Clinical and Sociodemographic Factors Associated with Telemedicine Engagement in an Urban Community Health Center Cohort During the COVID-19 Pandemic
Introduction
The emergence of the COVID-19 pandemic marked an inflection point in the adoption of telemedicine for the delivery of clinical care through telephone or video visits. Before the pandemic, telemedicine was circumscribed and infrequently used. Less than half of community health centers were using any telemedicine and among those who were, only 30% were using it for primary care and 20% were using it for chronic disease management.1 Despite the low telemedicine utilization, studies from the prepandemic period associated telemedicine with increased access to care,2,3 improved chronic disease and behavioral health management,4–6 and patient satisfaction.7 The gaps in who can access the benefits of telemedicine, however, are exacerbated by the “digital divide”—sociodemographic, language, and digital access barriers in who engages with digital technologies, including telemedicine.8–10 Patients with limited English proficiency, for example, were found to be half as likely to use telemedicine compared with patients proficient in English, even after controlling for internet use, and highlights the inequities perpetuated by the digital divide.10
Federal and state policy changes at the start of the pandemic heralded a new era for telemedicine.11 Telemedicine visits increased acutely in the initial months of the pandemic12 and persist at higher levels compared with prepandemic trends.13 Scholarship during the pandemic has largely focused on characterizing who is accessing telemedicine. Inequities in telemedicine use and in the differential use of audio-only over video modalities across various patient demographic characteristics, including race and ethnicity, age, and payer type have been identified.12,14–16 In a sample of community health center patients, there were racial and ethnic disparities in telemedicine use and lower utilization among Medicaid and uninsured patients.17
Findings from a recent national study, however, challenged expectations informed by prior studies and found that telemedicine use increased across all Medicare populations and the greatest increase occurred among individuals living in the most disadvantaged neighborhoods.18 As policymakers, health systems, and payers construct policies for a sustainable telemedicine care delivery model beyond the pandemic period, important equity considerations and clinical care evidence gaps remain.19–21
There is a need to characterize the clinical care being delivered through telemedicine. Although a few studies have queried the health conditions being assessed through telemedicine visits, they have been limited by small sample sizes16 or have not had a focus on safety net populations.22,23 Understanding the clinical conditions associated with telemedicine is a critical component necessary for building telemedicine programs that target chronic disease management among safety-net populations.19,24 To fill this gap, our study had two aims. Among a predominantly Latinx, Spanish-speaking population engaged in care at a large, multisite community health center we sought to (1) characterize associations between common chronic diseases and telemedicine use and to (2) assess for demographic and clinical factors associated with being a “high” utilizer of telemedicine.
Materials and Methods
STUDY DESIGN, SAMPLE, AND DATA SOURCES
We conducted a retrospective cohort study of adult patients seeking care at Fair Haven Community Health Care (FHCHC), a 16-site urban community health center in New Haven, Connecticut. FHCHC serves >31,000 unique patients yearly, 90% of whom are from minoritized racial and ethnic groups, almost half prefer a language other than English, and 27% are uninsured.25 We extracted clinical encounter-level data (encounter type [in-person or telemedicine], problem list diagnoses via International Classification of Diseases, Tenth Revision [ICD-10] codes) and patient demographic information (age during the patient’s first encounter in the pre-COVID-19 study period, gender, race and ethnicity, preferred language, health insurance type, electronic medical record portal activation, and annual household income) from FHCHC’s electronic medical record.
We included adult patients ≥18 years of age who met two requirements: (1) they had at least one in-person medical or behavioral health visit in the pre-COVID-19 study period defined as March 1, 2019–February 29, 2020, and (2) they had at least one visit (either an in-person or telemedicine visit) during the COVID-19 study period defined as March 1, 2020–March 10, 2021. We defined the beginning of the COVID-19 study period as March 1, 2020 to adequately capture the start of the pandemic period given that Connecticut declared a public health emergency ordinance on March 10, 2020.26 The study was exempted from full review by the Yale Human Investigation Committee.
OUTCOME VARIABLE
Our primary outcome variable was telemedicine visits during the COVID-19 period. We constructed two outcomes to characterize the degree of telemedicine engagement: those who had any telemedicine use, which we defined as having at least one telemedicine visit, and those who had high telemedicine use, defined as ≥3 telemedicine visits during the COVID-19 study period. Recognizing that there is currently a lack of evidence-based consensus surrounding how to define differing levels of telemedicine engagement for the management of chronic conditions, our study used an exploratory approach to define the threshold for high use at ≥3 telemedicine visits based on an initial assessment of the distribution of telemedicine visit frequency during the COVID-19 study period.
INDEPENDENT VARIABLES
Our independent variables included patient sociodemographic characteristics and clinical factors. We selected the following self-reported patient characteristics: age (18–44, 45–64, ≥65 years), gender (female, male), race and ethnicity (Asian, Black, Latinx, White, Other/unknown), preferred language (English, Spanish, other), health insurance type (Commercial, Medicaid, Medicare, no insurance), electronic health record portal activation (activated, not activated), and annual household income as a percent of the Federal Poverty Level (≤100%, 101–200%, >200%, unknown).
For clinical factors, we selected the following chronic conditions for which there was either a high prevalence in our sample (≥5%) or there is evidence of telemedicine’s clinical effectiveness: hypertension,27 chronic pulmonary disease,28 diabetes,4 obesity,29 alcohol use disorder and other substance use disorders,30 and depression.5 We defined the presence of a chronic condition as the presence of an active problem on the patient’s problem list during the study period by identifying ICD-10 codes that correspond to the Elixhauser Comorbidity Index31 for each of the chronic conditions. For diabetes, the ICD-10 codes capture patients with both complicated and uncomplicated diabetes. For hypertension, the ICD-10 codes capture patients with uncomplicated hypertension. Finally, we assessed the frequency of in-person office visits and emergency department visits (defined as any emergency department visit in the Yale-New Haven Health system, which is the only hospital system in New Haven city and the largest in New Haven County) during the 1-year pre-COVID-19 study period. We followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.32
STATISTICAL ANALYSIS
We performed descriptive statistics to characterize patient sociodemographic and clinical factors among those who completed at least one telemedicine visit and those who completed ≥3 telemedicine visits during the COVID-19 study period. We assessed bivariate associations between patient-level factors and each of our two outcomes of interest using chi-square tests. We built multivariable logistic regression models with all patient sociodemographic and clinical factors as independent variables and telemedicine use, classified as “any” (≥1 visit) and “high” (≥3 visits), as our dependent variables. We chose to include all patient factors as covariates in the multivariate models based on a theoretical understanding of their influence on telemedicine use derived from prior literature. We report unadjusted and adjusted odds ratios (aORs) and the corresponding 95% confidence intervals (CIs). We conducted two-sided statistical tests and a p-value of <0.05 was considered significant. All analyses were performed using SAS version 9.4.
Results
SAMPLE CHARACTERISTICS
Our sample consisted of 5,793 patients who sought care at FHCHC during the 1-year pre-COVID-19 period and during the COVID-19 study period (Table 1). Among this sample, 3,311 patients (57.2%) were 18–44 years old and 10.5% were ≥65 years old. The majority, 4,241 patients (73.2%), self-identified as Latino/Latina and 2,629 individuals (45.5%) preferred to receive care in Spanish. Twenty-seven percent of our sample was uninsured and 46% had Medicaid coverage. Regarding clinical factors, the prevalence of hypertension, chronic pulmonary disease, diabetes, and depression ranged from 15% to 20%. In addition, ∼20.5% of patients had ≥4 in-person visits and >10% had ≥3 emergency department visits during the pre-COVID-19 study period.
| CHARACTERISTIC/FACTOR | n (%) |
|---|---|
| Age (years) | |
| 18–44 | 3,311 (57.2) |
| 45–64 | 1,872 (32.3) |
| ≥65 | 610 (10.5) |
| Gender | |
| Female | 4,174 (72.1) |
| Male | 1,619 (28.0) |
| Race and ethnicity | |
| Asian | 42 (0.7) |
| Black | 825 (14.2) |
| Latinx | 4,241 (73.2) |
| Other/unknown | 96 (1.7) |
| White | 589 (10.2) |
| Preferred language | |
| English | 3,098 (53.5) |
| Spanish | 2,629 (45.4) |
| Other | 66 (1.1) |
| Health insurance | |
| Commercial | 427 (7.4) |
| Medicaid | 2,662 (46.0) |
| Medicare | 1,143 (19.7) |
| No insurance | 1,561 (27.0) |
| Portal use | |
| Active | 4,441 (76.7) |
| Not active | 1,352 (23.3) |
| Annual household income (% federal poverty level) | |
| ≤100% | 2,471 (42.7) |
| 101–200% | 1,148 (19.8) |
| >200% | 268 (4.6) |
| Unknown | 1,906 (32.9) |
| Presence of chronic conditions | |
| Hypertension | 1,195 (20.6) |
| Chronic pulmonary disease | 1,068 (18.4) |
| Diabetes | 871 (15.0) |
| Obesity | 1,588 (27.4) |
| Alcohol use disorder | 219 (3.8) |
| Substance use disorders | 505 (8.7) |
| Depression | 1,180 (20.4) |
| Emergency department visits during the pre-COVID-19 study period | |
| 0 | 3,205 (55.3) |
| 1–2 | 1,900 (32.8) |
| ≥3 | 688 (11.9) |
| Medical/behavioral in-person office visits during the pre-COVID-19 study period | |
| 1 | 2,286 (39.6) |
| 2–3 | 2,325 (40.1) |
| 4–5 | 797 (13.8) |
| ≥6 | 385 (6.7) |
TELEMEDICINE TRENDS
The distribution of patient sociodemographic and clinical factors with our telemedicine utilization outcomes are given in Table 2. Among our sample of 5,793 patients, 4,687 patients (80.9%) had ≥1 telemedicine visits and 1,053 patients (18.2%) had ≥3 telemedicine visits during the COVID-19 period. Among patients ≥65 years old, almost a quarter (24.6%) had ≥3 telemedicine visits and on further exploration of age trends we found increased percentages of older patients engaged with ≥3 telemedicine visits (Fig. 1).
Fig. 1. Percentage of participants utilizing telemedicine by age category during the COVID-19 study period.
| CHARACTERISTIC/FACTOR | PATIENTS WITH ANY (≥1) TELEMEDICINE VISITS | PATIENTS WITH HIGH (≥3) TELEMEDICINE VISITS | ||
|---|---|---|---|---|
| n (%)a | p | n (%)a | p | |
| Total | 4,687 (80.9) | 1,053 (18.2) | ||
| Age, years | <0.001 | <0.001 | ||
| 18–44 | 2,520 (76.1) | 433 (13.1) | ||
| 45–64 | 1,628 (87.0) | 470 (25.1) | ||
| ≥65 | 539 (88.4) | 150 (24.6) | ||
| Gender | 0.02 | 0.17 | ||
| Female | 3,345 (80.1) | 777 (18.6) | ||
| Male | 1,342 (82.9) | 276 (17.1) | ||
| Race and ethnicity | 0.05 | <0.001 | ||
| Asian | 35 (83.3) | 7 (16.7) | ||
| Black | 668 (81.0) | 158 (19.2) | ||
| Latinx | 3,407 (80.3) | 728 (17.2) | ||
| Other/unknown | 74 (77.1) | 7 (7.3) | ||
| White | 503 (85.4) | 153 (26.0) | ||
| Preferred language | 0.41 | 0.11 | ||
| English | 2,524 (81.5) | 593 (19.1) | ||
| Spanish | 2,108 (80.2) | 450 (17.1) | ||
| Other | 55 (83.3) | 10 (15.2) | ||
| Health insurance | <0.001 | <0.001 | ||
| Commercial | 340 (79.6) | 61 (14.3) | ||
| Medicaid | 2,163 (81.3) | 541 (20.3) | ||
| Medicare | 1,007 (88.1) | 261 (22.8) | ||
| No insurance | 1,177 (75.4) | 190 (12.2) | ||
| Portal use | 0.07 | 0.41 | ||
| Active | 3,616 (81.4) | 797 (18.0) | ||
| Not active | 1,071 (79.2) | 256 (18.9) | ||
| Annual household income (% federal poverty level) | 0.02 | <0.001 | ||
| ≤100% | 2,025 (82.0) | 506 (20.5) | ||
| 101–200% | 897 (78.1) | 162 (14.1) | ||
| >200% | 209 (78.0) | 36 (13.4) | ||
| Unknown | 1,556 (81.6) | 349 (18.3) | ||
| Presence of chronic conditions | ||||
| Hypertension | 1,067 (89.3) | <0.001 | 335 (28.0) | <0.001 |
| Chronic pulmonary disease | 925 (86.6) | <0.001 | 262 (24.5) | <0.001 |
| Diabetes | 786 (90.2) | <0.001 | 280 (32.2) | <0.001 |
| Obesity | 1,318 (83.0) | 0.01 | 317 (20.0) | 0.03 |
| Alcohol use disorder | 189 (86.3) | 0.04 | 55 (25.1) | 0.007 |
| Substance use disorders | 418 (82.8) | 0.26 | 149 (29.5) | <0.001 |
| Depression | 1,030 (87.3) | <0.001 | 324 (27.5) | <0.001 |
| Emergency department visits during the pre-COVID-19 study period | <0.001 | <0.001 | ||
| 0 | 2,524 (78.8) | 456 (14.2) | ||
| 1–2 | 1,577 (83.0) | 399 (21.0) | ||
| ≥3 | 586 (85.2) | 198 (28.8) | ||
| Medical/behavioral in-person office visits during the pre-COVID-19 study period | <0.001 | <0.001 | ||
| 1 | 1,799 (78.7) | 286 (12.5) | ||
| 2–3 | 1,881 (80.9) | 398 (17.1) | ||
| 4–5 | 651 (81.7) | 183 (23.0) | ||
| ≥6 | 356 (92.5) | 186 (48.3) | ||
We also found a dose–response relationship between health care utilization in the pre-COVID-19 study period and subsequent high (≥3) telemedicine use. For example, among patients who had only one medical or behavioral visit in the year before the pandemic, 12.5% had high telemedicine use; in contrast, among those with ≥6 medical or behavioral visits in the year before the pandemic, nearly half (48.3%) had high telemedicine use. Similarly, among patients who did not have any emergency department visits in the year before the pandemic, only 14.2% of them had high telemedicine use, whereas almost a third (28.8%) of patients who had ≥3 emergency department visits in the pre-COVID-19 period had high telemedicine use.
FACTORS ASSOCIATED WITH ANY (≥1) TELEMEDICINE USE
Results from our bivariate and multivariate analyses between clinical and sociodemographic factors and having at least one telemedicine visit are summarized in Table 3. In adjusted analyses, compared with those in the youngest age category of 18–44 years, older age was associated with having at least one telemedicine visit (45–64 years old: aOR, 1.69 [95% CI, 1.42–2.02]; ≥ 65 years old: aOR, 1.45 [95% CI, 1.04–2.02]). Race and ethnicity, preferred language of patient, and portal use were not significantly associated with having any telemedicine use in our sample after adjusting for demographic and clinical factors. Compared with patients with Medicaid, those with Medicare coverage were more likely to have any telemedicine use (aOR, 1.39 [95% CI, 1.10–1.76]).
| CHARACTERISTIC/FACTOR | ANY (≥1) TELEMEDICINE VISITS | HIGH (≥3) TELEMEDICINE VISITS | ||
|---|---|---|---|---|
| UNADJUSTED OR (95% CI) | aOR (95% CI) | UNADJUSTED OR (95% CI) | aOR (95% CI) | |
| Age, years | ||||
| 18–44 | Ref | Ref | Ref | Ref |
| 45–64 | 2.09 (1.79–2.45) | 1.69 (1.42–2.02) | 2.23 (1.93–2.58) | 1.74 (1.46–2.07) |
| ≥65 | 2.38 (1.84–3.09) | 1.45 (1.04–2.02) | 2.17 (1.76–2.67) | 1.50 (1.13–2.01) |
| Gender | ||||
| Female | Ref | Ref | Ref | Ref |
| Male | 1.20 (1.03–1.40) | 1.20 (1.02–1.41) | 0.90 (0.77–1.05) | 0.84 (0.71–1.00) |
| Race and ethnicity | ||||
| Asian | 1.22 (0.54–2.77) | 1.10 (0.46–2.67) | 0.97 (0.43–2.18) | 1.20 (0.47–3.04) |
| Black | 1.04 (0.86–1.26) | 0.90 (0.72–1.13) | 1.14 (0.94–1.38) | 0.96 (0.76–1.21) |
| Latinx | Ref | Ref | Ref | Ref |
| Other/unknown | 0.82 (0.51–1.33) | 0.75 (0.44–1.25) | 0.38 (0.18–0.82) | 0.39 (0.17–0.87) |
| White | 1.43 (1.13–1.82) | 1.10 (0.83–1.45) | 1.69 (1.39–2.07) | 1.34 (1.04–1.71) |
| Preferred language | ||||
| English | Ref | Ref | Ref | Ref |
| Spanish | 0.92 (0.81–1.05) | 1.01 (0.84–1.20) | 0.87 (0.76–1.00) | 0.98 (0.81–1.18) |
| Other | 1.14 (0.59–2.19) | 1.38 (0.67–2.84) | 0.75 (0.38–1.49) | 1.05 (0.48–2.33) |
| Health insurance | ||||
| Commercial | 0.90 (0.70–1.16) | 0.98 (0.75–1.29) | 0.65 (0.49–0.87) | 0.91 (0.67–1.23) |
| Medicaid | Ref | Ref | Ref | Ref |
| Medicare | 1.71 (1.39–2.09) | 1.39 (1.10–1.76) | 1.16 (0.98–1.37) | 0.94 (0.76–1.16) |
| No insurance | 0.71 (0.61–0.82) | 0.85 (0.71–1.02) | 0.54 (0.45–0.65) | 0.75 (0.61–0.92) |
| Portal use | ||||
| Active | Ref | Ref | Ref | Ref |
| Not active | 0.87 (0.75–1.01) | 0.68 (0.58–0.81) | 1.07 (0.91–1.25) | 0.89 (0.75–1.06) |
| Annual household income (% federal poverty level) | ||||
| ≤100% | Ref | Ref | Ref | Ref |
| 101–200% | 0.79 (0.66–0.94) | 0.85 (0.71–1.02) | 0.64 (0.53–0.77) | 0.78 (0.64–0.96) |
| >200% | 0.78 (0.57–1.06) | 0.79 (0.57–1.09) | 0.60 (0.42–0.87) | 0.74 (0.50–1.09) |
| Unknown | 0.98 (0.84–1.14) | 0.92 (0.78–1.08) | 0.87 (0.75–1.01) | 0.83 (0.70–0.98) |
| Presence of chronic conditions | ||||
| Hypertension | 2.25 (1.85–2.74) | 1.37 (1.09–1.72) | 2.11 (1.81–2.44) | 1.32 (1.10–1.59) |
| Chronic pulmonary disease | 1.66 (1.37–2.00) | 1.30 (1.06–1.59) | 1.62 (1.38–1.90) | 1.03 (0.86–1.23) |
| Diabetes | 2.42 (1.92–3.06) | 1.63 (1.26–2.10) | 2.54 (2.16–2.99) | 1.87 (1.55–2.26) |
| Obesity | 1.21 (1.04–1.41) | 1.06 (0.91–1.24) | 1.18 (1.02–1.36) | 1.03 (0.88–1.20) |
| Alcohol use disorder | 1.51 (1.02–2.23) | 1.20 (0.79–1.83) | 1.54 (1.13–2.10) | 0.94 (0.65–1.34) |
| Substance use disorders | 1.15 (0.90–1.46) | 0.81 (0.61–1.06) | 2.03 (1.66–2.49) | 1.58 (1.24–2.01) |
| Depression | 1.80 (1.49–2.16) | 1.40 (1.15–1.71) | 2.02 (1.74–2.34) | 1.27 (1.08–1.51) |
| Emergency department visits during the pre-COVID-19 study period | ||||
| 0 | Ref | Ref | Ref | Ref |
| 1–2 | 1.32 (1.14–1.53) | 1.18 (1.01 -1.37) | 1.60 (1.38–1.86) | 1.30 (1.11–1.53) |
| ≥3 | 1.55 (1.24–1.94) | 1.16 (0.91–1.48) | 2.44 (2.01–2.95) | 1.45 (1.16–1.81) |
| Medical/behavioral in-person office visits during the pre-COVID-19 study period | ||||
| 1 | Ref | Ref | Ref | Ref |
| 2–3 | 1.15 (0.99–1.32) | 1.13 (0.97–1.31) | 1.44 (1.23–1.70) | 1.39 (1.17–1.64) |
| 4–5 | 1.21 (0.98–1.48) | 1.12 (0.90–1.39 | 2.08 (1.70–2.56) | 1.83 (1.47–2.28) |
| ≥6 | 3.32 (2.25–4.91) | 2.69 (1.79–4.02) | 6.54 (5.17–8.27) | 5.07 (3.92–6.55) |
Regarding clinical factors, the presence of hypertension (aOR, 1.37 [95% CI, 1.09–1.72]), chronic pulmonary disease (aOR, 1.30 [95% CI, 1.06–1.59]), diabetes (aOR, 1.63 [95% CI, 1.26–2.10]), or depression (aOR, 1.40 [95% CI, 1.15–1.71]) as well as having ≥6 in-person office visits in the pre-COVID-19 study period, compared with patients who only had one in-person office visit (aOR, 2.69 [95% CI,1.79–4.02]) were all associated with having any telemedicine use.
FACTORS ASSOCIATED WITH HIGH (≥3) TELEMEDICINE USE
After adjusting for all patient factors (Table 3), older age was associated with higher likelihood of high (≥3) telemedicine use (45–64 years old: aOR, 1.74 [95% CI, 1.46–2.07]; ≥65 years old: aOR, 1.50 [95% CI, 1.13–2.01]). White patients, compared with Latinx patients, were also more likely to have high telemedicine use (aOR, 1.34 [95% CI, 1.04–1.71]) and being uninsured, compared with having Medicaid coverage, had lower odds of having high telemedicine use (aOR, 0.75 [95% CI, 0.61–0.92]). Patients with chronic conditions including hypertension (aOR 1.32 [95% CI, 1.10–1.59]), diabetes (aOR, 1.87 [95% CI, 1.55–2.26]), substance use disorders (aOR 1.58 [95% CI, 1.24–2.01]), or depression (aOR, 1.27 [95% CI, 1.08–1.51]) were more likely to have high telemedicine use, as were individuals who had a higher frequency of emergency department visits and in-person office visits in the pre-COVID-19 study period.
Discussion
In this study at an urban, multisite community health center we examined chronic conditions and patient sociodemographic characteristics associated with any (≥1) telemedicine use and high (≥3) telemedicine use during the first year of the COVID-19 pandemic. Our study is one of a few studies that has explored relationships between chronic conditions and telemedicine engagement among a predominantly Latinx and Spanish-speaking population. We found that chronic conditions including hypertension, diabetes, substance use disorders, and depression were independent predictors of having high telemedicine engagement after adjusting for prior health care utilization and patient sociodemographic factors. In addition, we found that older adults, males, and individuals covered by Medicare were more likely to have any telemedicine use. Older and White individuals were more likely to have high telemedicine engagement, whereas uninsured patients were less likely to have high telemedicine use.
In our study, common medical and behavioral health conditions were independently associated with having high telemedicine engagement. These findings suggest that telemedicine is being used as a tool for continuity of care and align with prior studies that have found that telemedicine is at least as effective as in-person care for the management of chronic conditions.33 Reviews from before the pandemic have demonstrated telemedicine benefits for diabetes care,4,6 blood pressure control,34 and in facilitating psychiatric consults, psychotherapy, and addiction treatment.5,30 In addition, our finding that individuals with substance use disorders, including opioid use disorder, were more likely to have high telemedicine engagement highlights the growing role of telemedicine in caring for this population.35
There have been few studies during the pandemic that have assessed telemedicine trends by specific chronic conditions. A study by Patel et al, among a sample of commercially insured and Medicare Advantage enrollees, found that common chronic conditions such as hypertension and diabetes had lower use of telemedicine, compared with mental health conditions such as depression, and had a drop in total visit volume in the first few months of the pandemic.23 In this context, our findings are different. We instead found increased telemedicine engagement among individuals with these chronic conditions, which offers reason to be hopeful that telemedicine is filling an important need in the management of chronic conditions during the pandemic.
Our results provide an initial step in evaluating how telemedicine is being used for primary care delivery and chronic disease management in a safety-net setting within the current policy and care delivery context. It is imperative that future research begins to assess the effectiveness of telemedicine for the management of chronic diseases by measuring its performance on quality, process, and health outcome metrics across different practice settings.
We found various sociodemographic differences in the use of telemedicine. Older adults in our cohort were more likely to have at least one telemedicine visit, and they were also more likely to have high telemedicine engagement. This finding differs from prior literature that has found an inverse relationship between age and telemedicine use.14,17 Although Bose et al found that overall telemedicine use increased among Medicare recipients, most of whom were over the age of 65, and among individuals living in the most disadvantaged neighborhoods, they also found decreasing odds of utilization with increasing age.
From our current study we are unable to ascertain what is predisposing older adults in our sample to higher telemedicine utilization. It is possible that the trend reflects increased ease in accessing care by overcoming transportation or distance barriers. Older adults are also more likely to have more medical comorbidities and higher telemedicine engagement may reflect increased health care utilization overall compared with younger patients. Irrespective of the underlying etiologies driving the trend, our results challenge the expectation that older adults are unlikely to use telemedicine and underscore the need for a patient-centered approach when eliciting patient preferences for types of visit modalities.
In our study, White patients were more likely, compared with Latinx patients, to have high telemedicine use. Racial and ethnic disparities in telemedicine use have been well documented in earlier studies. For example, Adepoju et al found that Hispanic patients and members of other minoritized racial and ethnic groups were less likely, compared with White patients, to utilize telemedicine in a sample of community health center patients.17
We also found differences in telemedicine utilization by patient insurance status. Patients with Medicare coverage were more likely to have at least one telemedicine visit, compared with patients with Medicaid, and patients who were uninsured were less likely to have high telemedicine engagement. These findings similarly align with literature that has found that payer type is significantly associated with telemedicine use.17 As we emerge from the public health emergency phase and payers formulate long-term reimbursement policies for telemedicine, it will be critical to continue to monitor the impact on telemedicine use. These findings also have important equity implications for safety-net clinics who serve disproportionate numbers of uninsured patients.
Our study has limitations. Our study’s analytic sample is drawn from patients seeking care at a single community health center in the northeast; generalizability of our findings to other populations may be limited. We were also unable to distinguish the type of telemedicine modality (audio phone call vs. video), which limits our ability to identify more precise trends in how this population is engaging with different telemedicine platforms. Finally, although we sought to identify chronic conditions associated with telemedicine use, we did not explore health outcomes to further elucidate how telemedicine is impacting chronic disease management. Despite these limitations, our study is representative of a predominantly Latinx and Spanish-speaking population from among a large multisite community health center. The focus on this specific safety-net population can inform the construction of a telemedicine care delivery model that is tailored for a population at risk of health inequities and who is often excluded from policy considerations.
Conclusions
In this community health center cohort, older adults and patients with common chronic conditions were more likely to have had high (≥3) telemedicine visits in the first year of the COVID-19 pandemic, suggesting that telemedicine is being effectively leveraged for continuity of care. Nonetheless, we identified persistent racial and ethnic inequities in who is engaging with telemedicine care. There is a need for a multifaceted approach to telemedicine clinical strategies that centers equity in access, patient experience, and in quality and health outcomes for chronic disease management among safety-net populations.
Authors’ Contributions
F.M. and B.J.O. contributed to the study conception and design. A.B. contributed to the data acquisition. P.R.S. contributed to the formal analysis of the study data. F.M., P.R.S., B.J.O. contributed to data interpretation. F.M. and B.J.O. contributed to the drafting of the article. All authors contributed to the critical review and revision of the article. All authors approved of the final version of the article.
Disclosure Statement
No competing financial interests exist.
Funding Information
This publication was made possible by the Yale National Clinician Scholars Program and by CTSA Grant No. TL1 TR001864 from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NIH.
References
- 1. . How the Rapid Shift To Telehealth Leaves Many Community Health Centers Behind During the COVID-19 Pandemic. Health Affairs Forefront; 2020. Available from: https://www.healthaffairs.org/do/10.1377/forefront.20200529.449762/full/ [Last Accessed: June 20, 2022]. Google Scholar
- 2. . Analysis of teladoc use seems to indicate expanded access to care for patients without prior connection to a provider. Health Aff (Millwood) 2014;33(2):258–264. Crossref, Medline, Google Scholar
- 3. Urban telemedicine enables equity in access to acute illness care. Telemed J E Health 2017;23(2):105–112. Link, Google Scholar
- 4. Evaluation of the clinical outcomes of telehealth for managing diabetes: A PRISMA-compliant meta-analysis. Medicine (Baltimore) 2018;97(43):e12962. Crossref, Medline, Google Scholar
- 5. Telemental health care, an effective alternative to conventional mental care: A systematic review. Acta Inform Med 2017;25(4):240–246. Crossref, Medline, Google Scholar
- 6. Mapping the evidence on the effectiveness of telemedicine interventions in diabetes, dyslipidemia, and hypertension: An umbrella review of systematic reviews and meta-analyses. J Med Internet Res 2020;22(3):e16791. Crossref, Medline, Google Scholar
- 7. Patients’ satisfaction with and preference for telehealth visits. J Gen Intern Med 2016;31(3):269–275. Crossref, Medline, Google Scholar
- 8. Bridging the digital divide in health care: The role of health information technology in addressing racial and ethnic disparities. Jt Comm J Qual Patient Saf 2011;37(10):437–445. Medline, Google Scholar
- 9. Profiles of a health information-seeking population and the current digital divide: Cross-sectional analysis of the 2015–2016 California health interview survey. J Med Internet Res 2019;21(5):e11931–e11931. Crossref, Medline, Google Scholar
- 10. Disparities in telehealth use among California patients with limited English proficiency. Health Aff (Millwood) 2021;40(3):487–495. Crossref, Medline, Google Scholar
- 11.
U.S . Department of Health and Human Services. Policy Changes During COVID-19. Available from: https://telehealth.hhs.gov/providers/policy-changes-during-the-covid-19-public-health-emergency/ [Last Accessed: May 3, 2022]. Google Scholar - 12. Telehealth use among safety-net organizations in California during the COVID-19 pandemic. JAMA 2021;325(11):1106–1107. Crossref, Medline, Google Scholar
- 13. Telehealth: A Quarter-Trillion-Dollar Post-COVID-19 Reality? McKinsey & Company; 2021. Available from: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality [Last Accessed: June 27, 2022]. Google Scholar
- 14. Patient characteristics associated with telemedicine access for primary and specialty ambulatory care during the COVID-19 pandemic. JAMA Netw Open 2020;3(12):e2031640–e2031640. Crossref, Medline, Google Scholar
- 15. Differences in the use of telephone and video telemedicine visits during the COVID-19 pandemic. Am J Manag Care 2021;27(1):21–26. Crossref, Medline, Google Scholar
- 16. Patient-level factors associated with utilization of telemedicine services from a free clinic during COVID-19. Telemed J E Health 2022;28(4):526–534. Link, Google Scholar
- 17. Utilization gaps during the COVID-19 pandemic: Racial and ethnic disparities in telemedicine uptake in federally qualified health center clinics. J Gen Intern Med 2022;37(5):1191–1197. Crossref, Medline, Google Scholar
- 18. Medicare beneficiaries in disadvantaged neighborhoods increased telemedicine use during the COVID-19 pandemic. Health Aff (Millwood) 2022;41(5):635–642. Crossref, Medline, Google Scholar
- 19. Addressing Equity in Telemedicine for Chronic Disease Management During the COVID-19 Pandemic. NEJM Catalyst; 2020. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0123 [Last Accessed: June 20, 2022]. Google Scholar
- 20. . Reducing disparities in telemedicine: An equity-focused, public health approach. Health Aff (Millwood) 2022;41(5):647–650. Crossref, Medline, Google Scholar
- 21. . Health management via telemedicine: Learning from the COVID-19 experience. J Am Med Inform Assoc 2021;28(11):2536–2540. Crossref, Medline, Google Scholar
- 22. Use and content of primary care office-based vs telemedicine care visits during the COVID-19 pandemic in the US. JAMA Netw Open 2020;3(10):e2021476–e2021476. Crossref, Medline, Google Scholar
- 23. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff (Millwood) 2021;40(2):349–358. Crossref, Medline, Google Scholar
- 24. Achieving equity in telehealth: “Centering at the margins” in access, provision, and reimbursement. J Am Board Fam Med 2021;34(Suppl.):S29–S32. Crossref, Medline, Google Scholar
- 25.
Health Resources & Services Administration . Health Center Program Uniform Data System (UDS) Data Overview; 2022. Available from: https://data.hrsa.gov/tools/data-reporting/program-data?grantNum=H80CS00741 [Last Accessed: June 27, 2022]. Google Scholar - 26.
Emergency Orders Issued by the Governor and State Agencies; 2020 . Available from: https://portal.ct.gov/Coronavirus/Pages/Emergency-Orders-issued-by-the-Governor-and-State-Agencies [Last Accessed: June 27, 2022]. Google Scholar - 27. Care coordination/home telehealth: The systematic implementation of health informatics, home telehealth, and disease management to support the care of veteran patients with chronic conditions. Telemed J E Health 2008;14(10):1118–1126. Link, Google Scholar
- 28. Pulmonary telemedicine—A model to access the subspecialist services in underserved rural areas. Int J Med Inform 2009;78(1):53–59. Crossref, Medline, Google Scholar
- 29. . The benefit of telemedicine in obesity care. Obesity (Silver Spring) 2022;30(3):577–586. Crossref, Medline, Google Scholar
- 30. Telemedicine-delivered treatment interventions for substance use disorders: A systematic review. J Subst Abuse Treat 2019;101:38–49. Crossref, Medline, Google Scholar
- 31. Comorbidity measures for use with administrative data. Med Care 1998;36(1):8–27. Crossref, Medline, Google Scholar
- 32. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann Intern Med 2007;147(8):573–577. Crossref, Medline, Google Scholar
- 33. . The Evidence Base for Telehealth: Reassurance in the Face of Rapid Expansion During the COVID-19 Pandemic. Agency for Healthcare Research and Quality: Rockville, MD, USA; 2020. Available from: https://effectivehealthcare.ahrq.gov/products/telehealth-expansion/white-paper [Last Accessed: September 29, 2022]. Google Scholar
- 34. Effect of telemedicine on quality of care in patients with coexisting hypertension and diabetes: A systematic review and meta-analysis. Telemed J E Health 2021;27(6):603–614. Link, Google Scholar
- 35. . Treating patients with opioid use disorder in their homes: An emerging treatment model. JAMA 2020;324(1):39–40. Crossref, Medline, Google Scholar