Division of Environmental Health, College of Public Health, The Ohio State University, Columbus, OH, United States of America.
Translational Data Analytics Institute, The Ohio State University, Columbus, OH, United States of America.
PLoS One. 2021 May 12;16(5):e0250324. doi: 10.1371/journal.pone.0250324. eCollection 2021.
An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.
We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.
A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26-35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.
Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.
阿片类药物治疗荒漠是指阿片类药物使用障碍患者获得药物辅助治疗和康复设施的机会有限的地区。我们探讨了阿片类药物治疗荒漠的概念,包括在潜在的空间可达性方面的种族差异,并使用来自俄亥俄州富兰克林县一家紧急医疗服务机构(哥伦布消防局)的高分辨率时空数据对此进行了应用。
我们从俄亥俄州哥伦布市富兰克林县的一家紧急医疗服务机构(哥伦布消防局)获得了个人层面的数据。阿片类药物过量事件是根据 2013 年 1 月 1 日至 2017 年 12 月 31 日期间使用纳洛酮的紧急医疗服务(EMS)运行情况确定的。潜在的空间可达性被定义为个体从发生阿片类药物过量的地点(这是其居住地点的替代测量指标)到最近的阿片类药物使用障碍(OUD)治疗提供者的旅行时间(以分钟计),该提供者提供了医学辅助治疗(MAT)。我们总体上按种族和四种类型的治疗提供者(任何类型的 OUD 药物治疗、丁丙诺啡、美沙酮或纳曲酮)估计可达性指标。如果到治疗提供者(任何类型的 OUD 药物治疗)的估计旅行时间超过给定阈值,则将该地区归类为阿片类药物治疗荒漠。我们使用基于多种交通方式(汽车和公共交通)的一系列阈值值和仅基于 EMS 运行到家庭/居住地点类型进行了敏感性分析。
最终分析中使用了来自 EMS 机构的数据共 6929 个地理编码的阿片类药物过量事件。大多数事件发生在 26-35 岁之间(34%),参与者被认定为白人成年人(56%)和男性(62%)。通过汽车和公共交通到达最近治疗提供者的中位数旅行时间和四分位距(IQR)分别为 2 分钟(IQR:3 分钟)和 17 分钟(IQR:17 分钟)。研究区域的几个社区获得阿片类药物治疗设施的机会有限,被归类为阿片类药物治疗荒漠。基于种族,大多数治疗提供者类型的公共交通旅行时间和基于美沙酮的治疗的汽车旅行时间在黑人成年人和白人成年人之间存在显著差异。
俄亥俄州哥伦布市的特定社区和不同种族群体在亚县一级存在获得阿片类药物治疗的机会不平等,可以使用当地公共安全数据(例如 EMS 运行情况)进行量化和可视化。确定阿片类药物治疗荒漠可以帮助多个利益相关者更好地规划和分配资源,以便为 OUD 获得更多的 MAT,从而减少阿片类药物流行带来的负担,同时更好地利用实时公共安全数据来应对已演变成公共安全危机的公共卫生流行病。