Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Comm...

Chukwuma B Duru, Uche R. Oluoha, Kelechi A. Uwakwe, Kelvin C. Diwe, Irene A. Merenu, Ifeadike O Chigozie, Anthony C. Iwu

American Journal of Public Health Research

Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Communities in Imo State, Nigeria

Chukwuma B Duru1,, Uche R. Oluoha2, Kelechi A. Uwakwe1, Kelvin C. Diwe1, Irene A. Merenu1, Ifeadike O Chigozie3, Anthony C. Iwu2

1Department of Community Medicine, Imo State University, Owerri, Imo State

2Department of Community Medicine, Imo State University Teaching Hospital, Orlu, Imo State

3Department of Community Medicine, Nnamdi Azikiwe University Nnewi Campus, Anambra State


Background: Malnutrition is a global issue with patterns and prevalence that vary significantly not only among different nations of the world but also in different region of a country. Methodology: This was a cross-sectional descriptive study carried out among under-five children in households in rural communities in Imo State. The multi-stage sampling technique was used for the selection of subjects. Data was collected by direct measurement of anthropometric parameters as well as the use of a semi-structured questionnaire to obtain caregivers’ information. Result: This mean age of the children was 21.0 ± 17.9 months. The mean weight, height, MUAC, and Head Circumference of the children were 10.6±4.4kg, 82.7±13.7cm, 20.2±3.6cm and 51.5±0.8cm respectively. The prevalence of overweight/obesity, underweight, wasting and stunting were, 9.8%, 28.6%, 23.6% and 28.1% respectively. Conclusion: Based on our findings, there is high prevalence of malnutrition among under-five children in the studied communities, thus there is need to institute appropriate control measures by the relevant authorities to reverse this problem owing to the fact that most of the causes of malnutrition are preventable.

Cite this article:

  • Chukwuma B Duru, Uche R. Oluoha, Kelechi A. Uwakwe, Kelvin C. Diwe, Irene A. Merenu, Ifeadike O Chigozie, Anthony C. Iwu. Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Communities in Imo State, Nigeria. American Journal of Public Health Research. Vol. 3, No. 6, 2015, pp 199-206.
  • Duru, Chukwuma B, et al. "Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Communities in Imo State, Nigeria." American Journal of Public Health Research 3.6 (2015): 199-206.
  • Duru, C. B. , Oluoha, U. R. , Uwakwe, K. A. , Diwe, K. C. , Merenu, I. A. , Chigozie, I. O. , & Iwu, A. C. (2015). Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Communities in Imo State, Nigeria. American Journal of Public Health Research, 3(6), 199-206.
  • Duru, Chukwuma B, Uche R. Oluoha, Kelechi A. Uwakwe, Kelvin C. Diwe, Irene A. Merenu, Ifeadike O Chigozie, and Anthony C. Iwu. "Prevalence and Sociodemographic Determinants of Malnutrition among Under-Five Children in Rural Communities in Imo State, Nigeria." American Journal of Public Health Research 3, no. 6 (2015): 199-206.

Import into BibTeX Import into EndNote Import into RefMan Import into RefWorks

1. Introduction

Despite awareness about the dire impact of malnutrition on health and the availability of health and nutrition interventions, malnutrition continues to be one of the leading causes of morbidity and mortality worldwide, particularly in developing countries. Globally it has been estimated that stunting, severe wasting and intra-uterine growth restriction together accounted for 2.2 million deaths of children aged under five [1]. In developing countries the prevalence of malnutrition is high with 1 out of 3 pre- school children affected [2]. Malnutrition refers to the various forms of under-nutrition, which are stunting, wasting and underweight.

Underlying causes of malnutrition as described in the United Nations International Children’s Emergency Fund (UNICEF) framework on child malnutrition include environmental, economic and socio-political factors, with poverty playing a major role [3].

Consequences of malnutrition include shorter adult height, less schooling and reduced economic productivity, and for women, off springs of lower birth weight [1]. Furthermore, the risks of developing overweight, obesity, diabetes and hypertension are increased among adults who suffered from under-nutrition during early childhood [4]. Nigeria is one of the leading economies in Africa and subscribes to initiatives such as the United Nations Millennium Development Goals (MDGs), one of the aims of which is to reduce the number of people suffering from hunger by 50% by 2015 [5]. However, despite this, Nigeria faces child malnutrition problems. According to 2013 Nigeria Demographic and Health Survey (NDHS), 37% of children under the age of five are considered to be short for their age or stunted, while 21% are severely stunted (below -3 SD). Eighteen percent (18%) of under-five Nigerians children are considered wasted or too thin for their height and 9% are severely wasted. Twenty-nine percent (29%) of Nigerian children are underweight (low weight for age), with 12% being severely underweight [6].

Various studies on child malnutrition on the African continent have shown that demographic, socio-economic and clinical profiles of malnourished children differ from one region to the next. Characteristics that were found to contribute to child malnutrition in the different regions ranged from the age of the child or the caregiver, family size and income and the caregivers education to underlying clinical conditions [7, 8, 9, 10, 11].

The aim of this study was therefore to determine the prevalence and factors associated with malnutrition among under-five year olds in rural communities Imo State, Nigeria.

2. Methodology

Study Area and Population: Imo state is one of the 36 states of Nigeria, created in 1976 with a population of about 4.2 million. It has 3 senatorial zones and 27 local government areas (LGAs) of which 22 are rural while 5 are urban as designated by the National Population Commission [12]. Nwangele is one of the rural Local Government Areas (LGAs) of Imo State with headquarters at Amaigbo. It has an area of 63 square kilometer and a population of 123,472 people according to the 2006 National Population Census (NPC) [12]. It was created on 4th December 1996 and presently has ten towns and eleven political wards. The main economic activities of the people are subsistence farming and petty trading. The LGA has 28 nursery and primary schools of which 6 are privately owned. There are 6 government owned secondary school and 4 private ones. The study area also has two institutions of higher learning namely; Imo State College of Nursing and Health Sciences, Amaigbo and School of Nursing, Saint Mary’s Joint Hospital, Amaigbo, owned by the state government and Catholic Church respectively. There are a total of 14 primary and secondary health facilities in the local government [12].

Study Design and Study Population: The study was a descriptive cross-sectional and the study population comprised children aged 0-59 months who reside in households from the selected rural communities.

Sample Size Estimation: Using the Cochrane formula for sample size estimation for cross-sectional studies in populations above 10,000 people and proportion of under-five-year olds in the rural areas who suffer from chronic malnutrition (stunted) in Nigeria according to the 2013 NDHS (43.0%) [6], the sample size that was used for this study was 406 children taking into consideration 10% attrition.

Where; n= Desired sample size, Z= 95% confidence level = 1.96, p= Proportion of the target population estimated to have chronic malnutrition= 0.43, q= 1-p = 0.57, d= 5% sampling error = 0.05

Sampling Technique: The sampling technique used was the multi-stage sampling technique. Stage 1: Involved the selection of Nwangele LGA from the list of rural LGAs in Imo state, using simple random sampling by balloting.

Stage 11: Involved the selection of wards that were studied from Nwangele LGA, of which 8 out of 11 wards in the LGA was selected using simple random sampling technique by balloting.

Stage 111: Involved the selection of enumeration areas to be studied. In each of the selected wards, two enumeration areas were selected using simple random sampling technique by balloting.

Stage 1V: Involved the selection of children and households to be studied. Only one child per household was selected and the mother or caregiver interviewed. In households with more than one eligible participant, simple random sampling by balloting was used to select the participant to be studied. Prominent places in the enumeration areas were located and moving in a clockwise direction, any household with eligible respondents were enrolled and interviewed until the required number for each enumeration was obtained. Twenty six children were selected in each enumeration area.

Selection Criteria: Only under-five children whose caregiver has been staying in the study area for at least 6 months prior to the study were enrolled and studied.

Data Collection Method and Analysis: Data was collected by direct measurement of anthropometric parameters; weight, height/length, head circumference and mid upper arm circumference (MUAC) and percentages of these parameters among the participants were compared with the expected calculated values for the child’s age using the appropriate formulas [13, 14]. Weight was recorded in kilograms to the nearest 0.1 kg using a standardized weighing scale. The height/length of the children was measured using a measuring board graduated to the nearest 0.1 cm. The head circumference and MUAC were measured using a tape rule.

The following classification was used for this survey [13, 14].

Mid Upper Arm Circumference (MUAC)

< 12.5 cm – Malnutrition

12.5 – 13.5 cm – At risk of malnutrition

≥ 14cm – Well Nourished

Modified Wellcome Working Party Classification [14].

Height for Age [13]

Based on above parameters, the children were classified as well nourished (normal), underweight, overweight, stunted or wasted. Also socio-demographic data were obtained in an interview using a semi-structured questionnaire developed by the researchers.

The data obtained were cleaned, validated manually and analysed using computer software (Epi Info 7.1). Frequency tables with percentages were generated. Bivariate analysis using the Chi-square was done where appropriate to test for significant association between variables. Results were considered significant when p value was < 0.05.

Ethical Approval: Ethical approval for this study was obtained from Imo State University Teaching Hospital Ethics Committee (IMSUTHEC). Approval was also given by the appropriate authorities in the Local Government Area and communities studied. Verbal informed consent was obtained from each caregiver before we commenced the study.

3. Results

Table 1. Socio-demographic characteristics of the children and caregivers

The mean age of the children was 21.0 ± 17.9 months with more than half of the children (52.7%) being within the ages of 0 – 12 months. Female children (51.2%) were slightly more than the male children (48.8%). Majority of these children (70.5%) had already been enrolled into school. A sizeable proportion of the participants (43.4%) were the last child in the household as at the time of the survey. Most of the mothers (61.6%) had only primary education while majority of the care giving (88.7%) was done by both parents. Majority of the mothers were either petty traders or farmers (68.0%) while the dominant occupations of the fathers were trading (43.8%) and civil service (36.9%). In this research, most of the persons interviewed were their mothers, 49.3% and fathers, 34.5%. Only 3.9% of the children had their mother deceased as at the time of this survey. Table 1

The mean weight, height, mid upper arm circumference (MUAC) and head circumference were; 10.6kg ± 4.4, 88.7cm ± 13.7, 20.2cm ± 3.5 and 51.5 ± 0.6 cm respectively. Underweight (28.6%) was the most common nutritional abnormality observed in this study. This was followed by stunting (28.1%), and wasting (23.6%). Overweight children were 9.6% only. Table 2.

Table 2. Nutritional Status of Subjects in Study Population

Table 3 revealed that prevalence of low weight for age (underweight) is fairly uniformly distributed among the age groups ranging from 21.3% in 25 - 36 months old, to 33.3% in 13 - 24 months age group. Variation of age with low weight for age is not statistically significant (χ2 = 4.24, df = 8, p = 0.835). Greater proportions of the males were underweight (34.3%) when compared to their female counterpart (23.1%). This difference was statistically significant (χ2 = 23.2, df = 2, p = 0.000). The prevalence of underweight was highest (65%) among children of mothers or caregivers with no formal education while those with tertiary education have the highest prevalence of children who were overweight (30%), (χ2 = 49.9, df = 6, p = 0.000). Underweight was found more in children that were yet to be enrolled in school, 29.2% and in those in pre-nursery classes, 31.9%, (χ2 = 25.0, df = 4, 0.000). Also maternal occupation (x2 = 39.8, df = 6, p = 0.000) and paternal occupation (χ2 = 25.3, df = 6, p = 0.000) showed statistically significant association with participants’ weight for age while there was no significant association between the position among siblings and weight for age (χ2 = 10.0, df = 6, p = 0.124).

Table 3. Socio-demographic Characteristics of Children and Nutritional Status (Weight for Age)

Table 4 showed that prevalence of stunting was predominant (44.4%) among the age group 13 – 24 months. This variation in stunting with age was statistically significant (χ2 = 17.0, df = 4, p = 0.002). More male children (35.4%) were significantly more stunted than their female counterparts (21.2%), (χ2 = 10.1, df = 1, p = 0.001). Prevalence of stunting was highest among under-five year olds whose mothers and caregivers had no formal education (65%), (χ2 = 26.6, df = 3, p = 0.000), among children yet to start school (37.5%), (χ2 = 8.95, df = 2, p = 0.011), and those that were last among the siblings (30.7%), (χ2 = 2.76, df = 3, p = 0.429). Maternal occupation was observed to affect height for age (χ2 = 23.6, df = 3, p = 0.000), however, variation of stunting with father’s occupation was not statistically significant, (χ2 = 5.51, df = 3, p = 0.138).

Table 4. Socio-demographic Characteristics of Children and Nutritional Status (Height for Age)

Also, the prevalence of wasting was higher (40.0%) among the age group 13 – 24 months. This difference was statistically significant, (χ2 = 18.3, df = 4, p = 0.001). Wasting was slightly higher among the male (26.3%) than the female (21.2%) gender, though this difference was not statistically significant, (χ2 = 1.47, df = 1, p = 0.226). The prevalence of wasting was highest (35.0%) among children whose mothers or caregivers had no formal education. The difference was statistically significant (χ2 = 13.6, df = 2, p = 0.003). In this study, pre-nursery children had the highest incidence of wasting (29.8%) when compared to other educational levels but this was not statistically significant (χ2 = 4.55, df = 2, p = 0.103). The position of the child in the household statistically affected the pattern of wasting (χ2 = 10.6, df = 3, p = 0.014), just like maternal occupation (χ2 = 13.3, df = 2, p = 0.003), however, occupation of the father did not statistically influence wasting (χ2 = 5.82, df = 3, p = 0.121). Table 5.

Table 5. Socio-demographic Characteristics of Children and Nutritional Status (Weight for Height)

4. Discussion

The proportion of underweight children in this study is 28.6%, this compares favourably with the national figure of 29.0% according to the 2013 Nigeria Demographic and Health Survey (NDHS). [6] The proportion of stunted children in our study was 28.1%, this is lower than the national figure of 37.0% for stunted under-fives. Our study also revealed a high proportion, (23.6%) of under-five-year olds considered too thin for their height (wasted). This figure is slightly more than the national average of 18.0%. [6] According to a joint UNICEF, WHO and The World Bank malnutrition data base for 2012, 56% of all stunted under-five-year olds lived in Asia and 36% in Africa; 67% of Asian under-five-year olds were underweight while it is 29% in Africa. Asia also has a higher proportion of wasted children (69%) when compared to Africa (28%), [15] despite having better economy than Africa; this could be explained by their large population size when compared to other continents.

Majority of the underweight, stunted and wasted children were in the age group 13 – 24 months. This is the period when most children are weaned off breast milk and supplementary feeds introduced. Other studies [11, 16] have confirmed that the single most important antecedent factor in the development of malnutrition in children is the introduction of complementary feeds. Studies by Olwedo et al [17] in Uganda, Emina et al [18] in the Demographic Republic of Congo and Irena et al [19] in Zambia have suggested that under-nutrition is more prevalent among boys than among girls. Likewise, our study found higher proportion of under-weight, stunted and wasted among males when compared to females. This is difficult to explain given that our society place higher premium on male children compared to the female ones. However, our study revealed no significant association between gender and wasting (p = 0.226). Other African studies in Botswana and Eastern Sudan [11, 20] concluded that there was no difference in proportions of under nourished male and female children.

Our study revealed that having more children was a significant predictor for childhood malnutrition. A similar finding was reported in studies done in Malaysia [21], Pakistan [22] and Vietnam [23]. The increased number of children in families placed a heavy burden on the scarce household resources, particularly on financial and food; it also reduced the time and quality of care received by the children [24].

Studies in other parts of the world have identified a number of risk factors and some of these have been consistent with findings of our study. An example is low maternal education [25, 26]; one year of maternal education has been associated with a 9% decrease in under-five mortality and children of better educated mothers, other things being equal, tend to be healthier [27]. One study concurs with the positive correlation between low maternal education and childhood malnutrition. A positive correlation between high maternal education and child’s nutritional status could be explained on the basis of improved socioeconomic status, health facility utilization and enhanced mothers’ empowerment in decision making [28, 29]. However, other researchers have not demonstrated this and they found children to be severely malnourished in spite of mothers’ high levels of education [30].

5. Conclusion

The prevalence of malnutrition was observed to be high in our study and most of the associated factors found were preventable. This is of grave public health problem as it affects both physical and mental development of the children and thus there is need to institute simple preventive measures at the community level like health promotion, education and school feeding programmes as these will go a long way to curb this menace among other measures.


[1]  Black RE, Allen HL, Bhutta ZA. Maternal and child under nutrition: global and regional exposures and health consequences. Lancet 2008; 371: 243-260.
In article      View Article
[2]  United Nations Sub – Committee on Nutrition. 5th Report on the World Nutrition Situation: Nutrition for Improved Outcomes, 2004. http: // Meeting/ SCN 3/SCN 5 Report. pdf (Accessed 13 January 2015)
In article      
[3]  United Nations International Children’s Emergency Fund (UNICEF). Strategy for Improved Nutrition of Children and Women in Developing Countries. New York: UNICEF, 1990.
In article      
[4]  Martins VTB, Florencio TMMT, Grillo LP, Franco MP, Martins PA, Clemente APG, Santos CDL, Vieira MF, Sawaya AL. Long lasting effects of under nutrition. Int J Environ Res Public Health 2011; 18: 1817-1846.
In article      View Article  PubMed
[5]  United Nations (UN) Millennium Project. Available at (Accessed 14 January 2015).
In article      
[6]  National Population Commission (Nigeria), MEASURE DHS, ICF Micro. Nigeria Demographic and Health Survey 2013. Preliminary Report Abuja and Calverton: NPC, MEASURES DHS and ICF Macro, 2013.
In article      
[7]  Smith CL, Haddad L. Explaining childhood mal-nutrition in developing countries, a cross-sectional analysis. International Food Policy Research Institute 2000. http: // (Accessed 10 January 2015).
In article      
[8]  Mahgoub OES, Nnyepi M, Bandeke T. Factors affecting prevalence of malnutrition among children under three years of age in Botswana. African Journal of Food Agriculture Nutrition and Development. 2006; 6(1): 1-9.
In article      View Article
[9]  Emina JB, Kandala N, Inungu J. The effect of maternal education on child nutritional status in the Democratic Republic of the Congo. Nairobi Kenya: African Population and Health Research Center, 2009.
In article      PubMed
[10]  Faber M, Wenhold F. Nutrition in Contemporary South Africa. Water SA. 2007; 33(3): 398-400.
In article      
[11]  Madondo A, MacIntyre UE, Ntuli B. The clinical and anthropometric profile of under-nourished children aged under 5 admitted to Nyangabgwe Referral Hospital in Botswana. South African Journal of Child Health. 2012; 6(4): 123-127.
In article      View Article
[12]  Imo State Ministry of Health; Health facilities in Nwangele LGA. Health and Welfare – Official portal for Nwangele LGA, Imo State, Nigeria. Available at (Accessed 28 July, 2013).
In article      
[13]  Azubuike JC, Nkanginieme KEO. Paediatrics and Child Health in a Tropic Region. 2ND Edition. African Educational Services Owerri 2007; 250.
In article      
[14]  Waterloo JC. Modified Wellcome Classification and Definition of Protein-Calorie Malnutrition. Bri Med J. 1972; 3: 566-569.
In article      View Article
[15]  Joint UNICEF-WHO-The World Bank Child Malnutrition Database: Estimates for 2012 and Launch of Interactive Data Dash Boards. Available at (Accessed 14th February 2015).
In article      
[16]  Lawoyin TO, Onadeko MO, Kolude O. Risk factors for malnutrition among under-five-year olds in an inner city community in Ibadan: A case-control study. Nigerian Journal of Paediatrics 2003; 30(1): 7-12.
In article      View Article
[17]  Olwedo MA, Mworozi E, Bachou H, Orash GC. Factors associated with malnutrition among children in internally displaced persons’ camps, Northern Uganda. Afr Health Sci 2008; 8: 244-252.
In article      PubMed
[18]  Emina JB, Kandala N, Inungu J. The effect of maternal education on child nutritional status in the Democratic Republic of the Congo. Nairobi, Kenya: African Population and Health Research Center, 2009.
In article      PubMed
[19]  Irena AH, Mwambazi M, Mulenga V. Diarrhoea is a major killer of children with severe acute malnutrition admitted to an in-patient set-up in Lusaka, Zambia. Nutr J. 2011; 10: 110-115.
In article      View Article  PubMed
[20]  Mahgoub HM, Adam I. Morbidity and mortality of severe malnutrition among Sudanese children in New Halfa Hospital, Eastern Sudan. Trans R Soc Trop Med Hyg. 2012; 106: 66-68.
In article      View Article  PubMed
[21]  Soon SD, Khor GL: Nutritional status of children aged one to six in Sg. Koyan FELDA in Pahang. Malays J. Nutr. 1995; 1: 115-128.
In article      
[22]  Khattak M, Ali S. Malnutrition and associated risk factors in pre-school children (2 – 5 years) in District Swabi (NWFP) – Pakistan Journal of Medical Science. 2010; 10: 34-39.
In article      View Article
[23]  Hien NN, Kam S: Nutritional status and the characteristics related to malnutrition in children under five years of age in Ngheen, Vietnam. J Prev Med Public Health. 2008; 41: 232-240.
In article      View Article  PubMed
[24]  Senbanjo IO, Olayiwola IO, Afolabi WA, Senbanjo OC. Maternal and child under-nutrition in rural and urban communities of Lagos state, Nigeria: the relationship and risk factors. BMC Res Notes. 2013, 6: 286.
In article      View Article  PubMed
[25]  Black RE, Brown KA, Beckes S, Alim A, Huq I. Longitudinal studies of infectious disease and physical growth of children in rural Bangladesh. Am J Epidemiol. 1982; 115: 315-324.
In article      PubMed
[26]  Caldwell JC. Education as a factor in mortality decline. An examination of Nigerian data. Population Studies. 1979; 33: 395-413.
In article      View Article
[27]  Canagarajah S, Ngwafon J, Thomas S. Evolution of Poverty and Welfare in Nigeria: 1985-92. The World Bank Policy Research Working Paper 1715. Washington DC 1997.
In article      
[28]  Goodburn GJ, Senapati S. Strategies educated mothers use to ensure health of their children. J. Trop. Paediat. 1990; 36: 17-23.
In article      View Article
[29]  Radebe BZ, Brady JP, Siziya S, Todd H. Maternal risk factors for childhood malnutrition in Mazowe district of Zimbabwe. Central African Journal of Medicine 1996; 42: 240-249.
In article      PubMed
[30]  Bwibo NO. Children in especially difficult circumstances. Primary Health Care Manual. UNICEF 1995; 13: 146-154.
In article      
  • CiteULikeCiteULike
  • MendeleyMendeley
  • StumbleUponStumbleUpon
  • Add to DeliciousDelicious
  • FacebookFacebook
  • TwitterTwitter
  • LinkedInLinkedIn