The Scientific World Journal
Volume 2014 (2014), Article ID 719673, 9 pages
http://dx.doi.org/10.1155/2014/719673

Research Article

Undernutrition and Its Correlates among Children of 3–9 Years of Age Residing in Slum Areas of Bhubaneswar, India

Ansuman Panigrahi and Sai Chandan Das

Department of Community Medicine, Kalinga Institute of Medical Sciences, KIIT University Bhubaneswar, Odisha 751024, India

Received 31 July 2014; Revised 7 November 2014; Accepted 30 November 2014; Published 17 December 2014

Academic Editor: Mieke Faber

Copyright © 2014 Ansuman Panigrahi and Sai Chandan Das. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Undernutrition among children is a major public health concern worldwide, more prevalent in Asia and Africa. It manifests itself in various forms such as wasting or stunting or underweight and retards physical and mental development, increases susceptibility to infection, and reduces educational attainment and productivity. The present study was undertaken to assess the level of wasting, stunting, and underweight and determine its associates among slum children of 3–9 years of age, residing in Bhubaneswar city, India. After obtaining informed consent, a total of 249 children from 249 households were studied and their parents/guardians were interviewed to collect all relevant information. 23.3%, 57.4%, and 45.4% of children were found to have wasting, stunting, and underweight, respectively. Variables like birth order of child, period of initiation of breastfeeding and mother’s education were found to be strong predictors of wasting, whereas toilet facility in household and practice of drinking water storage were significantly associated with stunting among slum children as revealed in multiple regression analysis. Thus, a multipronged approach is needed such as giving priority to improve education for slum community especially for women, creating awareness regarding benefits of early initiation of breastfeeding, small family size, and proper storage of drinking water, and providing toilet facility in slum households which could improve the nutritional status of slum children.

1. Introduction

Nutrition has been recognized as a basic pillar for social and economic development. Adequate nutrition is necessary in early childhood to ensure healthy growth, proper functioning of organs, strong immune system, and neurological and cognitive development [1]. Undernutrition is a leading contributor to infant, child, and maternal morbidity and mortality playing a role in about half of all child deaths [1, 2]. Undernutrition directly affects many aspects of children’s development such as retarding physical and mental development, increasing susceptibility to infections like sepsis, diarrhoea, pneumonia, and so forth, and further enhancing the probability of undernutrition [2, 3]. It also undermines education attainment and productivity, thereby affecting the economic growth. The prevalence of underweight children in India is among the highest in the world and is nearly double that of sub-Saharan Africa [4]. Undernutrition manifests itself in different forms in children such as wasting (indicator of acute undernutrition) or stunting (indicator of chronic undernutrition) or underweight (combined indicator for both acute and chronic undernutrition) [3].

According to WHO-UNICEF-World Bank report, it was estimated that in 2012 about 162 million, 99 million, and 51 million of under five children were stunted, underweight, and wasted, respectively, worldwide. More than half of these children lived in Asia [5]. According to the National Family Health Survey-3, among under five children, about 48%, 43%, and 20% are stunted, underweight, and wasted, respectively, whereas about 24% are severely stunted and 16% are severely underweight [6]. Odisha is one of the high prevalence states of India, where almost 50% of under five children are underweight [2, 4].

Urban slum dwellers are exposed to poor housing, overcrowding, poor quality drinking water, inadequate sanitation, which is further aggravated by their ignorance, illiteracy, and low socioeconomic status, and lack of access to basic health care facilities. Children living under such conditions are always at a high risk of developing health and nutritional problems [7]. Although numerous studies have been done in different states of India to assess the nutritional status of children, there are very limited studies involving children both in preschool age and in school age group, especially in Odisha, one of the high prevalence states of India. Thus, the present study was undertaken to ascertain the level of different forms of undernutrition such as wasting, stunting, and underweight and determine its associates among slum children of 3–9 years residing in Bhubaneswar city, Odisha.

2. Methodology

The present cross-sectional study was carried out in urban slums of Bhubaneswar, Odisha, India, involving children of age group (3–9 years) during the year 2013-2014. The sample size of 190 was calculated assuming the prevalence of undernutrition as 45% [8], precision of 10% at 95% confidence interval, with a design effect of 2. Multistage cluster random sampling technique was adopted to select the study population.

Bhubaneswar city has been divided into 5 geographical zones such as North, East, Central, West, and South, out of which 3 were selected randomly. From each zone, one ward was randomly chosen and then all the notified slums in the selected wards were considered for the study purpose. Overall, 6 slums were considered as study clusters. It was decided to include 40 households from each of the selected slum areas for the study purpose. In each household, only one child aged between 3 and 9 years having no known health disorder was randomly chosen as study subject. However, a total of 249 children of 3–9 years of age were included in the study. Their anthropometric measurements such as height and weight were taken by the investigators and trained field workers. A predesigned and pretested schedule was used to elicit relevant information regarding sociodemographic and individual characteristics like age, sex, education and occupation of parents, per capita monthly income, type of family, mother’s age at the time of birth of the child, birth order, and exclusive breastfeeding, and environmental information such as overcrowding, toilet facility, and drinking water storage.

Ethical approval was obtained from the Institutional Ethics Committee of Kalinga Institute of Medical Sciences, Bhubaneswar. For participation of the study subjects, parents/guardians were informed about the study objectives and verbal consent was obtained prior to inclusion in the study.

Weight and height measurements were made following standard operating procedures. Weight was measured to the nearest 0.5 kg in a standard weighing (bathroom) scale (model number PL8019, NOVA BS 1120/00). Weighing scale was calibrated every time before a new measurement was taken. Children were asked to stand still over the centre of the scale with minimal clothing and without footwear and hold their head up and face forward, arms hanging freely by the sides of the body, with palms facing the thigh. Height was measured to the nearest 0.1 cm with nonstretchable tape (TR-13–60′′ Tailor’s tape) which was fixed to a vertical smooth wall and the subject was asked to stand erect without footwear on a firm/level surface with his/her back against the wall, feet parallel, and hands hanging by the sides. Each measurement was done twice and the average of the two readings was recorded. Anthropometric data were analysed using WHO AnthroPlus version 1.0.4 software for assessing the growth of the children. The children were classified using the following categories.

(1)Wasting (acute undernutrition) is defined as a low weight for height. Children with scores (WHZ) <−2 are considered as wasted and those with WHZ <−3 are severely wasted.(2)Stunting (chronic undernutrition) is defined as a low height for age. Children with scores (HAZ) <−2 are considered as stunted and those with HAZ <−3 are severely stunted.(3)Underweight (mixed acute and chronic undernutrition) is defined as a low weight for age. Children with scores (WAZ) <−2 are considered as underweight and those with WAZ <−3 are severely underweight.(4)Thinness (measure of body fat) is defined as a low body mass index. Children with scores (BMIZ) <−2 are considered as thin and those with BMI <−3 are severely thin.

All the data were compiled and analysed using Statistical Package for Social Sciences (SPSS) software version 16.0 and appropriate statistical tests were applied. “” value <0.05 was considered as statistically significant. Univariate as well as multivariate analyses were carried out to test associations between various individual and sociodemographic characteristics and nutritional status of children. The variables found to have “” value <0.25 in univariate analysis were entered into logistic regression model. As multistage sampling technique was used for data collection, sample weights were calculated and taken into consideration during the data analysis in order to get more valid results.

3. Results

Overall, 249 slum children of age group between 3 and 9 years were surveyed. Majority of the study subjects were girls (68.3%) and the rest were boys (31.7%). Mean age with standard deviation (SD) of girls and boys was years and years, respectively. The mean height (with SD) of the girls was  cm, whereas that of boys was  cm. The mean weight (with SD) of the girls was  kg, whereas that of boys was  kg. The anthropometric measurements such as height and weight were found to be more in boys than in girls in all the age groups.

Table 1 shows the nutritional status among slum children. Out of 249 children, 58 (23.3%), 143 (57.4%), 113 (45.4%), and 57 (22.9%) were wasted/severely wasted, stunted/severely stunted, underweight/severely underweight, and thin/very thin, respectively. Overall, 65.5% of slum children were found to have undernutrition.

Table 1: Nutritional status of children (3–9 years) in urban slums of Bhubaneswar, India ().

Indicator
Number
Percentage

Weight for height

    Normal
191
76.7

    Wasted
44
17.7

    Severely wasted
14
5.6

Height for age

    Normal
106
42.6

    Stunted
64
25.7

    Severely stunted
79
31.7

Weight for age

    Normal
136
54.6

    Underweight
42
16.9

    Severely underweight
71
28.5

Body mass index

    Normal
192
77.1

    Thin
42
16.9

    Very thin
15
6.0

Univariate analysis showing association between wasting and various individual and sociodemographic characteristics is given in Table 2. The prevalence of wasting was found to be more in children in higher age category and more boys had wasting than girls, though these differences are statistically insignificant. More children with birth order ≥2 were suffering from wasting as compared to children having first birth order, which is statistically significant (). Higher proportions of wasted children were observed in mothers having lower education and vice versa. Hence, higher maternal education appeared to be associated with better child nutrition (). Almost similar pattern of child’s nutrition was found with father’s education. Further, households where the practice of storing drinking water was satisfactory had less number of wasted children (13.9%) as compared to 27.6% children in households with unsatisfactory practice of drinking water storage. This difference is statistically significant (). Other factors such as period of initiation of breastfeeding, mother’s age at birth of child, type of family, per capita monthly income, presence of overcrowding, and toilet facility in household were not found to be significantly associated with wasting among slum children.

Table 2: Univariate model showing association between wasting among slum children and various individual and sociodemographic characteristics.

Independent variable
Wasting
Total (%)
“” value*

Absent
Present

Number (%)
Number (%)

Age (in years) of child

    3–6
132 (82.5)
28 (17.5)
160 (64.3)
0.118

    6–9
59 (66.3)
30 (33.7)
89 (35.7)

Gender of child

    Female
135 (79.4)
35 (20.6)
170 (68.3)
0.117

    Male
56 (70.9)
23 (29.1)
79 (31.7)

Birth order of child

    First
74 (89.2)
09 (10.8)
83 (33.3)
0.010

    Second
87 (70.2)
37 (29.8)
124 (49.8)

    Third
30 (71.4)
12 (28.6)
42 (16.9)

Initiation of breastfeeding

    Within 1 hr
89 (82.4)
19 (17.6)
108 (43.4)
0.083

    Between 1 and 24 hrs
83 (75.5)
27 (24.5)
110 (44.2)

    After 24 hrs
19 (61.3)
12 (38.7)
31 (12.4)

Mother’s age (in years) at birth of child

    ≤20
57 (81.4)
13 (18.6)
70 (28.1)
0.231

    21–30
108 (74)
38 (26)
146 (58.6)

    ≥30
26 (78.8)
07 (21.2)
33 (13.3)

Mother’s education

    Illiterate
48 (68.6)
22 (31.4)
70 (28.1)
0.056

    Primary
54 (73.0)
20 (27.0)
74 (29.7)

    Middle
63 (82.9)
13 (17.1)
76 (30.5)

    High school and above
26 (89.7)
03 (10.3)
29 (11.7)

Father’s education

    Illiterate
51 (67.1)
25 (32.9)
76 (30.5)
0.071

    Primary
45 (70.3)
19 (29.7)
64 (25.7)

    Middle
58 (87.9)
08 (12.1)
66 (26.5)

    High school and above
37 (86.0)
06 (14.0)
43 (17.3)

Type of familya

    Joint
25 (80.6)
06 (19.4)
31 (12.4)
0.573

    Nuclear
166 (76.1)
52 (23.9)
218 (87.6)

Per capita monthly incomeb

    <Rs. 2000
29 (65.9)
15 (34.1)
44 (17.7)
0.262

    Rs. 2000–Rs. 4000
104 (78.2)
29 (21.8)
133 (53.4)

    ≥Rs. 4000
56 (80.6)
14 (19.4)
72 (28.9)

Overcrowding

    Absent
58 (80.6)
14 (19.4)
72 (28.9)
0.072

    Present
133 (75.1)
44 (24.9)
177 (71.1)

Toilet facility

    Absent
61 (75.3)
20 (24.7)
81 (32.5)
0.694

    Present
130 (77.4)
38 (22.6)
168 (67.5)

Drinking water storage

    Satisfactory
68 (86.1)
11 (13.9)
79 (31.7)
0.016

    Unsatisfactory
123 (72.4)
47 (27.6)
170 (68.3)

Significance is based on adjusted chi-square statistic.

aJoint family consists of two or more married couples and their children living together in the same household, all the men being related by blood of patrilineal descent. Nuclear family consists of the married couple and their dependent children, residing in the same household.

bOne US dollar is equivalent to Rs. 60 (approximately).

All the variables found to have “” value <0.25 in the univariate analysis were entered into logistic regression model (Table 3). When controlling for other variables, practice of drinking water storage lost its significance. However, birth order of child, period of initiation of breastfeeding, and mother’s education came out as strong independent predictors of wasting in slum children.

Table 3: Multiple logistic regression model showing association between wasting among slum children and various factors.

Independent variable
Dependent variable (wasting)
value*

Normal = reference category

Age (in years) of child

    3–6
−0.762
0.467
0.220

    6–9R

Gender of child

    Female
−0.480
0.619
0.053

    MaleR

Birth order of child

    First
−1.059
0.347
0.013

    Second
0.378
1.460
0.392

    Third or moreR

Initiation of breastfeeding

    Within 1 hr
−1.445
0.236
0.035

    Between 1 and 24 hrs
−0.713
0.490
0.151

    After 24 hrsR

Mother’s age (in years) at birth of child

    ≤20
0.190
1.209
0.522

    21–30
0.174
1.190
0.534

    ≥30R

Mother’s education

    Illiterate
1.179
3.250
0.024

    Primary
1.136
3.115
0.024

    Middle
0.558
1.747
0.329

    High school and aboveR

Father’s education

    Illiterate
1.036
2.817
0.208

    Primary
0.835
2.304
0.303

    Middle
−0.114
0.892
0.792

    High school and aboveR

Overcrowding

    Absent
−0.499
0.607
0.147

    PresentR

Drinking water storage

    Satisfactory
−0.909
0.403
0.099

    UnsatisfactoryR

Note: R = reference category, = regression coefficient (log odds ratio), and = odds ratio.

Model fit statistics: pseudo- squares—Cox and Snell = 0.188, Nagelkerke = 0.283, and Mcfadden = 0.191; classification table reports that the overall expected model performance is 81.1%; that is, 81.1% of the cases can be expected to be classified correctly by the model.

Table 4 depicts the association between stunting among slum children and various individual and sociodemographic characteristics. In contrast to wasting, the prevalence of stunting was more among children of lower age. However more boys (63.3%) were found to be stunted than their counterparts (54.7%). The mother’s age at child’s birth appeared to be significant risk factor for stunting in children as more children (71.4%) born to younger mothers (≤20 years of age) had stunting compared to children born to older women (). Households having toilet facilities had significantly less number of stunted children (52.4%) as compared to 67.9% children in households without toilet facilities (). It was also observed that the practice of storing drinking water satisfactorily in the households had a significant positive impact on the child nutrition () as more (63.5%) children were having stunting in households where practice of drinking water storage was unsatisfactory compared to less (44.3%) number of children in households having satisfactory storage of drinking water. Other factors such as birth order of the child, period of initiation of breastfeeding, parents’ education, type of family, per capita monthly income, and overcrowding were not found to significantly affect the status of stunting among slum children.

Table 4: Univariate model showing association between stunting among slum children and various individual and sociodemographic characteristics.

Independent variable
Stunting
Total (%)
“” value*

Absent
Present

Number (%)
Number (%)

Age (in years) of child

    3–6
63 (39.4)
97 (60.6)
160 (64.3)
0.190

    6–9
43 (48.3)
46 (51.7)
89 (35.7)

Gender of child

    Female
77 (45.2)
93 (54.7)
170 (68.3)
0.058

    Male
29 (36.7)
50 (63.3)
79 (31.7)

Birth order of child

    First
33 (39.8)
50 (60.2)
83 (33.3)
0.429

    Second
54 (43.5)
70 (56.5)
124 (49.8)

    Third
19 (45.2)
23 (54.8)
42 (16.9)

Initiation of breastfeeding

    Within 1 hr
44 (40.7)
64 (59.3)
108 (43.4)
0.528

    Between 1 and 24 hrs
50 (45.5)
60 (54.5)
110 (44.2)

    After 24 hrs
12 (38.7)
19 (61.3)
31 (12.4)

Mother’s age (in years) at birth of child

    ≤20
20 (28.6)
50 (71.4)
70 (28.1)
0.015

    21–30
72 (49.3)
74 (50.7)
146 (58.6)

    ≥30
14 (42.4)
19 (57.6)
33 (13.3)

Mother’s education

    Illiterate
23 (32.9)
47 (67.1)
70 (28.1)
0.093

    Primary
24 (32.4)
50 (67.6)
74 (29.7)

    Middle
39 (51.3)
37 (48.7)
76 (30.5)

    High school and above
20 (69.0)
09 (31.0)
29 (11.7)

Father’s education

    Illiterate
21 (27.6)
55 (72.4)
76 (30.5)
0.107

    Primary
29 (45.3)
35 (54.7)
64 (25.7)

    Middle
32 (48.5)
34 (51.5)
66 (26.5)

    High school and above
24 (55.8)
19 (44.2)
43 (17.3)

Type of familya

    Joint
10 (32.3)
21 (67.7)
31 (12.4)
0.327

    Nuclear
96 (44)
122 (56.0)
218 (87.6)

Per capita monthly incomeb

    <Rs. 2000
14 (31.8)
30 (68.2)
44 (17.7)
0.235

    Rs. 2000–Rs. 4000
53 (39.8)
80 (60.2)
133 (53.4)

    ≥Rs. 4000
39 (54.2)
33 (45.8)
72 (28.9)

Overcrowding

    Absent
34 (47.2)
38 (52.8)
72 (28.9)
0.387

    Present
72 (40.7)
105 (59.3)
177 (71.1)

Toilet facility

    Absent
26 (32.1)
55 (67.9)
81 (32.5)
0.019

    Present
80 (47.6)
88 (52.4)
168 (67.5)

Drinking water storage

    Satisfactory
44 (55.7)
35 (44.3)
79 (31.7)
0.007

    Unsatisfactory
62 (36.5)
108 (63.5)
170 (68.3)

Significance is based on adjusted chi-square statistic.

aJoint family consists of two or more married couples and their children living together in the same household, all the men being related by blood of patrilineal descent. Nuclear family consists of the married couple and their dependent children, residing in the same household.

bOne US dollar is equivalent to Rs. 60 (approximately).

All the variables found to have in the univariate analysis were entered into logistic regression model (Table 5). Variables like toilet facility and practice of drinking water storage in household came out as strong predictors of stunting among slum children, whereas mother’s age at birth of child lost its significance.

Table 5: Multiple logistic regression model showing association between stunting among slum children and various factors.

Independent variable
Dependent variable (wasting)
value*

Normal = reference category

Age (in years) of child

    3–6
0.334
1.396
0.207

    6–9R

Gender of child

    Female
−0.360
0.698
0.076

    MaleR

Mother’s age (in years) at birth of child

    ≤20
0.371
1.450
0.187

    21–30
−0.471
0.624
0.173

    ≥30R

Mother’s education

    Illiterate
1.023
2.783
0.146

    Primary
1.056
2.875
0.133

    Middle
0.370
1.447
0.605

    High school and aboveR

Father’s education

    Illiterate
0.984
2.674
0.134

    Primary
0.201
1.223
0.688

    Middle
−0.042
1.043
0.919

    High school and aboveR

Per capita monthly income

    <Rs. 2000
1.003
2.725
0.100

    Rs. 2000–Rs. 4000
0.634
1.885
0.190

    ≥Rs. 4000R

Toilet facility

    Absent
0.436
1.546
0.031

    Present

Drinking water storage

    Satisfactory
−0.822
0.439
0.047

    UnsatisfactoryR

Note: R = reference category, = regression coefficient (log odds ratio), and = odds ratio.

Model fit statistics: pseudo- squares—Cox and Snell = 0.180, Nagelkerke = 0.241, and Mcfadden = 0.145; classification table reports that the overall expected model performance is 71.1%; that is, 71.1% of the cases can be expected to be classified correctly by the model.

4. Discussion

Childhood undernutrition is a major public health problem in India especially in slums [9–12]. Starting with the positive aspect of results found in the present study, it was found that almost 90% of slum children were completely immunised till date, but the prevalence of undernutrition was still very high (65.5%). Taking different indicators of undernutrition into account, wasting (23.3%), stunting (57.4%), and underweight (45.4%), found in the children under study, is unacceptably high. Most studies worldwide have also reported high rates of undernutrition among slum children [8, 13–16]. The results of the present study are consistent with these findings.

Various studies have established the fact that boys are more likely to be stunted, wasted, and underweight than girls [17–20], which was also observed in the present study. In contrast, some other studies found no such association between gender and undernutrition [21–23].

Logistic regression analysis revealed the net effect of individual predictor on the dependent variable. It was observed that children of birth order of 3 or more were almost three times having wasting than children of first order. Children with higher birth order might get less attention and care compared to children of first order. Elkholy et al. revealed in their study that high means of wasting were found among the group of high birth order [24]. Association of undernutrition with high birth order has been shown by many studies [25–28]. The odds of having wasting rise 4.2 times in case of children whose mothers initiated breastfeeding after 24 hours of birth as compared to children who were breastfed within 1 hour of their birth. Early initiation of breastfeeding positively affects child’s overall nutritional status [29, 30]. Mother’s education was found to have a strong independent effect on child’s nutrition controlling the effects of other variables. Illiterate mothers and mothers with primary education were about thrice more likely to have wasted children as compared to mothers who had attained higher education (high school and above). It might be due to the reason that higher educated women can take independent decisions and have greater exposure to outside world and thus access to various resources which help them in securing proper nutrition of their children. They are more aware about proper nutrition, maintenance of hygiene, and various health issues as compared to uneducated or less educated women. Earlier studies have also documented the positive impact of mother’s education on wasting [31–33].

Two independent predictors of stunting were also identified. Children residing in households without having toilet facility were 1.5 times more at risk of developing stunting as compared to children in households with toilet facility. This finding has been corroborated in other studies [34–37]. Unavailability of toilet facility in households would promote open defecation which facilitates various water borne diseases that can negatively affect the health and nutrition of young children. Further, children in households with unsatisfactory practice of storing drinking water were 2.2 times more likely to have stunting than children living in households with satisfactory practice of drinking water storage. Merchant et al. showed in their study that children coming from homes with water and sanitation had a 17% greater chance of reversing stunting than those coming from homes without either facility [35]. Similar results have also been found in other studies [38, 39].

The estimation in the study is not a good measure of causal association between independent variables and nutrition of children because of cross-sectional nature of the study. There might be recall bias as some of the information was based on response of the study participants. In spite of these limitations, the study has certain important implications.

5. Conclusion and Recommendation

The present study was probably the first of its kind in Odisha to assess the nutritional status among the slum children both in preschool age and in primary school age group using WHO child growth standards. It is evident from the study that nutritional status of children shows a gloomy picture in slums of Odisha. Thus, utmost care and attention must be focused on these socioeconomically and disadvantaged children living in slums to attain the MDG goals. Sincere efforts must be undertaken to make a significant impact on child’s nutrition with multipronged approach such as giving priority to education for slum community especially for women, creating awareness regarding benefits of factors like early initiation of breastfeeding, limiting family size, proper storage of drinking water, and so forth, and providing toilet facility in the household. Similar studies need to be carried out among slum children of different regions to find out whether there are any ethnic and regional variations in the prevalence of undernutrition and also operational research can be planned regarding deliverance of multipronged strategies to curb this public health menace.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

The authors thank and express their gratitude to all the parents/guardians for providing their time and cooperation throughout the study.

References

Levels and trends in child malnutrition: UNICEF-WHO-The World Bank joint child malnutrition estimates, http://www.who.int/nutgrowthdb/jme_unicef_who_wb.pdf

K. Park, Park's Textbook of Preventive and Social Medicine, M/S Banarasidas Bhanot, Jabalpur, India, 22nd edition, 2013.

O. P. Ghai, V. K. Paul, and A. Bagga, Nutrition, Ghai Essential Pediatrics, CBS, New Delhi, India, 7th edition, 2009.

M. Gragnolati, M. Shekar, M. Dasgupta, C. Bredenkamp, and Y. K. Lee, “India’s undernourished children: a call for reform and action,” HNP, The World Bank, 2005, http://siteresources.worldbank.org/SOUTHASIAEXT/Resources/223546-1147272668285/IndiaUndernourishedChildrenFinal.pdf

Joint UNICEF-WHO-The World Bank Child Malnutrition Database: Estimates for 2012 and Launch of Interactive Data Dashboards, http://www.who.int/nutgrowthdb/jme_2012_summary_note_v2.pdf?ua=1

NFHS-3, http://www.rchiips.org/nfhs/NFHS-3%20Data/VOL-1/India_volume_I_corrected_17oct08.pdf

UN-HABITAT, “Defining slums: towards an operational definition for measuring slums,” Background Paper 2, Expert Group Meeting on Slum Indicators, United Nations, Nairobi, Kenya, October 2002, http://ww2.unhabitat.org/mediacentre/documents/sowcr2006/SOWCR%205.pdf

S. Mandal, V. R. Prabhakar, J. Pal, R. Parthasarathi, and R. Biswas, “An assessment of nutritional status of children aged 0–14 years in a slum area of Kolkata,” International Journal of Medicine and Public Health, vol. 4, no. 2, pp. 159–162, 2014.

S. Ghosh and D. Shah, “Nutritional problems in urban slum children,” Indian Pediatrics, vol. 41, no. 7, pp. 682–696, 2004.

S. Nandy, M. Irving, D. Gordon, S. V. Subramanian, and G. D. Smith, “Poverty, child undernutrition and morbidity: new evidence from India,” Bulletin of the World Health Organization, vol. 83, no. 3, pp. 210–216, 2005.

K. Bose, S. Biswas, S. Bisai et al., “Stunting, underweight and wasting among Integrated Child Development Services (ICDS) scheme children aged 3–5 years of Chapra, Nadia District, West Bengal, India,” Maternal & Child Nutrition, vol. 3, no. 3, pp. 216–221, 2007.

D. Kapur, S. Sharma, and K. N. Agarwal, “Dietary intake and growth pattern of children 9-36 months of age in an urban slum in Delhi,” Indian Pediatrics, vol. 42, no. 4, pp. 351–356, 2005.

M. Shreyaswi Sathyanath, Rashmi, and N. Udaya Kiran, “Prevalence and risk factors of undernutrition among under five children in a rural community,” Nitte University Journal of Health Science, vol. 3, no. 4, pp. 2249–7110, 2013.

S. V. Badami, S. Diwanji, K. Vijaykrishna et al., “Nutrtional status of underfive children in urban slums of Bagalkot,” Medica Innovetica, vol. 1, no. 2, pp. 28–34, 2012.

S. Bisai, K. Bose, and S. Dikshit, “Under-nutrition among slum children aged 3-6 years in Midnapore town, India,” The Internet Journal of Biological Anthropology, vol. 2, no. 2, Article ID 10564, 2008.

P. Sengupta, N. Philip, and A. I. Benjamin, “Epidemiological correlates of under-nutrition in under-5 years children in an urban slum of Ludhiana,” Health and Population: Perspectives and Issues, vol. 33, no. 1, pp. 1–9, 2010.

A. K. M. Shahabuddin, K. Talukder, M. Q.-K. Talukder et al., “Adolescent nutrition in a rural community in Bangladesh,” Indian Journal of Pediatrics, vol. 67, no. 2, pp. 93–98, 2000.

A. Hall, “The anthropometric status of schoolchildren in five countries in the Partnership for Child Development,” Proceedings of the Nutrition Society, vol. 57, no. 1, pp. 149–158, 1998.

M. S. Sebastián and S. Santi, “The health status of rural school children in the Amazon basin of Ecuador,” Journal of Tropical Pediatrics, vol. 45, no. 6, pp. 379–382, 1999.

N. J. S. Lwambo, S. Brooker, J. E. Siza, D. A. P. Bundy, and H. Guyatt, “Age patterns in stunting and anaemia in African schoolchildren: a cross-sectional study in Tanzania,” European Journal of Clinical Nutrition, vol. 54, no. 1, pp. 36–40, 2000.

M. Luthra and U. C. Parvan, “Epidemiological correlates of undernutrition in 0–5 year old children In rural field practice areas of SGRRIMandHS, Dehradun,” Indian Journal of Preventive & Social Medicine, vol. 41, no. 1-2, 2010, http://medind.nic.in/ibl/t10/i1/iblt10i1p8.pdf

M. K. Goel, R. Mishra, D. Gaur, and A. Das, “Nutrition surveillance in 1–6 years old children in urban slums of a city in Northern India,” The Internet Journal of Epidemiology, vol. 5, no. 1, pp. 122–131, 2007.

V. Bhatia, S. Puri, H. M. Swami, M. Gupta, and G. Singh, “Malnutrition among under six children in Chandigarh: scarcity in plenty,” Journal of Clinical and Diagnostic Research, vol. 6, pp. 483–487, 2007.

T. A. Elkholy, N. H. M. Hassanen, and Rasha, “Demographic, socio- economic factors and physical activity affecting the nutritional status of young children under five years,” Journal of American Science, vol. 7, no. 10, 2011.

D. Barman and S. Sarkar, “Social exclusion and child nutritional status among the scheduled population in India,” Developing Country Studies, vol. 3, no. 3, 2013.

L. K. Zottarelli, T. S. Sunil, and S. Rajaram, “Influence of parental and socioeconomic factors in stunting in children under 5 years in Egypt,” Eastern Mediterranean Health Journal, vol. 13, no. 6, pp. 1330–1342, 2007.

S. Das and H. Sahoo, “An investigation into factors affecting child undernutrition in madhya pradesh,” Anthropologist, vol. 13, no. 3, pp. 227–233, 2011.

A. Srivastava, S. E. Mahmood, P. M. Srivastava, V. P. Shrotriya, and B. Kumar, “Nutritional status of school-age children—a scenario of urban slums in India,” Archives of Public Health, vol. 70, no. 1, article 8, 2012.

E. N. Muchina and P. M. Waithaka, “Relationship between breastfeeding practices and nutritional status of children aged 0–24 months in Nairobi, Kenya,” African Journal of Food Agriculture Nutrition and Development, vol. 10, no. 4, 2010.

D. Kumar, N. K. Goel, P. C. Mittal, and P. Misra, “Influence of infant-feeding practices on nutritional status of under-five children,” Indian Journal of Pediatrics, vol. 73, no. 5, pp. 417–421, 2006.

D. Makoka, “The impact of maternal education on child nutrition: evidence from Malawi, Tanzania, and Zimbabwe,” Demographic and Health Surveys Working Papers no. 84, 2013.

E. W. H. Maiga, “The Impact of Mother's Education on Child Health and Nutrition in developing countries: Evidence from a Natural Experiment in Burkina Faso,” African Center for Economic Transformation, http://www.uneca.org/sites/default/files/page_attachments/maiga_the_impact_of_mothers_education_on_child_health_and_nutrition_in_develping_countries_0.pdf

A. S. Oyekale and T. O. Oyekale, “Do mothers’ educational levels matter in child malnutrition and health outcomes in Gambia and Niger,” UNICEF, http://www.saga.cornell.edu/saga/educconf/oyekale.pdf

G. Fink, I. Gunther, and K. Hill, “The effect of water and sanitation on childhealth : evidence from the demographic and health surveys 1986–2007,” International Journal of Epidemiology, vol. 40, no. 5, pp. 1196–1204, 2011.

A. T. Merchant, C. Jones, A. Kiure et al., “Water and sanitation associated with improved child growth,” European Journal of Clinical Nutrition, vol. 57, no. 12, pp. 1562–1568, 2003.

M. R. Vitolo, C. M. Gama, G. A. Bortolini, P. D. B. Campagnolo, and M. D. L. Drachler, “Some risk factors associated with overweight, stunting and wasting among children under 5 years old,” Jornal de Pediatria, vol. 84, no. 3, pp. 251–257, 2008.

D. Spears, A. Ghosh, and O. Cumming, “Open defecation and childhood stunting in India: an ecological analysis of new data from 112 districts,” PLoS ONE, vol. 8, no. 9, Article ID e73784, 2013.

G. Bantamen, W. Belaynew, and J. Dube, “Assessment of factors associated with malnutrition among under five years age children at Machakel Woreda, Northwest Ethiopia: a case control study,” Journal of Nutrition & Food Sciences, vol. 4, no. 1, article 256, 2014.

S. Demissie and A. Worku, “Magnitude and factors associated with malnutrition in children 6-59 months of age in pastoral community of Dollo Ado district, Somali region, Ethiopia,” Science Journal of Public Health, vol. 1, no. 4, pp. 175–183, 2013.