Division of Vital Statistics, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland.
Office of Analysis and Epidemiology, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland.
Am J Prev Med. 2018 Jul;55(1):72-79. doi: 10.1016/j.amepre.2018.03.020. Epub 2018 May 14.
Understanding the geographic patterns of suicide can help inform targeted prevention efforts. Although state-level variation in age-adjusted suicide rates has been well documented, trends at the county-level have been largely unexplored. This study uses small area estimation to produce stable county-level estimates of suicide rates to examine geographic, temporal, and urban-rural patterns in suicide from 2005 to 2015.
Using National Vital Statistics Underlying Cause of Death Files (2005-2015), hierarchical Bayesian models were used to estimate suicide rates for 3,140 counties. Model-based suicide rate estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences. Analyses were conducted in 2016-2017.
Posterior predicted mean county-level suicide rates increased by >10% from 2005 to 2015 for 99% of counties in the U.S., with 87% of counties showing increases of >20%. Counties with the highest model-based suicide rates were consistently located across the western and northwestern U.S., with the exception of southern California and parts of Washington. Compared with more urban counties, more rural counties had the highest estimated suicide rates from 2005 to 2015, and also the largest increases over time.
Mapping county-level suicide rates provides greater granularity in describing geographic patterns of suicide and contributes to a better understanding of changes in suicide rates over time. Findings may inform more targeted prevention efforts as well as future research on community-level risk and protective factors related to suicide mortality.
了解自杀的地理模式有助于为有针对性的预防工作提供信息。尽管州一级的年龄调整后自杀率的变化已经得到了很好的记录，但县级水平的趋势在很大程度上仍未得到探索。本研究使用小区域估计方法，生成稳定的县级自杀率估计值，以检查 2005 年至 2015 年期间自杀的地理、时间和城乡模式。
利用国家生命统计死因基本文件（2005-2015 年），使用分层贝叶斯模型估计了 3140 个县的自杀率。基于模型的自杀率估计值被映射出来，以探索地理和时间模式，并检查城乡差异。分析于 2016-2017 年进行。
对于美国 99%的县，后验预测的县级自杀率从 2005 年到 2015 年增加了>10%，其中 87%的县的增幅>20%。模型计算的自杀率最高的县一直位于美国西部和西北部，除了南加州和华盛顿部分地区。与更城市化的县相比，更多的农村县在 2005 年至 2015 年期间的自杀率估计值最高，而且随着时间的推移，增长率也最大。
绘制县级自杀率图提供了更详细的描述自杀地理模式的方法，并有助于更好地了解自杀率随时间的变化。这些发现可以为更有针对性的预防工作以及未来关于与自杀死亡率相关的社区一级风险和保护因素的研究提供信息。
