CONTEMPORARY METHODS
OF MORTALITY ANALYSIS
Lecture 2
Leonid Gavrilov
Natalia Gavrilova
Measures of Mortality





Crude Death Rate
Age-Specific Death Rates (AgeSpecific Mortality Rates)
Age-Adjusted Mortality Rates
(Standardized Mortality Rates)
Life Expectancy (at birth or other
age)
Measures of Infant Mortality
Crude Death Rate


Number of deaths in a population during a
specified time period, divided by the population
size "at risk" of dying during that study period.
For one-year period, Crude Death Rate,
CDR = Deaths in that year /mid-year population size
x 1,000 to adjust for standard-sized population of 1,000 persons
mid-year population = total population for July 1
Crude Death Rate Pros and Cons


Pros:
- Easy to calculate, and require less
detailed data than other mortality
measures
- Useful for calculation of the rate of
natural increase (crude birth rate minus
crude death rate)
Cons:
- Depends on population age structure
(proportions of younger and older people)
Trends in crude death rates (per
1,000) for Russia, USA and Estonia
Distribution of crude death rates
(per 1,000) in Russia, 2003
Age-Specific Death Rates (ASDR) or
Age-Specific Mortality Rates (ASMR)

Number of deaths in a specific age group during a
specified time period, divided by the size of this
specific age group during that study period.
Example:
For one-year study period, Age-Specific Death
Rates, ASDR for males at age 45-49 years =
= Deaths to males aged 45-49 in that year /
Number of males aged 45-49 at mid-year
x 1,000 to adjust for standard-sized population
of 1,000 persons of that age.
Age-Specific Death Rates
Pros and Cons


Pros:
- Allows to study mortality by age
(and sex)
Cons:
- Requires detailed data on deaths by
age (not always available for
developing countries, war and crisis
periods, historical studies)
Age-adjusted death rate (ADR),
standardized death rate (SDR) or
age-standardized death rate (ASDR)

Death rate expected if the studied
population had the age distribution of
another "standard" population (arbitrary
chosen for the purpose of comparison).
Calculated as weighted average (with
weights being proportions of the
"standard" population at each age)
Age-Adjusted Death Rate or
Age-Standardized Death Rate

Direct method of age standardization:
P si M ui
ADR =
i


Ps
Mui is mortality rate in the studied population
at age i
Psi – number of persons at age i in the standard
population. Ps – total standard population.
Age-Adjusted Death Rate or
Age-Standardized Death Rate


Pros:
- Allows comparison of death rates of
populations despite differences in their age
distribution
Cons:
- Requires data on death rates by age (not
always available for developing countries, war
and crisis periods, historical studies)
- Results of comparison may depend on the
arbitrary choice of standard.
Typical standard populations


European standard population
and World standard population
suggested by the World Health
Organization
In the United States: 1940 U.S.
standard population and 2000
U.S. standard population (applied
around 2003)
The Concept of Life Table

Life table is a classic demographic format of
describing a population's mortality experience
with age.
Life Table is built of a number of standard
numerical columns representing various
indicators of mortality and survival.
The concept of life table was first suggested in
1662 by John Graunt.
Before the 17th century, death was believed to be
a magical or sacred phenomenon that could not
and should not be quantified. The invention of
life table was a scientific breakthrough in
mortality studies.
Life Table


Cohort life table as a simple
example
Consider survival in the cohort of
fruit flies born in the same time
Number of dying, d(x)
Number of survivors, l(x)
Number of survivors at the
beginning of the next age interval:
l(x+1) = l(x) – d(x)
Probability of death in the age
interval:
q(x) = d(x)/l(x)
Probability of death, q(x)
Person-years lived in the interval, L(x)
Lx = x
lx + lx +
x
2
L(x) are needed to calculate life
expectancy. Life expectancy, e(x),
is defined as an average number of
years lived after certain age.
L(x) are also used in calculation of net
reproduction rate (NRR)
Calculation of life expectancy, e(x)
Life expectancy
at birth is
estimated as an
area below the
survival curve
divided by the
number of
individuals at
birth
Life expectancy, e(x)


T(x) = L(x) + … + Lω
where Lω is L(x) for the last age
interval.
Summation starts from the last age
interval and goes back to the age at
which life expectancy is calculated.
e(x) = T(x)/l(x)
where x = 0, 1, …,ω
Life Tables for Human Populations



In the majority of cases life tables for
humans are constructed for
hypothetic birth cohort using crosssectional data
Such life tables are called period life
tables
Construction of period life tables
starts from q(x) values rather than
l(x) or d(x) as in the case of
experimental animals
Formula for q(x) using
age-specific mortality rates
qx =
Mx
1 + (1
ax ) Mx
a(x) called the fraction of the last interval of life is
usually equal to 0.5 for all ages except for the first age
(from 0 to 1)
Having q(x) calculated, data for all other life table
columns are estimated using standard formulas.
Life table probabilities of death, q(x), for
men in Russia and USA. 2005
1
0
10
20
30
40
50
60
log(q(x))
0.1
0.01
0.001
0.0001
Age
Russia
USA
70
80
90
100
Period life table for hypothetical
population


Number of survivors, l(x), at the
beginning is equal to 100,000
This initial number of l(x) is called
the radix of life table
Life table number of survivors, l(x), for men
in Russia and USA. 2005.
120000
100000
80000
Russia
60000
USA
40000
20000
0
0
10
20
30
40
50
60
70
80
90 100
Life table number of dying, d(x), for men in
Russia and USA. 2005
Russia
USA
3500
3000
d(x)
2500
2000
1500
1000
500
0
0
10
20
30
40
50
Age
60
70
80
90
100
e(x)
Life expectancy, e(x), for men in Russia and
USA. 2005
80
70
60
50
40
30
20
10
0
Russia
USA
0
10
20
30
40
50
Age
60
70
80
90 100
Trends in life expectancy for men
in Russia, USA and Estonia
Trends in life expectancy for
women in Russia, USA and Estonia
Special methods based on life
table approach

Multiple decrement life tables

Cause-elimination life tables

Decomposition of life expectancy
Multiple decrement life tables



Multiple decrement life tables
Often used to construct life tables by
cause of death
In this case decrements are different
causes of death
Multiple decrement life tables
vs ordinary life tables






In an ordinary life table, membership in a well-defined
cohort can be terminated by a single attrition factor.
In a multiple-decrement life table, there are multiple
reasons for attrition (death).
Ordinary life tables can be used to answer questions about
longevity.
A multiple-decrement life table will be used to answer the
question, "What is the probability that a newborn will die
due to a specific cause before reaching age 65?“
In an ordinary life table of mortality it is assumed that
everyone eventually dies.
The multiple-decrement life table will provide the
probability that a person will eventually die due to a specific
cause.
Multiple decrement life table –
steps of construction


Construct an ordinary life table
Calculate probabilities of death from
cause k
k
x
q = qx
M
k
x
Mx
= qx
D
k
x
Dx
Multiple decrement life table –
steps of construction (continue)

Calculate number of decrements from cause
k in age interval (x, x+n):
k
x
d =q

k
x
lx
Calculate numbers of survivors to age y for
those who eventually die from cause k
during his/her life:
k
x
d
l =
x =y
k
x
Multiple decrement life table –
steps of construction (continue)



Calculate life-time probability of dying
from cause k :
lk/l0
Calculate mean expected age at death
from cause k by calculating Lkx and Tkx
Calculated as life expectancy in the
ordinary life table
Mean expected
age at death by
cause,
women, Russia
Васин С., доклад в Киеве
2006
Comparison
of mortality
structure for
Russia and
Western
countries
1965
Vassin, 2006
Comparison
of mortality
structure for
Russia and
Western
countries
2004 год
Васин С., доклад в Киеве
2006
Decomposition of life expectancy

Suggested by Andreev (1982),
Pollard (1982) and Arriaga (1984)
Decomposition by age
x
=
l
1
x
l
1
0
L
l
2
x
2
x
L
l
1
x
1
x
+
T
2
x +n
l
1
0
l
1
x
l
2
x
l
1
x +n
l
2
x +n
Where values lx, Lx, Tx represent standard functions
from ordinary life table, and notations 1 and 2
correspond to populations 1 and 2 respectively
(comparing populations).
Thus, we need first to calculate ordinary life
tables for populations 1 and 2
Decomposition by age (continue)
=
l
1
l 10
T
2
l2
T
1
l1
The last open age interval
Decomposition by contribution of
different causes of death
i
x
=
=
R
x
m
x
i (2 )
x
i (2 )
x
m
i (1 )
x
m (x2 )
m (x1 )
m
(2 )
x
R
i (1 )
x
m
(2 )
x
m
(1 )
x
m
(1 )
x
where Rix designates a proportion of deaths from
cause i in age group (x, x+n), which is Dix/Dx. In this
case Dix corresponds to the observed number of
deaths from cause i in age interval (x, x+n), and Dx is a
corresponding number of deaths from all causes.
Decomposition by causes of death
(continue)
Notations (1) and (2) correspond to comparing
populations. Values mx correspond to life table mortality
rates derived from ordinary life tables, because mx =
dx/Lx. In this formula value Δx corresponds to contribution
of differences in mortality from all causes of death in =age
interval (x, x+n) to the observed differences in life
expectancy. It can be shown that
i
x
x
i
e
(1 )
0
(2 )
x
e =
x
x
i
x
=
x
i
Decomposition of the U.S.-Russia
gap in life expectancy by cause
USA – 1999; Russia – 2001. Source: Shkolnikov et a. Mortality reversal in Russia.
Decomposition of the U.S.-Russia
gap in life expectancy by cause
USA – 1999; Russia – 2001. Source: Shkolnikov et a. Mortality reversal in Russia.
Additional reading
Preston S. H., Heuveline P., Guillot M.
Demography. Measuring and
modeling population processes.
Blackwell Publ., Oxford, 2001.
Cause elimination life tables
Cause elimination life table

Uses an additive property of hazard
rate
m x = m + m + ... + m
1
x

2
x
k
x
Chiang’s method (1978) – assumes
proportionality of hazard rates from
different causes
k
a
=r
k
a
, x a x+n
Main formula for cause elimination life
table
k
qx = 1
(1
1
qx )
r
In this formula notation –k means that probability of death is related
to cause elimination (not a power).
Proportionality ratio rk can be obtained from the observed number of
deaths in a particular age interval:
rk =
D kx
Dx
Mortality in Central Asia
An example of practical use of
demographic methods
Background on Mortality in
Russia
Before the World War II
Life expectancy (both sexes)
80
70
60
50
40
30
20
10
0
59
47
47
63
Russia
43
32
France
USA
1900
1938
Catching up with the West
Life expectancy in 1965
80
70
60
50
40
30
20
10
0
64.3 67.3 66.8
73.4 74.7 73.7
Russia
France
USA
Men
Women
Stagnation after 1965
In 1992 and 1998 Russia
experienced two serious
economic crises accompanied
by drop in personal income and
rapid impoverishment
Russia: Trends in life expectancy
Mortality reversal




Situation when the usual time trend of declining
mortality is reversed (mortality is increasing over
time).
Observed in sub-Saharan Africa (AIDS
epidemic), Eastern Europe, and FSU countries
including Russia.
Mortality Reversal in FSU countries and Russia is
particularly strong among male population, with
excess mortality at ages about 35-55 years.
Particularly high increase in mortality from
violence and accidents among manual workers
and low education groups.
Ethnic Differentials in Mortality
Based on the Study of Ethnic
Differentials in Adult
Mortality in Kyrgyzstan
Michel Guillot (PI), University of
Wisconsin-Madison
Natalia Gavrilova, University of Chicago
Tetyana Pudrovska, University of
Wisconsin-Madison
Demography, 2011, 48(3): 1081-1104
Background on Kyrgyzstan





Former Soviet republic; became
independent in 1991
Population: 5.2 million (2006)
Experienced a severe economic
depression after break-up of Soviet
Union
GNI per capita = 440 USD; 28th poorest
country in the world (2005)
48% of population below national
poverty line (2001)
2008 Workshop, Bishkek
Ethnic Groups in Kyrgyzstan



Native Central Asian groups: Kazakh,
Kyrgyz, Tajik, Turkmen, Uzbek (Sunni
Muslims)
Slavs: Russian, Ukrainian,
Bielorussian
Kyrgyzstan, 1999 census:
Central Asians: 79% of pop. (Kyrgyz 65%)
 Slavs: 14% of pop. (Russian 12%)

Recorded trends in adult mortality (20-60 years)
Kyrgyzstan, 40q20
0.30
0.10
0.20
q2060
0.10
0.20
q2060
0.30
0.40
Females
0.40
Males
1960
1970
1980
y ear
1990
2000
1960
1970
1980
y ear
1990
2000
russian
ky rgy z
russian
ky rgy z
slv
cas
slv
cas
Mortality paradox?

Soviet period: Russians/Slavs
occupied dominant positions in the
socio-economic structure of
Central Asian societies (Kahn
1993)
Mortality paradox?




Slavic females more educated than
Central Asian females (1989 and 1999
censuses)
Slavic males: educational advantage not
so clear – varies by age (1989 and 1999
censuses)
Slavic households less poor than Central
Asians (1993 World Bank poverty
survey)
Infant mortality lower among Slavs
(Soviet and post-Soviet period)
Proportion of individuals with post-secondary education,
by age and ethnicity, in 1989 census.
Females
SLAVIC (Russian, Ukrainian, Belorussian), 1989
CENTRAL ASIAN (Kyrgyz, Uzbek), 1989
0.300
Proportion higher education
0.250
0.200
0.150
0.100
0.050
0.000
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
Mortality paradox?




Slavic females more educated than
Central Asian females (1989 and 1999
censuses)
Slavic males: educational advantage not
so clear – varies by age (1989 and 1999
censuses)
Slavic households less poor than Central
Asians (1993 World Bank poverty
survey)
Infant mortality lower among Slavs
(Soviet and post-Soviet period)
Proportion of individuals with post-secondary education, by
age and ethnicity, in 1989 census. Males.
SLAVIC (Russian, Ukrainian, Belorussian), 1989
CENTRAL ASIAN (Kyrgyz, Uzbek), 1989
0.250
Proportion higher education
0.200
0.150
0.100
0.050
0.000
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
Mortality paradox?




Slavic females more educated than
Central Asian females (1989 and 1999
censuses)
Slavic males: educational advantage not
so clear – varies by age (1989 and 1999
censuses)
Slavic households less poor than Central
Asians (1993 World Bank poverty
survey)
Infant mortality lower among Slavs
(Soviet and post-Soviet period)
Mortality paradox?




Slavic females more educated than
Central Asian females (1989 and 1999
censuses)
Slavic males: educational advantage not
so clear – varies by age (1989 and 1999
censuses)
Slavic households less poor than Central
Asians (1993 World Bank poverty
survey)
Infant mortality lower among Slavs
(Soviet and post-Soviet period)
IMR by ethnicity, 1958-2003, Kyrgyzstan
30
20
10
IMR
40
50
Urban areas
1960
1970
1980
year
Central Asians
1990
Slavs
2000
Data

Unpublished population and death
tabulations since 1959



collected from local archives
Individual census records – 1999
Individual death records – 19981999

obtained from national statistical office
Possible explanations for
mortality paradox



Data artifacts
Migration effects (esp. 1989-99)
Cultural effects
Data artifacts?

Could the lower recorded mortality
among Central Asian adults be due to
lower data quality among them
(coverage of deaths, age
misreporting)?
Migration effects?


1/3 of Russian population has left
Kyrgyzstan since 1991
Could the increased disparity
between Russian and Kyrgyz adult
mortality be due to selective
migration (healthy migrant effect)?
Cultural effects?

Culture may affect mortality in various
ways:
individual health and lifestyle behaviors (e.g., diet,
smoking, alcohol, use of preventive care)
 family structure and social networks (denser social
networks may produce lower stress levels and
better health)


Could different cultural practices among
Slavs and Central Asians explain the
observed mortality differentials?
Data artifacts?

Intercensal estimates of death
registration coverage above age 60
(Guillot, 2004):
90+ % as early as 1959 in urban areas
 coverage in rural areas was low initially
(~50%) but caught up with urban areas in
1980s
 Total population: 92% for 1989-99 period


Adult deaths (20-59) usually better
reported than deaths 60+
Kyrgyzstan, 40q20, Urban areas
0.30
0.20
0.10
0.10
0.20
q2060
0.30
0.40
Females
0.40
Males
1960
1970
1980
y ear
1990
2000
1960
1970
1980
y ear
1990
2000
russian
ky rgy z
russian
ky rgy z
slv
cas
slv
cas
Health selection?
Russians in KG vs. Russia, 40q20
0.40
0.50
Females
0.10
0.20
0.30
q2060
0.30
0.20
0.10
q2060
0.40
0.50
Males
1960
1970
1980
y ear
Russians in KG
1990
2000
Russia
1960
1970
1980
y ear
Russians in KG
1990
2000
Russia
Cohort-specific changes in educational
attainment, Males, 1989-99
SLAVIC, 1989
SLAVIC, 1999
0.300
Proportion higher education
0.250
0.200
0.150
0.100
0.050
0.000
Age in 1989: 20-24
Age in 1999: 30-34
25-29
35-39
30-34
40-44
35-39
45-49
40-44
50-54
45-49
55-59
50-54
60-64
55-59
65-69
60-64
70-74
65-69
75-79
70-74
80-84
75-79
85-89
80-84
90-94
Cohort-specific changes in educational
attainment, Females, 1989-99
SLAVIC, 1989
SLAVIC, 1999
0.300
Proportion higher education
0.250
0.200
0.150
0.100
0.050
0.000
Age in 1989:
Age in 1999:
20-24
30-34
25-29
35-39
30-34
40-44
35-39
45-49
40-44
50-54
45-49
55-59
50-54
60-64
55-59
65-69
60-64
70-74
65-69
75-79
70-74
80-84
75-79
85-89
80-84
90-94
Cultural effects?


Analysis of causes of death by
ethnicity, 1998-99
Calculations based on micro-data
Deaths: vital registration (1998-99)
 Exposure: census (March 1999)
 Ages 20-59
 Ethnicity: Central Asians vs. Slavs
 ~20,000 death records; ~2.2 million
census records

Age-standardized Death Rates at
working ages (per 100000), 1998-99,
by cause and ethnicity, Males
Infectious/par. diseases
- incl. TB
Neoplasms
CVD
CA
Slavs
- incl. IHD
Respiratory diseases
Digestive diseases
Injuries/poisoning
Other causes
0
50
100
150
200
250
Contribution of causes of death to the difference
in life expectancy at working ages (40e20)
between Slavs and Central Asians
Males (total difference = 2.90 years)
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
au
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Age-standardized Death Rates at working
ages (per 100,000). Detailed Injuries, Males
50
45
40
35
Slavs
CA
30
25
20
15
10
5
0
Age-standardized Death Rates at
working ages (per 100,000), 1998-99,
by cause and ethnicity, Females
Infectious/par. diseases
- incl. TB
Neoplasms
CVD
- incl. IHD
Respiratory diseases
Digestive diseases
CA
Slavs
Injuries/poisoning
Other causes
0
10
20
30
40
50
60
70
80
Contribution of causes of death to the difference
in life expectancy at working ages (40e20)
between Slavs and Central Asians
Females (total difference = .28 years)
0.35
0.30
0.25
0.20
0.15
0.10
0.05
au
se
s
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s
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pl
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s.
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g
-0.10
Re
sp
ir a
to
ry
In
-0.05
Ne
o
fe
ct
io
ns
0.00
Age-standardized Death Rates at working
ages (per 100,000)
Detailed Injuries, Females
9
Slavs
CA
8
7
6
5
4
3
2
1
0
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i
s.
u
a
t.c
ts
ur
re
ng
i
i
n
c
f
.
n
e
y
tr
w
id
c
b
o
c
e
n
l
dr
se
ac
/e
l
de
.
u
i
t
a
or
us
ca
cc
nt
.
p
a
a
e
d
.
d
.c
ns
ci
c
lo
ci
c
a
l
c
c
a
a
tr
a
a
de
ci
i
su
Alcohol-related Causes of Death
(Chronic alcoholism, Alcohol psychoses, Alcohol cirrhosis of the
liver, Accidental poisoning by alcohol)
Age-standardized Death Rates at working ages (per
100,000)
50
45
CA
Slavs
40
35
30
25
20
15
10
5
0
Males
Females
Multivariate analysis







Do ethnic mortality differentials at adult ages
remain once we account for differences in
education and urban/rural residence?
Negative binomial regression
Dependent variable: deaths from all causes;
deaths by major cause (7)
Explanatory variables: exposure, dummy
variables for age, ethnicity, urban/rural
residence, education (3 cat.)
Males and Females analyzed separately
Model 1: age, ethnicity
Model 2: age, ethnicity, education, residence
Males, all causes of death
In
e
s.
ie
s
di
s.
di
ju
r
es
t iv
Di
g
y
Re
sp
ir a
to
r
CV
D
pl
as
m
s
ns
ca
us
es
fe
ct
io
Ne
o
In
Al
l
Risk Ratio Slavs/CA
Males
3.5
3.0
2.5
2.0
Model 1
1.5
Model 2
1.0
0.5
0.0
Risk Ratio Slavs/CA
Females
3.5
3.0
2.5
2.0
Model 1
Model 2
1.5
NS NS
1.0
NS
NS NS
NS NS
NS
0.5
au
se
s
ie
s
O
th
e
rc
ju
r
In
CV
Re
D
sp
ir a
to
ry
Di
s.
Di
ge
st
iv
e
Di
s.
pl
as
m
s
Ne
o
ns
fe
ct
io
In
Al
l
C
au
se
s
0.0
Conclusions



Excess mortality among adult Slavs
(Soviet and post-Soviet period) is
not likely due to data artifacts or
migration effects
Excess mortality due to important
ethnic differences in cause-specific
mortality – alcohol and suicide in
particular
Differences remain unexplained by
education or residence
Conclusions

Role of cultural characteristics?
Alcohol tied to cultural practices (“culture
of alcohol” among Russians; Impact of
Islam for Central Asians)
 Denser social networks and stronger social
support among Central Asian ethnic
groups?

Further developments
Divergent paths for adult mortality
in Russia and Central Asia:
Evidence from Kyrgyzstan


By M.Guillot, N.S.Gavrilova and
L.Torgashova
Presented at the annual meeting of the
European Association for Population
Studies (2011)
Age-standardized mortality rate, 40M20
Kyrgyzstan and Russia, 1981-2006
Age-standardized mortality rate, 40M20
Kyrgyzstan and Russia, 1981-2006


Study of autopsies in Barnaul during
1990-2004 (Zaridze et al., 2009)
Among 5732 autopsied men aged 3569 years who were reported to have
died from circulatory diseases 49%
had alcohol detected in their blood
and in 21% concentration of ethanol
was 4g/l and higher (lethal dose)
Of 5880 autopsied men aged 35-69
years who were reported to have died
from injuries 76% had alcohol in their
blood and in 38% concentration of
ethanol was 4g/l and higher
Codes used for the calculation of
cause-specific mortality in Russia
And Kyrgyzstan
Age-standardized mortality rate, 40M20
Kyrgyzstan, 1981-2006,
all causes and broad causes
Age-standardized mortality rate, 40M20
Kyrgyzstan, 1981-2006,
all causes and broad causes
40M20
(Russia) – 40M20 (Kyrgyzstan), 1989-1999, all
causes and strongly alcohol related causes
40M20
(Russia) – 40M20 (Kyrgyzstan), 1989-1999, all
causes and strongly alcohol related causes
40M20
(Russia) – 40M20 (Kyrgyzstan), 1979-2009, all
causes and strongly alcohol related causes
Framework for Understanding
Health Crisis in Russia vs. Central
Asia
Russia
Kyrgyzstan
(Central Asia?)
Infant
mortality
Declined
Stalled
Adult
mortality
Large increase
Moderate increase
Explanatory
framework
Greater importance Greater importance of
of detrimental adult health care
health behaviors
deterioration
Trends in Life Expectancy: Men
Russia
Kyrgyzstan
65.00
63.00
61.00
59.00
57.00
55.00
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
Life expectancy
67.00
Calendar year
Trends in Life Expectancy: Women
Russia
Kyrgyzstan
76.00
74.00
73.00
72.00
71.00
70.00
Calendar year
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
69.00
1980
Life expectancy
75.00
Workshop in Almaty,
Kazakhstan, July 2011


Organized by the United Nations
Population Fund (UNFPA)
Results of applying new methods to
demographic estimation of mortality
in Central Asian countries
Almaty is surrounded by mountains
Workshop in Almaty
‘Montmartre’ in Almaty
Acknowledgements



National Statistical Committee of the
Kyrgyz Republic
Zarylbek Kudabaev, Orozmat
Abdykalykov, Liudmila Torgashova,
Larissa Mimbaeva, Elena
Komandirova and Mikhail Denisenko
NICHD: R03 HD38752, R01
HD045531
Additional reading

Guillot M, Gavrilova N, Pudrovska T.
Understanding the "Russian
mortality paradox" in Central Asia:
Evidence from Kyrgyzstan.
Demography, 2011, 48(3): 10811104.