About

Gerald Bloomfield, MD, MPH, is the Associate Director for Research at the Duke Global Health Institute and an Associate Professor of Medicine as well as an Associate Research Professor of Global Health. He joined the faculty after completing his Cardiovascular Medicine fellowship training at Duke University Medical Center and Duke Clinical Research Institute. His educational background includes medical training, internal medicine residency, and a Master of Public Health degree from Johns Hopkins University. Dr. Bloomfield leads a longstanding research and capacity building program focused on cardiovascular global health, which encompasses work in under-resourced communities in the United States and in low- and middle-income country settings, including a partnership with Moi University in Eldoret, Kenya. His research interests include health policy and systems, health care access, HIV/AIDS, pediatrics, non-communicable diseases, cardiovascular disease, and epidemiology, with a particular focus on impacts of race and culture.

Research topics

• Internal medicine
• Medicine
• Environmental health
• Economic growth
• Gerontology
• Urology
• Immunology
• Virology
• Endocrinology
• Intensive care medicine
• Family medicine
• Nursing

Selected publications

• Risk of obesity, diabetes, hypertension, and major adverse cardiovascular events after a switch to an integrase inhibitor: a target trial emulation in REPRIEVE

  The Lancet HIV · 2026-03-28

  articleOpen access
  PublisherOA PDFDOI

• m-Lifestyle Intervention for Employees (m-LIfE): protocol for a pilot cluster randomized controlled trial on cardiovascular disease prevention strategies

  Pilot and Feasibility Studies · 2026-04-07

  articleOpen access

  INTRODUCTION: Cardiovascular diseases (CVDs) are the leading cause of mortality globally. Developing countries, including Pakistan, face a significant burden of CVD risk factors. Mobile health (mHealth) interventions have shown potential in promoting physical activity (PA) and reducing sedentary behavior; however, their use in CVD risk prevention, particularly among high-risk, urban sedentary employees remains underexplored. This study aims to develop and evaluate the feasibility and potential efficacy of a mobile-based Lifestyle Intervention for Employees (m-LIfE) to improve PA among sedentary bank employees in Karachi, Pakistan. METHODS AND ANALYSIS: A multiphase-sequential design will be conducted in two phases across branches of three commercial banks (one public and two private) in Karachi. In phase 1, a cross-sectional study will be conducted to estimate 10-year and lifetime risk for CVD events among bank employees, followed by focus groups discussions and interviews to explore employees' awareness, perceptions, and preferences regarding CVD prevention. These findings will inform the development of the m-LIfE app using a human-centered design (HCD) approach. In phase 2, a pilot cluster randomized controlled trial will be conducted to assess the feasibility (primary outcome) and potential efficacy of m-LIfE on PA (secondary outcome) over 12 weeks, with a 4-week post-intervention follow-up. Bank branches will serve as clusters, with four randomized to m-LIfE and four to routine care. The m-LIfE app will deliver healthy lifestyle content for behavior change, and routine-care participants will receive paper-based educational pamphlets on CVD prevention. DISCUSSION: m-LIfE will use an HCD approach to co-create the intervention with participants, ensuring contextual relevance and addressing barriers faced by sedentary employees. This approach complements global evidence while accounting for unique cultural, organizational, and individual factors shaping lifestyle behaviors in Pakistan. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov on 20th May 2025 (Identifier: NCT06981247).

  PublisherDOI

• Reciprocal innovation in implementation science and global health: reflections from the EXTRA-CVD (extending the HIV treatment cascade for cardiovascular disease prevention) study

  BMC Global and Public Health · 2026-03-18

  articleOpen access

  Reciprocal innovation, a model of sustained, multidirectional exchange in which health strategies are adapted, revisited, and refined across contexts, offers a compelling framework to rethink how implementation science can support global health equity by enabling dynamic, multidirectional learning across different contexts. Drawing on the EXTRA-CVD trial, a nurse-led cardiovascular disease prevention intervention designed to extend the HIV treatment cascade in United States (U.S.) HIV clinics, which adapted strategies informed by implementation research in Kenya and the U.S. Veterans Affairs health system, this perspective examines how reciprocal innovation can begin to emerge within existing research structures, as well as where opportunities for deeper exchange remain limited. We identify four operational domains of reciprocal innovation: care delivery strategies, end-user engagement, research methodologies, and research leadership and partnership. Across these domains, we describe how cross-context learning shaped intervention adaptation and site-level implementation in EXTRA-CVD, as well as missed opportunities where more intentional feedback, shared leadership, and methodological exchange could have strengthened multidirectional learning. Taken together, this work highlights both the potential and the practical challenges of reciprocal innovation in implementation research, emphasizing its role in moving beyond unidirectional knowledge transfer toward iterative, context-responsive learning. By embedding structures for iterative feedback, equity-centered governance, and multidirectional learning systems within research and implementation systems, future global partnerships can foster more inclusive, responsive, and sustainable health interventions.

  PublisherOA PDFDOI

• Mapping and evaluation of global and country-specific cardiovascular disease risk prediction models

  Figshare · 2026-03-17

  articleOpen access

  Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality, requiring precise risk prediction models for effective prevention and management. This review maps and evaluates globally utilized and country-specific CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, PREVENT, WHO/ISH Risk Charts, INTERHEART, and SCORE2. A structured literature search was conducted using PubMed and Google Scholar, from which 30 relevant studies were selected. Most of the models integrate traditional risk factors such as age, sex, blood pressure, cholesterol, and smoking status to estimate CVD risk. While these models demonstrate moderate to good discrimination (C-statistics ranging from 0.66 to 0.80) and validation, their applicability varies across populations, with concerns about overestimation or underestimation in non-original cohorts. Notably, the WHO/ISH and Globorisk models address global diversity by incorporating regional calibrations, making them suitable for low- and middle-income countries. Similarly, the country-specific risk scores outperform global models due to their incorporation of local socio-demographics. Limitations persist across existing models, including the underrepresentation of younger individuals, ethnic minorities, and the exclusion of emerging risk factors. Future efforts must prioritize the development of locally validated, population-specific models to support equitable and effective CVD risk assessment and prevention. Heart diseases and stroke are among the leading causes of illness and death worldwide. Doctors and health care providers use “risk prediction models” to estimate a person’s chance of developing a heart disease or/and stroke (cardiovascular diseases: CVD) in future typically within the next 10 years. These models usually consider factors like age, sex, blood pressure, cholesterol levels, and smoking. In this review, we looked at widely available CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, WHO/ISH charts, INTERHEART, SCORE2, and country-specific tools. We found that while these models work reasonably well overall, they do not always perform equally well in all populations. For example, some models may overestimate or underestimate risk when applied outside the country where they were developed. Models that are adapted for specific regions like WHO/ISH and Globorisk or developed within a country tend to provide more accurate predictions, especially in low- and middle-income countries. However, many models still miss certain groups, such as younger people or ethnic minorities, and do not include emerging risk factors. Future research needs to focus on creating and testing models that are locally relevant and fair, so that people everywhere can benefit from accurate CVD risk prediction and better prevention strategies.

  PublisherDOI

• Mapping and evaluation of global and country-specific cardiovascular disease risk prediction models

  Future Cardiology · 2026-03-17

  articleOpen access

  Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality, requiring precise risk prediction models for effective prevention and management. This review maps and evaluates globally utilized and country-specific CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, PREVENT, WHO/ISH Risk Charts, INTERHEART, and SCORE2. A structured literature search was conducted using PubMed and Google Scholar, from which 30 relevant studies were selected. Most of the models integrate traditional risk factors such as age, sex, blood pressure, cholesterol, and smoking status to estimate CVD risk. While these models demonstrate moderate to good discrimination (C-statistics ranging from 0.66 to 0.80) and validation, their applicability varies across populations, with concerns about overestimation or underestimation in non-original cohorts. Notably, the WHO/ISH and Globorisk models address global diversity by incorporating regional calibrations, making them suitable for low- and middle-income countries. Similarly, the country-specific risk scores outperform global models due to their incorporation of local socio-demographics. Limitations persist across existing models, including the underrepresentation of younger individuals, ethnic minorities, and the exclusion of emerging risk factors. Future efforts must prioritize the development of locally validated, population-specific models to support equitable and effective CVD risk assessment and prevention.

  PublisherOA PDFDOI

• Mapping and evaluation of global and country-specific cardiovascular disease risk prediction models

  Figshare · 2026-03-17

  articleOpen access

  Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality, requiring precise risk prediction models for effective prevention and management. This review maps and evaluates globally utilized and country-specific CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, PREVENT, WHO/ISH Risk Charts, INTERHEART, and SCORE2. A structured literature search was conducted using PubMed and Google Scholar, from which 30 relevant studies were selected. Most of the models integrate traditional risk factors such as age, sex, blood pressure, cholesterol, and smoking status to estimate CVD risk. While these models demonstrate moderate to good discrimination (C-statistics ranging from 0.66 to 0.80) and validation, their applicability varies across populations, with concerns about overestimation or underestimation in non-original cohorts. Notably, the WHO/ISH and Globorisk models address global diversity by incorporating regional calibrations, making them suitable for low- and middle-income countries. Similarly, the country-specific risk scores outperform global models due to their incorporation of local socio-demographics. Limitations persist across existing models, including the underrepresentation of younger individuals, ethnic minorities, and the exclusion of emerging risk factors. Future efforts must prioritize the development of locally validated, population-specific models to support equitable and effective CVD risk assessment and prevention. Heart diseases and stroke are among the leading causes of illness and death worldwide. Doctors and health care providers use “risk prediction models” to estimate a person’s chance of developing a heart disease or/and stroke (cardiovascular diseases: CVD) in future typically within the next 10 years. These models usually consider factors like age, sex, blood pressure, cholesterol levels, and smoking. In this review, we looked at widely available CVD risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, WHO/ISH charts, INTERHEART, SCORE2, and country-specific tools. We found that while these models work reasonably well overall, they do not always perform equally well in all populations. For example, some models may overestimate or underestimate risk when applied outside the country where they were developed. Models that are adapted for specific regions like WHO/ISH and Globorisk or developed within a country tend to provide more accurate predictions, especially in low- and middle-income countries. However, many models still miss certain groups, such as younger people or ethnic minorities, and do not include emerging risk factors. Future research needs to focus on creating and testing models that are locally relevant and fair, so that people everywhere can benefit from accurate CVD risk prediction and better prevention strategies.

  PublisherDOI

• Valvular Heart Disease Associations With Cardiac Biomarkers Using AI-Guided Echocardiography

  JACC Advances · 2025-12-16

  articleOpen accessSenior author

  BACKGROUND: Few studies have evaluated the prevalence or severity of mitral valve prolapse (MVP) and other valvular heart disease (VHD) in the rural U.S. South, where strategies for early detection are crucial for risk stratification and prevention. OBJECTIVES: We assessed the prevalence of MVP and other VHD in a rural U.S. South cohort and examined associations with cardiovascular disease (CVD) risk. We also evaluated associations between MVP severity, high-sensitivity cardiac troponin T, and N-terminal pro-B-type natriuretic peptide. METHODS: We conducted a cross-sectional analysis from the Risk Underlying Rural Areas Longitudinal study. Logistic regression assessed associations between participant characteristics and MVP, other VHD, or either. Weighted models assessed odds for MVP and other VHD by 10-year CVD risk categories using the Predicting Risk of CVD Events (PREVENT) score. Among a subset, we evaluated associations between MVP severity and cardiac biomarkers. RESULTS: Among 2,621 participants (68.7% women), MVP and other VHD were present in 1.9% and 11.2%, respectively. Compared to the low PREVENT risk group, odds of MVP were lower and odds of VHD were higher among borderline and intermediate/high groups. High-sensitivity cardiac troponin T was lower in MVP vs non-MVP (0.64; 95% CI: 0.58-0.71), without difference by severity of MVP. N-terminal pro-B-type natriuretic peptide was higher in participants with severe MVP than non-MVP (2.03; 95% CI: 1.49-2.78). CONCLUSIONS: MVP prevalence aligned with population-based epidemiologic studies. PREVENT risk category may identify individuals at higher risk for MVP and for other VHD. Future studies are needed to evaluate relationships between MVP/VHD status and clinical events.

  PublisherDOI

• Baseline ECG and Cardiovascular Outcomes in People With HIV: Insights From REPRIEVE

  Journal of the American Heart Association · 2025-12-11

  articleOpen accessSenior authorCorresponding

  BACKGROUND: With antiretroviral therapy, people with HIV (PWH) have an increased burden of cardiovascular disease. The REPRIEVE (Randomized Trial to Prevent Vascular Events in HIV) trial demonstrated that pitavastatin reduces major adverse cardiovascular events (MACEs) among PWH at low to moderate traditional atherosclerotic risk. Electrocardiographic abnormalities are common in PWH, but little is known about their association with MACEs. We sought to examine whether baseline electrocardiographic abnormalities are associated with increased MACE risk among a global primary cardiovascular disease prevention cohort of PWH in REPRIEVE. METHODS: In this observational analysis, entry electrocardiographic abnormalities were adjudicated and classified as major or minor abnormalities. Multivariable cause-specific Cox proportional hazards models assessed the association of electrocardiographic abnormalities with MACEs while stratifying for treatment effect. The model improvement with the addition of the ECG to a model with the pooled cohort equations risk score was examined. RESULTS: Among 7719 participants (median age, 50 years; 69% men), 49% had ≥1 electrocardiographic abnormality, with 3% classified as major. Over a median of 5.6 years, a major electrocardiographic abnormality was associated with a 2.42-fold (95% CI, 1.49-3.91) higher hazard of incident MACEs, whereas minor abnormalities were not. Specific abnormalities associated with MACEs were chamber enlargement and infarct/ischemia pattern. No significant subgroup- or treatment-related interaction was observed. Adding electrocardiographic findings to traditional risk factors increased the C-statistic modestly (+0.01). CONCLUSIONS: Among PWH in REPRIEVE, electrocardiographic abnormalities were common, but major electrocardiographic abnormalities were rare. Though major abnormalities were associated with increased hazard of MACEs, routine electrocardiographic screening is unlikely to improve the prediction of future cardiovascular events in this primary prevention population with low to moderate cardiovascular risk.

  PublisherDOI

• Statin Effects on Pericoronary Adipose Tissue Density in People With HIV

  JACC. Cardiovascular imaging · 2025-12-01

  articleOpen access
  PublisherOA PDFDOI

• Health-related quality of life among people with HIV at low-to-moderate risk for atherosclerotic cardiovascular disease in the REPRIEVE Trial

  AIDS · 2025-11-11

  articleOpen access

  BACKGROUND: There is limited evidence concerning the relationship between cardiometabolic characteristics and health-related quality of life (HRQoL), and potential effects of statin therapy among people with HIV (PWH). METHODS: The Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) enrolled PWH aged 40-75 years on antiretroviral therapy (ART) with low-to-moderate ASCVD risk. Coronary computed tomography angiography assessed coronary plaque among a subset of participants in the REPRIEVE Mechanistic Substudy at baseline and 24 months. The Short Form-36-Item Health Survey Version 2 was collected at baseline, and physical (PCS) and mental (MCS) component summary scores were determined. We explored the relationship of PCS and MCS with cardiometabolic characteristics, coronary atherosclerosis, and assessed change in score by treatment group (pitavastatin vs. placebo). RESULTS: Of 733 participants, median age was 51 years, 84% were male, 34% were Black non-Hispanic, and median years diagnosed with HIV was 15. At baseline, for participants randomized to pitavastatin vs. placebo the median PCS was 54.5 (Q1, Q3: 46.9, 57.7) vs. 54.1 (47.5, 58.0), and the median MCS was 52.9 (44.1, 57.6) vs. 52.8 (44.0, 57.9). In fully adjusted analyses, older age, Black non-Hispanic race/ethnicity, ART regimen class, elevated BMI, and cigarette smoking were associated with lower PCS. No clear trends were apparent with MCS. Between baseline and month 24, declines in PCS and MCS were minimal with no apparent difference by treatment group. CONCLUSIONS: Among this cohort of ART-treated PWH, baseline cardiometabolic risk factors were associated with worse self-reported physical HRQoL, with no apparent effect of statin therapy. TRIAL REGISTRATION: REPRIEVE; NCT02344290; https://clinicaltrials.gov/study/NCT02344290.

  PublisherOA PDFDOI

Recent grants

• Pathways to cardiovascular disease prevention and impact of specialty referral in under-represented racial/ethnic minorities with HIV

  NIH · $3.3M · 2018–2025

• NIH Grant K01TW008407

  NIH · $490k · 2015

Frequent coauthors

• Pamela S. Douglas

  Clinical Research Institute

  116 shared

• Eric J. Velazquez

  Yale University

  105 shared

• Kathleen V. Fitch

  Massachusetts General Hospital

  81 shared

• Steven Grinspoon

  Massachusetts General Hospital

  76 shared

• Markella V. Zanni

  Harvard University

  75 shared

• Julian T. Hertz

  Duke University

  73 shared

• Rajesh Vedanthan

  72 shared

• Nathan M. Thielman

  Duke Institute for Health Innovation

  71 shared

Labs

• Duke Global Health InstitutePI

Awards & honors

• School of Medicine Inclusive Excellence Award