Abstract
Objectives:
Little is known about the use of electronic health (eHealth) services supported by information technology in the United States among immigrants, a group that faces barriers in accessing care and, consequently, disparities in health outcomes. We examined differences in the use of eHealth services in the United States by immigration status in a nationally representative sample.
Methods:
We used data from the 2011-2015 National Health Interview Survey to assess use of eHealth services among US natives, naturalized citizens, and noncitizens. Our outcome variable of interest was respondent-reported use of eHealth services, defined as making medical appointments online, refilling prescriptions online, or communicating with health care professionals through email, during the past 12 months. We analyzed use of eHealth services, demographic characteristics, socioeconomic status, and health status among all 3 groups. We used multivariate logistic regression models to examine the association between immigration status and the likelihood of using eHealth services, adjusting for individual demographic, socioeconomic, and health characteristics.
Results:
Among 126 893 US natives, 18 763 (16.1%) reported using any eHealth services in the past 12 months, compared with 1738 of 15 102 (13.0%) naturalized citizens and 1020 of 14 340 (7.8%) noncitizens. Adjusting for socioeconomic factors reduced initial gaps: naturalized citizens (adjusted odds ratio [aOR] = 0.81; 95% confidence interval [CI], 0.75-0.87) and noncitizens (aOR = 0.81; 95% CI, 0.72-0.90) had approximately 20% lower odds of using eHealth services than did US natives. However, the differences varied by type of eHealth service. Immigrants with higher English-language proficiency were more likely to use eHealth services than were immigrants with lower English-language proficiency.
Conclusions:
Targeted interventions that reduce socioeconomic barriers in accessing technology and promote multilingual electronic portals could help mitigate disparities in use of eHealth services.
Keywords: eHealth services, immigrant, disparities, language barriers, minority health
Electronic health (eHealth) services, such as making medical appointments online, refilling prescriptions online, and communicating with health care professionals through email, are critically important to health care provision because information technology influences and shapes how physicians practice and communicate with patients. Such technologies are considered a potentially effective approach to help accomplish one of Healthy People 2020’s overarching goals, to “achieve health equity, eliminate disparities, and improve the health of all groups.”1 eHealth services can mitigate inaccessibility issues resulting from a shortage of health professionals and reduce patients’ costs associated with travel and loss of work time.2 Use of eHealth services in the United States has increased substantially during the past 15 years.3-6 More than 30% of US adults in 2014, compared with 7% in 2003, reported having communicated with their provider through various online channels, with email being the most common. Furthermore, approximately 7 in 10 adults indicated that they were likely to use eHealth technologies in the future.3,7
eHealth services, including technology-facilitated communication and prescription refills, are associated with better outcomes than are standard protocols across various clinical settings. For example, a randomized controlled trial showed that supplementary teleconferences reduced the likelihood of deterioration in chronic conditions by 50% among patients receiving skilled nursing care at home.8 An analysis of pharmacy claims data found that patients who refilled prescriptions online were more likely to adhere to physicians’ prescriptions than patients who did not.9 Also, email can improve the quality of communication and patient satisfaction.10,11 With emails, patients may ask questions that they do not feel comfortable asking during face-to-face visits, and communication via email automatically generates a record of physicians’ recommendations and instructions.11
Research examining the associations of demographic characteristics and socioeconomic status in the use of eHealth services found that people who were younger or had higher education levels were more likely than people who were older or had lower education levels to communicate with providers through electronic patient portals or emails.7,12-17 They were also more likely to acquire and track health information (eg, physician ratings and personal health records) on the internet.13 Only a few studies examined racial/ethnic differences in the use of eHealth services, and the results were mixed. For example, one study found that non-Hispanic white people were twice as likely as black people to send electronic messages to health professionals.15 Another study found no significant differences in conducting online health communication across racial/ethnic groups, such as looking for health information and using email or internet to communicate with physicians.13 Yet another study, using a US sample, found that disparities among racial/ethnic groups varied by online communication modes; for example, Hispanic people were more likely than non-Hispanic white people to communicate with health care providers through mobile applications or text messaging rather than email or fax.7
Little is known about the use of eHealth services in the United States among immigrants, a group that has faced long-standing barriers in accessing care and, consequently, disparities in some health outcomes.18-22 One study that examined the use of eHealth services among US immigrants found no differences in the use of eHealth services between US-born people and foreign-born people, although foreign-born people were more likely than US-born people to purchase medicine or vitamins online.13 However, the study did not focus on immigrants and did not account for important immigrant-related factors, including English-language proficiency. Studies in 2015 and 2016 examined the use of eHealth services among immigrants with chronic disease in Canada and Norway and highlighted the importance of designing culturally tailored eHealth tools.23,24 These studies suggest that eHealth systems not offering a preferred language other than English or the inability of patients to communicate with health professionals in English could limit US immigrants’ use of eHealth services. In another study, providers’ readiness to offer eHealth services was associated with reimbursement rates that varied across insurance programs and state policies.25 Public insurance not covering eHealth services or lack of insurance among immigrants might contribute to their underuse of eHealth services. The popularity of eHealth services in an immigrant’s country of origin may also influence his or her eHealth literacy and the likelihood of using those services.26
Our primary objective was to compare the use of eHealth services among noncitizens, naturalized citizens, and US natives. A secondary objective was to examine eHealth outcomes related to facilitating patients’ access to health care, including making medical appointments online, refilling prescriptions online, and communicating with health care professionals through email. A tertiary objective was to explore how English-language proficiency influences the likelihood of using eHealth services among immigrants.
Methods
Data and Sample
We used pooled cross-sectional data from the adult sample of the 2011-2015 National Health Interview Survey (NHIS) to measure eHealth service use by immigration status. The NHIS is an annual household survey that collects data on demographic characteristics, socioeconomic status, health service use, health status, and behaviors from a nationally representative sample of the US civilian, noninstitutionalized population.27 The 2011-2015 NHIS adult sample included 172 465 adult respondents aged ≥18. After we excluded respondents with any missing values (9.4% of the sample), our final analytical sample size was 156 355. Because the NHIS is a publicly available database and does not contain personally identifiable information, the University of Wisconsin–Milwaukee determined this study was exempt from human subjects review.
Measures
Our outcomes included 3 self-reported uses of eHealth services: making medical appointments online, refilling prescriptions online, and communicating with health care providers through email. Respondents were asked whether they had “scheduled a medical appointment on the internet,” “communicated with a health care provider by email,” and “filled a prescription on the internet” in the past 12 months. Each response was coded as a binary variable (1 = yes, 0 = no). We also created a composite measure indicating whether a respondent engaged in any of the 3 eHealth services during the past 12 months.
For immigration status, our primary independent variable, we categorized respondents into 3 groups based on their citizenship status and place of birth: US-born citizen (hereinafter, US native), naturalized citizen (ie, foreign-born people who had been granted US citizenship by the time of the survey), and noncitizen.
Demographic, socioeconomic, and health characteristics used as covariates in multivariate analyses were the following: age (18-29, 30-44, 45-64, and ≥65), sex (male or female), race/ethnicity (Hispanic, non-Hispanic white, non-Hispanic black, or non-Hispanic other [including Asian, American Indian/Alaska Native, and other races/ethnicities]), marital status (unmarried or married), educational attainment (<high school, high school, college, or graduate school), poverty status (family income <100% of the federal poverty level [FPL], 100%-199% FPL, or ≥200% FPL), health insurance (private, public, or no insurance), and having a usual source of care (yes or no). Research shows that immigrants are, on average, healthier than natives28; being healthier could be the reason why they use less health care, which highlights the importance of controlling health status when investigating immigrants’ use of eHealth services. In regression models, we adjusted for respondents’ having or not having (yes/no) any functional limitation and any of 5 common chronic conditions: arthritis, cancer, cardiovascular disease, diabetes, and stroke. We also included interviewees’ self-evaluation of English-language proficiency (speaks English very well, well, not well, or not at all).
Statistical Analyses
We first performed univariate analyses to describe the distribution of eHealth service use and other characteristics by immigration status, and then we examined the use of eHealth services by year during the study period. For both analyses, we used the Pearson χ2 test to examine whether gaps in the use of eHealth services were significantly different among US natives, naturalized citizens, and noncitizens. We considered P < .05 to be significant. We then used multivariate logistic regression models to assess the association between eHealth service use and immigration status. Lastly, we investigated the impact of English-language proficiency on eHealth service use among naturalized citizens and noncitizens. The NHIS started to evaluate English-language proficiency in the third quarter of 2013; we restricted our analyses of English-language proficiency to data from 2014-2015 because of the high rate of missing values (approximately 50%) in 2013 and the absence of these data in previous survey years. We accounted for the NHIS’s complex survey design in all analyses by using Stata version 14.29
Results
Of 156 335 respondents in our sample, 126 893 (81.2%) were US natives, 15 102 (9.7%) were naturalized citizens, and 14 340 (9.2%) were noncitizens (Table 1). Most noncitizens were Hispanic (61.6%), and most US natives were non-Hispanic white (76.6%). Among US natives, 16.1% reported using any eHealth services within the past 12 months, which was significantly higher than eHealth service use among naturalized citizens (13.0%, P < .001) and noncitizens (7.8%, P < .001). When examining specific eHealth services, we observed a similar pattern for patient-physician email communication and online prescription refill. Although the difference in scheduling medical appointments online between US natives and naturalized citizens was not significant, noncitizens were significantly less likely than US natives to make medical appointments online (4.8% vs 7.0%, P < .001). More than 40% of noncitizens, 17.3% of naturalized citizens, and 0.4% of US natives indicated they did not speak English well or at all (P < .001). Both naturalized citizens and noncitizens were more likely than US natives to be married, have <high school education, and have annual family incomes below the FPL. Almost half of noncitizens (44.1%) had no health insurance, which was nearly 4 times higher than US natives (11.7%). Compared with US citizens and naturalized citizens, noncitizens were also the least likely to have a usual source of care, any functional limitation, and any of the 5 common chronic conditions.
Table 1.
Use of electronic health (eHealth) services, demographic characteristics, socioeconomic characteristics, and health status of respondents, by immigration status, National Health Interview Survey, 2011-2015a
| Variable | US Native (n = 126 893) | Naturalized Citizen (n = 15 102) | Noncitizen (n = 14 340) | ||
|---|---|---|---|---|---|
| % (95% CI) | % (95% CI) | P Valueb | % (95% CI) | P Valueb | |
| eHealth service use | |||||
| Online appointment | 7.0 (6.7-7.2) | 6.9 (6.3-7.5) | .76 | 4.8 (4.4-5.4) | <.001 |
| Email communication | 8.2 (8.0-8.5) | 7.1 (6.6-7.6) | <.001 | 4.1 (3.6-4.6) | <.001 |
| Online prescription refill | 8.4 (8.2-8.6) | 5.5 (5.1-5.9) | <.001 | 2.6 (2.2-3.0) | <.001 |
| Any eHealth service | 16.1 (15.8-16.5) | 13.0 (12.3-13.8) | <.001 | 7.8 (7.1-8.5) | <.001 |
| English-language proficiencyc | |||||
| Speaks English very well | 97.9 (97.7-98.1) | 60.1 (58.4-61.7) | <.001 | 34.2 (32.4-36.1) | <.001 |
| Speaks English well | 1.8 (1.6-2.0) | 22.6 (21.2-24.1) | 22.5 (21.1-23.9) | ||
| Does not speak English well | 0.2 (0.2-0.2) | 12.8 (11.8-13.9) | 26.4 (24.8-27.9) | ||
| Does not speak English at all | 0.2 (0.1-0.2) | 4.5 (4.0-5.2) | 17.0 (15.6-18.4) | ||
| Age, y | |||||
| 18-29 | 22.5 (21.9-23.0) | 11.6 (10.8-12.5) | <.001 | 25.0 (23.8-26.1) | <.001 |
| 30-44 | 24.4 (24.0-24.7) | 27.5 (26.6-28.4) | 43.7 (42.5-44.8) | ||
| 45-64 | 35.1 (34.6-35.5) | 41.2 (40.1-42.3) | 25.7 (24.6-26.9) | ||
| ≥65 | 18.1 (17.8-18.5) | 19.7 (18.8-20.6) | 5.7 (5.1-6.2) | ||
| Sex | |||||
| Male | 48.4 (48.0-48.8) | 47.1 (46.1-48.1) | .03 | 51.4 (50.3-52.5) | <.001 |
| Female | 51.6 (51.2-52.0) | 52.9 (51.9-54.0) | 48.6 (47.5-49.7) | ||
| Race/ethnicity | |||||
| Non-Hispanic white | 76.6 (75.9-77.2) | 25.9 (24.7-27.1) | <.001 | 12.8 (11.8-13.8) | <.001 |
| Non-Hispanic black | 12.1 (11.7-12.6) | 9.3 (8.6-10.0) | 6.1 (5.5-6.7) | ||
| Hispanic | 7.7 (7.4-8.0) | 37.0 (35.7-38.4) | 61.6 (59.7-63.4) | ||
| Non-Hispanic otherd | 3.6 (3.4-3.9) | 27.8 (26.6-29.0) | 19.6 (18.3-21.0) | ||
| Marital status | |||||
| Unmarried | 48.4 (47.8-49.0) | 36.1 (35.0-37.1) | <.001 | 38.3 (37.1-39.5) | <.001 |
| Married | 51.6 (51.0-52.2) | 64.0 (62.9-65.0) | 61.7 (60.5-62.9) | ||
| Educational attainment | |||||
| <High school | 10.2 (9.9-10.5) | 17.2 (16.3. 18.1) | <.001 | 39.3 (37.8-40.9) | <.001 |
| High school | 26.2 (25.8-26.7) | 20.7 (19.8-21.7) | 22.4 (21.5-23.4) | ||
| College | 53.0 (52.5-53.5) | 48.0 (46.9-49.1) | 28.2 (27.0-29.4) | ||
| Graduate school | 10.6 (10.2-11.0) | 14.1 (13.3-14.9) | 10.1 (9.2-11.1) | ||
| Poverty status | |||||
| <100% FPL | 11.9 (11.5-12.3) | 13.5 (12.8-14.2) | <.001 | 28.4 (27.1-29.6) | <.001 |
| 100%-199% FPL | 16.7 (16.3-17.1) | 19.0 (18.2-19.8) | 29.9 (28.8-31.0) | ||
| ≥200% FPL | 71.4 (70.8-72.1) | 67.5 (66.4-68.6) | 41.8 (40.3-43.3) | ||
| Health insurance | |||||
| Private | 65.9 (65.3-66.5) | 60.1 (59.0-61.2) | <.001 | 36.8 (35.4-38.3) | <.001 |
| Public | 22.4 (22.0-22.9) | 26.2 (25.3-27.2) | 19.0 (18.0-20.1) | ||
| None | 11.7 (11.3-12.0) | 13.7 (13.0-14.4) | 44.1 (42.6-45.7) | ||
| Has a usual source of care | 86.1 (85.7-86.4) | 86.0 (85.2-86.8) | .83 | 64.8 (63.6-65.9) | <.001 |
| Has any functional limitatione | 36.6 (36.1-37.1) | 29.3 (28.3-30.3) | <.001 | 18.5 (17.6-19.4) | <.001 |
| Chronic condition | |||||
| Cardiovascular disease | 12.3 (12.0-12.6) | 8.9 (8.3-9.6) | <.001 | 4.5 (4.0-5.0) | <.001 |
| Stroke | 2.7 (2.6-2.9) | 2.2 (1.9-2.5) | <.001 | 1.2 (1.0-1.4) | <.001 |
| Cancer | 9.3 (9.1-9.6) | 5.9 (5.4-6.4) | <.001 | 1.9 (1.6-2.2) | <.001 |
| Diabetes | 9.1 (8.9-9.3) | 11.1 (10.5-11.8) | <.001 | 6.8 (6.2-7.3) | <.001 |
| Arthritis | 24.7 (24.3-25.1) | 17.9 (17.0-18.7) | <.001 | 7.1 (6.6-7.7) | <.001 |
Abbreviation: FPL, federal poverty level.
a Data source: National Center for Health Statistics.27
b Using Pearson χ2 test, with P < .05 considered significant.
c The National Health Interview Survey started to evaluate English-language proficiency in the third quarter of 2013; the analysis was restricted to data from 2014-2015 because of the high rate of missing values (approximately 50%) in 2013 and absence of data on English-language proficiency in previous survey years.
d Non-Hispanic other includes Asian, American Indian/Alaska Native, and other races/ethnicities.
e Indicated if respondents cannot perform daily activities (eg, walking and shopping) without any difficulties.
Gaps in the use of any eHealth services by naturalized citizens and noncitizens, compared with US natives, increased over time (Figure). The percentage of US natives using any eHealth service increased by 53% (from 13.6% in 2011 to 20.8% in 2015). In contrast, the increase among naturalized citizens and noncitizens was approximately 36% for both groups combined (increasing from 11.9% in 2011 to 16.2% in 2015 for naturalized citizens and from 7.6% to 10.4% for noncitizens).
Figure.
Use of electronic health (eHealth) services among adult respondents, by survey year and immigration status, National Health Interview Survey, 2011-2015.27 Error bars indicate 95% confidence intervals.
Model 1, which adjusted only for demographic characteristics, found lower likelihoods of using any eHealth service among both naturalized citizens (adjusted odds ratio [aOR] = 0.84; 95% confidence interval [CI], 0.78-0.90) and noncitizens (aOR = 0.54; 95% CI, 0.48-0.60) compared with US natives (Table 2). The magnitude of difference among types of eHealth services used varied. For example, naturalized citizens (aOR = 0.69; 95% CI, 0.63-0.76) and noncitizens (aOR = 0.37; 95% CI, 0.31-0.44) were less likely than US natives to refill a prescription online, whereas we found no differences between US natives and naturalized citizens for scheduling a medical appointment online. The odds did not change significantly after further controlling for health status (Model 2). Accounting for socioeconomic status (Model 3) explained a significant portion of the eHealth service gap between noncitizens and US natives. For example, the disparities in using any eHealth services, communicating with health care providers by email, and refilling prescriptions online between noncitizens and US natives were reduced from 45% to 19%, 41% to 16%, and 61% to 41%, respectively (Table 2).
Table 2.
Multivariate-adjusted relationship between use of electronic health (eHealth) services and immigration status among adult respondents, National Health Interview Survey, 2011-2015 (N = 156 335)a
| Model | aOR (95% CI) [P Value]b | |||
|---|---|---|---|---|
| Any eHealth Service | Online Appointment | Email Communication | Online Prescription Refill | |
| Model 1c | ||||
| US native | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) |
| Naturalized citizen | 0.84 (0.78-0.90) [<.001] | 1.00 (0.90-1.10) [.99] | 0.88 (0.80-0.97) [.01] | 0.69 (0.63-0.76) [<.001] |
| Noncitizen | 0.54 (0.48-0.60) [<.001] | 0.70 (0.61-0.81) [<.001] | 0.57 (0.49-0.67) [<.001] | 0.37 (0.31-0.44) [<.001] |
| Model 2d | ||||
| US native | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) |
| Naturalized citizen | 0.86 (0.80-0.92) [<.001] | 1.01 (0.92-1.12) [.80] | 0.90 (0.82-0.99) [.03] | 0.71 (0.65-0.79) [<.001] |
| Noncitizen | 0.55 (0.49-0.61) [<.001] | 0.71 (0.62-0.82) [<.001] | 0.59 (0.51-0.68) [<.001] | 0.39 (0.33-0.46) [<.001] |
| Model 3e | ||||
| US native | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) |
| Naturalized citizen | 0.81 (0.75-0.87) [<.001] | 0.97 (0.88-1.07) [.53] | 0.85 (0.77-0.93) [<.001] | 0.69 (0.63-0.76) [<.001] |
| Noncitizen | 0.81 (0.72-0.90) [<.001] | 1.04 (0.90-1.19) [.59] | 0.84 (0.73-0.96) [.01] | 0.59 (0.50-0.69) [<.001] |
Abbreviation: aOR, adjusted odds ratio.
a Data source: National Center for Health Statistics.27
b Using Pearson χ2 test, with P < .05 considered significant.
c Model 1 controlled for demographic characteristics.
d Model 2 controlled for demographic characteristics and health status.
e Model 3 controlled for demographic characteristics, health status, and socioeconomic status.
In analyses of the 2014-2015 NHIS data, English-language proficiency was another important predictor of using eHealth services (Table 3). Lower English-language proficiency was associated with decreasing odds of using eHealth services. For example, respondents who said they could not speak English at all were 81% (aOR = 0.19; 95% CI, 0.09-0.42) less likely than those who could speak English very well to use any eHealth services. For a given level of proficiency, the aORs were similar in magnitude for making a medical appointment online, communicating with health care providers by email, and refilling prescriptions online. With the exception of making a medical appointment online, which showed an increased gap (aOR = 1.30; 95% CI, 1.00-1.69; P = .049), accounting for English-language proficiency fully explained the differences in use of eHealth services between noncitizens and US natives. However, estimates between naturalized citizens and US natives were relatively stable.
Table 3.
Impact of English-language proficiency on use of electronic health (eHealth) services among adult respondents, National Health Interview Survey, 2014-2015 (N = 64 987)a
| aOR (95% CI) [P Value]b | ||||
|---|---|---|---|---|
| Variables | Any eHealth Service | Online Appointment | Email Communication | Online Prescription Refill |
| Immigration status | ||||
| US native | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) |
| Naturalized citizen | 0.86 (0.76-0.97) [.02] | 1.02 (0.88-1.18) [.79] | 0.84 (0.72-0.98) [.03] | 0.68 (0.58-0.79) [<.001] |
| Noncitizen | 1.06 (0.86-1.31) [.57] | 1.30 (1.00-1.69) [.049] | 1.05 (0.83-1.32) [.68] | 0.76 (0.57-1.01) [.06] |
| English-language proficiencyc | ||||
| Speaks English very well | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) |
| Speaks English well | 0.68 (0.58-0.81) [<.001] | 0.67 (0.52-0.85) [.001] | 0.61 (0.50-0.76) [<.001] | 0.69 (0.54-0.88) [.003] |
| Does not speak English well | 0.32 (0.23-0.44) [<.001] | 0.32 (0.21-0.50) [<.001] | 0.24 (0.14-0.39) [<.001] | 0.37 (0.22-0.61) [<.001] |
| Does not speak English at all | 0.19 (0.09-0.42) [<.001] | 0.18 (0.07-0.42) [<.001] | 0.14 (0.05-0.38) [<.001] | 0.26 (0.07-0.95) [.04] |
Abbreviation: aOR, adjusted odds ratio.
a Data source: National Center for Health Statistics.27
b Using Pearson χ2 test, with P < .05 considered significant. Models adjusted for demographic, socioeconomic, and health covariates.
c The National Health Interview Survey started to evaluate English-language proficiency in the third quarter of 2013; we restricted our analyses to 2014-2015 data because of the high rate of missing values (approximately 50%) in 2013 and absence of data on English-language proficiency in previous survey years.
In sensitivity analyses that restricted analytical samples to those who had visited health care professionals or had been prescribed medication within the past 12 months, the patterning of results and inferences suggesting lower odds of eHealth service used by naturalized citizens and noncitizens compared with US natives were the same.
Discussion
Our results of the lower likelihood of eHealth services use among naturalized citizens and noncitizens compared with US natives were somewhat similar to those from a study that found US-born natives to be 2.6 times more likely than foreign-born respondents to purchase medicine or vitamins online but not more likely to communicate with physicians through email or the internet.13 In our study, naturalized citizens had lower odds of using eHealth services than US natives, even after controlling for all covariates. One possible reason for this gap in the use of eHealth services is that naturalized citizens and noncitizens and those in racial/ethnic minority groups may have had less access to electronic channels than US natives.3,15,17 In a survey of Latino residents in northern Manhattan during August 2014 and June 2015, most respondents were born outside of the United States (84.5%) and about 50% did not have access to the internet or email.17 Another study also found that racial/ethnic minority populations, such as Latino and Filipino populations, had lower odds of having computers or smartphones than non-Hispanic white people.15 Also, using eHealth services often requires the purchase of personal digital equipment, such as computers or smartphones, and a monthly internet service fee. Because nearly 30% of noncitizen residents in our sample lived below the FPL, the cost of accessing digital devices and the internet may have been a substantial economic barrier. Although we controlled for income, we could not adjust for other financial factors, such as debt and extended family obligations. Hence, some of the remaining gap may be driven by socioeconomic factors.
Interestingly, our analyses showed significant differences between immigrants (ie, naturalized citizens and noncitizens) and US natives in communicating with health care providers through email and refilling prescriptions online rather than scheduling medical appointments online, after controlling for all demographic, health, and socioeconomic covariates. The type of eHealth service may influence its use by patients.30 For example, communicating with a health care provider through email may involve reading and understanding medical terminology, possibly depending on patients’ accurate descriptions of needs and symptoms. Seeking those online services necessitates high-level English-language skills, which could be challenging for immigrants whose native language is not English. Also, many immigrants may see providers who are not using eHealth services or not encouraging the use of eHealth services. Compared with eHealth services that involve a fair amount of physician-patient communication in English, an eHealth service such as making appointments through electronic patient portals, which may only require basic information input, is easier for immigrants with limited English-language proficiency and health literacy.17 One study reported that language barriers inhibited Chinese and Punjabi immigrants in Canada from using eHealth services for chronic disease self-management.24
Better command of English was also associated with higher odds of having used eHealth services among naturalized citizens and noncitizens in our study, which is consistent with research on the use of health care in general.31 English-language proficiency explained the persistent differences in eHealth use between US natives and noncitizens rather than between US natives and naturalized citizens after controlling for demographic, socioeconomic, and health covariates. The fact that English-language proficiency did not explain the differences in use of eHealth services between naturalized citizens and US natives may be because people who gain US citizenship through naturalization are required to meet a certain level of English-language proficiency. Our results point to the need to incorporate multilingual capabilities into eHealth technologies. Future research should also investigate other unexplained factors contributing to differences in eHealth use between naturalized citizens and US natives. In our sample, after adjusting for English-language proficiency, we found that noncitizens were more likely than US natives to make medical appointments online.
Health care professionals have now extensively adopted eHealth technologies to engage patients in continually monitoring health status and coordinating health care in the context of patient-centered medicine in the United States.32 Use of eHealth services has increased steadily, especially for chronic conditions. An important policy priority is to use eHealth services to help patients with chronic diseases, especially those in areas lacking health care resources, to closely monitor their conditions.4-6,33 Hence, it is concerning that the growth rate in use of eHealth services is slower among immigrants than among US natives. If this trend persists, disparities in self-management of chronic diseases and associated clinical outcomes will widen. Because immigrants with low socioeconomic status are more likely than immigrants with high socioeconomic status to rely on smartphones for internet access,34 a potential strategy to increase eHealth services use among immigrants is to maximize their smartphone-based user experience by providing translation-enhanced email communication.
Limitations
This study had several limitations. First, the NHIS did not provide detailed data on access to the internet, computers, or electronic devices or on eHealth literacy. However, we believe that adjusting for income and education level may partially reduce omitted variable bias. Second, we could not examine the disparities among US natives, naturalized citizens, and noncitizens in users’ experiences and satisfaction associated with eHealth service use. Immigrants with low English-language proficiency and limited health literacy may not achieve desirable outcomes even if they use eHealth services. Third, data on eHealth services use were self-reported and were not confirmed with providers; thus, they were subject to recall bias. Fourth, we were unable to control for providers’ capabilities to support eHealth services or the quality of the services. Most physicians may not understand emails written in languages other than English without the help of professional translators. They also may not prioritize responding to patients’ emails given constrained schedules.35 Future research should study the effect of physicians’ perceptions of eHealth service use among immigrants.
Conclusions
Our findings suggest that targeted interventions that reduce socioeconomic barriers and promote multilingual electronic portals should be explored to mitigate disparities in the use of eHealth services. Health care providers in the United States have already realized the importance of culturally competent health care facilitated by professional interpreters in clinical settings. This recognition should also extend to eHealth services. Future research should investigate noncitizens’ satisfaction with using eHealth services and the effect of geographic variation in eHealth policies on disparities between US natives and immigrants.
Footnotes
Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
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