measures of population health

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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rviv

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Life expectancy

Life expectancy and expected lifetime with and without long-standig illness

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rviv

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Years with long-standing illness

Years withoutlong-standing illness

Health expectancy by Sullivan's method

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Su

rviv

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ity Life table data

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

0

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-5 0 5 10log(Cotinine)

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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poor healthmedium healthexcellent health

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

valn

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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