This study examined the effects of neighborhood context on juvenile recidivism to determine if neighborhoods influence the likelihood of reoffending. Although a large body of literature exists regarding the impact of environmental factors on delinquency, very little is known about the effects of these factors on juvenile recidivism. The sample analyzed includes 7,061 delinquent male juveniles committed to community-based programs in Philadelphia, of which 74% are Black, 13% Hispanic, and 11% White. Since sample youths were nested in neighborhoods, a hierarchical generalized linear model was employed to predict recidivism across three general categories of recidivism offenses: drug, violent, and property. Results indicate that predictors vary across the types of offenses and that drug offending differs from property and violent offending. Neighborhood-level factors were found to influence drug offense recidivism, but were not significant predictors of violent offenses, property offenses, or an aggregated recidivism measure, despite contrary expectations. Implications stemming from the finding that neighborhood context influences only juvenile drug recidivism are discussed.
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http://dx.doi.org/10.1007/s10964-010-9518-5 | DOI Listing |
J Aging Health
January 2025
School of Public Policy & Maryland Population Research Center, University of Maryland, College Park, MD, USA.
Objectives: We determined if living in historically redlined neighborhoods was associated with level and change in cognitive functioning and if this association differed for Black and White older adults.
Methods: We linked the Health and Retirement Study 1998-2018 data to redlining scores from the Historic Redlining Indicator data. Our sample included adults aged 50 years and older (24,230 respondents, 129,618 person-period observations).
Health Place
January 2025
University of Edinburgh, Edinburgh, UK. Electronic address:
In the context of population ageing, multimorbidity is an increasingly prevalent public health issue that has a substantial impact on both individuals and healthcare systems. Alongside the literature looking at risk factors at the individual level, there is a growing body of research examining the role of neighbourhoods in the development of multimorbidity. However, most of this work has focused on physical features of place such as air pollution and green space, while social features of place have been largely overlooked.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United States of America.
Socioeconomic status (SES) is associated with well-being outcomes across studies; however, there is wide variation in its measurement, particularly in adolescence. One key difference in measures of SES concerns whether participants relay objective information-for example, years of education, household income-or subjective perceptions of socioeconomic status, either with or without reference to others or society. Although parents are often considered the best source of SES information-especially objective SES-within families, interviewing parents within the context of adolescent research is costly, time-consuming, and not always feasible.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Bruyère Health Research Institute, Ottawa, ON, Canada.
Background: Municipalities play a crucial role in population health due to their community connections and influence on health determinants. Community-campus engagement (CCE), that is, collaboration between academic institutions and communities, is a promising approach to addressing community health priorities. However, evidence of CCE's impact on population health remains limited.
View Article and Find Full Text PDFR Soc Open Sci
January 2025
Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK.
This study proposes a methodology and a proof of concept to target and prioritize mass retrofitting of residential buildings in the UK using open building datasets that combine fabric energy efficiency and fuel poverty to meet the net-zero targets. The methodological framework uses a series of multi-variate statistical and geospatial methods that consider urban, socio-economic and physical attributes. In addition, thermal imaging is implemented to provide insights at the building scale.
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