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Trajectories of alcohol use from Year 9 to Year 12

Trajectories of alcohol use from Year 9 to Year 12. Longitudinal Study of Young People in England (LSYPE): one-day introductory workshop 1 st October 2009 City University, London Gareth Hagger-Johnson, Bridgette Bewick, Robert West, Darren Shickle. Public health perspective.

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Trajectories of alcohol use from Year 9 to Year 12

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  1. Trajectories of alcohol use from Year 9 to Year 12 Longitudinal Study of Young People in England (LSYPE): one-day introductory workshop 1st October 2009 City University, London Gareth Hagger-Johnson, Bridgette Bewick, Robert West, Darren Shickle

  2. Public health perspective • Important to identify risky patterns early • Early alcohol use associated with • Heavier, more frequent usage • Dependence by age 30 • Worse educational outcomes • Partly mediated by impact on brain functioning • Very few longitudinal studies considering • Age of onset • Change over time • Educational outcomes

  3. Risk factors for problem drinking • Male gender • White ethnic minority status • Physical or sexual abuse • Negative attitudes toward schooling • Internalising symptoms (e.g. anxiety, depression) • Cigarette smoking • Antisocial behaviour • Family history of alcohol abuse • Poor parenting style • Low parental monitoring • Low socio-economic status

  4. Introduction • 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 policy

  5. 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) • 11,449 households at Wave Four (2007, year 12, age 16-17) + 352 • National Pupil Database • GCSE subjects and grade • Key Stage 3 results • Census data • Three data files • Family background • Parental attitudes • Young person

  6. 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

  7. Variables with public health relevance • Family background • Parental socio-economic status • Personal characteristics • Attitudes, experiences and behaviours • Smoking, alcohol and drug use • 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 and 4 only) • Self-reported health • Long standing illness or disability

  8. 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

  9. Combining two modelling strategies Mixture modelling,latent class analysis Latent growth curve modelling, trajectories y y1 y2 y3 y4 i s c x x

  10. Growth mixture modelling • Different trajectories • Different means y1 y2 y3 y4 i s c x

  11. Growth mixture modelling • Two-part growth mixture model (Olsen & Shafer, 2001) • u part (use = 1, non-use = 0) • Separates ‘never’ from ‘ever’ drinkers • Zeros represent never having drunk alcohol • y part (frequency of drinking) • Captures frequency of drinking, for ‘ever’ drinkers • Separately for males and females • Choose the number of latent classes • Bring covariates into model • Do they change the nature and number of latent classes? • Can covariates predict latent class membership? • Early predictions to design public health interventions • Practical usefulness (Muthén, 2008)

  12. Females: 2-class solution

  13. Males: 2-class solution

  14. Discussion • Evidence for heterogeneity in alcohol trajectories • Pupils do not follow a normative profile • Growth mixture modelling as a useful technique • Illustrates the importance of longitudinal data collection • Predictors of class membership and change • Demographic • Psychosocial • Prioritize interventions • Identify at-risk pupils • Future analysis plans

  15. 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-johnson@leeds.ac.uk

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