Prevalence and Community Variation in Harmful Levels of Family Conflict Witnessed by Children: Implications for Prevention
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Children’s reports of high family conflict consistently predict poor outcomes. The study identified criteria for high family conflict based on prospective prediction of increased risk for childhood depression. These criteria were subsequently used to establish the prevalence of high family conflict in Australian communities and to identify community correlates suitable for targeting prevention programs. Study 1 utilised a longitudinal design. Grade 6 and 8 students completed a family conflict scale (from the widely used Communities That Care survey) in 2003 and depression symptomotology were evaluated at a 1-year follow-up (International Youth Development Study, N = 1,798). Receiver-operating characteristic analysis yielded a cut-off point on a family conflict score with depression symptomatology as a criterion variable. A cut-off score of 2.5 or more (on a scale of 1 to 4) correctly identified 69 % with depression symptomology, with a specificity of 77.2 % and sensitivity at 44.3 %. Study 2 used data from an Australian national survey of Grade 6 and 8 children (Healthy Neighbourhoods Study, N = 8,256). Prevalence estimates were calculated, and multivariate logistic regression with multi-level modelling was used to establish factors associated with community variation in family conflict levels. Thirty-three percent of Australian children in 2006 were exposed to levels of family conflict that are likely to increase their future risk for depression. Significant community correlates for elevated family conflict included Indigenous Australian identification, socioeconomic disadvantage, urban and state location, maternal absence and paternal unemployment. The analysis provides indicators for targeting family-level mental health promotion programs.
KeywordsFamily conflict Prevalence Children Depression Health promotion
The authors are grateful for the financial support provided by the Australian National Health and Medical Research Council (project number, 334304) for HNS data collection, by the National Institute on Drug Abuse (R01-DA012140-05) for IYDS data collection, and the National Institute on Alcoholism and Alcohol Abuse (R01AA017188-01) and the Australian National Health and Medical Research Council (project number, 491241) for funding data analysis and paper writing on the IYDS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, National Institute on Alcoholism and Alcohol Abuse, or the National Institutes of Health. The authors express their appreciation and thanks to project staff and participants for their valuable contribution to the project.
- Alvarado, R., & Kumpfer, K. (2000). Strengthening America’s families. Juvenile Justice, 7, 8–18.Google Scholar
- Angold, A., Costello, E. J., Messer, S. C., Pickles, A., Winder, F., & Silver, D. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5, 237–249.Google Scholar
- Australian Bureau of Statistics. (2006). 2039.0–Information paper: An introduction to Socio-Economic Indexes for Areas (SEIFA). Accessed February, 2009 from http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/2039.
- Herrenkohl, T. I., Kosterman, R., Hawkins, J. D., & Mason, W. A. (2009). Effects of growth in family conflict in adolescence on adult depressive symptoms: Mediating and moderating effects of stress and school bonding. Journal of Adolescent Health, 44, 146–152.PubMedCentralCrossRefPubMedGoogle Scholar
- Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York: Routledge.Google Scholar
- Kish, L. (1965). Survey sampling. New York: Wiley.Google Scholar
- Kraemer, H. C. (1992). Evaluating medical tests: Objective and quantitative guidelines. Newbury Park: Sage.Google Scholar
- Marshall, N. L., & Tracy, A. J. (2009). After the baby: Work–family conflict and working mother’s psychological health Family Relations Interdisciplinary. Journal of Applied Family Studies, 58, 380–391.Google Scholar
- McKenzie, D. P., Toumbourou, J. W., Forbes, A. B., Mackinnon, A. J., McMaster, B. J., Catalano, R. F., et al. (2011). Predicting future depression in adolescents using the short mood and feelings questionnaire: A two-nation study. Journal of Affective Disorders, 134, 151–159.PubMedCentralCrossRefPubMedGoogle Scholar
- Messer, S. C., Angold, A., Costello, E. J., Loeber, R., Van Kammer, W., & Stouthamer-Loeber, M. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents: Factor composition and structure across development. International Journal of Methods in Psychiatric Research, 5, 251–262.Google Scholar
- Paradis, A. D., Reinherz, H. Z., Giaconia, R. M., Beardslee, W. R., Ward, K., & Fitzmaurice, G. M. (2009). Long-term impact of family arguments and physical violence on adult functioning at age 30 years: Findings from the Simmons longitudinal study. American Academy of Child and Adolescent Psychiatry, 48, 290–298.CrossRefGoogle Scholar
- Patton, G. C., Olsson, C. A., Bond, L., Toumbourou, J. W., Carlin, J. B., Hemphill, S. A., et al. (2008). Predicting female depression across puberty: A two-nation longitudinal study. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1424–1432.PubMedCentralCrossRefPubMedGoogle Scholar
- Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata (2nd ed.). College Station: Stata Press.Google Scholar
- Swets, J. A. (1995). Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Mahwah: Erlbaum.Google Scholar