Correlations Among Demographic Health Indicators
Subhagata Chattopadhyay, Pradeep Ray and Lesley Land
APuHC, SISTM, Australian School of Business, University of New South Wales, Sydney, NSW 2052, Australia
Keywords: Health data, Data mining, ANOVA, Regressions, QAR.
Abstract: Demographic health indicators such as crude birth rate, crude death rate, maternal mortality rate, infant
mortality rate (IMR), Adult literacy rate and many others are usually considered measures of a country’s
health status. These health indicators are often seen in an isolated manner rather than as a group of
associated events. Conventional statistical techniques often fail to mine inter-relations among these
indicators. This paper focuses on mining association-correlations among various demographic health
indicators under child immunization program, skilled obstetric practice, and IMR using both statistical and
Quantitative Association Rule (QAR) mining techniques. Relevant archived data from 10 countries located
in the Asia-Pacific region are used for this study. Finally the paper concludes that association mining with
QAR is more informative than that of statistical techniques. The reason may lie in its capability to generate
the association rules using a 2-D grid-based flexible approach. Finally it is concluded that such an approach
could be pioneering for engineering the hidden knowledge among various other health indicators.
Healthcare statistics of any country is one of the
most important reflectors to assess its state of socio-
economic growth. Better socio-economic growth in
the Western world is reflected through its better
healthcare status than the developing world. Various
indicators are used for healthcare assessment. Some
of these are crude birth rate, crude death rate,
maternal mortality rate, infant mortality rate (IMR),
adult literacy rate and so forth. These indicators are
available in various URLs in the WWW
ct.cfm) and therefore readily available. However, on
their own, the archived demographic data may
render a picture of a country’s healthcare status but
fails to provide much insight into possible
relationships between them. Given this scenario this
paper focuses on mining underlying relationships
between IMR and other indicators related to
maternal and child health. In this paper we argue
that the outcome of these indicators is more telling
than its usual tabular or graphical representations of
data values.
Analysis of a country’s healthcare practice remains a
potential field of research for population and social
scientists since last couple of decades. Various
health indicators are studied over a period of time.
El-Ghannam (2003) has shown that the highest mean
rate of child malnutrition was found in South Asia
region (57%), while the smallest mean rate was
found in Europe region (just 1%). In West Africa
region, the average of child mortality rate per 1000,
172 children, was the highest among all regions in
the world, while in Europe was found to be 14
children per 1000. The results of their studies reveal
positive associations between illiteracy rate,
unemployment, poverty, fertility rate, family size,
food consumption, maternal mortality rate,
population per physician. D’souza and Bryant
(1999), has also corroborated the above findings of
El-Ghannam (2003). D’souza and Bryant (1999)
found a positive correlation between huge
population that leads to insufficient food and
healthcare with IMR. In another study, Byass and
BilaBavi (2003) show that ‘2-child’ policy in
Vietnam has reduced the IMR quite considerably
because of lower rate of childbirth. Crude birth rate
poses to be another useful indicator of IMR (Hynes
et al, 2002 and Bhatia et al, 2002). Adult literacy
rate (also described by El-Ghannam, 2003) remains
another useful predictor of IMR. Hossain et al.
(2007) has observed that increased literacy rate
declines IMR. Studies performed by Hales et al.
(1999) and Wu and Chiang, 2001 show that GNP
per capita, gross domestic product, national health
expenditure, public social expenditure, and Gini
coefficient may influence the occurrence of IMR.
Authors of both the studies found that income-
Chattopadhyay S., Ray P. and Land L. (2008).
MINING ASSOCIATION - Correlations Among Demographic Health Indicators.
In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 315-320
DOI: 10.5220/0001880203150320
inequality remains the key barrier to improve IMR
and U5MR (Under 5 Mortality Rate). However, the
available literature has a couple of gaps – 1. The
analysis is made by conventional statistical
techniques that may sometimes be rigid to explain
the association-correlations among these indicators
and 2. No study has been undertaken to show
association-correlations among child immunization,
safe childbirth with that of IMR.
According to UNICEF, IMR is defined as death
of infants in a country per 1000 live births
htm1.) and is an important health indicator (King
and Zeng, 2001). However, IMR calculation varies
across countries. The variation may lie on how a
country defines ‘life birth’ and whether all deaths
(related to child birth) are included with in the
definition of IMR. To resolve the first issue, the
World Health Organization (WHO) defines a ‘live
birth’ as any baby born with clear demonstrations of
unassisted (and independent) signs of life, such as
breathing, voluntary movement, and/or auscultable
heartbeat (WHO, 1993). In order to minimize this
problem further, UNICEF (United Nations
Children’s Fund) uses a statistical methodology to
account for these reporting differences
ml). In USA every case of infant mortality is
reported while many other countries do not
(MacDorman et al., 2007). On the other hand, some
countries, e.g. Vietnam don't reliably register babies
who die within the first 24 hours of birth probably
due to cultural reasons (Huy et al., 2007). Thus,
despite of the super specialized neonatal care, USA
usually comes out with a higher IMR that is a
seemingly paradoxical finding. Therefore, the
second issue is still prevailing and invites research.
Assuming that better healthcare practice may
influence the IMR this paper aims to mine
associations among various other health indicators
and attempts to link it with IMR status. It studies
how different child immunization programs (OPV,
M, DPT and BCG) and assisted deliveries (ADR)
may be associated with each other and may be
linked with ‘IMR’. Archived data of ten
neighbouring Asian countries, such as Bangladesh,
Bhutan, India, Nepal, Thailand, Myanmar,
Indonesia, Maldives, Korea and Sri Lanka are
considered for the experiment. The possible
associations among these attributes are mined and in
turn correlated using statistical and Quantitative
Association Rule (QAR) mining techniques to note
which the better method of such kind of analysis is.
The layout of the paper is as follows. Section 2
illustrates the detailed methodology of the study.
Results are displayed and discussed in Section 3.
Conclusions are drawn and future extensions of the
study are discussed in Section 4.
The objective of the study is to mine the association
among a set of quantitative attributes (QA), such as
OPV, M, DPT, BCG, and ADR and link them to that
of a categorical attribute (CA) i.e., IMR. Archived
health data of ten Asian countries and are displayed
in Table 1.
Table 1: Country-wise Display of Attributes (%).
85 77 85 95 21.8 5.1
96 88 95 93 23.7 6.05
76 77 75 79 13 5.9
70 67 70 81 42.3 6.8
Sri Lanka
98 99 78 91 97 6.42
97 95 66 88 98 2.11
97 94 96 99 94.5 2.15
76 75 77 79 77.5 5.98
70 72 70 82 68.4 3.5
98 97 98 98 84 1.4
2.1 Statistical Data Mining
Statistical mining of the health data is performed in
MS EXCEL2003. It is done in three steps, Step-1:
Understanding the nature of data (central tendency
and levels of data dispersions) using descriptive
statistics, Step-2: Predicting of the similarity-
dissimilarities among the QA groups using one-way
ANOVA, and Step-3: Modelling the QA-CA
relationships using simple least square regressions
2.1.1 Descriptive Statistics
As the very first step of data mining, descriptive
statistics (Rastogi, 2006) have been chosen to
summarize the central tendency and data distribution
to get an idea about the nature of data. Results
obtained are discussed in section 4.
2.1.2 One-way ANOVA
It is a measure of difference between groups on
some variable. The steps of performing ANOVA is
discussed as follows,
ICSOFT 2008 - International Conference on Software and Data Technologies
Step-1: Stating null hypothesis that defines that
the groups under study are indifferent, measured
from the observed F scores that are calculated as
Where, MSTR and MSE indicate Mean Square
due to Treatments and Mean Square Error,
respectively, and
Step-2: Choosing a critical value (p-value) for
the test. We have chosen 0.05 for this study.
We used MS EXCEL-2003 for performing the
ANOVA test.
2.1.3 Simple Regressions
Simple regression is done for modelling the
relationships between each QA and the CA based on
the data of ten countries. Our aim is to mine the
relationships between each of the individual
quantitative attribute with that of the categorical
attribute using the following equation,
= (2)
Where, y = dependent variable (here IMR; j=1),
= intercept term,
= slope-coefficient of each
independent attribute (i=5) and
= error term,
which is the portion of the dependent variable that is
random, unexplained by any independent variable
itself. In regressions, we measure the model quality
looking at the distribution of the residuals and model
fitness by calculating the R-sq (correlation
coefficient values) and adjusted R-sq values
(Rastogi, 2006).
2.2 Association-correlation Mining
with QAR
QAR is a multidimensional Association Rule (AR)
mining technique, where the numeric attributes,
while mining are dynamically discretized for
satisfying some mining criteria, e.g. maximizing the
confidence of the mined rules (Han and Camber,
2006). As already mentioned, the objective of this
study is to mine AR using pair-wise quantitative
attributes for ten countries (i.e. observations). The 2-
D QAR grid thus generated can be generically
represented as follows,
min max min max
min max
( ," ... ") ( ," ... ")
(," ... ")
iii i i i
Where, X denotes the quantitative attribute and ‘i =
5’ (OPV, M, DPT, BCG, ADR). The steps of QAR
is as follows,
1. Binning:
2. Finding frequent predicate sets
3. AR generation, and
4. Correlation analysis using ‘lift’
These are described as follows.
2.2.1 Binning
Binning is the first and most important step for the
generation of 2-D grids (taking a pair of attributes
into account). Before fitting into the grid, the
attributes (in pair) are partitioned based on equal
range (called as equal-width binning). Two-D arrays
for each possible bin combinations involving pair of
QA are thus created. Each array cell holds the
corresponding count distribution for each possible
class of the categorical attribute based on the QA.
2.2.2 Finding Frequent Predicates
In this step we aim to find frequent predicate sets
those satisfy minimum support (s) and minimum
confidence (c), where support and confidence are
calculated using the following equations in
, and
respectively. Where, ‘av’ denotes the ‘average’ and
A, B are attributes.
The supports and confidence thus calculated for
all possible pairs using the following algorithm
For each frequent item-set ‘l’, generate all
non-empty subset of l
For every non-empty subset ‘s’ of ‘l’,
output the rule “s
(l-s)”, if csl >=)/(
Here, ‘c’ is the minimum confidence threshold
(King and Zeng, 2001). The ‘l’ denotes the
‘support_count l’ and ‘s’ indicates the
support_count s’. The ‘support_count’ (OPV
) is the number of countries containing both
higher than average values of OPV and M and
‘support_count’ (OPV
) denotes those countries
containing only higher than average values of OPV.
2.2.3 Association Rule Generation
The rules (AR) that satisfy minimum support and
minimum confidence may be denoted as ‘strong’
rules and all the strong rules are in turn, clustered.
AR, thus derived from this study is discussed in the
following section as well.
MINING ASSOCIATION - Correlations Among Demographic Health Indicators
2.2.4 Correlation Analysis using ‘Lift’
Lift is a correlation measure among a set of QA(s)
and is calculated as follows,
Lift (L) is interpreted as follows,
If L>1
Positive correlation
Strong negative correlation
L = 1
Nil correlation
This section displays and discusses the results
obtained from the experiments in two broad sections
1. Results of statistical data mining, and
2. Results of QAR-based data mining. Finally
these techniques are compared
3.1 Results of Statistical Data Mining
The results of statistical data mining are discussed in
the following subsections.
3.1.1 Results of Descriptive Statistics
From the values of mean, median, standard
deviations (stdev) we can state that the data are
almost normally distributed. Mean and median
values are close to each other in most of the
attributes. Skewness: it is seen that OPV, M, ADR
and IMR, i.e. >66% of the total attributes are
negatively skewed. In this study it is found that
skewness is well distributed across the attributes
ranging from –0.53 to +0.40, and 66% more towards
negative skewness. Kurtosis: Higher the kurtosis
more is the variance, which may be due to
infrequent extreme deviations, as opposed to
frequent modestly sized deviations.
3.1.2 Results of One-way ANOVA
One way or single factor ANOVA is performed on
these data set containing five quantitative attributes
(independent variables), one categorical attribute
(dependent variable) and ten observations (numbers
of countries).
The ANOVA result shows that the total sum of
squares (SS) is 14601.9 within the groups (WG)
where as the grouping accounts the SS 4522.9. The
null hypothesis was that there are no variations
among the groups of QA. Therefore, the null
hypothesis is rejected. ADR shows the highest
variation (1135). Given the small sample size
(number of observations N = 10), question may arise
whether such difference is by chance. It is explained
by the F statistic (3.48) with a p-value (0.014),
which is less than 0.05. Therefore based on these
observations we may conclude that there is indeed a
significant difference between the groups of the
quantitative parameters. In other term, as the value
of F is higher than F
, (2.57) that corroborates the
difference among the QA are significant. In the
following step simple regressions are attempted to
correlate the CA with QA(s).
3.1.3 Results of Simple Regressions
Simple regressions are performed to note the
relationships between each of the QA (n=5) with
that of the CA (n=1) based on 10 observations
(N=10) keeping CI as 95%. Based on the results
found after simple regressions, it is seen that the
correlation coefficient (R-sq) values are <50% for
each case.
3.2 Results of QAR-based Data Mining
Results obtained from QAR-based mining of health
data are discussed step-wise.
3.2.1 Results of Binning
Figure 1 has shown a typical 2-D grid [OPV, M],
using equal-width distributions (61-70; 71-80; 81-
90; 91-100). Now for the country, e.g. India, OPV
(70%) and M (67%) falls on the crossed grid (0,0)
while for Korea (OPV 97%, M 95%) it is the shaded
grid and represent the corresponding categorical
attribute i.e., IMR, 6.8% and 2.11%, respectively.
Similarly for other possible pairs (maximum number
of pairs =
), 2-D grids are created country-
wise and the corresponding IMR(s) could be mapped
easily. However this creates a fairly complex
scenario. The goodness of QAR is that it reduces
this complexity by accepting only those pairs where
the values are higher than the average (av) values.
For e.g., we may take only the higher values of
OPV, found in Bhutan, Sri Lanka, Korea, Thailand
and Maldives rather than taking all the values.
3.2.2 Finding Frequent Sets
At the first step, we identify the countries that
possess the QA values higher than the respective
‘av’. Using these values, then the minimum support
ICSOFT 2008 - International Conference on Software and Data Technologies
(s) and confidence (c) are calculated for the each
Figure 1: A 2-D Grid of ‘OPV-M’ Pair.
3.2.3 AR Generations
Suppose the data containing frequent predicate sets
‘l’={OPV, M, ADR} and the association rules, thus
generated as follows,
For this example, the non-empty subsets of ‘l’
according to countries are {ADR}, {OPV, M, ADR},
{OPV, M, ADR}, {OPV, M}, {OPV, M, ADR}, and
{OPV, M, ADR}. The resulting ‘c’ and ‘s’ can be
calculated as follows,
1. OPV^M
ADR, c = 5/5 = 100%; s = 5/10 =
50%; 2. OPV^ADR
M, c = 4/4 = 100%; s = 4/10
= 40%; 3. M^ADR
OPV, c = 4/5 = 80%; s =
4/10 = 40%; 4. OPV
M^ADR, c = 4/5 = 80%; s
= 4/10 = 40%; 5. M
OPV^ADR, c = 4/5 = 80%;
s = 4/10 = 40%; and 6. ADR
OPV^M, c = 4/6 =
66%; s = 4/10 = 40%.
Therefore, from the above values of ‘c it may be
stated that with minimum support (s) of 40%,
ADR is the strongest associations (c =
100%; s>40%). Similarly associations are calculated
for other combinations, e.g. DPT, M and ADR. In
this combination, the non-empty sets are {DPT,
{DPT, BCG, ADR}, and {DPT, ADR}. The resulting
‘s’ and ‘c’ values can then be calculated as follows,
ADR, c = 3/5 = 60%; s = 3/10
= 30%; 2. DPT ^ADR
BCG, c = 4/4 = 100%; s =
4/10 = 40%; 3. BCG^ADR
DPT, c = 3/4 = 75%;
s = 3/10 = 30%; 4. DPT
BCG^ADR, c = 4/4 =
100%; s = 4/10 = 40%; 5. BCG
DPT^ADR, c =
4/4 = 100%; s = 4/10 = 40%; 6. ADR
c = 3/5 = 60%; s = 3/10 = 40%.
Rule strength can be adjudged from the
minimum confidence assigned for a set of
combination. For these combinations, DPT
DPT^ADR have c = 100% and s>30%.
These rules are said to be strong rules because the
calculated confidence level is above the minimum
confidence and support is higher than the minimum
support. Similarly rules can be computed for
IMR. It is seen that
two countries, such as Bhutan and Thailand shows
>av OPV, M, DPT, BCG and ADR values whereas
five countries show higher IMR values. From these
information we can compute the minimum‘s’ and ‘c
values using equations 4 and 5, respectively as
IMR, c = 2/5 = 40%; s
= 5/10 = 50%. We may assume that at least one
country (observation) satisfies equation 4 and 5 to
calculate the minimum confidence (c) and support
level (s) and in that case c = (1/5)*100 = 20% and s
= (1/10)*100 = 10%, may be counted as at least one
country is showing level. In our study the computed
c and s values are more than these minimum values
and predict an association among all the attributes.
From this experiment we may infer that
ADR has got the strongest associations
among all the possible combinations within the
OPV-M 2-D grid. On the other hand, multiple strong
associations could be mined for the
DPT^ADR combinations. From these
association values we may predict that if a baby is
delivered under skilled supervision there is almost
100% possibility that it gets immunized with OPV
and Measles (M) vaccines and vice versa. From the
other sets of associations it may be inferred that safe
delivery under skilled obstetric supervision is
directly associated with BCG, DPT immunizations.
Therefore, we may frame a rule cluster that states if
skilled childbirth is directly associated with full
vaccinations and reflect a good maternal-and-child
health practice in any country.
3.2.4 Correlation Analysis using ‘Lift’
Using equation 6, correlations among the individual
QA are calculated. The results show that OPV, M,
DPT, BCG, ADR all are positively correlated with
each other (L<1 for all cases as s>c for all cases).
From the experimental results of OPV^M^DPT
IMR the predicted score of L<1
suggesting strong negative correlations, i.e. if OPV,
M, DPT, BCG and ADR rates (overall immunization
rates) become high, IMR declines.
The objective of this study is two-fold - firstly, to
engineer the underlying association-correlations
MINING ASSOCIATION - Correlations Among Demographic Health Indicators
among various vaccination program, safe childbirth
practice and IMR, and secondly to note which one of
the data mining techniques could be more suitable to
explain such relationships. From our experiment
based on the archived data of ten countries, we have
noticed the following,
One-way ANOVA result shows that OPV, M,
DPT, BCG and ADR are significantly dissimilar
from each other (p<0.05; F score>F
) and thus can
be suitably used for predictive modeling
(regressions) as different attributes though under the
same construct (child immunization program)
Simple regressions fail to predict any significant
correlation between any of the QA (OPV, M, DPT,
BCG and ADR) and the CA, i.e. IMR as indicated
by R-sq values <50% for each analysis. But the
attempt is said to be a good one as the residual plots
are almost linear in nature without any visible
QAR relies on 2-D grid combinations of QA and
generation of AR from confidence (c) level. From
the association rules, thus generated, it is found that
QAR is a better approach to engineer this kind of
data where direct relations cannot be statistically
predicted, but assumed. In this experiment it is
found that all the QA(s) are closely associated and
correlated with each other. From the experiment it is
found that with a combination of OPV, M and ADR
the IMR is quite low in Maldives and Thailand,
however in contrast to that Bhutan and Sri Lanka
shows a higher values. This may be due to influence
of other factors, e.g. general healthcare facilities,
literacy rate, crude birth rate and so forth. From the
other combinations DPT-BCG-ADR it is found that
rules 2,4,5 are strong rules that tells that if a baby is
born under a skilled health worker it receives DPT
and BCG vaccines and vice versa. It is true for
Thailand and Bangladesh i.e. this combination may
have reduced the IMR (but not with Bhutan).
From the ‘lift’ value, it may be observed that
there is a negative correlation between vaccination
and skilled childbirth with that of IMR, i.e. for
higher the number of immunisations and skilled
childbirth under supervisions, lower is the incidence
of IMR in a population.
However, it is important to mention here that
association mining based on a mathematical
approach may not always explain a real-world
scenario, as seen in the contrasting results of Bhutan
and Thailand. Therefore, consideration of other
health indicators could be considered in this type of
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