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This study examined a model for mental health consultation, training and support designed to enhance the benefits of publicly-funded recreational after-school programs in communities of concentrated urban poverty for children's academic, social, and behavioral functioning. We assessed children's mental health needs and examined the feasibility and impact of intervention on program quality and children's psychosocial outcomes in three after-school sites (n = 15 staff, 89 children), compared to three demographically-matched sites that received no intervention (n = 12 staff, 38 children). Findings revealed high staff satisfaction and feasibility of intervention, and modest improvements in observed program quality and staff-reported children's outcomes. Data are considered with a public health lens of mental health promotion for children in urban poverty.

Concern regarding the removal of African American children from their homes in McLean county and Peoria County Illinois, and placing them into Foster care have been the center of considerable debate and concern in the state of Illinois. As a result, a county- level (McLean and Peoria counties) investigation and intervention was performed. To evaluate the success of this intervention, we developed a mixed-effects Poisson regression model for the analysis of these data, and used it to obtain case-mix adjusted empirical Bayes estimates of county-specific changes (2000-2002) in Foster care placement rates in all counties in the state of Illinois. Results of the analysis revealed that four out of the 85 counties with African American residents in the state of Illinois exhibited significant decreases in Foster care placement rates relative to the change in the overall state-wide rate between 2000 and 2002. The two counties that received the intervention (McLean and Peoria) were among the four counties out of 85 counties that exhibited significant de- creases in Foster care placement rates (OR = 14.7, P < 0.0001). Conceptual and statistical aspects of this type of statistical profiling are presented and discussed.

In health research, count outcomes are fairly common and often these counts have a large number of zeros. In order to adjust for these extra zero counts, various modifications of the Poisson regression model have been proposed. Lambert (Lambert, D., Technometrics 34, 1-14, 1992) described a zero-inflated Poisson (ZIP) model that is based on a mixture of a binary distribution (πi ) degenerated at zero with a Poisson distribution (λi ). Depending on the relationship between πi and λi , she described two variants: a ZIP and a ZIP (τ ) model. In this paper, we extend these models for the case of clustered data (e.g., patients observed within hospitals) and describe random-effects ZIP and ZIP (τ ) models. These models are appropriate for the analysis of clustered extra-zero Poisson count data. The distribution of the random effects is assumed to be normal and a maximum marginal likelihood estimation method is used to estimate the model parameters. We applied these models to data from patients who underwent colon operations from 123 Veterans Affairs Medical Centers in the National VA Surgical Quality Improvement Program.