Topic 15 correlation

Nutrition & Child Health Status:
Correlation Analysis

Source: Babu and Sanyal (2009) 1

Malnutrition
• Importance:
 Knowledge of determinants of malnutrition : A pre -requisite for targeted
intervention programs and policies.
• Costs:
 Causes a great deal of human suffering (both physical and emotional).
 Apart from the human costs, chronic malnutrition has economic costs too.
Deficiencies in vitamin A, protein, iron and other micronutrients can cause
prolonged impairment, thus reducing productivity of human capital.
• Causes:
 Inadequate food intake, mother’s education and care, health status and
environmental factors. The immediate determinant of nutritional status is
dietary intake (calories, protein, fat, micronutrients, carbohydrates and
vitamins).
 Dietary intake must be sufficient in quantity and quality and nutrients must
be consumed in appropriate combinations for the child to absorb them.

Nutrition & Child Health Status:Correlation 2

Malnutrition
• Causes of Child malnutrition
Poverty - inadequate own food production, income
or in-kind transfers of food for gaining access to
food.
Women’s education and nutritional knowledge play
an equally important role. Women with at least
secondary education tend to have fewer children and
have better knowledge of feeding and caring
practices. These knowledge and skills improve the
caring practices and thereby positively influence the
nutritional status of the child.
Health environment and services - safe water,
sanitation, health care and environmental safety.

Policy Challenges
• Identify factors with positive or negative
association with child nutrition.
• It is important to understand the factors that
are associated strongly with child nutrition,
so that appropriate actions could be taken to
improve those causal factors thereby
improving child nutrition.


Policy Imperatives
• Knowledge on nature and extent of association
among different anthropometric indicators (weight
for age, height for age and weight for height Z-scores).
• Needed for program monitoring and evaluation for
the vulnerable segments of the population.
• If a significant portion of children of a representative
population group is found to be both underweight
and stunted and program managers identify lack of
child-care practices to be the primary cause, nutrition
interventions in the form of health education to the
care-giver may be appropriate in a given situation.


Data Requirement
• Cross Sectional Data
• Preferred approach as large representative samples and the
information on a range of topics can be obtained in a short
time period.
• Cost-effective compared to long-term longitudinal studies.
• Limitation:
– Unlike longitudinal surveys, they do not support
assessment of the direct effect of a particular episode of
illness on nutritional status of the child - assessment of
the impact of illness on growth attainment requires
knowledge of individual growth trends, which cannot be
determined from a single measurement.

Data Requirement
• Cross Sectional Data
• Limitation:
 Hence, cross-sectional measurements are unlikely to
reflect a consistent relationship of nutritional status with
reports of illness, whereas a series of measurements
obtained at different points in time are very likely to
demonstrate a direct causal relationship between
episodes of illness, especially diarrhea.
• Advantages:
 Cross-sectional data to analyze the correlation of
socioeconomic, demographic or environmental factors
with nutritional status.


Data description and methodology
• Determinants of Nutritional outcome:
Socioeconomic indicators that affect child
nutrition.
 CARE indicators.
 Community characteristics.
• Outcome variables:
Anthropometric indicators: Z-scores of height for
age (ZHA), weight for age (ZWA) and weight for
height(ZWH).

• Socio-economic variables:
• Per capita expenditure on food (PXFD): expenditure
on food is a critical variable in models of child
health and nutrition outcomes and is used as a
proxy for income.
• Education of the spouse (EDUCSPOUS): This is a
categorical variable which has a value ranging from
1 to 7 and measures the education level of the
mother in number of years. Higher values of this
variable indicate greater levels of education.


• CARE indicators:
• Clinic feeding (CLINFEED): a dichotomous variable
denoting whether the child is fed in a clinic or not.
• Breastfeeding (BFEEDNEW): also a dichotomous
variable denoting whether the child is breastfed or
not during his or her infanthood.


• Community characteristics:
• Drinking distance (DRINKDST): a categorical variable
assuming values from 1 to 5. Higher values of this variable
denote that the distance to a protected drinking source
for the household is higher.
• For example, the variable attains a value of 4 if distance to
a protected drinking source exceeds 3 km; greater the
distance to a protected water source the more is the
likelihood that children will suffer from malnutrition. This
is because by reducing the risk of bacterial infections and
diarrheal diseases, sanitation and clean water can
indirectly contribute in improving a child’s nutritional
status.


• Sanitation (LATERINE): a dichotomous variable
assuming two values 0 and 1, with 0 indicating
absence of latrine from the household. Sanitation
appears more important in nutritional outcomes
than presence of protected drinking source, since it
is directly related in preventing diarrhea, thereby
improving children’s nutritional status.


• DIARRHEA: Indicates whether the child has diarrhea
and is a dichotomous variable assuming two values
0 and 1. 1 indicates that the child had diarrhea
during the survey. Infections such as diarrhea can
reduce the nutrients in the body and thus increase
the likelihood of malnutrition further.


• Distance to a health facility (HEALTDST): a
categorical variable denoting the distance of the
household to a health clinic and assumes 4 values.
Higher values indicate that the household is located
farther from the nearest health center. For example,
a value of 4 indicates that the distance to the
nearest health clinic for the household is more than
10 km.


Correlation Analysis


Paired Data Set
• Issues:
Is there any relation?
If there is, what is it?
Can it be used for prediction?
Correlation is a measure of linear association
between two variables.


Positive Linear Association


Negative Linear Association


No Linear Association


Correlation and Association
• Correlation coefficient (r for sample
statistics):
• A measure of linear association: how far the
sample observation on a pair of variables fall
on a straight line.
• Not a good summary measure of association
if the scatter plot reveals non-linear patterns


Concepts in correlation analysis

• Suppose we have two random variables X and Y with means
X and Y and standard deviations Sx and SY respectively.
• Then, the correlation coefficient can be computed as
follows:

The correlation coefficient measures the strength of a linear
relationship between any two variables and is always between -1 and
+1. The closer the correlation is to +1 or -1, the closer it is to
a perfect relationship.


Correlation and Association
r is not a good measure if the data are
heteroscedastic.
r is not a good measure if there are outliers.
Strong correlation does not imply any cause-
effect relation and vice versa.


Football-shaped Scatter plots
• A good summary of football-shaped scatter
plots on variables X and Y:
• Sample mean of X
• Sample mean of Y
• Standard deviation of X
• Standard deviation of Y
• And sample correlation coefficient


Test Statistic t
• Test Statistic
r
t=
1–r2
n–2


Concepts in correlation analysis

One can also express r in terms of the regression
coefficients:


Inference about population parameters in
correlation
Let us assume a situation in which we have a random sample of n units
from a population with paired observations of X and Y for each unit.
We want to test the null hypothesis that the population correlation
coefficient ρ=0 against the alternative that ρ≠0. If the computed ρ
values in successive samples from the population were distributed
normally, we would have the standard error to perform the usual t-test
involving the normal distribution. Thus, we have the following statistic:

The standard error of r is given by

Note that the hypothesis testing procedure is in terms of r instead of r2.

Table 8.1 Frequency distribution of nutritional
indicators
Indicator Cases Per cent
No education 52
EDUCSPOUS Adult literacy training 2.8
Primary education 45.2
CLINFEED No 80.5
Yes 19.5
BFEEDNEW No 57.2
Yes 42.8
LATERINE No 61.1
Yes 38.9
DIARRHEA No 83.7
Yes 16.3
DRINKDST < 2 km 71.1
≥2 km 28.9
HEALTDDST < 2 km 19.8
≥2 km 80.2


Figure 8.1 Scatter plot of wasting with distance
to a drinking water source


Figure 8.2 Scatter plot of underweight with
distance to a drinking water source


Scatter Plots
• Figure 8.1: Scatterplots (a geometric representation) of
observations on incidence of wasting and distance to a
protected water source.
• Figure 8.2 : Underweight with distance to a protected
water source.
• Bivariate scatter plot: Display relationship between any
two quantitative variables.
• Both the incidences of wasting and underweight increase
as the distance to the protected water source increases.
• Could be due to increased risk of bacterial infections and
diarrheal diseases as the household is located farther
from a protected water source which, in turn, affects child
nutrition adversely.


Table 8.2 Pearson correlation coefficient among
the various Z-scores

Stunted height for Wasted weight for Underweight wt for
age Z-scores height Z-scores age Z-scores

Pearson correlation 1
Stunted height for
age Z-scores Sig. (2-tailed) .
N 235
-0.160(*)
Wasted weight for
height Z-scores Sig. (2-tailed) 0.014 .
N 235
0.640(**) 0.565(**)
Underweight wt for
age Z-scores Sig. (2-tailed) 0 0 .
N 235 250 276

Results
• Stunting (defined as height for age Z-score below 2) and
wasting (defined as weight for age Z-score below 2) are
very weakly correlated at the 1 per cent level.
• Stunting and wasting: Long-term and short-term
indicators of the nutritional status of the child; hence, the
low correlation would imply that the determinants of
short term and long-term factors of nutrition are different.
• Moderate correlation between weight for age
(underweight) and stunting and weight for age and
wasting would imply that significant monitoring and
evaluation are required, since there is a higher likelihood
that a significant proportion of children in Malawi
(especially in the rural areas) may suffer from long-term
malnutrition problems.

Policy Imperatives
1. Improve the educational level (especially that of
females).
2. Advocate better care practices (such as timely
introduction of breastfeeding and other
complementary feeding).
3. Improve sanitation facilities in communities where
they are lacking, so as to prevent diseases such as
diarrhea and other vector borne diseases.


Topic 15 correlation

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Topic 15 correlation