This paper is in the following e-collection/theme issue:

Breast Cancer (206) Demographics of Users, Social & Digital Divide (739) Telehealth and Telemonitoring (1671) Teleoncology (21)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55438, first published .
Assessing the Relationship Between Neighborhood Socioeconomic Disadvantage and Telemedicine Use Among Patients With Breast Cancer and Examining Differential Provisions of Oncology Services Between Telehealth and In-Person Visits: Quantitative Study

Assessing the Relationship Between Neighborhood Socioeconomic Disadvantage and Telemedicine Use Among Patients With Breast Cancer and Examining Differential Provisions of Oncology Services Between Telehealth and In-Person Visits: Quantitative Study

Assessing the Relationship Between Neighborhood Socioeconomic Disadvantage and Telemedicine Use Among Patients With Breast Cancer and Examining Differential Provisions of Oncology Services Between Telehealth and In-Person Visits: Quantitative Study

Authors of this article:

Jincong Q Freeman1, 2, 3 Author Orcid Image ;   Fangyuan Zhao1 Author Orcid Image ;   Frederick M Howard4 Author Orcid Image ;   Rita Nanda4 Author Orcid Image ;   Olufunmilayo I Olopade4, 5 Author Orcid Image ;   Dezheng Huo1, 5 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Department of Public Health Sciences, University of Chicago, Chicago, IL, United States

    2Center for Health and the Social Sciences, University of Chicago, Chicago, IL, United States

    3Cancer Prevention and Control Program, UChicago Medicine Comprehensive Cancer Center, Chicago, IL, United States

    4Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States

    5Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, IL, United States

    Corresponding Author:

    Dezheng Huo, MD, PhD

    Department of Public Health Sciences

    University of Chicago

    MC2000

    5841 S Maryland Ave

    Chicago, IL, 60637

    United States

    Phone: 1 773 702 2453

    Fax:1 773 702 2453

    Email: dhuo@health.bsd.uchicago.edu


    Background: Since the COVID-19 pandemic began, we have seen rapid growth in telemedicine use. However, telehealth care and services are not equally distributed, and not all patients with breast cancer have equal access across US regions. There are notable gaps in existing literature regarding the influence of neighborhood-level socioeconomic status on telemedicine use in patients with breast cancer and oncology services offered through telehealth versus in-person visits.

    Objective: We assessed the relationship between neighborhood socioeconomic disadvantage and telemedicine use among patients with breast cancer and examined differential provisions of oncology services between telehealth and in-person visits.

    Methods: Neighborhood socioeconomic disadvantage was measured using the Area Deprivation Index (ADI), with higher scores indicating greater disadvantages. Telemedicine and in-person visits were defined as having had a telehealth and in-person visit with a provider, respectively, in the past 12 months. Multivariable logistic regression was performed to examine the association between ADI and telemedicine use. The McNemar test was used to assess match-paired data on types of oncology services comparing telehealth and in-person visits.

    Results: The mean age of the patients with breast cancer (n=1163) was 61.8 (SD 12.0) years; 4.58% (52/1161) identified as Asian, 19.72% (229/1161) as Black, 3.01% (35/1161) as Hispanic, and 72.78% (845/1161) as White. Overall, 35.96% (416/1157) had a telemedicine visit in the past 12 months. Of these patients, 65% (266/409) had a videoconference visit only, 22.7% (93/409) had a telephone visit only, and 12.2% (50/409) had visits by both videoconference and telephone. Higher ADI scores were associated with a lower likelihood of telemedicine use (adjusted odds ratio [AOR] 0.89, 95% CI 0.82-0.97). Black (AOR 2.38, 95% CI 1.41-4.00) and Hispanic (AOR 2.65, 95% CI 1.07-6.58) patients had greater odds of telemedicine use than White patients. Compared to patients with high school or less education, those with an associate’s degree (AOR 2.67, 95% CI 1.33-5.35), a bachelor’s degree (AOR 2.75, 95% CI 1.38-5.48), or a graduate or professional degree (AOR 2.57, 95% CI 1.31-5.04) had higher odds of telemedicine use in the past 12 months. There were no significant differences in providing treatment consultation (45/405, 11.1% vs 55/405, 13.6%; P=.32) or cancer genetic counseling (11/405, 2.7% vs 19/405, 4.7%; P=.14) between telehealth and in-person visits. Of the telemedicine users, 95.8% (390/407) reported being somewhat to extremely satisfied, and 61.8% (254/411) were likely or very likely to continue using telemedicine.

    Conclusions: In this study of a multiethnic cohort of patients with breast cancer, our findings suggest that neighborhood-level socioeconomic disparities exist in telemedicine use and that telehealth visits could be used to provide treatment consultation and cancer genetic counseling. Oncology programs should address these disparities and needs to improve care delivery and achieve telehealth equity for their patient populations.

    JMIR Cancer 2024;10:e55438

    doi:10.2196/55438

    Keywords

    telemedicine; telehealth equity; Area Deprivation Index; neighborhood socioeconomic disadvantage; disparities; oncology services; treatment consultation; genetic counseling; in-person visits; breast cancer; mobile phone 


    Background

    In the United States, telemedicine use has risen over the years. According to the 2019 American Hospital Association annual survey, the percentage of telehealth programs implemented across hospitals increased from 43.1% in 2015 to 61.2% in 2017 [1]. Since the COVID-19 pandemic began, we have seen rapid and unprecedented growth in the demand for, and use of, telemedicine. A recent report from the Centers for Disease Control and Prevention has documented that the frequency of telehealth visits increased by 50% from 2019 to 2020 [2]. The increase in the use of telemedicine is also observed in populations of patients with cancer; for example, several studies conducted during 2020 and 2021 estimated that the prevalence of telemedicine use ranges from 34.9% to 64.9% among patients with breast or other cancers [3-11]. In 2020, the Centers for Medicare & Medicaid Services introduced policies that offered regulatory waivers and flexible reimbursement to Medicaid and Medicare providers for telehealth, contributing in part to the observed increase in telemedicine use and implementation [12,13]. In 2021, the American Society of Clinical Oncology performed a systematic literature review on telemedicine and published standards and recommendations for telehealth services and practices in the oncology setting [14]. Telemedicine helps facilitate access to health care and services for patients with cancer and their caregivers or family members. However, telehealth care and services are not equally distributed, and not all patients with cancer have equal access to telehealth care and services across different US regions. There are notable gaps in existing literature regarding the influence of neighborhood-level socioeconomic status (SES) on telemedicine use in patients with breast cancer and oncology services offered through telehealth versus in-person visits.

    Neighborhood-level SES is a fundamental component of the social determinants of health framework [15,16]. Neighborhood socioeconomic disadvantage has been shown to negatively affect health outcomes [17,18], access to care and preventive services [19,20], survival outcomes [18,21], and quality of life [22] among patients with cancer [23]. Previous investigations have also found neighborhood socioeconomic disadvantage to be associated with a lower likelihood of telemedicine use among patients in primary care and hematology or oncology clinics, as well as among outpatients [8,24-28]. A study of 627 patients with cancer experiencing financial distress during the COVID-19 pandemic reported a 3% decrease in the rate of telemedicine use per 10-unit increase in the Area Deprivation Index (ADI) [8], a validated composite measure of neighborhood-level SES [29,30]. Fassas et al [27] conducted a univariate analysis of 64 patients with head and neck cancer, revealing no significant differential interest in telehealth visits based on the ADI. Another study noted a higher percentage of telehealth visits among patients residing in the least socioeconomically deprived neighborhoods (54%) than those in the most deprived neighborhoods (46.1%) in a large cohort of patients with hematologic malignancies and patients with cancer from Kaiser Permanente [28]. These prior studies either lacked significant sample sizes or included heterogeneous populations of patients with cancer. Therefore, these findings may not be generalizable to the population of patients with breast cancer.

    In addition, whether provisions or receipts of oncology services differ between telemedicine and in-person office visits among patients with breast cancer is unclear. A recent retrospective analysis of 311 patients with cancer indicated that clinical practices, such as molecular test ordering and palliative care referrals, conducted through telehealth visits achieve similar efficiency to in-person visits [31]. A pilot study of 45 patients with advanced cancer in Mexico has suggested the feasibility of supportive care delivery via telemedicine [32]. Multiple studies have found telehealth provisions or visits to be feasible, effective, and safe for patient follow-ups after ambulatory or breast surgeries [33-36]. Earlier research has also demonstrated that, when comparing telehealth to in-person visits, patients with cancer in the United States, Canada, and Europe reported similar communication experiences or satisfaction with the oncology care consultations they received [7,37,38]. Moreover, telemedicine-based cancer genetic counseling has been shown to be feasible and effective and to achieve high degrees of satisfaction among providers as well as patients with colorectal, breast, or gynecologic cancer residing in remote or rural areas [39-42]. Although prior studies have elucidated the successful implementation of telemedicine and shown that certain types of cancer care and services delivered through telemedicine are equivalent to in-person office settings in mixed cohorts of patients with cancer, the results may not be applicable to patients with breast cancer. Furthermore, most of these studies were not able to examine the distributions of oncology services comparing telehealth and in-person visits because of small sample sizes and therefore are primarily descriptive. Understanding these associations can help oncology programs identify telehealth disparities and patient’s unmet needs, improve telemedicine practice and care delivery, reduce health disparities and inequities, and provide optimal support to patients with breast cancer.

    Objectives

    To fill these critical gaps in the literature, we undertook this study primarily seeking to evaluate (1) the association between neighborhood socioeconomic disadvantage and telemedicine use and (2) the differences in provisions of oncology services comparing telehealth and in-person office visits. The secondary objectives of this study were to describe (1) common perceived challenges or concerns related to telehealth visits and (2) patient satisfaction with oncology services delivered via telemedicine in this cohort of patients with breast cancer.


    Study Design and Population

    This study used a cross-sectional design and analyzed data from patients with breast cancer enrolled in the ongoing Chicago Multiethnic Epidemiologic Breast Cancer Cohort (ChiMEC), which is a hospital-based cohort established at the University of Chicago Medicine in 1993 [43]. From July to September 2022, a total of 1868 questionnaires were sent to ChiMEC participants who consented to follow-up surveys, of whom 1236 (66.17%) responded. The study survey is provided in Multimedia Appendix 1. For this analysis, of the 1236 respondents, we included 1163 (94.09%) patients who reported having had either telemedicine or in-person visits in the past 12 months.

    Ethical Considerations

    The University of Chicago Institutional Review Board reviewed and approved this study (approval 16352A). All participants provided written informed consent before taking part in the ChiMEC study and follow-up surveys.

    Measures

    Neighborhood socioeconomic disadvantage was defined by the ADI, a composite measurement of patients’ neighborhood-level income, education, employment, and housing quality based on linked zip codes and US Census block groups [29,30]. For this study, participants’ residential addresses were geocoded to census block groups and then linked with the corresponding ADI national ranking percentile, which ranks neighborhoods by socioeconomic disadvantage at the national level in the United States. ADI scores range from 1 to 100, with higher scores reflecting higher levels of neighborhood socioeconomic deprivation. We further categorized ADI scores into quartiles. The first quartile represented the least socioeconomically deprived neighborhoods, whereas the fourth quartile represented the most deprived neighborhoods.

    Telemedicine use was defined as having had a telehealth visit with a physician or other health providers in any specialty in the past 12 months and dichotomized as yes or no. For patients who used telemedicine, we asked whether their visits were conducted through telephone, videoconferencing, or both. Similarly, in-person visits were assessed by asking participants whether they had had an in-person office visit with a physician or other health providers in the past 12 months. Furthermore, participants were asked whether their telemedicine or in-person visits were related to 6 different types of oncology services: treatment consultation; review of laboratory, screening, and pathology test results; management of cancer symptoms and treatment side effects; cancer genetic counseling; cancer clinical trial follow-up; and informed consent for a cancer clinical trial. Common cancer symptoms and treatment side effects discussed during telehealth or in-person visits were also assessed, including hot flashes; chemotherapy-induced neuropathy, nausea, and vomiting; pain related to cancer or cancer treatment; depressive symptoms or mood changes; fatigue or tiredness; anxiety or stress; lymphedema; and insomnia or sleep problems.

    In addition, we asked participants to report any challenges or concerns when using telemedicine, such as technology difficulty or lack of comfort with technology, lack of electronic device (eg, desktop computer, laptop computer, smartphone, or iPad), lack of high-speed internet or slow internet connection at home, compromised patient-provider communication, compromised patient-provider relationship, telemedicine not being offered at the clinic or by a provider, cost, and telemedicine not being covered by health insurance. We then asked how satisfied participants were with their telehealth or in-person visits, using a 5-point Likert scale (ie, not at all, a little, somewhat, very, and extremely satisfied). Participants were also asked how likely they were to continue using telemedicine, using another 5-point Likert scale (ie, very unlikely, unlikely, neutral, likely, and very likely).

    Individual-level sociodemographic and clinicopathologic characteristics included age at survey, race, ethnicity, highest level of education, marital status, type of health insurance coverage, duration from cancer diagnosis to survey, Charlson comorbidity index (excluding breast cancer diagnoses), histologic type, American Joint Committee on Cancer stage group, molecular subtype, tumor grade, receipt of cancer treatment (chemotherapy, hormone therapy, or radiotherapy), and type of surgery. We obtained patients’ clinicopathologic information from electronic health records and the hospital cancer registry. Distance from residence to hospital (in miles) was geocoded and calculated by taking the differences of coordinates (longitudes or latitudes) between the patient’s address and the University of Chicago Medicine Comprehensive Cancer Center’s address based on the Haversine formula.

    Statistical Analysis

    We described patients’ characteristics using summary statistics. Means and SDs or medians and IQRs were calculated for continuous variables, and we used 2-tailed t tests, Wilcoxon rank sum tests, or Kruskal-Wallis tests to conduct bivariate analyses. For nominal data, we tabulated frequencies and percentages and compared the distributions using Pearson chi-square or Fisher exact tests. To examine the association between neighborhood socioeconomic disadvantage (continuous ADI scores) and telemedicine use, we fitted 3 separate multivariable logistic regression models. For modeling, we implemented a stepwise regression approach. Potential confounders were selected and adjusted for in the models based on a P value of <.10 from bivariate analyses or a priori knowledge. Model 1 included ADI, age at survey, race, ethnicity, duration from cancer diagnosis to survey, highest level of education, marital status, type of health insurance coverage, Charlson comorbidity index, and distance from residence to hospital. Model 2 was controlled for histologic type, American Joint Committee on Cancer stage, molecular subtype, and tumor grade, in addition to the covariates in model 1. Model 3 contained all variables in model 2 plus receipt of chemotherapy, hormone therapy, or radiotherapy, as well as type of surgery. Adjusted odds ratios (AORs) and corresponding 95% CIs were calculated. To evaluate the differences in types of oncology services between telemedicine and in-person office visits, we conducted McNemar tests on match-paired data of patients having both visit modalities. P values (2-tailed) <.05 were considered statistically significant. All statistical analyses were performed using Stata 17 (StataCorp LLC).


    Patient Characteristics

    Overall, the 1868 study surveys received 1236 (66.17%) responses. Of the 1236 participants who responded, 1163 (94.09%) had had either telemedicine or in-person visits in the past 12 months. These participants’ mean age was 61.8 (SD 12.0) years; 4.48% (52/1161) identified as Asian, 19.72% (229/1161) as Black, 3.01% (35/1161) as Hispanic, and 72.78% (845/1161) as White. Furthermore, 69.94% (747/1068) were married, 38.73% (450/1162) had a graduate or professional degree, 70.77% (823/1163) were privately insured, and 22.96% (267/1163) were on Medicaid or Medicare. The median distance from residence to hospital was 19.9 (IQR 9.5-32.3) miles, and the median duration from cancer diagnosis to survey was 6.5 (IQR 3.6-11.0) years. By ADI quartile, patients with breast cancer living in the most socioeconomically disadvantaged neighborhoods (fourth quartile) tended to be older, Black, at a lower level of education, and on Medicaid or Medicare (Table 1).

    Table 1. Characteristics of patients with breast cancer overall and by neighborhood socioeconomic disadvantage (n=1163).
    VariableTotal (n=1163)Area Deprivation IndexaP valueb


    		First quartile (n=381)Second quartile (n=376)Third quartile (n=252)Fourth quartile (n=99)
    Age (y) at survey, mean (SD)61.8 (12.0)60.9 (11.5)61.6 (11.7)62.0 (12.9)64.2 (12.5).68
    Age (y) at survey, n (%).03

    		<45107 (10.2)33 (9.5)37 (10.8)26 (11.4)7 (7.8)

    		45-54179 (17)64 (18.4)59 (17.3)38 (16.7)12 (13.3)

    		55-64308 (29.2)116 (33.3)99 (28.9)58 (25.4)22 (24.4)

    		≥65460 (43.6)135 (38.8)147 (43)106 (46.5)49 (54.4)
    Race and ethnicity, n (%)<.001

    		Asian52 (4.5)26 (6.8)14 (3.7)6 (2.4)3 (3)

    		Black229 (19.7)16 (4.2)40 (10.7)98 (38.9)56 (56.6)

    		Hispanic35 (3)5 (1.3)20 (5.3)5 (2)4 (4)

    		White845 (72.8)333 (87.6)301 (80.3)143 (56.7)36 (36.4)
    Highest level of education, n (%)<.001

    		High school, GEDc, or less115 (9.9)12 (3.1)45 (12)37 (14.7)16 (16.2)

    		Associate’s degree or some college259 (22.3)52 (13.6)86 (22.9)70 (27.8)44 (44.4)

    		Bachelor’s degree338 (29.1)127 (33.3)102 (27.2)69 (27.4)20 (20.2)

    		Graduate or professional degree450 (38.7)190 (49.9)142 (37.9)76 (30.2)19 (19.2)
    Marital status, n (%)<.001

    		Married747 (69.9)282 (80.3)259 (73.4)136 (59.6)36 (40.4)

    		Single or not married192 (18)44 (12.5)53 (15)59 (25.9)30 (33.7)

    		Divorced, separated, or widowed129 (12.1)25 (7.1)41 (11.6)33 (14.5)23 (25.8)
    Type of health insurance, n (%)<.001

    		Private823 (70.8)302 (79.3)276 (73.4)162 (64.3)49 (49.5)

    		Medicaid50 (4.3)5 (1.3)8 (2.1)17 (6.7)15 (15.2)

    		Medicare217 (18.7)54 (14.2)74 (19.7)55 (21.8)24 (24.2)

    		Other or unknown73 (6.3)20 (5.2)18 (4.8)18 (7.1)11 (11.1)
    Distance from residence to hospital (miles)d, median (IQR)19.9 (9.5-32.3)20.5 (10.9-31.9)22.5 (13.3-33.2)16.4 (4.6-30.5)11.9 (3.3-27.6)<.001
    Duration (y) from cancer diagnosis to survey, median (IQR)6.5 (3.6-11.0)6.8 (3.7-10.9)6.2 (3.6-10.3)6.5 (3.6-11.5)8.3 (4.2-11.6).61
    Duration (y) from cancer diagnosis to survey, n (%).63

    		≤3199 (17.1)58 (15.2)68 (18.1)48 (19)14 (14.1)

    		4-6319 (27.4)107 (28.1)107 (28.5)67 (26.6)23 (23.2)

    		≥7645 (55.5)216 (56.7)201 (53.5)137 (54.4)62 (62.6)
    Charlson comorbidity index, n (%).03

    		0994 (88.5)333 (90.5)335 (91.5)209 (85)77 (82.8)

    		162 (5.6)19 (5.2)11 (3)21 (8.5)6 (6.5)

    		≥267 (6.0)16 (4.3)20 (5.5)16 (6.5)10 (10.8)
    Histologic type, n (%).08

    		Ductal742 (80.2)247 (77.9)238 (79.9)159 (81.1)64 (88.9)

    		Lobular92 (10)38 (12)34 (11.4)15 (7.7)1 (1.4)

    		Ductal and lobular55 (6)19 (6)18 (6)12 (6.1)3 (4.2)

    		Other36 (3.9)13 (4.1)8 (2.7)10 (5.1)4 (5.6)
    AJCCe stage group, n (%).002

    		0200 (18.1)51 (14.2)69 (19)51 (21.1)21 (22.6)

    		I515 (46.5)189 (52.5)160 (44.1)104 (43)36 (38.7)

    		II271 (24.5)88 (24.4)91 (25.1)58 (24)24 (25.8)

    		III112 (10.1)31 (8.6)42 (11.6)24 (9.9)10 (10.8)

    		IV10 (0.9)1 (0.3)1 (0.3)5 (2.1)2 (2.2)
    Molecular subtype, n (%).06

    		HRf+/HER2g−571 (66.2)208 (69.3)180 (66.2)120 (65.6)35 (53.8)

    		HR+/HER+98 (11.4)34 (11.3)36 (13.2)15 (8.2)8 (12.3)

    		HR−/HER2+51 (5.9)12 (4)17 (6.2)19 (10.4)3 (4.6)

    		TNBCh142 (16.5)46 (15.3)39 (14.3)29 (15.8)19 (29.2)
    Tumor grade, n (%).047

    		1149 (14.3)59 (17.3)47 (13.8)27 (11.9)9 (10.3)

    		2471 (45.3)159 (46.6)146 (42.9)99 (43.8)42 (48.3)

    		3420 (40.4)123 (36.1)147 (43.2)100 (44.2)36 (41.4)
    Receipt of chemotherapy, n (%).92

    		No572 (54.3)190 (54.6)182 (53.2)125 (54.8)48 (53.3)

    		Yes482 (45.7)158 (45.4)160 (46.8)103 (45.2)42 (46.7)
    Receipt of hormone therapy, n (%).03

    		No341 (32.4)100 (28.7)113 (33)74 (32.5)39 (43.3)

    		Yes713 (67.7)248 (71.3)229 (67)154 (67.5)51 (56.7)
    Receipt of radiation therapy, n (%).08

    		No394 (37.4)140 (40.2)125 (36.5)83 (36.4)26 (28.9)

    		Yes660 (62.6)208 (59.8)217 (63.5)145 (63.6)64 (71.1)
    Type of surgery received, n (%).006

    		None13 (1.3)5 (1.5)3 (0.9)2 (0.9)3 (3.4)

    		Lumpectomy615 (59.3)185 (53.8)194 (57.4)146 (66.1)61 (68.5)

    		Mastectomy307 (29.6)116 (33.7)107 (31.7)50 (22.6)25 (28.1)

    		Bilateral mastectomy102 (9.9)38 (11.0)34 (10.1)23 (10.4)0 (0)

    aThe Area Deprivation Index (national ranking percentile) is a composite measure consisting of the domains of income, education, employment, and housing quality. It ranks neighborhoods by socioeconomic disadvantage at the national level and is scored from 1 to 100, with higher scores representing greater neighborhood socioeconomic deprivation.

    bP values were calculated using Kruskal-Wallis tests.

    cGED: General Educational Development Test.

    dDistance from residence to hospital was calculated by taking the differences of coordinates (longitudes or latitudes) between the patient’s address and the University of Chicago Medicine Comprehensive Cancer Center’s address based on the Haversine formula.

    eAJCC: American Joint Committee on Cancer.

    fHR: hormone receptor.

    gHER2: human epidermal growth factor receptor 2.

    hTNBC: triple-negative breast cancer.

    Telemedicine Use and Association With ADI

    Overall, 35.95% (416/1157) of the patients with breast cancer had a telehealth visit in the past 12 months (Table 2). By modality of telemedicine, 65% (266/409) of the clinic visits were conducted through videoconferencing only, followed by 22.7% (93/409) through telephone only and 12.2% (50/409) through both videoconferencing and telephone. The mean ADI score for telemedicine users was 37.7 (SD 24.2) compared to 39.5 (SD 24.0) for nonusers (Table 2). By ADI quartile, 38.3% (145/379) of the patients living in the least socioeconomically disadvantaged neighborhoods (first quartile) used telemedicine, followed by 37.9% (58/153), 35.1% (132/356), and 32.5% (81/249) in the fourth, second, and third quartiles, respectively. On multivariable regression analysis (model 3), higher ADI scores (per 10-unit increase) were associated with lower odds of telemedicine use (AOR 0.89, 95% CI 0.82-0.97; Table 3).

    Table 2. Characteristics of patients with breast cancer by telehealth visit (n=1157).
    VariableHad a telehealth visit in the past 12 monthsP valuea

    		No (n=741)Yes (n=416)

    Modality of telemedicine (n=409), n (%)—b

    		Telephone or audio call—93 (22.7)

    		Videoconference—266 (65)

    		Both—50 (12.3)
    Area Deprivation Indexc, mean (SD)39.5 (24.0)37.7 (24.2).18
    Area Deprivation Index, n (%).13

    		First quartile234 (61.7)145 (38.3)

    		Second quartile224 (64.9)132 (35.1)

    		Third quartile168 (67.5)81 (32.5)

    		Fourth quartile95 (62.1)58 (37.9)
    Age (y) at survey, mean (SD)62.2 (11.9)60.9 (12.2).09
    Age (y) at survey, n (%).04

    		<4556 (52.3)51 (47.7)

    		45-54121 (67.6)58 (32.4)

    		55-64201 (66.3)102 (33.7)

    		≥65299 (65.1)160 (34.9)
    Race and ethnicity, n (%).08

    		Asian37 (71.2)15 (28.9)

    		Black136 (60.2)90 (39.8)

    		Hispanic17 (48.6)18 (51.4)

    		White550 (65.3)292 (34.7)
    Highest level of education, n (%).002

    		High school, GEDd, or less92 (80)23 (20)

    		Associate’s degree or some college163 (63.4)94 (36.6)

    		Bachelor’s degree208 (61.9)128 (38.1)

    		Graduate or professional degree277 (61.8)171 (38.2)
    Marital status, n (%).70

    		Married481 (64.7)263 (35.4)

    		Single or not married116 (61.4)73 (38.6)

    		Divorced, separated, or widowed83 (64.3)46 (35.7)
    Type of health insurance, n (%).25

    		Private515 (63)302 (37)

    		Medicaid28 (56)22 (44)

    		Medicare147 (67.7)70 (32.3)

    		Other or unknown51 (69.9)22 (30.1)
    Distance (miles) from residence to hospitale, median (IQR)19.9 (9.8-32.3)20.4 (9.3-32.3).96
    Duration (y) from cancer diagnosis to survey, median (IQR)6.8 (3.7-0.9)6.3 (3.5-11.0).22
    Duration (years) from cancer diagnosis to survey, n (%).009

    		≤3109 (55)89 (45)

    		4-6217 (68)102 (32)

    		≥7415 (64.8)225 (35.2)
    Charlson comorbidity index, n (%).31

    		0635 (64.3)353 (35.7)

    		134 (54.8)28 (45.2)

    		≥244 (65.7)23 (34.3)
    Histologic type, n (%).27

    		Ductal459 (62.1)280 (37.9)

    		Lobular63 (68.5)29 (31.5)

    		Ductal and lobular38 (69.1)17 (30.9)

    		Other19 (52.8))17 (47.2)
    AJCCf stage group, n (%).26

    		0135 (68.5)62 (31.5)

    		I333 (64.9)180 (35.1)

    		II161 (59.6)109 (40.4)

    		III75 (61.5)47 (38.5)

    		IV5 (50)5 (50)
    Molecular subtype, n (%).91

    		HRg+/HER2h−358 (62.8)212 (37.2)

    		HR+/HER+64 (65.3)34 (34.7)

    		HR−/HER2+34 (66.7)17 (33.3)

    		TNBCi87 (62.1)53 (37.9)
    Tumor grade, n (%).10

    		187 (59.6)59 (40.4)

    		2316 (67.4)153 (32.6)

    		3258 (61.6)161 (38.4)
    Receipt of chemotherapy, n (%).19

    		No377 (66.4)191 (33.6)

    		Yes300 (62.5)180 (37.5)
    Receipt of hormone therapy, n (%).92

    		No217 (64.4)120 (35.6)

    		Yes460 (64.7)251 (35.3)
    Receipt of radiation therapy, n (%).68

    		No255 (65.4)135 (34.6)

    		Yes422 (64.1)236 (35.9)
    Type of surgery received, n (%).35

    		None6 (46.2)7 (53.8)

    		Lumpectomy404 (66)208 (34)

    		Mastectomy190 (62.5)114 (37.5)

    		Bilateral mastectomy68 (66.7)34 (33.3)

    aP values were calculated using 2-tailed t tests or Wilcoxon rank sum, Pearson chi-square, or Fisher exact tests, as appropriate.

    bNot applicable.

    cThe Area Deprivation Index (national ranking percentile) is a composite measure consisting of the domains of income, education, employment, and housing quality. It ranks neighborhoods by socioeconomic disadvantage at the national level and is scored from 1 to 100, with higher scores representing greater neighborhood socioeconomic deprivation.

    dGED: General Educational Development Test.

    eDistance from residence to hospital was calculated by taking the differences of coordinates (longitudes or latitudes) between the patient’s address and the University of Chicago Medicine Comprehensive Cancer Center’s address based on the Haversine formula.

    fAJCC: American Joint Committee on Cancer.

    gHR: hormone receptor.

    hHER2: human epidermal growth factor receptor 2.

    iTNBC: triple-negative breast cancer.

    In the same model (model 3), patients with breast cancer aged 45 to 54 years had lower odds of having a telehealth visit than those aged <45 years (AOR 0.49, 95% CI 0.27-0.91). Patients aged 55 to 64 years (AOR 0.63, 95% CI 0.36-1.12) or ≥65 years (AOR 0.63, 95% CI 0.34-1.18) also had a lower likelihood, but these differences were not statistically significant. Black (AOR 2.38, 95% CI 1.41-4.00) or Hispanic (AOR 2.65, 95% CI 1.07-6.58) patients had greater odds of telemedicine use than White patients. Compared to patients with high school or less education, those with an associate’s (AOR 2.67, 95% CI 1.33-5.35), bachelor’s (AOR 2.75, 95% CI 1.38-5.48), or graduate (AOR 2.57, 95% CI 1.31-5.04) degree had higher odds of telemedicine use in the past 12 months. Longer distance from residence to hospital (per 10-mile increase) was associated with greater odds of use of telemedicine, although this was not statistically significant (AOR 1.02, 95% CI 0.96-1.09; Table 3). Clinicopathologic and treatment factors were not significantly associated with telemedicine use (Table S1 in Multimedia Appendix 2). In subgroup analyses, ADI scores were not significantly different between videoconference and telephone visits (AOR 0.88, 95% CI 0.73-1.07). We also observed that patients with a graduate or professional degree had greater odds of using videoconference visits (AOR 5.78, 95% CI 1.03-32.55), and patients on Medicare had lower odds of videoconference visit use than privately insured patients (AOR 0.26, 95% CI 0.07-0.91; Table S2 in Multimedia Appendix 2).

    Table 3. Association between neighborhood socioeconomic disadvantage and telemedicine use in patients with breast cancer.
    VariableModel 1, adjusted odds ratioa (95% CI)P valueModel 2, adjusted odds ratiob (95% CI)P valueModel 3, adjusted odds ratioc (95% CI)P value
    Area Deprivation Indexd (continuous)e0.93 (0.87-0.99).030.89 (0.82-0.96).0050.89 (0.82-0.97).004
    Distance from residence to hospitale1.04 (0.99-1.10).131.03 (0.97-1.09).401.02 (0.96-1.09).48
    Age (y) at survey

    		<451.0 (reference)
    		1.0 (reference)
    		1.0 (reference)

    		45-540.55 (0.33-0.94).030.53 (0.29-0.97).040.49 (0.27-0.91).02

    		55-640.57 (0.35-0.93).020.64 (0.37-1.11).110.63 (0.36-1.12).11

    		≥650.65 (0.39-1.09).100.62 (0.34-1.13).120.63 (0.34-1.18).16
    Race and ethnicity

    		Asian0.55 (0.26-1.17).120.50 (0.20-1.22).130.50 (0.20-1.23).16

    		Black1.86 (1.21-2.86).0052.50 (1.48-4.20).0012.38 (1.41-4.00).001

    		Hispanic2.12 (1.02-4.41).042.85 (1.17-6.91).022.65 (1.07-6.58).03

    		White1.0 (reference)
    		1.0 (reference)
    		1.0 (reference)
    Highest level of education

    		High school, GEDf, or less1.0 (reference)
    		1.0 (reference)
    		1.0 (reference)

    		Associate’s degree or some college2.66 (1.47-4.81).0012.76 (1.40-5.44).0032.67 (1.33-5.35).006

    		Bachelor’s degree2.43 (1.35-4.38).0032.61 (1.33-5.10).0052.75 (1.38-5.48).004

    		Graduate or professional degree2.46 (1.39-4.38).0022.55 (1.32-4.93).0052.57 (1.31-5.04).006
    Duration (y) from cancer diagnosis to survey

    		≤31.0 (reference)
    		1.0 (reference)
    		1.0 (reference)

    		4-60.63 (0.41-0.96).030.67 (0.42-1.08).120.75 (0.46-1.21).24

    		≥70.67 (0.45-1.01).050.60 (0.38-0.96).040.65 (0.40-1.05).09

    aAdditionally adjusted for marital status, health insurance, and Charlson comorbidity index.

    bAdditionally adjusted for marital status, health insurance, Charlson comorbidity index, histologic type, stage, molecular subtype, and tumor grade.

    cAdditionally adjusted for marital status; type of health insurance; Charlson comorbidity index; histologic type; stage; molecular subtype; tumor grade; receipt of chemotherapy, hormone therapy, or radiotherapy; and type of surgery.

    dThe Area Deprivation Index (national ranking percentile) is a composite measure consisting of the domains of income, education, employment, and housing quality. It ranks neighborhoods by socioeconomic disadvantage at the national level and is scored from 1 to 100, with higher scores representing greater neighborhood socioeconomic deprivation.

    eOdds ratios were per 10-unit increase.

    fGED: General Educational Development Test.

    Comparisons of Provisions of Oncology Services Between Telehealth and In-Person Visits

    Figure 1 displays the breakdown of oncology services by visit type for the patients with breast cancer. Overall, 31.3% (130/416) of the patients used telemedicine for the purpose of treatment consultation; 22.4% (93/416) for reviewing laboratory, screening, and pathology test results; 13.5% (56/416) for managing cancer symptoms and treatment side effects; 4.3% (18/416) for cancer genetic counseling; and 3.4% (14/416) for cancer clinical trial follow-ups. Among patients who had in-person visits, reviewing laboratory, screening, and pathology test results was reported the most (322/1072, 30.04%), followed by treatment consultation (265/1072, 24.72%), management of cancer symptoms and treatment side effects (169/1072, 15.76%), genetic counseling (54/1072, 5.04%), and cancer clinical trial follow-ups (54/1072, 5.04%). After analyzing match-paired data (Table 4), we observed significant differences between telemedicine and in-person visits in the provision of management of cancer symptoms and treatment side effects; review of laboratory, screening, and pathology test results; and cancer clinical trial follow-ups. However, there were no significant differences in offering treatment consultation (45/405, 11.1% vs 55/405, 13.6%; P=.32) or cancer genetic counseling (11/405, 2.7% vs 19/405, 4.7%; P=.14) between telehealth and in-person visits (Table 4).

    Among the patients with breast cancer who reported management of cancer symptoms and treatment side effects (Figure 2), those with in-person visits had greater proportions of instances of discussions of fatigue (85/169, 50.3% vs 23/56, 41%), hot flashes (77/169, 45.6% vs 19/56, 34%), lymphedema (44/169, 26% vs 11/56, 20%), chemotherapy-induced neuropathy (42/169, 24.9% vs 11/56, 20%), or nausea and vomiting (27/169, 16% vs 5/56, 9%) than patients with telehealth visits, whereas a higher proportion of patients had discussed depressive symptoms through telemedicine than in-person visits (21/56, 38% vs 50/169, 29.6%). By modality of telemedicine, a higher percentage of patients used both telephone and video visits for treatment consultation than video visit or telephone visit alone (Table S3 in Multimedia Appendix 2). In addition, there were no significant differences in the distributions of various methods of managing cancer symptoms and treatment side effects across the 3 telemedicine modalities (Table S4 in Multimedia Appendix 2).

    ‎
    Figure 1. Percentages of oncology services by type of visit among patients with breast cancer.
    Table 4. Analysis of match-paired data on oncology services by type of visit among patients with breast cancer (n=405).
    Type of visit and variableTelemedicine visits, n (%)P valuea

    		NoYes

    In-person visits, n (%)

    		Treatment consultation.32


    		No222 (54.8)45 (11.1)


    		Yes55 (13.6)83 (20.5)

    		Management of treatment side effects or cancer symptoms<.001


    		No295 (72.8)15 (3.7)


    		Yes54 (13.3)41 (10.1)

    		Review of laboratory, screening, and pathology test results<.001


    		No224 (55.3)26 (6.4)


    		Yes90 (22.2)65 (16)

    		Cancer genetic counseling.14


    		No368 (90.9)11 (2.7)


    		Yes19 (4.7)7 (1.7)

    		Cancer clinical trial follow-up visits.046


    		No379 (93.6)4 (1)


    		Yes12 (3)10 (2.5)

    aP values were calculated using the McNemar test.

    ‎
    Figure 2. Percentages of cancer symptoms and treatment side effects discussed during telemedicine or in-person visits reported by patients with breast cancer.

    Telemedicine Use Challenges or Concerns and Satisfaction

    Compared with the patients with breast cancer who did not use telemedicine, those who did reported a higher percentage of technology difficulty or lack of comfort with technology (51/416, 12.3% vs 21/741, 2.8%; P<.001), compromised patient-provider communication (28/416, 6.7% vs 16/741, 2.2%; P<.001), and compromised patient-provider relationship (35/416, 8.4% vs 12/741, 1.6%; P<.001; Table 5). Furthermore, of the telemedicine users, 93.7% (132/141) and 95.5% (301/315) reported being somewhat to extremely satisfied with their telephone visit and videoconference visit experiences, respectively, and 61.8% (254/411) were likely or very likely to continue using telemedicine (Table S5 in Multimedia Appendix 2). In addition, no significant differential distributions of satisfaction with telehealth visits across all oncology services were observed, stratified by modality of telemedicine (Tables 6 and 7) or overall (Table S6 in Multimedia Appendix 2).

    Table 5. Percentages of challenges or concerns regarding telemedicine use reported by patients with breast cancer (n=1157).
    VariablesOverall (n=1157), n (%)Had a telehealth visit in the past 12 monthsP valuea


    		No (n=741), n (%)Yes (n=416), n (%)

    Technology difficulty or lack of comfort with technology<.001

    		No1085 (93.8)720 (97.2)365 (87.7)

    		Yes72 (6.2)21 (2.8)51 (12.3)
    Lack of an electronic device (eg, desktop computer, laptop computer, smartphone, or iPad).20

    		No1151 (99.5)739 (99.7)412 (99)

    		Yes6 (0.5)2 (0.3)4 (1)
    Lack of high-speed internet or slow internet connection at home.06

    		No1133 (97.9)730 (98.5)403 (96.9)

    		Yes24 (2.1)11 (1.5)13 (3.1)
    Compromised patient-provider communication<.001

    		No1113 (96.2)725 (97.8)388 (93.3)

    		Yes44 (3.8)16 (2.2)28 (6.7)
    Compromised patient-provider relationship<.001

    		No1110 (95.9)729 (98.4)381 (91.6)

    		Yes47 (4.1)12 (1.6)35 (8.4)
    Not being offered at the clinic or by a provider.80

    		No1140 (98.5)729 (98.4)411 (98.8)

    		Yes17 (1.5)12 (1.6)5 (1.2)
    Cost.30

    		No1148 (99.2)737 (99.5)411 (98.8)

    		Yes9 (0.8)4 (0.5)5 (1.2)
    Not being covered by health insurance.22

    		No1146 (99)736 (99.3)410 (98.6)

    		Yes11 (1)5 (0.7)6 (1.4)

    aP values were calculated using Pearson chi-square or Fisher exact tests.

    Table 6. Percentages of satisfaction with telephone visit by oncology service among patients with breast cancer (n=141).
    Were your telemedicine visits related to the following oncology services?How satisfied were you with your telephone visit with physicians or other health care providers?P valuea

    		Not at all (n=2), n (%)A little (n=7), n (%)Somewhat (n=28), n (%)Very (n=62), n (%)Extremely (n=42), n (%)

    Treatment consultation.70

    		No1 (1)5 (5)20 (19.8)44 (43.6)31 (30.7)

    		Yes1 (2)2 (5)8 (20)18 (45)11 (27.5)
    Review of laboratory, screening, and pathology test results.92

    		No1 (1.0)3 (3.0)25 (24.8)41 (40.6)31 (30.7)

    		Yes1 (2.5)4 (10.0)3 (7.5)21 (52.5)11 (27.5)
    Management of treatment side effects or cancer symptoms.22

    		No1 (0.8)7 (5.7)24 (19.5)51 (41.5)40 (32.5)

    		Yes1 (5.6)0 (0)4 (22.2)11 (61.1)2 (11.1)
    Cancer genetic counseling.58

    		No2 (1.5)7 (5.3)26 (19.6)57 (42.9)41 (30.8)

    		Yes0 (0)0 (0)2 (25)5 (62.5)1 (12.5)
    Cancer clinical trial follow-up visits.95

    		No2 (1.4)7 (5.0)28 (20.0)61 (43.6)42 (300)

    		Yes0 (0)0 (0)0 (0)1 (100)0 (0)

    aP values were calculated using Wilcoxon rank sum tests.

    Table 7. Percentages of satisfaction with videoconference visit by oncology service among patients with breast cancer (n=315).
    Were your telemedicine visits related to the following oncology services?How satisfied were you with your videoconference visit with physicians or other health care providers?P valuea

    		Not at all (n=8), n (%)A little (n=6), n (%)Somewhat (n=56), n (%)Very (n=157), n (%)Extremely (n=88), n (%)

    Treatment consultation.78

    		No4 (2)5 (2.5)36 (17.7)104 (51)55 (27)

    		Yes4 (3.6)1 (0.9)20 (18)53 (47.8)33 (29.7)
    Review of laboratory, screening, and pathology test results.77

    		No6 (2.4)4 (1.6)47 (19.1)117 (47.6)72 (29.3)

    		Yes2 (2.9)2 (2.9)9 (13)40 (58)16 (23.2)
    Management of treatment side effects or cancer symptoms.20

    		No8 (3)6 (2.2)50 (18.5)132 (48.9)74 (27.4)

    		Yes0 (0)0 (0)6 (13.3)25 (55.6)14 (31.1)
    Cancer genetic counseling.98

    		No7 (2.3)6 (2)54 (17.9)150 (49.8)84 (27.9)

    		Yes1 (7.1)0 (0)2 (14.3)7 (50)4 (28.6)
    Cancer clinical trial follow-up visits.48

    		No8 (2.7)6 (2)54 (17.9)146 (48.5)87 (28.9)

    		Yes0 (0)0 (0)2 (14.3)11 (78.6)1 (7.1)

    aP values were calculated using Wilcoxon rank sum tests.


    Principal Findings

    This study built on previous work investigating the relationship between neighborhood socioeconomic disadvantage and telemedicine use among patients with breast cancer and comparing provisions of oncology services between telehealth and in-person office visits. Telemedicine use has expanded dramatically since the COVID-19 pandemic began. However, not all patients with breast cancer benefited from the expansion; as our study uncovered, patients with higher ADI scores (ie, lower neighborhood-level SES) were less likely to have used telemedicine in the past 12 months. Although patients cited technology difficulty or lack of comfort with technology as well as compromised patient-provider communication and compromised patient-provider relationship as common challenges or concerns, they expressed high degrees of satisfaction with telehealth experiences and willingness to continue using telehealth care and services in the future. In addition, both telemedicine and in-person visits were equally likely to deliver treatment consultation and cancer genetic counseling, while services that were more likely to be offered in person were management of cancer symptoms and treatment side effects; review of laboratory, screening, and pathology test results; and cancer clinical trial follow-ups.

    One key study finding is that patients with breast cancer living in greater socioeconomically disadvantaged neighborhoods were less likely to use telemedicine for care and services, consistent with previous studies of patients in primary care, adult outpatient, and hematology or oncology settings [8,24-28]. A descriptive study suggested no correlation between ADI scores and interest in telehealth visits among 64 patients with head and neck cancers (interested: median ADI score=4.0 vs not interested: median ADI score=5.0; P=.79) [27], but the small sample size limited the reliability of these findings. Lee et al [28] analyzed a cohort of 341,089 patients with hematologic malignancies and patients with cancer, demonstrating a significant difference in the distribution of telemedicine use between patients living in the most socioeconomically disadvantaged neighborhoods and those living in the least socioeconomically disadvantaged neighborhoods (46.1% vs 54%) [28]. However, the proportion of patients with breast cancer as well as adjusted estimates are unknown due to the descriptive nature of this analysis. In another study, Hassan et al [8] observed a 3% decrease in the number of telemedicine visits per 10-unit increase in the ADI score among patients with cancer who were financially distressed, whereas an 11% decrease in telemedicine use was observed in our cohort of patients with breast cancer. Our estimated effect is larger possibly due to our large sample size and the heterogeneous group of patients with breast cancer. Our findings indicate that neighborhood-level SES may have a bigger impact on the use of telemedicine among patients with breast cancer. Neighborhood socioeconomic disadvantage is associated with lower access to telehealth care and services. Strategies to enhance the accessibility of telehealth are needed to reduce neighborhood-level socioeconomic disparities in telemedicine use among patients with breast cancer, particularly among patients living in socioeconomically deprived neighborhoods and regions.

    Consistent with prior research in patients with breast, gynecologic, or other cancers [3,4,11], we found that >1 in 3 patients with breast cancer had had a telehealth visit in the past 12 months. With respect to race and ethnicity, Black or Hispanic patients were more than twice as likely as their White counterparts to have used telemedicine. In previous studies, Campos-Castillo and Anthony [44] observed that Black or Latinx American adults were more likely to have telehealth visits, and Reed et al [45] documented a higher likelihood of patients using both telephone and video visits for primary care. However, a study at an outpatient oncology care center revealed that Asian or Hispanic patients were less likely than White patients to have used telemedicine, while no significant difference existed between Black and White patients [11]. These inconsistent results are possibly due to sampling variability and the heterogeneous characteristics of patient populations in oncology and other clinical settings. Nevertheless, our finding indicates that patients belonging to racial and ethnic minority groups with breast cancer may have unique and greater needs for telehealth. Similarly, compared to patients with high school or less education, those with an associate’s, bachelor’s, or graduate degree had >2-fold greater odds of telemedicine use. Older age groups were associated with a lower likelihood of telemedicine use. These findings are well documented in the literature and highlight the influence of individual-level SES on the use of telemedicine. Telemedicine may have the potential to improve telehealth care and service use among patients belonging to racial and ethnic minority groups with breast cancer. Oncology programs should also ensure that patients who are older and those with lower levels of education have equal access to telehealth care and services.

    Another notable finding is that patient-reported treatment consultations and cancer genetic counseling services occur with an equal frequency when comparing telemedicine and in-person office visits. Our study supports existing literature on the feasibility and acceptability of teleoncology consultations and telehealth-based cancer genetic counseling among providers and their patients with cancer [7,31-42]. However, only a few prior studies compared these oncology services between telemedicine and in-person visits, and most samples were small. A univariate analysis of 509 patients with cancer from the University of Texas MD Anderson Cancer Center reported a similar distribution of patients seeking integrative oncology consultation between telehealth and in-person settings [37]. McDonald et al [46] illustrated no significant differences in providing cancer genetic services (in-person vs telegenetics) by residential area or perceived cancer risk. We also observed that patients were particularly more likely to join treatment consultations through videoconference than telephone. However, we found significant variations between telehealth and in-person office visits in provisions of management of cancer symptoms and treatment side effects; review of laboratory, screening, and pathology test results; and cancer clinical trial follow-ups. In the subgroup of patients with breast cancer who reported management of cancer symptoms and treatment side effects, more than one-third of the patients discussed depressive symptoms or mood change (23/56, 41%) or anxiety or stress (20/56, 36%) during telehealth visits with their providers. This finding indicates that telemedicine may be in greater need among these patients. Taken together, our data demonstrate the variations in provisions of oncology services between telemedicine and in-person visits among patients with breast cancer. Cancer programs and providers may consider tailoring the delivery of care and services according to patients’ care needs and preferences. Future research will be necessary to explore reasons or factors that explain these variations and whether there are unmet telehealth needs in patients with breast cancer experiencing certain mental health symptoms.

    With regard to challenges or concerns related to telemedicine use, we found that significantly higher proportions of the patients with breast cancer who had telehealth visits cited technology difficulty or lack of comfort with technology, compromised patient-provider communication, or compromised patient-provider relationship. Possibly, the telemedicine nonusers in our study did not have firsthand experience of technology difficulty. Our finding is aligned with previous research [27] but not with 2 cross-sectional studies [9,38] that reported similar patient-provider communication experiences when comparing telehealth and in-person visits. Analyses are further needed to determine the correlations between telemedicine use and these challenges or concerns among patients with breast cancer. Despite these challenges or concerns, patients reported a remarkably high level of satisfaction with their telemedicine use experiences (390/407, 95.8%), and 61.8% (254/411) expressed the willingness to continue telemedicine use, congruent with prior studies in patients with cancer [6,7,10,33,34]. However, prior research evaluated only overall satisfaction, whereas we found a similar level of satisfaction by type of oncology service or across various cancer symptoms and treatment side effects discussed during telehealth visits, irrespective of the modality of telemedicine. These findings offer a practical implication for telehealth implementation and care and service delivery, but future research in telemedicine program evaluation is necessary.

    Limitations

    Several limitations of this study should be considered. First, the survey data were per self-report, which is prone to recall error or social desirability. However, this bias is likely very minimal because our research staff had limited to no interaction with the participants that would influence the survey responses. Second, the study sample included only patients with breast cancer who were willing to respond to our survey. The proportions of self-reported visits for oncology services and perceived challenges or concerns related to telemedicine use have probably been underestimated. Our estimated effect of neighborhood socioeconomic disadvantage on telehealth use in patients with breast cancer may also be underestimated. Third, this study evaluated broader access to telehealth care and services, including oncology; therefore, the results may not be specific to breast oncology. However, it is important to point out that, regardless of treatment status and duration since diagnosis, patients have other care and service needs across the cancer care continuum and different clinical settings. Fourth, distance from residence to hospital was not associated with telemedicine use. It was calculated based on the Haversine formula, which did not account for travel time, traffic patterns, lack of transportation, road conditions, weather, and other environmental factors. In addition, we were not able to assess other unmeasured potential confounding factors, such as the availability and density of telehealth clinics or cancer programs in the geographic area and local technology or digital infrastructure, that could affect the associations or variability we observed in this analysis. Thus, this warrants future research. Finally, participants in the ChiMEC study may not be representative of all patients with breast cancer nationally, which limits the generalizability of our findings.

    Conclusions

    In conclusion, our findings from ChiMEC patients with breast cancer offer insights into the impact of neighborhood socioeconomic disadvantage on telemedicine use and comparing provisions of oncology services between telehealth and in-person office visits, underscoring the importance of identifying neighborhood-level socioeconomic disparities and patients’ unmet needs for telemedicine. Oncology programs should address these disparities and needs to improve care delivery and achieve telehealth equity for their patient populations. Meanwhile, as cancer centers and research organizations further embrace telemedicine and other digital platforms, it is essential to tackle patients’ perceived challenges or concerns and consider allocating these platforms to particular care and services, such as genetic counseling, treatment consultation, and management of depressive symptoms and anxiety, to provide high-quality telehealth care and services to patients with breast cancer.

    Acknowledgments

    The authors are grateful to patients enrolled in the Chicago Multiethnic Epidemiologic Breast Cancer Cohort and the study participants who responded to our survey. The authors also appreciate their research staff’s assistance and support for the project.

    This study is supported by the Breast Cancer Research Foundation (BCRF-23-071), the Department of Defense (W81XH2210791), the National Cancer Institute (P20CA233307 and K08CA283261), the Susan G Komen Breast Cancer Foundation (TREND21675016 and SAC210203), and the National Institute on Aging (T32AG000243 and R24AG066588). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute and the National Institute on Aging.

    Data Availability

    The data sets generated and analyzed during this study are not publicly available due to patient confidentiality and privacy concerns but are available from the corresponding author on reasonable request.

    Authors' Contributions

    JQF was responsible for conceptualization, methodology, design, survey development, data analysis, formatting tables and figures, and writing the initial manuscript. FZ was responsible for methodology, design, survey development, research implementation, and data collection. FMH was responsible for conceptualization, methodology, and design. RN was responsible for methodology, design, survey development, and research implementation. OIO was responsible for methodology, design, and research implementation. DH was responsible for conceptualization, methodology, design, survey development, research implementation, and overall supervision. All authors contributed to the interpretations of the findings; writing, reviewing, and editing the manuscript; and the approval of the final manuscript and submission.

    Conflicts of Interest

    RN has advisory board involvement with, and research funding from, Arvinas, AstraZeneca, BeyondSpring Pharmaceuticals, Celgene, FujiFilm, Genentech, Gilead, Infinity, iTeos, Merck, OBI Pharma, OncoSec, Pfizer, Relay Therapeutics, Seagen, Sun Pharma, and Taiho Pharma. OIO has financial relationships with CancerIQ, 54gene, HealthWell Solutions, and Tempus, as well as research funding from Ayala Pharmaceuticals, Cepheid, Color Genomics, Novartis, and Genentech. All other authors declare no other conflicts of interest.

    Multimedia Appendix 1

    Survey questions regarding telehealth and in-person office visits.

    DOCX File , 25 KB
    Multimedia Appendix 2

    Associations between neighborhood socioeconomics disadvantage and telemedicine use (video conference versus telephone); comparing provisions of oncology services by modality of telemedicine; and patient satisfaction with telemedicine.

    DOCX File , 50 KB
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    ‎
    ADI: Area Deprivation Index
    AOR: adjusted odds ratio
    ChiMEC: Chicago Multiethnic Epidemiologic Breast Cancer Cohort
    SES: socioeconomic status

    Edited by T de Azevedo Cardoso; submitted 12.12.23; peer-reviewed by W Tang, K Natsuhara; comments to author 05.03.24; revised version received 20.03.24; accepted 24.04.24; published 18.07.24.

    Copyright

    ©Jincong Q Freeman, Fangyuan Zhao, Frederick M Howard, Rita Nanda, Olufunmilayo I Olopade, Dezheng Huo. Originally published in JMIR Cancer (https://cancer.jmir.org), 18.07.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.