THE ASSOCIATION BETWEEN MATERIAL DEPRIVATION AND RELATIVE RISK OF INFANT MORTALITY IN PENINSULAR...

International Journal of Arts & Sciences,

CD-ROM. ISSN: 1944-6934 :: 4(17):257–269 (2011)

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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.


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


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


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.


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

��


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


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.


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


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)


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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