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