Missing child maltreatment data implications
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Implications of non-response and loss to follow up and some possible solutions. James Doidge PhD Candidate, University of South Australia Prof Leonie Segal, Supervisor, University of South Australia A. Prof Paul Delfabbro, Supervisor, University of Adelaide ATP Collaborators Dr Ben Edwards, AIFS Prof John Toumbourou, Deakin Dr Daniel Higgins, AIFS Ms Suzanne Vassall
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Missing child maltreatment data implications
- 1. Missing data in research on child maltreatment Implications of non-response and loss to follow-up, and some possible solutions PhD Candidate James Doidge, University of South Australia Supervisors Prof Leonie Segal, University of South Australia A/Prof Paul Delfabbro , University of Adelaide ATP Collaborators Dr Ben Edwards, AIFS Prof John Toumbourou, Deakin Dr Darryl Higgins, AIFS Ms Suzanne Vassallo, AIFS British Association for the Study and Prevention of Child Abuse and Neglect | Congress 2015 James.Doidge@unisa.edu.au
- 3. The Australian Temperament Project 2,443 participants and their families/teachers/nurses 15 waves of data collection over 29 years from birth from: Parents (all waves) Cohort members (since age 11) Nurses (1st wave) Teachers (3 waves) From the available data, 369 fields were selected, representing ~130 variables of interest, reflecting10 underlying domains: Indicators of child maltreatment Risk factors for child maltreatment (economic factors, social factors, parental mental health and substance use, child health, child temperament) Consequences of child maltreatment (economic, social, physical health and mental health) BASPCAN Congress 2015 | James Doidge | University of South Australia
- 4. Loss to follow-up and non-response in the Australian Temperament Project 2443 responded ~3000 selected for recruitment ~557 did not participate ~1500 still in contact ~943 lost to follow-up 980 responded ~557 did not respond 953 completed all questions about child maltreatment 27 did not complete all questions about child maltreatment 140 with complete information on all risk factors and outcomes 813 with missing information on some risk factors or outcomes Wave 1 Wave 14 (all waves) BASPCAN Congress 2015 | James Doidge | University of South Australia
- 5. Child maltreatment and non-response by parents and children in waves prior and subsequent to self-report 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Relative odds of any maltreatment w.r.t. non- response (95% CI) Wave of follow-up BASPCAN Congress 2015 | James Doidge | University of South Australia
- 6. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge6 Predict retention in wave 2 using variables from wave 1 Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 7. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge7 Predict retention in wave 3 using variables from waves 1 and 2 amongst respondents to wave 2. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 8. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge8 Predict retention in wave 4 using variables from waves 1 and 3 amongst respondents to wave 3. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 9. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge9 Predict retention in wave 5 using variables from waves 1 and 4 amongst respondents to wave 4. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 10. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge10 Predict retention in wave 6 using variables from waves 1 and 5 amongst respondents to wave 5. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 11. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge11 Predict retention in wave 7 using variables from waves 1 and 6 amongst respondents to wave 6. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 12. Stepped inverse-probability weighting with non-monotone missing data BASPCAN Congress 2015 | James Doidge12 Predict retention in final wave using variables from waves 1 and previous wave (no non-respondents by definition). Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 5 Pattern 6 Pattern 7 Pattern 8
- 13. Wave 1 variable s Wave 2 variable s Wave 3 variable s Wave 4 variable s Wave 12 variable s Wave 13 variable s Wave 14 variable s Wave 15 variable s People lost after wave 1 People lost after wave 2 People lost after wave 3 People lost after wave 4 People lost after wave 12 People lost after wave 13 People lost after wave 14 People not Combined inverse probability weighting and multiple imputation (IPW/MI) Wave 1 variable s Wave 2 variable s Wave 3 variable s Wave 4 variable s Wave 12 variable s Wave 13 variable s Wave 14 variable s Wave 15 variable s People lost after wave 1 People lost after wave 2 People lost after wave 3 People lost after wave 4 People lost after wave 12 People lost after wave 13 People lost after wave 14 People not IPW Wave 1 variable s Wave 2 variable s Wave 3 variable s Wave 4 variable s Wave 12 variable s Wave 13 variable s Wave 14 variable s Wave 15 variable s People lost after wave 1 People lost after wave 2 People lost after wave 3 People lost after wave 4 People lost after wave 12 People lost after wave 13 People lost after wave 14 People not IPW MI BASPCAN Congress 2015 | James Doidge | University of South Australia
- 14. Implications for estimates of the prevalence of child maltreatment Type of child maltreatment Prevalence (%) Complete cases (n = 951) Weighted excluding missingness Weighted including missingness Participants with weight > 5 (n = 58) Emotional abuse 17.0 18.4 19.7 27.9 Emotional abuse (very true) 3.4 3.3 3.6 3.3 Neglect 2.8 2.8 2.8 1.6 Physical abuse 5.9 6.6 7.2 16.4 Sexual abuse 5.9 6.7 7.1 11.5 Witnessed domestic violence 4.4 4.8 4.7 4.9 Any child abuse or neglect 24.3 25.2 26.9 37.9 Any child abuse or neglect (emotional = very true) 16.3 16.9 17.6 26.2 Multiple maltreatment 8.3 9.0 9.8 19.0 Multiple maltreatment (emotional = very true) 4.2 4.7 4.9 6.9 BASPCAN Congress 2015 | James Doidge | University of South Australia
- 15. Implications of missing data for estimates of risk factors for child maltreatment Economic factors Social factors Parental mental health Child health Child temperament Any Emo Phy Neg Sex WDV 1.0 0.9 1.2 1.0 0.7 0.7 Any Emo Phy Neg Sex WDV 1.1 1.1 1.2 1.4 1.2 1.1 1.0 1.0 0.7 1.5 0.8 0.8 0.9 0.9 0.8 0.8 0.5 0.9 1.5 1.3 1.5 1.2 1.2 1.6 0.9 1.1 0.9 1.1 1.0 1.0 1.1 1.1 1.2 1.2 1.8 1.1 1.3 1.0 0.7 0.7 0.7 1.0 1.2 1.3 1.3 1.2 1.5 0.6 1.2 1.1 1.1 1.1 1.6 1.2 0.9 1.3 1.3 1.3 0.9 1.1 1.1 1.3 1.1 1.0 0.9 1.2 1.1 1.2 1.2 0.5 0.8 0.8 0.8 0.7 1.2 0.9 0.9 1.2 1.1 0.9 1.2 1.4 1.3 1.1 1.2 1.0 0.7 1.3 1.5 1.1 1.1 1.0 0.6 1.0 1.0 1.1 1.1 0.9 1.1 0.9 1.0 1.0 1.1 1.2 0.9 1.5 1.0 1.1 1.0 1.3 0.9 1.3 1.4 1.1 1.1 1.4 1.1 0.8 1.6 0.8 0.9 1.0 0.8 0.8 0.7 0.5 0.6 0.4 1.0 1.0 1.0 1.0 1.2 1.1 1.0 0.5 0.5 0.7 0.5 0.6 0.6 1.2 1.1 1.2 1.2 1.1 1.5 1.1 1.1 0.8 1.3 1.5 0.7 1.1 1.2 1.1 1.0 0.9 0.9 0.9 1.0 1.0 0.9 1.0 1.1 1.2 1.2 1.4 1.4 1.4 1.5 1.0 1.1 0.8 0.8 1.3 1.2 1.1 1.1 1.0 0.7 1.2 1.2 1.4 1.3 0.8 1.0 0.7 0.9 1.1 1.0 0.8 0.7 1.2 0.5 1.3 1.3 1.0 1.1 0.8 0.7 1.8 1.6 0.3 1.7 0.5 1.1 1.1 1.0 1.1 1.0 1.1 1.0 0.8 1.1 0.9 0.9 0.8 0.9 1.1 1.3 1.0 0.7 1.2 1.2 1.1 0.9 0.8 1.1 0.9 Gender = male Young parents Parental education & occupation Green = underestimation by complete case analysis Red = overestimation by complete case analysis Number = ratio of odds ratios (weighted/imputed:CCA) BASPCAN Congress 2015 | James Doidge | University of South Australia
- 16. Any Emo Phy Neg Sex WDV Any Emo Phy Neg Sex WDV Any Emo Phy Neg Sex WDV Any Emo Phy Neg Sex WDV 1.2 1.2 1.1 1.3 1.5 0.9 2.5 2.6 1.3 1.8 2.7 1.0 1.0 0.9 0.9 0.7 0.9 0.6 0.9 0.8 0.6 1.0 0.7 0.6 1.1 1.0 1.2 1.5 1.6 0.8 2.0 2.3 1.7 1.5 2.4 1.0 1.0 1.0 1.0 0.8 0.9 1.0 1.4 1.4 1.2 1.2 1.3 1.1 0.9 0.9 0.8 0.8 0.7 0.6 1.2 1.2 1.1 0.8 1.9 1.5 1.0 1.0 1.1 1.4 0.8 0.9 1.1 1.0 1.0 1.0 1.1 0.8 1.1 0.9 1.4 1.0 1.0 0.7 1.6 1.5 2.1 2.3 1.6 2.3 1.0 0.9 1.1 0.9 0.8 1.0 0.7 0.7 0.6 1.3 0.9 0.7 0.9 0.8 0.9 0.8 0.7 0.7 0.8 0.7 1.0 0.8 0.9 1.0 0.9 0.9 0.9 1.0 1.0 1.1 1.1 1.0 1.1 1.0 1.2 0.8 0.9 0.8 1.1 1.1 1.0 0.6 0.8 0.8 1.2 1.0 0.6 1.5 0.8 0.8 0.9 0.9 0.8 0.9 0.9 1.0 1.0 0.9 1.0 1.0 0.9 0.7 1.2 0.7 0.7 0.7 1.4 1.1 1.8 1.5 0.7 1.5 0.8 0.7 0.8 0.7 0.9 0.7 0.8 0.7 0.5 0.9 1.0 0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.9 0.9 1.1 1.1 1.0 1.3 1.0 1.0 1.0 0.8 1.0 1.2 0.8 0.9 1.0 0.7 0.5 1.5 1.1 1.0 1.0 0.7 0.7 0.6 1.0 1.0 1.4 1.3 1.4 2.2 0.9 0.9 0.9 0.9 0.9 0.9 0.7 0.7 0.3 0.5 1.0 0.6 0.7 0.6 0.6 0.9 0.7 0.7 0.9 0.9 0.6 0.6 0.9 1.0 lower lowerhigherlower lower lower 0.8 0.6 2.1 0.9 0.5 0.7 0.6 0.7 1.1 1.0 0.5 0.8 0.9 0.7 0.8 0.7 1.0 0.9 higherlowerhigherlower lowerhigher 1.1 1.1 1.0 0.9 1.1 0.8 0.7 0.7 1.0 1.1 0.7 0.7 1.1 1.2 1.0 0.9 1.1 1.2 higherhigherhigherlowerhigherhigher 1.1 1.1 1.3 0.9 1.2 1.0 0.9 0.8 0.9 0.8 0.7 0.7 1.5 1.7 1.0 1.3 1.5 1.9 lower lowerhigherhigherhigherhigher 0.8 0.7 0.6 1.1 1.0 1.1 0.8 0.8 0.9 0.9 0.7 0.7 1.0 1.1 1.4 1.1 1.3 1.9 0.7 0.6 0.5 0.6 1.0 0.4 0.9 0.8 1.0 1.0 0.9 0.7 1.3 1.3 1.3 1.2 1.4 1.4 0.7 0.6 0.5 0.6 1.0 0.4 lower lower lowerhigherhigherlower lower lower lower lower lower lower 0.8 0.7 1.3 1.6 0.6 1.0 lower lowerhigherhigherhigherlower higherhigherhigherlowerhigherhigher 1.2 1.1 0.6 1.0 1.6 0.5 lower lowerhigherlower lowerhigher higherhigherhigherlowerhigherhigher 1.2 1.1 0.6 1.0 1.6 0.5 lower lower lowerhigherlower lower higherhigherhigherlowerhigherhigher 0.7 0.7 0.8 1.8 1.1 0.8 lower lower lowerhigherlower lower 1.2 1.1 0.7 0.8 1.6 0.6 lower lowerhigherhigherhigherlower 1.3 0.4 1.0 1.0 1.2 1.0 lower lower lower lower lower lower 0.8 0.8 0.7 0.8 0.8 0.7 lower lower lowerhigherlower lower 1.0 1.0 0.6 1.0 0.5 0.6 0.4 0.5 0.4 1.0 1.0 0.5 0.9 0.8 0.8 1.0 1.0 0.8 higherlower lowerhigherhigherhigher higherlowerhigherhigherhigherhigher higherlowerhigherlower lower lower higherhigherhigherhigherhigherlower Implications of missing data for estimates of outcomes of child maltreatment Economic Social Education Criminal behaviour Green = underestimation by complete case analysis Red = overestimation by complete case analysis Number = ratio of odds ratios (weighted/imputed:CCA) Physical health Mental health BASPCAN Congress 2015 | James Doidge | University of South Australia
- 17. Strength of the missing at random assumption Modelled covariates Odds ratio for any maltreatment with respect to: Non-response by parents Non-response by cohort Partial response Overall missingness 1 (none) 3.96*** 1.55* 4.87** 5.50*** 2 Sex 3.98*** 1.56* 4.89** 5.53*** 3 Sex and economic factors 2.18* 1.48 1.45 2.58* 4 Sex, economic and other social factors 2.09 1.49 0.90 2.27 5 Sex, economic and other social factors, parental mental health and substance use 1.97 1.46 0.71 2.06 6 Sex, economic and other social factors, parental mental health and substance use, child health and temperament 2.38* 1.48 0.59 2.39 BASPCAN Congress 2015 | James Doidge | University of South Australia
- 18. Summary BASPCAN Congress 2015 | James Doidge | University of South Australia Prevalence is likely to be underestimated because of missing data, but not greatly. The assumption that missing data will ‘conservatively’ bias estimates of risk or consequences cannot be justified. Most estimates were fairly robust to missing data, but there are some patterns and some strong biases (in both directions) that warrant consideration. The missing at random assumption is unlikely to hold in research on child maltreatment. Sensitivity analysis and/or follow-up of non-respondents is usually warranted. When possible, incorporate measures of missingness into analyses. These findings are likely to be generalisable to other types of population-based research, and possibly to other types of missingness (e.g. non-participation).
Notas del editor
- Accepted title: “Missing data in child maltreatment: implications of sample selection, non-response and loss to follow-up, and some possible solutions”
- INTRODUCE YOURSELF ASK ABOUT BACKGROUNDS
- While it looks like the association was low to begin with, this is actually obscured by the fact that in waves 2 and 3 the researchers surveyed only 2/3 of the cohort, and I was unable to distinguish between parents who chose not to respond and parents who were never asked.
- Wave 1 data to predict loss to follow-up at wave 2
- Non-respondents to wave 2 get a partial weight of 1 for this wave.