Magdalena Cerdá1, Melissa Tracy, Jennifer Ahern, Sandro Galea. 1. Magdalena Cerdá, Melissa Tracy, and Sandro Galea are with the Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY. Jennifer Ahern is with the Department of Epidemiology, University of California, Berkeley.
Abstract
OBJECTIVES: As a case study of the impact of universal versus targeted interventions on population health and health inequalities, we used simulations to examine (1) whether universal or targeted manipulations of collective efficacy better reduced population-level rates and racial/ethnic inequalities in violent victimization; and (2) whether experiments reduced disparities without addressing fundamental causes. METHODS: We applied agent-based simulation techniques to the specific example of an intervention on neighborhood collective efficacy to reduce population-level rates and racial/ethnic inequalities in violent victimization. The agent population consisted of 4000 individuals aged 18 years and older with sociodemographic characteristics assigned to match distributions of the adult population in New York City according to the 2000 U.S. Census. RESULTS: Universal experiments reduced rates of victimization more than targeted experiments. However, neither experiment reduced inequalities. To reduce inequalities, it was necessary to eliminate racial/ethnic residential segregation. CONCLUSIONS: These simulations support the use of universal intervention but suggest that it is not possible to address inequalities in health without first addressing fundamental causes.
OBJECTIVES: As a case study of the impact of universal versus targeted interventions on population health and health inequalities, we used simulations to examine (1) whether universal or targeted manipulations of collective efficacy better reduced population-level rates and racial/ethnic inequalities in violent victimization; and (2) whether experiments reduced disparities without addressing fundamental causes. METHODS: We applied agent-based simulation techniques to the specific example of an intervention on neighborhood collective efficacy to reduce population-level rates and racial/ethnic inequalities in violent victimization. The agent population consisted of 4000 individuals aged 18 years and older with sociodemographic characteristics assigned to match distributions of the adult population in New York City according to the 2000 U.S. Census. RESULTS: Universal experiments reduced rates of victimization more than targeted experiments. However, neither experiment reduced inequalities. To reduce inequalities, it was necessary to eliminate racial/ethnic residential segregation. CONCLUSIONS: These simulations support the use of universal intervention but suggest that it is not possible to address inequalities in health without first addressing fundamental causes.
Authors: Gemma Phillips; Adrian Renton; Derek G Moore; Christian Bottomley; Elena Schmidt; Shahana Lais; Ge Yu; Martin Wall; Patrick Tobi; Caroline Frostick; Angela Clow; Karen Lock; Mark Petticrew; Richard Hayes Journal: Trials Date: 2012-07-06 Impact factor: 2.279
Authors: Katherine M Keyes; Ava Hamilton; Jeffrey Swanson; Melissa Tracy; Magdalena Cerdá Journal: Am J Public Health Date: 2019-06 Impact factor: 9.308