Gender Specific Effects of Early

Report
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
Disease free LE
DFLE
DemFLE
Cog imp-free LE
Active LE (ADL)
Many measures of health = many health expectancies!
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)
Probability to be in good or
excellent health
Andreyev et al., Bull.WHO, 2003
Probability to be in good or
excellent health
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”
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
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
New trends in health surveys
• Harmonization of surveys at world
scale
• Biomarker collection
• Large-scale study of health and
retirement of older americans
• Survey of more that 22000 americans
older than 55 years every 2 years.
Started in 1992
HRS-harmonizing studies
• UK English Longitudinal Study of
Ageing (ELSA)
• Study on Health, Ageing and
Retirement in Europe (SHARE)
• WHO Study on global AGEing and
adult health (SAGE) including Russia
• Отдельные исследования в
Мексике, Китае, Индии, Японии,
Корее, Ирландии
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 of CCBAR:
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
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
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.
NSHAP Biomeasures
McClintock
Laboratory
(Cytology)
“Laboratory Without
Walls”
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!
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/
Is sex an “integral part”
of health at older ages?
What is health?
Subjective measures
Functional measures
Biomeasures
What aspects of health are most highly
associated with sexual function at
older ages?
SEX
HEALTH
National survey conducted in 1994/95
7,189 Americans aged 25-74
core national sample (N=3,485)
city oversamples (N=957)
Strata: age, self-reported health status
Control variables: partner status, partner health,
race, education
Domains of Inquiry








Social Networks
Physical Health
Sexuality
Personal beliefs
Work and
Finances
Children
Marriage
Religion
Childhood family
background
Psychological
turning
Community
involvement
Neighborhood
Life overall
Proportion of Sexually Active Women
by age and self-rated physical health
90
Percentage sexually active
80
70
60
50
poor health
medium health
excellent health
40
30
20
10
0
25-54
55-64
65-74
MIDUS study
SEXUALITY AND HEALTH
Self-rated physical health is higher among
sexually active women
Women with very good and excellent
health are more sexually active at all ages
Satisfaction with sexual aspect of life is
higher among women with very good and
excellent health compared to women with
poor health
How to Compare Sexual
Activity Across Populations?
We suggest to use a new measure – Sexually
Active Life Expectancy (SALE)
Calculated using the Sullivan method
Based on self-reported prevalence of having
sex over the last 6 months (MIDUS and
NSHAP studies)
Life tables for the U.S. population in 1995
and 2003 (from Human Mortality
Database)
Prevalence of Sexual Activity
by Age and Gender (MIDUS 1)
100
90
Prevalnce, %
80
70
60
50
Males
Females
40
30
20
10
0
25 30 35 40 45 50 55 60 65 70
Age
Prevalence of Sexual Activity
by Age and Gender (MIDUS 1)
Men and women having intimate partner
100
90
Prevalnce, %
80
70
60
50
Males
Females
40
30
20
10
0
25 30 35 40 45 50 55 60 65 70
Age
Publication on sexuality
Lindau, Gavrilova, British Medical Journal, 2010, 340, c810
Life expectancy and sexually
active life expectancy (SALE)
Based on the MIDUS study
Sexually active life expectancy
and self-rated health
Based on the MIDUS study

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