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Ethnic Differentials in Mortality. Based on the Study of Ethnic Differentials in Adult Mortality in Central Asia. Michel Guillot (PI), University of Wisconsin-Madison Natalia Gavrilova, University of Chicago Tetyana Pudrovska, University of Wisconsin-Madison. Background on Kyrgyzstan.

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based on the study of ethnic differentials in adult mortality in central asia

Based on the Study of Ethnic Differentials in Adult Mortality in Central Asia

Michel Guillot (PI), University of Wisconsin-Madison

Natalia Gavrilova, University of Chicago

Tetyana Pudrovska, University of Wisconsin-Madison

background on kyrgyzstan
Background on Kyrgyzstan
  • Former Soviet republic; became independent in 1991
  • Population: 5.2 million (2006)
  • Experienced a severe economic depression after break-up of Soviet Union
  • GNI per capita = 440 USD; 28th poorest country in the world (2005)
  • 48% of population below national poverty line (2001)
ethnic groups in kyrgyzstan
Ethnic Groups in Kyrgyzstan
  • Native Central Asian groups: Kazakh, Kyrgyz, Tajik, Turkmen, Uzbek (Sunni Muslims)
  • Slavs: Russian, Ukrainian, Bielorussian
  • Kyrgyzstan, 1999 census:
    • Central Asians: 79% of pop. (Kyrgyz 65%)
    • Slavs: 14% of pop. (Russian 12%)
mortality paradox
Mortality paradox?
  • Soviet period: Russians/Slavs occupied dominant positions in the socio-economic structure of Central Asian societies (Kahn 1993)
mortality paradox8
Mortality paradox?
  • Slavic females more educated than Central Asian females (1989 and 1999 censuses)
  • Slavic males: educational advantage not so clear – varies by age (1989 and 1999 censuses)
  • Slavic households less poor than Central Asians (1993 World Bank poverty survey)
  • Infant mortality lower among Slavs (Soviet and post-Soviet period)
proportion of individuals with post secondary education by age and ethnicity in 1989 census females
Proportion of individuals with post-secondary education, by age and ethnicity, in 1989 census. Females
mortality paradox10
Mortality paradox?
  • Slavic females more educated than Central Asian females (1989 and 1999 censuses)
  • Slavic males: educational advantage not so clear – varies by age (1989 and 1999 censuses)
  • Slavic households less poor than Central Asians (1993 World Bank poverty survey)
  • Infant mortality lower among Slavs (Soviet and post-Soviet period)
slide11

Proportion of individuals with post-secondary education, by age and ethnicity, in 1989 census. Males.

mortality paradox12
Mortality paradox?
  • Slavic females more educated than Central Asian females (1989 and 1999 censuses)
  • Slavic males: educational advantage not so clear – varies by age (1989 and 1999 censuses)
  • Slavic households less poor than Central Asians (1993 World Bank poverty survey)
  • Infant mortality lower among Slavs (Soviet and post-Soviet period)
mortality paradox14
Mortality paradox?
  • Slavic females more educated than Central Asian females (1989 and 1999 censuses)
  • Slavic males: educational advantage not so clear – varies by age (1989 and 1999 censuses)
  • Slavic households less poor than Central Asians (1993 World Bank poverty survey)
  • Infant mortality lower among Slavs (Soviet and post-Soviet period)
slide16
Data
  • Unpublished population and death tabulations since 1959
    • collected from local archives
  • Individual census records – 1999
  • Individual death records – 1998-1999
    • obtained from national statistical office
possible explanations for mortality paradox
Possible explanations for mortality paradox
  • Data artifacts
  • Migration effects (esp. 1989-99)
  • Cultural effects
data artifacts
Data artifacts?
  • Could the lower recorded mortality among Central Asian adults be due to lower data quality among them (coverage of deaths, age misreporting)?
cultural effects
Cultural effects?
  • Culture may affect mortality in various ways:
    • individual health and lifestyle behaviors (e.g., diet, smoking, alcohol, use of preventive care)
    • family structure and social networks (denser social networks may produce lower stress levels and better health)
  • Could different cultural practices among Slavs and Central Asians explain the observed mortality differentials?
data artifacts22
Data artifacts?
  • Intercensal estimates of death registration coverage above age 60 (Guillot, 2004):
    • 90+ % as early as 1959 in urban areas
    • coverage in rural areas was low initially (~50%) but caught up with urban areas in 1980s
    • Total population: 92% for 1989-99 period
  • Adult deaths (20-59) usually better reported than deaths 60+
migration effects
Migration effects?
  • 1/3 of Russian population has left Kyrgyzstan since 1991
  • Could the increased disparity between Russian and Kyrgyz adult mortality be due to selective migration (healthy migrant effect)?
cultural effects28
Cultural effects?
  • Analysis of causes of death by ethnicity, 1998-99
  • Calculations based on micro-data
    • Deaths: vital registration (1998-99)
    • Exposure: census (March 1999)
    • Ages 20-59
    • Ethnicity: Central Asians vs. Slavs
    • ~20,000 death records; ~2.2 million census records
age standardized death rates at working ages per 100000 1998 99 by cause and ethnicity males
Age-standardized Death Rates at working ages (per 100000), 1998-99, by cause and ethnicity, Males
slide30

Contribution of causes of death to the difference in life expectancy at working ages (40e20) between Slavs and Central Asians Males (total difference = 2.90 years)

age standardized death rates at working ages per 100 000 1998 99 by cause and ethnicity females
Age-standardized Death Rates at working ages (per 100,000), 1998-99, by cause and ethnicity, Females
slide33

Contribution of causes of death to the difference in life expectancy at working ages (40e20) between Slavs and Central Asians Females (total difference = .28 years)

slide35

Alcohol-related Causes of Death(Chronic alcoholism, Alcohol psychoses, Alcohol cirrhosis of the liver, Accidental poisoning by alcohol) Age-standardized Death Rates at working ages (per 100,000)

multivariate analysis
Multivariate analysis
  • Do ethnic mortality differentials at adult ages remain once we account for differences in education and urban/rural residence?
  • Negative binomial regression
  • Dependent variable: deaths from all causes; deaths by major cause (7)
  • Explanatory variables: exposure, dummy variables for age, ethnicity, urban/rural residence, education (3 cat.)
  • Males and Females analyzed separately
  • Model 1: age, ethnicity
  • Model 2: age, ethnicity, education, residence
conclusions
Conclusions
  • Excess mortality among adult Slavs (Soviet and post-Soviet period) is not likely due to data artifacts or migration effects
  • Excess mortality due to important ethnic differences in cause-specific mortality – alcohol and suicide in particular
  • Differences remain unexplained by education or residence
conclusions41
Conclusions
  • Role of cultural characteristics?
    • Alcohol tied to cultural practices (“culture of alcohol” among Russians; Impact of Islam for Central Asians)
    • Denser social networks and stronger social support among Central Asian ethnic groups?
slide42

Обследования населения, биомаркеры и продолжительность здоровой жизни

Н.С. Гаврилова

population surveys
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
international surveys in russia and fsu
International Surveys in Russia and FSU
  • Russia Longitudinal Monitoring Survey (RLMS)

http://www.cpc.unc.edu/rlms/

  • Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition.

http://www.measuredhs.com

http www cpc unc edu projects rlms
http://www.cpc.unc.edu/projects/rlms

16 раундов обследования

demographic and health surveys
Demographic and Health Surveys
  • Child Health - vaccinations, childhood illness
  • Education - highest level achieved, school enrollment
  • Family Planning knowledge and use of family planning, attitudes
  • Female Genital Cutting - prevalence of and attitudes about female genital cutting
  • Fertility and Fertility Preferences - total fertility rate, desired family size, marriage and sexual activity
  • Gender/Domestic Violence - history of domestic violence, frequency and consequences of violence
  • HIV/AIDS Knowledge, Attitudes, and Behavior - knowledge of HIV prevention, misconceptions, stigma, higher-risk sexual behavior
  • HIV Prevalence - Prevalence of HIV by demographic and behavioral characteristics
  • Household and Respondent Characteristics- electricity, access to water, possessions, education and school attendance, employment
  • Infant and Child Mortality - infant and child mortality rates
  • Malaria - knowledge about malaria transmission, use of bednets among children and women, frequency and treatment of fever
  • Maternal Health - access to antenatal, delivery and postnatal care
  • Maternal Mortality - maternal mortality ratio
  • Nutrition - breastfeeding, vitamin supplementation, anthropometry, anemia
  • Wealth/Socioeconomics - division of households into 5 wealth quintiles to show relationship between wealth, population and health indicators
  • Women's Empowerment - gender attitudes, women’s decision making power, education and employment of men vs. women
dhs sample designs
DHS sample designs
  • The sample is generally representative:
  • At the national level
  • At the residence level (urban-rural)
  • At the regional level (departments, states)

The sample is usually based on a stratified two-stage cluster design:

  • First stage: Enumeration Areas (EA) are generally drawn from Census files
  • Second stage: in each EA selected, a sample of households is drawn from an updated list of households
slide48
DHS охватывает следующие страны б.СССР
  • Азербайджан
  • Казахстан (1995, 1999)
  • Кыргызстан (1997)
  • Молдова (2005)
  • Туркменистан (2000)
  • Узбекистан (1995, 2002)
slide49

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

slide50
Goals:
    • 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
slide51
Why?
  • Need for move from interdisciplinary data COLLECTION to integrated data ANALYSIS
  • Barriers
    • Models/methods
    • Rules of academe
    • Reviewers/editors
slide52
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
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?
slide55

Public Dataset

http://www.icpsr.umich.edu/NACDA/

nshap collaborators
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
Study Timeline
  • Funding: NIH / October, 2003
  • Pretest: September – December, 2004
  • Wave I Field Period: June 2005 – March 2006
  • Wave I Analysis: Began October, 2006
slide60

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.

slide61

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
slide63

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
nshap biomeasures
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
principles of minimal invasiveness
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
Applying Biomeasures in NSHAP

++ = Very well suited -- = Poorly suited

nshap biomeasures68
NSHAP Biomeasures

“Laboratory Without Walls”

Salimetrics

(Saliva Analysis)

McClintock Laboratory

(Cytology)

McDade Lab Northwestern

(Blood Spot Analysis)

UC Cytopathology

(Cytology)

Jordan Clinical Lab

Magee Women’s Hospital

(Bacterial, HPV Analysis)

slide69

Salivary Biomeasures

  • Sex hormone assays
      • Estradiol
      • Progesterone
      • DHEA
      • Testosterone
  • Cotinine
slide70

Salivary Sex Hormones (preliminary analysis)

Frequency

Frequency

Frequency

log(estradiol)

log(testosterone)

log(progesterone)

Units: pg/ml

slide71

Salivary Cotinine

  • Nicotine metabolite
  • Objective marker of tobacco exposure, including second-hand
  • Non-invasive collection method (vs. serum cotinine)
slide72

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

slide73

Dried Blood Spots

  • C-Reactive Protein (CRP)
  • Epstein-Barr Virus (EBV) Antibody Titers

Thanks, Thom and McDade Lab Staff!

slide74

Self-Report Measures

  • Demographic Variables:
    • Age
    • Race/Ethnicity
    • Education
    • Insurance Status
slide75

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
slide76

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
slide78

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?

more information on biomarkers is available at the ccbar website
More Information on Biomarkers is Available at the CCBAR website

http://biomarkers.uchicago.edu/

living longer but healthier
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).
quality or quantity of life
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
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!

what is the best measure85
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
slide87

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

calculation of health expectancy sullivan method
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)
slide90
Вероятность быть здоровым в зависимости от возраста Мужчины

Andreyev et al., Bull.WHO, 2003

slide91
Вероятность быть здоровым в зависимости от возраста Женщины

Andreyev et al., Bull.WHO, 2003

slide93

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

}

slide94

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?”

slide95

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”