International Journal of Arts & Sciences,
CD-ROM. ISSN: 1944-6934 :: 4(17):257–269 (2011)
Copyright c© 2011 by InternationalJournal.org
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THE ASSOCIATION BETWEEN MATERIAL DEPRIVATION AND
RELATIVE RISK OF INFANT MORTALITY IN PENINSULAR
MALAYSIA
Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
Universiti Kebangsaan Malaysia, Malaysia
The aim of this paper is to investigate the association between material deprivation index and
relative risk of infant mortality for 81 administrative districts in Peninsular Malaysia. This
investigation is essential for the regional planning and development by policy makers in order
to identify and conduct possible efforts to reduce the socioeconomic inequality and health
disparities across the different regions in the country. We begin this investigation by
developing index of material deprivation based on several indicators available from census
data and expected value of infant mortality based on the number of infant mortality and live
birth for each administrative district using the administrative registration data of the years
1991 and 2000. Since Bayesian hierarchical modeling is flexible as it allows the idea of
“borrowing information”, we apply the technique to estimate the expected values of material
deprivation index and relative risk of infant mortality for each individual administrative
district. Association between material deprivation and relative risk of infant mortality is
studied based on the two measures found using graphical methods involving caterpillar charts
and superimposition of choropleth maps. In addition, Pearson product moment correlation is
computed to determine the overall association. The graphical methods indicate that there exist
some tendency where some districts which experienced high relative risk of infant mortality
also observed high index of material deprivation and vice versa. The overall results showed
that there are moderate positive correlations between material deprivation and relative risk of
infant mortality for the years 1991 and 2000, as given by the values of correlation coefficient
values of 0.48 and 0.45 respectively, indicating no significant temporal change over the two
periods.
Keywords: Material Deprivation, Infant Mortality, Bayesian Hierarchical
INTRODUCTION
The national report indicates that over the period from 1972 to 2000, the overall infant mortality
rate for Peninsular Malaysia has shown a declining trend (Jabatan Perangkaan Malaysia, 1972-
2000). However, Rahman (2008) considers that the figure is less realistic since the
administrative districts are assumed independent. Accordingly, she applied the locally weighted
average and empirical Bayesian approaches where one district borrow information from the
257
258 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
neighbouring districts and found out that the relative risk of infant mortality is high in the east
coast area and low in the west coast area. In addition to this finding, it would be more
informative to study the confounding factor which is related to infant mortality. Based on the
study carried out by Joyce et al. (2000), for example, which analyzed the data of fetal and infant
mortality rate across 364 English Local Authorities, it was found that area deprivation is a
confounding factor to predict the stillbirth and infant mortality. Furthermore, Oakley et al. (2009)
found in their working paper that socioeconomic status and deprivation was strongly associated
with the deaths of children under one year in England and Wales during the years 2005 and
2006. Consequently, we consider that it is worthwhile to investigate the association between the
material deprivation and relative risk of infant mortality in Peninsular Malaysia.
The goal of this paper is to determine the index of material deprivation and relative risk of
infant mortality for each district and study the association between these two indices. The index
of material deprivation is found by applying the arcsin transformation on the selected indicators,
normalization on the transformed indicators, summing of the Z scores and finally, applying the
hierarchical Bayesian approach on these sums. The relative risk of infant mortality is found by
first finding the expected infant mortality and followed by applying the Bayesian hierarchical
model on these expected values. Bayesian hierarchical method is preferable than classical
method because Bayesian hierarchical model is quite flexible since each parameter refers to
specific groups or units is allowed to borrow strength from the corresponding parameters of other
groups or units with similar characteristics (Ntzoufras, 2009). Therefore, Bayesian techniques
have been commonly used in modeling of count data such as in evaluation of road safety
measures (Ibrahim and Metcalfe, 1993), in ranking of institutional performance (Spiegelhalter et
al., 2003), on forecasting of shrimp growth in commercial aquaculture practice (Yu and Leung,
2010) and on estimating the growth of northern abalone (Zhang et al., 2009).
The structure of this paper is as follows. The next section is devoted on the background of
material deprivation index. In addition, the drawback of the use of standard mortality rate as a
measure of relative risk is also discussed. This is followed by the application of Bayesian
hierarchical model for estimating the expected values of material deprivation index and relative
risk of infant mortality for each individual administrative district. The association between
material deprivation and relative risk of infant mortality is presented by using graphical methods
and the degree of association is suggested by the computed value of the coefficient of
correlation. Finally, discussion and conclusion will appear in the last section.
LITERATURE REVIEW
The deprivation concept has its origin from Britain in the late 1960s, where it emerged on issues
of economic and social inequality in Britain (Norris, 1979). Deprivation is different from
poverty, whereby deprivation is viewed as a state of observable and demonstrable disadvantage
relative to local community or the wider society or nation to which an individual, family or
group belongs (Townsend, 1987). Using data in 414 local government districts in the north of
England, Congdon and Southall (2005) found that infant mortality gradients were strongly
associated with indicators used in the socioeconomic deprivation observed during the different
time periods.
In this study, the socioeconomic deprivation measurement that is being used as the
confounding factor of infant mortality is material deprivation. Material deprivation indices were
being introduced since 1980 and are widely used in public health care, both in epidemiological
analyses and in allocation of resources (Davey et al., 2001). The three main purposes why
The Association between Material Deprivation and Relative Risk.... 259
material deprivation indices have been developed in the area of public health are, firstly, social
determinants of health are numerous and cannot be summarized in one single measure, secondly,
inequalities in health need to be measured and monitored in order to promote actions to reduce
such inequalities, and thirdly, most health administrative data bases do not contain
socioeconomic information on individual (Pampalon et al., 2009). Many researchers have
identified that there exist a relationship between material deprivation indices and infant mortality
(Saunders and Fox, 1996; Joyce et al., 2000; Singh and Kogan, 2007; Oakley et al., 2009).
Table 1. Variables and transformation technique considered by the three different indices Jarman UPA8, Townsend
index and Carstair index.
Description Index of Material Deprivation
Jarman Townsend Carstairs
Variable
Unemployment √ √� √ (Male)
Overcrowding √� √� √
No housing tenure √� √�
No owning car √� √� √
Elderly alone √�
Children under 5 √�
Lone parent households √�
Households social class 5 √�
Households social class 4 & 5 √
Transformation used Arcs in
100
x Log(x +1) None
There are some well-known and commonly used material deprivation indices, namely, Jarman
Underprivileged Area Score, Townsend Index of Deprivation and Carstairs Deprivation Index, as
proposed by Jarman (1983), Townsend (1987) and Carstairs and Morris (1989) respectively. All
indices found using these methods are calculated based on census data. Table 1 summarizes the
list of variables and transformation technique that are often used for the three common indices.
MATERIALS AND METHODS
Data
The data analyzed in this study was provided by Department of Statistics, Malaysia. The data
analyzed comprises of census data and administrative registration data for 81 administrative
districts in Peninsular Malaysia for the years 1991 and 2000. Variables of interest in the census
data are percentage of private household without owning a car, percentage of private household
without owning house, percentage of private household without access to pipe water and
percentage of economically active residents aged 15 to 64 who are unemployed. Meanwhile, the
data extracted from administrative registration records are number of infant mortality and
number of live birth for the same year.
260 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
Material Deprivation
We used arcsin transformation on the variables of interest extracted from census data, i.e.
TVij= ijarcsin V 100 , where TVij is the transformed value of variable j for district i, in order to
stabilize the variances and reduce the skewness, so that the data is approximately normal.
Following Townsend method, the four transformed variables for each ith administrative district
where i=1,…, 81 are standardized as i ijZ =(TV -TV S )j j , where jTV is the mean and Sj is the
standard deviation for each jth explanatory variables where j=1,.., 4. Finally, we used simple
additive method where four of these Zj scores are given an equal weight and summed to obtain
the material deprivation index, denoted Di =4
j
j
Z∑ .
Since the four explanatory variables have been transformed into normal random variable, Di also
follows a normal distribution with unknown parameters mean i
µ and variance 2
iσ , which can be
written as: ( ) ( ) ( )1
22 2 22| , 2 exp 1 2i i i i i i i
p D Dµ σ πσ µ σ−
− = − −
; where ( ),iD = −∞ ∞
Relative Risk of Infant Mortality
Traditionally, standardized mortality/morbidity ratio is a measurement which is commonly used
to estimate the relative risk of disease within each tract (Lawson et al., 2003). By applying this
idea, we produce standardized infant mortality/number of live birth as an estimate of the relative
risk of infant mortality within each administrative district. We define Oi and Ni as the observed
number of infant mortality and number of live birth in the ith administrative district respectively.
Thus, the expected number of infant mortality for the ith district denoted as Ei, is given by 81 81
1 1
i i i i
i i
E N O N= =
=
∑ ∑ and the relative risk of infant mortality ratio is defined as
i i iO Eθ =
(Langford et al., 1999). However, this standardized infant mortality (SIMR) method has some
drawbacks because of the influence of the dominator Ei. Small changes in Ei can yield a large
change in the estimation of i
θ and a small value of Ei could cause the value of i
θ to be very
large. Therefore, to prevent the problems associated with SIMR, various smoothing alternatives
have been proposed to reduce the extra variability brought about the changes in the value of Ei.
In this study for smoothing the SIMR, we used Bayesian hierarchical model as proposed by
Lawson and Browne (2003) for estimating the relative risk. Since the occurrence of infant
mortality Oi is a Poisson process, we can say that Oi follows a Poisson distribution with
parameter i i
Eθ which can be written as:
( ) ( ) ( )| ! exp ; where 0,1,2,...i i i i i i i i i
p O E E O E Oθ θ θ = =
Application of Bayesian Hierarchical Models
Material Deprivation IndexNormal-normal model
The basic mechanism in the Bayesian approach is the posterior is proportional to the prior
multiply by the likelihood. Since material deprivation can be assumed to follow a normal
The Association between Material Deprivation and Relative Risk.... 261
distribution, therefore we choose normal-normal model as our Bayesian hierarchical model for
index of material deprivation which is written as follows:
Level 1: Di | µ i, σi2, Di~N (µ i, τi); τi= 1/ σi
2 Likelihood
Level 2: µ i |µo, µ i ~N (µo, τ0), τi|0.1, 0.1~ IG (0.1, 0.1) Prior
Level 3: µo |0, 0.000001~N (0, 0.000001), τ0|0.1, 0.1~ IG (0.1, 0.1) Hyperprior
Relative Risk of Infant Mortality
Poisson-lognormal model
It is noted in by Lawson (2003) that a log-normal model for relative risk is more flexible than
poisson gamma model which allowed for spatial correlation. Therefore for this paper, we apply
the Poisson-lognormal method to produce the posterior mean for the relative risk of infant
mortality for 1991 and 2000 as follows:
Level 1: Oi | Ei iθ , Oi~Poisson (
i iEθ ) Likelihood
Level 2: logi
θ =α+vi; where vi =random effect, vi~N (0, τv) Prior
Level 3: α |0, 0.000001~N (0, 0.000001), τv|0.5, 0.0005~ IG (0.5, 0.0005) Hyperprior
where α is an overall level of the relative risk and vi is the uncorrelated heterogeneity of random
effect.
The analysis is carried out with R freeware statistical package (version 2.9.1), WinBUGS
(version 1.4) and ArcGIS (version 9.3).
ANALYSIS AND RESULTS
After carrying out 1000 update burn-in and followed by a further 10000 updates for both the
material deprivation and relative risk of infant mortality models using the Win BUGS program,
the summary of the resultant posterior mean and standard deviation for each AD for the years
1991 and 2000 are shown in Table 2. The overall mean is slightly lower for the year 2000 when
compared to the year 1991 for both indices of material deprivation and relative risk of infant
mortality indicating an improvement. However, it is found that the standard deviation for the
year 2000 is slightly larger indicating that there is an increase in disparities for the more recent
period as compared to the earlier year.
The association of material deprivation and relative risk of infant mortality across 81
administrative districts in Peninsular Malaysia is illustrated in Figure 1 using the caterpillar
charts. The points on the charts indicate the posterior mean, while the horizontal lines are the
95% credible intervals with respect to material deprivation or relative risk of infant mortality for
each individual administrative district. The middle line represents the overall mean. For material
deprivation charts as shown in Figure 1a and 1b, any points below the middle line indicate
affluent areas and any points above the middle line indicate deprived areas when compared to the
overall average level of material deprivation for the peninsula. Meanwhile, for relative risk of
infant mortality charts as shown in Figure 1c and 1d, those points below the middle line indicate
262 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
low risk area and those points above the middle line indicate high risk area when compared to
the overall average level of relative risk for the peninsula.
Table 2: Results based normal-normal model and Poisson-lognormal model for material deprivation and relative
risk of infant mortality respectively for the years 1991 and 2000.
Material Deprivation Relative Risk of Infant Mortality
Mean_91 Sd_91 Mean_00 Sd_00 Mean_91 Sd_91 Mean_00 Sd_00
Mean -0.052 1.428 -0.107 1.797 1.019 0.032 1.012 0.054
Minimum -1.134 0.908 -1.579 1.085 0.459 0.045 0.102 0.007
1st Quartile -0.799 0.982 -1.025 1.154 0.922 0.103 0.889 0.135
Median -0.190 1.120 0.003 1.357 1.022 0.137 1.051 0.196
Mean -0.052 1.201 -0.108 1.440 1.048 0.135 1.090 0.198
3rd
Quartile 0.806 1.385 1.035 1.697 1.150 0.159 1.292 0.249
Maximum 1.040 1.735 1.366 2.107 1.653 0.241 2.523 0.417
Another method to gauge the level of association between material deprivation and relative
risk of infant mortality is to conduct a spatial quartile analysis. The two indices are divided into
four quartiles. The quartile categories for material deprivation are arranged in increasing
deprivation with quartile one being the most affluent and quartile four being the most deprived.
Meanwhile, the quartile categories for relative risk of infant mortality are arranged in increasing
risk with quartile one being the lowest and quartile four being the highest. Table 3 gives the
classification of administrative districts for each state and one federal territory according to these
quartiles.
In order to visualize the results in Table 3 spatially, the results for material deprivation and
relative risk of infant mortality are mapped. We used choropleth maps (see Figure 2 a, b, c and d)
to represent spatial distribution by quartiles ranking for material deprivation and relative risk of
infant mortality for both census years 1991 and 2000 across administrative districts in Peninsular
Malaysia. The states are designated by the thick black lines and the administrative districts by the
thinner black lines. In 1991, as shown in Figure 2a, deprived fourth quartile can be seen
distributed mainly in the northeast part of Peninsular Malaysia where Kelantan and Terengganu
states are located. The inner part and southeast of Pahang state and northeast of Kedah state also
fall into this quartile. In contrast, majority administrative districts along the west coast from
Perlis state to the southwest part of peninsular Malaysia fall into the first and second quartiles.
The third quartile covered 67% and 50% administrative districts for Perak and Pahang states
respectively. The detail performance of each state and Kuala Lumpur Federal Territory
according to quartile range for both indices for the years 1991 and 2000 are summarized in
Table 3.
For the year 2000, as given in Figure 2b, it is observed that most northeast region, apart
from several ADs in Terengganu state, appears to fall into the same quartile as in the year 1991
(see Figure 2a). In year 1991, all administrative districts in Pulau Pinang and Melaka states and
Kuala Lumpur Federal Territory fall in the first quartile and this situation remains the same for
year 2000. In Figure 2c, there appears to be random spatial distribution while in Figure 2d, there
appears to be clustering. The spatial distribution of posterior mean of relative risk of infant
mortality for the year 1991 seems to have some association with material deprivation of 1991.
The Association between Material Deprivation and Relative Risk.... 263
a b
c d
Figure 1: (a) and (b) displaying the ranking of posterior mean and associated 95% credible intervals for material
deprivation indices for the 81 administrative districts (AD) for 1991 and 2000 respectively. (c) and (d) displaying
the ranking of posterior mean and associated 95% credible interval relative risk of infant mortality for 81
administrative districts (AD) for 1991 and 2000 respectively.
�
Caterpillar Plot RRIM2000: Posterior Theta
RR 0.0 1.0 2.0 3.0 4.0
AD
�
Caterpillar Plot IMD1991: Pos terior Mean
IMD -5.0 -2.5 0.0 2.5 5.0
AD
�
Caterpillar Plot RRIM1991: Pos terior Theta
RR 0.0 1.0 2.0 3.0
AD
� Caterlillar Plot IMD2000:Posterior Mean
IMD -5.0 -2.5 0.0 2.5 5.0
AD
��
264 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
Table 3: Allocation number of administrative district for each quartile index of material deprivation (MD) and
relative risk of infant mortality (RRIM) years 1991 and 2000.
State Total
ADs
1991 2000
Quartile for MD Quartile for RRIM Quartile for MD Quartile for RRIM
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Johor 8 2 5 1 1 4 3 4 3 1 2 4 2 Kedah 11 1 2 4 4 5 4 2 3 2 5 1 5 1 1 4
Kelantan 10 1 9 1 2 7 1 9 1 9
Melaka 3 3 1 2 3 2 1
Negeri Sembilan 7 4 3 3 3 1 1 4 2 2 4 1 Pahang 10 3 5 2 3 2 4 1 1 4 5 1 5 4
Pulau Pinang 5 5 4 1 5 1 2 2 0
Perak 9 1 2 6 5 3 1 1 5 2 1 3 3 1 2
Perlis 1 1 1 1 1 Selangor 9 5 3 1 2 3 3 1 4 4 1 5 2 2 Terengganu 7 2 5 1 2 4 3 4 2 1 3 1
W.P.Kuala Lumpur 1 1 1 1 1
Overall, based on the choropleth maps, the most deprived and high relative risk of infant
mortality areas were found to be more clustered in the northeast region when the two indices are
considered for year 2000 as compared to the year 1991.
In order to have a clearer picture of the level of association between material deprivation and
relative risk of infant mortality, Table 4 gives the top twenty administrative districts that are most
deprived and experience most severe relative risk of infant mortality. The results in Table 4 are
then mapped in Figure 3 by superimposing the top twenty administrative districts that
experienced the most deprived and most high relative risk of infant mortality. Figures 3a and 3b
are the results of superimposing relative risk of infant mortality on the top 20 deprived
administrative districts for the census years 1991 and 2000 respectively. Figure 3c and 3d are the
results of superimposing material deprivation on the top 20 high risk administrative districts for
the census years 1991 and 2000 respectively.
It is clear from Figure 3 that the northeast region observed the highest level of relative risk
of infant mortality and being the most deprived region of the peninsula over the two period of
time.
In addition, the correlation between the two indices is determined using Pearson product
moment correlation and the results are provided in Table 5. From Table 5, for the years 1991 &
2000, there exist a significant moderately positive correlation between material deprivation and
relative risk of infant mortality. The two measure of correlation are quite close, indicating no
significant change over the two periods.
DISCUSSION AND CONCLUSION
This article used Bayesian hierarchical model which bring idea of shrink’s estimates toward
the overall mean by borrowing information from other areas to obtain the posterior mean for two
indices namely index of material deprivation and relative risk of infant mortality across 81
administrative districts in Peninsular Malaysia for the years 1991 and 2000. We analyzed these
two indices separately and found that material deprivation has moderate positive correlation with
relative risk of infant mortality for both census years. This implies that there are other risk
The Association between Material Deprivation and Relative Risk.... 265
factors which were not included in this analysis would be appeared to have a greater influence to
explain the spatial patterns of relative risk of infant mortality. Various studies have highlighted
child’s birth weight or parity, maternal social class, marital status, smoking, ethnic group,
teenage pregnancy, lower socioeconomic status, housing type and various measures of material
deprivations are amongst the modifiable risk factors and non modifiable risk factors that cause
infant mortality (Saunders and Fox, 1996; Blair et al., 2006; McManus et al., 2010). Williams
(1997) has distinguished those infant mortality influential factors as basic causes and surface
causes. Basic causes have dimensions such as culture, biology, racism, economic structures,
political and legal factor whilst surface causes are such as health practices, psychosocial
resources and medical care.
Figure 2: (a) and (b) Spatial distribution of the Index of Material Deprivation (IMD) by quartile rankings using
Bayesian Hierarchical normal-normal model across 81 administrative districts. (c) and (d) Spatial distribution of
relative risk of infant mortality (RRIM) by quartile ranking using Bayesian Hierarchical Poisson-lognormal model
across 81 administrative districts.
266 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
Table 4: Top twenty administrative districts, Peninsular Malaysia that are most deprived and experience most
severe relative risk of infant mortality for the years 1991 and 2000.
Rank MD 1991 RRIM 1991 MD 2000 RRIM 2000
1 Machang Seremban Machang Cameron Highlands
2 Bachok Cameron Highlands Ulu Perak Pekan
3 Marang Larut Dan Matang Besut Batang Padang
4 Tanah Merah Melaka Tengah Setiu Marang
5 Dungun Baling Baling Yan
6 Rompin Jerantut Rompin Kuala Langat
7 Hulu Terengganu Barat Daya Pasir Mas Pasir Puteh
8 Gua Musang Temerloh Lipis Kluang
9 Sik Batang Padang Pekan Hilir Perak
10 Pasir Puteh Jempol Tanah Merah Kuala Terengganu
11 Lipis Batu Pahat Jeli Jerantut
12 Baling Marang Pasir Puteh Kuala Kangsar
13 Besut Kemaman Bachok Kemaman
14 Tumpat Gombak Tumpat Jeli
15 Pendang Kuala Krai Hulu Terengganu Temerloh
16 Pasir Mas Kuala Langat Kuala Krai Maran
17 Jeli Dungun Gua Musang Kuala Selangor
18 Kuala Krai Setiu Dungun Hulu Terengganu
19 Kemaman Sik Bentong Melaka Tengah
20 Padang Terap Pasir Mas Jerantut Kota Bharu
Note: 1 indicates the most deprived and most severe level of relative risk of infant mortality
administrative district in Peninsular Malaysia.
It should be emphasized that the purpose of the current study is not to identify causes of infant
mortality in detail, but rather to examine geographically the association of spatial distribution by
quartile ranking level of material deprivation with spatial distribution by quartile ranking degree
of relative risk of infant mortality across 81 administrative districts in Peninsular Malaysia for
the two census years 1991 and 2000.
In conclusion, the overall results showed that there exist moderate association between
material deprivation and relative risk of infant mortality in Peninsular Malaysia for the two
census years 1991 and 2000. In addition, the coefficient of determination values ranged from
20% to 23% as shown in Table 5 implying that material deprivation may present as a necessary
risk factor but not a sufficient risk factor to explain the total variation in infant mortality.
However, this study will hopefully prompt those researcher involved in public health, decision
makers and health care consumers for further investigation the relationship of socioeconomic
level with degree of health status.
We believe Bayesian hierarchical models are capable of making more accurate prediction
than the simple nonlinear model and we note the need for further research into this and currently
The Association between Material Deprivation and Relative Risk.... 267
embarking on the detailed study which aims to investigate space-time effect for exploratory
analysis.
Figure 3: (a) and (b), show the superimpose association between top 25% deprived areas and degree relative risk of
infant mortality for both census years 1991 and 2000 across the 81 administration districts whilst (c) and (d), show
the superimpose association top 25% high relative risk of infant mortality with level of deprivation for both census
years 1991 and 2000 across the 81 administration districts.
Table 5: Pearson product moment coefficient of correlation between material deprivation and RRIM for years 1991
and 2000.
Year Coefficient of
Correlation (R)
p-value Coefficient of
Determination (R2)
268 Soo-Fen Fam, Abdul Aziz Jemain and Kamarulzaman Ibrahim
1991 0.48 4.905e-06 23.04
2000 0.45 2.906e-05 20.25
ACKNOWLEDGMENTS
The authors would like to thank Malaysian Statistics Department for providing the data to make
this study possible. The first author is grateful to Public Service Department of Malaysia and
Department of Statistics, Malaysia for their sponsorship and support for this research.
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