The Intersection of Neighborhood Environment and Adverse Childhood Experiences: Methods for Creation of a Neighborhood ACEs Index

Krista Schroeder, Levent Dumenci, David B. Sarwer, Jennie G. Noll, Kevin A. Henry, Shakira F. Suglia, Christine M. Forke and David C. Wheeler
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Krista Schroeder: Department of Nursing, Temple University College of Public Health, Philadelphia, PA 19122, USA
Levent Dumenci: Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA 19122, USA
David B. Sarwer: Department of Social and Behavioral Sciences, Center for Obesity Research and Education, Temple University College of Public Health, Philadelphia, PA 19122, USA
Jennie G. Noll: Department of Human Development and Family Studies, Penn State College of Health and Human Development, University Park, PA 16802, USA
Kevin A. Henry: Department of Geography and Urban Studies, Temple University College of Liberal Arts, Philadelphia, PA 19122, USA
Shakira F. Suglia: Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
Christine M. Forke: Master of Public Health Program, Perelman School of Medicine, University of Pennsylvania, Center for Violence Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
David C. Wheeler: Department of Biostatistics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA

IJERPH, 2022, vol. 19, issue 13, 1-19

Abstract: This study evaluated methods for creating a neighborhood adverse childhood experiences (ACEs) index, a composite measure that captures the association between neighborhood environment characteristics (e.g., crime, healthcare access) and individual-level ACEs exposure, for a particular population. A neighborhood ACEs index can help understand and address neighborhood-level influences on health among individuals affected by ACEs. Methods entailed cross-sectional secondary analysis connecting individual-level ACEs data from the Philadelphia ACE Survey ( n = 1677) with 25 spatial datasets capturing neighborhood characteristics. Four methods were tested for index creation (three methods of principal components analysis, Bayesian index regression). Resulting indexes were compared using Akaike Information Criteria for accuracy in explaining ACEs exposure. Exploratory linear regression analyses were conducted to examine associations between ACEs, the neighborhood ACEs index, and a health outcome—in this case body mass index (BMI). Results demonstrated that Bayesian index regression was the best method for index creation. The neighborhood ACEs index was associated with higher BMI, both independently and after controlling for ACEs exposure. The neighborhood ACEs index attenuated the association between BMI and ACEs. Future research can employ a neighborhood ACEs index to inform upstream, place-based interventions and policies to promote health among individuals affected by ACEs.

Keywords: adverse childhood experiences; geospatial; index; methods; neighborhood; obesity; spatial; trauma; neighborhood ACEs index (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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