children who are malnourished in comparison with those who have an education. In addition, the percentage malnourished children living in rural areas is higher than urban areas (CSO, 2015:159). Section 3 presents the data methodology, and section 4 discusses the regression results. Using the linear regression model and the probit model, the results show males are more likely to be stunted and underweight than females. Furthermore, the presence of diarrhoea plays a role in child nutritional status. Diarrhoea increases the probability of a child being wasted in comparison to a child who does not have diarrhoea. Other sanitation measures, such as drinking water source and toilet facility are insignificant yet sanitation should be considered when improving child nutritional status. Section 4 concludes by giving policy recommendations. The main limitation of the study was finding a suitable proxy for household food security and the inclusion of sufficient variables that affect child's nutritional status. Therefore, one recommendation is that for future study, additional information that corresponds to anthropometric measure should be included. Another recommendation is economic empowerment of poor households and households located in rural areas because most malnourished children live in such households and the encouraging child caregivers to give children a diverse group of healthy food

Nutritional Status of Zambian Children
Chronic malnutrition or stunting is a major problem experienced by Zambian children. Nationally, there are approximately 40 percent of children under 5 years who are stunted and 17 percent, who are severely stunted (CSO, 2015:157). Table 1 presents the nutritional  status of children in Zambia under the age of 5,  controlling for various background characteristics, expressed in percentages. Within each age group, children aged 18-23 months have the highest percentage of stunting at 54 percent and the lowest are those less than 6 months. Furthermore, stunting is higher in males at approximately 42 percent than females at 38 percent.
The mother's educational attainment also has an impact on the stunting levels. Stunting in children is higher at about 45 percent for mothers who have no education level and is lowest at about 18 percent for mothers who have more than secondary education.

Table1: Child nutritional status in Zambia
Height-for-age 1 Weight-for-height  Figure 1 illustrates the nutritional status of children in urban and rural areas. As is indicated, the percentage of stunting in both areas is high at 42 percent in rural areas and 36 percent in urban areas. This is higher than the percentage of children who are wasted or underweight. The percentage of children who are underweight is higher in rural areas at 16 percent than in urban areas at 13 percent. For wasting, the percentage difference between rural and urban areas is small though it is higher in urban areas than rural areas.

Data Description
The data used in this analysis is from the 2013-2014 Zambia Demographic Health Survey (ZDHS). The representative sample for the 2013-2014 ZHDS included 18,052 households. The survey was conducted in the whole country.
In this survey, the dataset on children is used. It includes information on each child of eligible women under the age of 5 years (0-59 months).
Other information include immunisation coverage, vitamin A supplementation, and recent occurrences of diarrhoea, fever and cough as well as treatment. The dataset also includes community characteristics such as sanitation, source of drinking water, the toilet facility and household characteristics such as maternal education, assets, household head occupation and residential type. Height and weight measurements represented in z-score standard deviationsare included in the dataset.

Model Description
In the analysis of the impact of household food security on nutritional status of Zambian children, linear probability regressionmodels (LPM) and probit regression methods are used. The sampling methods are first adjusted to control for the effects of sample design of the survey. This is done by sampling weights, clustering and stratification. Three LPM regressions and probit regression models are created with child nutritional outcomes as the dependent variables for each regression.
In this research, the household production function is used as the base model as specified by Adewara and Visser, 2011:5; Robert,2014:5; and Babu and Sanyal, 2012:182. The child's nutritional status outcome is affected by the household's decision of inputs such as nutritional intake; care; and health environment and services. It is also affected by available resources and information constraints faced by the household.Therefore, the functional form of child nutritional status is: (1)

Equation 1 expressed as a linear regression function;
(2) is the child nutritional status outcomes determined by anthropometric measures weightfor-height and weight-for-age. A child that has a weight-for-height and weight-for-age Z-scores below -2 standard deviations is considered wasted and underweight respectively. A dichotomous variable for wasting is created where the value 1 is assigned to a weight-forheight Z-score that is ≤ -2 and 0 when ≥ -2. The dichotomous variable for underweight is 1 if the weight-for-age is ≤ -2 and 0 when ≥ -2.
, is a vector of child specific characteristics such as age (in months), gender, birth order and multiple births (child has siblings). Gender is a dichotomous variable for males where male takes the value 1 and females the value 0. Birth order and multiple births are included as proxies of childcare. The rationale is that the more children in a household, the less childcare can be given to a single child by the caregiver. In addition, the birth order of a child is important because the younger a child is, the more care would be needed to be given by the caregiver in comparison to an older child.
, is a vector of community characteristics that consists of water source, type of toilet facility and diarrhoea. These community characteristics should be considered because in as much as a child may live in a household where they have access to food; the child may not be able to retain the necessary nutrients due to poor sanitation.
Drinking water source includes thirteen options in the survey which are piped into a dwelling, piped into a yard, public tap/standpipe, tube well/borehole, (un)protected dug well, (un)protected spring, river/dam, rainwater, bottled water, tanker truck and cart with small tank. These options are categorised into two groups, namely improved drinking water source and unimproved water source according to the WHO/UNICEF Joint Monitoring Programme (JMP) method (WHO and UNICEF, 2013:12). An improved drinking water source includes piped into dwelling, piped into yard, pubic tap/stand pipe tube well/borehole, protected well, protected spring and river water. Unimproved water source includes unprotected well, unprotected spring, river/dam, tanker truck, cart with a small tank and bottled water.
Toilet facility responses include flush to piped sewer, flush to septic tank, flush to pit latrine, flush to somewhere, flush don't know, ventilated improved pit latrine, pit latrine with(out) slab, compositing toilet, hanging toilet/hanging latrine and no facility/bush/field. Toilet facility is also grouped into improved toilet facility and unimproved toilet facility according to the JMP method (WHO and UNICEF, 2013:12). An improved toilet facility includes flush to piped sewer, flush to septic tank, flush to pit latrine, ventilated improved pit latrine, pit latrine with slab and composting toilet. Unimproved toilet facilities include flush to somewhere, flush do not know, pit latrine without slab, hanging toilet and no facility/bush/field. Diarrhoea is a dichotomous variable indicating whether the child had diarrhoea recently where the value 1 indicates that the child had diarrhoea recently and 0 the child did not have diarrhoea recently.
, is a vector of household specific characteristics such as type of residential place, maternal education, household head occupation, household assets and household size. The type of residential place consists of whether the child is located in a rural or urban area. A dichotomous variable for type of residential place is created where rural takes the value 1 ifthe child lives in a rural residence and the value 0 if a child lives in an urban residence. Maternal education consists of the educational level of a mother that is no education, primary, secondary and higher education, which take the values 0, 1, 2 and 3 respectively. The rationale for including it is that mothers influence hygienic practices and types of foods consumed hence it is expected that higher education levels should positively influence these practices which also in turn improve the nutritional status of a child.
The household assets and household head occupation are proxies for income. In relation to household assets, the wealth index in the survey is used. It is a continuous variable comprising of options of poorest, poorer, middle, richer and richest. Unfortunately, the data did not contain information concerning the occupation of the household head. It is expected that children who come from poorer households may not be able to access the appropriate food required for the improvement of their nutritional status.
The rationale for including household size is that in larger households, food demands are higher than in smaller households therefore it may be difficult for supply of food to meet demand. Children who come from larger households will be affected by the ability to meet demand for food.
, is the Women Dietary Diversity Score (WDDS). It is a proxy of household food security, which reflects food availability, and the probability of micronutrient adequacy of a diet. It is determined by enquiring of the respondent whether they gave the child a range of different foods during the previous day or night. These foods are cereals, white roots and tubers; dark green leafy vegetables; other vitamin A rich foods and vegetables; organ meat; meat and fish; eggs; legumes, nuts and seeds; milk and milk products. To generate the WDDS, each food is dichotomous variable indicating whether a child was given that particular food where 1 is the value assigned if the child was given the type of food and 0 the child was not given. These dichotomous variables are added up to create a score ranging from 0 to 9 then households are categorised into three groups where a score ≤ 3 food groups are lowest dietary diversity, 4-5 food groups are medium dietary diversity and ≥ 6 are high dietary diversity.
, is the effect of unobservable variables on child's nutrition.

Child
Nutritional Outcomes: Anthropometric Measurements Where W is weight of the child, X is the median weight-for-height of the reference population and is the standard deviation weight-for-height of the reference population (Robert, 2014:8). The weight-for-height index measures body mass in terms of body height or length. A zscore measure below -2standard deviation from the WHO child growth standard population median indicates that a child is wasted. Wasting reflects the lack of adequate nutrition in the short-term immediately after the survey and may be because of inadequate intake of food or recent illness that has caused loss of weight of the child. Children's weight-for-height z-score that is below -3 standard deviations are referred to as severely wasted and those above + 2 standard deviations, overweight or obese (CSO, 2015:156-157).
Where W is weight of the child, MW is the median weight-for-age of the reference population and is the standard deviation weight-for-age of the reference population (Adewara and Visser, 2011:7).The weight-forage index is a combination of the height-for-age and weight-for height measurements. It measures both chronic and acute undernutrition. Children below -2 standard deviations from the reference population median are classified as underweight and those below -3 standard deviations, severely underweight (CSO, 2015:157).

Study Limitations
A major limitation of this study is that using a suitable proxy for food security is difficult hence making causal inferences was challenging. The shortcoming of the woman's dietary diversity score is that it considers one aspect of food security, which is food access, rather than the other aspects of food availability, food utilization and food stability. Furthermore, the woman's dietary diversity score only captures short-term access of food that is food given to a child during the previous day or night. A dietary diversity score that captures access of food over a month or year would be more preferable than a day because inferences can be made on anthropometric measure of stunting which measures chronic malnutrition. Another limitation of the study is that the dataset did not include sufficient variables that affect child's nutritional status. The variables that can be included are home health practices, health-seeking behaviours by caregivers, access to a health facility, utilization of healthcare, maternal care, household head occupation and education. The inclusion of these variables would have improved the dataset and the results generated and hence reduce potential bias. For household characteristics, living in a rural area was significant for the wasted coefficient and insignificant for the underweight coefficient. The probability of a being wasted child reduces by about 5 percent and is significant at the 5 percent significance level. This is similar to the findings of the CSO (2014: 159) where wasting is higher in urban areas than rural areas. This is probably because of the increasing urban poverty though urban children have nutritional advantages such as access to health care, improved water and toilet sanitation in comparison to rural children (Smith et al., 2004:1).As compared to poorest households, a household with the richest wealth index significantly reduces the probability of being wasted by about 11 percent. Generally, the probability of a child being malnourished reduces as the wealth index increases that is from poorer to richer.

Descriptive Statistics of Estimation Sample
In terms of the woman's dietary diversity score, when compared to low dietary diversity, the probability of a child being wasted increases by about4 percent for medium dietary diversity at a 1 percent significance level. Therefore, this result suggests that the probability of wasting increases as the woman's dietary diversity changes from low dietary diversity to medium dietary diversity. This is seen in the positive relationship between medium dietary diversity and wasting. These results are similar to those found by Mofya-Mukaka and Kulhgatz (2015:12) who indicated a positive relationship between high levels of diversification and wasting. Still interpretation of this result must be done with caution. For the underweight measure, none of the woman's dietary diversity scores is significant.

Limitations of the Linear Probability Model
The main problems with using the linear probability model are the non-normality and heteroscedasticity of the error term; the possibility of the dependent variables lying outside the 0 to 1 probability range; and low Rsquared values (Gujarati, 2004:584-586). Since the dependent variables are dichotomous variables, the error term also takes on two values and hence follows a Bernoulli distribution. The mean and variance are p and 1 − respectively where p is the probability of success occurring. This indicates that the variance is a function of the mean and leading to heteroscedastic error variance (Gujarati, 2004:584-585).
Another problem arising is that the predicted probabilities may be outside the 0 to 1 probability range, hence making interpretation difficult (Gujarati, 2004:586;Wooldridge 2012:251). Furthermore, the R-squared values in linear probability models are usually very low as can be seen in the linear probability models in table 3.Other variables that could be explaining the nutrition measures are household head occupation, maternal care, health-seeking behaviour by caregivers, access to a health facility and utilization of healthcare. Unfortunately, the survey data did not include such information. Table 4 presents the abridged probit regression model and its marginal effects where wasted and underweight are dependent variables. Full detailed information of the probit regression model and probit marginal effects are in appendix A.2 and A.3 respectively. The first column shows that for the male dummy, there is a positive relationship between a male child and the underweight measure. The probit marginal effect indicates that the probability of a male child being underweight is 0.2 percent. This is also consistent with the finding of CSO (2014:159) where males are more likely to be underweight than females.

Coefficient Estimation Using Probit Models
Drinking water and toilet facility are insignificant for measures of the probit model and the probit marginal effect. In comparison to a child not having diarrhoea, the probit coefficient effect and marginal effect of diarrhoea is positive for all determinants but is only significant for the wasted determinant at the 1 percent statistical level. This shows that diarrhoea increases the probability of a child being wasted and hence community characteristics relating to sanitation play a role in child nutrition.
Similar to the linear probability model, the probit marginal coefficient for living in a rural area is 5 percent and significant at the 5 percent significance level. This is similar to the findings of the CSO (2014: 159) where wasting is higher in urban areas than rural areas. The underweight measure is insignificant in both the probit model and the probit marginal effects. For the wealth index, the results show that for both the probit model and the marginal effects there is a negative relationship between a child being wasted and the wealth index. This suggests that children born in wealthier households are less likely to be wasted in comparison to those born in poor households. In the probit model, household size is significant only for the underweight measure at a 10 percent significance level. The positive coefficient may indicate that in more crowded households, children are more likely to be underweight probably because of the need to share household resources such as food. Notes: Linearized standard errors in parentheses. a Omitted category of coefficient and standard error. *** p<0.01, ** p<0.05, * p<0.1 indicates coefficient significance at 1%, 5% and 10% levels respectively.

Probit model Probit marginal effects
The probit model results show that in comparison to the low dietary diversity, there is a positive relationship between a wasted child and the medium dietary diversity score. For the medium diversity score, the coefficient magnitude is about 0.10 and is significant at a 10 percent significance level. The marginal effects show the probability of a child being wasted increases by about 4 percent for medium dietary diversity in comparison to low dietary diversity though this is significant at the 1 percent significance level. For the underweight measure, none of the dietary diversity scores are significant in both the probit model and the marginal effects. This is probably because the underweight measure is a chronic measure of malnutrition. The probit model and the marginal effects show that in comparison to low dietary diversity, the high dietary diversity is negatively related to wasting and the underweight measure and reduces the probability of a child being malnourished but the results are insignificant for all measures.

Limitations of the Probit Model
The main disadvantage of using probit models is that the coefficients are difficult to interpret (Wooldridge, 2012: 583). This is because the dependent variables of wasting and underweight depend on unobserved components of utility or latent variable that are normally distributed. The normal distributions in some situations are inappropriate and may lead to inaccurate forecasts (Train, 2002:101).

CONCLUSION AND RECOMMENDATIONS
Zambia is an agricultural country and though it may have sufficient supply of food, many children suffer from malnutrition (Mofya-Mukuka and Kuhlgatz, 2015:2). In this study, the2013-2014 Zambia Demographic Health Survey (ZDHS) is used. Child nutrition is determined using anthropometric measurements.
Overall the data shows that male children are more likely to be malnourished that females. Children born to mothers with no education are more likely to be malnourished than those born to mothers who have education. Furthermore, the percentage malnourished children living in rural areas is higher than urban areas (CSO, 2015:159).
To analyse the impact of household food security on nutritional status of Zambian children, the linear probability regression model and the probit model are used. The dependent variables for both models are wasting and underweight, which are regressed on child specific characteristics, household characteristics, community characteristics and the woman's dietary diversity score which is a proxy for food security. The main limitation of the linear probability model is the possibility of the dependent variables lying outside the 0 to 1 probability range and a low R-squared (Gujarati, 2004: 584-586). For the probit model, the main limitation is the difficulty of interpreting the coefficients (Wooldridge, 2012:583).
The results from both models show that male children are more likely to be underweight in comparison to females. For community characteristics, the presence of diarrhoea plays a significant role in child nutritional status that is the wasting measurement. Though the other variables relating to community characteristics such as drinking water source and toilet facility were insignificant, they still play an important role in child nutritional status.
For the woman's dietary diversity score, the linear probability model and probit model show that wasting is positively related to the medium dietary diversity. The marginal effects show the probability of a child being wasted increases by about 4 percent for medium dietary diversity in comparison to low dietary diversity.This means that a diet of 4-5 food group is more likely to increase the probability of wasting. The probability that a child is malnourished reduces as dietary diversity increases that is from medium dietary diversity to high dietary diversity. Therefore, if a child is to increase their consumption from 4-5 food groups to ≥ 6 food groups, the probability of the occurrence of malnutrition can reduce.

RECOMMENDATIONS
 Additional questions included in the survey to create more variables that explain the dependent variables of wasting and underweight. These questions should include the capturing of a more suitable dietary diversity score, proxy for food security and other variables.
The dietary diversity score should capture access of food over longer period such as a month or year rather than a day so that inferences can be made on anthropometric measure of stunting. Therefore, respondents can keep a dairy where they record the type of food that they give to their children and when they give the children the food.
A suitable proxy for food security that captures other aspects of food security such as food availability, food utilization and food stability should be included in the dataset. Therefore, the survey questionnaire should have questions that enable information on food utilization, food access, food availability and food stability to be obtained.
Other variables that affect child nutrition should be included in the dataset. The variables that can be included are home health practices, health-seeking behaviours by caregivers, access to a health facility, and utilization of healthcare, maternal care, household head occupation and education. The inclusion of these variables can generate better results hence reduce potential bias.
 Economic empowerment of poor households and households located in rural areas are to be considered because most malnourished children live in such households. Therefore, to support such households, nutritional programmes can be set up to incentivise them to give children a diverse group of healthy food (Masiye et al., 2010:40). One such programme may provide discounts on health foods items in stores or markets for individuals who are members of the programme. The food items can include fruits, vegetables, carbohydraterich foods, protein-rich foods, diary and dairy alternatives, legumes and lentils, oils, spreads, nuts and seeds. Still there should be continuous monitoring and evaluation of the nutritional programmes and its beneficiaries.
 Launch and sustenance of a nutritional education program, which has the goal of educating households on the importance of healthy food groups and awareness of the dangers of malnutrition (Sealey-Potts and Potts, 2014:5).
 Macro public health and micro public health to prevent water-borne diseases such as diarrhoea should be undertaken. The government and other stakeholders should continue to embark on macro public health projects such as the filtration and chlorination of water supplies, the improvement of drainage and sanitation systems and building of improved toilet facilities. Households in Zambia should carry out micro public health such as the boiling of utensils such as bottles used by children, boiling water and milk, washing hands and protection of food from insects such as flies that carry diseases (Cutler et al., 2006:102).