aims of the project
DESCRIPTION
Current differentiation of demographic processes in Lithuania: a census-linked study with population register data Domantas Jasilionis, Aušra Maslauskaitė, Aiva Jasilionien ė, Vlada Stankūnienė and Dalia Ambrozaitienė , Giedrė Smailytė, France Meslé, Jacques Vallin , Vladimir Shkolnikov. - PowerPoint PPT PresentationTRANSCRIPT
Current differentiation of demographic processes in Lithuania: a census-linked study with population register
data
Domantas Jasilionis, Aušra Maslauskaitė, Aiva Jasilionienė, Vlada Stankūnienė
andDalia Ambrozaitienė, Giedrė Smailytė, France Meslé, Jacques Vallin,
Vladimir Shkolnikov
The research is funded by EU structural assistance to Lithuanian under the measure VP-1-3.1-ŠMM-07-K “Support to Research Activities of Scientists and Other Researchers (Global
Grant)” project Nr. VP-1-3.1-ŠMM-07-K-02-067
AIMS OF THE PROJECT
• to create integrated census-linked longitudinal databases combining population census, demographic register, and contextual data.
• to obtain new very important for Lithuania and other EU countriesscientific evidence for complex assessment of demographic differentials and their impact on sustainability of demographic trends.
• on the basis of new reliable scientific evidence and innovative methodological solutions to create and disseminate methodological recommendations for development of studies on demographic differentials.
Focus on methods: formal demography, epidemiology, statistics spatial analyses multilevel approach
Frequency dataset: numbers of demographic events and person year of exposureby each combination of categories of available variables.
Marriage Birth by parity
Emigration
DeathNo event
Time2001 OR 2011 CENSUS
Beginning of the observation
Socio-demographic and socio-economic characteristics
End of observation
Linkage between census and follow-up for dem. events
Period or cohort demographic indicators by socio-demographic groupsPuasson regression coefficients (rate ratios)
DivorceCancer diagnosis
CENTRAL POPULATION REGISTER:Death, Birth, Marriage, Divorce,
Migration
CANCER REGISTER:Cancer incidence
CAUSE OF DEATH REGISTER
The key advantages of population-level census-linked data for studying demographic differentials:
=> Representativeness: covers entire population. Surveys often exclude some (vulnerable) parts of populations.
=> Sample size: substantial numbers of demographic events and person years of exposure for statistically robust estimations of demographic rates for sociodemographic groups.
=> More reliable data for studying mortality differentials: a census-linked approach allows to avoid numerator- denominator bias which is typical for cross-sectional studies using death record information about sociodemographic status of deceased.
Lithuanian census-linked data and their use in research on death, fertility and family events
Examples of studies
Studies in progress1.Socioeconomic and sociodemographic mortality differentials.
2.Socioeconomic differences in cancer incidence and survival.
3.Socioeconomic and sociodemographic fertility differentials.
4.Socioeconomic differences in divorce risk.
5.Individual and contextual determinants of emigration.
Future studies1.Migration patterns and determinants.
2.Family formation.
3.Changes in demographic differentials 2001-2011.
STUDY #1: NUMERATOR-DENOMINATOR BIAS IN CROSS-SECTIONAL
CENSUS-UNLINKED MORTALITY DATA
Source: Jasilionis, Stankuniene, Ambrozaitiene, Jdanov, Shkolnikov, 2012.
STUDY #2: HIGH MORTALITY AND ITS DETERMINANTS IN LITHUANIA
MALES
64
65
66
67
68
69
70
71
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Life expectancy
Lithuania
Estonia
Latvia
FEMALES
74
75
76
77
78
79
80
81
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Life expectancy
Estonia
Latvia
Lithuania
MALES FEMALES
Lithuania Estonia Lithuania Estonia
Causes amenable to medical intervention -0.3 0.6 0.2 0.9
Ischaemic heart dis. -0.4 0.9 -0.1 1.3
Other (remaining) -1.3 0.5 -0.2 0.4
TOTAL CHANGE -1.9 2.0 -0.1 2.6
Lagging behind Estonia in reforming health care?
Contributions of amenable causes and IHD to LEB changesbetween 2000 and 2007.
A delayed “cardiovascular revolution”?Trends in SDRs for ischaemic heart disease, 2000-2010.
100
1000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
MALES
Lithuania
Latvia
Estonia
100
1000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
FEMALES
Lithuania
Latvia
Estonia
Importance of health inequalities (1)
Economic argument: improving the health of the poor helps them to extract themselves from poverty. Ill-health is an obstacle for economic progress (WHO, 1999). Good health of all individuals in the society should be an ultimate goal of economic development.
Ultimately ethical issues – a socio-economic disadvantage and adverse health conditions in some populations should be considered on moral grounds, not in terms of economic return. Most of health inequalities between and within countries are not genetic differences, nor they are biologically inevitable. Therefore, the inequalities can be reduced by appropriate policies in public health, health systems, and other areas (Leon & Walt, 2001).
Source: Whitehead, 1992.
Importance of health inequalities (2):Demographic and economic arguments
Mackenbach, Meerding, Kunst (2011):
EVERY YEAR inequality related losses to health in the European Union amount for:
More than 700 thou. avoidable deaths;33 mil. prevalent cases of ill-health.Lithuania: ~20 thou. avoidable deaths at working ages in 2001-05.
EVERY YEAR inequality related ECONOMIC losses to health amount:
1.4% of GDP (or €141 billion) – through avoidable loss of labour productivity; 5% of the costs of social security systems; 20% of the costs of healthcare systems.
DIVIDED LITHUANIA: male life expectancy by 24 pop groups
Difference between ~10% males with the highest e(30)and ~10% males with the lowest e(30) – 20 years!
Distribution of male life expectancy at age 30 by 24 four-dimensional groups in Lithuania, 2001-2004.
Example:1st group – married Lithuanian men with higher education, residing in urban areas.
Source: Jasilionis et al., 2007.
STUDY #2: SOCIOECONOMIC DIFFERENCES IN ADULT MORTALITY IN LITHUANIA
Poisson regression adult (30-59 years) mortality rate ratios for suicide, by occupation. Lithuania, 2001-2005.
Source: Jasilionis, Stankūnienė, 2012.
STUDY #3: SOCIOECONOMIC DIFFERENCES IN CANCER INCIDENCE AND
MORTALITY
INCIDENCE MORTALITY
Higher (ref. gr.) 1 1
Secondary 0.70 (0.64–0.76) 1.18 (0.93–1.48)
Lower than secondary 0.49 (0.46–0.53) 1.40 (1.13–1.72)
Source: Smailyte, Jasilionis, Krilaviciute et al. (2012) / Cancer Epidemiology.
Opposite educational gradients in prostate cancer incidence and mortality, Lithuanian males, aged 40-79, 2001-2004
STUDY #4: DETERMINANTS OF FERTILITY IN LITHUANIA
Using registers to improve quality of fertility statisticsSolving a “mystery” of TFR in 2011 (=1.8!!!)
TFR (2010) = 1.5 (includes mothers and births de facto abroad but registered as in
Lithuania)
TFR (2011) =1.8 (excludes mothers abroad, but includes births abroad and/or mothers
residing abroad)
After correction TFR (2011)=1.55
Identification of status of mothers’ residential status using registers:
Central Population Register (last address, registered movements)Social Security Register (Social benefits)
Health Insurance Register (Using health care services) Tax Register (Regular employment or income, taxes)
STUDY #4: DETERMINANTS OF FERTILITY IN LITHUANIA
Trends in Total Fertility Rate, 2001-2012.
1.00
1.10
1.20
1.30
1.40
1.50
1.60
1.70
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
TFR
STUDY #4: DETERMINANTS OF FERTILITY IN LITHUANIA
Source: Jasilioniene, Stankūnienė, Maslauskaitė et al. 2012.
Ethnic differentials in parity-specific Total Fertility Rate (TFR) and Mean Age at Birth (MAB), 2001-2002.
STUDY #5: SOCIOECONOMIC DETERMINANTS OF DIVORCE IN LITHUANIA
Poisson regression relative first divorce risks by ECONOMIC ACTIVITY ECONOMIC ACTIVITY STATUSSTATUS adjusted for all control variables. Lithuania, 2001-2003
MODEL: Additionally controlled for duration of marriage, marriage cohort, age at first marriage, number of children, education, ethnicity, place of residence, place of birth.
FEMALES MALES
Active, employed 1 1
Active, unemployed 1.02 1.43***1.43***
Inactive, disabled 0.98 1.28***1.28***
Inactive, housewife/house husband 0.74***0.74*** 1.09
Source: Maslauskaitė, Jasilionienė, Jasilionis, Stankūnienė, Shkolnikov, 2013.
The study is based on 41 thou. first (legal) divorces and 3.18 million person-years of marriage years of exposure.
First divorce risks by economic activity status for females residing: 1) in large cities, 2) other urban areas, 3) rural areas.
Controlled for duration of marriage, marriage cohort, age at first marriage, number of children, education, ethnicity, place of residence, place of birth.
Large cities Other urban
Rural
Active, employed 1 1 1
Active, unemployed 0.88***0.88*** 1.04 1.39***1.39***
Inactive, disabled 0.86 1.01 1.25*
Inactive, housewife 0.63***0.63*** 0.84***0.84*** 0.82***0.82***
Source: Maslauskaitė, Jasilionienė, Jasilionis, Stankūnienė, Shkolnikov, 2013.
Emigration determinants
Source: Kluesener, Jasilionis, Grigoriev, Stankuniene, 2013.
Trends in crude emigration and net-migration rates (per 1000), 2001-2012
Emigration determinants
Source: Kluesener, Jasilionis, Grigoriev, Stankuniene, 2013.
Poisson regression emigration rate ratios by socio-demographic and socio-economic variables, Lithuanian males and females aged 20-64, 2011-2012
Males Females MRR P values CI- CI+ MRR sig CI- CI+ Education Higher (ref.) 1.00 1.00 Secondary 1.13 0.000 1.07 1.19 0.96 0.109 0.92 1.01 Lower than secondary 1.06 0.080 0.99 1.14 0.87 0.000 0.81 0.93 Economic activity Employed (ref.) 1.00 1.00 Unemployed 1.47 0.000 1.40 1.55 1.56 0.000 1.47 1.64 Inactive, disabled 0.18 0.000 0.14 0.25 0.17 0.000 0.12 0.24 Other inactive 0.96 0.239 0.90 1.03 1.06 0.019 1.01 1.12 Ethnicity Lithuanian (ref.) 1.00 1.00 Russian 1.21 0.000 1.12 1.31 1.16 0.000 1.07 1.25 Polish 0.80 0.000 0.72 0.87 0.82 0.000 0.75 0.90 Other 0.89 0.032 0.80 0.99 0.90 0.026 0.82 0.99 Experience of life abroad for 1 year No experience (ref.) 1.00 1.00 Life abroad for 1 yr 1.69 0.000 1.58 1.81 1.81 0.000 1.68 1.95