Presentation on theme: "Current differentiation of demographic processes in Lithuania: a census-linked study with population register data Domantas Jasilionis, Aušra Maslauskaitė,"— Presentation transcript:
Current differentiation of demographic processes in Lithuania: a census-linked study with population register data Domantas Jasilionis, Aušra Maslauskaitė, Aiva Jasilionienė, Vlada Stankūnienė and Dalia Ambrozaitienė, Giedrė Smailytė, France Meslé, Jacques Vallin, Vladimir Shkolnikov The research is funded by EU structural assistance to Lithuanian under the measure VP ŠMM-07-K “Support to Research Activities of Scientists and Other Researchers (Global Grant)” project Nr. VP ŠMM-07-K
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 countries scientific 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 exposure by each combination of categories of available variables. Marriage Birth by parity Emigration Death No event Time 2001 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 groups Puasson regression coefficients (rate ratios) Divorce Cancer 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 progress 1.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 studies 1.Migration patterns and determinants. 2.Family formation. 3.Changes in demographic differentials
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
Lagging behind Estonia in reforming health care? Contributions of amenable causes and IHD to LEB changes between 2000 and 2007.
A delayed “cardiovascular revolution”? Trends in SDRs for ischaemic heart disease,
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 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, Example: 1 st 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, Source: Jasilionis, Stankūnienė, 2012.
STUDY #3: SOCIOECONOMIC DIFFERENCES IN CANCER INCIDENCE AND MORTALITY Source: Smailyte, Jasilionis, Krilaviciute et al. (2012) / Cancer Epidemiology. Opposite educational gradients in prostate cancer incidence and mortality, Lithuanian males, aged 40-79,
STUDY #4: DETERMINANTS OF FERTILITY IN LITHUANIA Using registers to improve quality of fertility statistics Solving 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,
STUDY #4: DETERMINANTS OF FERTILITY IN LITHUANIA Source: Jasilioniene, Stankūnienė, Maslauskaitė et al Ethnic differentials in parity-specific Total Fertility Rate (TFR) and Mean Age at Birth (MAB),
STUDY #5: SOCIOECONOMIC DETERMINANTS OF DIVORCE IN LITHUANIA ECONOMIC ACTIVITY STATUS Poisson regression relative first divorce risks by ECONOMIC ACTIVITY STATUS adjusted for all control variables. Lithuania, MODEL: Additionally controlled for duration of marriage, marriage cohort, age at first marriage, number of children, education, ethnicity, place of residence, place of birth. FEMALESMALES Active, employed 11 Active, unemployed *** Inactive, disabled *** Inactive, housewife/house husband 0.74***1.09 Source: Maslauskaitė, Jasilionienė, Jasilionis, Stankūnienė, Shkolnikov, 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 citiesOther urban Rural Active, employed 111 Active, unemployed 0.88*** *** Inactive, disabled * Inactive, housewife 0.63***0.84***0.82*** Source: Maslauskaitė, Jasilionienė, Jasilionis, Stankūnienė, Shkolnikov, 2013.
Emigration determinants Source: Kluesener, Jasilionis, Grigoriev, Stankuniene, Trends in crude emigration and net- migration rates (per 1000),
Emigration determinants Source: Kluesener, Jasilionis, Grigoriev, Stankuniene, Poisson regression emigration rate ratios by socio-demographic and socio- economic variables, Lithuanian males and females aged 20-64,