nutritional assessment of children ages 0-5...
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NUTRITIONAL ASSESSMENT OF CHILDREN AGES 0-5 YEARS IN RURAL PUNJAB, INDIA
by
Daniel Aaron Eike
A Senior Honors Thesis Submitted to the Faculty of The University of Utah
In Partial Fulfillment of the Requirements for the
Honors Degree in Bachelor of Science
In
Health Promotion and Education
Approved: ______________________________ Dr. Tejinder Pal Singh Thesis Faculty Supervisor
_____________________________ Les Chatelain Chair, Department of Health Promotion and Education
_______________________________ Anita Leopardi Honors Faculty Advisor
_____________________________ Sylvia D. Torti, PhD Dean, Honors College
December 2016
Copyright © 2016 All Rights Reserved
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ABSTRACT
Malnutrition is a major health risk that affects nearly 50% of children in India.
The state of Punjab has been termed the “breadbasket” of India because of its massive
agricultural production. Despite this, children throughout Punjab still suffer from high
rates of malnutrition.
The purpose of this study is to provide a baseline for malnutrition among children
(ages 0-5) in 9 rural villages in the Fatehgarh Sahib District of Punjab. Weight and height
were measured for children throughout the nine villages. This data was analyzed using
WHO Anthro Software to determine malnutrition based on four indicators: weight for
age, height for age, weight for height, and BMI for age. Among all children surveyed
(n=463), 53% were found to be at risk or underweight according to the weight for age
indicator. Results were also compared to demographic factors including: gender, school,
sibling and caste.
Results indicate a need for further intervention measures. Possible solutions to
this problem include community outreach and collaboration between local NGOs, district
officials and village leaders; health and nutrition counseling; follow-up and referral
services for malnourished children; and improving the role of the family in child nutrition
(especially the role of the father).
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TABLE OF CONTENTS
ABSTRACT ii
INTRODUCTION 1
METHODS 5
RESULTS 9
DISCUSSION 14
LIMITATIONS 17
CONCLUSION AND WAY FORWARD 18
REFERENCES 22
1
INTRODUCTION
Worldwide, malnutrition accounts for approximately 3.1 million child deaths (45% of all
child deaths) per year ("2015 World Hunger and Poverty," n.d.). Poor nutrition restricts
child immune systems from functioning properly and exacerbates other health issues and
diseases. Measles, malaria, pneumonia, and diarrhea lead to disproportionate deaths in
children and all have underlying roots in child malnutrition.
Southern Asia (India, Pakistan, and Bangladesh) is the most severely impacted
region of the world for poor nutrition. An estimated 276 million people are
undernourished in this region (“2015 World Hunger and Poverty,” n.d.) The Indian state
of Punjab is known as the “Breadbasket of India” due to its massive agricultural
production relative to the remainder of the country. Wages in this region are also
generally higher, which should correlate to reasonably better health outcomes and
nutrition status. However, this is not the case as nearly 1 in 2 children are undernourished
and 1 in 3 are stunted (The Ministry of Statistics and Programme Implementation:
Government of India, 2012). The Indian government has implemented an intervention
program to improve child nutrition since the 1970s. This program is known as Integrated
Child Development Services (ICDS); its purpose is to establish Anganwadi Centers
(AWC) in rural villages to provide six basic services: supplementary nutrition, nutrition
and health education, preschool education, health check-up, immunization, and referral
services. A trained Anganwadi Worker (AWW) is assisted by an Anganwadi Helper
(AWH) to staff each Anganwadi Center. AWW’s and AWH’s are typically female and
are known for their position in each village.
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The ICDS has been criticized as being ineffective. One criticism points out that
the ICDS scheme focuses heavily on supplementary nutrition while neglecting the
importance of health literacy and nutritional education (Gragnolati, 2006). AWW’s are
often overwhelmed with heavy workloads, yet they lack the required resources to
effectively produce their desired impact. Each village in this study has one AWC per
1000 population. Six villages have one AWC, two villages have two AWCs, and one
village has three AWCs.
Malnutrition leads to many short term and long term consequences for individuals
and communities alike. Child malnutrition is related to physiological and cognitive
impairments and can also restrict a child’s access to education, which affects future
growth and productivity levels. Economic burden related to malnutrition is estimated to
cost India $2.5 billion annually (Gragnolati, 2006).
The University of Utah has teamed up with Mehar Baba Charitable Trust, a local
NGO in Punjab, and PGIMER School of Public Health to form a partnership under the
name Bassi Pathana Community Collaborative Development Project (BP-CCDP). This
partnership works to address major health issues in rural villages of the Fatehgarh Sahib
district of Punjab. With resources and supervision from the University of Utah, MBCT
has successfully conducted baseline growth monitoring on ~80% of all under-five
children in nine select villages.
This baseline data was collected as part of an ongoing child nutrition intervention
project being carried out by BP-CCDP. This project utilizes Anganwadi Centers as the
focal point to address child malnutrition. Nine villages in the Fatehgarh Sahib District of
Punjab were selected for this project. The overarching goal is to reduce child malnutrition
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by five percentage points from the baseline through improvements in Anganwadi Center
facilities and services, family counseling, and medical follow-up. The data used in this
research paper was collected between May and August 2015 as baseline for the ongoing
nutrition intervention project.
Background of Region
This study gathers nutrition data on 463 children in nine rural villages in the
Fatehgarh Sahib District of Punjab, India. The population of Punjab only makes up ~2%
of the population of India. However, this region produces nearly 20% of India’s wheat
and 9% of India’s rice (Punjab- the leader in agricultural sector, 2013). This has
benefitted landowners in this region, whom make up about 2/3 of the population of rural
villages. Increased agricultural production has boosted employment opportunities,
income, and available food to landowners (Gulati, 2010). The Fatehgarh Sahib District
specifically had a population of 599,814 in 2011. Its sex ratio is 871 females per 1000
males and its literacy rate is 80.3% (Fatehgarh Sahib District: census 2011 data). Sikhism
is the dominant religion in Fatehgarh Sahib and the remainder of Punjab. Sikhs make up
71% of the population of Fatehgarh Sahib and Hindus make up 25% of the population
(Fatehgarh Sahib District: census 2011 data). A large majority of children included in this
study come from Sikh families.
Cultural traditions have a heavy influence on food habits among people in this
region. Male children tend to be offered more nutritious food choices and larger
quantities than female children and even the mother (Gulati, 2010). The National Family
Health Survey (NFHS) reports that the state of Punjab has experienced minimal change
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in the nutritional status of under-three children between the results from the 1991-1992
NFHS-2 and the 2005-2006 NFHS-3 (Lokshin, et. al., 2005). Under-three children are an
important subgroup to consider in this study due to crucial cognitive and physical
development that occurs between birth and 36 months of age.
This study investigates the prevalence of malnutrition and overall nutritional
status of under-five children in nine select villages of the rural Fatehgarh Sahib District,
Punjab, India through quantitative data and qualitative observations collected from the
BP-CCDP nutrition intervention project.
Research Question 1: What is the overall prevalence of malnutrition among under-five
children in the nine villages that were included in this study?
Research Question 2: Which malnutrition indicators will have the best and worst
nutrition outcomes among under-five children in this study?
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Research Question 3: What is the correlation of demographic factors such as caste,
school and sex on malnutrition among under-five children?
METHODS
Between May and August 2015, MBCT dispatched teams of 2-4 trained workers
per day to canvas nine select villages. An initial goal of 100% coverage of 0-5 children
was revised to 80% coverage due to time limitations with the project. Overall, the teams
gathered growth data on 463 children in the nine villages. This data allowed MBCT to
create a database with a nutrition log for each individual child that can be updated and
analyzed with each subsequent growth monitoring check.
MBCT teams carried paper forms to collect data on each child. This data was later
transferred into WHO Anthro software for analysis. Each paper form contained a series
of categories for each child: name, father’s name (important for identification purposes),
birth date, caste, school, sex (male/female), height (cm), and weight (kg).
Data Collection Process
General census data in this area is unreliable and often incomplete. For this
reason, teams were required to utilize local leaders to locate and collect data on each
individual child. While in each village, MBCT team members worked directly with local
AWW’s, AWH’s, and Sarpanches (Village Leader). These senior members assisted with
locating each child for growth monitoring checks. MBCT team members also helped
teach AWW’s and AWH’s the growth monitoring process, as this is also a key service
that is often neglected in Anganwadi Centers.
MBCT team members employed three basic strategies to conduct growth-
monitoring checks:
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Conducting measurements in a central location: The team utilized a central location such
as Anganwadi Centers or Sikh Gurudwaras. Stationed at this location, a team member or
community member will canvas the village and bring children and parents to the central
location where they are measured for height and weight. This method was typically used
on the initial visit to each village, as it was the most efficient process.
Home Station: In this method, the team would conduct growth measurements within a
single home. A team member and the community member recruits children from
neighboring homes to be measured at that home, then the team will move down to
another home and repeat the process. This method was utilized after conducting
measurements in a central location.
Door to Door: The team walks with a senior community member to each home. Growth
measurements are carried out within the home. This method is least efficient and was
typically carried out to measure children that could not be covered in the central location.
Instruments
Height and weight data was collected using tools provided by SPH-PGIMER.
• Infantometer- used to conduct length measurements of infants.
• Height Stand- used to conduct height measurements of children over 92 cm.
• Measuring tape- used to conduct height measurements of children under 92 cm.
• Digital Weighing Balance- used to conduct weight measurements of standing
children and infants (if weighing mother with child, then mother without child,
and subtracting to find the weight of the child alone).
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• Baby Scale- Supine-position scale used to conduct weight measurements of
infants under 10 kg.
Tool used by MBCT teams for growth data collection:
Growth Monitoring Field Recording Tool
Village Name Name
Father's Name Sex S/R DOB
Weight (kg)
Height (cm) Caste
School Attended
Sibling Notes
1 Akash Singh
Baljeet Singh M S 24/4/13 10.9 84 SC AWC
1 of 3 Example
2
S/R refers to standing or recumbent scale used for measuring child’s weight. This is
necessary for Z-score calculation within WHO Anthro. Caste options are General
Population (GC), Scheduled Caste (SC), or Backwards Class (BC). School options are
Anganwadi Center (AWC), public, private, or none. Sibling number allowed team
members to record the sibling number and total number of siblings in each family. A
notes section was included for MBCT team members to record any qualitative
observations from field visits.
Data Analysis
Raw data collected by MBCT teams was entered into WHO Anthro 2007 software. This
software, developed by the World Health Organization, analyzes height and weight data
for under-5 children. It utilizes 2007 standards to determine each child’s z-score relative
to the mean for four indicators:
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1. Height for Age: This indicator is particularly useful to help identify stunting in a
child’s growth due to illness or under nutrition (World Health Organization,
2008).
2. Weight for Age: This indicator compares a child’s body weight to his/her age on a
particular day. It is useful to help determine if the child is underweight or severely
underweight and is therefore commonly used. However, it must also be
considered that a child’s weight could be low due to stunting, thinness, or both
(World Health Organization, 2008).
3. Weight for Length/Height: This is a measure of a child’s current weight relative
to his/her height or length. This indicator can assist with identification of wasted
and severely wasted children. “Wasting” refers to acute and severe weight loss
caused by food shortage, illness, or chronic under nutrition (World Health
Organization, 2008).
4. BMI for Age: This indicator demonstrates a child’s body mass index relative to
current age. This indicator is largely useful for identifying obesity on the
individual level. It has been included in this study because it illustrates the general
trend of BMI levels for the population surveyed (World Health Organization,
2008).
Z-scores calculated from WHO Anthro were analyzed using STATA Software.
Regression analysis was performed on z-scores to identify trends from caste, school, and
sex variables.
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RESULTS
MBCT teams collected growth data on approximately 80% of under-five children in the 9
villages. This data was collected between May and August 2015.
Overall, there were 463 children measured (N=463). Among these, there are 215 females
and 248 males. Scheduled Caste children make up the majority of the children surveyed
with 54.2% (n=169); General Population children make up 37.8% (n=118) and
Backwards Class children represent 8.0% (n=25) of all children in this study. 45.6% of
children do not attend school. This is because most under-three children do not attend any
formal school. Of the children that do attend school, 74 (24.3% of total) attend an AWC,
70 (23.0%) attend private school and 22 (7.1%) attend public school.
Z-Scores: -1>-2 represents at-risk children, -2>-3 represents malnourished children, and
below -3 represents severely malnourished children for each indicator.g
Weight for Age Results
Table 1a. Weight for Age Z-Score Distribution
Category (Z-‐Score) n=463 Percentage Healthy (Greater than -‐1) 219 47% At Risk (-‐1>-‐2) 140 30% Underweight (-‐2>-‐3) 73 16% Severely Underweight (Below -‐3)
31 7%
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Table 1b. Weight for Age Regression Analysis Table
Weight for Age Regression*
Coefficient Standard Error
R Squared
Observations (n)
Sex Insignificant at P<0.05 AWC vs Other -‐0.530 0.149 0.040 305 Backwards Class vs other castes
-‐0.544 0.243 0.061 312
Scheduled Caste vs other castes
-‐0.580 0.132 0.061 312
Oldest sibling** Insignificant at P<0.05 313
*Demographic data (school, caste, sibling number) not available for all children in study (N=463).
**Oldest sibling or only child.
Graph 1. Percentage of children at risk, underweight, or severely underweight by village.
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Weight for Height Results
Table 2a. Weight for Height Z-score distribution
Category (Z-‐Score) n=460* Percentage Healthy (Greater than -‐1) 230 50% At Risk (-‐1>-‐2) 144 31% Wasting (-‐2>-‐3) 56 12% Severe Wasting (Below -‐3)
30 7%
*Height Data missing for 3 children
Regression analyses of demographic factors were insignificant for the Weight for Height
indicator.
Graph 2. Percentage of children at risk, wasted, or severely wasted by village
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Height for Age Results
Table 3a. Height for Age Z-Score Distribution
Category (Z-‐Score) n=460* Percentage Healthy (Greater than -‐1) 244 53% At Risk (-‐1>-‐2) 127 28% Stunted (-‐2>-‐3) 71 15% Severely Stunted (Below -‐3) 18 4% *Height Data missing for 3 children
Table 3b. Height for Age Regression Analysis Results
Height for Age Regression*
Coefficient Standard Error
R Squared
Observations (n)
Sex Insignificant at P<0.05
AWC vs Other -‐0.637 0.209 0.030 305 Backwards Class vs other castes
Insignificant at P<0.05
Scheduled Caste vs other castes
-‐0.702 0.185 0.045 312
Oldest Sibling** 0.365 0.176 0.014 313 *Demographic data (school, caste, sibling number) not available for all children in study (N=463).
**Oldest sibling or only child.
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Graph 3. Percentage of children at risk, stunted, or severely stunted in each village.
BMI for Age Results
Table 4a. BMI for Age Z-Score Distribution
Category (Z-‐Score) n=457* Percentage Healthy (Greater than -‐1) 243 53% At Risk (-‐1>-‐2) 127 28% Low BMI (-‐2>-‐3) 59 13% Severely Low BMI (Below -‐3) 31 7% *Height or exact birthdate missing for 6 children
Regression analyses of demographic factors were insignificant for the BMI for Age
indicator.
Regression analysis results found sex (male/female) to be a statistically
insignificant factor (P<0.05) in determining malnutrition among children in this study for
all four indicators.
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Average Results by Village
Table 5. Average Malnutrition Indicator Z-Score by village
Village Code
Weight for Age
Weight for Height
Height for Age
BMI for Age
Observations (n)
DM -‐1.71 -‐1.81 -‐0.70 -‐1.80 29 SM -‐1.50 -‐1.18 -‐1.25 -‐1.09 12 MJ -‐1.08 -‐1.13 -‐0.49 -‐1.11 28 JW -‐1.06 -‐0.73 -‐1.07 -‐0.62 64 BS -‐1.37 -‐1.06 -‐1.09 -‐1.06 69 BL -‐0.96 -‐0.86 -‐0.61 -‐0.86 50 FN -‐1.08 -‐0.95 -‐0.79 -‐0.82 75 FZ -‐0.86 -‐0.77 -‐0.60 -‐0.72 66 LM -‐1.25 -‐1.31 -‐0.66 -‐1.26 70
The two worst z-scores in each indicator category are highlighted in red. The two best
scores in each category are highlighted in green.
DISCUSSION
This study illustrates the overall prevalence of malnutrition among under-five
children in this region. Low weight is the most significant issue this population is
currently facing, as only 47% of children (Table 1a) in the sample are within the healthy
weight range (weight for age z-score between -1 and 1). Results from each of the three
other malnutrition indicators demonstrate a prevalence of at-risk or malnourished
children to be nearly 50% (Tables 2a, 3a, 4a). The rates of malnutrition prevalence found
in this study are similar to estimates from The Ministry of Statistics and Programme
Implementation (2012).
The data collected in this study illustrate the magnitude of under nutrition among
children in this region. Regression analysis of the weight for age indicator found a
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significant correlation in caste and school with worse nutrition outcomes. Scheduled
Caste and Backwards Class children were both determined to be related with worse
weight for age outcomes when compared to other castes; with regression coefficients of -
0.57 and -0.54, respectively (Table 1b). Scheduled caste children are also more likely to
be stunted, with a regression coefficient of -0.70 (P<0.5). Higher likelihood of low-
weight and stunting is likely due to a range of disparities being faced by Scheduled Caste
and Backwards Class families. Scheduled Caste and Backwards Class families tend to
have a lower socio-economic status and are less educated than General Population
families. These families tend to suffer from unequal distribution of resources and
inability to access services that are accessible to general population. MBCT teams noted
that general population families displayed increased interest in child nutrition and health
during growth monitoring visits when compared to scheduled caste and backwards class
families. One major goal of the BP-CCDP nutrition intervention program is improving
and increasing the role of the father in child nutrition and health.
An important component of the BP-CCDP malnutrition intervention project is the
use of Anganwadi Centers as the focal point as a means to improve child nutrition
outcomes in these nine villages. The project addresses malnutrition by improving
conditions and services, and also boosting attendance and access to the 13 AWCs
throughout these nine villages. AWCs operate as a tool to improve child nutrition,
especially among less economically privileged families in rural villages. As such, this
study analyzed the relationship of each malnutrition indicator with AWC attendance
when compared to other/no school attendance. The regression analysis results show a
statistically significant relationship between poor weight and stunting outcomes and
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AWC attendance (Tables 1b and 3b; n=305). These results are not surprising, as these
children also tend to be from lower socioeconomic classes (scheduled caste and
backwards class). Anganwadi Workers reported that some children that attended the
AWC were only receiving 1-2 meals per day; one of which is a meal provided by the
AWC as part of the supplementary nutrition program (AWC Service). However, even
meals provided at AWCs are often not up to the standards of the ICDS… AWCs in these
villages often do not have the resources to provide children a hot-cooked meal each day.
Instead, AWCs frequently only provide children with a “take-home ration” of panjeeri
(nutritional supplement made from whole wheat flour). These rations are not nutritious
and insufficient for the children attending AWCs, especially if their families are also
unable to provide nutritious food for their children at home due to financial or other
reasons. Many families consider AWCs as similar to “daycare” and don’t fully take
advantage of the resources and services provided. By treating these centers as such, their
overall efficacy is reduced. Parents (mostly mothers) drop their children at the AWC in
the morning and pick them up after a few hours after they have been served that day’s
nutrition rations. Parents do not often fully take advantage of nutrition counseling, growth
monitoring, and other services available to them at the AWC. The BP-CCDP project aims
to improve and increase access to these resources for the parents of AWC children.
AWCs are also negatively stigmatized and under-utilized by general population families.
The BP-CCDP project intends to boost awareness and utilization of AWCs and their
services by general population families.
Families in Punjab traditionally favor the first-born child. Therefore, health and
nutrition outcomes for the oldest sibling tend to be better than those of younger children
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in the family (Gulati, 2010). Data from this study was insignificant (P<0.05) to show any
correlation between the first-born/only child and improved nutrition status for three of the
four malnutrition indicators. However, regression analysis results determined that
oldest/only children tend to be less stunted according to the Height for Age indicator
(Table 3b).
One notable outcome of this study is that sex had no statistically significant
correlation to any of the malnutrition indicators that were assessed. This was surprising,
as male children in this region generally receive preferential treatment in health and
nutrition.
Table 5 shows the average z-score in each village for the four malnutrition
indicators assessed. Smaller villages such as DM and SM favored poorly for most
indicators. Larger villages such as JW and FZ favored better for all indicators, yet their
average z-scores are at least still 0.5 standard deviations below the mean for all indicators
based on the WHO standards. Further follow-up research will be required to analyze the
socio-economic factors that may play a role in nutrition outcomes for each individual
village.
LIMITATIONS
Limitations to this product include, but are not limited to, a number of factors.
Caste, school, and sibling data was missing for a large proportion of the sample, because
it was not collected for the initial villages that were covered during data collection.
Therefore, regression analysis results on this data were limited to the villages in which
those factors were collected.
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Data collected for this project was recorded on paper forms by MBCT teams.
This data was transferred to WHO Anthro for malnutrition indicator analysis and the Z-
Scores were later transferred to an Excel master document for further analysis. A
consistent process was utilized to handle the data, however there is a possibility of human
error during the data entry or transfer.
MBCT teams were fully trained to collect growth data in the field, but there is risk
of user-error during data collection as well.
MBCT team members relied on senior village members such as AWWs, AWHs,
and Sarpanches (village leader) to locate and record growth data for individual children
in each village. Team members relied on local information to determine coverage of
children in each village. For this reason, some children may have been missed during data
collection. Overall, it is estimated that 80% of under-five children in the nine villages
were covered.
CONCLUSION & WAY FORWARD
This study illustrates the magnitude of child malnutrition and offers some insight
into some of the factors that may influence its prevalence in Punjab. The data and results
from this study are crucial to help understand the current problem in these select nine
villages and other villages in this region.
Malnutrition rates in this region are incredibly high, despite the abundance of
food produced here. The overarching malnutrition problem is incredibly complex and is
influenced by a multitude of environmental, socio-economic, cultural and other factors. It
would be impossible to blame one single factor as the underlying cause of malnutrition.
However, as argued countless times before this study was conducted, poverty is the most
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important disparity influencing malnutrition (Malhotra, 2012). Scheduled caste and
backwards class individuals are considered to be a lower social tier compared to general
population. For this reason, scheduled caste and backwards class families tend to be from
a lower socio-economic background and many families fall below the poverty line
(Scheduled caste population in Punjab, n.d.). The literacy rate among the scheduled caste
population is 64.8% compared to the rate of 75.8% for the total population in Punjab.
Results from this study indicate that children belonging to scheduled caste or backwards
class were more likely to be stunted or underweight. These results suggest that socio-
economic status is a considerable determinant of malnutrition among under-five children
in this study.
As stated before, child malnutrition is a major compounding factor that can lead
to physical and cognitive impairment, compromised immune system, decreased school
attendance and performance, lower future income, and even early death. Improving
nutrition in this region is a complex and vast task. The results from this study and the raw
data collected by MBCT teams are excellent resources for the improvement of child
malnutrition in the nine selected villages as part of the BP-CCDP malnutrition
intervention project. This data acts as a baseline for future growth monitoring visits in
these villages. Bi-annual growth monitoring data should be collected in these villages and
the results compared to the baseline data analyzed in this study to check for improvement
of child malnutrition conditions in this population. BP-CCDP partners should counsel
parents and families about the importance of growth monitoring and the meaning of each
nutrition indicator can take place during future growth monitoring visits. BP-CCDP
Parents should be able to properly measure their children and plot the information on a
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growth chart to visualize the growth progress of their children. To do this, parents must
be engaged and concerned with their child’s nutrition. Materials should be provided (by
an NGO or otherwise) to assist parents with growth monitoring and also provide them
with child health and nutrition information. Also, as stated before, Anganwadi Workers
and Anganwadi Helpers should assist teams with growth monitoring and also fully
understand its importance for their own practice at the AWC.
Inclusion of AWWs, AWHs, and other senior village members in future growth
monitoring and other nutrition field visits is crucial to build rapport with members of
each village. NGOs and academic partners can be seen as outsiders while conducting
field visits, so this rapport will allow parents to better understand the implications of
child nutrition and growth monitoring. In addition, this project requires proper
collaboration and communication between NGOs, academic partners, and district
officials. A number of players have influence over intervention programs and their
effectiveness. In order to have a successful outcome, the ongoing project must allow all
players to operate smoothly with a like-minded goal.
Improvement of AWC infrastructure, resources, and services will serve as a
catalyst to benefit the nutrition outcomes of children attending. This includes improving
sanitation and hygiene, nutrition resources. Focus should be placed on monitoring AWC
children and counseling families about nutrition and its importance to child health.
Children attending AWCS are often from scheduled caste and backwards class families
with low socio-economic status.
Contact sessions, community meetings, and other outreach must be carried out to
promote the awareness and understanding of malnutrition in these 9 villages. These
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outreach events must involve direct contact with families. Involvement and
empowerment of community members will help create a sustainable process to address
malnutrition in these villages. In order to improve child malnutrition, residents must first
become aware of the issue and understand its consequences. They must then have a
desire to create change and improve nutrition conditions for themselves and their
families. Extrinsic intervention from NGOs and academic partners will not be sufficient
to create sustainable improvement to child nutrition and health in this area. Instead, this
intervention should act as support for a community-based approach that enables residents
of these villages to be invested in their own nutrition.
Children falling below two standard deviations from the mean for any
malnutrition indicator should receive follow-up treatment and a referral to a medical
professional to rule out or begin treatment of any nutrition-related health problems.
Parents of any child falling below one standard deviation from the mean for any
malnutrition indicator should receive direct nutrition counseling and education. This data
will allow BP-CCDP partners to carry out individual follow-up with children that fall into
these categories.
The role of the family in child nutrition is a top concern. Counseling and outreach
must emphasize the role of the father in child nutrition. Fathers are often not concerned
with responsibilities such as child nutrition and leave this role to the mother. As the father
is likely the top wage earner, he has direct influence over the types and quantity of food
consumed by the family. Fathers and mothers (and all caretakers) should be fully engaged
and invested in the nutrition of their children to guarantee the best health outcomes for
their families.
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Future research in this area could explore further factors that influence child
malnutrition in this region. This could involve an in-depth analysis of the impact of
socio-economic status on child nutrition. Also, a qualitative investigation of nutrition
literacy and practices of families could provide excellent insight into how the family role
and cultural practices influence nutrition outcomes. Other studies could compare rates of
malnutrition and the prevalence of related health issues among children. Also, follow-up
growth monitoring studies should be carried out in order to track the nutritional status of
the under-five population included in this study.
REFERENCES
Fatehgarh Sahib District : census 2011 data. (n.d.). Retrieved July 9, 2016, from Census
2011 website: http://www.census2011.co.in/census/district/593-fatehgarh-
sahib.html
Gragnolati, M. (2006). India’s Undernourished Children : A Call for Reform and Action.
In India’'s Undernourished Children. Washington: Wiley Subscription Services,
Inc., A Wiley Company. doi:10.1002/yd.20038
Gragnolati, M., Bredenkamp, C., & Gupta, M. (2006). ICDS and persistent
undernutrition: Strategies to enhance the impact. Economic and Political ...,
1193–1201. Retrieved from
http://www.bpni.org/Article/ICDS_and_persistent_undernutrition.pdf
Gulati, J. K. (2010). Child Malnutrition: Trends and Issues. Anthropologist, 12(2), 131–
23
140. Retrieved from http://www.krepublishers.com/02-Journals/T-Anth/Anth-12-
0-000-10- Web/Anth-12-2-000-10-Abst-PDF/Anth-12-2-131-10-513-Gulati-J-
K/Anth-12-2-131-10- 513-Gulati-J-K-Tt.pdf
International Institute for Population Sciences (IIPS) and Macro International. (2007).
National family health survey (NFHS-3) (Vol. 1). doi:10.1016/S0140-6736(05)17806
Lokshin, M., Gupta, M. D., Gragnolati, M., & Ivaschenko, O. (2005). Improving child
nutrition? The Integrated Child Development Services in India". Development
and Change, 613-640.
Malhotra, N. (2012). Inadequate feeding of infant and young children in India: Lack of
nutritional information or food affordability? Public Health Nutrition, 16(10), 1–
9. doi:10.1017/S1368980012004065
Miller, J., Roberts, E., Robinson, W., & Verde, S. (2015). Addressing child malnutrition
in rural India: Anganwadi Centers and the Bassi Pathana Community
Collaborative Development Project (Unpublished master's thesis). University of
Utah, Salt Lake City, UT.
Punjab- the leader in agricultural sector, Agriculture Today, 2013. Retrieved from:
http://agropedia.iitk.ac.in/content/punjab-leader-agricultural-sector
Scheduled caste population in Punjab. (n.d.). In Department of welfare of scheduled
castes (SC) and backwards classes (BC). Retrieved July 7, 2016, from
http://welfarepunjab.gov.in/SCpopulation.html
The Ministry of Statistics and Programme Implementation: Government of India. (2012).
Children in India 2012- A Statistical Appraisal, 1–225.
24
World Health Organization. (2014). Nutrition. Retrieved June 25, 2016, from
http://www.who.int/nutrition/en/
World Health Organization. (n.d.-b). Training course and other tools. Retrieved June 23,
2016, from http://www.who.int/childgrowth/training/en/
World Health Organization. Training Course on Child Growth Assessment. Geneva,
WHO, 2008.
World Health Organization. (n.d.-a). The WHO Child Growth Standards. Retrieved June
26, 2016, from http://www.who.int/childgrowth/en/
Why Invest in Nutrition ? (n.d.), 21–41. Retrieved from: http://siteresources.worldbank.or
g/SOUTHASIAEXT/Resources/223546-1147272668285/undernourished_executi
ve_summary.pdf
2015 world hunger and poverty facts and statistics. (n.d.). Retrieved June 28, 2016, from
http://www.worldhunger.org/2015-world-hunger-and-poverty-facts-and-
statistics/#children1
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Name of Candidate: Daniel Aaron Eike
Birth date: May 14, 1994
Birth place: Everett, Washington
Address: 11567 Jordan Farms Rd. South Jordan, UT 84095