Raymond Greenberg

Dr. Raymond Greenberg

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 May 2022 in JMIR Public Health and Surveillance
Verified
Journal Article
Open access
UCUan-I Chen
Uan-I Chen
HXHua Xu
Hua Xu
TKTrudy Millard Krause
Trudy Millard Krause
RGRaymond Greenberg
Raymond Greenberg
XDXiao Dong
Xiao Dong
XJXiaoqian Jiang
Xiaoqian Jiang
Background: Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making. Objective: This study aims to assess the factors associated with COVID-19–related deaths from a large, national, individual-level data set. Methods: A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic. Results: A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19–related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46). Conclusions: This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19–related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.

29 April 2025 in PLOS ONE
Verified
Journal Article
Open access
PDF file
CSCaroline M. Schaefer
Caroline M. Schaefer
TKTrudy Millard Krause
Trudy Millard Krause
GDGeorge L. Delclos
George L. Delclos
RGRaymond Greenberg
Raymond Greenberg
Background: A retrospective cohort study was undertaken to assess the relationship between initial disease severity of COVID-19 and the risk of post-acute symptoms. The COVID-19 cohort was compared against influenza and pneumonia cohorts to assess whether risk of post-acute symptoms differed. Methods: Administrative health claims data were obtained for commercially insured and Medicare Advantage covered adults (≥ 18 years) with symptomatic laboratory-confirmed COVID-19 diagnosed in 2020 (n=121,205), and similar cohorts of influenza (n=20,844) and pneumonia (n=29,052) patients diagnosed prior to the pandemic. Post-acute symptoms were assessed at four weeks, three and six months following initial diagnosis. Results: Among the patients with COVID-19, the likelihood of any post-acute symptom increased with initial disease severity, and was also influenced by the presence of comorbidities, especially rheumatoid arthritis, ischemic heart disease and asthma. The specific post-acute symptoms varied by age, with increased risks of anxiety and headache among the young, whereas the elderly experienced increased brain fog and fatigue. When compared against the influenza and pneumonia cohorts, all three groups experienced post-acute symptoms, with a strong relationship to disease severity, and only partial resolution over the six-month observation period. Those with influenza were less likely than those with COVID-19 to experience post-acute symptoms while those with pneumonia were more likely to have post-acute illness than those with COVID-19. Conclusions: Using a large national dataset, we found that COVID-19 symptomology could not be described by previously seen influenza or pneumonia symptomology and differences exist in the prevalence of symptoms as well as time to resolution, better characterizing “long COVID” and identifying that these differences are unique to COVID-19.

6 November 2025 in Cancers
Verified
Journal Article
Open access
PDF file
DLDavid S. Lopez
David S. Lopez
EPEfstathia Polychronopoulou
Efstathia Polychronopoulou
OAOmer Abdelgadir
Omer Abdelgadir
RGRaymond Greenberg
Raymond Greenberg
LCLindsay Grey Cowell
Lindsay Grey Cowell
YKYong_Fang Kuo
Yong_Fang Kuo
Background/Objectives: Metformin is one of the most frequently used concomitant medications among prostate cancer (PCa) patients. However, the effects of metformin on all-cause and PCa-specific mortality among men diagnosed with advanced/metastatic PCa treated with androgen-deprivation therapy (ADT) remain poorly understood, but they may be specifically explained by emulating a target trial. Methods: We emulated a target trial of metformin therapy and survival using observational data on 7361 patients diagnosed with advanced PCa, who were treated with ADT, from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2008–2019), with completed follow-up until 2020. We included patients with diabetes, and participants were assigned as either “initiator of metformin within 6 months after advanced PCa diagnosis” or “non-initiator of metformin.” We estimated mortality risks using Cox proportional hazards models with adjustment for risk factors via inverse probability weighting using both intention-to-treat and per-protocol analyses. Results: Over 13 years of follow-up, with a maximum 3 years of follow-up after PCa diagnosis, all-cause mortality occurred in 52 metformin initiators (47.7%) versus 3052 non-initiators (42.1%), while PCa-specific mortality occurred in 36 initiators (33.0%) versus 1919 non-initiators (26.5%). In the intention-to-treat analysis, metformin initiation was not associated with all-cause mortality (Hazard Ratio [HR] = 1.38, 95% CI: 0.98–1.95) or PCa-specific mortality (HR = 0.99, 95% CI: 0.63–1.55). Similarly, in per-protocol analysis, there was no evidence of risk reduction with all-cause (HR = 1.20, 95% CI = 0.80–1.81) or PCa-specific mortality (HR = 1.45, 95% CI = 0.88–2.38) after adjusting for time-varying covariates and allowing a 30-day gap for metformin discontinuation, adjusted for via inverse probability weighting. Conclusions: Our findings align with prior randomized trials showing no survival benefit of metformin in advanced PCa patients receiving ADT. Timing of metformin discontinuation also showed no significant effect. However, the small size of the metformin initiator group precluded subgroup analyses for hormone-sensitive (HSPC) and castrate-resistant prostate cancer (CRPC), limiting our ability to explore potential differential effects.

2 April 2021 in JMIR Preprints
Verified
Preprint
Open access
UCUan-I Chen
Uan-I Chen
HXHua Xu
Hua Xu
TKTrudy Millard Krause
Trudy Millard Krause
RGRaymond Greenberg
Raymond Greenberg
XDXiao Dong
Xiao Dong
XJXiaoqian Jiang
Xiaoqian Jiang
BACKGROUND Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making. OBJECTIVE This study aims to assess the factors associated with COVID-19–related deaths from a large, national, individual-level data set. METHODS A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic. RESULTS A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19–related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46). CONCLUSIONS This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19–related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.