Linas Beth S, Latkin Carl, Genz Andrew, Westergaard Ryan P, Chang Larry W, Bollinger Robert C, Kirk Gregory D
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Drug Alcohol Depend. 2015 Jun 1;151:250-7. doi: 10.1016/j.drugalcdep.2015.03.031. Epub 2015 Apr 5.
We assessed patterns of illicit drug use using mobile health (mHealth) methods and subsequent health care indicators among drug users in Baltimore, MD.
Participants of the EXposure Assessment in Current Time (EXACT) study were provided a mobile device for assessment of their daily drug use (heroin, cocaine or both), mood and social context for 30 days from November 2008 through May 2013. Real-time, self-reported drug use events were summed for individuals by day. Drug use risk was assessed through growth mixture modeling. Latent class regression examined the association of mHealth-defined risk groups with indicators of healthcare access and utilization.
109 participants were a median of 48.5 years old, 90% African American, 52% male and 59% HIV-infected. Growth mixture modeling identified three distinct classes: low intensity drug use (25%), moderate intensity drug use (65%) and high intensity drug use (10%). Compared to low intensity drug users, high intensity users were younger, injected greater than once per day, and shared needles. At the subsequent study visit, high intensity drug users were nine times less likely to be medically insured (adjusted OR: 0.10, 95%CI: 0.01-0.88) and at greater risk for failing to attend any outpatient appointments (aOR: 0.13, 95%CI: 0.02-0.85) relative to low intensity drug users.
Real-time assessment of drug use and novel methods of describing sub-classes of drug users uncovered individuals with higher-risk behavior who were poorly utilizing healthcare services. mHealth holds promise for identifying individuals engaging in high-risk behaviors and delivering real-time interventions to improve care outcomes.
我们使用移动健康（mHealth）方法评估了马里兰州巴尔的摩市吸毒者的非法药物使用模式以及随后的医疗保健指标。
“当前暴露评估（EXACT）”研究的参与者在2008年11月至2013年5月的30天内获得了一部移动设备，用于评估他们每日的药物使用情况（海洛因、可卡因或两者皆有）、情绪和社会背景。按天汇总个人实时自我报告的药物使用事件。通过生长混合模型评估药物使用风险。潜在类别回归分析了mHealth定义的风险组与医疗保健获取和利用指标之间的关联。
109名参与者的年龄中位数为48.5岁，90%为非裔美国人，52%为男性，59%感染了艾滋病毒。生长混合模型确定了三个不同的类别：低强度药物使用（25%）、中等强度药物使用（65%）和高强度药物使用（10%）。与低强度药物使用者相比，高强度使用者更年轻，每天注射超过一次，并且共用针头。在随后的研究随访中，相对于低强度药物使用者，高强度药物使用者获得医疗保险的可能性低九倍（调整后的比值比：0.10，95%置信区间：0.01 - 0.88），未能参加任何门诊预约的风险更高（调整后的比值比：0.13，95%置信区间：0.02 - 0.85）。
对药物使用的实时评估以及描述吸毒者亚类别的新方法揭示了行为风险较高且医疗保健服务利用率较低的个体。移动健康有望识别出从事高风险行为的个体并提供实时干预措施以改善护理结果。
