Faith-placed cardiovascular health promotion: a framework for contextual and organizational factors underlying program success.

The objective of this study was to assess the literature on faith-placed cardiovascular health promotion in order to construct a framework of factors meant to facilitate effective program design. Data source was empirical studies on the contextual and organizational factors underlying faith-placed cardiovascular program performance. Study inclusion criteria were papers reported from 1984 to 2003 that include contextual and organizational variables. Success factors identified in the literature fall under the following clusters: faith support, secular support, partnership (and obstacles to it), faith organization capabilities, secular organization capabilities and caring intervention. Each cluster consists of several factors, whose relative weights cannot be ascertained from the present state of the literature. These clusters of factors can be interrelated through a simple framework that is useful in program design.

Geographical variation of cerebrovascular disease in New York State: the correlation with income.

BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. RESULTS: We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075-1.22 x 10(-4)(income)+ 8.068x10(-10)(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each 10,000 dollars increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. CONCLUSION: Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors.