NORC at The University of Chicago Sex, health, and years of sexually active life gained due to good health Natalia S. Gavrilova NORC at The University of Chicago Chicago, USA Although scientific discovery has had a major impact on disease, illness, and disability at older ages, much less is known about health in later life. Women outnumber men in the older population, yet broad scale gerontological survey research is heavily male-centered. The National Social Life Health and Aging Project was designed by an interdisciplinary team of biomedical and social science researchers to understand the mechanisms through which social life and relationships influence health, in addition to illness, at older ages. Because the study was designed to explore sexuality at older ages, and in reflection of the diverse disciplinary orientations of the researchers, data on many previously untouched issues pertinent to older women's health have been collected. 1

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

Introduction to: NSHAP

NSHAP Design Overview Wave 1: In 2005-06 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 The NSHAP study interviewed 3005 community-residing adults ages 57-85 between July 2005 and March 2006. This involved a population-based sample with oversampling of Black and Hispanic individuals. The study included a 120 minute in- home face-to-face questionnaire conducted in English and Spanish and collection of 17 different biological measures. In addition, a s questionnaire was left behind for self-completion. 4

Domains of Inquiry Demographics Social Medical Women’s Health 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 Because this data set will become available for public use in 2008, and many of you share my interest in studying issues of aging, let me briefly tell you about the spectrum of data we collected. Here, you see listed all of the interview domains. 5

NSHAP Biomeasures Cooperation This slide summarizes the measures we collected. As you can see, cooperation rates were excellent, exceeding 90% for almost all. Among the more demanding measures, including saliva collection, HIV testing, fingerstick blood samples, and vaginal self- swabs, cooperation rates were also very high. 6

NSHAP Biomeasures Salimetrics McClintock Laboratory (Saliva) Analysis) “Laboratory Without Walls” Salimetrics (Saliva) Analysis) McClintock Laboratory (Cytology) Jordan Clinical Lab Magee Women’s Hospital (Bacterial, HPV Analysis) McDade Lab Northwestern (Blood Spot Analysis) UC Cytopathology (Cytology)

Salivary Biomeasures Sex hormone assays Cotinine Estradiol Progesterone DHEA Testosterone Cotinine Saliva collection involved production of approximately 2 milliliters of saliva into a small, code-labeled polystyrene collection vial via a 5-centimeter section of a household plastic straw. The salivary specimens were stored on ice and transported on cold packs to Salimetrics, LLC (State College, PA). After transport, specimens were stored at -40°C, thawed for testing, and centrifuged. An appropriate volume of clear sample was pipetted and quantitatively analyzed for the presence of cotinine using the High Sensitivity Salivary Cotinine Enzyme Immunoassay Kit according to manufacturer’s instructions. 8

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 Because this data set will become available for public use in 2008, I list here all of the domains of inquiry.

MIDUS study

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)

Prevalence of Sexual Activity by Age and Gender (MIDUS 1) Men and women having intimate partner

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

Is Sex an Important Predictor of Mortality? Wave 2 of NSHAP is able to answer this question. In 2010 and 2011, nearly 3,400 interviews were completed for Wave 2 with Wave 1 Respondents, Wave 1 Non-Interviewed Respondents, and their spouses or cohabiting romantic partners. NSHAP study collected information on the number of deaths between two waves.

Sexual inactivity and 5-year mortality NSHAP data. Men Multivariate logistic regression, N=2672 Variable Odds ratio 95% CI P-value No sexual activity past 12 months 2.41 1.51- 3.84 <0.001 Age 1.07 1.04-1.10 Living with partner 0.64 0.41-1.02 0.062 Smoking 1.49 0.94-2.37 0.090 Poor/Fair SRH 1.80 1.14-2.84 0.011 College or higher education 0.79 0.52-1.18 0.248 19

Sexual inactivity and 5-year mortality NSHAP data. Women Multivariate logistic regression, N=2498 Variable Odds ratio 95% CI P-value No sexual activity past 12 months 0.97 1.51- 3.84 0.933 Age 1.10 1.25-2.32 <0.001 Living with partner 0.84 1.21-2.31 0.535 Smoking 4.57 1.27-3.40 Poor/Fair SRH 2.45 1.00-1.84 0.001 College or higher education 0.60 0.63-1.19 0.051 20

Results were not changed if we used “interest in sex” variable (thinking about sex once a week or more often) instead of sexual inactivity

Biomarkers tested as mortality predictors C-reactive protein (CRP) DHEA Testosterone (T) Estradiol (E2) Glycated hemoglobin (HbA1C) Note: HbA1C turned out to be non-significant predictor of mortality

Selected biomarkers and 5-year mortality NSHAP data. Men Multivariate logistic regression, N=2287 Variable Odds ratio 95% CI P-value CRP (log) 1.51 1.15-1.98 0.003 DHEA (log) 0.85 0.64-1.15 0.294 Testosterone (log) 1.64 0.96-2.81 0.069 Estradiol (log) 1.00 0.77-1.31 0.976 Sexual inactivity 2.14 1.27-3.59 0.004 Controlled for age and smoking status 23

Selected biomarkers and 5-year mortality NSHAP data. Women Multivariate logistic regression, N=2117 Variable Odds ratio 95% CI P-value CRP (log) 0.93 0.65-1.33 0.688 DHEA (log) 0.70 0.49-1.01 0.057 Testosterone (log) 0.71 0.30-1.67 0.430 Estradiol (log) 1.58 1.14-2.21 0.006 Sexual inactivity 1.63 0.60-4.41 0.333 Controlled for age and smoking status 24

More Information on Biomarkers is Available at the CCBAR website http://biomarkers.uchicago.edu/ Acknowledgements This study was supported by NIH grants. Study on SALE has been jointly conducted with Dr. Stacy Lindau, MD, MAPP at the ObGyn Dept., University of Chicago