Telehealth is an important source of health care during the COVID-19 pandemic. Evidence is scarce regarding disparities in telehealth utilization in the United States. We aimed to investigate the prevalence and factors associated with telehealth utilization among US adults. Our data came from the Health, Ethnicity, and Pandemic Study, a nationally representative survey conducted in October 2020, with 2554 adults ≥ 18 and an oversample of racial/ethnic minorities. Telehealth utilization was measured as self-reported teleconsultation with providers via email, text message, phone, video, and remote patient monitoring during the pandemic. Logistic regressions were performed to examine the association between telehealth use and factors at the individual, household, and community levels. Overall, 43% of the sample reported having used telehealth, representing 114.5 million adults in the nation. East and Southeast Asians used telehealth less than non-Hispanic Whites (OR = 0.5, 95% CI: 0.3–0.8). Being uninsured (compared with private insurance: OR = 0.4, 95% CI: 0.2–0.8), and those with limited broadband coverage in the community (OR = 0.5, 95% CI: 0.3–0.8) were less likely to use telehealth. There is a need to develop and implement more equitable policies and interventions at both the individual and community levels to improve access to telehealth services and reduce related disparities.

Background: The documented efficacy and promise of telemedicine in diabetes management does not necessarily mean that it can be easily translated into clinical practice. An important barrier concerns patient activation and engagement with telemedicine technology. Objective: To assess the importance of patient activation and engagement with remote patient monitoring technology in diabetes management among patients with type 2 diabetes. Methods: Ordinary least squares and logistic regression analyses were used to examine how patient activation and engagement with remote patient monitoring technology were related to changes in hemoglobin A1c (HbA1c) for 1,354 patients with type 2 diabetes monitored remotely for 3 months between 2015 and 2017. Results: Patients with more frequent and regular participation in remote monitoring had lower HbA1c levels at the end of the program. Compared to patients who uploaded their biometric data every 2 days or less frequently, patients who maintained an average frequency of one upload per day were less likely to have a postmonitoring HbA1c > 9% after adjusting for selected covariates on baseline demographics and health conditions. Conclusions: Higher levels of patient activation and engagement with remote patient monitoring technology were associated with better glycemic control outcomes. Developing targeted interventions for different groups of patients to promote their activation and engagement levels would be important to improve the effectiveness of remote patient monitoring in diabetes management.

Objective: To investigate the impact of the COVID-19 pandemic on multiple lifestyle changes among adults in the United States (USA). Methods: We conducted a survey, the Health, Ethnicity, and Pandemic (HEAP) Study, in October 2020 among USA adults. Participants were selected from the United States using 48 sampling strata, including age, race, ethnicity, education, and gender, and were asked to report five lifestyle behaviors (i.e., exercise time, screen time, fast-food meal consumption, alcohol drinking, and cigarette smoking) before and during the COVID-19 pandemic. The associations of sociodemographic factors with each lifestyle change were estimated using weighted multivariable logistic regression models. Results: All 2709 HEAP participants were included in this study. Compared to pre-pandemic, the time spent on exercise decreased (32.06 vs. 38.65 min/day; p < 0.001) and screen time increased (6.79 vs. 5.06 h/day; p < 0.001) during the pandemic. The percentage of individuals who reported consuming fast-food meals ≥3 times/week decreased from 37.7% before the pandemic to 33.3% during the pandemic. The percentage of heavy drinkers (≥5 times/week) increased from 20.9% before the pandemic to 25.7% during the pandemic. Among smokers, heavy smoking (≥11 cigarettes/day) increased from 5.8% before the pandemic to 7.9% during the pandemic. We also identified subgroups who were more vulnerable to adverse influences from the pandemic, including racial/ethnic minority groups and young adults. Conclusions: The COVID-19 pandemic had negative impacts on multiple lifestyle behaviors among Americans. Mitigating such negative impacts of COVID-19 requires effective interventions, particularly for some vulnerable subgroups.

Hubei province in China has completed cycle of quarantine-resumption in 23^rd Janauary and 8^th April 2020, providing a unique opportunity as for now to assess its intervention impact and the pattern of SARS-COV-2 transmission during the quarantine period. In this study, we evaluate the impact of the metropolitan-wide quarantine on the trend and transmission route of the COVID-19 epidemic in Hubei, China. The intervention reduces more than 70% of new infections in both households and the public space, as well as the deaths caused by COVID-19 pneumonia. Household transmission is the dominant route of disease spread regardless of quarantine. This will provide important evidence and scientific insights to other worldwide countries that are currently under quarantine.

Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000-240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55-94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic. Delaying vaccination rollout for 1-2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained. The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.