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Projection of Life Expectancy at Birth of Malaysian Population: A Modelling Approach

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The purpose of this study is to project life expectancies (LEs) by sexes and ethnic groups for Malaysia. The secondary data of LE by sex and ethnic groups for Malaysia were extracted from the Department of Statistics, Government of Malaysia. The exponential growth model was employed to fulfill the objective. Projections of LEs for male and female of Malaysia by ethnicity were estimated by using exponential growth model for the years 2014-2050. The study investigated that the LEs for male and female of Malaysia by ethnicity are showing increasing trend. Results revealed that LEs for the female is greater than that of the male for each ethnic group. The projected LEs for male and female of Malaysia in 2050 would be 79.13 and 84.05 years, respectively. The population of Malaysia will tend to live long. The Government should have clear information about the number of elderly population ensuring their healthy environment.
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Original Article
Projection of Life Expectancy at Birth of Malaysian Population:
A Modelling Approach
Md. Rafiqul Islam,1,2 Mahendran Shitan,2,3 Md. Nazrul Islam Mondal*1
Abstract
The purpose of this study is to project life expectancies (LEs) by sexes and ethnic groups for
Malaysia. The secondary data of LE by sex and ethnic groups for Malaysia were extracted
from the Department of Statistics, Government of Malaysia. The exponential growth model
was employed to fulfill the objective. Projections of LEs for male and female of Malaysia by
ethnicity were estimated by using exponential growth model for the years 2014-2050. The
study investigated that the LEs for male and female of Malaysia by ethnicity are showing
increasing trend. Results revealed that LEs for the female is greater than that of the male
for each ethnic group. The projected LEs for male and female of Malaysia in 2050 would be
79.13 and 84.05 years, respectively. The population of Malaysia will tend to live long. The
Government should have clear information about the number of elderly population
ensuring their healthy environment.
Key words: Life expectancy, ethnic groups in Malaysia, exponential growth model,
Malaysia
(Journal of The Indian Academy of Geriatrics, 2017; 13:68-72)
INTRODUCTION
Life expectancy (LE) is defined as the mean
number of additional years that a person of a
specific age will live when the age-specific mortality
rates remain constant over the course of the
individual's lifetime and it is an alternative
measure of mortality in the human population.1 LE
is the principal health indicator of a nation. On the
contrary, education and health are the leading
sectors for social development in the developing
country like Malaysia. In Malaysia LE is more or
less similar in international comparison with the
median of LE for male and female being 72 and 77
years respectively in 2015.2 LE is not the measure
of the elderly population, but it represents the
present situation of the socioecon-omic development
of a country. In Malaysia, LE is increasing over
time similar to developed countries. Like many
other countries, women live longer than men in
Malaysia, and this gender gap in LE has been
increasing over time. The longer LE for female does
not necessarily mean that they are healthier than
that of men.
A number of studies using self-reported health
status measure indicate higher prevalence of
functional limitations and poor health among
women population suggesting that the additional
years may not necessarily be lived in healthy
1Department of Population Science and Human
Resource Development, Rajshahi University, Rajshahi,
Bangladesh; 2Laboratory of Computational Stati-stics
and Operations Research, Institute for Mathem-atical
Research, University Putra Malaysia, Serdang,
Selangor, Malaysia; 3Department of Mathematics,
Faculty of Science, University Putra Malaysia,
Serdang, Selangor, Malaysia.
*Corresponding author: Dr Md. Nazrul Islam
Mondal, Professor, Department of Population Science
and Human Resource Development, Rajshahi
University, Rajshahi 6205, Bangladesh, E-mail:
nazrulupm@gmail.com
Projection of Life Expectancy at Birth 69
conditions.3-6 Projections using exponential growth
model had been employed in recent studies.7-11 LE is
increasing every year which may imply that the
aged population is growing not only in Malaysia but
also all over the world. For this reason, correct
information regarding increased LE in future is
needed to take several steps for development of
education, health, environment and others sectors
in Malaysia as well as in the world as a whole.
Therefore, the study has attempted to apply
exponential growth model to examine the projection
of LEs for male and female separately and the
different ethnic groups in Malaysia for the years
2014-2050.
DATA AND METHODS
The secondary data on LE for male and female
of Malaysia by ethnic groups were taken from the
Department of Statistics, Government of Malaysia.
There are different types of methods of carrying out
population projections but primarily two methods
namely the Cohort-Component Method and the
mathematical approach are used. In developed
nations, the Cohort-Component Method is one of
the widely used method of population projections
because of the availability of sufficient data. In
developing countries having insufficient data,
perhaps a mathematical approach might be more
appropriate to make projections. For this,
exponential growth model is considered for the
projection of LEs of Malaysia.
For the projection of LEs for male and female
of Malaysia by ethnic groups, exponential growth
model is considered and the mathematical
formulation of this model is addressed by
12 tt PP =
{
}
)(exp 12 ttr , ........................... (1)
where, 1
t
Pthe initial LE at time t1 (2001); 2
t
P,
the terminal LE at time t2 (2013);
r
, the annual
growth rate during the interval.12
The estimation of
r
is computed from
equation 1 as follows.
r
=
()
1
2
12
ln
1
t
t
P
P
tt . ............................... (2)
Years 2001 and 2013 are considered as the
initial and the terminal points respectively in
estimating the growth rates of LEs by using
equation 2. This process is carried out successively
37 times for each case.
Moreover, the LE at birth for both sexes in
2013 and 2050 have been estimated using the
following formula
s
ese
e
m
f
+
+
=1
0
0
0
0 ;
Where, m
e0 is the LE at birth for male, f
e0 is
the LE at birth for female and s is the sex ratio at
birth at the same year. It is noted that sex ratio is
chosen as 1.029 in both cases.
RESULTS
In Malaysia, the LEs for the years 2001-2013
are presented by sexes and by ethnicity in Table 1.
In the case of total population, the LEs were 72.60
years for males and 77.20 years for females in 2013.
In this study, three ethnic groups, viz. Bumiputera,
Chinese, and Indian were considered. In the case of
ethnic groups, the LEs were 73.67 years (male,
71.30; female, 76.10) for Bumiputera; 77.32 years
(male, 75.00; female, 79.70) for Chinese; and 71.99
years (male, 67.90; female, 76.20) for Indian in 2013
(Table 1). The projected LEs up to the years 2050 by
sexes and by ethnicity are presented in Table 2. The
results revealed that the LEs in 2050 would be
81.56 years (male, 79.13; female, 84.05) for total
population; 80.53 years (male, 77.15; female, 84.00)
for Bumiputera; 83.84 years (male, 82.21; female,
85.52) for Chinese; and 78.51 years (male, 72.74;
females, 84.45) for Indian ethnic groups (Table 2).
The LEs for females would be higher compared to
males, and the LE (83.87 years) for ethnic Chinese
population would be the highest compared to other
ethnic populations.
DISCUSSION
The main objective of this study was to project
the LEs of Malaysian populations by ethnicity and
by sex. The exponential growth model was used as
the mathematical tool to project the LEs. The
projections were made up to the year 2050. The
results revealed that the LEs of Malaysian
population is increasing with time. Women tend to
live longer than men, and it is reflected in
differences in LEs. In this study, the LE was
considered the average number of years a person
can expect to live given existing mortality patterns.
The LE is considered the most commonly used
indicator of a population's general health status.
Nevertheless, LE should be recognised a measure of
the length of life rather than the quality of life, as it
does not account for the full burden of illness and
disability.
70 Journal of The Indian Academy of Geriatrics, Vol. 13, No. 2, June, 2017
Table 1. Life expectancy by ethnic groups and sex in Malaysia during 2001-2013
Total Bumiputera Chinese Indian Year
Male Female Male Female Male Female Male Female
2001 70.6 75.1 69.5 73.7 72.8 77.9 66.4 73.7
2002 70.8 75.3 69.6 73.9 73.0 78.1 66.8 74.1
2003 70.9 75.6 69.7 74.2 73.1 78.3 66.8 74.5
2004 71.1 75.9 69.9 74.4 73.3 78.5 67.2 75.1
2005 71.4 76.2 70.1 74.7 73.6 78.9 67.6 75.4
2006 71.5 76.3 70.2 74.7 73.8 79.0 67.9 75.5
2007 71.5 76.3 70.2 74.8 73.9 79.2 67.9 75.7
2008 71.6 76.4 70.2 74.8 73.9 79.3 67.9 75.7
2009 71.6 76.5 70.2 74.8 74.0 79.5 68.0 75.8
2010 71.9 76.6 70.7 75.4 74.4 79.1 67.6 75.7
2011 72.1 76.8 70.9 75.7 74.6 79.5 67.8 75.8
2012 72.4 77.0 71.1 75.9 74.9 79.7 67.9 76.2
2013 72.6 77.2 71.3 76.1 75.0 79.7 67.9 76.2
Source: The website of Department of Statistics, Govt. of Malaysia.
Table 2. Projection of life expectancy by ethnic groups and sex in Malaysia during 2014-2050
Total Bumiputera Chinese Indian Year
Male Female Male Female Male Female Male Female
2014 72.77 77.38 71.45 76.30 75.19 79.85 68.03 76.41
2015 72.94 77.56 71.60 76.51 75.37 80.00 68.15 76.62
2016 73.11 77.73 71.76 76.71 75.56 80.16 68.28 76.84
2017 73.28 77.91 71.91 76.92 75.75 80.31 68.41 77.05
2018 73.45 78.09 72.06 77.12 75.94 80.46 68.53 77.27
2019 73.62 78.27 72.22 77.33 76.12 80.62 68.66 77.48
2020 73.79 78.45 72.37 77.54 76.31 80.77 68.79 77.70
2021 73.96 78.63 72.53 77.74 76.50 80.92 68.92 77.91
2022 74.14 78.81 72.68 77.95 76.69 81.08 69.05 78.13
2023 74.31 78.99 72.84 78.16 76.88 81.23 69.18 78.35
2024 74.48 79.18 72.99 78.37 77.08 81.39 69.30 78.57
2025 74.66 79.36 73.15 78.58 77.27 81.54 69.43 78.78
2026 74.83 79.54 73.30 78.79 77.46 81.70 69.56 79.00
2027 75.01 79.72 73.46 79.00 77.65 81.85 69.69 79.22
2028 75.18 79.91 73.62 79.21 77.84 82.01 69.82 79.44
2029 75.36 80.09 73.77 79.42 78.04 82.16 69.95 79.67
2030 75.53 80.28 73.93 79.63 78.23 82.32 70.08 79.89
2031 75.71 80.46 74.09 79.85 78.43 82.48 70.21 80.11
2032 75.88 80.65 74.25 80.06 78.62 82.64 70.34 80.33
2033 76.06 80.83 74.40 80.27 78.82 82.79 70.48 80.56
2034 76.24 81.02 74.56 80.49 79.01 82.95 70.61 80.78
2035 76.41 81.20 74.72 80.70 79.21 83.11 70.74 81.01
2036 76.59 81.39 74.88 80.92 79.40 83.27 70.87 81.23
2037 76.77 81.58 75.04 81.14 79.60 83.43 71.00 81.46
2038 76.95 81.77 75.20 81.35 79.80 83.58 71.13 81.68
2039 77.13 81.95 75.36 81.57 80.00 83.74 71.27 81.91
2040 77.31 82.14 75.52 81.79 80.20 83.90 71.40 82.14
2041 77.49 82.33 75.68 82.01 80.40 84.06 71.53 82.37
Projection of Life Expectancy at Birth 71
Total Bumiputera Chinese Indian Year
Male Female Male Female Male Female Male Female
2042 77.67 82.52 75.84 82.23 80.60 84.22 71.67 82.60
2043 77.85 82.71 76.01 82.45 80.80 84.38 71.80 82.83
2044 78.03 82.90 76.17 82.67 81.00 84.54 71.93 83.06
2045 78.21 83.09 76.33 82.89 81.20 84.71 72.07 83.29
2046 78.40 83.28 76.49 83.11 81.40 84.87 72.20 83.52
2047 78.58 83.47 76.66 83.33 81.60 85.03 72.34 83.75
2048 78.76 83.67 76.82 83.56 81.80 85.19 72.47 83.99
2049 78.95 83.86 76.98 83.78 82.01 85.35 72.61 84.22
2050 79.13 84.05 77.15 84.00 82.21 85.52 72.74 84.45
In 1980, the LE in Malaysia was 65.0 years,
but by 2015, it had increased 74.50 (male, 72;
female, 77) years.2 The longevity can be attributed
to many factors, including rising living standards,
improved lifestyle and better education, as well as
greater access to quality health services which are
significantly increased in Malaysia. Thus, the
demographic changes, socioeconomic inequalities,
and availability of health factors influence LE.13-16
In Malaysia, the population expansion and
demographic transition since the 1980s were
accompanied by major socioeconomic develop-
ment.17 After the long years of socioeconomic
expansion, Malaysia transformed into a developed
nation. Consequently, the economic development
deter-mines the improvements in social conditions
and increases LE. Increases in LEs have also been
attributed to improvements in sanitation and
access to clean water.18 In Malaysia, access to
improved sanitation reached 96% which is
significantly high among the developing countries
of Asia. An increase in LE was driven mainly by an
improvement in sanitation during the nineteenth
and early twentieth centuries.19
Gender differences in mortality and LE vary
by countries. In most countries, men live shorter
lives than women, sometimes by a margin of as
much as ten years. This study identified that the
LEs for females irrespective of ethnicity would be
higher compared to males in Malaysia. The death
rates for women were found lower than those for
men at all ages. More boys than girls die in infancy
and during each subsequent year of life; mortality
rates for males exceed those for females. As a
result, the gender gap would be widened in this
century. Factors that influence gender differences
in mortality include biological factors such as
hormonal influences on physiology and behaviour
and environmental factors such as cultural
influences on gender differences in health
behaviours. The importance of specific factors may
reflect the environmental context.
The Malaysian Chinese consists of people of
full or partial Chinese- particularly Han Chinese
ancestry who were born in or immigrated to
Malaysia. Most are descendants who arrived
between the early and the mid-20th century.
Malaysia is home to the second largest community
of overseas Chinese in the world after Thailand.
Within Malaysia, they are usually simply referred
to as Chinese and represent the second largest
ethnic group in Malaysia after the ethnic Malay
majority. The ethnic Chinese populations are the
socioeconomically well established middle class
ethnic group and traditionally dominate the
business and commerce sectors of the Malaysian
economy. This study identified that the ethnic
Chinese population especially women live longer
than that of other ethnic groups. Malaysian
Chinese women are more health conscious, and
they are relatively more aware of healthy dietary
habits and give priority to a healthy lifestyle. They
are very particular about their health, and are
active from young to old age, they go for regular
medical check-ups and are physically active, more
hardworking, especially the older generation.
Remarkably, the older people are found more
conscious about their health compared to others
and tend to exercise, eat healthy food and pursue
healthy lifestyles.
CONCLUSIONS
The LEs for male and female population of
Malaysia by ethnic groups were estimated using
exponential growth model up to year 2050. It was
found that the LEs for male and female populations
of Malaysia by ethnic groups showed gradually
increasing trend over time. It was also observed
that population of Malaysia is tending to have a
longer LE. These might be used as predicted LEs
for the male and female population of Malaysia by
ethnic groups for further higher study. The LEs for
male and female population of Malaysia in 2050
72 Journal of The Indian Academy of Geriatrics, Vol. 13, No. 2, June, 2017
will be 79.13 and 84.05 years respectively. Hence it
may be concluded that LEs for female population is
longer than male population in Malaysia. As a
consequence, the population of Malaysia will tend
to experience serious ageing related problems in the
future. Therefore, especially government and policy
makers should have a clear understanding of the
number of aged population to ensure their healthy
environment. More research works are needed in
this area.
ACKNOWLEDGEMENTS
The authors are very grateful to the
Laboratory of Computational Statistics and
Operations Research, Institute for Mathematical
Research (INSPEM) and to the University Putra
Malaysia (UPM) for granting a Visiting Scientist
Fellowship to complete this study.
Conflicts of interest: None declared.
REFERENCES
1. Chiang L. The Life Table and its construction,
Introduction to stochastic processes in biostatistics.
New York: John Wiley and Sons 1968; 189-214.
2. PRB 2016. World Population Data Sheet. Population
Reference Bureau (PRB), 1875, Connecticut Ave.,
NW, Suite 520, Washington, DC; USA.
3. Parahyba I, Veras R, Melzer, D. Disability among
elderly women in Brazil. Rev Saude Publica 2005;
39(3):383-391.
4. Lima M, Barros A, Cesar G, et al. Health-related
quality of life among the elderly: a population-based
study using sf-36 survey. Cad Saude Publica 2009;
25 (10):2159-2167.
5. Zunzunegui V, Alvarado E, Beland F, Vissandjee B.
Explaining health differences between men and
women in later life: A cross-city comparison in Latin
America and the Caribbean. Soc Sci Med 2009;
68:235-242.
6. Szwarchwald L, Mota C, Damacena N, Pereira S.
Health inequalities in Rio de Janeiro, Brazil: Lower
healthy life expectancy in socioeconomically disadv-
antaged areas. Am J Public Health 2011; 101(3): 517-
523.
7. Islam M R. Predicting population for male of rural
area in Bangladesh. Journal of Statistical Research
of Iran 2007; 4(2):227-238.
8. Islam M R. Modelling and Projecting Population for
Muslim of Urban Area in Bangladesh. International
Journal of Probability and Statistics, 2012; (1):04-10.
9. Islam M R, Beg ABM, Rabiul Alam. Modelling and
predicting population of Dhaka district of
Bangladesh. International J. of Mathematics and
Computation 2009; 4(9):69-80.
10. Islam M R, Beg ABM, Rabiul Alam. Modelling and
predicting urban male population of Bangladesh:
Evidence from census data. International e Journal
of Mathematics and Engineering 2010; 1(1):86-95.
11. Islam M R, Hoque M.N. Mathematical modelling and
projecting population of Bangladesh by age and sex
from 2002 to 2031. Emerging Techniques in Applied
Demography, Applied Demography Series 4, 2015;
Chapter 5: 53-60.
12. Shryock H S J S, Siegel and Associates. 1975. The
Methods and Materials of Demography, Vol. I & II,
U.S. Government Printing Office, Washington, DC;
USA.
13. Mondal MNI, Shitan M. Impact of socio-health
factors on life expectancy in the low and lower
middle-income countries. Iranian J Publ Health
2013a; 42(12):1354-1362.
14. Mondal MNI, Shitan M. Factors affecting the
HIV/AIDS epidemic: an ecological analysis of global
data. African Health Sciences 2013b; 13(2):301-310.
15. Mondal MNI, Shitan M. Relative importance of
demographic, socioeconomic and health factors on life
expectancy in low- and lower-middle-income
countries. J Epidemiol 2014; 24(2):117-124.
16. Mondal MNI, Ullah MMMN, Islam MR, Rahman MS,
et al. Sociodemographic and health determinants of
inequalities in life expectancy in the least developed
countries. International Journal of MCH and AIDS
2015; 3(2):96-105.
17. World Bank 2010. World Development Indicators.
World Bank (WB); H Street NW, Washington DC,
USA.
18. Mondal MNI, Shitan M. Author’s response:
Regarding the relative importance of demographic,
socioeconomic and health factors on life expectancy
in low- and lower-middle-income countries. J
Epidemiol 2015; 25(6):460.
19. Oeppen J, Vaupel JW. Broken limits to life
expectancy. Science 2002; 296, 1029.
... These findings conform to the study of Malaysian population (Islam et al., 2017). In this study, actually the LE was treated as the average number of years of a person who can expect to live with rising living standards, enhanced lifestyle and improved quality education, as well as greater access to quality as well as available health services which are significantly increased LE in Bangladesh, especially in the urban area of Bangladesh. ...
... These findings conform to the study of Malaysian population (Islam et al., 2017). In this study, actually the LE was treated as the average number of years of a person who can expect to live with rising living standards, enhanced lifestyle and improved quality education, as well as greater access to quality as well as available health services which are significantly increased LE in Bangladesh, especially in the urban area of Bangladesh. ...
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... However, currently, there are several studies such as (Shair et al., 2019). Islam et al (2017) which focus on forecasting life expectancy. Thus, this work aims to offer additional literature on forecasting Malaysia life expectancy in the future by using Box-jenkins model. ...
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Dear Prof. Inoue, We would like to thank Dr. Selck for his interest in our article, “Relative Importance of Demographic, Socioeconomic and Health Factors on Life Expectancy in Low- and Lower-Middle-Income Countries”.1 We are very grateful to him for his letter2 having scholastic suggestions regarding our study. In that study, an attempt was made to identify the pathways by which demographic changes, socioeconomic inequalities, and availability of health factors influence life expectancy in low- and lower-middle-income countries (n = 91). The response variable was life expectancy, and the explanatory variables were demographic events (total fertility rate and adolescent fertility rate), socioeconomic status (mean years of schooling and gross national income per capita), and health factors (physician density and human immunodeficiency virus [HIV] prevalence). Path analysis was used to determine the direct, indirect, and total effects of these factors on life expectancy. Variables that had the most significant effects on life expectancy in the most recent previous studies were chosen.3–6 However, in his letter,2 Dr. Selck claimed that we failed to either reference or test for the assertion that, “the current increases in life expectancy have been attributed to improvements in sanitation.” Actually, the complete sentence was: “Increases in life expectancy have been attributed to improvements in sanitation and access to clean water; medical advances, including childhood vaccines; and massive increases in agricultural production.” The preceding sentence was “Wide variations in life expectancy still exist between high- and low-income countries” which demanded the next sentence to clarify the stipulation of this study. However, we do agree with the suggestion and believe a reference is merited. Further, Selck2 stated that the analysis of that study1 included recent data for poor- and middle-income countries that only focused on fertility, schooling, income, physician density, and HIV prevalence. Selck2 subsequently recommended that ‘sanitation’ and other factors (eg, vaccination) be included in the multivariate regression model to improve results. The suggestion is good, and we do believe that the inclusion of sanitation and vaccination as determinant factors would make the paper more relevant. We hope to incorporate the suggested factors in our next paper.