Ethnic Differentials in Mortality
Based on the Study of Ethnic
Differentials in Adult
Mortality in Central Asia
Michel Guillot (PI), University of
Wisconsin-Madison
Natalia Gavrilova, University of Chicago
Tetyana Pudrovska, University of
Wisconsin-Madison
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)
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)?
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
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)?
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
se
s
O
th
e
rc
ju
ri
es
In
C
V
R
D
es
pi
ra
to
ry
D
is
.
D
ig
es
t iv
e
D
is
.
pl
as
m
s
N
eo
In
fe
ct
io
ns
0.0
ho
m
un
de
te
rm
in
ed
su
ic
id
e
.
ic
id
ac
e
cid
.p
oi
al
so
lo
ni
ng
th
er
ac
ci
d.
tra
ca
ns
us
po
.
rt
ac
ci
ac
de
ci
nt
de
s
nt
al
dr
ow
ac
c.
ni
ca
ng
us
./e
ac
le
ct
c.
r.c
m
ec
ur
ha
n.
su
ffo
ca
t.
ot
he
r
in
ju
ry
ac
ci
d.
po
is
on
./a
lco
h
Age-standardized Death Rates at working
ages (per 100,000). Detailed Injuries, Males
50
45
40
Slavs
CA
35
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
O
th
e
rc
ju
rie
s
In
Di
s.
e
es
t iv
CV
D
pl
as
m
s
Di
s.
Di
g
-0.10
Re
sp
ir a
to
ry
In
-0.05
Ne
o
fe
ct
io
ns
0.00
.
su
ic
id
e
.a
cc
id
en
t.c
tra
au
ns
s.
po
rt
ac
ci
ac
de
ci
nt
d.
s
ca
us
e
ac
by
c.
fir
ca
e
us
./e
le
ac
ct
ci
r.c
de
ur
nt
al
dr
ow
ni
ng
al
lo
o.
ic
id
ac
e
ci
d.
po
iso
in
ju
ni
ry
ng
un
de
te
rm
in
ed
ho
m
ac
ci
d.
po
is
on
./a
lco
h
Age-standardized Death Rates at working
ages (per 100,000)
Detailed Injuries, Females
9
8
7
Slavs
CA
6
5
4
3
2
1
0
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?

Обследования населения,
биомаркеры и
продолжительность
здоровой жизни
Н.С. Гаврилова
Population surveys



Provide more detailed information on
specific topics compared to censuses
Cover relatively small proportion of
population (usually several
thousand)
Population-based survey – random
sample of the total population;
represents existing groups of
population
International Surveys in Russia
and FSU


Russia Longitudinal Monitoring Survey
(RLMS)
http://www.cpc.unc.edu/rlms/
Demographic and Health Surveys (DHS)
are nationally-representative household
surveys that provide data for a wide range
of monitoring and impact evaluation
indicators in the areas of population,
health, and nutrition.
http://www.measuredhs.com
http://www.cpc.unc.edu/projects/rlms
16 раундов обследования
Demographic and Health Surveys
















Child Health - vaccinations, childhood illness
Education - highest level achieved, school enrollment
Family Planning knowledge and use of family planning, attitudes
Female Genital Cutting - prevalence of and attitudes about female genital cutting
Fertility and Fertility Preferences - total fertility rate, desired family size, marriage
and sexual activity
Gender/Domestic Violence - history of domestic violence, frequency and
consequences of violence
HIV/AIDS Knowledge, Attitudes, and Behavior - knowledge of HIV prevention,
misconceptions, stigma, higher-risk sexual behavior
HIV Prevalence - Prevalence of HIV by demographic and behavioral characteristics
Household and Respondent Characteristics- electricity, access to water, possessions,
education and school attendance, employment
Infant and Child Mortality - infant and child mortality rates
Malaria - knowledge about malaria transmission, use of bednets among children and
women, frequency and treatment of fever
Maternal Health - access to antenatal, delivery and postnatal care
Maternal Mortality - maternal mortality ratio
Nutrition - breastfeeding, vitamin supplementation, anthropometry, anemia
Wealth/Socioeconomics - division of households into 5 wealth quintiles to show
relationship between wealth, population and health indicators
Women's Empowerment - gender attitudes, women’s decision making power,
education and employment of men vs. women
DHS sample designs
The sample is generally representative:
 At the national level
 At the residence level (urban-rural)
 At the regional level (departments, states)
The sample is usually based on a stratified
two-stage cluster design:
 First stage: Enumeration Areas (EA) are
generally drawn from Census files
 Second stage: in each EA selected, a
sample of households is drawn from an
updated list of households

DHS охватывает следующие
страны б.СССР






Азербайджан
Казахстан (1995, 1999)
Кыргызстан (1997)
Молдова (2005)
Туркменистан (2000)
Узбекистан (1995, 2002)
Biomarkers in Population-Based
Aging and Longevity Research
Natalia Gavrilova, Ph.D.
Stacy Tessler Lindau, MD, MAPP
CCBAR Supported by the National Institutes of Health (P30 AG012857)
NSHAP Supported by the National Institutes of Health (5R01AG021487)
including:
National Institute on Aging
Office of Research on Women's Health
Office of AIDS Research
Office of Behavioral and Social Sciences Research

Goals:
Foster interdisciplinary research community
 Establish means of exchanging rapidly
evolving ideas related to biomarker collection
in population-based health research
 Translation to clinical, remote, understudied
areas

Why?


Need for move from interdisciplinary
data COLLECTION to integrated data
ANALYSIS
Barriers
Models/methods
 Rules of academe
 Reviewers/editors

Why?

Growing emphasis on value of
interdisciplinary health research
NIH Roadmap Initiative
 NAS report


Overcome barriers of unidisciplinary
health research
Concern for health disparities
 Response bias in clinical setting
 Self-report in social science research

What is needed?


Methods and models for analytic
integration
Streamlining data collection
Advances in instruments
 Minimally invasive techniques
 Best practices
 Concern for ethical issues
 Central coordination?

Introduction to:
Public Dataset
http://www.icpsr.umich.edu/NACDA/
NSHAP Collaborators

Co-Investigators








Linda Waite, PI
Ed Laumann
Wendy Levinson
Martha McClintock
Stacy Tessler Lindau
Colm O’Muircheartaigh
Phil Schumm
NORC Team

Stephen Smith and
many others



Collaborators
 David Friedman
 Thomas Hummel
 Jeanne Jordan
 Johan Lundstrom
 Thomas McDade
Ethics Consultant
 John Lantos
Outstanding
Research Associates
and Staff
Affiliated Investigators and
Labs
LAB
SPECIMENS
ASHA
Test results
Lundstrom, Sweden
Olfaction
Hummel, Germany
Gustation
Magee Women’s Hospital,
Jeanne Jordon
Vaginal Swabs,
TM
Orasure
McClintock Lab,
Univ. Chicago
Vaginal Cytology
McDade Lab,
Northwestern Univ.
Blood Spots
Salimetrics
Saliva
USDTL*
Urine
Corporate Contributions and
Grants
Item
Company/Contact Information
Smell pens
Martha McClintock, Institute for Mind and
Biology at the University of Chicago
OraSure collection device
Orasure Technologies
Digital scales
Sunbeam Corporation
Blood pressure monitors
A & D Lifesource
Vision charts
David Freidman, Wilmer Eye Institute at the
Johns Hopkins Bloomberg School of Public
Health
Filter paper for blood spot
collection
Schleicher & Schuell Bioscience
Blood pressure cuff (large
size)
A & D Lifesource
OraSure Western Blot Kit
Biomerieux Company
HPV kits
Digene Laboratory
Boxes of swabs
Digene Laboratory
2-point discriminators
Richard Williams
Study Timeline




Funding: NIH / October, 2003
Pretest: September – December,
2004
Wave I Field Period: June 2005 –
March 2006
Wave I Analysis: Began October,
2006
He, W., Sengupta, M., Velkoff, V. A., DeBarros, K. A. (2005). 65+ In the United States: 2005. Current Population
Reports: Special Studies, U. S. Census Bureau.
NSHAP Design Overview




Interview 3,005 community-residing
adults ages 57-85
Population-based sample, minority
over-sampling
75.5% weighted response rate
120-minute in-home interview
Questionnaire
 Biomarker collection


Leave-behind questionnaire
Est. Pop. Distributions (%)
AGE
57-64
65-74
75-85
RACE/ETHNICITY
White
African-American
Latino
Other
RELATIONSHIP STATUS
Married
Other intimate relationship
No relationship
SELF-RATED HEALTH
Poor/Fair
Good
Very good/Excellent
Men
(n=1455)
Women
(n=1550)
43.6
35.0
21.4
39.2
34.8
26.0
80.6
9.2
7.0
3.2
80.3
10.7
6.7
2.2
77.9
7.4
14.7
55.5
5.5
39.0
25.5
27.5
47.0
24.2
31.5
44.3
Domains of Inquiry

Demographics






Basic Background
Information
Marriage
Employment and Finances
Religion




Social





Networks
Social Support
Activities, Engagement
Intimate relationships,
sexual partnerships
Physical Contact
Medical


Physical Health
Medications, vitamins,
nutritional supplements
Mental Health
Caregiving
HIV
Women’s Health




Ob/gyn history, care
Hysterectomy,
oophorectomy
Vaginitis, STDs
Incontinence
NSHAP Biomeasures


Blood: hgb, HgbA1c, CRP, EBV
Saliva: estradiol, testosterone,
progesterone, DHEA, cotinine

Vaginal Swabs: BV, yeast, HPV, cytology

Anthropometrics: ht, wt, waist

Physiological: BP, HR and regularity

Sensory: olfaction, taste, vision, touch

Physical: gait, balance
NSHAP Biomeasures Cooperation
Measure
Height
Weight
Blood pressure
Touch
Smell
Waist circumference
Distance vision
Taste
Get up and go
Saliva
Oral fluid for HIV test
Blood spots
Vaginal swabs
Eligible
Respondents
2,977
2,977
3,004
1,502
3,004
3,004
1,505
3,004
1,485
3,004
972
2,493
1,550
Cooperating
Respondents
2,930
2,927
2,950
1,474
2,943
2,916
1,441
2,867
1,377
2,721
865
2,105
1,028
* Person-level weights are adjusted for non-response by age and urbanicity.
Cooperation
Rate*
98.6%
98.4%
98.4%
98.4%
98.3%
97.2%
96.0%
95.9%
93.6%
90.8%
89.2%
85.0%
67.6%
Principles of Minimal
Invasiveness

Compelling rationale: high value to individual health,
population health or scientific discovery

In-home collection is feasible

Cognitively simple

Can be self-administered or implemented by single data
collector during a single visit

Affordable

Low risk to participant and data collector

Low physical and psychological burden

Minimal interference with participant’s daily routine


Logistically simple process for transport from home to
laboratory
Validity with acceptable reliability, precision and accuracy
Lindau ST and McDade TW. 2006. Minimally-Invasive and Innovative Methods for Biomeasure Collection in
Population-Based Research. National Academies and Committee on Population Workshop. Under Review.
Applying Biomeasures in
NSHAP
Uses of
Biomeasures
Population-Based
Sample
Clinic-Based
Sample
++
++
-
++
--
++
To determine effectiveness
of intervention
++
+
To identify biological
correlates or mechanisms of
social/environmental
conditions
++
--
To detect and monitor risk
for disease, pre-disease,
disease, mortality OR to
quantify and monitor
function
To recruit or exclude people
from study
To determine efficacy of
intervention
++ = Very well suited
-- = Poorly suited
NSHAP Biomeasures
“Laboratory Without
Walls”
McClintock Laboratory
(Cytology)
UC Cytopathology
(Cytology)
Jordan Clinical Lab
Magee Women’s Hospital
(Bacterial, HPV Analysis)
Salimetrics
(Saliva Analysis)
McDade Lab
Northwestern
(Blood Spot Analysis)
Salivary Biomeasures

Sex hormone assays
Estradiol
 Progesterone
 DHEA
 Testosterone


Cotinine
Frequency
Frequency
Frequency
Salivary Sex Hormones
(preliminary analysis)
log(estradiol)
Units: pg/ml
log(progesterone)
log(testosterone)
Salivary Cotinine



Nicotine metabolite
Objective marker of tobacco exposure,
including second-hand
Non-invasive collection method (vs. serum
cotinine)
Distribution of Salivary Cotinine
Classification of Smoking Status by Cotinine Level in Females
Cut-points based on distribution among smokers
.2
Occasional
Fraction
.15
Nonsmoker
Passive
Regular
.1
10 ng
15 ng
34 ng
10% M
103 ng
30% M
344 ng
M
.05
0
-5
0
log(Cotinine)
M = mean cotinine among female who report current smoking
Bar on left corresponds to cotinine below level of detection
5
10
Dried Blood Spots

C-Reactive Protein (CRP)

Epstein-Barr Virus (EBV) Antibody Titers
Thanks, Thom and
McDade Lab Staff!
Self-Report Measures

Demographic Variables:

Age

Race/Ethnicity

Education

Insurance Status
Self-Report Measures

Social/Sexuality Variables:

Spousal/other intimate partner status

Cohabitation

Lifetime sex partners

Sex partners in last 12 months

Frequency of sex in last 12 months

Frequency of vaginal intercourse

Condom use
Self-Report Measures

Health Measures:

Obstetric/Gynecologic history
Number of pregnancies
 Duration since last menstrual period
 Hysterectomy


Physical health
Overall health
 Co-morbidities


Health behaviors
Tobacco use
 Pap smear, pelvic exam history


Cancer
Challenges
Specimen Storage
First enrollment
July, 2005
Last enrollment
March 2006
Specimens collected and
sent to lab
When does a
study end?
Initial storage (pre-assay)
Interim storage (post-assay)
Continued storage (post-assay)
Destruction?
Storage for
future use?
More Information on Biomarkers
is Available at the CCBAR website
http://biomarkers.uchicago.edu/
Measures of Population Health
Living longer but healthier?

Keeping the sick and frail alive


Delaying onset and progression


expansion of morbidity (Kramer, 1980).
compression of morbidity (Fries, 1980, 1989).
Somewhere in between: more
disability but less severe

dynamic equilibrium (Manton, 1982).
WHO model of health transition (1984)
Quality or quantity of life?
Health expectancy
 partitions years of life at a particular
age into years healthy and unhealthy
 adds information on quality
 is used to:




monitor population health over time
compare countries (EU Healthy Life Years)
compare regions within countries
compare different social groups within a population
(education, social class)
What is the best measure?
Health Expectancy
Healthy LE
(self rated health)
HLE
Disability free LE
DFLE
Disease free LE
DemFLE
Cog imp-free LE
Active LE (ADL)
Many measures of health = many health expectancies!
What is the best measure?
Depends on the question
 Need a range of severity



Performance versus self-report


dynamic equilibrium
cultural differences
Cross-national comparability

translation issues
Estimation of
health
expectancy
by Sullivan’s
method
Life expectancy
expectancy and expected lifetime with and without
long-standig illness
1.0
Survival probability
probability
0.9
Years with longstanding illness
0.8
0.7
0.6
0.5
0.4
Years without
Life expectancy
long-standing illness
0.3
0.2
0.1
0.0
0
10
20
30
40
50
60
Age
70
80
90
100
110
Health expectancy by Sullivan's method
1,0
Survival probability
0,9
Life table data
0,8
0,7
0,6
Prevalence data
on health status
0,5
0,4
Unhealthy
0,3
Healthy
0,2
0,1
0,0
0
10
20
30
40
50
60
Age
70
80
90
100
110
Calculation of health expectancy
(Sullivan method)



Lxh = Lx x πx
Where πx - prevalence of healthy
individuals at age x
Lxh - person-years of life in healthy
state in age interval (x,x+1)
Вероятность быть здоровым в
зависимости от возраста
Мужчины
Andreyev et al., Bull.WHO, 2003
Вероятность быть здоровым в
зависимости от возраста
Женщины
Andreyev et al., Bull.WHO, 2003
Choice of
health
expectancy
indicators
Self-rated health
Interview question:
“How do you rate your present state of health in general?”
Answer categories:
 Very good
 Good
 Fair
 Poor
 Very poor
}
}
Dichotomised
Long-standing illness
Interview question:
“Do you suffer from any long-standing illness, longstanding after-effect of injury, any handicap, or other
long-standing condition?”
Long-lasting restrictions (if “yes” to the following questions)
First question:
“Within the past 2 weeks, has illness, injury or ailment
made it difficult or impossible for you to carry out your
usual activities?”
Second question:
“Have these difficulties/restrictions been of a more
chronic nature? By chronic is meant that the
difficulties/restrictions have lasted or are expected to
last 6 months or more”