Projects
The Chicago Center for Diabetes Translation Research (CDTR) provides investigators with funding to lead pilot projects and feasibility studies that address diabetes, with an emphasis on research that addresses health inequity. Below view the current and past projects funded by the center for examples of the work we support. If you have questions about the application process for Chicago CDTR grants, find answers here.
Current Grant Awardees
Client & Staff Perspectives on Nutrition & Chronic Disease Prevention in Food PantriesPI: Jenny Jia, MD, MS, Northwestern University Feinberg School of Medicine
The purpose of this project is to:
- Convene meetings with community charitable food stakeholders to help refine and finalize interview questions for pantry clients on stigma and cultural relevance in food pantries.
- Examine client experience using semi-structured interviews with pantry clients that explore stigma, cultural relevance and clients' influence on healthy food selection at food pantries.
- Explore staff members’ perspectives on how to address stigma and cultural relevance experienced by pantry clients in the design of nutrition behavioral interventions in food pantries.
Innovating Diabetes Screening in Emergency Departments & Linkage Services (IDEAL): Enhancing Linkage to CarePI: Angela Kong, PhD, MPH, RD, University of Illinois Chicago
The purpose of this project is to:
- Identify critical barriers and facilitators to care linkage from the perspective of patients and healthcare staff implementing Project IDEAL to inform the development of implementation strategies supporting these processes.
- Pilot test strategies supporting linkage to care and assess the implementation of these strategies and their preliminary impact on care initiation.
Patient & Clinician Perspectives on Preventing & Managing Type 2 Diabetes Among Older Adults Engaged in HIV CarePI: Allison Pack, PhD, MPH, Northwestern University Feinberg School of Medicine
The purpose of this project is to:
- Explore patients’ knowledge and information sources for type 2 diabetes (T2DM), their perceived risks and any actions taken to prevent T2DM.
- Identify patient perspectives on disease self-management and potential intervention strategies for enhanced management of HIV+T2DM.
- Determine clinician perspectives on T2DM prevention and strategies for enhancing coordinated care of patients with HIV+T2DM.
Impact of Telehealth on Diabetes Care in Community Health Centers During the COVID-19 PandemicPI: Jodi Simon, PhD, Alliance Chicago
The purpose of this project is to:
- Describe how telehealth utilization is related to HbA1c levels and active engagement of adult diabetic patients throughout the COVID-19 pandemic.
- Use mixed methods to explore the impact of social determinants of health on telehealth utilization during the COVID-19 pandemic.
- Develop a set of community health center-specific telehealth recommendations to better serve diabetic patients’ complex needs.
Improving Diabetes Risk Communication Among Latino Hispanic PatientsPI: Nathan Walter, PhD, MA, Northwestern University School of Communication
The purpose of this project is to:
- Assess the unique messaging needs and responses of Hispanic/Latinx individuals to diabetes risk communication.
- Experimentally test (using a randomized controlled trial) the efficacy of vicarious affirmation, embedded within three national prediabetes awareness campaigns, to foster receptivity to risk and prevention information among Latinx Americans.
Past Grant Awardees
Longitudinal Patterns of Cardiometabolic Risk Factors, Gestational Diabetes & DiabetesPI: Sadiya Khan, MD, MS, Northwestern University Feinberg School of Medicine
The purpose of this project is to:
- Describe rates of gestational diabetes (GDM) and identify longitudinal patterns of cardiometabolic risk factors in early-adult life that are related to or provide protection from development of GDM and future incident GDM.
- In a subset of the Alliance cohort, link maternal and child health records between Alliance and the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) to examine associations with newborn and childhood cardiometabolic health.
Adaptation & Integration of a Telehealth Diabetes Discharge Toolkit for the COVID-19 EraPI: Amisha Wallia, MD, MS, Northwestern University Feinberg School of Medicine
The purpose of this project is to:
- Adapt the Diabetes (DM) Discharge Toolkit, using novel learning design methods, to create the Telehealth DM Discharge Toolkit for DM patients (COVID-19 +/-) requiring new and/or additional DM medication(s) who are being discharged from hospital to home.
- Conduct a feasibility pilot study of integration of the Telehealth DM Discharge Toolkit into the new hospital discharge processes of DM patients (COVID-19 +/-) requiring new and/or additional DM medication(s) in transition from hospital to home.
Machine-Learning Predictive Modeling of Increasing Cost-Related Medication Non-Adherence Among Medicare Diabetes Patients at High Risk of Hospitalization During a Coronavirus PandemicPIs: James Zhang, PhD, and David Meltzer, MD, PhD, University of Chicago
The purpose of this project is to:
- Test the hypothesis that there is an increased cost-related medication non-adherence (CRN) rate among Medicare diabetes patients at high risk of hospitalization during coronavirus pandemic, using a sample of 427 subjects enrolled in the Comprehensive Care Physician study with predominantly African American enrollees residing in the South Side of urban Chicago. We propose to use interrupted time series analysis to detect the difference in the CRN rates before and during the coronavirus pandemic.
- Develop a machine-learning predictive model to identify the factors that have a more significant role in predicting the increased CRN rates during the coronavirus pandemic, including disability status, gender, comorbidity conditions, functional status, family size, marital status, Medicare-Medicaid dual eligibility and mental health scores.
- Improve the Andersen healthcare utilization model by weighing the impact of predictors for CRN among diabetes patients during the pandemic based on the relative importance metric obtained from the machine-learning predictive modeling.