denotes emergency department, and IQR interquartile range. † Race was determined by the clinical team. ‡ Obesity was defined as a body-mass index (the weight in kilograms divided by the square of the height in meters) of 30 or higher.

PURPOSE SARS-CoV-2 (COVID-19) is a systemic infection. Patients with cancer are immunocompromised and may be vulnerable to COVID-related morbidity and mortality. The objectives of this study were to determine if patients with cancer have worse outcomes compared with patients without cancer and to identify demographic and clinical predictors of morbidity and mortality among patients with cancer. METHODS We used data from adult patients who tested positive for COVID-19 and were admitted to two New York–Presbyterian hospitals between March 3 and May 15, 2020. Patients with cancer were matched 1:4 to controls without cancer in terms of age, sex, and number of comorbidities. Using Kaplan-Meier curves and the log-rank test, we compared morbidity (intensive care unit admission and intubation) and mortality outcomes between patients with cancer and controls. Among those with cancer, we identified demographic and clinical predictors of worse outcomes using Cox proportional hazard models. RESULTS We included 585 patients who were COVID-19 positive, of whom 117 had active malignancy, defined as those receiving cancer-directed therapy or under active surveillance within 6 months of admission. Presenting symptoms and in-hospital complications were similar between the cancer and noncancer groups. Nearly one half of patients with cancer were receiving therapy, and 45% of patients received cytotoxic or immunosuppressive treatment within 90 days of admission. There were no statistically significant differences in morbidity or mortality ( P = .894) between patients with and without cancer. CONCLUSION We observed that patients with COVID-19 and cancer had similar outcomes compared with matched patients without cancer. This finding suggests that a diagnosis of active cancer alone and recent anticancer therapy do not predict worse COVID-19 outcomes and therefore, recommendations to limit cancer-directed therapy must be considered carefully in relation to cancer-specific outcomes and death.

Background and Purpose: Social determinants of health (SDOH) have been previously associated with incident stroke. Although SDOH often cluster within individuals, few studies have examined associations between incident stroke and multiple SDOH within the same individual. The objective was to determine the individual and cumulative effects of SDOH on incident stroke. Methods: This study included 27 813 participants from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, a national, representative, prospective cohort of black and white adults aged ≥45 years. SDOH was the primary exposure. The main outcome was expert adjudicated incident stroke. Cox proportional hazards models examined associations between incident stroke and SDOH, individually and as a count of SDOH, adjusting for potential confounders. Results: The mean age was 64.7 years (SD 9.4) at baseline; 55.4% were women and 40.4% were blacks. Over a median follow-up of 9.5 years (IQR, 6.0–11.5), we observed 1470 incident stroke events. Of 10 candidate SDOH, 7 were associated with stroke ( P <0.10): race, education, income, zip code poverty, health insurance, social isolation, and residence in one of the 10 lowest ranked states for public health infrastructure. A significant age interaction resulted in stratification at 75 years. In fully adjusted models, among individuals <75 years, risk of stroke rose as the number of SDOH increased (hazard ratio for one SDOH, 1.26 [95% CI, 1.02–1.55]; 2 SDOH hazard ratio, 1.38 [95% CI, 1.12–1.71]; and ≥3 SDOH hazard ratio, 1.51 [95% CI, 1.21–1.89]) compared with those without any SDOH. Among those ≥75 years, none of the observed effects reached statistical significance. Conclusions: Incremental increases in the number of SDOH were independently associated with higher incident stroke risk in adults aged <75 years, with no statistically significant effects observed in individuals ≥75 years. Targeting individuals with multiple SDOH may help reduce risk of stroke among vulnerable populations.

Researchers have developed missing data handling techniques for estimating interaction effects in multiple regression. Extending to latent variable interactions, we investigated full information maximum likelihood (FIML) estimation to handle incompletely observed indicators for product indicator (PI) and latent moderated structural equations (LMS) methods. Drawing on the analytic work on missing data handling techniques in multiple regression with interaction effects, we compared the performance of FIML for PI and LMS analytically. We performed a simulation study to compare FIML for PI and LMS. We recommend using FIML for LMS when the indicators are missing completely at random (MCAR) or missing at random (MAR) and when they are normally distributed. FIML for LMS produces unbiased parameter estimates with small variances, correct Type I error rates, and high statistical power of interaction effects. We illustrated the use of these methods by analyzing the interaction effect between advanced cancer patients’ depression and change of inner peace well-being on future hopelessness levels.

Background: Despite potential harm that can result from polypharmacy, real-world data on polypharmacy in the setting of heart failure (HF) are limited. We sought to address this knowledge gap by studying older adults hospitalized for HF derived from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). Methods: We examined 558 older adults aged ≥65 years with adjudicated HF hospitalizations from 380 hospitals across the United States. We collected and examined data from the REGARDS baseline assessment, medical charts from HF-adjudicated hospitalizations, the American Hospital Association annual survey database, and Medicare’s Hospital Compare website. We counted the number of medications taken at hospital admission and discharge; and classified each medication as HF-related, non-HF cardiovascular-related, or noncardiovascular-related. Results: The vast majority of participants (84% at admission and 95% at discharge) took ≥5 medications; and 42% at admission and 55% at discharge took ≥10 medications. The prevalence of taking ≥10 medications (polypharmacy) increased over the study period. As the number of total medications increased, the number of noncardiovascular medications increased more rapidly than the number of HF-related or non-HF cardiovascular medications. Conclusions: Defining polypharmacy as taking ≥10 medications might be more ideal in the HF population as most patients already take ≥5 medications. Polypharmacy is common both at admission and hospital discharge, and its prevalence is rising over time. The majority of medications taken by older adults with HF are noncardiovascular medications. There is a need to develop strategies that can mitigate the negative effects of polypharmacy among older adults with HF.

Background: Some reports suggest that obesity could be a risk factor for complications in coronavirus disease 2019 (COVID-19) (1). Several mechanisms could explain this. First, adipocytes, which activate the inflammatory cascade, can increase risk for thromboembolism and susceptibility to the cytokine storm described in COVID-19 (2). Second, obesity negatively affects lung mechanics, which could predispose obese persons to more severe respiratory distress and failure (3). Finally, obesity can alter mitochondrial bioenergetics in lung epithelial cells and increase risk for acute lung injury (4). However, some have suggested an obesity paradox in some critical illnesses, including acute respiratory distress syndrome, where patients with obesity may have improved outcomes; whether this phenomenon occurs in patients with COVID-19 is unclear (5). Objective: To study the association between obesity and outcomes among a diverse cohort of 1687 persons hospitalized with confirmed COVID-19 at 2 New York City hospitals. Methods and Findings: This retrospective observational cohort study included consecutive adults with confirmed COVID-19 who were hospitalized between 3 March and 15 May 2020 at an 862-bed quaternary referral center or a 180bed community hospital in New York City. We excluded 46 patients who did not have height or weight data available to calculate body mass index (BMI). Patient data were manually abstracted (1) from the electronic health record through 6 June 2020. We determined BMI on the basis of the most recent height and weight listed in the electronic health record. Height and weight were collected during hospitalization for 95.5% of the cohort; the remaining BMIs were collected during ambulatory encounters within 3 months of hospitalization. We defined BMI categories as underweight (<18.5 kg/m 2), normal (18.5 to 24.9 kg/m 2), overweight (25.0 to 29.9 kg/m 2), mild to moderate obesity (30.0 to 39.9 kg/m 2), and morbid obesity (≥40.0 kg/m 2). To examine the association between BMI and in-hospital mortality, we used a Cox proportional hazards model adjusted for age, sex, race, smoking, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, end-stage renal disease, coronary artery disease, heart failure, and cancer. These characteristics were chosen on the basis of risk factors for severe COVID-19 identified by the Centers for Disease Control and Prevention. We also examined for effect modification by age, sex, and race. To examine the association between BMI and respiratory failure, defined as a need for invasive mechanical ventilation, we used a Fine and Gray model to account for the competing risk for death and adjusted for the same 12 variables used in the model for mortality. We excluded the underweight group from this analysis because of low numbers. Finally, we repeated the adjusted Cox proportional hazards model analysis for mortality among persons with respiratory failure, again excluding the underweight group. To account for missing data (12% for race), we did multiple imputation.

Background: Social determinants of health (SDH) are individually associated with incident coronary heart disease (CHD) events. Indices reflecting social deprivation have been developed for population management, but are difficult to operationalize during clinical care. We examined whether a simple count of SDH is associated with fatal incident CHD and nonfatal myocardial infarction (MI). Methods: We used data from the prospective longitudinal REasons for Geographic And Racial Differences in Stroke cohort study, a national population-based sample of community-dwelling Black and white adults age ≥45 years recruited from 2003-7. Seven SDH from the five Healthy People 2020 domains included social context (Black race, social isolation); education (educational attainment); economic stability (annual household income); neighborhood (living in a zip code with high poverty); and healthcare (lacking health insurance, living in one of the 9 US states with the least public health infrastructure). Outcomes were expert adjudicated fatal incident CHD and nonfatal MI. Results: Of 22,152 participants free of CHD at baseline, 58.8% were women, 42.0% were Blacks, 20.6% had no SDH, 30.6% had 1, 23.0% had 2, and 25.8% had ≥3. There were 463 fatal incident CHD events and 932 nonfatal MIs over median 10.7 years [IQR 6.6-12.7]. Fewer SDH were associated with nonfatal MI than with fatal incident CHD. The age-adjusted incidence per 1000 person-years increased with the number of SDH for both fatal incident CHD (0 SDH 1.30, 1 SDH 1.44, 2 SDH 2.05, ≥3 SDH 2.86) and nonfatal MI (0 SDH 3.91, 1 SDH 4.33, ≥2 SDH 5.44). Compared to those without SDH, crude and fully adjusted hazard ratios (HR) for fatal incident CHD among those with ≥3 SDH were 3.00 (95% CI 2.17, 4.15) and 1.67 (95% CI 1.18, 2.37), respectively; and that for nonfatal MI among those with ≥2 SDH were 1.57 (95% CI 1.30, 1.90) and 1.14 (0.93, 1.41), respectively. Conclusions: A greater burden of SDH was associated with a graded increase in risk of incident CHD, with greater magnitude and independent associations for fatal incident CHD. Counting the number of SDH may be a promising approach that could be incorporated into clinical care to identify individuals at high risk of CHD.

Background: During the height of the coronavirus (COVID-19) pandemic, there was an unprecedented demand for "virtual visits," or ambulatory visits conducted via video interface, in order to decrease the risk of transmission. Objective: To describe the implementation and evaluation of a video visit program at a large, academic primary care practice in New York, NY, the epicenter of the COVID-19 pandemic. Design and participants: We included consecutive adults (age > 18) scheduled for video visits from March 16, 2020 to April 17, 2020 for COVID-19 and non-COVID-19 related complaints. Intervention: New processes were established to prepare the practice and patients for video visits. Video visits were conducted by attendings, residents, and nurse practitioners. Main measures: Guided by the RE-AIM Framework, we evaluated the Reach, Effectiveness, Adoption, and Implementation of video visits. Key results: In the 4 weeks prior to the study period, 12 video visits were completed. During the 5-weeks study period, we completed a total of 1,030 video visits for 817 unique patients. Of the video visits completed, 42% were for COVID-19 related symptoms, and the remainder were for other acute or chronic conditions. Video visits were completed more often among younger adults, women, and those with commercial insurance, compared to those who completed in-person visits pre-COVID (all p < 0.0001). Patients who completed video visits reported high satisfaction (mean 4.6 on a 5-point scale [SD: 0.97]); 13.3% reported technical challenges during video visits. Conclusions: Video visits are feasible for the delivery of primary care for patients during the COVID-19 pandemic.