摘要：Objectives. We sought to better determine the way in which neighborhood disadvantage affects the shape of the age–crime curve. Methods. We used data from the Pittsburgh Youth Study (PYS), a 14-year longitudinal study, to compare the age–crime curves of individuals in neighborhoods of different disadvantage. We visually compared observed age–crime curves, and then used generalized linear mixed models to test for differences in curve parameters. Results. Adjusted for individual risk factors, the mixed models found that the parameters for interactions of neighborhood disadvantage with both linear age and quadratic age were significant ( P < .05) and consistent with higher and longer age–crime curves in more disadvantaged neighborhoods. This implied that compared with boys in advantaged neighborhoods, rates of violence among boys in disadvantaged neighborhoods rose to higher levels that were sustained significantly longer. Conclusions. These results suggested that residing in a disadvantaged neighborhood during early adolescence may have an enduring effect on the shape of the age–crime curve throughout an individual's life. One of the most consistent findings in crime research has been the variation in offending over age, described as the age–crime curve. 1 In the age–crime curve for violence, prevalence increases in early adolescence, peaks in the late teens, and decreases more slowly into older ages. 1 The same curve properties have been observed in longitudinal data for individuals and cross-sectional population data. 2 – 4 Understanding the parameters of the curve is paramount for the continued development of public health-based youth violence prevention programs. When following a cohort over time, the curve provides insights into the developmental progression of violence in an individual. 4 , 5 When looking at population data at a cross section in time, the curve provides a snapshot of prevalence rates of violence in a community. 6 , 7 Understanding the factors contributing to variation in the shape of the curve is important for targeting individual prevention (cohort data), as well as for predicting and preventing future violence epidemics (cross-sectional data). Though the general shape of the age–crime curve is robust, 1 , 8 variations are observed for specific subpopulations in start and end points and the height and age of peaks. 3 , 4 , 6 , 7 , 9 – 11 How and if the parameters of the curve (reflecting onset, rate of involvement, and desistance) vary have been less understood. Neighborhood-level studies found a higher prevalence of violence in disadvantaged neighborhoods, 12 – 15 although results were mixed in individual-level studies. 16 – 18 No research quantitatively examined how specific parameters of the curve varied across neighborhoods. In disadvantaged neighborhoods, neighborhood effects might have resulted in curves that peaked higher and remained close to that peak to produce a “longer” curve. Reflecting earlier entry into or later desistance, longer curves involve sustained high prevalence rates as more individuals remain involved in violence over a longer period. This has been important in public heath for predicting possible future violence epidemics. For instance, problem behaviors often precedes violence and were measured at the school, neighborhood, or community level and have the potential to become valuable predictors of youth violence rates. What is needed to exploit that potential is a more precise understanding of a cohort's age of entry, peak, and exit from the age–crime curve to allow for identification of which behaviors can be used for prediction. In this article, we examined whether residing in disadvantaged neighborhoods during early adolescence (when neighborhood and peers replace family as the primary milieu of social interaction) affected the shape of the age–crime curve. The neighborhood effect was estimated while controlling for individual risk factors. First, we described the shape of the age–crime curve across neighborhoods with varying levels of disadvantage. Second, we tested whether disadvantage: (1) affected the curve's linear component, which determined how quickly the curve rose to, and fell from, a peak, and (2) affected the quadratic component, which determined a peak's location and sharpness. We hypothesized that both the linear and quadratic parts of the age–crime curve differed across neighborhood disadvantage levels. We utilized both descriptive techniques and generalized linear mixed models (GLM) to test these effects in repeated measures of violence of individuals.
