Health Information Technology Use Among Persons With Self-reported Atherosclerotic Cardiovascular Disease: Analysis of the 2011-2018 National Health Interview Survey

Abstract

Background Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality in the United States. Health information technologies (HITs) have recently emerged as a viable intervention to mitigate the burden of ASCVD. Approximately 60% of US adults report searching the internet for health information; however, previous research has not examined the prevalence of general technology or HIT use among adults with and without ASCVD. In addition, social determinants in HIT use among adults with ASCVD are not well understood. Objective The aim of this study was to evaluate the prevalence and social determinants of HIT use among US adults with versus without self-reported ASCVD. Methods We pooled cross-sectional data from the 2011-2018 National Health Interview Survey (NHIS) to examine the general technology and HIT use among adults aged ≥18 years with and without self-reported ASCVD (coronary heart disease, stroke, or both). General technology use was defined as mobile phone ownership, internet use, and computer use. HIT use was defined as looking up health information on the internet, filling a web-based prescription, scheduling a medical appointment on the internet, communicating with a health care provider by email, or using web-based group chats to learn about health topics. We evaluated sociodemographic differences in HIT use among respondents by using Poisson regression. Analyses were weighted according to NHIS standards. Results A total sample of 256,117 individuals were included, of which 2194 (0.9%) reported prior ASCVD. Among adults with prior ASCVD, the mean age was 70.6 (SD 11.5) years, and 47.4% (1048/2194) of the adults were females. General technology use differed between participants with and without prior ASCVD, with 36.0% (614/1826) and 76.2% (157,642/213,816) indicating internet usage and 24.6% (374/1575) and 60.7% (107,742/184,557) indicating using a computer every day, respectively. Similarly, adults with ASCVD were less likely to use HIT than those without ASCVD (515/2194, 25.1% vs 123,966/253,923, 51.0%; P<.001). Among adults with prior ASCVD, social determinants that were associated with HIT use included younger age, higher education, higher income, being employed, and being married. Conclusions HIT use was low among adults with a history of ASCVD, which may represent a barrier to delivering care via emerging HIT. Given the associations with social determinants such as income, education, and employment, targeted strategies and policies are needed to eliminate barriers to impact HIT usage.

Trends and factors associated with use of digital health technology among adults with serious psychological distress in the United States: A secondary data analysis of the National Health Interview Survey

“…Findings indicate that among people with SPD, older age, male sex, lower education level, and lower income, were associated with lower odds of using DHT to interact with the healthcare system. These findings are consistent with previous research which suggests that trends in DHT use across the general population is increasing but disparities in DHT engagement still exist, which could potentially exacerbate health disparities 13,15 . The finding that individuals with higher education level and high-income high SES background are more likely to use DHT matches observations from past studies 16 .…”

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review

“…A complete extraction table of the studies may be found in Table S1 . 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 The study characteristics are summarized in Table 1 , the SDOH domains addressed by each study are summarized in Table 2 , and the primary uses of the SDOH data among each study are summarized in Table 3…”

“…Most studies used a cross‐sectional study design (n=17), retrospective cohort studies (n=13), and geographical and spatial analysis (n=12), whereas less common study designs included case studies (n=3), clinical trials (n=3), and randomized control trials (n=2). Thirty‐three studies used linear or logistic regression methods, 35 , 37 , 38 , 39 , 40 , 41 , 42 , 46 , 47 , 48 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 62 , 64 , 65 , 66 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 80 , 81 and 14 studies applied spatial analysis to understand the relationship between SDOH and CVD outcomes. 35 , 36 , 37 , 38 , 45 , 50 , 53 , 54 , 58 , 59 , 60 , 61 Machine learning, including data mining, gradient boosting machine, extreme gradient boosting, phase type random forests, Breiman's random forest, support vector machine, and tree‐based classification were used in 4 stu...…”

Sustainable Digital Transformation in Healthcare: Advancing a Digital Vascular Health Innovation Solution

“…Aneurysms can occur anywhere in the body but are mostly seen in the aorta, the body's largest artery. Treatment options for aneurysms may include close monitoring, medication to control blood pressure, or surgery to repair or remove the affected vessel [16]. Stroke is another serious condition that may require vascular healthcare.…”