Background

For nearly half of the period between 1999 and 2019, rates of rural overdose death surpassed those in urban areas. Despite this substantial increase, little attention has been given to rural overdose or the contextual factors that predict risk of fatal overdose in rural vs. urban communities.

Methods

Risk terrain modeling was used to assess 2016–2017 overdose deaths in two urban and two rural Ohio counties. Spatial models incorporated criminal incidents and features of the built environment that have been previously associated with fatal overdose. The efficacy of spatial models was evaluated through the Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI*).

Results

Spatial models in rural counties were more influenced by past instances of crime, whereas risk in urban counties was determined by both crime and the built environment. Taken together, models accurately predicted 76% of 2018 overdoses. Rural models were overall more accurate, primarily in the areas predicted as having the highest risk of future overdose deaths. The predictive accuracy and efficiency of rural models varied more than those of urban models.

Conclusions

It is feasible to apply risk terrain modeling to predict fatal overdose in rural areas. Though the underlying contextual risk factors and patterns of predicted risk differ between rural and urban areas, both can be utilized to place treatment and prevention resources more accurately for targeted intervention.

中文翻译：

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犯罪和预测致命过量风险的建筑环境特征：俄亥俄州农村和城市县与风险地形模型的比较

背景

1999年至2019年的近一半时间里，农村地区吸毒过量死亡率超过了城市地区。尽管数量大幅增加，但很少有人关注农村过量用药或预测农村与城市社区致命过量用药风险的背景因素。

方法

使用风险地形模型评估了俄亥俄州两个城市和两个农村县 2016 年至 2017 年的服药过量死亡情况。空间模型结合了犯罪事件和建筑环境的特征，这些特征以前曾与致命的过量用药有关。空间模型的有效性通过预测准确性指数（PAI）和预测效率指数（PEI*）进行评估。

结果

农村县的空间模型更多地受到过去犯罪事件的影响，而城市县的风险则由犯罪和建筑环境共同决定。总的来说，模型准确预测了 2018 年 76% 的用药过量事件。农村模型总体上更准确，主要是在预测未来药物过量死亡风险最高的地区。农村模型的预测准确性和效率比城市模型差异更大。

结论

应用风险地形模型来预测农村地区致命的药物过量是可行的。尽管农村和城市地区潜在的背景风险因素和预测风险模式不同，但两者都可以用来更准确地放置治疗和预防资源，以进行有针对性的干预。