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Lantus Solostar (A-S Medication Solutions): FDA Package Insert, Page 6
Page 6: A-S Medication Solutions: LANTUS is indicated to improve glycemic control in adult and pediatric patients with diabetes mellitus. LANTUS is a...
Lantus Solostar (Page 6 of 8)
14.4 Additional Clinical Studies in Adults with Diabetes Type 1 and Type 2
Different Timing of LANTUS Administration in Diabetes Type 1 and Diabetes Type 2
The safety and efficacy of once daily LANTUS administered either at pre-breakfast, pre-dinner, or at bedtime were evaluated in a randomized, controlled clinical study in adult patients with type 1 diabetes (Study H, n=378). Patients were also treated with insulin lispro at mealtime. The average age was 41 years. All patients were White (100%) and 54% were male. The mean BMI was approximately 25.3 kg/m 2. The mean duration of diabetes was 17 years.
LANTUS administered at pre-breakfast or at pre-dinner (both once daily) resulted in similar reductions in HbA1c compared to that with bedtime administration (see Table 12). In these patients, data are available from 8-point home glucose monitoring. The maximum mean blood glucose was observed just prior to LANTUS injection regardless of time of administration. In this study, 5% of patients in the LANTUS-breakfast group discontinued treatment because of lack of efficacy. No patients in the other two groups (pre-dinner, bedtime) discontinued for this reason.
The safety and efficacy of once daily LANTUS administered pre-breakfast or at bedtime were also evaluated in a randomized, active-controlled clinical study (Study I, n=697) in patients with type 2 diabetes not adequately controlled on oral antidiabetic therapy. All patients in this study also received glimepiride 3 mg daily. The average age was 61 years. The majority of patients were White (97%) and 54% were male. The mean BMI was approximately 28.7 kg/m 2. The mean duration of diabetes was 10 years. LANTUS given before breakfast was at least as effective in lowering HbA1c as LANTUS given at bedtime or NPH insulin given at bedtime (see Table 12).
Table 12: Study of Different Times of Once Daily LANTUS Dosing in Type 1 (Study H) and Type 2 (Study I) Diabetes Mellitus Treatment durationTreatment in combination with Study H24 weeksInsulin lispro Study I24 weeksGlimepiride LANTUSBefore Breakfast LANTUSBefore Dinner LANTUSBedtime LANTUSBefore Breakfast LANTUSBedtime NPHBedtime * Intent-to-treat † Not applicable Number of subjects treated * 112 124 128 234 226 227 HbA1c Baseline mean 7.6 7.5 7.6 9.1 9.1 9.1 Mean change from baseline -0.2 -0.1 0.0 -1.3 -1.0 -0.8 Basal insulin dose (Units) Baseline mean 22 23 21 19 20 19 Mean change from baseline 5 2 2 11 18 18 Total insulin dose (Units) – – – NA † NA † NA † Baseline mean 52 52 49 – – – Mean change from baseline 2 3 2 – – – Body weight (kg) Baseline mean 77.1 77.8 74.5 80.7 82 81 Mean change from baseline 0.7 0.1 0.4 3.9 3.7 2.9
Progression of Retinopathy Evaluation in Adults with Diabetes Type 1 and Diabetes Type 2
LANTUS was compared to NPH insulin in a 5-year randomized clinical study that evaluated the progression of retinopathy as assessed with fundus photography using a grading protocol derived from the Early Treatment Diabetic Retinopathy Scale (ETDRS). Patients had type 2 diabetes (mean age 55 years) with no (86%) or mild (14%) retinopathy at baseline. Mean baseline HbA1c was 8.4%. The primary outcome was progression by 3 or more steps on the ETDRS scale at study endpoint. Patients with prespecified postbaseline eye procedures (pan-retinal photocoagulation for proliferative or severe nonproliferative diabetic retinopathy, local photocoagulation for new vessels, and vitrectomy for diabetic retinopathy) were also considered as 3-step progressors regardless of actual change in ETDRS score from baseline. Retinopathy graders were blinded to treatment group assignment.
The results for the primary endpoint are shown in Table 13 for both the per-protocol and intent-to-treat populations, and indicate similarity of LANTUS to NPH in the progression of diabetic retinopathy as assessed by this outcome. In this study, the numbers of retinal adverse events reported for LANTUS and NPH insulin treatment groups were similar for adult patients with type 1 and type 2 diabetes.
Table 13: Number (%) of Patients with 3 or More Step Progression on ETDRS Scale at Endpoint LANTUS (%) NPH (%) Difference * , † (SE) 95% CI for difference * Difference = LANTUS – NPH † Using a generalized linear model (SAS GENMOD) with treatment and baseline HbA1c strata (cutoff 9.0%) as the classified independent variables, and with binomial distribution and identity link function Per-protocol 53/374 (14.2%) 57/363 (15.7%) -2.0% (2.6%) -7.0% to +3.1% Intent-to-Treat 63/502 (12.5%) 71/487 (14.6%) -2.1% (2.1%) -6.3% to +2.1%
The ORIGIN Study of Major Cardiovascular Outcomes in Patients with Established CV Disease or CV Risk Factors
The Outcome Reduction with Initial Glargine Intervention study (i.e., ORIGIN) was an open-label, randomized, 2-by-2, factorial design study. One intervention in ORIGIN compared the effect of LANTUS to standard care on major adverse cardiovascular (CV) outcomes in 12,537 adults ≥ 50 years of age with:
Abnormal glucose levels (i.e., impaired fasting glucose [IFG] and/or impaired glucose tolerance [IGT]) or early type 2 diabetes mellitus and
Established CV disease or CV risk factors at baseline.
The first coprimary endpoint was the time to first occurrence of a major adverse CV event defined as the composite of CV death, nonfatal myocardial infarction, and nonfatal stroke.
The second coprimary endpoint was the time to the first occurrence of CV death or nonfatal myocardial infarction or nonfatal stroke or revascularization procedure or hospitalization for heart failure.
Patients were randomized to either LANTUS (N=6,264) titrated to a goal fasting plasma glucose of ≤95 mg/dL or to standard care (N=6,273). Anthropometric and disease characteristics were balanced at baseline. The mean age was 64 years and 8% of patients were 75 years of age or older. The majority of patients were male (65%). Fifty nine percent were Caucasian, 25% were Latin, 10% were Asian and 3% were Black. The median baseline BMI was 29 kg/m 2. Approximately 12% of patients had abnormal glucose levels (IGT and/or IFG) at baseline and 88% had type 2 diabetes. For patients with type 2 diabetes, 59% were treated with a single oral antidiabetic drug, 23% had known diabetes but were on no antidiabetic drug and 6% were newly diagnosed during the screening procedure. The mean HbA1c (SD) at baseline was 6.5% (1.0). Fifty-nine percent of the patients had had a prior CV event and 39% had documented coronary artery disease or other CV risk factors.
Vital status was available for 99.9% and 99.8% of patients randomized to LANTUS and standard care respectively at end of study. The median duration of follow-up was 6.2 years (range: 8 days to 7.9 years). The mean HbA1c (SD) at the end of the study was 6.5% (1.1) and 6.8% (1.2) in the LANTUS and standard care group respectively. The median dose of LANTUS at end of study was 0.45 U/kg. Eighty-one percent of patients randomized to LANTUS were using LANTUS at end of the study. The mean change in body weight from baseline to the last treatment visit was 2.2 kg greater in the LANTUS group than in the standard care group.
Overall, the incidence of major adverse CV outcomes was similar between groups (seeTable 14). All-cause mortality was also similar between groups.
Table 14: Cardiovascular Outcomes in ORIGIN in Patients with Established CV Disease or CV Risk Factors – Time to First Event Analyses LANTUSN=6,264 Standard CareN=6,273 LANTUS vs Standard Care n(Events per 100 PY) n(Events per 100 PY) Hazard Ratio (95% CI) Coprimary endpoints CV death, nonfatal myocardial infarction, or nonfatal stroke 1041(2.9) 1013(2.9) 1.02 (0.94, 1.11) CV death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure or revascularization procedure 1792(5.5) 1727(5.3) 1.04 (0.97, 1.11) Components of coprimary endpoints CV death 580 576 1.00 (0.89, 1.13) Myocardial Infarction (fatal or nonfatal) 336 326 1.03 (0.88, 1.19) Stroke (fatal or nonfatal) 331 319 1.03 (0.89, 1.21) Revascularizations 908 860 1.06 (0.96, 1.16) Hospitalization for heart failure 310 343 0.90 (0.77, 1.05)
In the ORIGIN study, the overall incidence of cancer (all types combined) or death from cancer (Table 15) was similar between treatment groups.
Table 15: Cancer Outcomes in ORIGIN – Time to First Event Analyses LANTUSN=6,264 Standard CareN=6.273 LANTUS vs Standard Care n(Events per 100 PY) n(Events per 100 PY) Hazard Ratio (95% CI) Cancer endpoints Any cancer event (new or recurrent) 559(1.56) 561(1.56) 0.99 (0.88, 1.11) New cancer events 524(1.46) 535(1.49) 0.96 (0.85, 1.09) Death due to Cancer 189(0.51) 201(0.54) 0.94 (0.77, 1.15)
| https://medlibrary.org/lib/rx/meds/lantus-solostar-1/page/6/ |
(PDF) Nutritional status of children in India: Household socio-economic condition as the contextual determinant
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Nutritional status of children in India: Household socio-economic condition as the contextual determinant
August 2010
International Journal for Equity in Health 9(1):19
DOI: 10.1186/1475-9276-9-19
License
CC BY 2.0
Authors:
Md. Hafizur Rahman
Abstract and Figures
Despite recent achievement in economic progress in India, the fruit of development has failed to secure a better nutritional status among all children of the country. Growing evidence suggest there exists a socio-economic gradient of childhood malnutrition in India. The present paper is an attempt to measure the extent of socio-economic inequality in chronic childhood malnutrition across major states of India and to realize the role of household socio-economic status (SES) as the contextual determinant of nutritional status of children.
Using National Family Health Survey-3 data, an attempt is made to estimate socio-economic inequality in childhood stunting at the state level through Concentration Index (CI). Multi-level models; random-coefficient and random-slope are employed to study the impact of SES on long-term nutritional status among children, keeping in view the hierarchical nature of data.
Across the states, a disproportionate burden of stunting is observed among the children from poor SES, more so in urban areas. The state having lower prevalence of chronic childhood malnutrition shows much higher burden among the poor. Though a negative correlation (r = -0.603, p < .001) is established between Net State Domestic Product (NSDP) and CI values for stunting; the development indicator is not always linearly correlated with intra-state inequality in malnutrition prevalence. Results from multi-level models however show children from highest SES quintile posses 50 percent better nutritional status than those from the poorest quintile.
In spite of the declining trend of chronic childhood malnutrition in India, the concerns remain for its disproportionate burden on the poor. The socio-economic gradient of long-term nutritional status among children needs special focus, more so in the states where chronic malnutrition among children apparently demonstrates a lower prevalence. The paper calls for state specific policies which are designed and implemented on a priority basis, keeping in view the nature of inequality in childhood malnutrition in the country and its differential characteristics across the states.
Prevalence of Malnutrition among children (0-59 months) across fifteen major states of India (NFHS-3) …
Trend in Malnutrition in India among Children (0-35 months) …
Scatter plot showing relationship between NSDP and CI for Stunting, across the states. …
Conceptual Framework …
Trend in Malnutrition in India among Children (0-35 months) …
Figures - uploaded by
Nutritional status of children in India: household
socio-economic condition as the contextual
determinant
Barun Kanjilal
1
, Papiya Guha Mazumdar
2*
, Moumita Mukherjee
2
, M Hafizur Rahman
3
Abstract
Background: Despite recent achievement in economic progress in India, the fruit of development has failed to
secure a better nutritional status among all children of the country. Growing evidence suggest there exis ts a socio-
economic gradient of childhood malnutrition in India. The present paper is an attempt to measure the extent of
socio-economic inequality in chronic childhood malnutrition across major states of India and to realize the role of
household socio-economic status (SES) as the contextual determinant of nutritional status of children.
Methods: Using National Family Health Survey-3 dat a, an attempt is made to estimate socio-economic inequality
in childhood stunting at the state level throug h Concentration Index (CI). Multi-level models; random-coefficient
and random-slope are employed to study the impact of SES on long-term nutrit ional status among children,
keeping in view the hierarchical nature of data.
Main findings: Across the states, a disproportionate burden of stunting is observed among the children from poor
SES, more so in urban areas. The state having lower prevalence of chronic childhoo d malnutrition shows much
higher burden among the poor. Though a negative correlation (r = -0.603, p < .001) is establ ished between Net
State Domestic Product (NSDP) and CI values for stunting; the development indicator is no t always linearly
correlated with intra-state inequality in malnutrition prevale nce. Results from multi-level models however show
children from highest SES quintile posses 50 percent better nutritional status than those from the poorest quintile.
Conclusion: In spite of the declining trend of chronic childhood malnutrition in India, the concerns remain for its
disproportionate burden on the poor. The socio-economic gradi ent of long-term nutritional status among children
needs special focus, more so in the states where chronic malnutrition among children apparently demons trates a
lower prevalence. The paper calls for state specific policies which are designed and implemented on a priority
basis, keeping in view the nature of inequality in childhood malnutrition in the country and its differential
characteristics across the states.
Introduction
Despite recent achievement in economic progress in
India [1], the frui t of development has failed to secu re a
better nutritional stat us of children in the country [2-5].
India presents a typical scen ario of South-Asia, fitting
the adage of ‘ Asian Enigma ’ [6]; where progress in child-
hood malnutrit ion seems to have sunken into an appa r-
ent undernutrition trap, lagging far behind the other
Asian countries characterized by similar levels of eco-
nomic development [7-10].
Exhibiting a sluggish declining trend over the past
decade and a half, the recent estimate from the National
Family Health Survey -3 (NFHS-3)- the unique source
for tracking the status of ch ild malnutrition in India
[11]- indicates about 46 percent of the children under
5 years of age are moderately to severely underweight
(thin for age), 38 percent ar e moderately to severely
stunted (short for age), and approximately 19 percent
are moderatel y to severely wasted (th in for height) [12].
The decline in prevalence however becomes unimpres-
sive with the average levels marked by wide inequality
* Correspondence: [email protected]
2
Future Health Systems India, Institue of Health Management Research,
Kolkata, India
Full list of author information is available at the end of the article
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
http://www.equityhealthj.com/content/9/1/19
© 2010 Kanjila l et al; licensee BioMed Ce ntral Ltd. This is an Open Ac cess article distribu ted under the terms of the Cr eative Commons
Attribution L icense (http://creati vecommons.org/lice nses/by/2.0), which perm its unrestricted use, di stribution, and repro duction in
any medium, p rovided the original wor k is properly cited.
in childhood malnutrition across the states and various
socio-economic groups [2,3,13,14]. Growing evidence
suggests [13] that in India the gap in prevalence of
underweight children among the rich and the poor
households is inc reasing over the years with wide re gio-
nal differentials. From this specific context, the paper is
an attempt to study the specific interplay between
household socio-economic conditions and the nutri-
tional status for In dian children (particular in respe ct to
stunting, whi ch is an indicator for long-t erm nutritional
status), considering controls for various other estab-
lished predi ctors of the chronic child m alnutrition lying
at individual, maternal, household and community
characteristics.
Socio-economic inequality in childhood malnutrition:
Contextualizing the extent in India
Socio-economic differences in morbidity and mortality
rates across the wo rld have received its due attent ion in
the recent years [15-17 ]. Such differentials in health sta-
tus in-fact ar e found pervasive across na tions cross-cut-
ting stages of development [18-29]. Studies have
identified poverty as the chief determinant of malnutri-
tion in develop ing countries that perpet uates into inter-
generational transfer of poor nutritional status among
children and prevents social improvement and equity
[30,31]. Nut ritional status of und er-five children in par -
ticular is often co nsidered as one of the most important
indicator of a household ’ s living standard and also an
important determi nant of child survival [32]. The deter-
ministic studies in India while exploring the impact of
covariates o n the degree of childhood ma lnutrition sug-
gests an important nexus shared with household socio-
economic status [2,25,33-41]. The two-way causality of
poverty and under nutrition seems to pose a ver y signif-
icant pretext for malnutrition in India like other devel-
oping nations, where poverty and economic insecurity,
coupled by constrained access to economic resources
permeate malnourishment am ong the children [42-46].
Thus, economi c inequality constitutes t he focal point of
discussion while studying malnut rition and deserves sui-
table analytical treatment to examine its interplay with
other dimensions of malnutrition and to prioritize
appropriate programme intervention. Such attempt to
the best of our knowledge is still awaited, using recent
nationwide survey data in India.
In this backdrop, the p aper attempts to shed lights on
two specific objectives: 1) to find out the extent of
socio-economic inequality in chronic childhood malnu-
trition, across the major states of India, separated for
urban and rural locations, and 2) to understand the con-
ditional impact of househol d socio-economic condition
on nutritional status of children per se, controlling for
various other important cov ariates. The conceptual
framework (F igure 1) of the study is based o n review of
existing literature on the topic and adapts from various
existing framework on determinants of childhood mal-
nutrition in general [47], adding a special emphasis on
household soci o-economic status as the key exp lanatory
variable.
Methodology
Data
The paper uses the National Family Health Survey
(NFHS) 3rd round data (2005-06) for study and analysis.
Similar to NFHS-1 and NFHS-2, NFHS-3 was designed
to provide estimates of important maternal and child
health indicators including nutritional status for young
children (under five years for NFHS-3), following stan-
dard anthropometric components. The survey was con-
ducted following stratified s ampling technique, details
on the sampling procedure can be found at IIPS, 2007
[12]. Of the total 43,737 children for whom NFHS-3
provides height-for-age z-score (HAZ ), a subset of
24,896 child ren was considered; th ose were alive, hailed
from fifteen major states and had the HAZ score within
the range of -5 to +5 standard deviation from the
WHO-NCHS reference population.
We have also used secondary data from Handbook of
Indian Economy 2004-05 [48], for the statistics on per
capita Net State Domestic Product (NSDP), for the fif-
teen major states.
Methods used
The study uses two analytical methods for studying the
objectives. The first objective is catered through the
measurement of co ncentration index and underst anding
its linkage with the state level indicator of economic
development. While for study ing the second objective,
multi-level regression model have been employed.
Further details on methodology are presented below.
Concentration Index
The widely used standard tool that examines the magni-
tude of socio-economic inequality in any health out-
come, i.e. Concentration Index (CI) [49] is employed to
study the extent of inequity in chronic child malnutri-
tion across the states of India. The tool has been univer-
s a l l yu s e db yt h ee c o n o m i s t st om e a s u r et h ed e g r e eo f
inequality in various health system indicators, such as
health outcome, health care utilization and financing.
The value of CI ranges between -1 to +1, hence, if there
is no socio-ec onomic differential th e value returns zero.
A negative value implies that the relevant health variable
is concentrated among the poor or disadvantaged people
while the opposite is true for its positive values, when
poorest are assigned the lowest value of the wealth-
index. A zero CI implies a s tate of horizontal equity
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
http://www.equityhealthj.com/content/9/1/19
Page 2 of 13
which is defined as equal treatm ent for equal needs (For
further readings on application of CI in malnutrition
refer to Wagstaff & Watanab e 2000) [50]. CI values cal-
culated for stunting help us fi nd the possible concentra-
tion among rich and poor children below five years of
age during NFHS-3.
Multi-level regression
Due to the stratified nature of data in NFHS [12], the
children are naturally nested into mothers, mothers are
nested into households, hou seholds are into Primary
Sampling Units (PSUs) and PSUs into states. Hence
keeping in view this hierarchically clustered nature, the
paper uses multi-level regr ession model to estimate
parameter for nutritional status among children to avoid
the likely under-estimatio n of parameters from a single
level model [51]. Since here siblings are expected to
share certain common char acteristics of the mother and
the household (mother ’ s education and household eco-
nomic status for e.g.) and children from a particular
community or village have in common community level
factors such as availability of health facilities and out-
comes, it can be reasonably asserted that unobserved
heterogeneity in the outcome variable is also correlated
at the cluster levels [52-54]. This amounts to an estima-
tion problem employing conve ntional OLS estimators,
which gives efficient estimates only when the commu-
nity level covariates and the household level covariates
are uncorrelated with the individual and maternal effects
covariates.
Researchers have ad opted fixed effects models to esti-
mate nutrition models and control for unobservable
variables at the cluster level, which leads to the diffi-
culty that if the fixed effect is differenced away, then
the effect of those variables that do not vary in a cluster
will be lost in the estimation process [54]. Allowing the
contextual effects in our analysis of the impact of
household socio-economic status on child undernutri-
tion, we adopt an al ternative approach of using multi le-
vel models.
Individual Characteristics of Child
• Age
• Sex
• Birth Order
• Size at birth
• Child feeding practice
Mother Specific
• Education
• Occupation
• Nutritional Status
(BMI)
• Anemia status
• Healthcare related
autonomy
Household Characteristics
Household Specific
• Ethnicity
• Socio-economic status
Health Service Uptake
• Status of
Institutional
Delivery
• Status of
immunization
Place of Residence
• Urban
• Rural
State
Distant
Intermediate
Proximate
Chronic Malnutrition among Children
Figure 1 Conceptual Framework
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
Broadly, we test the two types of multilevel models
following the practice in co ntemporary literature; the
variance comp onents (or random interce pt) models and
the random coefficients (or random slopes) models. As
in above, STATA routines for hie rarchical linear models
using maximum likelihood estimators for linear mixed
models were used for both model forms.
The variance-components model correct for the
problem of correlated observations in a cluster, by
introducing a random effect at each cluster. In other
words, subjects within the same cluster are allowed to
have a shared random intercept. We consider two clus-
ters, i.e., community and household, since in most of
the cases NFHS provides information on children of
one mother chosen from a particular household. Thus,
we have,
zx
ij ij i ij
=′ + +
where z
ij
is the HAZ score for the child(re n) from the
j
th
household in the i
th
community. b is a vector of
regression coefficients corresponding to the effects of
fixed covariates x
ij
, which are the observed characteris-
tics of the child, the household and the community.
Where, ‘ i ’ is a random community effect denoting the
deviation of community i ’ s mean z-score from the grand
mean, ‘ j ’ is a random household effect that represents
deviation of household ij ’ s mean z-score from the i
th
community mean. The error terms δ
i
and μ
ij
are
assumed to be normal ly distributed with zero mean and
variances s
2 c
and s
2, h
respectively. As per our argu-
ments above, these terms are non-zero and estimated by
variance components models. To the extent that the
greater homogeneity of within-cluster observations is
not explained by the observed covariates, s
2 c
,a n d s
2, h
will be larger [55].
To evaluate the appropriateness of the multilevel
models, we test whether the variances of the random
part are different from zero over households and com-
munities. The resulting estimates from the models can
be used to assess the Intra Class Correlation (ICC) i.e.,
the extent to which child undernutrition is correlated
within households and communities, before and after
we have accounted for the obs erved effects of covariates
. A significantly different ICC from zero suggests
appropriateness of random effect models [54]. The ICC
coefficient describes the pro portion of variation that is
attributable to the higher level source of variation. The
correlations between the anthropometric outcomes of
children in the same commu nity and in the same family
are respectively:
cc c h
22 2
=+ /( )
Following this, the total variability in the individual
HAZ scores can be divided into its two components;
variance in children ’ s nutritional status among house-
holds within communities, and variance among commu-
nities. By including covariates at each le vel, the variance
components models allow to examine the extent to
which observed differences in the anthropometric scores
are attributable to factors operating at each level. Thus,
the variance components model described above intro-
duces a random intercept at each level or cluster assum-
ing a constant effect of each of the covariates (on the
If additionally, we consider the effect of certain covari-
ates to vary across the clusters (for e.g, differential
impact of household socio-economic status or mother ’ s
education across households and/or communities), we
need to introduce a random effect for the slopes as well,
leading to a random coefficients model. Under these
assumptions, the covariance of the disturbances, and
therefore the total variance at each level depend on the
values of the predictors [55].
As mentioned earlier, a subse t of 24,896 children have
been considered for the analysis from the hierarchically
clustered NFHS-3 da taset. Hence, our multilevel model s
are based on observations on 24,896 children from
18,078 households distributed in 2,440 communities/
clusters (PS Us). Inclusion of separ ate levels for children
and mothers were considered not necessary since these
were almost unitary to the number of households.
The analysis is presented in the form of five models,
apart from the conventional OLS model wit hout consid-
ering the cluster random effects, primaril y as a compari-
son: Model_Null is the null model, where the HAZ z
scores is the dependant variable with no covariates
and richest household asset quintile, other covariates are
introduced in a phased manner. Such as, Model_Kids
introduces ch ild specific predictors (be ing purely indivi-
dual attributes); Model_Moms introduces the mother-
specific cov ariates. Model_Full is t he full model with all
the model covariates at respective levels. These models
are three-level random intercept models with the two
clusters: community, and households. In Model_Ran-
dom_Slope, we introduce a random coefficient for
socio-economic status at the household level. We settled
for the random coefficient in the form of wealth quintile
dummies. The covariates included as controls in our
analytical models, with the primary aim of isolating the
effect of income or socioeconomic status (SES) on
chronic child undernutriti on are described below. In the
multilevel framework most of these variables can be
classified as individual-specific, household-specific or
community-specific covariates.
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
Variables in the regression model
As mentioned earlier, the paper uses height for age
(stunting) as the key outcome var iable, which is an indi-
cator of chronic nutritional status capable of reflecting
long-term deprivation of food [56] following the estab-
lished practice o f anthropometric measures of malnu tri-
tion. The measure is expressed in the form of z-scores
standard deviation (SD) from the median of the 2006
WHO International Reference Populati on. This continu-
ous standard deviation of HAZ score is capable of pro-
viding expected change in the value of the response
variable due to one unit change in the regressors regard-
less of whether a child is stunted or not [57]. Hence, the
present approach differs from the usual practice of
employing a dichotomous variable on probability of a
child being chronically malnourished (0 = otherwise, 1 =
stunted). Since here the attempt is not to model prob-
ability of stunting, but instead using a deterministic
model the paper attempts to find out the influencing
role of household asset o n childhood nutrition in fifteen
major states of India.
Explanatory variables
Asset quintile as the proxy for household socio-economic
status
Following the standard approach of assessing economic
status of the household [28], the paper uses household
asset index provided by the NFHS-3. The survey pro-
vides the household wealth index based on thirty-three
household characteristics and ownership of household
assets using a Principal Component analysis (for details
on the methodology refer to IIPS 2007) [12]. In the
paper we divided the household index into quintiles
based on the asset scores adj usted by sample weights.
Separate quintiles were developed for rural and urban
areas of each state by using state-specific sample
weights, to avoid questions on comparability [28].
Other explanatory variables used as controls
Apart from the above mentioned asset index, other
determinants of childhood malnutrition are chosen
based on approaches in literature and presented in the
conceptual framework (Figure 1) of the study [47]. We
consider certain indi vidual characteristics of child as the
proximate covar iate of chronic malnutrit ion. These pre-
disposing factors include child ’ s characteristics similar to
other studies, such as, child ’ s age in months, quadratic
form of age to elimi nate the effect on z-score [38 ] since
there exists non-linearity between age and HAZ, sex of
the child, birth order, size of child at birth (as a proxy
of birth weight) [57], incidence of recent illness, com-
plete doses of immunization and recommended feeding
practice; denoted by exclusive breast feeding for infants
below six months of age, introduction of nutritional
supplements along with or without breastmilk after six
months. In view of information provided by NFHS on
child feeding, we considered a child is introduced to
supplementary food, where ver the child was reported
having given any food-stuff irrespective of its breast
feeding status, a day preceding the survey date.
The controls on mother ’ s characteristics includes;
years of in terms of education, body mass index (BMI),
mothers status of anemia, autonomy for seeking medical
help for self [58,59] and place of birth for the child of
interest. On the household level, except for asset quin-
tile, controls was incl uded for household ethnicity, since
a large number of earlier st udies found a significant
linkage between scheduled tribe/scheduled caste house-
holds and childhood undernutrition [2,14]. Community
characteristic is regarded as the distant covariate of
child malnutrition in t he model and include rural-urban
Figure 2 Trend in Malnutrition in India among Children (0-35 months)
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
http://www.equityhealthj.com/content/9/1/19
place of residence and state. Keeping in mind the large
scale variation in childhood mortality and morbidity, the
states are consider ed for each of the models as contro ls,
or as fixed effects in multilevel models.
Results
Extent of socio-economic inequity in childhood stunting
As mentioned earlier, the successive waves of NFHS in
India indicates a declining trend in the prevalence of
child malnutrition among children aged below three
years (Figure 2).
Except for wasting, across the two different established
anthropometric measures of malnutrition; stunting and
underweight, a consistent decline is evident during
1992-2005 period (Figure 2). Overall, NFHS-3 reveals a
different ial scenario of chil d malnutrition acros s the fif-
teen major states of India (Table 1).
To describe further, the state of Kerala showed the low-
est prevalence of stunting among children (25 percent)
across all the major states, where the rural-urban differ-
ential is virt ually nonexistent. Whereas the opposit e side
of the spectrum, more than half the children below five
years were stunted in Uttar P radesh (57 percen t), Bihar
(56 percent), Gujarat (52 percent) and Madhya Pradesh
(50 percent) (Table 1). The rural-urban differentials are
also considerably high in these states, along with West
Bengal; which showed the highest (19 percent) d ifferen-
tial between rural-urba n prevalence of child malnutrition
which is unfavorable for rural areas, during NFHS-3.
Overall, all the three indicators of malnourishment are
found highly correlated with each other and hence it
was worthwhile to explore their association with the
incidence of poverty in the s tates, following the estab-
lished line of argument. It c an be said that the optimal
growth of the ch ildren (having standard height for thei r
age and weight for their height) have been strongly
associated with economic status of the population.
Table 1 Prevalence of Malnutrition among children (0-59 months) across fifteen major states of India (NFHS-3)
States/Country Underweight Stunting Wasting
Rural Urban Total Rural Urban Total Rural Urban Total
Haryana 41.3 34.6 39.6 48.1 38.3 45.7 19.7 17.3 19.1
Punjab 26.8 21.4 24.9 37.5 35.1 36.7 9.2 9.2 9.2
Rajasthan 42.5 30.1 39.9 46.3 33.9 43.7 20.3 20.8 20.4
Uttar Pradesh 44.1 34.8 42.4 58.4 50.1 56.8 15.2 12.9 14.8
Bihar 57 47.8 55.9 56.5 48.4 55.6 27.4 25.2 27.1
Orissa 42.3 29.7 40.7 46.5 34.9 45.0 20.5 13.4 19.5
West Bengal 42.2 24.7 38.7 48.4 29.3 44.6 17.8 13.5 16.9
Assam 37.7 26.1 36.4 47.8 35.6 46.5 13.6 14.2 13.7
Gujarat 47.9 39.2 44.6 54.8 46.6 51.7 19.9 16.7 18.7
Andhra Pradesh 34.8 28 32.5 45.8 36.7 42.7 13 10.7 12.2
Karnataka 41.1 30.7 37.6 47.7 36 43.7 18.2 16.5 17.6
Kerala 26.4 15.4 22.9 25.6 22.2 24.5 18.2 10.9 15.9
Tamilnadu 32.1 27.1 29.8 31.3 30.5 30.9 22.6 21.6 22.2
All 15 states 44.1 32.2 41.1 49.7 39.2 47.04 19.9 16.6 19.04
Source: Authors ’ Calculation from NFHS 3 unit data
Table 2 Concentration Index Values for Stunting across
States and Urban-Rural Locations, India, NFHS-3
States/Country Concentration Index (Stunting)
Rural Urban Total
Haryana -0.118** -0.257** -0.151**
Punjab -0.211** -0.259** -0.212**
Rajasthan -0.069** -0.182** -0.106**
Madhya Pradesh -0.032 -0.133** -0.063**
Bihar -0.082** -0.131** -0.094**
Orissa -0.169** -0.267** -0.183**
West Bengal -0.112** -0.3** -0.168**
Assam -0.101** -0.253** -0.116**
Gujarat -0.087** -0.132** -0.115**
Maharashtra -0.12** -0.167** -0.146**
Andhra Pradesh -0.104** -0.134** -0.14**
Karnataka -0.076** -0.185** -0.127**
Kerala -0.204** -0.061 -0.165**
Tamil Nadu -0.075 -0.196** -0.131**
All 15 states -0.092** -0.177** -0.121**
ALL INDIA -0.098** -0.169** -0.126**
Source: Authors ’ Calculation from NFHS 3 unit data
Significant at ***p < 0.01, ** p < 0.05, and * p < 0.10.
The CI values for chronic malnutrition in respect to
fifteen major st ates and at the country level cons istently
return negative values, reflecting a heavy burden of
malnutrition among the poor in India (Table 2).
The above table (Table 2) confirms the fact that across
children from poorer households share the higher bur-
den of sub-optimal gr owth due to undernourishment. It
needs speci al mention that chroni c malnutrition among
children is more concentrat ed among urban poor com-
paring their count erpart living in rural areas. This trend
is consistent across all thirt een states, except for Bihar
and Kerala; wh ere concentration of stun ting is observed
higher among poor children from rural areas.
It is also seen in a similar vein that aggregate eco-
nomic status of a population is associated with child
nutritional status. CI values for stunting and Net State
Domestic Product (NSDP; considered as the indicator
for economic devel opment for the aggregate level of the
state) share an inverse association (Figure 3), common
for most of the states.
Overall, the negative corre lation established between
CI values for stunting and NSDP per capita stands at
r = -0.603 (p < .001). The scatter plot of NSDP per
capita and CI values for stunted children across the
states (Figure 3) emerges few specific patterns. The
states like, Bihar, Uttar Pradesh, Madhya Pradesh and
Rajasthan exhibit a typical situation where per capita
0.134
(0.074)
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
http://www.equityhealthj.com/content/9/1/19
Page 8 of 13
per-capita NSDP as compared to the national average,
the state exhibits a noticeably higher burden of chronic
malnourishment among the poor.
Role of household socio-economic conditions
determining long-term nutritional status among children
The results shows (Table 3), significant association
between househ old asset quintiles and nutri tional status
of children.
Given the form of the dependent variable in the sub-
sequent models a higher coefficient indicates better
nutritional status among children from better off socio-
economic status quintiles (SES). It shows (Table 3)
nearly 50 percent better nutritional status (0.31 -
(-0.18)) among children from richest SES quintiles, com-
pared to ones those from the poorest quintile.
The variance component mo dels (i.e., Model_kids,
Model_moms and Model_full) and the random slope
model (Table 3) also support such finding. By introdu-
cing covariates at each level, the variance component
models allow to examine the extent to which observed
differences in the HAZ scores are attributed to the fac-
tors operating at each level. With the introduction of
child ’ s individual characteristics in the Mode l_kids along
with the state level fixed effect, the impact of richest &
poorest SES quintiles become much stronger. The result
s h o w so v e r8 0p e r c e n t( 0 . 5 4- (-0.29)) higher incidence
of worse nutritional status among children in the poor-
est quintile, than the ones hailing from richest SES
group. However, such richest-poorest gap decreases
with the phased introduction of covariates related to
mother ’ s characteristics, household ethnicity and place
of residence in the models (Table 3). Finally, similar to
the initial estimate by OLS, the variance component
models and random sl ope model indicates that the chil -
dren with the most favorable SES background enjoy
almost 50 percent better nutritional status than their
counterpart from the poorest SES groups.
The calculated ICC coefficient values presented in
Table 4 differ from zero. This indicates that child nutri-
tion is indeed correlated with households and commu-
nities (PSUs ). The ICC for household lev el shows much
higher correlation than the case of PSUs.
The lower panel of the Table 4 shows how the resi-
dual variance is distributed across PSUs and households.
Estimates f rom model 1(null model) , which contains no
observed covariates, indicate that the variation in
height-for-age has substanti al group level components.
The total variance 0. 548 (combined for PSU and house-
holds estimates), of which 63 percent is attributed to
household level variation i n anthropometric scores.
Consistent wit h this observation on null model var iance
decomposition, other model specifications show similar
variance distribution pattern across state, PSU and
household levels.
Estimation of househo ld random effects (Table 4) indi-
cates that h ousehold heterog eneity is accounte d for only
partially by the covariates in our model (Model_Full &
Model_random_slope). In other words the significantly
different values of s
2
c
and s
2
h
indicates that the
Table 3 Association ( b s) fro m Ordinary Least Squares and Multilevel Linea r Regression Models (Main Effects) between
Child Stunting (Height for Age) and Household Socio-Economic Status, controlling for various other covariates; Fifteen
Major States, India, NFHS-3 (Continued)
Madhya Pradesh 0.025
(0.056)
- 0.062
(0.071)
0.069
(0.067)
0.032
(0.067)
0.034
(0.067)
Gujarat -0.307***
(0.063)
- -0.281***
(0.079)
-0.272***
(0.074)
-0.314***
(0.075)
-0.311***
homogeneity withi n cluster observations is not explai ned
by the observed covariates s pecified in the model. The
intra-household correlation remains as large as 0.242
suggesting that the height outcomes of two children
belonging to the same family are more ho mogenous than
those of two children chosen at random, even after
adjusting for other observed covariates (Model_Full).
household level denotes existence of higher homogeneity
at the household level. These results further imply that
choice of one-level model with the similar data set
might yield underestimation of parameters.
Discussion
Successive waves of NFHS brings to the fore wid espread
under nutrition among the Indian children, however it
shows a declining trend duri ng the inter survey period.
Though, the latest estimates as provided by the NFHS 3,
highlights the cont inuance of high overall levels of child
malnutrition in India. As we find here, prevalence of
states and also across rural and urban areas. It needs
special mention that chronic malnutrition among chil-
dren is more concentrated among urban poor compared
to their counterpart living in rural areas (Table 2) where
inequalitie s are not as great but overall lev els of malnu-
trition are higher. This trend is consistent across all
thirteen states, except for Bihar and Kerala; where con-
centration of stunting is observed higher among poor
children from rural areas.
The intra-stat e inequality in child m alnutrition is stark
as we find through the divergent values of the Concentra-
tion Index highlighting the disproportionate burden
among the poor. The variance component models clearly
show clustering o f observation at community and ho use-
hold levels. In othe r words, for the fifteen major states in
India, children in the households that shared similar
communities do posses similar n utritional status. Intra-
household corr elation is the most substantia l, comparing
intra-PSU correlation. In other words, children from a
cluster or community do not se em to share stronger cor-
relation in terms of their nutritional status. But, at the
household level the observations are not independent. It
implies the fact that children belonging to a particular
household do share certain common characteristics while
growing up. The children who belong to households
from the poorest SES quintile have higher prevalence of
worse nutrit ional status. While, on t he contrary the chil-
dren hailing from richest asset quintile households are
associated with better nutrit ional status. The finding is
supportive of many earlier observations made based on
NFHS data [2]. Such association is consistent across the
different models applied to the researc h (Table 4); recon-
firming better nutritional status among children with
favourable household socio -economic background, even
after controlling for a range of individual, maternal and
community characteristics. T his further emphasizes the
impact of differential available resources to the families
that act as a major determinant of malnutrition. The
finding is supportive of studies conducted even in other
countries [60]. Hence the gradient of household socio-
economic status remains as a crucial determi nant of level
of nutritional achievement among children. Betterment
of such condition thus is exp ected to improve growth of
children likely through better nutritional intake and
reduced morbidity.
Table 4 Random Coefficients, Intra-class correlation and Variance Decomposition estimates from comparative models
Null_model Model_Kids Model_Mom Model_Full Model_Random_Slope
Random Effects
s
2 c
(Community - PSU) 0.202 0.091 0.056 0.055 0.055
(S.E.) (0.014) (0.009) (0.008) (0.008) (0.008)
Proportions of overall (null model) explained by the covariates of
the model (in %)
55.189 72.149 72.850 72.875
s
2 h
(Household) 0.346 0.462 0.436 0.431 0.366
(S.E.) (0.027) (0.025) (0.025) (0.025) (0.030)
Proportions of overall (null model) explained by the covariates of
the model (in %)
-33.578 -26.086 -24.575 -5.683
Residual
2
1.877 1.516 1.514 1.518 1.516
(S.E.) (0.029) (0.024) (0.025) (0.025) (0.025)
Intra-class correlation
r (PSU) 0.083* 0.044* 0.028* 0.027* 0.028*
r (household) 0.226* 0.267* 0.245* 0.242* 0.217*
Variance Decomposition (in %)
PSU 36.9 16.4 11.4 11.3 13.1
Household 63.1 83.6 88.6 88.7 86.9
Significance level: * p<0 5
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
However, at the more macro level i t is seen that abso-
lute levels of malnutrition prevalence across th e states is
not necessarily linearly correlated with the intra-state
inequality in malnutrition prevalence. In other words,
states that records higher prevalence of childhood mal-
nutrition are not always reflective of the disproportion-
ate burden shared by the poorest households.
Mazumdar (fo rthcoming) [61], while exploring the link-
age between povert y and inequality with child malnu tri-
tion in India suggests a po ssible conformation of
malnutrition inequality wi th overall socioeconomic
inequality that exists in the states. We too identify a
similar pattern; though with overall economic develop-
ment measured throug h NSDP is found to be negatively
correlated with the proportion of stunted children in the
state, emphasizing the role of development that pro-
motes equity in better nutritional outcome; the pattern
cannot be generalized.
In states like Punjab and Kerala with better develop-
ment, a typical scenario emerges. Here, higher inequality
in malnutrition pr evalence can be observed at the lower
levels of percentage of stun ted children. On the other
hand, states like Madhya Pradesh, Bihar, Gujarat and
Rajasthan the states wit h less economic develop ment or
at par with the national average, though have consider-
ably high prevalence of malnutrition exhibited lower
values of the concentration index suggesting lower levels
of inequality. It is particularly since a higher average
implies prevalence of malnutrition irrespective of SES
with fewer differentials. Hence a clear gradient of mal-
nutrition inequality, biased against the poor is more pro-
nounced in states where absolute levels of malnutrition
are low. This is largely due to the overall inequality in
household ass et [50,62] among the states, wi th the poor
accounting for a major share of the malnourished
children.
On the other hand, the states with higher levels of
child malnutrition, generally tend to have a uniform dis-
nomic distribution (Mazumdar forthcoming )[ 6 1 ] ,a n d
the poor in states with lower observed levels sharing a
higher disproportionate burden, vis-à-vis the poor in the
former group of states.
The situation in Orissa is however the worst and do es
not confirm to any of the pattern discussed above. Here,
with much lower per-capita NSDP as compared to the
national average, the state exhibits a noticeably higher
burden of chronic malnourishment among the poor.
Hence, perhaps economic development cannot be con-
sidered as the straightforward indicator for removing
overall disparity in various input and outcome indicators
among different income bracket, especially in a country
like India. It is argued that reduction in child malnutri-
tion does not seem to depend so much on economic
growth of a state per se or even on the efforts at redu-
cing income poverty at the state level [3]. Achieving bet-
ter nutritional status among children is found sharing
close nexus with the household socio-economic condi-
tions, efforts to influence households ’ economic status
thus might prove to be b eneficial. Alternatively, one can
only think of successf ul targeted interventions to ensur e
nutritiona l status among chil dren those belong to unfa-
vorable asset bracket.
Nevertheless, this issue of malnutrition and poverty
deserves special treatment incorporating other para-
meters reflecting the possible predictors of overall socio-
economic inequality and its bearing on malnutrition
inequality among the states, as future research in this
area. Attempts can be worthwhile to know the reasons
why the states with better economic development
coupled with noticeable success in arresting the overall
level of chronic child malnutrition, have failed to
remove its disproportion ate prevalence across the socio-
economic classes. It can be said that prevalence of
worse nutritio nal status among children in Indi a cannot
be addressed with utmost success unless, inequality in
prevalence across soci o-economic classes ar e taken care
of. A more state spec ific policy should be designed o n a
priority basis, to arrest such unequal prevalence.
Conclusions
Regional hete rogeneity in malnutrit ion across the major
states and rural-urban locations are observed to be
widespread during NFHS-3. The concerns amplify with
the disproportionate burden of malnutrition among
malnutrition is seen to be at the lower level, but where
they are experiencing better status of economic develop-
ment. Multilevel analyses with introduction of controls
on various covariates continue to indicate the household
SES-undernutrition gradi ent. Hence, an appropriate pol-
icy guideline that focuses on altering the nutritional
intake among the poor children, especially in the states
with apparent lower prevalence of childhood malnutri-
tion is need of the hour. In the high prevalence states
much stronger programme are awaited to reduce the
overall level. More focused programme attention tar-
geted at the poor to enhance the level of nutrition and
behavior al changes, through i nterventions lik e the posi-
tive deviance approach in a state like Orissa should be
further expanded in the near future.
Acknowledgements
The authors acknowledge the scientific support extended by ‘ Future Health
Systems: Innovations for equity ’ (http://www.futurehealthsystems.org) a
research program consortium of researchers from Johns Hopkins University
Bloomberg School of Public Health (JHSPH), USA; Institute of Development
Studies (IDS), UK; Center for Health and Population Research (ICDDR, B),
Bangladesh; Indian Institute of Health Management Research (IIHMR), India;
Kanjilal et al . International Journal for Equity in Health 2010, 9 :19
59. Shroff M, Griffiths P, Adair L, Suchindran C, Bentley M: Maternal autonomy
is inversely related to child stunting in Andhra Pradesh, India. Maternal
and Child Nutrition 2009, 5 :64-74.
60. Beenstock M, Sturdy P: The determinants of infant mortality in regional
India. World Development 1990, 18(3) :443-453.
61. Mazumdar S: Determinants of Inequality in Child Manutrition in India:
The Poverty-Undernutrition Linkage. Asian Population Studies 2010.
62. Van De Poel E, Hosseinpoor AR, Speybroeck N, van Ourti T, Vega J:
Socioeconomic Inequality in Malnutrition in Developing Countries.
Bulletin of the World Health Organization 2008, 86 :282-291.
doi:10.1186/1475-9276-9-19
Cite this article as: Kanjilal et al .: Nutritional status of children in India:
household socio-economic condition as the contextual determinant.
... Nearly 165 million children, under the age of five, are malnourished in LMICs [4]. The proportion of undernourished children in 2017 accounted for 20.4% in Africa,
11
.4% in certain regions in Asia, and 6.1% in Latin America. Evidence indicates an increase in undernourishment and severe food insecurity in almost all the regions of Africa and South America, whereas, the undernourishment situation in Asia is observed to be stable [7]. ...
... A malnourished child is ten times more likely to die than a well-nourished child from a preventable cause [9]. The nutritional status of under-5 children in India, estimated using the anthropometric data on height and weight collected in the fifth round of the National Family Health Survey (NFHS-5;2019-20), indicates that 35·4% are stunted (height-for-age deficit), 32·08% are underweight Dialogues in Health 2 (2023) 100135 (weight-for-age deficit), and 19·35% are wasted (weight-for-height deficit) [10] India's progress in childhood nutrition status does not commensurate with its economic progress, and it is worse off than most Sub-Saharan nations
[11]
This paradoxical situation in India, as in most other South-Asian countries, is called the 'Asian Enigma'. ...
... Most studies treat tribal communities as an egalitarian group with a collective conscience, and, fail to capture and address the question of differentiation and inequality among them. Even though since Independence, various publicly financed initiatives have significantly improved the overall living conditions of the Tribal population across India, the inequality across all social classes and within ST communities continues
[11,
22]. Such inequalities are also reflected in the health status of the ST population. ...
Wealth inequalities in nutritional status among the tribal under-5 children in India: A temporal trend analysis using NFHS data of Jharkhand and Odisha states - 2006-21
Umakant Dash
... It is observed from NFHS-3 and NFHS-4 reports that the scenario of child malnutrition in West Bengal is lower than the national average, but the prevalence of malnutrition is not evenly distributed across the districts of the state (IIPS, 2017(IIPS, , 2021. Moreover, there are different studies done considering the role of bio-demographic factors in the prevalence of child malnutrition across the country
(Kanjilal et al., 2010;
Bisai et al., 2014;Panigrahi & Das, 2014;Amruth et al., 2015;Rengma et al., 2016;Ansuya et al., 2018; Aayog, 2021a), has been considered to study the prevalence of child malnutrition (Fig. 1). The proportion of rural populations in the district is 87.26%, which is quite high. ...
... But in this globalized world of the twenty-first century, the incidence of undernutrition is experienced by a wide range of pockets across the globe (UNICEF et al., 2019). Several studies observed that the bio-demographic and socioeconomic factors, such as BMI of mother, marriage age of mother, child birth weight, number of siblings, birth order, food intake, family income, education of parents, sanitation facility, source of drinking water, have a direct bearing in the prevalence of malnutrition of children (de Onis & Blossner, 1997;Cummins, 1998;Phillips, 2006;
Kanjilal et al., 2010;
Panigrahi & Das, 2014;Amruth et al., 2015;Rengma et al., 2016;Ansuya et al., 2018;De & Chattopadhyay, 2019;Islam & Biswas, 2020;Agarwal et al., 2021). In this present study, a total number of fifteen bio-demographic and socio-economic parameters are adopted as independent variables to find out the significant predictors of child malnutrition (Table 3). ...
... The prevalence of wasted children is observed in several works in different proportions across India, such as Arambag, West Bengal (2 to 6 years) 50% (Mandal et al., 2008); Paschim Medinipur, West Bengal (2 to 13 years) 22.70% (Bisai & Mallick, 2011); Paschim Medinipur, West Bengal (1 to 14 years) 19.40% (Bisai et al., 2008b); Darjeeling, West Bengal (5 to 12 years) 26.50% (Debnath et al., 2018); Odisha (1 to 6 years) 25% (Goswami, 2016); Assam (5 to 12 years) 17.15% ; Bhubaneswar, Orissa (3 to 9 years) 23.29% (Panigrahi & Das, 2014); Sullia, Karnataka (5 to 11 years) 26.50% (Amruth et al., 2015). It was documented in different studies that the variation in the prevalence of wasting mainly results from socio-economic-backgrounds and growth patterns of different age groups of children (Alemayehu et al., 2015;Amruth et al., 2015;Bisai et al., 2008aBisai et al., , 2008bDas & Bose, 2009;Debnath et al., 2018;Islam & Biswas, 2020;
Kanjilal et al., 2010;
Panigrahi & Das, 2014;Rengma et al., 2016;Singh, 2020;Stiller et al., 2020). The study does not observe any statistically significant difference in sex and age groups of children in the prevalence of wasting; it may be due to the low socio-economic profile of the households (Ghosh et al., 2021;Mandal, 2021;Mandal & Ghosh, 2019;Mandal et al., 2017). ...
Rural child health in India: the persistent nature of deprivation, undernutrition and the 2030 Agenda
... A high-quality diet is typically comprised of regular consumption of fruits, vegetables, whole grains, lean sources of protein, and dairy products, and infrequent consumption of foods rich in sugar, salt, and fat that are low in nutritional density. On a shortterm basis, a high-quality diet in preschoolers is positively related to better cognitive development and a lower prevalence of childhood overweight and obesity [1]
[2]
[3][4][5] . Meanwhile, in the longer term, diet quality during preschool may act as a lifelong predictor for an individual's risk of having poor or good health during adolescence and beyond 1-3 . ...
... Meanwhile, in the longer term, diet quality during preschool may act as a lifelong predictor for an individual's risk of having poor or good health during adolescence and beyond 1-3 . In the era of economic achievement and the advancement of the health industrial revolution, it has not yet guaranteed a better diet quality and nutrition status among all preschool children in the country
2,
3 . must be studied because it is an indicator and predictor of their future wellbeing and health conditions in adulthood 4 . ...
FAMILY FOOD CHOICES MOTIVE AMONG MALAYSIAN PRESCHOOL CHILDREN’S PARENTS
It is important to determine the factors influencing the family, specifically the parent's food choice motives (FFCMs). These factors are perceived to relate to the nutritional status, eating habits of the children and, subsequently, their future well-being. This study aimed to determine the FFCMs factors (including health concerns, natural content, sensory appeal, convenience, weight control, price, mood, and familiarity) of the parents who had preschool children in Selangor, Malaysia. A cross-sectional study was conducted among seventy-six pairs of mothers and children aged 4 to 6 years in six selected preschools in the Klang Valley, Selangor. A set of self-administered questionnaires measuring demographic data, dietary records, and FFCMs of the parents were answered by the mother, and anthropometric measurements of the children were then taken. The mean FFCMs score found that "health" (mean 3.5 ± 0.53) was reported as the most important factor in parents' ’food choices than the "familiarity" factor (mean 2.78 ± 0.67). Compared to the ethnic groups, both Chinese and Indians mostly chose "natural content”, compared to Malay parents who chose "health" (3.55 ± 0.50) as an important factor to consider when choosing food. In conclusion, this study showed that by determining the most important factors influencing a family’s food choices, it is likely to improve the nutritional status and well-being of children and their family members. Thus, this study proposed the utilization of FFCMs as an instrument to design and develop food- and nutrition-related interventions for further studies.
... Reducing poverty and increasing access to services for those in need are crucial to improving childhood health and nutritional outcomes. Kanjilal, Mazumdar, Mukherjee and Rahman
[5]
found that stunting disproportionately affects children from poor socio-economic backgrounds across all Indian states, and that there is a negative correlation between Net State Domestic Product and stunting prevalence. Larrea and Kawachi, [6] investigated the effect of economic inequality on chronic child malnutrition while considering various household and individual determinants in Ecuador. ...
A Study on Income Inequality and Nutritional Status of Children in Rural Areas of Jammu and Kashmir: Evidence from Jammu District, India
Children nutritional status is a powerful indicator of nutrition security and well-being of individual and reflects the nutritional and poverty situation of household. Good nutritional status of children under five years of age is very crucial for the foundation of a healthy life. This study is a small attempt to highlight the extent of malnutrition in rural areas of Jammu district on the basis of households’ economic status. It empirically investigates the relationship between income inequality and nutritional status of children under age of five years of age (stunting) in the rural areas of Jammu district of Jammu and Kashmir. To determine the prevalence of undernutrition among different income groups of rural households, the study uses the data of a primary household survey. In order to measure the level of undernutrition (stunting), gender-specific anthropometric z-scores for height-for-age are calculated by using new child growth standards which are developed by the World Health Organisation (WHO). The study finds that children from households in the poorest quintile have significantly higher odds of stunting compared to those in the highest income quintile. The study revealed a negative association between income and the prevalence of stunting in the study area. It means the proportion of stunted children under five years of age decreases as the level of income increases among rural households. The study suggests that the government can help improve the nutritional status of children by taking important policy initiatives that addresses income inequality and poverty in rural areas.
... In spite of the implementation of Revamped Public Distribution System and Targeted Public Distribution System since the 1990s, and the enactment of the National Food Security Act, 2013, India is suffering from a serious and acute level of hunger and malnutrition. According to a report of the National Family Health Survey, approximately 38% of children under the age of 5 years are categorized within the range from moderate to severely stunted (based on height for age); overall, 46% of the children are categorized within the range from moderate to severely underweight (based on weight for age), and around 19% are categorized within the range from moderate to severely futile based on weight for height (Rajaram et al. 2007;
... Therefore, it is necessary to design region-specific public health policies (Yu et al., 2019) to find early intervention strategies and mitigate the human resource and economic burden of those regions and ultimately to India by investigating the nutritional status. Several studies are there in this regard in different regions (Swain et al., 2018; on different age groups
Agarwalla et al., 2015;Padma et al., 2016) but most of the previous studies on nutritional status from Meghalaya, North-east India and Garos of East Garo Hills are conducted on children (Rao et al., 2005;Chyne et al., 2017;Singh et al., 2019;Meitei, 2020) and a very few on women (Chyne et al., 2017;Nongrum et al., 2021) while, research related to the prevalence of malnutrition among the adults (men and women) of North-eastern part of India, particularly from the Garo tribes of Meghalaya are meagre. ...
| https://www.researchgate.net/publication/45628665_Nutritional_status_of_children_in_India_Household_socio-economic_condition_as_the_contextual_determinant |
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