Background

The emergence of fentanyl around 2013 represented a new, deadly stage of the opioid epidemic in the USA. We aimed to develop a statistical regression approach to identify counties at the highest risk of high overdose mortality in the subsequent years by predicting annual county-level overdose death rates across the contiguous USA and to validate our approach against observed overdose mortality data collected between 2013 and 2018.

Methods

We fit mixed-effects negative binomial regression models to predict overdose death rates in the subsequent year for 2013–18 for all contiguous state counties in the USA (ie, excluding Alaska and Hawaii). We used publicly available county-level data related to health-care access, drug markets, socio-demographics, and the geographical spread of opioid overdose as model predictors. The crude number of county-level overdose deaths was extracted from restricted US Centers for Disease Control and Prevention mortality records. To predict county-level overdose rates for the year 201X: (1) a model was trained on county-level predictor data for the years 2010–201(X–2) paired with county-level overdose deaths for the year 2011–201(X–1); (2) county-level predictor data for the year 201(X–1) was fed into the model to predict the 201X county-level crude number of overdose deaths; and (3) the latter were converted to a population-adjusted rate. For comparison, we generated a benchmark set of predictions by applying the observed slope of change in overdose death rates in the previous year to 201(X–1) rates. To assess the predictive performance of the model, we compared predicted values (of both the model and benchmark) to observed values by (1) calculating the mean average error, root mean squared error, and Spearman's correlation coefficient and (2) assessing the proportion of counties in the top decile (10%) of overdose death rates that were correctly predicted as such. Finally, in a post-hoc analysis, we sought to identify variables with greatest predictive utility.

Findings

Between 2013 and 2018, among the 3106 US counties included, our modelling approach outperformed the benchmark strategy across all metrics. The observed average county-level overdose death rate rose from 11·8 per 100 000 people in 2013 to 15·4 in 2017 before falling to 14·6 in 2018. Our negative binomal modelling approach similarly identified an increasing trend, predicting an average 11·8 deaths per 100 000 in 2013, up to 15·1 in 2017, and increasing further to 16·4 in 2018. The benchmark model over-predicted average death rates each year, ranging from 13·0 per 100 000 in 2013 to 18·3 in 2018. Our modelling approach successfully ranked counties by overdose death rate identifying between 42% and 57% of counties in the top decile of overdose mortality (compared with 29% and 43% using the benchmark) each year and identified 194 of the 808 counties with emergent overdose outbreaks (ie, newly entered the top decile) across the study period, versus 31 using the benchmark. In the post-hoc analysis, we identified geospatial proximity of overdose in nearby counties, opioid prescription rate, presence of an urgent care facility, and several economic indicators as the variables with the greatest predictive utility.

Interpretation

Our model shows that a regression approach can effectively predict county-level overdose death rates and serve as a risk assessment tool to identify future high mortality counties throughout an emerging drug use epidemic.

Funding

National Institute on Drug Abuse.

中文翻译：

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确定美国新出现的芬太尼流行期间面临高服药过量死亡率负担风险的县：一项预测统计模型研究

 背景

2013 年左右芬太尼的出现代表了美国阿片类药物流行的一个新的致命阶段。我们的目标是开发一种统计回归方法，通过预测美国本土每年县级用药过量死亡率来确定未来几年用药过量死亡率最高风险的县，并根据 2013 年至 2013 年期间收集的观察到的用药过量死亡率数据验证我们的方法。 2018.

 方法

我们拟合混合效应负二项式回归模型来预测 2013-18 年美国所有邻近州县（即不包括阿拉斯加和夏威夷）下一年的用药过量死亡率。我们使用与医疗保健可及性、药品市场、社会人口统计以及阿片类药物过量的地理分布相关的公开县级数据作为模型预测因子。县级用药过量死亡的粗略数字是从美国疾病控制和预防中心有限的死亡率记录中提取的。为了预测 201X 年县级用药过量率：(1) 根据 2010-201(X-2) 年县级预测数据与 2011-201(X-2) 年县级用药过量死亡人数配对训练模型。 X-1); （2）将201（X-1）年县级预测数据输入模型，预测201X年县级服药过量死亡粗数； (3)后者被转换为人口调整率。为了进行比较，我们通过将前一年观察到的服药过量死亡率变化斜率应用到 201(X-1) 比率，生成了一组基准预测。为了评估模型的预测性能，我们通过以下方式将预测值（模型和基准）与观测值进行比较：(1) 计算平均误差、均方根误差和斯皮尔曼相关系数；(2) 评估比例正确预测的药物过量死亡率前十分之一 (10%) 的县。最后，在事后分析中，我们试图找出具有最大预测效用的变量。

 发现

2013 年至 2018 年间，在美国 3106 个县中，我们的建模方法在所有指标上均优于基准策略。观察到的县级平均服药过量死亡率从 2013 年的每 10 万人 11·8 上升至 2017 年的 15·4，然后在 2018 年下降至 14·6。我们的负二项模型方法同样发现了上升趋势，预测平均为 11 · 2013 年每 10 万人中有 8 人死亡，2017 年高达 15·1，2018 年进一步增加至 16·4。基准模型每年高估平均死亡率，范围从 2013 年的每 10 万人 13·0 人到2018 年 18·3。我们的建模方法成功地根据药物过量死亡率对各县进行了排名，每年识别出药物过量死亡率最高十分之一的县中的 42% 至 57%（相比之下，使用基准时为 29% 和 43%），并确定了 194 个研究期间有 808 个县出现药物过量爆发（即新进入前十分之一），而使用基准的县中有 31 个。在事后分析中，我们确定了附近县用药过量的地理空间邻近性、阿片类药物处方率、紧急护理设施的存在以及几个经济指标作为具有最大预测效用的变量。

 解释

我们的模型表明，回归方法可以有效预测县级用药过量死亡率，并作为风险评估工具来确定整个新兴吸毒流行病中未来的高死亡率县。

 资金

国家药物滥用研究所。