Hill Ryan M, Cacace Sam C, Starkey Austin, Hendley Tyler, Bolner Jackson, Cummings Angela, Hayes Angie, Cramer Robert J
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA.
, 236 Audubon Hall, 1105 Field House Drive, Baton Rouge, LA, 70802, USA.
BMC Public Health. 2025 Apr 17;25(1):1453. doi: 10.1186/s12889-025-22490-0.
The 2024 National Strategy for Suicide Prevention has called for the development of community-based suicide prevention resources, and improved existing prevention efforts. In line with such efforts, Hill and colleagues developed the Geospatial Identification of Elevated Suicide Risk model that estimates the relative prevalence of adolescent suicide risk within specific geographical areas. The current study seeks to further evaluate and refine the model for use as a tool to evaluate risk and protective factors at the neighborhood level.
Drawing from multiple sources, data was collected detailing adolescent suicidal ideation, suicide attempts, suicide fatalities, and census tract characteristics. Utilizing data resulting from an initial pool of 74,883 suicidal ideation and attempt screens found in electronic health records, suicidal ideation and attempt rates were calculated, described, and mapped onto relevant census tracts via the Census Geocoder. Once mapped, a total of 1,098 census tracts were examined for criterion validity and minimum data evaluations.
Data indicate that rates of positive suicide risk screens are relatively normally distributed when using a minimum cell size of at least n = 5, with additional improvements at n = 10 screens per census tract. Of 48,928 records with completed screens and patient address data listed in the electronic health record, 44,776 addresses (91.5%) were matched to U.S. census tracts via the Census Geocoder database. When evaluating criterion validity, the simultaneous multivariate logistic regression revealed that the model did not fit well to the data, and suicide attempts and suicidal ideation only predicted 0.02% of the variance in the probability of suicide fatality. Finally, a classification tree revealed that a minimum of 10 data points were required to delineate between high and low-risk census tracts.
The refined model may act as a helpful tool to evaluate neighborhood level risk and protective factors. Findings suggest a prevention-oriented, as opposed to risk prediction, approach to suicide risk management at the community level may be needed; such an approach would prioritize community connectedness, adequate mental health support services, and reduction of community-level risk factors (e.g., substance misuse), among others.
《2024年国家自杀预防战略》呼吁开发基于社区的自杀预防资源，并改进现有的预防措施。为响应这些努力，希尔及其同事开发了自杀风险升高的地理空间识别模型，该模型可估计特定地理区域内青少年自杀风险的相对患病率。本研究旨在进一步评估和完善该模型，以用作评估邻里层面风险和保护因素的工具。
从多个来源收集数据，详细记录青少年自杀意念、自杀未遂、自杀死亡情况以及普查区特征。利用电子健康记录中最初的74,883次自杀意念和未遂筛查数据，计算、描述自杀意念和未遂率，并通过人口普查地理编码器将其映射到相关普查区。映射完成后，共对1,098个普查区进行了效标效度和最小数据评估。
数据表明，当使用至少n = 5的最小单元格大小时，自杀风险筛查阳性率相对呈正态分布，每个普查区n = 10次筛查时会有进一步改善。在电子健康记录中列出的48,928条完成筛查且有患者地址数据的记录中，44,776个地址（91.5%）通过人口普查地理编码器数据库与美国普查区匹配。在评估效标效度时，同时进行的多变量逻辑回归显示该模型与数据拟合不佳，自杀未遂和自杀意念仅预测了自杀死亡概率方差的0.02%。最后，一棵分类树显示，至少需要10个数据点来区分高风险和低风险普查区。
完善后的模型可能是评估邻里层面风险和保护因素的有用工具。研究结果表明，社区层面的自杀风险管理可能需要一种以预防为导向而非风险预测的方法；这种方法将优先考虑社区联系、充足的心理健康支持服务以及减少社区层面的风险因素（如药物滥用）等。
