Swietek Karen, Jones Kelley A, Bettger Janet Prvu, French Alexis, Maslow Gary, Norman Katherine S, Lake Ashley D, Carvalho Marissa, Cholera Rushina, Freed Salama S, Tchuisseu Yolande Pokam, Repka Samantha, Whitaker Rebecca G
Health Care Evaluation Department, NORC at the University of Chicago, Cambridge, Massachusetts, USA.
Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
Telemed J E Health. 2024 Aug;30(8):e2319-e2326. doi: 10.1089/tmj.2023.0563. Epub 2024 May 10.
Increased availability of telehealth can improve access to health care. However, there is evidence of persistent disparities in telehealth usage, as well as among people from minoritized racial and ethnic groups and rural residents. The objective of our work was to explore the degree to which disparities in telehealth use for behavioral health (BH) and musculoskeletal (MSK) related services during the COVID-19 pandemic are explained by observed beneficiary- and area-level characteristics. Using North Carolina Medicaid claims data of Medicaid beneficiaries with BH or MSK conditions, we apply nonlinear regression-based decomposition analysis-based models developed by Kitagawa, Oaxaca, and Blinder to determine which observed variables are associated with racial, ethnic, and rural inequalities in telehealth usage. In the BH cohort, we found statistically significant differences in telehealth usage by race in the adult population, and by race, Hispanic ethnicity, and rurality in the pediatric population. In the MSK cohort, we found significant inequities by Hispanic ethnicity and rurality among adults, and by race and rurality among children. Inequalities in telehealth use between groups were small, ranging from 0.7 percentage points between urban and rural adults with MSK conditions to 3.8 percentage points between white adults and people of color among those with BH conditions. Overall, we found that racial and ethnic inequalities in telehealth use are not well explained by the observed variables in our data. Rural disparities in telehealth use are better explained by observed variables, particularly area-level broadband internet use. For inequalities between rural and urban residents, our analysis provides observational evidence that infrastructure such as broadband internet access is an important driver of differences in telehealth use. For racial and ethnic inequalities, the pathways may be more complex and difficult to measure, particularly when relying on administrative data sources in place of more detailed data on individual-level socioeconomic factors.
远程医疗服务的可及性提高有助于改善医疗保健的获取。然而，有证据表明，在远程医疗的使用方面存在持续的差异，在少数族裔和农村居民中也是如此。我们这项工作的目的是探讨在新冠疫情期间，行为健康（BH）和肌肉骨骼（MSK）相关服务的远程医疗使用差异在多大程度上可由观察到的受益人和地区层面的特征来解释。利用北卡罗来纳州医疗补助受益人的医疗补助申请数据，这些受益人患有BH或MSK疾病，我们应用了由北川、瓦哈卡和布林德开发的基于非线性回归的分解分析模型，以确定哪些观察到的变量与远程医疗使用中的种族、族裔和农村不平等相关。在BH队列中，我们发现成年人群体中按种族划分的远程医疗使用存在统计学上的显著差异，在儿童群体中按种族、西班牙裔族裔和农村地区划分也存在显著差异。在MSK队列中，我们发现成年人中按西班牙裔族裔和农村地区划分存在显著不平等，儿童中按种族和农村地区划分存在显著不平等。不同群体之间远程医疗使用的不平等程度较小，患有MSK疾病的城乡成年人之间的差异为0.7个百分点，患有BH疾病的白人成年人与有色人种之间的差异为3.8个百分点。总体而言，我们发现我们数据中观察到的变量并不能很好地解释远程医疗使用中的种族和族裔不平等。远程医疗使用中的农村差异能更好地由观察到的变量来解释，特别是地区层面的宽带互联网使用情况。对于城乡居民之间的不平等，我们的分析提供了观察证据，表明宽带互联网接入等基础设施是远程医疗使用差异的一个重要驱动因素。对于种族和族裔不平等，其途径可能更复杂且难以衡量，特别是当依赖行政数据源而非关于个人层面社会经济因素的更详细数据时。
