Socio economic influences on self rated health trajectories evidence from four oecd countries
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Socio-economic influences on self-rated health trajectories: evidence from four OECD countries. Amanda Sacker Peggy McDonough Diana Worts. Mplus Users Meeting Meeting 8 th June 2009. Background. The life course and the welfare state

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Socio economic influences on self rated health trajectories evidence from four oecd countries

Socio-economic influences on self-rated health trajectories: evidence from four OECD countries

Amanda Sacker

Peggy McDonough

Diana Worts

Mplus Users Meeting Meeting 8th June 2009


Background
Background evidence from four OECD countries

  • The life course and the welfare state

    • Societies with weak social safety nets have worse population health than those with strong supports

    • Problem: based almost exclusively on aggregate, cross-sectional measurements of health

  • Comparative individual health dynamics and their social patterning


Aims of study
Aims of study evidence from four OECD countries

  • Describe average national trajectories of self-rated health over a 7-year period

  • Identify socio-economic determinants of cross-sectional and longitudinal health

  • Compare cross-national patterns


Welfare state typologies
Welfare state typologies evidence from four OECD countries

  • Esping Andersen (1990)

    • “three worlds of welfare”

    • decommodification, social stratification, private-public mix

    • liberal, conservative, social-democratic

  • Castles & Mitchell (1993)

    • 2 X 2 cross-classification

    • aggregate welfare expenditure, benefit equality

    • liberal, conservative, non-right hegemony, radical


Castles mitchell s typology
Castles & Mitchell’s typology evidence from four OECD countries


The data
The data evidence from four OECD countries

  • Four panel surveys

    • US Panel Study of Income Dynamics

    • British Household Panel Survey

    • German Socio-Economic Panel Survey

    • Danish panel from the European Community Household Panel Survey

  • Respondents of working age throughout the follow-up period

  • Covariates measured in 1994

  • Health reported 1995-2001


Methods
Methods evidence from four OECD countries

  • Latent growth curves

    • Linear growth curves by elapsed time controlling for age/age squared at baseline

    • Covariate effects on intercept and slope

  • Graphing health over time

    • Aging vectors

    • Synthetic cohort trajectories

  • Estimate health trajectories

    • Compound effect of covariates


The latent growth curve model
The latent growth curve model evidence from four OECD countries

. . . . . . . . . . . . . . .

SRH

1995

SRH

1996

SRH

2001

0

1

6

1

1

1

Slope

Intercept

Age

Gender

Ethnicity

Marital

status

Employ

status

Occup

class

Educ

Income


The latent growth curve model1
The latent growth curve model evidence from four OECD countries


Mplus syntax
Mplus syntax evidence from four OECD countries

VARIABLE:

NAMES ARE (omitted)

USEVARIABLES ARE srhth95 srhth96 srhth97 srhth98

srhth00 srhth01 rout94 missoc94 minority single94

excoup94 unempl94 outlf94 meded94 lowed94 female

agecen agesq logposp94;

MISSING ARE ALL (-999);

WEIGHT IS XRWGT94;

STRAT IS strata;

CLUSTER IS psu;

CATEGORICAL ARE srhth95 srhth96 srhth97 srhth98

srhth00 srhth01;

CENTERING IS GRANDMEAN (rout94 missoc94 minority

single94 excoup94 unempl94 outlf94 meded94 lowed94

female logposp94);

DEFINE:

agecen = agecen/10;

agesq = agecen^2;

ANALYSIS:

TYPE IS COMPLEX;

MODEL:

i s | srhth95@0 srhth96@.1 srhth97@.2 srhth98@.3

srhth00@.5 srhth01@.6;

i s ON rout94 - logposp94;


Self rated health
Self-rated health evidence from four OECD countries

  • US

    “Would you say [your/his/her] health in general is excellent, very good, good, fair, or poor?”

  • Britain

    “Please think back over the last 12 months about how your health has been. Compared to people of your own age, would you say that your health has on the whole been excellent, good, fair, poor or very poor?”

  • Germany

    “How would you describe your current health, very good, good, satisfactory, poor, bad?”

  • Denmark

    “How is your health in general, very good, good, fair, bad, very bad?”


Socio economic influences on self rated health trajectories evidence from four oecd countries

Aging vector graphs evidence from four OECD countries


Stata syntax
Stata syntax evidence from four OECD countries

** data file with one record for each synthetic birth cohort

** age centred on 40 years in 10 year units

** estimates imported from Mplus growth curve model

** calculate self-rated health in 1995 and 2001 for each birth cohort

gen srh_1 = -i_mean - (i_age*agecen + i_agesq*agecen^2)

gen srh_2 = -i_mean - (i_age*agecen + i_agesq*agecen^2) ///

- 0.6*(s_mean + s_age*agecen + s_agesq*agecen^2)

** figure with arrows from health in 1995 to health in 2001 for those aged 25-57 in 1994

gen age_1 = age

gen age_2 = age+6

graph twoway (pcarrow srh_1 age_1 srh_2 age_2), ///

xtitle(Age) ytitle("Predicted SRH Z-score", size(large)) ///

xtick(25(5)65) xlabel(25(5)65, labsize(large)) xtitle(,size(large)) ///

ytick(-1(.5).1) ylabel(-1(.5)1,angle(0) labsize(large)) ///

title("a) United States", size(vlarge)) legend(label(1 "1995 to 2001")) ///

saving("US aging vector.gph", replace)


Socio demographic covariates
Socio-demographic covariates evidence from four OECD countries

  • Age

  • Gender

  • Ethnicity

  • Marital status

  • Education

  • Occupational class

  • Employment status

  • Income


Intercept regressed on covariates
Intercept regressed on covariates evidence from four OECD countries

* p < 0.05 ** p < 0.005 *** p < 0.0005


Slope regressed on covariates
Slope regressed on covariates evidence from four OECD countries

* p < 0.05 ** p < 0.005 *** p < 0.0005


Intercept regressed on covariates interactions with age
Intercept regressed on covariates: evidence from four OECD countriesinteractions with age

* p < 0.10 ** p < 0.05 *** p < 0.005


Age by ethnicity effects on baseline health
Age by ethnicity evidence from four OECD countrieseffects on baseline health


Age by employment status effects on baseline health
Age by employment status effects on baseline health evidence from four OECD countries


Aggregate effects
Aggregate effects evidence from four OECD countries

  • Average ideal type

    • Mean values for all covariates

  • Advantaged ideal type

    • Male, majority ethnic group, cohabiting, tertiary educated, employed, non-routine occupational class, above median income

  • Disadvantaged ideal type

    • Female, minority ethnic group, no longer living with partner, lower secondary education, unemployed, routine occupational class, below median income


Growth curves by levels of advantage
Growth curves by levels of advantage evidence from four OECD countries


Substantive conclusions
Substantive conclusions evidence from four OECD countries

  • Socio-economic influences on trajectories of self-rated health broadly consistent with welfare typologies

  • Results suggest

    • Health of minority groups may be more affected by aggregate welfare expenditure

    • Work and health may be more affected by benefit equality


Methodological conclusions
Methodological conclusions evidence from four OECD countries

  • Latent variable modelling cannot overcome differences in question wording and response labelling

    • Cannot make between-country comparisons about mean levels of health and rate of change

  • Ageing vectors useful for identifying cohort effects


Discussion
Discussion evidence from four OECD countries

  • Causality?

    • Lack of relationship between covariates and changes in health

    • Common finding

  • Violations of measurement invariance

    • How far can anchoring vignettes help?

    • Other adjustment procedures?