measures of population health
DESCRIPTION
Measures of Population Health. Living longer but healthier?. Keeping the sick and frail alive expansion of morbidity (Kramer, 1980). Delaying onset and progression compression of morbidity (Fries, 1980, 1989). Somewhere in between: more disability but less severe - PowerPoint PPT PresentationTRANSCRIPT
Measures of Population Health
Living longer but healthier?
Keeping the sick and frail alive expansion of morbidity (Kramer, 1980).
Delaying onset and progression 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 Disability free LE Disease free LE (self rated health) DFLE DemFLE
HLE Cog imp-free LE
Active LE (ADL)
Many measures of health = many health expectancies!
Estimation ofhealth
expectancyby Sullivan’s
method
Life expectancy
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Life expectancy
Life expectancy and expected lifetime with and without long-standig illness
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Years with long-standing illness
Years withoutlong-standing illness
Health expectancy by Sullivan's method
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Prevalence data on health status
Healthy
Unhealthy
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 ofhealth
expectancyindicators
Interview question:“How do you rate your present state of health in general?”
Answer categories:Very goodGoodFairPoorVery poor
Self-rated health
}
}Dichotomised
Interview question:
“Do you suffer from any long-standing illness, long-
standing after-effect of injury, any handicap, or other
long-standing condition?”
Long-standing illness
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?”
Long-lasting restrictions (if “yes” to the following questions)
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
dynamic equilibrium
Performance versus self-report 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
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
Natalia Gavrilova, Ph.D.
Stacy Tessler Lindau, MD, MAPP
Biomarkers in Population-Based Aging and Longevity 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:
http://www.icpsr.umich.edu/NACDA/
Public Dataset
NSHAP Collaborators
Co-Investigators Linda Waite, PIEd LaumannWendy LevinsonMartha McClintockStacy Tessler
LindauColm
O’MuircheartaighPhil Schumm
NORC TeamStephen Smith and
many others
CollaboratorsDavid FriedmanThomas Hummel Jeanne Jordan Johan LundstromThomas 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, OrasureTM
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.
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
NSHAP Design Overview
Men Women(n=1455) (n=1550)
AGE 57-64 43.6 39.2 65-74 35.0 34.8 75-85 21.4 26.0RACE/ETHNICITY White 80.6 80.3 African-American 9.2 10.7 Latino 7.0 6.7 Other 3.2 2.2RELATIONSHIP STATUS Married 77.9 55.5 Other intimate relationship 7.4 5.5 No relationship 14.7 39.0SELF-RATED HEALTH Poor/Fair 25.5 24.2 Good 27.5 31.5 Very good/Excellent 47.0 44.3
Est. Pop. Distributions (%)
Medical Physical Health Medications, vitamins,
nutritional supplements Mental Health Caregiving HIV
Women’s Health Ob/gyn history, care Hysterectomy,
oophorectomy Vaginitis, STDs Incontinence
Demographics Basic Background
Information Marriage Employment and
Finances Religion
Social Networks Social Support Activities, Engagement Intimate relationships,
sexual partnerships Physical Contact
Domains of Inquiry
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
Self-Report Measures
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 Eligible
Respondents Cooperating Respondents
Cooperation Rate*
Height 2,977 2,930 98.6% Weight 2,977 2,927 98.4% Blood pressure 3,004 2,950 98.4% Touch 1,502 1,474 98.4% Smell 3,004 2,943 98.3% Waist circumference 3,004 2,916 97.2% Distance vision 1,505 1,441 96.0% Taste 3,004 2,867 95.9% Get up and go 1,485 1,377 93.6% Saliva 3,004 2,721 90.8% Oral fluid for HIV test 972 865 89.2% Blood spots 2,493 2,105 85.0% Vaginal swabs 1,550 1,028 67.6% * Person-level weights are adjusted for non-response by age and urbanicity.
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
accuracyLindau 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.
McClintock Laboratory(Cytology)
Jordan Clinical LabMagee Women’s Hospital(Bacterial, HPV Analysis)
McDade Lab Northwestern(Blood Spot Analysis)
Salimetrics(Saliva Analysis)
“Laboratory Without Walls”
UC Cytopathology(Cytology)
NSHAP Biomeasures
Sex hormone assays
Salivary Biomeasures
EstradiolProgesteroneDHEATestosterone
Cotinine
Salivary Sex Hormones (preliminary analysis)
log(progesterone)
Freq
uen
cy
log(estradiol)
Freq
uen
cy
log(testosterone)
Freq
uen
cy
Units: pg/ml
Nicotine metabolite Objective marker of tobacco exposure,
including second-hand Non-invasive collection method (vs.
serum cotinine)
Salivary Cotinine
10 ng 15 ng 34 ng 10% M
103 ng 30% M
344 ng M
Nonsmoker Passive
Occasiona
lRegular
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M = mean cotinine among female who report current smoking
Bar on left corresponds to cotinine below level of detection
Cut-points based on distribution among smokersClassification of Smoking Status by Cotinine Level in Females
Distribution of Salivary Cotinine
C-Reactive Protein (CRP)
Epstein-Barr Virus (EBV) Antibody
Titers
Dried Blood Spots
Thanks, Thom and McDade Lab Staff!
Challenges
First enrollment
March 2006
Specimens collected and sent to lab
When does a When does a study end?study end?
July, 2005
Last enrollment
Initial storage (pre-assay)
Interim storage (post-assay)
Continued storage (post-assay)
Storage for Storage for future use?future use?
Destruction?Destruction?
Specimen Storage
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 measuresFunctional measuresBiomeasures
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
Childhood family background
Psychological turning
Community involvement
NeighborhoodLife overall
Social Networks
Physical Health
Sexuality Personal
beliefs Work and
Finances Children Marriage Religion
Proportion of Sexually Active Womenby age and self-rated physical health
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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 methodBased 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)
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Pre
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Age
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Prevalence of Sexual Activity by Age and Gender
(MIDUS 1)Men and women having intimate
partner
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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