Transcript Gender Specific Effects of Early

CONTEMPORARY METHODS OF MORTALITY ANALYSIS
Measures of Population Health
Lecture 5
Living longer but healthier?

Keeping the sick and frail alive


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
– dynamic equilibrium
• Performance versus self-report
– 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)
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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
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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

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Performance versus self-report
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dynamic equilibrium
cultural differences
Cross-national comparability

translation issues
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
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
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
A 30-40 minute telephone survey
Number of respondents: 4,242
A 114 page mail survey
Number of respondents: 3,690
Domains of Inquiry
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Social Networks
Physical Health
Sexuality
Personal beliefs
Work and
Finances
Children
Marriage
Religion
Childhood family
background
Psychological
turning
Community
involvement
Neighborhood
Life overall
80%
Currently
Sexually Active
With Partner
84%
AGE 25-54
(n=1,436)
31%
56%
37%
60%
AGE 55-64
(n=414)
AGE 65-74
(n=237)
IS SEX IMPORTANT?
“How much thought and effort do you put into the
sexual aspect of your life?”
60
Percentage (by age)
50
40
25-54 (n=1,210)
55-64 (n=365)
30
65-74 (n=186)
20
10
0
None
Some
Moderate
Responses
Much
Very
Much
CONTROL OVER SEXUAL ASPECT OF LIFE
“How would you rate the amount of control you
have over the sexual aspect of your life?”
Percentage (by age)
50
40
25-54 (n=1,210)
30
55-64 (n=365)
20
65 - 74 (n=186)
10
0
None
Some
Moderate
Responses
Much
Very
Much
Self-rated Health
by age and sexual activity
mean self-rated health (10-grade)
7.6
7.4
7.2
sexually active
sexually non-active
7
6.8
6.6
6.4
25-54
55-64
65-74
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
Satisfaction with sexual aspect of life
by age and self-rated health
satisfaction with sex life (10-grade)
6
5
4
poor health
good health
excellent health
3
2
1
0
25-54
55-64
65-74
MIDUS: Sexuality and Lifecourse
Health Events
• 52.9% actively engaged in a sexual
relationship, all with a male partner.
• Sexually inactive women report lower
sex life satisfaction (2.30 vs 5.70,
p<0.0001)
• Sexually active women more likely to
report good physical health than
sexually inactive women (57.3% vs.
42.7%, p<.05).
Multivariate Models
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Logistic regression model of the
likelihood of having sex in the past six
months, conditional on having a
current marital or other romantic
relationship.
Linear regression model of the rating
of sexual aspect of life , conditional on
having a current marital or other
romantic relationship.
Results of logistic regression model
Dependent variable: having sex in the past 6 months
Covariate
Odds
ratio
95% CI
P-value
Age (vs 25-54)
55-64
0.18
(0.11-0.31)
<0.001
65-74
0.06
(0.04-0.12)
<0.001
Good health
0.72
(0.37-1.40)
0.334
Excellent health
1.13
(0.54-2.36)
0.748
Good partner’s health
1.89
(1.06-3.36)
0.030
Excellent partner’s health
2.14
(1.22-3.75)
0.008
Respondent’s health (vs poor)
Partner’s health (vs poor)
Education and race were not significantly associated with sex activity
Results of linear regression model
Dependent variable: 10-grade rating of the sex aspect of life
Covariate
Coeffici
ent
95% CI
P-value
Age (vs 25-54)
55-64
-0.71
(-1.13 - -0.30) <0.001
65-74
-1.71
(-2.32 - -1.11) <0.001
Respondent’s self-rated
health (vs poor)
Good health
0.12
(-0.40-0.64)
0.641
Excellent health
0.47
(-0.04-0.98)
0.072
Good health
1.11
(0.60-1.61)
<0.001
Excellent health
1.87
(1.40-2.35)
<0.001
Partner’s health (vs poor)
African-Americans rated sex life significantly better than whites. Education NS.
Other Results of the
Multivariate Models
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HRT and obesity are negatively
associated with both sexual activity
and rating of the sexual aspect of life
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Self-rated emotional health is
positively associated with the rating of
the sexual aspect of life but not with
the sexual activity
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
Bidirectional Relationship
?
Sexuality
Health
Biological /
Physiological
Mechanisms
TIME
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
LE and SALE at Age 30
(MIDUS 1)
60
50
50.44
44.73
35.26
40
29.12
Males
Females
30
20
10
0
LE
SALE
Sexually Active Life Expectancy
at Age 30 (MIDUS 1)
40
38.15
35.26
35
39.13
29.12
30
25
Males
Females
20
15
10
5
0
All
With partner
Percent of Expected Life
Without Sexual Activity
at Age 30 (MIDUS 1)
32.4
35
27.1
30
25
21.2
20
14.7
15
10
5
0
All
With partner
Males
Females
Comparison with other surveys
• NSHAP - National Social Life, Health,
and Aging Project, is an in-home
survey of 3,000 persons aged 57 to
84 that collect biomarkers of health
and physiological functioning to
better characterize the health of
survey participants. Rich source of
data on sexuality at older ages.
• MIDUS-2 – second wave of the
MIDUS study conducted in 20042006
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
Study Timeline
•
Funding: NIH / October, 2003
•
Pretest: September – December,
2004
•
Wave I Field Period: June 2005 –
March 2006
•
Wave I Analysis: Began October,
2006
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
•
– Physical Health
– Basic Background
Information
– Marriage
– Employment and Finances
– Religion
•
– Medications, vitamins,
nutritional supplements
– Mental Health
– Caregiving
Social
– Networks
– Social Support
– Activities, Engagement
– Intimate relationships,
sexual partnerships
– Physical Contact
Medical
– 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
Sexually Active Life Expectancy
at Age 55
16
15.55
14.77
14
12
10.36
10.06
10
Males
Females
8
6
4
2
0
MIDUS-1
NSHAP
Percent of Expected Life
Without Sexual Activity
at Age 55
70
63.1
62.8
60
50
40
39.4
32.4
Males
Females
30
20
10
0
MIDUS-1
NSHAP
Sexually Active Life Expectancy
at Age 55
25
20.11
21.24
20
15.55
15
Males
Females
10.06
10
5
0
MIDUS-1
MIDUS-2
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
Conclusions
• Proportion of women having sex partner
declines with age
• Amount of control over sexual aspect of
life declines with age
• Self-rated physical health is higher among
sexually active women
• Women have lower sexually active life
expectancy compared to men