Department of Health Behavior and Health Education, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Fay W. Boozman College of Public Health, Southern Public Health and Criminal Justice Research Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
J Urban Health. 2023 Dec;100(6):1149-1158. doi: 10.1007/s11524-023-00795-y. Epub 2023 Nov 27.
We sought to investigate temporal trends in telehealth availability among outpatient mental health treatment facilities and differences in the pace of telehealth growth by state urbanicity and rurality. We used the National Mental Health Services Survey (2015-2020) to identify outpatient mental health treatment facilities in the US (N = 28,989 facilities; 2015 n = 5,018; 2020 n = 4,889). We used logistic regression to model telehealth, predicted by time, state rurality (1 to 10% rural, 10 to < 20%, 20 to < 30%, or [Formula: see text] 30%), and their interaction, and adjusted for relevant covariates. We estimated the predicted probability of telehealth based on our model. We estimated effects with and without data from 2020 to assess whether the rapid and widespread adoption of telehealth during the COVID-19 pandemic changed the rural/urban trajectories of telehealth availability. We found that telehealth grew fastest in more urban states (year*rurality interaction p < 0.0001). Between 2015 and 2020, the predicted probability of telehealth in more urban states increased by 51 percentage points (from 9 to 61%), whereas telehealth in more rural states increased by 38 percentage points (from 23 to 61%). Predicted telehealth also varied widely by state, ranging from more than 75% of facilities (RI, OR) to below 20% (VT, KY). Health systems and new technological innovations must consider the unique challenges faced by urban populations and how best practices may be adapted to meet the growing urban demand. We framed our findings around the need for policies that minimize barriers to telehealth.
我们试图调查门诊心理健康治疗机构中远程医疗服务可用性的时间趋势,以及各州城市性和农村性对远程医疗发展速度的差异。我们使用了国家心理健康服务调查(2015-2020 年)来确定美国的门诊心理健康治疗机构(N=28989 个机构;2015 年 n=5018;2020 年 n=4889)。我们使用逻辑回归模型来预测远程医疗,预测因素是时间、州农村性(1%至 10%农村、10%至<20%农村、20%至<30%农村或[公式:见正文]30%农村)以及它们的交互作用,并调整了相关的协变量。我们根据我们的模型估计了远程医疗的预测概率。我们在有无 2020 年数据的情况下估计了效果,以评估 COVID-19 大流行期间远程医疗的快速广泛采用是否改变了远程医疗可用性的农村/城市轨迹。我们发现,在更城市化的州,远程医疗的增长速度最快(年份*农村性交互作用 p<0.0001)。在 2015 年至 2020 年期间,更城市化的州的远程医疗预测概率增加了 51 个百分点(从 9%增加到 61%),而更农村化的州的远程医疗则增加了 38 个百分点(从 23%增加到 61%)。远程医疗的预测概率也因州而异,范围从超过 75%的机构(RI、OR)到低于 20%的机构(VT、KY)。卫生系统和新的技术创新必须考虑到城市人口面临的独特挑战,以及如何调整最佳实践以满足不断增长的城市需求。我们根据需要制定了政策框架,以尽量减少远程医疗的障碍。