Ethnic differentials in mortality
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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 l.jpg

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 l.jpg
Background on Kyrgyzstan Mortality in Central Asia

  • 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 l.jpg
Ethnic Groups in Kyrgyzstan Mortality in Central Asia

  • 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%)



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Mortality paradox? Mortality in Central Asia

  • Soviet period: Russians/Slavs occupied dominant positions in the socio-economic structure of Central Asian societies (Kahn 1993)


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Mortality paradox? Mortality in Central Asia

  • 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 l.jpg
Proportion of individuals with post-secondary education, by age and ethnicity, in 1989 census. Females


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Mortality paradox? age and ethnicity, in 1989 census.

  • 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)


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


Mortality paradox12 l.jpg
Mortality paradox? age and ethnicity, in 1989 census. Males.

  • 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 l.jpg
Mortality paradox? age and ethnicity, in 1989 census. Males.

  • 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)


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IMR by ethnicity, 1958-2003, Kyrgyzstan age and ethnicity, in 1989 census. Males.


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Data age and ethnicity, in 1989 census. Males.

  • 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


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Possible explanations for mortality paradox age and ethnicity, in 1989 census. Males.

  • Data artifacts

  • Migration effects (esp. 1989-99)

  • Cultural effects


Data artifacts l.jpg
Data artifacts? age and ethnicity, in 1989 census. Males.

  • Could the lower recorded mortality among Central Asian adults be due to lower data quality among them (coverage of deaths, age misreporting)?


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Cultural effects? age and ethnicity, in 1989 census. Males.

  • 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?


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Data artifacts? age and ethnicity, in 1989 census. Males.

  • 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 l.jpg
Migration effects? age and ethnicity, in 1989 census. Males.

  • 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)?


Health selection l.jpg
Health selection? age and ethnicity, in 1989 census. Males.




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Cultural effects? 1989-99

  • 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 l.jpg
Age-standardized Death Rates at working ages (per 100000), 1998-99, by cause and ethnicity, Males


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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 l.jpg
Age-standardized Death Rates at working ages (per 100,000), 1998-99, by cause and ethnicity, Females


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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)


Age standardized death rates at working ages per 100 000 detailed injuries females l.jpg
Age-standardized Death Rates at working ages (per 100,000) expectancy at working ages (Detailed Injuries, Females


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


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Multivariate analysis expectancy at working ages (

  • 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


Males all causes of death l.jpg
Males, all causes of death expectancy at working ages (


Risk ratio slavs ca males l.jpg
Risk Ratio Slavs/CA expectancy at working ages (Males


Risk ratio slavs ca females l.jpg
Risk Ratio Slavs/CA expectancy at working ages (Females

NS

NS

NS

NS

NS

NS

NS

NS


Conclusions l.jpg
Conclusions expectancy at working ages (

  • 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 l.jpg
Conclusions expectancy at working ages (

  • 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?


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Обследования населения, биомаркеры и продолжительность здоровой жизни

Н.С. Гаврилова


Population surveys l.jpg
Population surveys биомаркеры и продолжительность здоровой жизни

  • 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


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International Surveys in Russia and FSU биомаркеры и продолжительность здоровой жизни

  • 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 l.jpg
http://www.cpc.unc.edu/projects/rlms биомаркеры и продолжительность здоровой жизни

16 раундов обследования


Demographic and health surveys l.jpg
Demographic and Health Surveys биомаркеры и продолжительность здоровой жизни

  • 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 l.jpg
DHS sample designs биомаркеры и продолжительность здоровой жизни

  • 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


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DHS биомаркеры и продолжительность здоровой жизниохватывает следующие страны б.СССР

  • Азербайджан

  • Казахстан (1995, 1999)

  • Кыргызстан (1997)

  • Молдова (2005)

  • Туркменистан (2000)

  • Узбекистан (1995, 2002)


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Biomarkers in Population-Based Aging and Longevity Research биомаркеры и продолжительность здоровой жизни

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


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  • Goals: биомаркеры и продолжительность здоровой жизни

    • 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


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Why? биомаркеры и продолжительность здоровой жизни

  • Need for move from interdisciplinary data COLLECTION to integrated data ANALYSIS

  • Barriers

    • Models/methods

    • Rules of academe

    • Reviewers/editors


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Why? биомаркеры и продолжительность здоровой жизни

  • 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 l.jpg
What is needed? биомаркеры и продолжительность здоровой жизни

  • Methods and models for analytic integration

  • Streamlining data collection

    • Advances in instruments

    • Minimally invasive techniques

    • Best practices

    • Concern for ethical issues

    • Central coordination?


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Introduction to: биомаркеры и продолжительность здоровой жизни


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Public Dataset биомаркеры и продолжительность здоровой жизни

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


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NSHAP Collaborators биомаркеры и продолжительность здоровой жизни

  • 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


Affiliated investigators and labs l.jpg
Affiliated Investigators and Labs биомаркеры и продолжительность здоровой жизни


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Corporate Contributions and Grants биомаркеры и продолжительность здоровой жизни


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Study Timeline биомаркеры и продолжительность здоровой жизни

  • Funding: NIH / October, 2003

  • Pretest: September – December, 2004

  • Wave I Field Period: June 2005 – March 2006

  • Wave I Analysis: Began October, 2006


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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.


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NSHAP Design Overview (2005).

  • 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



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Domains of Inquiry (2005).

  • 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 l.jpg
NSHAP Biomeasures (2005).

  • 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



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Principles of Minimal Invasiveness (2005).

  • 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.


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Applying Biomeasures in NSHAP (2005).

++ = Very well suited -- = Poorly suited


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NSHAP Biomeasures (2005).

“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)


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Salivary Biomeasures (2005).

  • Sex hormone assays

  • Estradiol

  • Progesterone

  • DHEA

  • Testosterone

  • Cotinine


  • Slide70 l.jpg

    Salivary Sex Hormones (preliminary analysis) (2005).

    Frequency

    Frequency

    Frequency

    log(estradiol)

    log(testosterone)

    log(progesterone)

    Units: pg/ml


    Slide71 l.jpg

    Salivary Cotinine (2005).

    • Nicotine metabolite

    • Objective marker of tobacco exposure, including second-hand

    • Non-invasive collection method (vs. serum cotinine)


    Slide72 l.jpg

    Distribution of Salivary Cotinine (2005).

    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 l.jpg

    Dried Blood Spots (2005).

    • C-Reactive Protein (CRP)

    • Epstein-Barr Virus (EBV) Antibody Titers

    Thanks, Thom and McDade Lab Staff!


    Slide74 l.jpg

    Self-Report Measures (2005).

    • Demographic Variables:

      • Age

      • Race/Ethnicity

      • Education

      • Insurance Status


    Slide75 l.jpg

    Self-Report Measures (2005).

    • 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 l.jpg

    Self-Report Measures (2005).

    • 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


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    Challenges (2005).


    Slide78 l.jpg

    Specimen Storage (2005).

    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?


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    More Information on Biomarkers is Available at the CCBAR website

    http://biomarkers.uchicago.edu/



    Living longer but healthier l.jpg
    Living longer but healthier? website

    • 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 l.jpg
    Quality or quantity of life? website

    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 l.jpg
    What is the best measure? website

    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 l.jpg
    What is the best measure? website

    • Depends on the question

    • Need a range of severity

      • dynamic equilibrium

    • Performance versus self-report

      • cultural differences

    • Cross-national comparability

      • translation issues


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    Estimation of websitehealth expectancyby Sullivan’s method


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    Life expectancy website

    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


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    Calculation of health expectancy (Sullivan method) website

    • 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)


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    Вероятность быть здоровым в зависимости от возраста Мужчины

    Andreyev et al., Bull.WHO, 2003


    Slide91 l.jpg
    Вероятность быть здоровым в зависимости от возраста Женщины

    Andreyev et al., Bull.WHO, 2003


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    Choice of зависимости от возраста Женщиныhealth expectancyindicators


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    Self-rated health зависимости от возраста Женщины

    Interview question:

    “How do you rate your present state of health in general?”

    • Answer categories:

    • Very good

    • Good

    • Fair

    • Poor

    • Very poor

    }

    Dichotomised

    }


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    Long-standing illness зависимости от возраста Женщины

    Interview question:

    “Do you suffer from any long-standing illness, long-standing after-effect of injury, any handicap, or other long-standing condition?”


    Slide95 l.jpg

    Long-lasting restrictions зависимости от возраста Женщины(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”


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