A qualitative assessment of multi-level nutrition strategies for reducing racial and ethnic disparities in diabetes

The purpose of this study is to examine the stages of program realization of the interventions that the Bronx Health REACH program initiated at various levels to improve nutrition as a means for reducing racial and ethnic disparities in diabetes. This study was based on secondary analyses of qualitative data collected through the Bronx Health REACH Nutrition Project, a project conducted under the auspices of the Institute on Urban Family Health, with support from the Centers for Disease Control and Prevention (CDC). Local human subjects' review and approval through the Institute on Urban Family Health was required and obtained in order to conduct the Bronx Health REACH Nutrition Project. ^ The study drew from two theoretical models—Glanz and colleagues' nutrition environments model and Shediac-Rizkallah and Bone's sustainability model. The specific study objectives were two-fold: (1) to categorize each nutrition activity to a specific dimension (i.e. consumer, organizational or community nutrition environment); and (2) to evaluate the stage at which the program has been realized (i.e. development, implementation or sustainability). ^ A case study approach was applied and a constant comparative method was used to analyze the data. Triangulation of data based was also conducted. Qualitative data from this study revealed the following principal findings: (1) communities of color are disproportionately experiencing numerous individual and environmental factors contributing to the disparities in diabetes; (2) multi-level strategies that targeted the individual, organizational and community nutrition environments can appropriately address these contributing factors; (3) the nutrition strategies greatly varied in their ability to appropriately meet criteria for the three program stages; and (4) those nutrition strategies most likely to succeed (a) conveyed consistent and culturally relevant messages, (b) had continued involvement from program staff and partners, (c) were able to adapt over time or setting, (d) had a program champion and a training component, (e) were integrated into partnering organizations, and (f) were perceived to be successful by program staff and partners in their efforts to create individual, organizational and community/policy change. As a result of the criteria-based assessment and qualitative findings, an ecological framework elaborating on Glanz and colleagues model was developed. The qualitative findings and the resulting ecological framework developed from this study will help public health professionals and community leaders to develop and implement sustainable multi-level nutrition strategies for addressing racial and ethnic disparities in diabetes. ^

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^