Growth mixture modelling of alcohol consumption in young people

1
Growth mixture modelling of alcohol consumption
in young people

• UK Mplus Users Meeting
• 8th-9th June 2009
• Gareth Hagger-Johnson, Bridgette Bewick, Robert
  West, Darren Shickle

2
Data set

• Longitudinal Survey of Young People in England
  (LSYPE)
• Next steps
• Funding
• The Department for Children, Schools and Families
  (DCSF)
• Designed by
• Centre for Longitudinal Studies (CLS)
• National Centre for Social Research (NatCen)
• Cohort study
• Year 9 English pupils born 01/09/89 to 31/08/90
• Assessed annually from 2004 (year 9, age 13/14)
  to 2014
• Primary aim
• Transition through education into work, evaluate
  impact of educational policy

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Data linkage

• Waves 1 to 3 currently available
• 15,770 households at Wave One (2004, year 9, age
  13-14)
• 13,539 households at Wave Two (2005, year 10, age
  14-15)
• 12,439 households at Wave Three (2006, year 11,
  age 15-16)
• National Pupil Database
• GCSE subjects and grade
• Key Stage 3 results
• Census data
• Three data files
• Family background
• Parental attitudes
• Young person

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Sampling procedure

• Two stage probability proportional to size (PPS)
• With disproportionate stratification
• Sampling units
• Schools
• Deprived vs. non-deprived
• Deprived over-sampled by 1.5
• Pupils within schools
• Major ethnic minority groups (Indian Pakistani
  Bangladeshi Black African Black Caribbean and
  Mixed) over-sampled at pupil level (n 1000)
• Design efficiency 78.4
• Accurate assessments of national quantities
• With simple random sample as large as achieved

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Variables available

• Family background
• Parental socio-economic status
• Personal characteristics
• Attitudes, experiences and behaviours
• Attainment in education
• Parental employment
• Income and family environment as well as local
  deprivation
• The school(s) the young person attends/has
  attended
• Health data
• Birth weight
• Psychological distress (GHQ-12 wave 2 only)
• Self-reported health (wave 2 and 3 only)
• Long standing illness or disability

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Alcohol items in LSYPE

• Thinking about the last 12 months, about how
  often did you usually have an alcoholic drink?
  Was it...
• 6. Most days
• 5. Once or twice a week
• 4. 2 or 3 times a month
• 3. Once a month
• 2. Once every couple of months or
• 1. Less often?
• Have you ever had a proper alcoholic drink? That
  is a whole drink, not just a sip. Please do not
  count drinks labelled low alcohol.
• 1. Yes, 2. No

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Frequency counts
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Planned analysis strategy

• Link data and extract substantive variables
• Simple latent curve model
• Growth mixture model
• Two-part growth mixture model (Olsen Shafer,
  2001)
• u part (use 1, non-use 0)
• Random zeros represent zero or low activity
• y part (frequency of drinking)
• Multiple Imputation using Chain Equations (MICE)
• R package
• Bring covariates into model
• Number and nature of trajectory classes subject
  to change
• Can covariates predict latent class membership?
• Early predictions to design public health
  interventions
• Practical usefulness (Muthén, 2008)

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Academic Unit of Public HealthLeeds Institute of
Health SciencesFaculty of Medicine and
HealthUniversity of LeedsCharles Thackrah
Building101 Clarendon RoadLeeds, United
KingdomLS2 9LJwww.leeds.ac.uk/lihsg.hagger-jo
hnson_at_leeds.ac.uk