Racial and ethnic disparities in telehealth use before and after California's stay-at-home order.

Introduction: Telehealth can potentially improve the quality of healthcare through increased access to primary care. While telehealth use increased during the COVID-19 pandemic, racial/ethnic disparities in the use of telemedicine persisted during this period. Little is known about the relationship between health coverage and patient race/ethnicity after the onset of the COVID-19 pandemic. Objective: This study examines how differences in patient race/ethnicity and health coverage are associated with the number of in-person vs. telehealth visits among patients with chronic conditions before and after California's stay-at-home order (SAHO) was issued on 19 March 2020. Methods: We used weekly patient visit data (in-person (N = 63, 491) and telehealth visits (N = 55, 472)) from seven primary care sites of an integrated, multi-specialty medical group in Los Angeles County that served a diverse patient population between January 2020 and December 2020 to examine differences in telehealth visits reported for Latino and non-Latino Asian, Black, and white patients with chronic conditions (type 2 diabetes, pre-diabetes, and hypertension). After adjusting for age and sex, we estimate differences by race/ethnicity and the type of insurance using an interrupted time series with a multivariate logistic regression model to study telehealth use by race/ethnicity and type of health coverage before and after the SAHO. A limitation of our research is the analysis of aggregated patient data, which limited the number of individual-level confounders in the regression analyses. Results: Our descriptive analysis shows that telehealth visits increased immediately after the SAHO for all race/ethnicity groups. Our adjusted analysis shows that the likelihood of having a telehealth visit was lower among uninsured patients and those with Medicaid or Medicare coverage compared to patients with private insurance. Latino and Asian patients had a lower probability of telehealth use compared with white patients. Discussion: To address access to chronic care management through telehealth, we suggest targeting efforts on uninsured adults and those with Medicare or Medicaid coverage, who may benefit from increased telehealth use to manage their chronic care.

Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data.

Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of MCAR require large sample sizes n and/or large group sample sizes n(i), and they usually fail when applied to non-normal data. Hawkins (1981) proposed a test of multivariate normality and homoscedasticity that is an exact test for complete data when n(i) are small. This paper proposes a modification of this test for complete data to improve its performance, and extends its application to test of homoscedasticity and MCAR when data are multivariate normal and incomplete. Moreover, it is shown that the statistic used in the Hawkins test in conjunction with a nonparametric k-sample test can be used to obtain a nonparametric test of homoscedasticity that works well for both normal and non-normal data. It is explained how a combination of the proposed normal-theory Hawkins test and the nonparametric test can be employed to test for homoscedasticity, MCAR, and multivariate normality. Simulation studies show that the newly proposed tests generally outperform their existing competitors in terms of Type I error rejection rates. Also, a power study of the proposed tests indicates good power. The proposed methods use appropriate missing data imputations to impute missing data. Methods of multiple imputation are described and one of the methods is employed to confirm the result of our single imputation methods. Examples are provided where multiple imputation enables one to identify a group or groups whose covariance matrices differ from the majority of other groups.