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Determinants of Malnutrition among Children Aged 6-59 Months in Trans-Mara East Sub-County, Narok County, Kenya

Edward olodaru Ole Tankoi*, Stephen Amolo Asito and Samson Adoka

Department of Public Health, Jaramogi Oginga Odinga University of Science and Technology, Ukwala-Bondo Rd, Bondo, Kenya

*Corresponding Author:
Edward olodaru Ole Tankoi
Jaramogi Oginga Odinga University of Science and Technology
Ukwala-Bondo Rd, Bondo, Kenya
Tel: +254 57 2501804
E-mail: [email protected]

Received date: March 21, 2016; Accepted date: October 28, 2016; Published date: October 31, 2016

Citation: Ole Tankoi EO, Asito SA, Adoka S (2016) Determinants of Malnutrition among Children Aged 6-59 Months in Trans-Mara East Sub-County, Narok County, Kenya. Int J Pub Health Safe 1:116

Copyright: © 2016 Ole Tankoi EO, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Malnutrition is associated with a lot of morbidity and more than one-third deaths in children under 5 years globally. A majority of those who suffer from the brunt of malnutrition are in developing countries. Of note Kenya is one of the countries with the greatest burden of malnutrition associated with rapid nutrition, economic and social transitions. However, there is a paucity of data on malnutrition and the factors related to it in children in rural settings. This study therefore examined the prevalence and predictors of malnutrition among children aged 6-59 months in Trans-Mara East sub-county in Narok county. The study employed a descriptive cross-sectional design and data was collected using a semi-structured questionnaire. Analysis was done using multivariate logistic regression. Of the 350 children enrolled in this study, 31%, 22% and 8% of the children were stunted, underweight and wasted, respectively. Besides, 9% and 4% of the children suffered from overweight and obesity respectively. The key determinants for stunting were number of children in the household (adjusted Odds Ratio (aOR): 1.86; 95%CI: 1.01-3.43), mother being a house wife (aOR: 3.63; 95%CI: 1.08-12.24), and being poor (aOR: 3.33; 95%CI: 1.44-7.68). For obesity, the predictors were child age with 12-23 months (Crude Odds Ratio: 2; 95%CI: 0.175-22.8); 24-35 months (odds ratio of 2.22; 95%CI: 0.22-22.3), child gender with males more likely to be obese relative to females (OR: 3.27; 95%CI: 0.856-12.5). This study indicates that there is double burden of malnutrition in rural settings characterized by high prevalence of under nutrition and low prevalence of over nutrition. The results of this study will be useful for the Ministry of Health and other developmental partners targeting child nutrition in formulating context-specific interventions that are optimized according to the level of food insecurity within different settings.

Keywords

Child malnutrition; Double burden malnutrition; Rural settings; Predictors

Introduction

Malnutrition or malnourishment is a condition associated with either consuming a diet without enough (under nutrition) or too much nutrients (over nutrition) resulting in different types of health problems [1]. However, in less developed countries, food malnutrition is often used specifically to refer to under nutrition [2]. Under nutrition can be due to lack of protein or deficiency in other dietary nutrients [3]. Moreover, the protein energy malnutrition can manifest as either marasmus, which is lack of proteins and other dietary nutrients whereas kwashiorkor, is just protein deficiency [4]. Several studies have indicated that under nutrition during pregnancy or before the first two years of life impacts negatively on both physical and mental development of children [5]. The main driver of malnutrition in developing countries is poverty leading to food insecurity while in developed countries it is the abundance of food leading to malnutrition associated with obesity [6]. However, recent studies indicate that even in developing countries there is double burden of malnutrition where children are presenting with both under nutrition and over nutrition [7]. The burden of obesity has begun to shift to the poor, and the dual burden can be observed within low income communities [8].

Globally, over 10 million children under the age of 5 years die every year from preventable and treatable illnesses despite effective health interventions with malnutrition contributing to a half of these deaths [9-11]. Child malnutrition is still one of the most serious health problems with countries in sub-Saharan Africa and South Asia. It is estimated that nearly 3.1 million children die annually either directly or indirectly as a result of malnutrition [9]. Data indicate that the burden of malnutrition is much higher in South Asia and Africa relative to other parts of the world [12]. This has led nearly 165 million children to be affected by chronic restriction of potential growth [10]. Of note, chronic malnutrition has been a persistent problem for young children in Sub-Saharan Africa and the number of undernourished (low weight for age) people of all ages in sub-Saharan Africa increased from about 90 million in 1970 to 225 million in 2008, and is projected to add another 100 million by 2015 [13]. Although the proportion of children with stunted growth has declined from 35% in 2000 to 30% in 2008 to 2009 [14], Kenya is one of 34 countries with the highest burden of child malnutrition in the world [9]. Of interest is that stunting is more pronounced in children from 0-59 months [2]. However, there is a paucity of data on the prevalence of malnutrition in Trans-Mara East sub-county, an area with food insecurity [15]. Therefore, this study determined the prevalence of malnutrition in Trans-Mara East subcounty among children aged 6-59 months.

It has been demonstrated that malnourished children have lowered resistance to infection; therefore, they are more likely to die from common childhood ailments such as diarrhoea l diseases and respiratory infections [2]. In addition, malnourished children that survive are likely to suffer from frequent illness, which adversely affects their nutritional status and locks them into a vicious cycle of recurring sickness, faltering growth and diminished learning ability [2,4]. In developing countries, malnutrition is a major health problem [16]. Frequent and chronic attacks of malnutrition in early childhood have a potential negative impact on the physical and mental growth of children [17]. Besides these impacts, there is a possibility that malnutrition may expose children to chronic diseases, which further exacerbates the high rate of child morbidity and mortality [18]. Moreover, malnutrition is associated with stunting (low height-for-age) which can cause chronic restriction of a child’s potential growth [19]. Specifically, it refers to children from the ages of 0-59 months who are below 2 standard deviations from the median height-for-age determined by the World Health Organization (WHO) Child Growth Standards. Along with wasting (low weight-for-height) and underweight (low weight-forage), stunting is an indicator of under nutrition [4,20,21]. In addition, malnutrition can result in severe acute malnutrition (SAM) defined by WHO and UNICEF by a weight-for-height index (WHZ) less than -3 z-score or a mid-upper arm circumference (MUAC) less than 115 mm, or presence of edema in children age 1-5 years [6]. Therefore, this study carried out anthropometric measurements to determine the most common type of malnutrition among children aged 6-59 months in Trans-Mara East sub-county.

Studies have further shown that several factors are context specific and are associated with malnutrition [2,9]. Significantly, the causal factors for stunting in children less than 5 years old, varies with age and are ecologically linked with each other [2,9]. This includes environmental factors in households such as household food security and healthy household environment that are important in long term in preventing stunting in children [2,9,22]. In addition, the other household environment related to child nutrition includes the knowledge and perception of caregivers, care givers age and food insecurity [23], child health and food selection [24], and household socio-economic status [25], infestations with ecto-parasites [2]. Studies have also shown that gender of the child is a determinant of malnutrition with females being more likely to suffer from malnutrition relative to males [2]. On the hand infestation with jiggers and ringworms and endo-parasites like tapeworms are also predictors of malnutrition [26]. These intrahousehold environmental factors contribute to the neglect of children’s needs, especially their nutritional status from birth to preschool [24]. Furthermore, the intra-household environment is affected by environmental, cultural, and historical factors in the communities’ where the mothers’ live [2].

The prevalence of childhood obesity has increased considerably in recent years [1]. Recent systematic review evidence revealed a transition towards increasing proportions of overweight over time among school-aged children (5 to 17 years) in sub-Sahara Africa [27]. Overweight is problematic in children due to the resulting increased risk of obesity in adulthood, physical and psychosocial morbidity, and premature mortality in adulthood. Childhood overweight is also associated with impaired social and economic productivity in adulthood [28]. Consequently, the concern for a growing prevalence of non-communicable diseases in SSA is worrying [14]. Therefore, this study was designed to determine the influence of household environmental factors on the nutritional status of children aged 6-59 months in a community with a high-level of food insecurity in Trans- Mara East sub-county.

Problem Statement

Malnutrition is associated with a lot of morbidity and mortality especially in children under 5 years old. Studies have shown that rural communities bear the brunt of malnutrition. More importantly, although the proportion of children with stunted growth has declined from 35% in 2000 to 30% in 2008 to 2009, Kenya is still one of 34 countries with the highest burden of child malnutrition in the world and the levels of under nutrition has persisted for decades in Kenya. Moreover, the levels of wasting and stunting have remained majorly unaltered for about 20 years at between 6% and 7% for wasting and 30% and 35% for stunting, however, a recent survey has shown that there has been a great improvement with stunting currently at 26%, underweight at 11% and wasting at 4%. Of note studies indicated that malnutrition is a very common phenomenon in rural communities and those living in peri-urban or urban poor settings because of poverty and food insecurity, but not all children suffer from malnutrition even in food insecure situations, suggesting that other unique context specific factors may be critical in driving malnutrition. Hence there is a need to study these context-specific issues that influences malnutrition in children. Therefore, this study was designed to evaluate the determinants of malnutrition in Trans-Mara East sub-county. Of note, this sub-county was formerly mainly populated by Maasai community but there has been immigration to this region by other tribes. This has led into shift of Maasai lifestyle from pastoralism to agro-pastoralism. Moreover, high rates of population growth and in-migration and immigration has impacted on pastoral lands and food security. Hence there is need to understand how these factors have impacted on malnutrition in this region.

Objectives

Broad objective: To determine determinants of malnutrition among children aged 6-59 months in Trans-Mara East sub-county.

Specific objective:

1. To determine the prevalence of malnutrition among children aged 6-59 months in Trans-Mara East sub-county

2. To determine maternal and child characteristics associated with malnutrition among children aged 6-59 months in Trans-Mara East sub-county

3. To determine the predictors of malnutrition among children aged 6-59 months in Trans-Mara East sub-county

Justification

Frequent and chronic attacks of malnutrition in early childhood have a potential negative impact on the physical and mental growth of children [17]. It affects children in many ways, predisposing them to different infectious diseases, psychosocial mal-development, and cognitive deficiencies. Besides these impacts, there is a possibility that malnutrition may expose children to chronic diseases which further exacerbates the high rate of child morbidity and mortality [18]. Also, even though the government and other health stakeholders have introduced a number of nutritional intervention programs to address the problem of malnutrition in various counties, not all of them are based on scientifically proven association. In Narok county a survey carried out on the nutritional baseline indicators, did not explore factors associated with malnutrition [29]. A rapid assessment of the development situation in 2004, ranked the area (Kirindon, the now Trans-Mara East sub-county) as the poorest division, with 40 percent (20,000) of the total poor (51,200) in the district. The assessment identified development issues that included poor education standards, poor health status, inadequate clean water supply, high rates of HIV and AIDS, poor infrastructure, inadequate food security, and insecurity due to ethnic and clan conflicts [15]. These factors potentially changed due to social and economic disparities. Although risk factors for malnutrition have been identified, individual factors potentially change in specific areas over time and a current characterization of risk factors provides the basis for preventive intervention programs. The present study sought to establish the determinants of malnutrition.

Methodology

Research design

This study was a cross-sectional descriptive survey using a semistructured questionnaire and measurements of weight and height to determine the nutritional status of children aged 6-59 months and also examine the determinants of malnutrition among these children.

Study variables

Dependent variable: Malnutrition indicated by stunting, wasting underweight overweight and obesity.

Independent variables: Determinants assessed as independent variables.

Socio-economic and demographic variables: Head of household, marital status, ethnicity, religion, family size, income, education, occupation, ownership of livestock and size of farm land.

Child characteristics: Age, sex, birth order, place of delivery, gestational age, type of birth, breastfeeding status and sickness status (fever and diarrhea).

Child caring practices: Feeding mode, health care seeking and immunization.

Maternal characteristics: Age, number of children ever born, ANC visits, extra food during pregnancy/lactation, health status during pregnancy, use of extra food during pregnancy or lactation and decision-making on use of money.

Environmental health condition: Water source, sanitation means, mode of water treatment and main source of cooking fuel.

Study area

The study was carried out in Trans-Mara East sub-county in Narok county. The sub-county has a population of 110,000 by 2009 census in Kenya, with a growth rate of 3% annually, and a total number of 20,759 households [14]. The total number of children under five is 18,590 (16.9% of population) in which 16,731 falls between 6-59 months (90% of under-fives).

It covers an area of 320.5 km2 with population density of 332 and longitude 35.054536 and latitude -0.960695. Recent data indicated that 4.4% of children under the age of 5 years suffer from severe acute malnutrition (Narok county, 2013). Majority of the cases came from Trans-Mara East sub-county.

Study population

Target population: All households which had children aged 6-59 months in Trans-Mara East sub-county.

Inclusion criteria: This study included households with children aged 6 to 59 months. In addition, primary caregivers’ that is the person responsible for the day-to-day care and wellbeing of an infant/child between the age of 6 and 59-months, including biological mothers, grandparents, aunts, and others in cases where the biological parents were deceased or unavailable. The study was only done in households where the household heads gave informed consent.

Exclusion criteria: This study excluded households with children below 6 months or above 59 months of age, or whose caretakers did not consent. Moreover, children who were visitors to the households in the area of study were excluded. The study also excluded the following children from the analysis: those whose caregivers were unable to answer the questions due to hearing disability; those who were severely sick; and those whose birth date were not appropriate or not known.

Sample size determination

Since the aim of this study was to determine the prevalence of malnutrition in Trans-Mara-East sub-county. The sample size calculation was therefore based on data from previous study that indicated that the national prevalence estimate for stunting which is 35% [7,30]. Fisher’s formula was used to calculate the sample size at 95% significance level as follows [31],

n=Z2Pq/d2

where n is the desired sample size (if the targeted population is greater than 10,000); Z is the critical value associated with level of significance usually 1.96 corresponding to 95%; P is proportion of target population estimated to have a particular characteristic. P, the national prevalence estimate for stunting was given as 35% [7,14,31]; d is the margin of error i.e., 5%=0.05.

q=1-p.

n=1.962 × p (1-p) ÷ d2=sample size required

n=1.962 × 0.35 (1-0.35) ÷ 0.0502=(3.8416 × 0.2275)=(0.873964) ÷ (0.0025)=350 children

The sampling technique: Multi stage cluster sampling technique was used because it involved a wide geographical area (sub-county) and came up with six clusters based on the existing administrative units called locations. Probability proportionate to size (PPS) sampling was used to compute the cluster sample. This was then followed by simple random sampling to select children from the households. From the 197 existing villages, 17 were selected by simple random sampling.

Sampling procedure: Clusters were formed based on the 6 existing locations. In each cluster, the sample was computed through probability proportional to size (PPS) taking the number of households in that particular cluster (location) as the enumerator over the total number of households in the entire Sub-county, multiplied by 350 (The determined sample size) (Figure 1). Using the total number of villages in the cluster as the numerator and the overall number of villages in the sub-county as denominator multiplied by the calculated cluster sample computed by PPS, the required number of villages to be visited out of the sampling frame of 197 villages was obtained. In each cluster, based on the obtained sample, According to Mugenda [31], a sample size of between 10% and 30% is a good representation of the target population and hence the 30% was adequate for sampling.

public-health-safety-Schematic-presentation

Figure 1: Schematic presentation of sampling procedure.

After establishment of the required number of villages within the clusters, simple random sampling technique was applied to select the villages that were visited. Eventually, households with eligible children were randomly selected based on the determined sample size per cluster. Enumerators administered the questionnaire after obtaining consent from the caregiver and thereafter took the anthropometrical measurements.

Cluster Sample=(A/Z × 350)

where A is the cluster total number of households and Z is total number of households in the entire sub-county.

Formula used to calculate sample size of each selected cluster (location):

ni=(n × Ni)/N

where ni is sample size of each selected cluster; n is total sample size; Ni is total number of household in each cluster; N is total number of household in all clusters.

The clusters were households with eligible children were randomly selected based on the calculated/determined sample size per cluster. Enumerators administered the questionnaire after obtaining consent from the caregiver and thereafter took the anthropometrical measurements.

Data collection tools/instruments

Data collection procedures: The questionnaire was pre-tested and refined on the basis of the feedback obtained from the pre-test. Data were collected using semi-structured questionnaire and anthropometric measurement. Twelve data collectors who were able to communicate in the local language were recruited. Training was provided for data collectors and two supervisors for one day. Interview was conducted with mothers/care takers of the children to fill the questionnaire. In households with more than one child of age between 6-59 months, one child was selected randomly by lottery method.

The correct age of a child was confirmed using his/her maternal and child health (MCH) handbook or by the response from the caregiver where the MCH handbook was not available.

Anthropometric data: The anthropometric data were collected using the procedure stipulated by the WHO [20]. Before taking anthropometric data for children, their age should first be determined in order to ensure that, was within the target population. A local event such as circumcision time of an age group was used to establish the birth period if the child’s card was not available [32].

Height/length measurement: Body length of children age up to 23 months was measured without shoes and the height was read to the nearest 0.1 cm by using a horizontal wooden length board with the infant in recumbent position. However, height of children 24 months and above was measured using a vertical wooden height board by placing the child on the measuring board, and child standing upright in the middle of board. The child’s head, shoulders, buttocks, knees and heels touching the board.

Weight measurement: Weight was measured by electronic digital weight scale with minimum/lightly/clothing and no shoes. Calibration was done before weighing every child by setting it to zero. In case of children age below two years, the mother was first weighed alone then she was weighed with the baby and the baby's weight was calculated by subtracting the mothers’ weight from the combined mother/baby weight. Edema was checked and noted on data sheet because children with edema were severely malnourished. In order to determine the presence of bilateral edema, normal thumb pressure was applied to the two feet for three seconds whether a shallow print or pint remains on both feet when the thumb is lifted.

In determining children’s nutritional status, World Health Organization (WHO) child growth standards of 2006 [20] was used to calculate Z-scores for the anthropometric indicators. Children were considered wasted, stunted or underweight when their WHZ/WLZ, HAZ/LAZ and WAZ score was less than -2 SD respectively. A Z score of +2 SD (i.e. 2 SD above the median) and +3 SD or more was considered overweight and obesity respectively.

Household food security level measurements: The household food security level was measured using the household food insecurity access scale (HFIAS) with scores ranging from 0 to 27 by household level [33]. The HFIAS scores obtained from households were categorized into 4 levels of food insecurity, namely, “food secure,” “mildly food insecure,” “moderately food insecure,” and “severely food insecure,” based on the HFIAS guideline [34]. The household socioeconomic status (SES) was parameterized by the principle component analysis (PCA) method using house properties confirmed by the questionnaire: property owned, source of drinking water, sanitation method used and hand washing facilities. The score in the first PCA component was used as an asset index of SES status for each household [35]. According to the PCA-based asset index, households will be divided into 4 groups, the first quartile SES group was considered the poorest and the fourth quartile SES group was the richest in the study area.

Data analysis

Malnutrition status was determined by univariate logistic regression analyses. Multiple logistic regression analysis was also conducted to control confounding factors by backward stepwise selection with 0.2 of significant level of removal from the model. STATA version 13 software was used for the analysis.

In order to obtain comparable estimates of malnutrition indices across other surveys, data were analyzed using the proposed procedures and cut-offs by the World Health Organization. Using the WHO method, overweight was defined as weight-for-height z-score>1 (corresponding approximately to the 84th percentile) and obesity as weight-for-height z-score>2 (corresponding to approximately 98th percentile).

Ethical considerations

Ethical clearance was obtained from Board of Post-Graduate Studies (BPS) of Jaramogi Oginga Odinga University of Science and Technology and Research Ethic Committee University of Eastern Africa, Baraton, Eldoret. It was also granted by ministry of Health office, Trans-Mara East sub-county administrative unit. Verbal consent from parents/ care taker of the study subjects was obtained and the objective of the study was explained to them. Privacy and confidentiality of collected information was ensured at all level.

Results

Demographic and socio-economic characteristics of households

From the total planned study subjects, complete response was obtained for 350 (100%) (Table 1). As indicated in Table 1, 277 (79.1%) households (HH) were headed by males and a majority of the respondents were married 290 (82.9%). In the study area, most of the respondents interviewed were of the Kipsigis ethnic group 349 (98.3%) and 99.4% were Christians. In addition, 335 (95.7%) had more than four family members in their respective household sand slightly more than two-thirds of the households had 2-3 children under five years of age 244 (69.7%). Concerning educational status, 112 (32%) of mothers and 105 (30%) of fathers cannot read and write, 215 (61.4%) and 236 (67.4%) of fathers and mothers respectively had attained primary education compared to 8 (2.3%) of fathers and 1 (2.3%) of mothers who had attained a higher level of education. Close to three-quarters of the mothers 252 (72.0%) were housewives and 58 (16.5%) were farmers compared to 242 (69.1%) of fathers who are farmers. Regarding livestock, 264 (75.4%) of HHs had livestock, where 214 (61.1%) had 1-5 livestock and 20 (5.7%) had more than 10 livestock. On the other hand, 320 (91.4%) of the households possessed farm land, and out of them 202 (57.7%) had more than 1 Hectare. Majority of the HHs, 226 (64.6%) had a monthly income of Kenya shillings 3,000-9,000.

Characteristics   Frequency Percent
Head of HHs Male 277 79.1
Female 73 20.9
Marital status Married 290 82.9
Divorced 6 1.7
Others 54 15.4
Ethnicity Kipsigis 349 99.7
Luhya 1 0.3
Religion Christianity 348 99.4
Others 2 0.6
Family size 1-3 members 15 4.3
>4 members 335 95.7
HHs with under 5 years’ children 1 106 30.3
2-3 244 69.7
Can’t read and write 112 32
Can read and write 238 68
Paternal education Primary education 236 67.4
Secondary education 29 8.3
Higher education 1 0.3
No education 80 22.9
Can’t read and write 105 30
Can read and write 245 70
Maternal Education Primary education 215 61.4
Secondary education 22 6.3
Higher education 8 2.3
Housewife only 252 72
Occupation of caregiver Farmer 58 16.5
Merchant/Trade 31 8.9
Others 9 2.6
Occupation of child’s Father Farmer 242 69.1
Gov’t employee 15 4.3
Merchant/Trade 56 16
Others 2 0.6
No job 35 10
Monthly income (in KShs) Less than KSh 3000 80 22.9
3000-9000 226 64.6
>9000 44 12.5
Decision making on use of money Mainly husband 81 23.1
Mainly woman 127 36.3
Only husband 20 5.7
Both jointly 122 34.9
Ownership of livestock Yes 264 75.4
No 86 24.6
Livestock per household 1-5 214 61.1
6-10 30 8.6
>10 20 5.7
Ownership of land Yes 320 91.4
No 30 8.6
Land by hectare per HH Below 1 hectare 118 43.7
Above1 hectare 202 57.7

Table 1: Demographic and socio-economic characteristics of households in Trans-Mara East sub-county.

Prevalence of malnutrition among children aged 6-59 months

The study also assessed the levels of malnutrition (Table 2). It was found that slightly more than a quarter of the participants were healthy 89 (25%). For the remaining children, 108 (31%), 78 (22%), 32 (9%), 29 (8%) and 14 (4%) of children were stunted, underweight, overweight, wasted or obese respectively (Figure 2).

Variable Underweight Stunted Wasted Overweight Obese Normal
Gender
Male 44 (56.41%) 65 (60.19%) 11 (37.93%) 15 (46.88%) 11 (78.57%) 46 (51.69%)
Female 34 (43.59%) 43 (39.81%) 18 (62.07%) 17 (53.12%) 3 (21.43%) 43 (48.31%)
Age (m)
6-11 months 7 (12.73%) 6 (7.32%) 8 (30.77%) 4 (19.05%) 1 (7.69%) 8 (12.9%)
12-23 months 10 (18.18%) 16 (19.51%) 6 (23.08%) 6 (28.57%) 3 (23.08%) 13 (20.97%)
24-35 months 18 (32.73%) 22 (26.83%) 5 (19.23%) 5 (23.81%) 5 (38.46%) 18 (29.03%)
36-47 months 20 (36.36%) 38 (46.34%) 7 (26.92%) 6 (28.57%) 4 (30.77%) 23 (37.1%)

Table 2: Malnutrition status by gender of children.

public-health-safety-Prevalence-malnutrition

Figure 2: Prevalence of malnutrition among children aged 6-59 months.

The study further assessed the malnutrition status by gender of which females had higher malnutrition status in underweight, stunted and obese at 44 (56.41%), 65 (60.19%) and 11 (78.57%) respectively. The males had higher levels of malnutrition at wasted 18 (62.07%) and overweight at 17 (53.12%). Slightly more than half of the females 46 (51.69%) were healthy. The study also looked at the children’s age and their malnutrition status. It was found that 20 (36.36%) and 18 (32.73%) of underweight children, 38 (46.34%) and 22 (26.83%) of stunted children, and, 23 (37.10%) and 18 (29.03%) of healthy children were aged between 36-47 months and 24-35 months respectively. In addition, 4 (19.05%) and 6 (28.57%) of overweight children, and 8 (30.77%) and 6 (23.08%) of wasted children were aged between 6-11 months and 12-23 months respectively.

Prevalence of malnutrition types by socio-demographic characteristics

This study also assessed the prevalence of the various types of malnutrition by the socio-demographic characteristics (Table 3). Of children aged 6-11 months, 7 (20.59%) were underweight, 6 (17.65%) were stunted and 8 (23.53%) were wasted. Of those aged 36-47 months; 20 (20.00%) were underweight, 38 (38.00%) were stunted and 24 (24.00%) were healthy. Under gender, 44 (22.92%) were underweight and 65 (33.85%) were stunted in males compared to 34 (21.52%) and 43 (27.2%) in females. In socio-economic status, 15 (19.48%) were underweight and 22 (28.57%) were underweight in the poorest category, 20 (31.75%) were underweight and 26 (41.27%) were stunted in the very poor category. There was no statistical significant association found on age with p=0.211, gender p=0.088, mothers age p=0.41, household head p=0.702, marital status at p=0.086. However, socio economic status had a statistical significant association to malnutrition p=0.02.

Variables Underweight Stunted Wasted Healthy Overweight Obesity p value
Child age (m)             0.211
6-11 7 (20.59) 6 (17.65) 8 (23.53) 8 (23.53) 4 (11.76) 1 (2.94)  
12-23 10 (18.87) 16 (30.19) 6 (11.32) 12 (22.64) 6 (11.32) 3 (5.66)  
24-35 18 (24.66) 22 (30.14) 5 (6.85) 18 (24.66) 5 (6.85) 5 (6.85)  
36-47 20 (20.00) 38 (38.00) 7 (7.00) 24 (24.00) 7 (7.00) 4 (4.00)  
48-59 23 (25.56) 26 (28.89) 3 (3.33) 27 (30.00) 10 (11.11) 1 (1.11)  
Child gender             0.088
Boy 44 (22.92) 65 (33.85) 11 (5.73) 47 (24.48) 14 (7.29) 11 (5.73)  
Girl 34 (21.52) 43 (27.22) 18 (11.39) 42 (26.58) 18 (11.39) 3 (1.90)  
Mothers age (y)             0.41
15-19 6 (26.09) 6 (26.09) 4 (17.39) 5 (21.74) 2 (8.70) 0 (0.00)  
20-29 29 (17.26) 49 (29.17) 15 (8.93) 49 (29.17) 18 (10.71) 8(4.76)  
30-39 29 (25.22) 42 (36.52) 5 (4.35) 25 (21.74) 9 (7.83) 5 (4.35)  
40-49 14 (31.82) 11 (25.00) 5 (11.36) 10 (22.73) 3 (6.82) 1 (2.27)  
Household head              
Father 60 (21.66) 89 (32.13) 22 (7.94) 73 (26.35) 23 (8.30) 10 (3.61)  
Mother 18 (24.66) 19 (26.03) 7 (9.59) 16 (21.92) 9 (12.33) 4 (5.48)  
Marital status             0.702
Married 65 (22.41) 89 (30.69) 24 (8.28) 77 (26.55) 25 (8.62) 10 (3.45)  
Single 4 (16.67) 6 (25.00) 2 (8.33) 8 (33.33) 2 (8.33) 2 (8.33)  
Divorced/Widowed/Separated 9 (25.00) 13 (36.11) 3 (8.33) 4 (11.11) 5 (13.89) 2 (5.56)  
Number of children in household             0.086
1 23 (21.70) 25 (23.58) 5 (4.72) 37 (34.91) 11 (10.38) 5 (4.72)  
2 38 (20.77) 66 (36.07) 15 (8.20) 41 (22.40) 15 (8.20) 8 (4.37)  
3 17 (27.87) 17 (27.87) 9 (14.75) 11 (18.03) 6 (9.84) 1 (1.64)  
Decision maker on income             0.562
Mother 31 (24.41) 38 (29.92) 15 (11.81) 26 (20.47) 9 (7.09) 8 (6.30)  
Mainly father 17 (20.99) 29 (35.80) 7 (8.64) 19 (23.46) 8 (9.88) 1 (1.23)  
Father only 4 (20.00) 6 (30.00) 1 (5.00) 7 (35.00) 1 (5.00) 1 (5.00)  
Both 26 (21.31) 35 (28.69) 6 (4.92) 37 (30.33) 14 (11.48) 4 (3.28)  
Socio-economic status             0.02
Poorest 15 (19.48) 22 (28.57) 7 (9.09) 24 (31.17) 6 (7.79) 3 (3.90)  
Very poor 20 (31.75) 26 (41.27) 6 (9.52) 5 (7.94) 4 (6.35) 2 (3.17)  
Poor 16 (22.86) 25 (35.71) 5 (7.14) 11 (15.71) 9 (12.86) 4 (5.71)  
Less poor 19 (25.00) 15 (19.74) 9 (11.84) 24 (31.58) 7 (9.21) 2 (2.63)  
Least poor 8 (12.50) 20 (31.25) 2 (3.13) 25 (39.06) 6 (9.38) 3 (4.69)  

Table 3: Prevalence of malnutrition status by socio-demographic characteristics.

Prevalence of malnutrition types by health and dietary status

Of those who had diarrhea in the past two weeks, 19 (17.76%), 35 (32.71%) 13 (12.15%) were underweight, stunting and wasting respectively though there was no statistically significant difference to those who had no diarrhea (p=0.181). Of those who had fever, 25 (20.83%), 39 (32.50%) and 14 (11.67%) were underweight, stunting and wasting respectively though there was no statistically significant difference to those who had no fever (p=0.485). Edema presence had a significant association with malnutrition status (p=0.002) (Table 4).

Variables Underweight Stunting Wasting Normal Overweight Obesity p value
Diarrhea past 2 weeks             0.181
Yes 19 (17.76) 35 (32.71) 13 (12.15) 25 (23.36) 8 (7.48) 7 (6.54)  
No 59 (24.28) 73 (30.04) 16 (6.58) 64 (26.34) 24 (9.88) 7 (2.88)  
Fever past 2 weeks             0.485
Yes 25 (20.83) 39 (32.50) 14 (11.67) 27 (22.50) 9 (7.50) 6 (5.00)  
No 53 (23.04) 69 (30.00) 15 (6.52) 62 (26.96) 23 (10.00) 8 (3.48)  
Edema presence             0.002
Yes 0 (0.00) 6 (54.55) 3 (27.27) 0 (0.00) 0 (0.00) 2 (18.18)  
No 78 (23.01) 102 (30.09) 26 (7.67) 89 (26.25) 32 (9.44) 12 (3.54)  
Dietary intake
Daily feeding frequency             0.4567
<3 times 10 (22.22) 17 (37.78) 1 (2.22) 15 (33.33) 2 (4.44) 0 (0.00)  
3 times 36 (23.38) 42 (27.27) 14 (9.09) 41 (26.62) 14 (9.09) 7 (4.55)  
>3 times 32 (21.19) 49 (32.45) 14 (9.27) 33 (21.85) 16 (10.60) 7 (4.64)  
Exclusive breast feeding (m)             0.055
1-3 35 (21.88) 52 (32.50) 13 (8.13) 40 (25.00) 14 (8.75) 6 (3.75)  
4-5 14 (20.90) 20 (29.85) 5 (7.46) 22 (32.84) 5 (7.46) 1 (1.49)  
6 13 (16.67) 22 (28.21) 4 (5.13) 26 (33.33) 8 (10.26) 5 (6.41)  
7-12 16 (35.56) 14 (31.11) 7 (15.56) 1 (2.22) 5 (11.11) 2 (4.44)  

Table 4: Prevalence of malnutrition by health and dietary status.

Care givers demographic and behavioral characteristics

The study had 168 (48.0%) of the care givers aged between 20- 29 years, 115 (32.9%) aged between 30-39 years and 44 (12.5%) aged between 40-49 years. Close to two-thirds of the mothers had given their first birth aged 12-19 years 224 (64.0%) followed by 20-27 years at 124 (35.4%). In addition, 105 (30.0%) of caregivers, had 1-2 previously born children. Majority of the caregivers, 236 (67.4%) had visited the ANC more than 4 times at a health facility, 158 (45.1%) of the caregivers do use family planning method of which 118 (74.7%) of them use Depo- Provera while 35 (22.2%) use other family planning methods (Table 5).

Characteristics Frequency Percent
Age of mother (years)    
15-19 23 6.6
20-29 168 48
30-39 115 32.9
40-49 44 12.5
Age at first birth (years)    
12-19 224 64
20-27 124 35.4
36-43 2 0.6
Total child born before    
No 56 16
1-2 105 30
3-4 98 28
5-6 68 19
7 and above 23 7
How many times did you visit health facility for ANC    
Once 29 8.3
Twice 73 20.9
At least 4 times 236 67.4
No visit made 12 3.4
Family planning used    
Yes 158 45.1
No 192 54.9
Types of family planning used    
Pills 5 3.2
Depo-provera 118 74.7
Others 35 22.2

Table 5: Caregivers demographic and behavioral characteristics.

Behavioral and clinical characteristics of children

The children interviewed in the study comprised of 192 (54.9%) males who are slightly more than half of the sample. Moreover, close to three-quarters of the children were above 24 months of age, that is 73 (20.9%), 100 (28.6%) and 90 (25.7%) were aged between 24-35 months, 36-47 months and 48-59 months respectively. Majority of the children were delivered at home 245 (70.0%) and 326 (9.1%) of the children had gestational age at 9 months. In addition, 335 (95.7%) were single births. Furthermore, 268 (76.6%) of children were not being breast fed of which 208 (77.6%) caregivers cited that child was above breast feeding age as the reason and 25 (9.3%) cited maternal pregnancy. On clinical characteristics, 107 (30.6%) of the children experienced diarrhea in the past 2 weeks prior to the study, of which 52 (48.6%) of them had more than 5 episodes and 25 (23.4%) had two episodes of diarrhea. Lastly 120 (34.3%) of the children were reported to have experienced an episode of fever in the past two weeks (Table 6).

Variables Frequency Percent
Child sex    
Male 192 54.9
Female 158 45.1
Child age (in months)  
6-11 34 9.7
12-23 53 15.1
24-35 73 20.9
36-47 100 28.6
48-59 90 25.7
Place of delivery    
Home 245 70
Health facility 105 30
Gestational Age at birth    
Less than 9 months 4 1.2
At 9 months 326 93.1
Greater than 9 months 20 5.7
Types of birth  
Single 335 95.7
Twin 15 4.3
Still breastfeed child    
Yes 82 23.4
No 268 76.6
Reason for not feed breast (n=268)    
Maternal health problems 8 3
Refusal of child 20 7.5
Maternal pregnancy 25 9.3
Workload 7 2.6
Child ‘s above breastfeeding age 208 77.6
Diarrhea    
Yes 107 30.6
No 243 69.4
Frequency of diarrhea    
1 episode 13 12.1
2 episode 25 23.4
3-4 episode 17 15.9
³ 5 episode 52 48.6
Fever    
Yes 120 34.3
No 230 65.7

Table 6: Behavioral and clinical characteristics of children.

Feeding practices of children

The study also looked at the children’s feeding practices whereby 264 (75.4%) of the children were breast fed immediately after birth, 237 (67.7%) of children had water with sugar as pre-lactation food, 120 (34.3%) and 117 (33.4%) of children started complementary feeds at 4-5 and 1-2 months respectively. In addition, 211 (60.3%) had their complementary feeds in fluid forms, 154 (44.0%) and 151 (43.1%) were fed three or more than three times daily respectively. The methods of feeding comprised mostly of cups 218 (62.3%) and bottles 82 (23.4%). Furthermore, 160 (45.7%) had exclusive breast feeding for a period of 1-3 months compared to 78 (22.3%) who had exclusive breast feeding for 6 months. Lastly, 293 (83.7%) children were given vitamin A (Table 7).

Characteristics Frequency Percent
Initiation of breastfeeding of child    
Immediately 264 75.4
After 1 hour to 24 hours 76 21.7
After a day 10 2.9
Pre-lactation food/fluids kind    
Honey 5 1.4
Water with sugar 237 67.7
Plain water 81 23.1
Fruit juice 3 0.9
Butter 5 1.4
None 19 5.4
Age complementary feeding started (in months)    
1-3 117 33.4
4-5 120 34.3
6 92 26.3
7-12 21 6
Form complementary in addition to BF    
In fluid form 211 60.3
Semi solid form 139 39.7
Frequency of feeding/day    
<3 times 45 12.9
3 times 154 44
>3 times 151 43.1
Method of feeding    
Bottle 82 23.4
Cup 218 62.3
Spoon 32 9.1
Hand 18 5.2
Length of EBF child (in months)    
1-3 160 45.7
4-5 67 19.1
6 78 22.3
7-12 45 12.9
Immunization    
Yes 316 90.3
No 34 9.7
Vitamin A supplementation    
Yes 293 83.7
No 57 16.5

Table 7: Children feeding practices.

Feeding patterns by wealth index

The research also looked at feeding patterns and its association with the wealth index. Close to half, 124 (46.9%) of the least poor began breast feeding initiation immediately, 57 (75.0%) of the very poor began 1 hour to 2 hours. On giving other drinks than breast milk, 3 (60%) of the least poor were given honey and 110 (46.4%) sugar. In addition, 58 (71.6%) and 2 (66.7%) in the very poor category were given water and fruit juice respectively. On assessing the age of complementary feeding 43 (46.7%) of the least poor started at 6 months, 67 (57.2%) and 38 (31.6%) of the very poor began at 1-2 and 4-5 months respectively. On the form of complementary feeding 104 (42.9%) of the least poor were given liquid/semi-solid food compared to 76 (54.6%) of the very poor. There was statistical significant difference in breast feeding initiation, complementary feeding and age, frequency of feeding, feeding method, length of exclusive breast feeding and use of vitamin a (p<0.0001) (Table 8).

  Wealth index n (%)  
Variable Very poor Poor Least poor p value
Breast feeding initiation       <0.0001
Immediately 79 (29.92) 61 (23.11) 124 (46.97)  
1 hour to 2 hours 57 (75.00) 9 (11.84) 10 (13.16)  
After a day 4 (40.00) 0 (0.00) 6 (60.00)  
Other drinks other than breast milk       <0.0001
Honey 2 (40.00) 0 (0.00) 3 (60.00)  
Sugar 73 (30.80) 54 (22.78) 110 (46.41)  
Water 58 (71.60) 11 (13.58) 12 (14.81)  
Fruit juice 2 (66.67) 1 (33.33) 0 (0.00)  
Butter 0 (0.00) 0 (0.00) 5 (100.00)  
None 5 (26.32) 4 (21.05) 10 (52.63)  
Age of complementary feeding (m)       <0.0001
1-2 67 (57.26) 10 (8.55) 40 (34.19)  
4-5 38 (31.67) 31 (25.83) 51 (42.50)  
6 26 (28.26) 23 (25.00) 43 (46.74)  
7-12 9 (42.86) 6 (28.57) 6 (28.57)  
Form of complementary       <0.0001
Liquid/Fluid 64 (30.33) 43 (20.38) 104 (42.29)  
Semi solid 76 (54.68) 27 (19.42) 36 (25.90)  
Frequency of feeding (daily)       <0.0001
<3 22 (48.89) 2 (4.44) 21 (46.67)  
3 78 (50.65) 24 (15.58) 52 (33.77)  
>3 40 (26.49) 44 (29.14)    
Feeding method       <0.0001
Bottle 17 (20.73) 14 (17.07) 51 (62.20)  
Cup 98 (44.95) 48 (22.02) 72 (33.03)  
Spoon 16 (50.00) 2 (6.25) 14 (43.75)  
Hand 9 (50.00) 6 (33.33) 3 (16.67)  
Length of exclusive breast feeding       <0.001
1-3 91 (56.88) 22 (13.75) 47 (29.38)  
4-5 15 (22.39) 13 (19.40) 39 (58.21)  
6 18 (23.08) 20 (25.64) 40 (51.28)  
7-12 16 (35.56) 15 (33.33) 14 (31.11)  
Immunization       0.052
Yes 122 (38.61) 61 (19.30) 133 (42.09)  
No 18 (52.94) 9 (26.47) 7 (20.59)  
Vitamin A       <0.0001
Yes 91 (31.06) 66 (22.53) 136 (46.42)  
No 49 (85.96) 4 (7.02) 4 (7.02)  

Table 8: Feeding patterns by wealth index.

Environmental characteristics of households

The study also assessed the environmental health characteristics of the households. The main sources of drinking water used by households was river 187 (53.4%) followed by shallow well 67 (19.1%), water tap 45 (12.9%) and borehole 41 (11.7%). Within the study sample, almost 73 (20.9%) of households had to cover a distance greater than 30 minutes to fetch water from these sources. In regard to water consumption per household per day, 40 (11.4%) HHs used less than 40 liters and 287 (82%) used 40-80 litter per day. Concerning treatment of drinking water in households, majority of HHs 207 (59.1%) instituted no form of water treatment to make it safe for drinking. About sanitary facilities, majority of households 310 (88.6%) had latrine. In hand washing facilities, only 164 (46.9%) households washed hands using both soap and water, whereas 80 (22.9%) used water only, as 106 (30.3%) don’t wash at all. Regarding waste disposal system, largely 178 (50.9%) dispose in common pit, whereas open field disposal accounted for 30.9% (108). The most widely used source of cooking fuel at household was firewood, accounting for 346 (98.9%) (Table 9).

Characteristics Frequency Percent
Source of drinking water    
River 187 53.4
Water tap 45 12.9
Rain water 10 2.9
Shallow well 67 19.1
Borehole 41 11.7
Water used in HH per day by liters    
<40 40 11.4
40-80 287 82
>80 23 6.6
Time to obtain drinking water (round trip)    
<15 minutes 152 43.4
15-30 minutes 125 35.7
>30 minutes 73 20.9
HHs water treatment before drinking    
Boiling 58 16.6
Filtering 78 22.3
Use of chlorine tabs/guard 7 2
No Form of treatment 207 59.1
Place of fecal disposal    
Open field 11 3.1
Sanitary latrine 310 88.6
In bush 29 8.3
Materials used to wash hands after visiting a toilet    
Using water only 80 22.9
Using soap and water 164 46.9
Don’t wash at all 106 30.3
Method of disposal of HHs waste    
Open field disposal 108 30.9
In a pit 62 17.7
Common pit 178 50.9
Composing 2 0.8
Burning 50 14.3
Cooking fuel    
Animal dung 1 0.3
Charcoal 3 0.8
Firewood 346 98.9

Table 9: Environmental characteristics of households.

Determinants of malnutrition among children aged 6-59 months

Correlates of malnutrition types by socio-demographic characteristics of the household and children: Wasting was statistically significant for children who were aged between 48-59 months as they were 0.11 less likely to be wasted to those aged between 6-11 months (OR: 0.11; 95%CI: 0.02-0.52). Having 2 or 3 number of children in a household was statistically associated with being unhealthy compared to having one (OR: 0.54; 95%CI: 0.32-0.91) and (OR: 0.41; 95%CI: 0.19-0.88) respectively. Statistical significance was also reflected in stunting for those who had 2 children (OR: 2.4; 95%CI: 1.26-4.51), and 3 children in wasting (OR: 6.06; 95%CI: 1.68-21.86). On mothers who had no education, their children were 5.5 times more likely to suffer from stunting than those who had secondary education (OR: 5.47; 95%CI: 1.49-20.10) and this was statistically significant. Mothers who were decision makers in the family were 3.6 times more likely to have children in their households suffering from wasting compared to household where both parents were decision makers (OR: 3.56; 95%CI: 1.22-10.39). On socio-economic status, those who were very poor were more likely to have children who are underweight (OR: 6.4; 95%CI: 1.98-20.69) and stunting (OR: 5.67; 95%CI: 1.85-17.36) compared to the poorest. Furthermore, the very poor and poor were less likely to have unhealthy children compare to the poorest (OR: 0.19; 95%CI: 0.07-0.54) and (OR: 0.41; 95%CI: 0.18-0.92) respectively. Children who were exclusively breast fed for 7-12 months were more likely to experience underweight, stunting and wasting (OR: 32.0; 95%CI: 3.81- 268.5), (OR: 16.55; 95%CI: 2.01-136.0) and (OR: 45.50; 95%CI: 4.36- 474.6) respectively (Table 10).

  Crude Odds Ratios (95%CI)
Healthy Underweight Stunting Wasting Overweight Obese
Variables n=350 n=167 n=197 n=118 n=121 n=103
Child age            
6-11 months ref. ref. ref. ref. ref. ref.
12-23 months 0.951 (0.343,2.640) 0.952 (0.255,3.553) 1.778 (0.486,6.500) 0.5 (0.125,1.999) 1 (0.212,4.709) 2 (0.175,22.80)
24-35 months 1.064 (0.409,2.763) 1.143 (0.342,3.819) 1.63 (0.477,5.565) 0.278 (0.0689,1.119) 0.556 (0.117,2.634) 2.222 (0.222,22.23)
36-47 months 1.026 (0.411,2.564) 0.952 (0.294,3.085) 2.111 (0.652,6.839) 0.292 (0.0801,1.062) 0.583 (0.135,2.527) 1.333 (0.129,13.74)
48-59 months 1.393 (0.560,3.466) 0.974 (0.306,3.096) 1.284 (0.392,4.210) 0.111** (0.0237,0.520) 0.741 (0.182,3.011) 0.296 (0.0166,5.288)
Child gender            
Male 0.895 (0.553,1.450) 1.156 (0.628,2.131) 1.351 (0.766,2.382) 0.546 (0.232,1.288) 0.695 (0.308,1.567) 3.277 (0.856,12.55)
Female ref. ref. ref. ref. ref. ref.
Mothers age (y)            
15-19 0.944 (0.280,3.186) 0.857 (0.204,3.610) 1.091 (0.252,4.714) 1.6 (0.293,8.735) 1.333 (0.165,10.74) 1 (1,1)
20-29 1.4 (0.642,3.053) 0.423 (0.166,1.074) 0.909 (0.354,2.335) 0.612 (0.181,2.073) 1.224 (0.302,4.959) 1.633 (0.183,14.55)
30-39 0.944 (0.411,2.172) 0.829 (0.313,2.190) 1.527 (0.568,4.107) 0.4 (0.0947,1.689) 1.2 (0.268,5.369) 2 (0.207,19.34)
40-49 ref. ref. ref. ref. ref. ref.
Household head            
Father 1.275 (0.689,2.359) 0.731 (0.343,1.555) 1.027 (0.493,2.138) 0.689 (0.251,1.888) 0.56 (0.218,1.436) 0.548 (0.152,1.970)
Mother ref. ref. ref. ref. ref. ref.
Marital status            
Married 0.723 (0.298,1.757) 1.688 (0.486,5.862) 1.541 (0.512,4.637) 1.247 (0.248,6.274) 1.299 (0.259,6.522) 0.519 (0.0965,2.798)
Single ref. ref. ref. ref. ref. ref.
Divorced/Widowed/Separated 0.250* (0.0653,0.957) 4.5 (0.837,24.18) 4.333 (0.928,20.24) 3 (0.348,25.87) 5 (0.655,38.15) 2 (0.201,19.91)
Religion            
Christian 0.338 (0.0209,5.468) 1 (1,1) 1.216 (0.0750,19.72) 1 (1,1) 1 (1,1) 1 (1,1)
Muslim/Others ref. ref. ref. ref. ref. ref.
Number of children in household            
1 ref. ref. ref. ref. ref. ref.
2 0.538* (0.317,0.914) 1.491 (0.754,2.950) 2.382** (1.256,4.517) 2.707 (0.896,8.177) 1.231 (0.502,3.015) 1.444 (0.434,4.806)
3 0.410* (0.191,0.882) 2.486 (0.991,6.237) 2.287 (0.918,5.697) 6.055** (1.677,21.86) 1.835 (0.552,6.098) 0.673 (0.0709,6.383)
Mothers education            
None 0.475 (0.185,1.218) 0.688 (0.214,2.204) 5.469* (1.488,20.10) 1.094 (0.254,4.713) 3.125 (0.317,30.79) 2.906 (0.882,9.579)
Primary 0.677 (0.299,1.535) 0.903 (0.350,2.331) 2.702 (0.806,9.059) 0.726 (0.203,2.592) 4.032 (0.490,33.17) 1 (1,1)
Secondary ref. ref. ref. ref. ref. ref.
Post-secondary 0.475 (0.0466,4.839) 1 (0.0546,18.30) 5 (0.348,71.90) 1 (1,1) 10 (0.317,315.3) 1 (1,1)
Fathers education            
None 0.379 (0.0836,1.717) 1.1 (0.199,6.088) 6.3 (0.616,64.43) 0.6 (0.111,3.255) 1.35 (0.123,14.82) 2.325 (0.720,7.509)
Primary 0.675 (0.157,2.912) 0.79 (0.153,4.089) 2.806 (0.284,27.75) 0.366 (0.0745,1.793) 1.016 (0.100,10.31) 1 (1,1)
Secondary 0.444 (0.0730,2.708) 1 (0.120,8.306) 5.25 (0.400,68.95) 1 (1,1) 0.75 (0.0321,17.51) 1 (1,1)
Post-secondary ref. ref. ref. ref. ref. ref.
Mothers occupation            
Housewife only 0.938 (0.399,2.204) 1.032 (0.352,3.030) 2.168 (0.676,6.946) 0.295* (0.0930,0.935) 1.677 (0.333,8.440) 0.516 (0.0928,2.869)
Farmer 0.831 (0.301,2.289) 1.319 (0.375,4.636) 2.215 (0.588,8.340) 0.527 (0.130,2.143) 0.615 (0.0718,5.276) 1.231 (0.182,8.330)
Merchant ref. ref. ref. ref. ref. ref.
Teacher 2.875 (0.346,23.92) 1 (1,1) 0.8 (0.0566,11.30) 1 (1,1) 2 (0.115,34.82) 1 (1,1)
Other work 11.50* (1.114,118.7) 1 (1,1) 1 (1,1) 1 (1,1) 1 (0.0683,14.64) 1 (1,1)
Fathers occupation            
Farmer 1.239 (0.536,2.867) 0.503 (0.191,1.324) 1.682 (0.556,5.091) 0.697 (0.167,2.915) 0.646 (0.177,2.364) 0.492 (0.0886,2.735)
Gov't employee 1.688 (0.445,6.395) 0.667 (0.145,3.075) 0.8 (0.135,4.745) 1 (1,1) 0.4 (0.0342,4.681) 0.8 (0.0566,11.30)
Trader 0.825 (0.295,2.307) 0.727 (0.216,2.444) 2.061 (0.560,7.577) 2.182 (0.444,10.73) 0.909 (0.184,4.500) 0.727 (0.0838,6.314)
Not applicable ref. ref. ref. ref. ref. ref.
Other work ref. ref. ref. ref. ref. ref.
Decision maker on income            
Mother 0.591 (0.332,1.055) 1.697 (0.823,3.498) 1.545 (0.783,3.050) 3.558* (1.219,10.39) 0.915 (0.345,2.428) 2.846 (0.775,10.45)
Mainly father 0.704 (0.370,1.339) 1.273 (0.558,2.904) 1.614 (0.770,3.383) 2.272 (0.669,7.716) 1.113 (0.397,3.116) 0.487 (0.0508,4.666)
Only father 1.237 (0.457,3.351) 0.813 (0.216,3.065) 0.906 (0.277,2.962) 0.881 (0.0914,8.492) 0.378 (0.0425,3.352) 1.321 (0.128,13.66)
Both ref. ref. ref. ref. ref. ref.
Socio-economic status            
Poorest ref. ref. ref. ref. ref. ref.
Very poor 0.190** (0.0678,0.535) 6.400** (1.980,20.69) 5.673** (1.854,17.36) 4.114 (0.960,17.63) 3.2 (0.652,15.70) 3.2 (0.419,24.42)
Poor 0.412* (0.184,0.920) 2.327 (0.854,6.343) 2.479 (0.993,6.191) 1.558 (0.403,6.020) 3.273 (0.932,11.49) 2.909 (0.554,15.27)
Less poor 1.019 (0.515,2.018) 1.267 (0.524,3.061) 0.682 (0.287,1.622) 1.286 (0.412,4.013) 1.167 (0.342,3.985) 0.667 (0.102,4.354)
Least poor 1.416 (0.706,2.840) 0.512 (0.184,1.427) 0.873 (0.383,1.991) 0.274 (0.0517,1.455) 0.96 (0.272,3.393) 0.96 (0.176,5.231)
Child Health Condition
Diarrhea past 2 weeks            
Yes 0.853 (0.501,1.450) 0.824 (0.412,1.649) 1.227 (0.665,2.266) 2.08 (0.875,4.943) 0.853 (0.339,2.150) 2.56 (0.815,8.045)
No ref. ref. ref. ref. ref. ref.
Fever past 2 weeks            
Yes 0.787 (0.469,1.321) 1.083 (0.562,2.087) 1.298 (0.713,2.362) 2.143 (0.910,5.050) 0.899 (0.368,2.195) 1.722 (0.545,5.444)
No ref. ref. ref. ref. ref. ref.
Edema presence            
Yes ref. ref. ref. ref. ref. ref.
No ref. ref. ref. ref. ref. ref.
Dietary Intake
Daily feeding frequency            
<3 times 1.378 (0.674,2.818) 0.759 (0.304,1.899) 1.106 (0.489,2.504) 0.195 (0.0236,1.616) 0.39 (0.0792,1.925) 1 (1,1)
3 times ref. ref. ref. ref. ref. ref.
>3 times 0.771 (0.456,1.304) 1.104 (0.570,2.139) 1.449 (0.783,2.685) 1.242 (0.520,2.969) 1.42 (0.606,3.326) 1.242 (0.396,3.899)
Feeding mode            
Bottle 1.644 (0.935,2.892) 0.543 (0.257,1.147) 0.721 (0.371,1.401) 0.401 (0.137,1.180) 0.923 (0.377,2.263) 0.154 (0.0189,1.250)
Cup ref. ref. ref. ref. ref. ref.
Spoon 2.125 (0.970,4.655) 0.627 (0.236,1.668) 0.625 (0.249,1.567) 0.174 (0.0213,1.420) 1 (1,1) 0.333 (0.0394,2.821)
Hand 0.708 (0.197,2.548) 1.255 (0.267,5.900) 2.25 (0.578,8.759) 1 (1,1) 1.6 (0.248,10.32) 1 (1,1)
Exclusive breast feeding (m)            
1-3 0.667 (0.369,1.204) 1.75 (0.782,3.917) 1.536 (0.762,3.099) 2.112 (0.621,7.188) 1.138 (0.419,3.090) 0.78 (0.216,2.821)
4-5 0.978 (0.488,1.957) 1.273 (0.495,3.273) 1.074 (0.468,2.464) 1.477 (0.353,6.186) 0.739 (0.211,2.587) 0.236 (0.0256,2.178)
6 ref. ref. ref. ref. ref. ref.
7-12 0.0455** (0.00593,0.349) 32.00** (3.814,268.5) 16.55** (2.013,136.0) 45.50** (4.362,474.6) 16.25* (1.648,160.2) 10.4 (0.785,137.8)

Table 10: Correlates of malnutrition versus socio-demographic characteristics of the household and children.

Correlates and determinants of stunting in comparison to other health status in children: For univariate logistic regression, all the variables that were considered as factors that might influence stunted growth were included as shown in Table 11. They include: child’s age and gender, parents age marital status, occupation and education status, head of household, number of children in the household and the socioeconomic status and the child health condition and dietary intake. Variables that had a p<0.25 were put in the final model. The results from the stepwise multiple regression model of stunted children on household areas also shown in Table 11. The following factors remained and were incorporated into the regression model: child age, number of children in household, mothers’ education status, mother’s occupation, father’s occupation and socioeconomic status (SES). Among children aged between 36-47 months they were 3.1 times more likely to have stunted growth to those aged between 6-11 months (OR: 3.11; 95%CI: 1.07-9.02; p: 0.037). Moreover, household with 2 children were 1.8 times more likely to have stunted growth compared to households with one child (OR: 1.86; 95%CI: 1.01-3.43; p: 0.045). In addition, mothers who were housewives were 3.6 times more likely to have children with stunted growth compared to mothers who were merchants (OR: 3.63; 95%CI: 1.08-12.24; p: 0.037). Finally, those who were very poor (OR: 3.33; 95%CI: 1.44-7.69; p: 0.005) and poor (OR: 3.52; 95%CI: 1.41-8.82; p: 0.007) were 3.3 and 3.5 times more likely to have stunted children.

Variables Crude OR (95%CI) p value Adjusted OR (95%CI) p value
Child age (m)   0.2325    
6-11 ref.      
12-23 2.02 (0.70-5.82) 0.194 2.15 (0.68-6.76) 0.192
24-35 2.01 (0.73-5.55) 0.176 2.11 (0.69-6.41) 0.189
36-47 2.86 (1.08-7.54) 0.034 3.11 (1.07-9.02) 0.037
48-59 1.90 (0.70-5.12) 0.207 1.92 (0.65-5.68) 0.238
Child gender   0.1797    
Boy 1.37 (0.86-2.17)   1.22 (0.73-2.05) 0.445
Girl ref.      
Mothers age (y)   0.4083    
15-19 1.05 (0.33-3.36) 0.923    
20-29 1.24 (0.58-2.64) 0.585    
30-39 1.73 (0.79-3.77) 0.171    
40-49 ref.      
Household head   0.3095    
Father 1.35 (0.75-2.40)      
Mother ref.      
Marital status   0.6512    
Married 1.33 (0.51-3.46) 0.561    
Single ref.      
Divorced/Widowed/Separated 1.70 (0.54-5.34) 0.367    
Religion   0.5722    
Christian ref.      
Muslim/Others 2.25 (0.14-36.34)      
Number of children in household   0.0712    
1 ref.      
2 1.83 (1.06-3.14) 0.029 1.86 (1.01-3.43) 0.045
3 1.25 (0.61-2.56) 0.539 1.14 (0.49-2.64) 0.755
Mothers education   0.0101    
None 4.86 (1.55-15.26) 0.007 3.36 (0.94-11.98) 0.092
Primary 2.48 (0.83-7.34) 0.104 2.41 (0.75-7.73) 0.138
Secondary ref.      
Post-Secondary 4.17 (0.52-33.26) 0.178 -  
Fathers education   0.0902    
None 4.45 (0.53-37.51) 0.169    
Primary 2.59 (0.31-21.47) 0.379    
Secondary 4.08 (0.41-40.46) 0.229    
Post-secondary ref.      
Mothers occupation   0.2305    
Housewife 2.60 (0.96-7.01) 0.059 3.63 (1.08-12.24) 0.037
Farmer 2.34 (0.77*7.08) 0.132 3.00 (0.82-10.94) 0.095
Merchant ref.      
Teacher 1.73 (0.15-20.23) 0.661 2.92 (0.08-106.67) 0.559
Other -   -  
Fathers occupation   0.141    
Farmer 2.48 (0.99-6.21) 0.053 2.40 (0.85-6.82) 0.1
Employee 1.21 (0.26-5.64) 0.81 2.01 (0.31-13.03) 0.465
Trader 2.11 (0.74-6.01) 0.163 2.70 (0.78-9.38) 0.117
Other -      
Not applicable/None ref.      
Decision maker on income   0.745    
Mother 1.06 (0.61-1.83) 0.831    
Mainly father 1.38 (0.76-2.53) 0.286    
Father only 1.07(0.38-3.00) 0.56    
Both ref.      
Socio-economic status   0.069    
Poorest ref.      
Very poor 1.76 (0.87-3.55) 0.117 3.33 (1.44-7.69) 0.005
Poor 1.39 (0.69-2.78) 0.354 3.52 (1.41-8.82) 0.007
Less poor 0.61 (0.29-1.30) 0.204 1.72 (0.66-4.52) 0.27
Least poor 1.13 (0.55-2.34) 0.729 2.52 (0.99-6.43) 0.053
Child Health Condition
Diarrhea past 2 weeks   0.615    
Yes 1.13 (0.70-1.84)      
No ref.      
Fever past 2 weeks   0.635    
Yes 1.12 (0.70-1.81)      
No ref.      
Edema presence        
Yes 2.79 (0.83-9.34)      
No ref.      
Dietary Intake
Daily feeding frequency   0.348    
<3 times 1.62 (0.80-3.26) 0.177    
3 times ref.      
>3 times 1.28 (0.78-2.09) 0.324    
Feeding mode   0.376    
Bottle 1.05 (0.611.84) 0.849    
Cup ref.      
Spoon 1.09 (0.49-2.44) 0.827    
Hand 2.41 (0.91-6.34) 0.076    
Exclusive breast feeding (m)   0.9199    
1-3 1.23 (0.68-2.22) 0.502    
4-5 1.08 (0.53-2.22) 0.828    
6 ref.      
7-12 1.15 (0.52-2.56) 0.733    

Table 11: Odds Ratios (OR) for child stunting among the whole child group using univariate and multiple logistic regression.

Correlates and determinants of overweight in comparison to other health status in children: The study also did a multivariable logistic regression on those who were overweight. It was found that children whose household heads were fathers were less likely to be overweight (aOR: 0.750; 95%CI: 0.139-4.052; p: 0.738). Children whose parents were married (aOR: 3.485; 95%CI: 0.345-35.746; p: 0.293) and divorced/widowed or separated (aOR: 4.29; 95%CI: 0.350-52.690; p: 0.255) had higher chance of being overweight compared to single/never married parents. Furthermore, those who were very poor and poor had a higher likelihood of being overweight i.e., (aOR: 3.405; 95%CI: 0.549-25.120; p: 0.188) and (aOR: 3.548; 95%CI: 0.781-16.126; p: 0.101) respectively. Children who were breastfed for over 6 months also has a higher chance of being overweight compared to those breastfed for 6 months (aOR: 14.402; 95%CI: 1.158-179.056; p: 0.0308). Lastly, children who were fed more than three times in a day were more likely to be overweight compared to those fed only 3 times (aOR: 1.252; 95%CI: 0.431-3.638; p: 0.680) (Table 12).

Variable aOR (95%CI) p value
Household head    
Father 0.750 (0.139-4.052) 0.738
Mother ref.  
Marital status    
Married 3.485 (0.340-35.746) 0.293
Single ref.  
Divorced/Widowed/Separated 4.29 (0.350-52.690) 0.255
Socio-economic status    
Poorest ref.  
Very poor 3.405 (0.549-25.120) 0.188
Poor 3.548 (0.781-16.126) 0.101
Less poor 1.385 (0.309-6.203) 0.67
Least poor 1.368 (0.306-6.127) 0.682
Exclusive breast feeding (m)    
1-3 1.222 (0.398-3.751) 0.726
4-5 0.835 (0.212(3.296) 0.797
6 ref.  
7-12 14.4015 (1.158-179.056) 0.038
Daily feeding frequency    
< 3 times 0.489 (0.094-2.551) 0.396
3 times ref.  
> 3 times 1.252 (0.431-3.638) 0.68

Table 12: Odds Ratios (OR) for child overweight among the whole child group using univariate and multiple logistic regression.

Correlates and determinants of obesity in comparison to other health status in children: Children who were males were more likely to obese compared to the females (aOR: 2.994; 95%CI: 0.641-13.983; p: 0.163). Households whose decision maker was only the father had a higher likelihood of having obese children (aOR: 4.969; 95%CI: 0.283- 87.358; p: 0.273) compared to those who both parents were decision makers. Children who were breastfed for over 6 months also has a higher chance of being overweight compared to those breastfed for 6 months (aOR: 2.198; 95%CI: 0.105-46.159; p: 0.612). Furthermore, those fed by bottle or cup were less likely to be overweight (aOR: 0.091,95%CI=0.007-1.382; p: 0.074) and (aOR: 0.450; 95%CI: 0.0413- 4.888; p: 0.512) respectively (Table 13).

Obesity    
Variable aOR (95%CI) p value
Child gender    
Male 2.994 (0.641-13.983) 0.163
Female ref.  
Decision maker on income    
Mother 2.978 (0.589-15.066) 0.187
Mainly father 0.276 (0.023-3.382) 0.314
Only father 4.969 (0.283-87.358) 0.273
Both ref.  
Diarrhea past 2 weeks    
Yes 3.022 (0.652-14.013) 0.158
No ref.  
Exclusive breast feeding (m)    
1-3 0.346 (0.0598-2.006) 0.237
4-5 0.095 (0.007-1.382) 0.085
6 ref. 0.612
7-12 2.198 (0.105-46.159)  
Feeding mode    
Bottle 0.091 (0.007-1.257) 0.074
cup ref.  
Spoon 0.450 (0.0413-4.888) 0.512
Hand -  

Table 13: Odds Ratios (OR) for child obesity among the whole child group using univariate and multiple logistic regression.

Discussion

This study found very high levels of under nutrition particularly stunting and underweight among children aged 6-59 months living the rural set up studied. This is not surprising as previous studies had reported similar finding in poor urban settings in Nairobi indicating that household socioeconomic factors play a critical role in predisposing children to under nutrition [7,36]. Consistent with previous findings that stunting is higher in urban centers relative to rural setting in many countries [25], this study reported lower levels of stunted children relative two previous studies in Kibra slums in Nairobi [7,36]. Indeed, previous studies have reported a higher prevalence of child under nutrition and mortality in urban slums compared to rural areas [7,37]. Moreover, under nutrition is a common phenomenon in East Africa with data indicating the level of stunting stands at 50% [30]. However, the level of underweight and wasted children was relative higher in the current study setting relative to the previous studies and even the national averages that stands at 26%, 11% and 4% for stunting, underweight and wasting respectively [38]. These data indication is that there are differences in maternal nutrition in urban and rural setting that may be predisposing children to different forms of under nutrition. Indeed, several studies have reported that there is a relationship between household food insecurity and under nutrition in children [39-41]. While under nutrition was the major problem in the study area, this study also reported that 9% of the children were overweight and 4% were obese which is below the national prevalence of obesity that stands at 5% [7,38,42], indicating that even rural set ups in low resource countries have also started to suffer from the double burden of malnutrition. Although this study did not look at maternal anthropometric measurement, studies have indicated that maternal obesity during pregnancy may result in an obese child [43], hence studies on child nutrition status should also focus on maternal nutritional status. Of note is that despite a paucity of data on the level of obesity in developing countries recent data indicate that it has increased from rise from 4% in 1990 to 7% currently [7,44], but Kenya is not off the track in meeting World Health Assembly on child obesity [7,45]. However, the increase in obesity levels can be associated with food transition or shifts in dietary patterns from traditional food systems to western type food systems that include cereal based high-energy diets [46]. The transition from rural to an urban lifestyle is associated with increased levels of overweight and obesity which has been linked to dramatic changes in lifestyle. In many developed countries, higher calorie intakes and lower calorie expenditure have already resulted in a rapid increase in the prevalence of overweight, obesity and related non-communicable diseases [47]. Many developing countries including Kenya are in the process of undergoing similar transition with increase in prevalence, often in addition to an on-going problem of under nutrition [48]. In deed data shows that mothers who are highly educated tend to have obese children mainly due to over nutrition with food rich in sugars and saturated fats due to their high income [49].

Previous studies have shown that malnutrition especially undernutrition is associated with several factors including gender of the child, age of the child, mothers’ education status and socioeconomic status of the family [2,7]. Consistent with these studies the present study also found that children aged between 36-47 months are 3.1 times more likely to have stunted growth relative to those aged 6-11 months. This can be due to the fact that as the children grow, the caregiver or mother stops giving breast milk and complementary food and shifts to improper feeding practices like providing tea or porridge with milk which are low in nutritive value [50]. Of note is that porridge causes negative nutrient-nutrient interactions and malabsorption in children due to immaturity of infant’s gut and is also rich in phytates and tannins that binds to available nutrients leading to reduction of their bioavailability [51,52] ultimately leading to stunting among children within this age group. Children then become chronically undernourished at this age. In this scenario, education focused on caregivers’ feeding habits of complementary food for 2 to 3-year-old children can help prevent childhood stunting [50]. Further studies might be necessary to determine the right types of interventions for each community with the problems of childhood stunting and food insecurity. These data indicate that both chronic and acute child malnutrition, develop during the weaning period and rise sharply thereafter.

Education is one of the most important resources that enable women to provide appropriate care for their children. Education of women is believed to have an impact on health and specifically nutritional status of children since it provides the mother with the necessary skills for child care, increase awareness and improve uptake and health care seeking behavior of the caregivers in the community. Additionally, it also changes traditional beliefs about diseases causation, and use of contraceptives for birth spacing [53]. This study found that children from mothers with no education were 5.5 timely more likely to be stunted relative to those children whose mothers had secondary education. These findings are consistent with earlier findings that indicated that maternal education is a critical determinant of undernutrition among children [53]. Together these data indicate that educated mothers are better aware on the nutritional requirements of their children and they usually provide improved health care as a result of their awareness. In addition, the results of this study show that the number of children within households is an important determinant of under-nutrition. These findings are consistent with previous findings that showed that increased number of children negatively influenced nutritional status of children [2].

This study also found that children who were exclusively breast fed for 7-12 months in this study were more likely to experience underweight, stunting and wasting. These data indicate that delay in initiation to weaning has a negative effect on the nutritional status of the children. Indeed, previous studies have shown that protein energy malnutrition (PEM) usually manifests at 6-24 months are associated with delayed introduction of solid foods [54]. Moreover, conversely, late introduction of adequate complementary foods as recommended, places children at risk for stunting or underweight [55]. Thus, late introduction of complementary feeding has a set back to the children nutritional status. However, a recent study in Nairobi Kenya showed that there is no association between exclusive breast feeding within six-month breast feeding and children nutritional status but this in an urban setting where there is low prevalence of exclusive breast feeding [7]. These studies indicate that there is need for comparative studies between urban and rural settings in Kenya to understand the effects of exclusive breast-feeding on nutritional status of children. Of note, studies have shown childcare practices that are important to the wellbeing of the child [42,56].

Significantly this study also found that immunization of children has a protective effect on children nutritional status. This is consistent with studies that found that immunization status is significantly associated with the nutritional status of children [57]. It is noteworthy to note that in the current study although 67.4% of the mothers had made four ante-natal care (ANC) visits during pregnancy to a health facility though there were still high prevalence of under-nutrition among children from the study area indicating that apart from health facility related factors other factors may be critical predictors of under nutrition. Consistent with this view this study found that, children from families who do not treat their drinking water by boiling, filtering and using chlorine were more likely affected by wasting relative to those from family that treat water. In fact, a previous study in western Kenya showed that, more children who drank water that was not consistently treated in households were wasted [58]. Of note is that 59.1% of the families did not treat their drinking water. However, this problem is not limited to the study area alone since a previous study indicated that rural households do not use an appropriate treatment method to ensure that water is safe for drinking relative urban households [17]. This may lead to high prevalence of diarrhea and waterborne diseases at households’ level might increase the prevalence of malnutrition directly or indirectly.

Several studies have found association between being obesity and several factors including the child gender, children’s’ age and maternal occupation [7,49,59]. Consistent with these studies this study also found that obesity is associated with child age with those between 12- 23 months and 24-35 months being more likely to be obese. In addition, this study found that being male, mothers age, maternal marital status, father education, mothers being decision maker and exclusive breastfeeding for 7-12 months. Data have shown that in limited or poor resource settings that are high chances of children being exposed to high-energy dense foods leading to obesity. Furthermore, high level of poverty and food insecurity in a given setting can either lead to mothers accessing lower quality of food having wasted children while those accessing lower quality of food limited in dietary diversity, fruit and vegetable and energy dense food predisposes children to obesity [7,56]. Hence the double burden of malnutrition in poor rural setting should also include the types of food children are exposed to so that it can inform guidelines of weaning children.

Conclusion

The empirical result of this study confirms a coexistence of child double burden malnutrition in the rural setting. In this study, 31%, 22% and 8% of the children were stunted, underweight and wasted, respectively. Analysis also revealed that 9% and 4% of the children suffered from overweight and obesity respectively. The predictors for stunting were number of children in the household, mother being a house wife and being poor. For obesity, the predictors were child age with 12-23 months and 24-35 months, child gender with males more likely to be obese relative to females. The study has demonstrated that poor livelihoods and low socio-economic status, high levels of food insecurity and consumption of high energy dense foods, poor access to water and environ-mental sanitation and health care services, and poor child feeding practices in this settings are key determinants of malnutrition. More importantly, addressing poverty is likely to lead to improvements in the nutritional status of the children. In light of these results it is imperative that policymakers pay utmost attention to the constraints that beset child nutrition.

Recommendations for action

New and innovative strategies will be required to counter the growing double burden of malnutrition (DBM). Collaboration across sectors, complemented by an effective coordination mechanism, should join the efforts of those within and outside the nutrition community to address the DBM. Improving county-level capacity to coordinate nutrition actions is critical.

1. To reduce the existing high rate of malnutrition in the area, the study suggests targeting of community units with health education programs and provision of clean water, including health promotion to create demand and advocate for behavior change.

2. Assist partner agencies to ensure the availability of medical treatment to all existing supplementary feeding programs.

3. Advocate for community lead total sanitation (CLTS) to address sanitation problem in Trans-Mara East sub-county.

4. Improve nutritional surveillance and response through capacity building of the Ministry of Health and partners on nutrition.

5. Conduct further nutrition surveys to establish if the factors are consistent over time or if there have been any changes, considering the rapidly changing economic and sociodemographic characteristics of the population, influenced by technological advances and rural-urban migration, among other factors to inform health and nutritional interventions across the sub-county.

6. Plan for outreach services (immunization) to ensure that children and expectants mothers to access health care services. Integrate deworming with other services during the outreach and also organize for periodic deworming program. The periodic deworming programs should be included in public health strategies because malnutrition makes children more susceptible to parasitic infections and diminishes the immune response to infections. Some parasitic infections influence nutritional status through a subtle reduction in digestion and absorption of nutrients, chronic inflammation and loss of nutrients.

Recommendations for future research

This study did not look at maternal anthropometrical measurement, however studies have indicated that maternal obesity during pregnancy may result in an obese child, hence a study on child nutritional status alongside with maternal nutritional status need to be done. A further study is also imperative to determine the right types of interventions for each community with the problems of childhood malnutrition and food insecurity. A comparative study between urban and rural settings in Kenya or Narok county is needed to understand the determinants on nutritional status of children in the two different settings.

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