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Latent Class Analysis of the Breadth, Severity and Stability of Child Health Inequalities . Mensah FK, Nicholson JM, Headley L, Carlin JB, Berthelsen D, Wake M. NHMRC Capacity Building Grant, MCRI, RCH and University of Melbourne Professors Melissa Wake, John Carlin

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latent class analysis of the breadth severity and stability of child health inequalities
Latent Class Analysis of the Breadth, Severity and Stability of Child Health Inequalities

Mensah FK, Nicholson JM, Headley L, Carlin JB, Berthelsen D, Wake M

slide2

NHMRC Capacity Building Grant, MCRI, RCH and University of Melbourne

Professors Melissa Wake, John Carlin

Professor Jan Nicholson, Parenting Research Centre, Melbourne

Liz Headley, Victorian Public Health Training Scheme and Monash University

Professor Donna Berthelsen, Queensland University of Technology

Gender Equality Network, Economic and Social Research Council UK

Dr Wendy Sigle-Rushton, London School of Economics

Professors John Hobcraft and Kathleen Kiernan, Department of Social Policy and Social Work and Institute of Effective Education, University of York

Acknowledgments

multiple outcomes of health inequality
Multiple Outcomes of Health Inequality

Models have often focused on single outcomes

  • fail to consider breadth of impact and address “multifinality”

Alternative strategies

  • summary indices
  • multiple outcome models

Example to follow: Impact of socioeconomic position on children’s risks for multiple problems in physical, socio-emotional and cognitive functioning

slide4
Two national cohorts of ~5,000 children each

Infants (0-1 yrs) & Kindergarten children (4-5 yrs) in 2004

Followed up every two years

Conducted by Australian Govt. Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), Australian Institute of Family Studies (AIFS), and the Australian Bureau of Statistics (ABS) with advice from a national research consortium

www.aifs.gov.au/growingup/

The Longitudinal Study of Australian Children (LSAC)

latent class analysis lca
Latent Class Analysis (LCA)
  • Groups children to reflect key differences
  • Accounts for inter-correlations between measures
  • Similar to cluster analysis, but is model based (probabilistic)
  • Gives model fit statistics to determine number of categories
  • Uses specialist software such as Mplus

E.g. Multiple problem behaviours in adolescence

Fergusson et al. 1994, Journal of Child Psychology & Psychiatry 35 (6) 1123-1140

Subtypes and severity of peer victimisation

Nylundet al. 2007, Child Development 78 (6) 1706-1722

classification k cohort age 4 5
Classification (K cohort age 4-5)

ESTIMATED CLASS PROPORTIONS AND COUNTS

CLASSIFICATION QUALITY (Entropy) 0.809

CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

a further technique for classification after lca
A further technique for classification after LCA

Technique of random draws on the basis of probabilities

http://www.statmodel.com/download/meantest2.pdf

Takes into account both bias and uncertainty

Similar principles to multiple imputation

Implemented in Mplus as a default

Can also be implemented in Stata

Feeds into descriptive analysis and regression models

conclusions continuation and discussion
Conclusions, continuation and discussion

Clear and reproducible groups reflecting degree of difficulties

Powerful social grading

Strong continuity from early to mid-childhood

Potential to examine predictors, prognosis and associated costs

For discussion:

Criteria for evaluating validity

Utility of this type of indicator beyond academic research