Background The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients. Objectives This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area. Methods Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption. Results A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31–1.37), and have private insurance (OR 1.43; CI, 1.41–1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28–1.35), and have public insurance (OR 1.30; CI 1.27–1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, p = 0.01) had a strong correlation to video telemedicine adoption rate. Conclusion Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.

This study was funded by a COVID-19 Rapid Response Grant to ZZ from the Medical College of Wisconsin Cardiovascular Center through the Cullen Run Foundation, a startup fund to ZZ from the Medical College of Wisconsin and Blood Research Institute through the Advancing a Healthier Wisconsin Endowment, a Fellow Scholar Award to ZZ from the American Society of Hematology, and a Career Development Award (19CDA34660043) to ZZ from the American Heart Association. The Medical College of Wisconsin Clinical Research Data Warehouse was supported by the NIH National Center for Advancing Translational Sciences (to Clinical & Translational Science Institute of Southeast Wisconsin, UL1TR001436). Background. Alteration in blood triglyceride levels have been found in patients with coronavirus disease 2019 (COVID-19). However, the association between hypertriglyceridemia and mortality in COVID-19 patients is unknown. Objective. To investigate the association between alteration in triglyceride level and mortality in hospitalized COVID-19 patients. Methods. We conducted a retrospective study of 600 hospitalized patients with COVID-19 diagnosis (ICD10CM:U07.1) and/or SARS-CoV-2 positive testing results between March 1, 2020 and December 21, 2020 at a tertiary academic medical center in Milwaukee, Wisconsin. De-identified data, including demographics, medical history, and blood triglyceride levels were collected and analyzed. Of the 600 patients, 109 patients died. The triglyceride value on admission was considered the baseline and the peak was defined as the highest level reported during the entire period of hospitalization. Hypertriglyceridemia was defined as greater than 150 mg/dl. Logistic regression analyses were performed to evaluate the association between hypertriglyceridemia and mortality. Results. There was no significant difference in baseline triglyceride levels between non-survivors (n=109) and survivors (n=491) [Median 127 vs. 113 mg/dl, p =0.213]. However, the non-survivors had significantly higher peak triglyceride levels during hospitalization [Median 179 vs. 134 mg/dl, p <0.001]. Importantly, hypertriglyceridemia independently associated with mortality [odds ratio=2.3 (95% CI: 1.4-3.7, p =0.001)], after adjusting for age, gender, obesity, history of hypertension and diabetes, high CRP, high leukocyte count and glucocorticoid treatment in a multivariable logistic regression model. Conclusions. Hypertriglyceridemia during hospitalization is independently associated with 2.3 times higher mortality in COVID-19 patients. Prospective studies are needed to independently validate this retrospective analysis.

Objective The objective of this study was to determine the impact of patient demographics and socioeconomic factors on the utilization of tertiary rhinology care services in an upper Midwestern academic medical center. Study Design Retrospective review of electronic health records. Setting Academic medical center. Methods The electronic health record of our academic center was interrogated for the demographics and diagnosis of chronic rhinosinusitis (CRS) among adult patients seen by fellowship-trained rhinologists from 2000 to 2019. Patient characteristics (age, sex, race, insurance status) and population-level data (median income and education level) were compared with utilization of tertiary rhinology services for CRS. Utilization rates were calculated for each regional zip code and correlated with census data for median income and education. The association between determinants of health and tertiary rhinology utilization was assessed by multivariate regression analyses. Results A total of 8325 patients diagnosed with CRS used tertiary rhinology services. Patients were older (median, 58.9 years) and more likely to be female (57.6%), White (85%), and privately insured (60%) when compared with patients seen across our hospital system ( P < .001). Adjusted analyses showed median income, education level, and White race to be independently correlated with tertiary care utilization. Private insurance alone was not an independent contributing factor to access. Conclusion Utilization of tertiary rhinology services correlated with income, race, and education level. Private insurance was not an independent factor. These results highlight social differences in determinants of access to tertiary otolaryngologic care.

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