Gracia Enrique, López-Quílez Antonio, Marco Miriam, Lladosa Silvia, Lila Marisol
Am J Epidemiol. 2015 Jul 1;182(1):58-66. doi: 10.1093/aje/kwv016. Epub 2015 May 15.
We examined whether neighborhood-level characteristics influence spatial variations in the risk of intimate partner violence (IPV). Geocoded data on IPV cases with associated protection orders (n = 1,623) in the city of Valencia, Spain (2011-2013), were used for the analyses. Neighborhood units were 552 census block groups. Drawing from social disorganization theory, we explored 3 types of contextual influences: concentrated disadvantage, concentration of immigrants, and residential instability. A Bayesian spatial random-effects modeling approach was used to analyze influences of neighborhood-level characteristics on small-area variations in IPV risk. Disease mapping methods were also used to visualize areas of excess IPV risk. Results indicated that IPV risk was higher in physically disordered and decaying neighborhoods and in neighborhoods with low educational and economic status levels, high levels of public disorder and crime, and high concentrations of immigrants. Results also revealed spatially structured remaining variability in IPV risk that was not explained by the covariates. In this study, neighborhood concentrated disadvantage and immigrant concentration emerged as significant ecological risk factors explaining IPV. Addressing neighborhood-level risk factors should be considered for better targeting of IPV prevention.
我们研究了社区层面的特征是否会影响亲密伴侣暴力(IPV)风险的空间差异。分析使用了西班牙巴伦西亚市(2011 - 2013年)1623起与相关保护令有关的IPV案件的地理编码数据。社区单元为552个人口普查街区组。借鉴社会失序理论,我们探究了三种背景影响因素:集中性劣势、移民集中程度和居住稳定性。采用贝叶斯空间随机效应建模方法来分析社区层面特征对IPV风险小区域差异的影响。还使用疾病映射方法来直观显示IPV风险过高的区域。结果表明,在环境杂乱破败、教育和经济水平较低、公共秩序和犯罪率较高以及移民高度集中的社区,IPV风险更高。结果还揭示了IPV风险中存在空间结构上的剩余变异性,而协变量无法解释这一点。在本研究中,社区集中性劣势和移民集中程度成为了解释IPV的重要生态风险因素。为了更有针对性地预防IPV,应考虑解决社区层面的风险因素。