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BACKGROUND: Although research shows that digital health tools (DHT) are increasingly integrated with healthcare in the United States, very few studies have investigated the rural-urban differences in DHT adoption at the national level. Individuals in rural communities experience disproportionately greater rates of chronic diseases and face unique challenges in accessing health care. Studies have shown that digital technology can improve access and support rural health by overcoming geographic barriers to care. OBJECTIVE: To evaluate the rates of ownership and preferences for utilization of DHT as a measure of interest among rural adults compared to their urban counterparts in the United States using a National Inpatient Survey. METHODS: Data was drawn from the 2019 (n= 5438) iteration of the Health Information National Trends Survey (HINTS 5 cycle 3). Chi-square tests and weighted multivariable logistic regressions were conducted to examine rural-urban differences regarding ownership, usage, and use of digital health tools to interact with health care systems while adjusting for health-related characteristics and sociodemographic factors. RESULTS: The ownership rates of digital health technology (DHT) devices, including tablets, smart phones, health apps, and wearable devices, were comparable between rural and urban residents. For tablets, the ownership rates were 54.52% among rural residents and 60.24% among urban residents, with an adjusted odds ratio (OR) of 0.87 (95% confidence interval {CI}: 0.61, 1.24). The ownership rates of health apps were 51.41% and 53.35% among rural and urban residents, respectively, with an adjusted OR of 0.93 (95% CI: 0.62, 1.42). For smartphones, the ownership rates were 81.64% among rural residents and 84.10% among urban residents, with an adjusted OR of 0.81 (95% CI: 0.59, 1.11). Additionally, rural residents were equally likely to use DHT in managing their healthcare needs. Both groups were equally likely to have reported their smart device as helpful in discussions with their healthcare providers (OR 0.90; 95% CI 63 - 1.30; p = 0.572). Similarly, there were similar odds of reporting that DHT had helped them to track progress on a health-related goal (e.g., quitting smoking, losing weight, or increasing physical activity) (OR 1.17; 95% CI 0.75 - 1.83; p = 0.491), and to make medical decisions (OR 1.05; 95% CI 0.70 - 1.59; p = 0.797). However, they had lower rates of internet access and were less likely to use DHT for communicating with their healthcare providers. CONCLUSION: We found that rural residents are equally likely as urban residents to own and use DHT to manage their health. However, they were less likely to communicate with their health providers using DHT. With increasing use of DHT in healthcare, future research that targets reasons for geographical digital access disparities is warranted.
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Background There is a scarcity of studies delineating the trends of cardiovascular interventions in the hospitalized population stratified by body mass index (BMI). Our study aimed to study the burden of cardiovascular interventions and outcomes by BMI. Methods We retrospectively analyzed the Nationwide Inpatient Sample (NIS) database between January 2016 and December 2020. We identified the population of interest using the International Classification of Diseases, Tenth Revision (ICD-10) code. We studied the BMI in five categories: "healthy weight" (HW; BMI < 19.9-24.9 kg/m2), "overweight" (OV; BMI = 25-29.9 kg/m2), "obesity class one" (OB1; BMI = 30-34.9 kg/m2), "obesity class two" (OB2; BMI = 35-39.9 kg/m2), and "obesity class three" (OB3; BMI > 40 kg/m2). Results There were 5,654,905 hospitalizations with an ICD-10 code related to BMI within this study period. The HW group had 1,103,659 (19.5%) hospitalizations, the OV group had 462,464 (8.2%), the OB1 group had 1,095,325 (19.4%), the OB2 group had 1,036,682 (18.3%), and the OB3 group had 1,956,775 (34.6%) hospitalizations. The mean age of the population with obesity was as follows: OB1 = 61 years (SD = 16); OB2 = 58 years (SD = 15.9); and OB3 = 55 years (SD = 15.5). The mean ages of the HW and OV groups were 68 years (SD = 16.6) and 65 years (SD = 16.1), respectively. In the HW group, there were 948 (8.1%) hospital admissions for aortic valve replacement (AVR), 54 (11%) for aortic valve repair (AVRr), 737 (15.9%) for mitral valve replacement (MVRr), 12 (17.1%) for mitral valve repair (MVR), 79 (2.2%) for left atrial appendage (LAA) closure, and 3390 (5.2%) for percutaneous coronary intervention (PCI). The OV group had 1049 (8.9%) hospital admissions for AVRs, 42 (9%) for AVRr, 461 (10%) for MVRr, four (5.7%) for MVR, 307 (8.6%) for LAA closure, and 5703 (8.8%) for PCIs. The OB1 group had 3326 (28.4%) hospital admissions for AVR, 125 (26.9%) for AVRr, 1229 (26.7%) for MVRr, 23 (32.9%) for MVR, 1173 (32.9%) for LAA, and 20,255 (31.3%) for PCI, while the OB2 group had 2725 (23.3%) hospital admissions for AVR, 105 (22.6%) for AVRr, 898 (19.4%) for MVRr, 11 (15.7%) for MVR, 933 (26.2%) for LAA, and 16,773 (25.9%) for PCI. Lastly, the OB3 group had 3626 (31%) hospital admissions for AVR, 139 (29.9%) for AVRr, 1285 (27.8%) for MVRr, 20 (28.6%) for MVR, 1063 (29.9%) for LAA, and 18,589 (28.7%) for PCI. Conclusion Our study supports the evidence of increased cardiovascular interventions with increasing BMI. Albeit, an inconsistent presentation across the spectrum of cardiovascular diseases and outcomes, for example, equal or better outcomes in obese cohorts compared to the healthy weight population undergoing PCI. However, the increasing cardiovascular intervention burden in the youngest studied population suggests a rise in the cardiovascular disease burden among the young and partially explains their better outcomes. Steps to include weight management for these patients are paramount.
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Melanoma is a skin cancer arising from melanocytes, the cells responsible for synthesizing melanin pigment, which gives the skin its color. Early diagnosis and treatment of melanoma increase survival rates. Clinical examination and biopsy are the primary tools used to diagnose melanoma. However, distinguishing between pre-malignant melanocytic lesions and early invasive melanoma histopathologically remains challenging. Therefore, additional modalities such as a detailed clinical history, imaging, genetic testing, and biomarkers have been applied to diagnose melanoma. This review discusses the current trends in biomarker advancements over the last 10 years to assist in the early detection and diagnosis of melanoma. Biomarkers such as melanoma-associated antigens (MAAs), S100B, microRNAs (miRNAs), and circulating tumor cells (CTCs) have the potential to aid in the detection, diagnosis, and prognosis of melanoma. However, the application of biomarkers in the diagnosis of melanoma is still evolving.