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 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 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

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 The Longitudinal Study of Australian Children (LSAC)

Physical, Psychosocial and Cognitive Difficulties at 4-5 (Birth cohort)

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) Subtypes and severity of peer victimisation Nylund et al. 2007, Child Development 78 (6)

Study design Age 4-5Age 6-7Age 8-9 K cohort (wave 1) (N=3,711)K cohort (wave 2) (N=2,563)K cohort (wave 3) (N=3,080) B cohort (wave 3) (N=3,608) (replication sample) Indicators and Measures Global health measureExcellent / Very good / Good / Fair or Poor Special health care needsNo / Yes (condition lasting for at least 12 months requiring medicines, medical care, health or educational services ) Asthma diagnosisNo / Yes Sleep difficulties0,1,2+ (getting to sleep, sleeping alone, waking during the night) Injuries0,1,2+ in the last year Body mass indexUnderweight / Normal / Overweight / Obese Peds Quality of LifePhysical, Social, Emotional and School Scores Strengths and DifficultiesPro-social, Conduct, Emotional, Hyperactivity and Peers Scores Peabody Picture vocabulary testDirect Assessment of Language Development Who am I? (Age 4-5)Direct Assessment of School Readiness Teacher rating scales (Age 6-7, 8-9)Language and Literacy, Mathematical Thinking

Model fit parameters (K cohort age 4-5) P value Classes

Mean score forHealthiest (59.5%) Moderate (33.4%) Severe (7.2%) Peds Quality of Life:Physical Social Emotional School Strengths and difficultiesPro-social Conduct difficulties Emotional difficulties Hyperactivity Difficulties With Peers LanguagePPVT School readinessWho am I Profiles from 3 class model (K cohort age 4-5)

Percent of children withHealthiest (59.5%) Moderate (33.4%) Severe (7.2%) Fair or poor health Special health care need Asthma diagnosis or more sleep problems or more injuries Underweight Overweight/obese Profiles from 3 class model (K cohort age 4-5)

Replication and consistency of 3 class structure B cohortK cohort Age 4-5 (%) Age 6-7 (%)Age 8-9 (%) (N=3,608)(N=3,711)(N=2,563)(N=3,080) Healthiest Moderate Severe Common sample across 3 waves of the K cohort N=1,685 Age 4-5 (%)Age 6-7 (%)Age 8-9 (%) Healthiest Moderate Severe

Classification (K cohort age 4-5) ESTIMATED CLASS PROPORTIONS AND COUNTS CLASSIFICATION QUALITY(Entropy) CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP 1(Healthiest) (Moderate) (Severe) (Healthiest) (Moderate) (Severe)

A further technique for classification after LCA Technique of random draws on the basis of probabilities 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

Health Inequalities - K cohort age 4-5

Stability through childhood (K cohort) Age 6-7Age 8-9 Healthiest (%) Moderate (%) Severe (%) Healthiest (%) Moderate (%) Severe (%) Age 4-5Healthiest Moderate Severe Age 6-7Healthiest Moderate Severe

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