Prevention Science最新文献

Policies that provide economic support, such as the earned income tax credit (EITC), improve peoples' ability to meet their basic needs and reduce stress, which can reduce violence perpetration. Refundable state-level EITCs have been associated with decreases in multiple forms of violence (e.g., child abuse and neglect); however, it is unknown whether there is an association between the EITC and violent crime as captured by Uniform Crime Reports. Crime and violence remain a pressing concern for many communities across the nation. Using a longitudinal data set, we conducted fixed-effects regression models with year and state specified as fixed effects, to determine whether variations in generosity of state-level EITCs are related to the rate of violent crime. After adjusting for demographic covariates, refundable state-level EITCs remained significantly associated with reductions in criminal homicide compared to states without an EITC. As many states attempt to combat crime and prevent violence in their communities, anti-poverty measures such as the EITC provide a promising strategy for reducing the social and economic costs associated with violence.

Preventing young people's cigarette smoking is a major public health priority, and smoking is especially prevalent in vocational schools. Well-enforced comprehensive school tobacco policies accompanied by preventive efforts show potential to reduce smoking, but the implementation process is crucial to achieve the intended effect. We investigate whether and how implementation fidelity of a multi-component smoking prevention intervention impacted student smoking outcomes after 4-5 months among students in Danish vocational education and training (national age range 15-65 years, mean 25.6) and preparatory basic education (national age range 15-25 years, mean 17.6) institutions using questionnaire data from a cluster-RCT. The intervention included a smoke-free school hours policy, educational curriculum, and class competition. We calculated an overall implementation fidelity measure combining staff-reported school-level delivery (fidelity) and student-reported receipt (participation, responsiveness), and used multilevel regression models to analyze associations with smoking outcomes (smoking daily, regularly, and during school hours). We supplemented the analysis with restricted cubic spline regression. Additionally, we stratified the analyses by school types and analyzed associations between implementation fidelity of the separate intervention components and smoking outcomes. High implementation was associated with lower odds of regular smoking (OR: 0.37, 95% CI: 0.18-0.78) and smoking during school hours, but not daily smoking, and these associations varied between the school settings. When analyzed separately, implementation fidelity of the components did not affect the outcomes significantly. Our findings underline the need to support the implementation process of school tobacco policy interventions to ensure the intended effects of reducing students' smoking.

We examine and compare the relationship between minimum wage increases and youth homicide rates in three groups: all youth, White youth only, and Black youth only. Using 2001-2019 mortality data from the National Vital Statistics System (NVSS) for all 50 states and Washington DC, we apply a difference in differences (DD) design to compare the change in youth homicides across states with varying changes in the state-specific minimum wage. With the inclusion of state-specific linear time trends, we find that a $1 increase in minimum wage leads to a significant 4% reduction (RR = 0.96, 95%CI [0.92, 0.99]) in homicides among White youth, but no significant reduction among Black youth (RR = 0.98, 95%CI [0.91, 1.04]). Findings are consistent with research on marginalization-related diminished returns for Black youth. While minimum wage increases are a promising step to reduce youth homicides overall, reducing homicide disparities experienced by Black youth requires additional components. Future research should examine policies with the specific intention to dismantle structural racism.

Multi-level, place-based interventions have proven effective at promoting a range of health behaviors, including tobacco control and discouraging the uptake of tobacco products. This paper describes the implementation and impact of a 3-year, multi-level tobacco prevention and control program at a community-college minority-serving institution (MSI) on the Texas Gulf Coast within the context of a broader multi-sector, cross-functional health coalition. The intervention studied included a tobacco-free policy, a large-scale communication campaign highlighting parts of the intervention and prevention and cessation resources. The intervention was bolstered by the support of a community-led Steering Committee and tobacco control experts. Results from the first 3 years of implementation show that tobacco-free policies were largely supported by community members, awareness of the policy increased over time, and tobacco prevention and cessation resources were successfully embedded into campus norms. This multi-component approach shows how a community college was able to effectively reach students and staff on their campus to increase awareness of both the campus tobacco-free policy and the availability of tobacco prevention and cessation resources. Additionally, it also offers lessons for future tobacco prevention and control work in higher education.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.