Aboriginal Children and Their Caregivers Living with Low Income: Outcomes from a Two-Generation Preschool Program

The development of preschool children of Aboriginal heritage is jeopardized by the inter-generational transmission of risk that has created, and continues to create, social disadvantage. Early intervention programs are intended to mitigate the impact of social disadvantage. Yet, evidence of the effectiveness of these programs for children of Aboriginal heritage is limited. The purpose of this study was to examine the effects of a two-generation, multi-cultural preschool program on 45 children of Aboriginal heritage and their caregivers. We used a single-group, pretest (program intake)/posttest (program exit) design with follow-up when the children were 7 years old. We used an observational measure of child receptive language (Peabody Picture Vocabulary Test–III) and caregiver-reported measures of child development (Nipissing District Developmental Screen), risk for child maltreatment (Adult-Adolescent Parenting Inventory; AAPI), parenting stress (Parenting Stress Index; PSI), self-esteem (Rosenberg Self-Esteem scale; RSE), and life skills (Community Life Skills scale; CLS). Using paired t-tests we found statistically significant increases in child receptive language scores between intake and exit, and repeated-measures ANOVA showed that these improvements were maintained up to age 7 years. For caregivers, Pearson’s correlations demonstrated that risk for child maltreatment, parenting stress, self-esteem, and life skills were stable over time. Results of this study suggest that children of Aboriginal heritage can benefit from participation in a two-generation, multi-cultural preschool program. Their caregivers may have received greater benefit if issues of intergenerational transmission of the negative influences of residential schools were addressed as part of programming

Individual and social determinants of multiple chronic disease behavioral risk factors among youth

<p>Abstract</p> <p>Background</p> <p>Behavioral risk factors are known to co-occur among youth, and to increase risks of chronic diseases morbidity and mortality later in life. However, little is known about determinants of multiple chronic disease behavioral risk factors, particularly among youth. Previous studies have been cross-sectional and carried out without a sound theoretical framework.</p> <p>Methods</p> <p>Using longitudinal data (n = 1135) from Cycle 4 (2000-2001), Cycle 5 (2002-2003) and Cycle 6 (2004-2005) of the National Longitudinal Survey of Children and Youth, a nationally representative sample of Canadian children who are followed biennially, the present study examines the influence of a set of conceptually-related individual/social distal variables (variables situated at an intermediate distance from behaviors), and individual/social ultimate variables (variables situated at an utmost distance from behaviors) on the rate of occurrence of multiple behavioral risk factors (physical inactivity, sedentary behavior, tobacco smoking, alcohol drinking, and high body mass index) in a sample of children aged 10-11 years at baseline. Multiple behavioral risk factors were assessed using a multiple risk factor score. All statistical analyses were performed using SAS, version 9.1, and SUDAAN, version 9.01.</p> <p>Results</p> <p>Multivariate longitudinal Poisson models showed that social distal variables including parental/peer smoking and peer drinking (Log-likelihood ratio (LLR) = 187.86, degrees of freedom (DF) = 8, <it>p </it>< .001), as well as individual distal variables including low self-esteem (LLR = 76.94, DF = 4, <it>p </it>< .001) increased the rate of occurrence of multiple behavioral risk factors. Individual ultimate variables including age, sex, and anxiety (LLR = 9.34, DF = 3, <it>p </it>< .05), as well as social ultimate variables including family socioeconomic status, and family structure (LLR = 10.93, DF = 5, <it>p </it>= .05) contributed minimally to the rate of co-occurrence of behavioral risk factors.</p> <p>Conclusions</p> <p>The results suggest targeting individual/social distal variables in prevention programs of multiple chronic disease behavioral risk factors among youth.</p

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p