A comparison of two analytic methods for the identification of neighborhoods as intervention and control sites for community-based programs

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Robert E. Aronson, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Study Objective: Interest in community as the focus of public health interventions is growing. However, choosing intervention and comparison neighborhoods when designing community based programs poses a challenge to program planners. Ideally, intervention neighborhoods should be chosen based upon risk profiles and demonstrated need for the program. Multiple sources of data that tap into neighborhood characteristics might be used to facilitate the selection of intervention and comparison neighborhoods for program implementation and evaluation. Design: We present and compare selected characteristics of two analytic methods that can be used to create perinatal risk profiles of neighborhoods within cities. For our example, we used information from several sources of routinely available data and used census tract level low birthweight as our intervention or outcome variable. Main Results: At the neighborhood level, we found average household wealth of the census tract, proportion of births to women with late or no prenatal care, proportion of teen births per census tract, per capita crime rates, proportion of housing violations, and number of community organizations as being important factors identifying neighborhoods at risk for high rates of low birthweight births. Advantages of both methods are discussed and risk profiles generated from either method can be used not only to identify high risk areas of the city for adverse perinatal outcomes but also for the identification of intervention and comparison neighborhoods for implementation of community based programs.

Additional Information

Evaluation and Program Planning, 20(4): 405-414
Language: English
Date: 1997
community interventions, evaluation design, control group, low birth weight, infant mortality, regression analysis

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