Promoting Policy, Systems, and Environment Change to Prevent Chronic Disease: Lessons Learned From the King County Communities Putting Prevention to Work Initiative.

Context: Initiatives that convene community stakeholders to implement policy, systems, environment, and infrastructure (PSEI) change have become a standard approach for promoting community health.

Objective: To assess the PSEI changes brought about by the King County, Washington, Communities Putting Prevention to Work initiative and describe how initiative structures and processes contributed to making changes.

Design: The impact evaluation used a logic model design, linking PSEI changes to longer-term behavioral impacts in healthy eating active living and tobacco use and exposure.

Increasing access to farmers markets for beneficiaries of nutrition assistance: evaluation of the farmers market access project.

Introduction: Increased acceptance of nutrition benefits at farmers markets could improve access to nutritious foods for low-income shoppers. The objective of this study was to evaluate a pilot project to increase participation by farmers markets and their vendors in the Supplemental Nutrition Assistance Program (SNAP) and Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).

Methods: The intervention targeted 9 markets in lower-income regions of King County, Washington.

Initiating policy, systems, and environmental changes for childhood obesity prevention by engaging six faith-based organizations.

Little is known about how to engage faith-organizations, especially churches, when using policy, systems, and environmental (PSE) change approaches for prevention. This article documents the PSE changes implemented by engaging 6 faith organizations, with an estimated reach of 3500 members, for 18 months. Timeline (n = 6), focus group (n = 6), report (n = 18), and observational meeting (n = 16) and event (n = 5) summaries were analyzed using content analysis.

An introduction to tree-structured modeling with application to quality of life data.

Background: Investigators addressing nursing research are faced increasingly with the need to analyze data that involve variables of mixed types and are characterized by complex nonlinearity and interactions. Tree-based methods, also called recursive partitioning, are gaining popularity in various fields. In addition to efficiency and flexibility in handling multifaceted data, tree-based methods offer ease of interpretation.