Harris Andrew, Jain Amit, Dhanjani Suraj A, Wu Catherine A, Helliwell Lydia, Mesfin Addisu, Menga Emmanuel, Aggarwal Shruti, Pusic Andrea, Ranganathan Kavitha
From the Department of Orthopaedic Surgery, The Johns Hopkins University.
Harvard Medical School.
Plast Reconstr Surg. 2023 Mar 1;151(3):677-685. doi: 10.1097/PRS.0000000000009939. Epub 2022 Nov 29.
Because of the expansion of telehealth services through the 2020 Coronavirus Aid, Relief, and Economic Security (CARES) Act, the potential of telemedicine in plastic surgery has gained visibility. This study aims to identify populations who may have limited access to telemedicine.
The authors created a telemedicine literacy index (TLI) using a multivariate regression model and data from the US Census and Pew Research Institute survey. A multivariate regression model was created using backwards elimination, with TLI as the dependent variable and demographics as independent variables. The resulting regression coefficients were applied to data from the 2018 US Census at the county level to create a county-specific technological literacy index (cTLI). Significance was set at P < 0.05.
On multivariable analysis, the following factors were found to be significantly associated with telemedicine literacy: age, sex, race, employment status, income level, marital status, educational attainment, and urban or rural classification. Counties in the lowest tertile had significantly lower median annual income levels ($43,613 versus $60,418; P < 0.001) and lower proportion of the population with at least a bachelor's degree (16.7% versus 26%; P < 0.001). Rural areas were approximately three times more likely to be in the lowest cTLI compared with urban areas ( P < 0.001). Additional associations with low cTLI were Black race ( P = 0.045), widowed marital status ( P < 0.001), less than high school education ( P = 0.005), and presence of a disability ( P = 0.01).
These results highlight disadvantaged groups at risk of being underserved with telehealth. Using these findings, key stakeholders may be able to target these communities for interventions to increase telemedicine literacy and access.
由于通过2020年《冠状病毒援助、救济和经济安全(CARES)法案》扩大了远程医疗服务,整形手术中远程医疗的潜力已得到关注。本研究旨在确定可能难以获得远程医疗服务的人群。
作者使用多元回归模型以及美国人口普查和皮尤研究中心调查的数据创建了一个远程医疗素养指数(TLI)。使用向后排除法创建了一个多元回归模型,以TLI作为因变量,人口统计学特征作为自变量。将所得回归系数应用于2018年美国县级人口普查数据,以创建特定县的技术素养指数(cTLI)。显著性设定为P<0.05。
在多变量分析中,发现以下因素与远程医疗素养显著相关:年龄、性别、种族、就业状况、收入水平、婚姻状况、教育程度以及城乡分类。处于最低三分位数的县的年中位数收入水平显著较低(43,613美元对60,418美元;P<0.001),且拥有至少学士学位的人口比例较低(16.7%对26%;P<0.001)。与城市地区相比,农村地区处于最低cTLI的可能性大约高三倍(P<0.001)。与低cTLI相关的其他因素包括黑人种族(P = 0.045)、丧偶婚姻状况(P<0.001)、高中以下教育程度(P = 0.005)以及残疾(P = 0.01)。
这些结果突出了在远程医疗服务方面可能得不到充分服务的弱势群体。利用这些发现,关键利益相关者或许能够针对这些社区进行干预,以提高远程医疗素养并增加远程医疗服务的可及性。