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Technology access, use, socioeconomic status, and healthcare disparities among African Americans in the US

PMCID: PMC12153058

PMID: 40503485

Abstract

Background Healthcare disparities remain a significant challenge in addressing equitable healthcare access and outcomes for minority populations, including African Americans. Rooted in systemic racism and historical exclusion, these inequities persist as part of broader structural violence. Leveraging health technology holds promise in addressing these disparities by enhancing access to care, improving its quality, and reducing inequities. However, the association between health technology access, use, socioeconomic status (SES), and healthcare disparities among African Americans remains underexplored. This study aims to explore the potential role of technology in mitigating healthcare disparities by investigating the associations between technology access, healthcare technology use, socioeconomic status (SES), and health disparities among African Americans. Methods Using data from the Health Information National Trends Survey (HINTS) Wave 6 dataset, a sample of 815 African Americans was analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Findings The results of the study showed that technology access had a significant positive effect on healthcare technology use ( β = 0.260, p < 0.000). Technology access ( β = −0.086, p = 0.034) and healthcare technology use ( β = −0.180, p < 0.001) demonstrated a significant negative effect on healthcare disparity, respectively. Results also revealed SES had a significant positive effect on technology access ( β = 0.424, p < 0.001). Additionally, SES was found to significantly moderate the relationship between technology access and healthcare disparities, indicating variability in the impact of technology access based on SES levels among African Americans. Conclusion These findings highlight the potential of technology in mitigating healthcare disparities among African Americans. By promoting enhanced health technology access and utilization, particularly in lower SES populations, the healthcare outcomes for vulnerable communities can be significantly improved. Policymakers, healthcare providers, and technology developers are encouraged to collaborate in providing conducive conditions for the adoption and use of technology to advance healthcare equity.

Full Text

In recent years, advancements in technology have transformed various aspects of society, including the healthcare sector. These advancements have revolutionized healthcare delivery, enabling access to improved personalized healthcare, enhanced patient outcomes, and more efficient care systems (1, 2). However, despite these advancements, significant disparities in healthcare outcomes persist among marginalized communities, particularly African Americans (3–7). The Centers for Disease Control and Prevention (CDC) defines health disparities as preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations (8). Technology inequity, characterized by unequal access to and utilization of healthcare technologies, has emerged as a critical factor contributing to these disparities (9).
Healthcare inequities faced by African Americans are not incidental but rooted in systemic racism and historical patterns of exclusion (3, 5, 10). Saidiya Hartman’s ‘Afterlife of Slavery’ highlights how skewed life chances, by extension technological exclusion, and healthcare disparities are enduring legacies of slavery and racial capitalism (11). Similarly, Ruth Wilson Gilmore’s framing of racism as ‘the production of premature death’ (12) underscores how systemic barriers, such as limited access to telehealth, electronic health records, and wearable technologies, disproportionately deny Black populations equitable opportunities for healthcare (13). While efforts have been made to address these disparities, the rapid pace of technological advancements has introduced a new dimension to the problem (14, 15). Technology inequity exacerbates healthcare disparities among African Americans, as they often encounter limited access to essential technologies, such as electronic health records (EHRs), telehealth services, mobile health applications, and wearable devices, and also experience delayed access to needed care, and discrimination (16, 17).
To comprehend the multifaceted impact of technology on healthcare disparities among African Americans, it is crucial to explore the underlying factors that perpetuate this divide. Socioeconomic factors, including income disparities, education, occupation, and lack of health insurance coverage, play a significant role in limiting access to and utilization of healthcare technologies (14). Low socioeconomic status (SES) individuals, including many African Americans, often face barriers to technology access due to limited financial resources, lower digital literacy levels, and inadequate technology infrastructure in their communities. Additionally, continued systemic discrimination (18, 19), delayed access to needed care (20, 21), and health risk factors (18) may contribute to the divide, further impeding African Americans’ ability to leverage technology for improved health outcomes.
Several studies have demonstrated the detrimental effects of technology inequity on healthcare disparities (22) among African Americans (23–26). Research by Mulia et al. indicated that Hispanic/Latinx and African American patients had reduced access to telehealth services compared to their white counterparts, resulting in delayed or inadequate healthcare access (27). Furthermore, Heiney et al. found that African Americans faced barriers to utilizing mobile health applications, limiting their ability to engage in self-management and preventive care practices (28).
Using data from the National Cancer Institute (NCI)’s Health Information National Trends Survey (HINTS) Wave 6 dataset, this study analyzed responses from 815 African Americans. HINTS 6 is a cross-sectional survey of non-institutionalized civilian adults aged 18+ years in the United States collected from March 7, 2022, to November 8th, 2022 with the goal of investigating the need for, access to, and use of health-related information and health-related behaviors, perceptions and knowledge (29, 30).
The mailing protocol for HINTS 6 followed a modified Dillman approach (31) with all selected households receiving a total of four mailings: an initial mailing, a reminder postcard, and two follow-up mailings.
The relationship between socioeconomic status (SES), technology access, and healthcare disparities is complex and multifaceted. SES, which encompasses factors such as income, education, and occupation, plays a significant role in determining an individual’s access to technology and subsequent healthcare disparities. Many studies have established a strong relationship between socioeconomic factors and how they influence an individual’s access to technology (32, 33). Higher-income individuals are more likely to afford smartphones, computers, and internet access, which are essential for utilizing healthcare technology such as telehealth services or health apps (34). On the other hand, lower-income individuals may face barriers due to the cost of devices, internet access, or limited availability of technology infrastructure in their communities (35). Consequently, disparities in technology access may contribute to disparities in healthcare outcomes. Furthermore, the digital divide, driven by socioeconomic factors, exacerbates healthcare disparities. For instance, lower-income individuals, including African Americans who are more likely to experience economic challenges, may lack the resources or skills to effectively use health technology. This limits their ability to access online health information, engage in telehealth visits, or effectively manage their healthcare. As a result, they may experience delays in accessing care, receive suboptimal healthcare services, or have poorer health outcomes compared to individuals with higher SES (14, 36). SES is closely linked to health literacy, which refers to an individual’s ability to access, understand, and use health information to make informed decisions about their health (37, 38). Individuals with lower SES are more likely to have lower health literacy levels, which can hinder their utilization of healthcare technology. Limited health literacy skills may make it difficult for individuals to navigate complex digital platforms, understand health-related information, or effectively communicate with healthcare providers through technology (37, 38). Another factor that could be influenced by SES is provider-patient communication. Technology-mediated interactions, such as telehealth visits or patient portals, may impact provider-patient communication differently based on SES. Individuals with lower SES may face challenges in effectively communicating their health concerns, understanding medical jargon, or asking necessary questions through these platforms. These communication barriers can impede the delivery of patient-centered care and contribute to disparities in healthcare quality and outcomes (39). Education and technological literacy have the potential to influence the level of SES of an individual (33). Education, another component of SES, influences technological literacy and digital skills. Higher levels of education are associated with better technology proficiency (33, 40, 41), including the ability to navigate digital platforms, access online health resources, and use health technology effectively. Individuals with lower education levels may experience difficulties in adopting and benefiting from healthcare technology, leading to disparities in healthcare access, utilization, and health outcomes (42, 43). Thus, it is imperative to understand how SES plays into technology access and use and how they consequently impact healthcare disparity among African Americans. Respondents’ education level and household income were used to evaluate socioeconomic status. For education, respondents were asked about their highest level of education. The highest level of education was categorized as “Less than high school,” “high school graduate,” “some college,” “bachelor’s degree,” and “post-baccalaureate degree.” Similarly, respondents’ annual household income was assessed with 1 representing “$0 to $9,999”, 2 “$10,000 to $14,999”, 3 “$15,000 to $19,999”, 4 “$20,000 to $34,999”, 5 “$35,000 to $49,999”, 6 “$50,000 to $74,999”, 7 “$75,000 to $99,999”, 8 “$100,000 to $199,999” and 9 as “$200,000 or more”.
Technology and its associated advancements have long been trumpeted to influence the kind of healthcare individuals receive and the quality of such care (44). Nonetheless, through what has been known as the “Digital Divide,” technology might either assist in making things better or perhaps worse. For some populations, having access to digital information might help with self-care and maintaining good health (32). However, the racial and ethnic groups that experience the largest injustices, particularly Blacks and Hispanics, have significantly varying access to digital resources depending on socioeconomic level. The future will depend on improving digital health equality. Broadband access represents one specific major concern. Over 21 million Americans lack broadband access (45). While cities like New York have broadband infrastructure covering 99.9% of the population, 2.2 million adults there do not have a home broadband subscription (46). In more rural areas, such as the mountains of Appalachia in states such as Tennessee, Kentucky, and West Virginia, there are large areas with no or limited broadband access (46). According to the Public Policy Institute of California (PPIC), though broadband has grown slightly from 84% in 2019 to 85% in 2020, racial and ethnic disparities in access persist with 81% of Latino, 83% of Black, 87% of white, and 88% of Asian households report having broadband access at home in 2021 (33). Thus, disparities in broadband infrastructure, driven by uneven geographical deployment, economic affordability issues, and historical digital redlining, constitute critical barriers that disproportionately affect marginalized communities, including African Americans. Aside from broadband access, telehealth access is critically essential in promoting equitable healthcare. The COVID-19 pandemic has facilitated and increased the importance and use of telehealth (46). Because of the risk of person-to-person viral transmission, organizations around the country switched most outpatient care to telehealth essentially overnight (46). Black patients and poorer patients were much more likely to receive telephonic as opposed to video visits (46). The following questions were posed to respondents to measure technology access to access to basic cell phones, smartphones, and access and use of the internet rated on a “Yes”/“No” scale; (i) “Have a basic cell phone?,” (ii) “Have a smartphone?” and (iii) “Do you ever go online to access the Internet or World Wide Web, or to send and receive email?.” Based on the above, this study posits that: H3: Technology access has a significant negative effect on healthcare disparities, H4: Technology access has a significant positive effect on health technology, and H7: There is a significant negative indirect effect of SES on H_DISPARITY through the sequential mediation of TECH_ACC.
Health technology has the potential to significantly impact healthcare outcomes for African Americans, as it may help improve access to care, enhance patient engagement, and promote health equity. For instance, during the COVID-19 pandemic the use of telehealth became more prevalent, helping individuals manage their chronic diseases by enhancing patient-provider communication, promoting medication adherence, and enabling self-monitoring of health conditions (47, 48). However, it is crucial to acknowledge that disparities in health technology use may also contribute to existing healthcare disparities (49), particularly issues of access due to structural and systemic racism. Thus, to measure the health technology use among African Americans, this study evaluated the frequency of watching a health-related video on a social media site like YouTube in the last 12 months. The responses were assessed on a 5-likert scale with 1 as “Almost every day,” 3 as “A few times a month” and 5 as “Never.” Respondents were further asked how often they interacted with people who have similar health or medical issues as them on social media or online forums and how often they shared general health-related information on social media for example, a news article in the last 12 months, respectively. Finally, respondents were asked to indicate whether they had received care from a doctor or health professional using telehealth in the past 12 months using a scale of 1–4, with 1 indicating Yes, by video, 2 as “Yes, by phone call (voice only with no video),” 3 as “Yes, some by video and some by phone call” and 4 as “No telehealth visits in the past 12 months.”
Due to its robustness of estimations and statistical power (50, 51), the partial least squares (PLS) based on structural equation modeling (SEM) was employed to test and validate the hypothesized model with the aid of SmartPLS 4.0 (52). The model’s validity was determined by analyzing the measurement and structural models.
The measurement model was evaluated using internal consistency reliability, convergent validity, and discriminant validity. This was done using the outer loadings (≥0.70 for reliability), Average of Variance Extracted (AVE; ≥0.50 indicating convergent validity), Composite Reliability (CR; ≥0.70 indicating internal consistency), Fornell and Larcker criterion (square root of AVE exceeding inter-construct correlations indicating discriminant validity), and Heterotrait-Monotrait Ratio of correlation (HTMT; <0.85 demonstrating discriminant validity) (53–55).
The collinearity assessment (VIF) (53, 54), path coefficient (β; showing relationship strength and direction), t-statistics (statistical significance) (53), and the model fit index were used to evaluate the structural model for the main model, mediation, and moderation analysis. The moderating effect was further evaluated using the simple slope analysis. Model fit analysis was performed using the Standardized Root Mean Square Residual (SRMR ≤0.10 acceptable), discrepancy function (d_ULS and d_G), chi-square statistic, and the Normed Fit Index (NFI) (56, 57).
Table 1 shows the summary statistics for the respondents’ demographics. The majority of respondents were females, and the most occurring age was 50–64 years old. Most respondents had some college education, with the majority having only one employment. Lastly, the most observed household income range was between $20,000 and $34,999.
The results for the outer loadings, internal consistency reliability, convergent validity, and discriminant validity are presented in Table 2 above. The outer loadings for all constructs showed values greater than the acceptable threshold of 0.70, except hTU2, which had an outer loading value of 0.694. Also, composite reliability values for all the constructs were greater than 0.70, suggesting strong internal consistency reliability (38). AVE values were all above the recommended level of 0.50 (55), as shown in Table 2. Figure 1 shows the various constructs with their respective loadings. Results of HTMT showed all the constructs had HTMT values less than the threshold of 1. Additionally, the square root of the AVE of all the constructs was greater than their correlation with other constructs, and the diagonal items were larger than the entries in corresponding columns and rows, hence satisfying the Fornell and Larcker criterion (55).
Results of the structural model as shown in Table 3 revealed a statistically significant relationship between SES → TECH_ACC (β = 0.424, t-statistics = 16.444, p < 0.001), TECH_ACC → H_DISPARITY (β = −0.086, t-statistics = 1.828, p-value = 0.034), TECH_ACC → hTECH_USE (β = 0.260, t-statistics = 11.363, p < 0.001), and hTECH_USE → H_DISPARITY (β = −0.180, t-statistics = 4.458, p < 0.001), thus, supporting hypothesis H2, H3, H4, and H5, respectively. However, SES → H_DISPARITY (β = −0.021, t-statistics = 0.503, p-value = 0.307) was not significant, therefore not supporting H1. Mediation results shows a partial mediation SES → TECH_ACC → H_DISPARITY (β = −0.036, t-statistics = 1.794, p-value = 0.036) and TECH_ACC → hTECH_USE → H_DISPARITY (β = −0.047, t-statistics = 4.332, p < 0.001), hence satisfying H7 and H8, respectively. The path diagram for the bootstrapped results is shown in Figure 2 below.
The moderating effect of SES x TECH_ACC → H_DISPARITY (β = 0.097, t-statistics = 2.674, p-value = 0.004) produced a statistically significant result, thus, satisfying H6. The simple slope analysis of the moderating effect is shown in Figure 3.
This study aimed to investigate the relationships between socioeconomic status (SES), technology access, health technology use, and healthcare disparities. The findings revealed a positive association between SES and technology access, suggesting that individuals with higher socioeconomic status are more likely to have more access to technology. This aligns with previous research indicating that socioeconomic factors play a crucial role in determining technology access (32, 33, 58). The statistically significant relationship between SES and technology access highlights the role of structural racism in shaping economic opportunities and digital inclusion for African Americans. These disparities are not simply socioeconomic but are rooted in what Ruth Wilson Gilmore terms ‘group-differentiated vulnerability to premature death.’ (12) Communities with limited access to health technology face compounded disadvantages, reflecting patterns of exclusion embedded in racialized spatial dynamics.
Consistent with previous studies (14, 59, 60), this study found a negative association between technology access and healthcare disparities. Individuals with better technology access were found to experience reduced healthcare disparities. This finding underscores the potential of technology in bridging healthcare gaps and improving access to care, especially for underserved populations, if, and only if, systemic barriers to access and utilization are dismantled. Telehealth specifically plays a crucial role in addressing healthcare disparities by providing access to medical care for individuals in underserved communities. It enables remote consultations, reduces wait times, and facilitates continuity of care, particularly for managing chronic diseases. Mobile health applications and electronic health records further support patient engagement by providing real-time health monitoring and improved access to medical information (47, 61). Thus, by providing remote access to healthcare services, technology can overcome geographical barriers and ensure timely delivery of care to individuals who may face challenges in accessing traditional healthcare facilities.
The study also found a positive association between technology access and health technology use, which is consistent with previous research (44, 62–64), suggesting that individuals with better technology access are more likely to engage with health technology tools. This finding emphasizes the importance of ensuring equitable technology access to facilitate health technology adoption and engagement among diverse populations. By leveraging health technology, individuals can actively manage their health, access educational resources, and engage in shared decision-making with healthcare providers, potentially leading to improved health outcomes (65).
Furthermore, the negative association between health technology use and healthcare disparities supports the notion that health technology can help reduce disparities in healthcare outcomes. Individuals who actively utilize health technology may benefit from improved health management, increased access to information, and better communication with healthcare providers (66, 67). These factors contribute to more equitable healthcare experiences and outcomes.
The interaction effect between SES and technology access on healthcare disparities highlights the importance of considering the interplay between socioeconomic factors and technology access. This finding suggests that addressing both SES disparities and technology access is crucial in reducing healthcare disparities. Government initiatives such as telehealth subsidies, broadband expansion programs, and Medicaid coverage for virtual healthcare services have the potential to improve access to telehealth for low-income populations (68). These policies have the potential to reduce financial and technological barriers that limit healthcare access for marginalized groups (69). Thus, strategies should focus not only on enhancing technology access but also on addressing underlying socioeconomic inequalities to achieve equitable healthcare outcomes for all individuals.
Regarding the mediation analysis, this study found that the joint mediation of technology access and health technology use in the association between SES and healthcare disparities was statistically significant, suggesting that health technology use plays a crucial role in reducing healthcare disparities among individuals with different socioeconomic backgrounds (70, 71).

Sections

"[{\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref1\", \"ref2\", \"ref3\", \"ref8\", \"ref9\"], \"section\": \"Introduction\", \"text\": \"In recent years, advancements in technology have transformed various aspects of society, including the healthcare sector. These advancements have revolutionized healthcare delivery, enabling access to improved personalized healthcare, enhanced patient outcomes, and more efficient care systems (1, 2). However, despite these advancements, significant disparities in healthcare outcomes persist among marginalized communities, particularly African Americans (3\\u20137). The Centers for Disease Control and Prevention (CDC) defines health disparities as preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations (8). Technology inequity, characterized by unequal access to and utilization of healthcare technologies, has emerged as a critical factor contributing to these disparities (9).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref3\", \"ref5\", \"ref10\", \"ref11\", \"ref12\", \"ref13\", \"ref14\", \"ref15\", \"ref16\", \"ref17\"], \"section\": \"Introduction\", \"text\": \"Healthcare inequities faced by African Americans are not incidental but rooted in systemic racism and historical patterns of exclusion (3, 5, 10). Saidiya Hartman\\u2019s \\u2018Afterlife of Slavery\\u2019 highlights how skewed life chances, by extension technological exclusion, and healthcare disparities are enduring legacies of slavery and racial capitalism (11). Similarly, Ruth Wilson Gilmore\\u2019s framing of racism as \\u2018the production of premature death\\u2019 (12) underscores how systemic barriers, such as limited access to telehealth, electronic health records, and wearable technologies, disproportionately deny Black populations equitable opportunities for healthcare (13). While efforts have been made to address these disparities, the rapid pace of technological advancements has introduced a new dimension to the problem (14, 15). Technology inequity exacerbates healthcare disparities among African Americans, as they often encounter limited access to essential technologies, such as electronic health records (EHRs), telehealth services, mobile health applications, and wearable devices, and also experience delayed access to needed care, and discrimination (16, 17).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref14\", \"ref18\", \"ref19\", \"ref20\", \"ref21\", \"ref18\"], \"section\": \"Introduction\", \"text\": \"To comprehend the multifaceted impact of technology on healthcare disparities among African Americans, it is crucial to explore the underlying factors that perpetuate this divide. Socioeconomic factors, including income disparities, education, occupation, and lack of health insurance coverage, play a significant role in limiting access to and utilization of healthcare technologies (14). Low socioeconomic status (SES) individuals, including many African Americans, often face barriers to technology access due to limited financial resources, lower digital literacy levels, and inadequate technology infrastructure in their communities. Additionally, continued systemic discrimination (18, 19), delayed access to needed care (20, 21), and health risk factors (18) may contribute to the divide, further impeding African Americans\\u2019 ability to leverage technology for improved health outcomes.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref22\", \"ref23\", \"ref27\", \"ref28\"], \"section\": \"Introduction\", \"text\": \"Several studies have demonstrated the detrimental effects of technology inequity on healthcare disparities (22) among African Americans (23\\u201326). Research by Mulia et al. indicated that Hispanic/Latinx and African American patients had reduced access to telehealth services compared to their white counterparts, resulting in delayed or inadequate healthcare access (27). Furthermore, Heiney et al. found that African Americans faced barriers to utilizing mobile health applications, limiting their ability to engage in self-management and preventive care practices (28).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref29\", \"ref30\"], \"section\": \"Study population and design\", \"text\": \"Using data from the National Cancer Institute (NCI)\\u2019s Health Information National Trends Survey (HINTS) Wave 6 dataset, this study analyzed responses from 815 African Americans. HINTS 6 is a cross-sectional survey of non-institutionalized civilian adults aged 18+ years in the United States collected from March 7, 2022, to November 8th, 2022 with the goal of investigating the need for, access to, and use of health-related information and health-related behaviors, perceptions and knowledge (29, 30).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref31\"], \"section\": \"Study population and design\", \"text\": \"The mailing protocol for HINTS 6 followed a modified Dillman approach (31) with all selected households receiving a total of four mailings: an initial mailing, a reminder postcard, and two follow-up mailings.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref32\", \"ref33\", \"ref34\", \"ref35\", \"ref14\", \"ref36\", \"ref37\", \"ref38\", \"ref37\", \"ref38\", \"ref39\", \"ref33\", \"ref33\", \"ref40\", \"ref41\", \"ref42\", \"ref43\"], \"section\": \"Socioeconomic status (SES)\", \"text\": \"The relationship between socioeconomic status (SES), technology access, and healthcare disparities is complex and multifaceted. SES, which encompasses factors such as income, education, and occupation, plays a significant role in determining an individual\\u2019s access to technology and subsequent healthcare disparities. Many studies have established a strong relationship between socioeconomic factors and how they influence an individual\\u2019s access to technology (32, 33). Higher-income individuals are more likely to afford smartphones, computers, and internet access, which are essential for utilizing healthcare technology such as telehealth services or health apps (34). On the other hand, lower-income individuals may face barriers due to the cost of devices, internet access, or limited availability of technology infrastructure in their communities (35). Consequently, disparities in technology access may contribute to disparities in healthcare outcomes. Furthermore, the digital divide, driven by socioeconomic factors, exacerbates healthcare disparities. For instance, lower-income individuals, including African Americans who are more likely to experience economic challenges, may lack the resources or skills to effectively use health technology. This limits their ability to access online health information, engage in telehealth visits, or effectively manage their healthcare. As a result, they may experience delays in accessing care, receive suboptimal healthcare services, or have poorer health outcomes compared to individuals with higher SES (14, 36). SES is closely linked to health literacy, which refers to an individual\\u2019s ability to access, understand, and use health information to make informed decisions about their health (37, 38). Individuals with lower SES are more likely to have lower health literacy levels, which can hinder their utilization of healthcare technology. Limited health literacy skills may make it difficult for individuals to navigate complex digital platforms, understand health-related information, or effectively communicate with healthcare providers through technology (37, 38). Another factor that could be influenced by SES is provider-patient communication. Technology-mediated interactions, such as telehealth visits or patient portals, may impact provider-patient communication differently based on SES. Individuals with lower SES may face challenges in effectively communicating their health concerns, understanding medical jargon, or asking necessary questions through these platforms. These communication barriers can impede the delivery of patient-centered care and contribute to disparities in healthcare quality and outcomes (39). Education and technological literacy have the potential to influence the level of SES of an individual (33). Education, another component of SES, influences technological literacy and digital skills. Higher levels of education are associated with better technology proficiency (33, 40, 41), including the ability to navigate digital platforms, access online health resources, and use health technology effectively. Individuals with lower education levels may experience difficulties in adopting and benefiting from healthcare technology, leading to disparities in healthcare access, utilization, and health outcomes (42, 43). Thus, it is imperative to understand how SES plays into technology access and use and how they consequently impact healthcare disparity among African Americans. Respondents\\u2019 education level and household income were used to evaluate socioeconomic status. For education, respondents were asked about their highest level of education. The highest level of education was categorized as \\u201cLess than high school,\\u201d \\u201chigh school graduate,\\u201d \\u201csome college,\\u201d \\u201cbachelor\\u2019s degree,\\u201d and \\u201cpost-baccalaureate degree.\\u201d Similarly, respondents\\u2019 annual household income was assessed with 1 representing \\u201c$0 to $9,999\\u201d, 2 \\u201c$10,000 to $14,999\\u201d, 3 \\u201c$15,000 to $19,999\\u201d, 4 \\u201c$20,000 to $34,999\\u201d, 5 \\u201c$35,000 to $49,999\\u201d, 6 \\u201c$50,000 to $74,999\\u201d, 7 \\u201c$75,000 to $99,999\\u201d, 8 \\u201c$100,000 to $199,999\\u201d and 9 as \\u201c$200,000 or more\\u201d.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref44\", \"ref32\", \"ref45\", \"ref46\", \"ref46\", \"ref33\", \"ref46\", \"ref46\", \"ref46\"], \"section\": \"Technology access (TECH_ACC)\", \"text\": \"Technology and its associated advancements have long been trumpeted to influence the kind of healthcare individuals receive and the quality of such care (44). Nonetheless, through what has been known as the \\u201cDigital Divide,\\u201d technology might either assist in making things better or perhaps worse. For some populations, having access to digital information might help with self-care and maintaining good health (32). However, the racial and ethnic groups that experience the largest injustices, particularly Blacks and Hispanics, have significantly varying access to digital resources depending on socioeconomic level. The future will depend on improving digital health equality. Broadband access represents one specific major concern. Over 21 million Americans lack broadband access (45). While cities like New York have broadband infrastructure covering 99.9% of the population, 2.2 million adults there do not have a home broadband subscription (46). In more rural areas, such as the mountains of Appalachia in states such as Tennessee, Kentucky, and West Virginia, there are large areas with no or limited broadband access (46). According to the Public Policy Institute of California (PPIC), though broadband has grown slightly from 84% in 2019 to 85% in 2020, racial and ethnic disparities in access persist with 81% of Latino, 83% of Black, 87% of white, and 88% of Asian households report having broadband access at home in 2021 (33). Thus, disparities in broadband infrastructure, driven by uneven geographical deployment, economic affordability issues, and historical digital redlining, constitute critical barriers that disproportionately affect marginalized communities, including African Americans. Aside from broadband access, telehealth access is critically essential in promoting equitable healthcare. The COVID-19 pandemic has facilitated and increased the importance and use of telehealth (46). Because of the risk of person-to-person viral transmission, organizations around the country switched most outpatient care to telehealth essentially overnight (46). Black patients and poorer patients were much more likely to receive telephonic as opposed to video visits (46). The following questions were posed to respondents to measure technology access to access to basic cell phones, smartphones, and access and use of the internet rated on a \\u201cYes\\u201d/\\u201cNo\\u201d scale; (i) \\u201cHave a basic cell phone?,\\u201d (ii) \\u201cHave a smartphone?\\u201d and (iii) \\u201cDo you ever go online to access the Internet or World Wide Web, or to send and receive email?.\\u201d Based on the above, this study posits that: H3: Technology access has a significant negative effect on healthcare disparities, H4: Technology access has a significant positive effect on health technology, and H7: There is a significant negative indirect effect of SES on H_DISPARITY through the sequential mediation of TECH_ACC.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref47\", \"ref48\", \"ref49\"], \"section\": \"Health technology use (hTECH_USE)\", \"text\": \"Health technology has the potential to significantly impact healthcare outcomes for African Americans, as it may help improve access to care, enhance patient engagement, and promote health equity. For instance, during the COVID-19 pandemic the use of telehealth became more prevalent, helping individuals manage their chronic diseases by enhancing patient-provider communication, promoting medication adherence, and enabling self-monitoring of health conditions (47, 48). However, it is crucial to acknowledge that disparities in health technology use may also contribute to existing healthcare disparities (49), particularly issues of access due to structural and systemic racism. Thus, to measure the health technology use among African Americans, this study evaluated the frequency of watching a health-related video on a social media site like YouTube in the last 12\\u202fmonths. The responses were assessed on a 5-likert scale with 1 as \\u201cAlmost every day,\\u201d 3 as \\u201cA few times a month\\u201d and 5 as \\u201cNever.\\u201d Respondents were further asked how often they interacted with people who have similar health or medical issues as them on social media or online forums and how often they shared general health-related information on social media for example, a news article in the last 12\\u202fmonths, respectively. Finally, respondents were asked to indicate whether they had received care from a doctor or health professional using telehealth in the past 12\\u202fmonths using a scale of 1\\u20134, with 1 indicating Yes, by video, 2 as \\u201cYes, by phone call (voice only with no video),\\u201d 3 as \\u201cYes, some by video and some by phone call\\u201d and 4 as \\u201cNo telehealth visits in the past 12\\u202fmonths.\\u201d\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref50\", \"ref51\", \"ref52\"], \"section\": \"Statistical analysis\", \"text\": \"Due to its robustness of estimations and statistical power (50, 51), the partial least squares (PLS) based on structural equation modeling (SEM) was employed to test and validate the hypothesized model with the aid of SmartPLS 4.0 (52). The model\\u2019s validity was determined by analyzing the measurement and structural models.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref53\"], \"section\": \"Statistical analysis\", \"text\": \"The measurement model was evaluated using internal consistency reliability, convergent validity, and discriminant validity. This was done using the outer loadings (\\u22650.70 for reliability), Average of Variance Extracted (AVE; \\u22650.50 indicating convergent validity), Composite Reliability (CR; \\u22650.70 indicating internal consistency), Fornell and Larcker criterion (square root of AVE exceeding inter-construct correlations indicating discriminant validity), and Heterotrait-Monotrait Ratio of correlation (HTMT; <0.85 demonstrating discriminant validity) (53\\u201355).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref53\", \"ref54\", \"ref53\", \"ref56\", \"ref57\"], \"section\": \"Statistical analysis\", \"text\": \"The collinearity assessment (VIF) (53, 54), path coefficient (\\u03b2; showing relationship strength and direction), t-statistics (statistical significance) (53), and the model fit index were used to evaluate the structural model for the main model, mediation, and moderation analysis. The moderating effect was further evaluated using the simple slope analysis. Model fit analysis was performed using the Standardized Root Mean Square Residual (SRMR \\u22640.10 acceptable), discrepancy function (d_ULS and d_G), chi-square statistic, and the Normed Fit Index (NFI) (56, 57).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"tab1\"], \"section\": \"Results\", \"text\": \"Table 1 shows the summary statistics for the respondents\\u2019 demographics. The majority of respondents were females, and the most occurring age was 50\\u201364\\u202fyears old. Most respondents had some college education, with the majority having only one employment. Lastly, the most observed household income range was between $20,000 and $34,999.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"tab2\", \"ref38\", \"ref55\", \"tab2\", \"fig1\", \"ref55\"], \"section\": \"Results\", \"text\": \"The results for the outer loadings, internal consistency reliability, convergent validity, and discriminant validity are presented in Table 2 above. The outer loadings for all constructs showed values greater than the acceptable threshold of 0.70, except hTU2, which had an outer loading value of 0.694. Also, composite reliability values for all the constructs were greater than 0.70, suggesting strong internal consistency reliability (38). AVE values were all above the recommended level of 0.50 (55), as shown in Table 2. Figure 1 shows the various constructs with their respective loadings. Results of HTMT showed all the constructs had HTMT values less than the threshold of 1. Additionally, the square root of the AVE of all the constructs was greater than their correlation with other constructs, and the diagonal items were larger than the entries in corresponding columns and rows, hence satisfying the Fornell and Larcker criterion (55).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"tab3\", \"fig2\"], \"section\": \"Results\", \"text\": \"Results of the structural model as shown in Table 3 revealed a statistically significant relationship between SES\\u202f\\u2192\\u202fTECH_ACC (\\u03b2\\u202f=\\u202f0.424, t-statistics\\u202f=\\u202f16.444, p\\u202f<\\u202f0.001), TECH_ACC\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f\\u22120.086, t-statistics\\u202f=\\u202f1.828, p-value\\u202f=\\u202f0.034), TECH_ACC\\u202f\\u2192\\u202fhTECH_USE (\\u03b2\\u202f=\\u202f0.260, t-statistics\\u202f=\\u202f11.363, p\\u202f<\\u202f0.001), and hTECH_USE\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f\\u22120.180, t-statistics\\u202f=\\u202f4.458, p\\u202f<\\u202f0.001), thus, supporting hypothesis H2, H3, H4, and H5, respectively. However, SES\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f\\u22120.021, t-statistics\\u202f=\\u202f0.503, p-value\\u202f=\\u202f0.307) was not significant, therefore not supporting H1. Mediation results shows a partial mediation SES\\u202f\\u2192\\u202fTECH_ACC\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f\\u22120.036, t-statistics\\u202f=\\u202f1.794, p-value\\u202f=\\u202f0.036) and TECH_ACC\\u202f\\u2192\\u202fhTECH_USE\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f\\u22120.047, t-statistics\\u202f=\\u202f4.332, p\\u202f<\\u202f0.001), hence satisfying H7 and H8, respectively. The path diagram for the bootstrapped results is shown in Figure 2 below.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"fig3\"], \"section\": \"Results\", \"text\": \"The moderating effect of SES x TECH_ACC\\u202f\\u2192\\u202fH_DISPARITY (\\u03b2\\u202f=\\u202f0.097, t-statistics\\u202f=\\u202f2.674, p-value\\u202f=\\u202f0.004) produced a statistically significant result, thus, satisfying H6. The simple slope analysis of the moderating effect is shown in Figure 3.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref32\", \"ref33\", \"ref58\", \"ref12\"], \"section\": \"Discussion\", \"text\": \"This study aimed to investigate the relationships between socioeconomic status (SES), technology access, health technology use, and healthcare disparities. The findings revealed a positive association between SES and technology access, suggesting that individuals with higher socioeconomic status are more likely to have more access to technology. This aligns with previous research indicating that socioeconomic factors play a crucial role in determining technology access (32, 33, 58). The statistically significant relationship between SES and technology access highlights the role of structural racism in shaping economic opportunities and digital inclusion for African Americans. These disparities are not simply socioeconomic but are rooted in what Ruth Wilson Gilmore terms \\u2018group-differentiated vulnerability to premature death.\\u2019 (12) Communities with limited access to health technology face compounded disadvantages, reflecting patterns of exclusion embedded in racialized spatial dynamics.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref14\", \"ref59\", \"ref60\", \"ref47\", \"ref61\"], \"section\": \"Discussion\", \"text\": \"Consistent with previous studies (14, 59, 60), this study found a negative association between technology access and healthcare disparities. Individuals with better technology access were found to experience reduced healthcare disparities. This finding underscores the potential of technology in bridging healthcare gaps and improving access to care, especially for underserved populations, if, and only if, systemic barriers to access and utilization are dismantled. Telehealth specifically plays a crucial role in addressing healthcare disparities by providing access to medical care for individuals in underserved communities. It enables remote consultations, reduces wait times, and facilitates continuity of care, particularly for managing chronic diseases. Mobile health applications and electronic health records further support patient engagement by providing real-time health monitoring and improved access to medical information (47, 61). Thus, by providing remote access to healthcare services, technology can overcome geographical barriers and ensure timely delivery of care to individuals who may face challenges in accessing traditional healthcare facilities.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref44\", \"ref62\", \"ref65\"], \"section\": \"Discussion\", \"text\": \"The study also found a positive association between technology access and health technology use, which is consistent with previous research (44, 62\\u201364), suggesting that individuals with better technology access are more likely to engage with health technology tools. This finding emphasizes the importance of ensuring equitable technology access to facilitate health technology adoption and engagement among diverse populations. By leveraging health technology, individuals can actively manage their health, access educational resources, and engage in shared decision-making with healthcare providers, potentially leading to improved health outcomes (65).\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref66\", \"ref67\"], \"section\": \"Discussion\", \"text\": \"Furthermore, the negative association between health technology use and healthcare disparities supports the notion that health technology can help reduce disparities in healthcare outcomes. Individuals who actively utilize health technology may benefit from improved health management, increased access to information, and better communication with healthcare providers (66, 67). These factors contribute to more equitable healthcare experiences and outcomes.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref68\", \"ref69\"], \"section\": \"Discussion\", \"text\": \"The interaction effect between SES and technology access on healthcare disparities highlights the importance of considering the interplay between socioeconomic factors and technology access. This finding suggests that addressing both SES disparities and technology access is crucial in reducing healthcare disparities. Government initiatives such as telehealth subsidies, broadband expansion programs, and Medicaid coverage for virtual healthcare services have the potential to improve access to telehealth for low-income populations (68). These policies have the potential to reduce financial and technological barriers that limit healthcare access for marginalized groups (69). Thus, strategies should focus not only on enhancing technology access but also on addressing underlying socioeconomic inequalities to achieve equitable healthcare outcomes for all individuals.\"}, {\"pmc\": \"PMC12153058\", \"pmid\": \"40503485\", \"reference_ids\": [\"ref70\", \"ref71\"], \"section\": \"Discussion\", \"text\": \"Regarding the mediation analysis, this study found that the joint mediation of technology access and health technology use in the association between SES and healthcare disparities was statistically significant, suggesting that health technology use plays a crucial role in reducing healthcare disparities among individuals with different socioeconomic backgrounds (70, 71).\"}]"

Metadata

"{\"section-at-acceptance\": \"Life-course Epidemiology and Social Inequalities in Health\"}"