Sociodemographic disparities in the use of cardiovascular ambulatory care and telemedicine during the COVID-19 pandemic

Patient-, Provider-, and Facility-Level Contributors to the Use of Cardiology Telehealth Care in the Veterans Health Administration: Retrospective Cohort Study

BACKGROUND

Telehealth (care delivered by phone or video) comprises a substantial proportion of cardiology care delivered in the Veterans Health Administration (VHA). Little is known about how factors specific to patients, clinicians, and facilities contribute to variation in cardiology telehealth use.

OBJECTIVE

The aim of this study is to estimate the relative extent to which patient-, clinician-, and facility-level factors affect cardiology telehealth use in VHA.

METHODS

This was a retrospective, nation-wide cohort study of veterans' use of VHA cardiology telehealth care during the first 2 years of the COVID-19 pandemic (March 11, 2020, to March 10, 2022). We constructed multilevel, multivariable, logistic regression models of patient-level cardiology telehealth use (telephone or video-based care). Models included random effects for the patient, the patient's main cardiology provider, and the patient's primary facility (ie, VHA medical center) for specialty care and fixed effects for patient sociodemographic and clinical characteristics.

RESULTS

Our analytic cohort comprised 223,809 veterans with 989,271 encounters among 2235 unique clinicians. The veterans' average age was 70.2 years, and 3.4% (n=7616) were women. Of the 989,271 encounters, 4.2% (n=41,480) were video based and 34.3% (n=338,834) were phone based. Adjusted odds of telehealth use were slightly higher for women versus men (adjusted odds ratio [AOR] 1.08, 95% CI 1.05-1.10), individuals identifying as Hispanic or Latino versus not Hispanic or Latino (AOR 1.46, 95% CI 1.43-1.49), and those with medium and long drive times versus short drive time (AOR 1.11, 95% CI 1.10-1.12 and AOR 1.09, 95% CI 1.07-1.10, respectively). Further, 40.5% of the variation in a veteran's likelihood of using cardiology telehealth care was found at the patient level, 30.8% at the clinician level, and 7% at the facility level.

CONCLUSIONS

The largest share of the attributable variability in VHA cardiology telehealth use in this cohort was explained by the patient, followed closely by the clinician. Little variability was attributed to the primary facility through which the veteran received their cardiology care. These results suggest that policy solutions intended to improve equity of cardiology telehealth care use in VHA may be most impactful when directed at patients and clinicians.

Patterns of Telehealth Visits After the COVID-19 Pandemic Among Individuals With or at Risk for Cardiovascular Disease in the United States

BACKGROUND

Prior studies have shown that cardiovascular disease (CVD) can be effectively managed through telehealth. However, there are little national data on the use of telehealth in people with CVD or CVD risk factors. We aimed to determine the prevalence of telehealth visits and visit modality (video versus audio-only) in people with CVD and CVD risk factors. We also assessed their rationale and satisfaction with telehealth visits.

METHODS AND RESULTS

A nationally representative sample of 6252 participants from the 2022 Health Information National Trends Survey 6 was used. We defined the CVD risk categories as having no self-reported CVD (coronary heart disease or heart failure) or CVD risk factors (hypertension, diabetes, obesity, or current smoking), CVD risk factors alone, and CVD. Multivariable logistic regression, adjusting for major sociodemographic factors, assessed the relationship between CVD risk and telehealth uptake. The weighted prevalence of using telehealth was 50% (95% CI, 44%-56%) for individuals with CVD and 40% (95% CI, 37%-43%) for those with CVD risk factors alone. Individuals with CVD had the highest odds of using any telehealth (audio-only or video) (adjusted odds ratio [OR], 2.02 [95% CI, 1.39-2.93]) when compared with those without CVD or CVD risk factors. Notably, 21% (95% CI, 16.3%-25.6%) of patients with CVD used audio-only visits (adjusted OR, 2.38 [95% CI, 1.55-3.64]) compared with patients without CVD or CVD risk factors.

CONCLUSIONS

In a nationally representative survey, there was high prevalence of any (video or audio-only) telehealth visits in people with CVD, and audio-only visits comprised a significant proportion of telehealth visits in this population.

Telemedicine Disparities in Ambulatory Cardiology Visits in a Large Academic Health System

Background

The COVID-19 pandemic prompted rapid expansion of telemedicine to access subspecialty care. However, potential disparities in access to telemedicine in cardiology remain to be fully characterized.

Objectives

The authors aimed to study whether telemedicine visit modality (video or audio only) differed by sociodemographic characteristics in the outpatient cardiology population of a large academic health center.

Methods

We conducted a retrospective cross-sectional study of telemedicine encounter data from all outpatient cardiology telemedicine visits from January 1, 2020, to December 31, 2021. We examined unique patients' first telemedicine encounter during the study period. The primary outcome was visit modality, video versus audio-only visit. Predictors of audio-only visit modality were assessed using adjusted logistic regression analyses.

Results

There were 47,961 total adult cardiology telemedicine encounters among 39,381 unique patients. Of all encounters, 20.4% were audio only. Odds of audio-only visit modality increased with age, with the highest odds of audio-only visits in patients aged >75 years (OR: 3.4; 95% CI: 2.8-4.2). Non-White race (OR: 1.2; 95% CI: 1.1-1.3), lack of private insurance (Medicaid OR: 2.8; 95% CI: 2.5-3.1 and Medicare OR: 1.7; 95% CI: 1.5-1.8), and higher social deprivation index quintile (social deprivation index 5, most deprived, OR: 2.0; 95% CI: 1.9-2.2) were also associated with increased odds of audio-only modality.

Conclusions

We identified sociodemographic disparities in telemedicine visit modality in a large outpatient cardiology population. These findings highlight the important role of audio-only visits in accessing telemedicine, and opportunities to narrow the digital health divide.

Disparities in Visual Field Testing Frequency Among Subjects With Glaucoma

Purpose

Prior evidence suggests racial disparities in the utilization of visual field testing (VFT) for the diagnosis and monitoring of glaucoma. In this study, we considered the effect of baseline glaucoma severity and socioeconomic disadvantage along with other potential confounders such as test reliability, ancillary tests, and glaucoma surgeries on racial disparity in the frequency of VFT.

Methods

The records of all subjects with a diagnosis of glaucoma who received VFT at an academic, tertiary care facility from January 2018 to December 2021 were accessed. Analysis was performed to compare VFT frequency, the total number of office visits (DoS), and the ratio of VFT frequency to DoS (VFT/DoS) across self-reported races while controlling for sex, age, socioeconomic disadvantage (Area Deprivation Index), VF reliability indicators and baseline mean deviation, optical coherence tomography frequency, and glaucoma surgeries.

Results

Among the 2654 subjects (1515 White, 782 Black, and 357 Asian) included in this study, Black subjects had the worst socioeconomic status and disease severity at baseline. They also experienced a 3% lower VFT/DoS ratio compared to White subjects (P = 0.031). Asian subjects had a 5% lower VFT/DoS ratio compared to White subjects (P = 0.015).

Discussion

We identified racial disparity in performing VFT in subjects with glaucoma even when multiple confounders were considered. Further investigation is necessary to identify other race-associated factors to work toward reducing racial disparities in VFT.

Translational Relevance

Black and Asian subjects with glaucoma receive fewer VFT per visit compared to White subjects even when considering socioeconomic disadvantage and disease severity.

Impact of the COVID-19 pandemic on cardiovascular mortality and contrast analysis within subgroups

Background

An increase in deaths has been perceived during the pandemic, which cannot be explained only by COVID-19. The actual number of deaths far exceeds the recorded data on deaths directly related to SARS-CoV-2 infection. Data from early and short-lived pandemic studies show a dramatic shift in cardiovascular mortality. Grounded in the post-pandemic era, macroscopic big data on cardiovascular mortality during the pandemic need to be further reviewed and studied, which is crucial for cardiovascular disease prevention and control.

Methods

We retrieved and collected data associated with cardiovascular disease mortality from the National Vital Statistic System from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform based on the ICD-10 codes. We applied regression analysis to characterize overall cardiovascular disease mortality trends from 2010 to 2023 and built a time series model to predict mortality for 2020-2023 based on mortality data from 2010 to 2019 in order to affirm the existence of the excess deaths by evaluating observed vs. predicted mortality. We also conducted subgroup analyses by sex, age and race/ethnicity for the purpose of obtaining more specific sociodemographic information.

Results

All-cause age-standardised mortality rates (ASMRs) for CVD dramatically increased between 2019 and 2021[annual percentage change (APC) 11.27%, p < 0.01], and then decreased in the following 2021-2023(APC: -7.0%, p < 0.01). Subgroup analyses found that the ASMR change was most pronounced in Alaska Indians/Native American people (APC: 16.5% in 2019-2021, -12.5% in 2021-2023, both p < 0.01), Hispanics (APC: 12.1% in 2019-2021, -12.2% in 2021-2023, both p < 0.05) and non-Hispanic Black people (APC:11.8% in 2019-2021, -10.3% in 2021-2023, both p < 0.01)whether during the increasing or declining phase. Similarly, the ASMR change was particularly dramatic for the 25-44 age group (APC:19.8% in 2019-2021, -15.4% in 2021-2023, both p < 0.01) and males (APC: 11.5% in 2019-2021, -7.6% in 2021-2023, both p < 0.01). By the end of 2023, the proportion of COVID-related excess death remained high among the elderly (22.4%), males (42.8%) and Alaska Indians/Native American people(39.7%). In addition, we did not find the presence of excess deaths in the young (25-44) and middle-aged cohort (45-64) in 2023, while excess deaths remained persistent in the elderly.

Conclusions

All-cause ASMRs for CVD increased notably during the initial two years of the COVID-19 pandemic and then witnessed a decline in 2021-2023. The cohorts (the young, males and minorities) with the steepest rise in mortality decreased at the fastest rate instead. Previous initiatives to promote cardiovascular health were effective, but further research on cardiovascular healthcare for the elderly and racial disparities should be attached to priority considering the presence of sociodemographic differences in CVD death.

Telehealth Expansion, Internet Speed, and Primary Care Access Before and During COVID-19

Importance

Primary care (PC) receipt is associated with better health outcomes. How telehealth expansion and internet speed are associated with PC use is unclear.

Objective

To examine the association of telehealth and internet speed with PC use across sociodemographic determinants of health.

Design, Setting, and Participants

This cohort study performed difference-in-differences regression of the change in in-person and telehealth PC visits between pre-COVID-19 public health emergency (PHE) (June 1, 2019, to February 29, 2020) and an initial (March 1, 2020, to May 31, 2020) and prolonged (March 1, 2020, to December 31, 2021) PHE period among continuously enrolled nonpregnant, nondisabled Wisconsin Medicaid beneficiaries aged 18 to 64 years. Data were analyzed from March 2022 to March 2023.

Exposure

PHE-induced telehealth expansion.

Main Outcomes and Measures

Change in PC telehealth (using Current Procedural Terminology codes) visits: (1) count; (2) visit share completed by telehealth; (3) percentage of PHE-induced visit decline offset by telehealth. High-speed internet (HSI) defined as living in a census block group with a median block maximum download speed of 940 megabits per second or greater (June 2020 Federal Communications Commission broadband data); other census block groups classified as low-speed internet (LSI).

Results

In the total cohort of 172 387 participants, 102 989 (59.7%) were female, 103 848 (60.2%) were non-Hispanic White, 34 258 (19.9%) were non-Hispanic Black, 15 020 (8.7%) were Hispanic, 104 239 (60.5%) were aged 26 to 45 years, and 112 355 (66.0%) lived in urban counties. A total of 142 433 (82.6%) had access to HSI; 72 524 (42.1%) had a chronic condition. There was a mean (SD) of 0.138 (0.261) pre-PHE PC visits per month. In the pre-PHE period, visit rates were significantly higher for female than male participants, non-Hispanic White than non-Hispanic Black individuals, urban than rural residents, those with HSI than LSI, and patients with chronic disease than patients without. In the initial PHE period, female participants had a greater increase in telehealth visits than male participants (43.1%; 95% CI, 37.02%-49.18%; P < .001), share (2.20 percentage point difference [PPD]; 95% CI, 1.06-3.33 PPD; P < .001) and offset (6.81 PPD; 95% CI, 3.74-9.87 PPD; P < .001). Non-Hispanic Black participants had a greater increase in share than non-Hispanic White participants (5.44 PPD; 95% CI, 4.07-6.81 PPD; P < .001) and offset (15.22 PPD; 95% CI, 10.69-19.75 PPD; P < .001). Hispanic participants had a greater increase in telehealth visits than Non-Hispanic White participants (35.60%; 95% CI, 25.55%-45.64%; P < .001), share (8.50 PPD; 95% CI, 6.75-10.26 PPD; P < .001) and offset (12.93 PPD; 95% CI, 6.25-19.60 PPD; P < .001). Urban participants had a greater increase in telehealth visits than rural participants (63.87%; 95% CI, 52.62%-75.11%; P < .001), share (9.13 PPD; 95% CI, 7.84-10.42 PPD; P < .001), and offset (13.31 PPD; 95% CI; 9.62-16.99 PPD; P < .001). Participants with HSI had a greater increase in telehealth visits than those with LSI (55.23%; 95% CI, 42.26%-68.20%; P < .001), share (6.61 PPD; 95% CI, 5.00-8.23 PPD; P < .001), and offset (6.82 PPD; 95% CI, 2.15-11.49 PPD; P = .004). Participants with chronic disease had a greater increase in telehealth visits than those with none (188.07%; 95% CI, 175.27%-200.86%; P < .001), share (4.50 PPD; 95% CI, 3.58-5.42 PPD; P < .001), and offset (9.03 PPD; 95% CI, 6.01-12.04 PPD; P < .001). Prolonged PHE differences were similar. Differences persisted among those with HSI.

Conclusions and Relevance

In this cohort study of Wisconsin Medicaid beneficiaries, greater telehealth uptake occurred in groups with higher pre-PHE utilization, except for high uptake among Hispanic and non-Hispanic Black individuals despite low pre-PHE utilization. HSI did not moderate disparities. These findings suggest telehealth and HSI may boost PC receipt, but will generally not close utilization gaps.

Drivers of telemedicine in primary care clinics at a large academic medical centre

BACKGROUND

COVID-19 disrupted healthcare routines and prompted rapid telemedicine implementation. We investigated the drivers of visit modality selection (telemedicine versus in-person) in primary care clinics at an academic medical centre.

METHODS

We used electronic medical record data from March 2020 to May 2022 from 13 primary care clinics (N = 21,031 new, N = 207,292 return visits), with 55% overall telemedicine use. Hierarchical logistic regression and cross-validation methods were used to estimate the variation in visit modality explained by the patient, clinician and visit factors as measured by the mean-test area under the curve (AUC).

RESULTS

There was significant variation in telemedicine use across clinicians (ranging from 0-100%) for the same visit diagnosis. The strongest predictors of telemedicine were the clinician seen for new visits (mean AUC of 0.79) and the primary visit diagnosis for return visits (0.77). Models based on all patient characteristics combined accounted for relatively little variation in modality selection, 0.54 for new and 0.58 for return visits, respectively. Amongst patient characteristics, males, patients over 65 years, Asians and patient's with non-English language preferences used less telemedicine; however, those using interpreter services used significantly more telemedicine.

CONCLUSION

Clinician seen and primary visit diagnoses were the best predictors of visit modality. The distinction between new and return visits and the minimal impact of patient characteristics on visit modality highlights the complexity of clinical care and warrants research approaches that go beyond linear models to uncover the emergent causal effects of specific technology features mediated by tasks, people and organisations.