Violence Prevention Research Program, Department of Emergency Medicine, University of California Davis, Sacramento, California, USA
Center for Gun Violence Prevention and Policy, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Inj Prev. 2022 Feb;28(1):61-67. doi: 10.1136/injuryprev-2020-044056. Epub 2021 Feb 8.
To estimate the long-term impact of Safe Streets Baltimore, which is based on the outreach and violence interruption model, on firearm violence.
We used synthetic control methods to estimate programme effects on homicides and incidents of non-fatal penetrating firearm injury (non-fatal shootings) in neighbourhoods that had Safe Streets' sites and model-generated counterfactuals. Synthetic control analyses were conducted for each firearm violence outcome in each of the seven areas where Safe Streets was implemented. The study also investigated variation in programme impact over time by generating effect estimates of varying durations for the longest-running programme sites.
Synthetic control models reduced prediction error relative to regression analyses. Estimates of Safe Streets' effects on firearm violence varied across intervention sites: some positive, some negative and no effect. Beneficial programme effects on firearm violence reported in prior research were found to have attenuated over time.
For highly targeted interventions, synthetic control methods may provide more valid estimates of programme impact than panel regression with data from all city neighbourhoods. This research offers new understanding about the effectiveness of the intervention over extended periods of time in seven neighbourhoods. Combined with existing evaluation literature, it also raises questions about contextual and implementation factors that might influence programme outcomes.
评估基于外展和暴力干预模式的巴尔的摩安全街道计划对枪支暴力的长期影响。
我们使用合成控制方法,根据 Safe Streets 计划实施的社区和模型生成的反事实情况,估计该计划对凶杀案和非致命性穿透性枪支伤害(非致命性枪击)事件的影响。针对 Safe Streets 实施的七个地区中的每个枪支暴力结果进行了合成控制分析。该研究还通过生成最长运行计划地点的不同持续时间的效果估计值,研究了计划效果随时间的变化。
与回归分析相比,合成控制模型降低了预测误差。Safe Streets 对枪支暴力的影响估计因干预地点而异:有些是积极的,有些是消极的,有些则没有影响。先前研究报告的对枪支暴力有益的计划效果随着时间的推移而减弱。
对于高度针对性的干预措施,合成控制方法可能比使用所有城市社区数据的面板回归提供更有效的计划影响估计。这项研究提供了关于该干预措施在七个社区的长时间内的有效性的新认识。结合现有的评估文献,它还提出了一些关于可能影响计划结果的背景和实施因素的问题。