Transcript 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.

Обследования населения,
биомаркеры и
продолжительность
здоровой жизни
Н.С. Гаврилова
Population surveys
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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
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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
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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
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DHS охватывает следующие
страны б.СССР
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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
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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
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Why?
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Need for move from interdisciplinary
data COLLECTION to integrated data
ANALYSIS
Barriers
Models/methods
 Rules of academe
 Reviewers/editors
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Why?
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Growing emphasis on value of
interdisciplinary health research
NIH Roadmap Initiative
 NAS report
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Overcome barriers of unidisciplinary
health research
Concern for health disparities
 Response bias in clinical setting
 Self-report in social science research
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What is needed?
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Methods and models for analytic
integration
Streamlining data collection
Advances in instruments
 Minimally invasive techniques
 Best practices
 Concern for ethical issues
 Central coordination?
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Outline
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NSHAP study as an example of a
population-based study collecting
biomarkers
Theoretical Foundations for
Integrated Health Research
CCBAR website
Introduction to:
Public Dataset
http://www.icpsr.umich.edu/NACDA/
NSHAP Collaborators
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Co-Investigators
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Linda Waite, PI
Ed Laumann
Wendy Levinson
Martha McClintock
Stacy Tessler Lindau
Colm O’Muircheartaigh
Phil Schumm
NORC Team
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Stephen Smith and
many others
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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
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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.
The Interactive
Biopsychosocial
Model (IBM)
Lindau ST, Laumann EO, Levinson W, Waite
LJ. Perspectives in Biology & Medicine. (2003)
Conceptual Framework
INTERACTIVE BIOPSYCHOSOCIAL MODEL
Sexuality
Health
Biological/
Physiological
Mechanisms
TIME
HYPOTHESIS:
Positive sexual relationships promote health and mitigate
illness as people age.
Integrated Health Research
Social
Factors
Biological /
Physiologica
l
Mechanisms
Health
TIME
Social
Factors
Biological /
Physiologic
al
Mechanisms
NSHAP Design Overview
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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
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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
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Demographics
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Basic Background
Information
Marriage
Employment and Finances
Religion
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Social
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Networks
Social Support
Activities, Engagement
Intimate relationships,
sexual partnerships
Physical Contact
Medical
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Physical Health
Medications, vitamins,
nutritional supplements
Mental Health
Caregiving
HIV
Women’s Health
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Ob/gyn history, care
Hysterectomy,
oophorectomy
Vaginitis, STDs
Incontinence
NSHAP Biomeasures
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Blood: hgb, HgbA1c, CRP, EBV
Saliva: estradiol, testosterone,
progesterone, DHEA, cotinine
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Vaginal Swabs: BV, yeast, HPV, cytology
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Anthropometrics: ht, wt, waist
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Physiological: BP, HR and regularity
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Sensory: olfaction, taste, vision, touch
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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
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Compelling rationale: high value to individual health,
population health or scientific discovery
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In-home collection is feasible
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Cognitively simple
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Can be self-administered or implemented by single data
collector during a single visit
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Affordable
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Low risk to participant and data collector
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Low physical and psychological burden
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Minimal interference with participant’s daily routine
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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
Applying Biomeasures in
NSHAP
Uses of
Biomeasures
Population-Based
Sample
Clinic-Based
Sample
To detect and monitor risk
for disease, pre-disease,
disease, mortality OR to
quantify and monitor
function
RISK: Genital HPV
Tobacco use
Obesity
FUNCTION: Mucosal
integrity
Sex hormone metabolism
++
-
++
--
++
Future public health
interventions:
e.g. smoking cessation, HPV
vaccine, new hearing
devices
+
To recruit or exclude people
from study
To determine efficacy of
intervention
To determine effectiveness
of intervention
To identify biological
correlates or mechanisms of
++ = Very well suited
social/environmental
Hypertension
Impaired Glucose
-- = Poorly suited
--
Disciplinary Use of
Biomeasures
Population-Based
Sample
Clinic-Based
Sample
To detect and monitor risk
for disease, pre-disease,
disease, mortality OR to
quantify and monitor
function
RISK: Genital HPV
Tobacco use
Obesity
EPIDEMIOLOGY
FUNCTION: Mucosal
integrity
Sex hormone metabolism
++
-
++
--
++
Future public health
EPIDEMIOLOGY
+
To recruit or exclude people
from study
To determine efficacy of
intervention
To determine effectiveness
of intervention
BIOMEDICINE
Uses of
Biomeasures
interventions:
e.g. smoking cessation, HPV
vaccine, new hearing
devices
BIODEMOGRAPHY/ANTHROPOLOGY
To identify biological
correlates or mechanisms of
social/environmental
++ = Very well suited
Hypertension
Impaired Glucose
Metabolism -- = 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
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Sex hormone assays
Estradiol
 Progesterone
 DHEA
 Testosterone
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Cotinine
Frequency
Frequency
Frequency
Salivary Sex Hormones
(preliminary analysis)
log(estradiol)
Units: pg/ml
log(progesterone)
log(testosterone)
Salivary Cotinine
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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
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C-Reactive Protein (CRP)
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Epstein-Barr Virus (EBV) Antibody
Titers
Thanks, Thom and
McDade Lab Staff!
Self-Report Measures
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Demographic Variables:
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Age
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Race/Ethnicity
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Education
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Insurance Status
Self-Report Measures
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Social/Sexuality Variables:
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Spousal/other intimate partner status
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Cohabitation
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Lifetime sex partners
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Sex partners in last 12 months
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Frequency of sex in last 12 months
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Frequency of vaginal intercourse
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Condom use
Self-Report Measures
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Health Measures:
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Obstetric/Gynecologic history
Number of pregnancies
 Duration since last menstrual period
 Hysterectomy
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Physical health
Overall health
 Co-morbidities
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Health behaviors
Tobacco use
 Pap smear, pelvic exam history
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Cancer
Challenges
Human Subjects Issues
in Data Analysis
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Additional HPV testing considered “future
use”
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Genotyping of Hybrid Capture 2 negative
specimens
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Consent for additional genotyping?
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Commercial availability of genotyping
assays (experimental vs. FDA-approved)
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/
CCBAR Website Objectives
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Central resource for collecting, monitoring,
and disseminating the most recent
developments
Virtual research collaborative, establishing
a means of exchanging rapidly evolving
ideas related to all aspects of biomarker
collection in population-based research
Educate public about integrated
population-based health research
Studies Collecting Biomarkers in
Population Settings
Local, regional, national and
international studies and
investigators involved in populationbased health research involving
collection of biological and physical
measures.
Web-based search of
studies collecting
specific biomarkers
Studies collecting
C-reactive protein
Measures of Population Health
Self-Rated Health
Information from population surveys
Healthy Life Expectancy in Russia and USA
80
64.1
70
60
67.2
71.3
52.8
50
Men
Women
40
30
20
10
0
Russia
USA
Expected Years in Poor Health
8.5
9
7.7
8
7
6
7.4
5.5
5
Men
Women
4
3
2
1
0
Russia
USA
Russia vs. Canada:
Health Status
90
80
70
60
50
40
Russia
Canada
30
20
10
0
Good or Very Good
Self-reported health. Both sexes
60
50
40
Very good/good
30
Medium
20
Poor/very poor
10
0
Ukraine 2000
Russia 1998
Risk factors including lifestyle
Survival of men by alcohol consumption level
1
Survival
0,75
Life span:
0,5
-3,8
0,25
-5,0
0
40
45
50
.
55
60
65
70
75
Age
Never
Moderate
High(>168 g/week )
Russian Lipid Clinics Study
80
Survival of women by alcohol consumption level
Survival
1
0,75
Lifespan:
0,5
-2,5
0,25
-8,6
.
0
40
44
48
52
56
В о з р а с т
Never
60
64
68
72
76
80
д о ж и т и я (г о д ы)
Moderate
High (>84 g/week)
Russian Lipid Clinics Study
84
Prevalence of hypertension
Women ( 41%)
Men ( 39%)
%
100
АД>=140/90 мм рт.ст.
80
60
40
.
20
0 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+
Age groups (years)
Russian representative sample
Prevalence of smoking
80
%
Women (9,7%)
Men (63,2%)
60
40
.
20
0
20-29
30-39 40-49
50-59
60-69 70-79
Age group
Russian representative sample
80-89
Responses of smokers and non-smokers about health
effects of active smoking (Ukraine)
100
90
80
70
60
Non-smokers
50
Smokers
40
30
20
10
0
Good for
health
Helps to relax
No effect
Sometimes
causes disease
Bad for health
Living longer but healthier?
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Keeping the sick and frail alive
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Delaying onset and progression
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expansion of morbidity (Kramer, 1980).
compression of morbidity (Fries, 1980, 1989).
Somewhere in between: more
disability but less severe
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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:
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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
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Performance versus self-report
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dynamic equilibrium
cultural differences
Cross-national comparability
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translation issues
Cross-sectional versus
longitudinal data
X-sectional versus
longitudinal data
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The simplest method of calculating a
health expectancy is Sullivan’s method
(Sullivan 1971) with:
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prevalence of the health state from a crosssectional survey
a standard life table for the same period
Multi-state life tables require longitudinal
data on transitions between health states
and death
HE with cross-sectional data
Mortality data
Age specific
prevalence of
ill-health (e.g.
disability)
Life table
Life
expectancy
LE free of
disability
LE with
disability
HE with longitudinal data
Baseline
No disability
Disability
Follow-up
No disability
Disability
Dead
X-sectional versus longitudinal

Cross-sectional
+ easiest for trends
- life tables not available for subgroups
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Longitudinal
+ explicitly estimates incidence and
recovery providing better future forecasts
- cost, attrition
Not either/or but must include institutional population
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
Вклад плохого здоровья и смертности в
различия по продолжительности
здоровой жизни между Россией и
Западной Европой. Мужчины
Andreyev et al., Bull.WHO, 2003
Вклад плохого здоровья и смертности в
различия по продолжительности
здоровой жизни между Россией и
Западной Европой. 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”
Total survival, survival without disability and survival
without chronic disease, France 1981-1991, females
10 0 0 0 0
90000
80000
70 0 0 0
60000
50 0 0 0
S urvie t o t ale 19 8 1
40000
S urvie s ans malad i 19 8 1
30000
S urvie s ans incap acit é 19 8 1
20000
S urvie t o t ale 19 9 1
10 0 0 0
S urvie s ans malad ie 19 9 1
S urvie s ans incap acit é 19 9 1
0
0
20
40
60
80
10 0
Future potential of HLE

Are social and regional inequalities
widening?


Diseases more or less disabling?


effect of greater access to education in new
cohorts
saving lives v reducing disability
Living longer healthier?

new cohorts with more ethnic minority elders
Issues
Must
have
total
population
including those in institutions
 Cultural differences in self-report?
 Accurate translation to underlying
concepts
for
cross
national
comparability
