Measures of Population Health

1. Measures of Population Health

2. 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).

3. WHO model of health transition (1984)

4. 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)

5. 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!

6. Estimation ofhealth expectancyby Sullivan’s method

7. Life expectancy 1.0 0.9 0.8 0.7 0.6 0.5 Survival probability 0.4 Life expectancy 0.3 0.2 0.1 0.0 0 10 20 30 40 50 60 70 80 90 100 110 Age

9. 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)

10. Probability to be in good or excellent health Andreyev et al., Bull.WHO, 2003

11. Probability to be in good or excellent health Andreyev et al., Bull.WHO, 2003

12. Choice ofhealth expectancyindicators

13. 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 }

14. Long-standing illness Interview question: “Do you suffer from any long-standing illness, long-standing after-effect of injury, any handicap, or other long-standing condition?”

15. 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”

17. 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

18. 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

19. New trends in health surveys • Harmonization of surveys at world scale • Biomarker collection

20. 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

21. 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

22. 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 AgingOffice of Research on Women's Health Office of AIDS ResearchOffice of Behavioral and Social Sciences Research

23. 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

24. Why? • Need for move from interdisciplinary data COLLECTION to integrated data ANALYSIS • Barriers • Models/methods • Rules of academe • Reviewers/editors

25. 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

26. 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?

27. Introduction to:

28. Public Dataset http://www.icpsr.umich.edu/NACDA/

29. 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

30. Affiliated Investigators and Labs

31. Corporate Contributions and Grants

32. Study Timeline • Funding: NIH / October, 2003 • Pretest: September – December, 2004 • Wave I Field Period: June 2005 – March 2006 • Wave I Analysis: Began October, 2006

33. 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.

34. 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

35. Est. Pop. Distributions (%)

36. Domains of Inquiry • 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

37. Self-Report Measures • Demographic Variables: • Age • Race/Ethnicity • Education • Insurance Status

38. 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

39. 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

40. 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

41. NSHAP Biomeasures Cooperation

42. 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.

43. NSHAP Biomeasures “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)

44. Salivary Biomeasures • Sex hormone assays • Estradiol • Progesterone • DHEA • Testosterone • Cotinine

45. Salivary Sex Hormones (preliminary analysis) Frequency Frequency Frequency log(estradiol) log(testosterone) log(progesterone) Units: pg/ml

46. Salivary Cotinine • Nicotine metabolite • Objective marker of tobacco exposure, including second-hand • Non-invasive collection method (vs. serum cotinine)

47. Distribution of Salivary Cotinine Classification of Smoking Status by Cotinine Level in Females Cut-points based on distribution among smokers .2 Occasional .15 Nonsmoker Passive Regular .1 Fraction 10 ng 15 ng 34 ng 103 ng 344 ng 10% M 30% M M .05 0 -5 0 5 10 log(Cotinine) M = mean cotinine among female who report current smoking Bar on left corresponds to cotinine below level of detection

48. Dried Blood Spots • C-Reactive Protein (CRP) • Epstein-Barr Virus (EBV) Antibody Titers Thanks, Thom and McDade Lab Staff!

49. Challenges

50. Specimen Storage First enrollment Last enrollment July, 2005 March 2006 When does a study end? Specimens collected and sent to lab Initial storage (pre-assay) Interim storage (post-assay) Continued storage (post-assay) Destruction? Storage for future use?