Our past research has highlighted four community domains that may impact the development of diabetes in our region, as well as management of the disease:
- Social environment, particularly the level of community socioeconomic deprivation
- Food environment, in terms of food outlets present in a community
- Physical activity environment, including walkability and the fitness and recreational environment
- Chronic environmental contamination, including the presence of abandoned coal mine lands, unconventional natural gas development, and industrial food animal production
Our first study includes 17 years of electronic health record data on more than 130,000 patients with type 2 diabetes, combined with secondary data on characteristics of Pennsylvania communities. In this new study, we are enriching electronic health record data with questionnaire, morphometric, and biomarker data on a subset of 1000 patients with diabetes. This data will allow us to evaluate the pathways by which the four community domains influence type 2 diabetes, whether through stress, sleep quality, mental health, or physical activity and dietary behaviors. Through this work, we aim to identify modifiable contextual factors with the greatest influence on type 2 diabetes in Pennsylvania to inform targeted disease prevention and management strategies.
This 5-year study is part of a larger network funded by the CDC, The Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network. This collaborative network includes, Geisinger, Johns Hopkins University, New York University School of Medicine, Drexel University, and the University of Alabama at Birmingham. Additional information about The Diabetes LEAD Network can be found here: http://diabetesleadnetwork.org/
Her first paper, that found that the greater the burden of AMLs in communities, the higher the community socioeconomic deprivation, was published in the peer-reviewed journal ISRN Public Health. Her second paper found that higher AML burden was associated with worse diabetes early in the disease course and worse progression over time in 28,000 Geisinger diabetic patients using hemoglobin A1c levels, a biomarker of diabetes severity and control.
- Hirsch AG, Nordberg CM, Bandeen-Roche K, Pollak J, Poulsen MN, Moon KA, Schwartz BS. Urban-rural differences in health care utilization and COVID-19 outcomes in patients with Type 2 diabetes. Preventing Chronic Disease. 2022; 19: 220015. DOI: http://dx.doi.org/10.5888/pcd19.220015. Appendices.
- Hirsch AG, Durden TE, Nordberg C, Berger A, Schwartz BS. Associations of four community factors with longitudinal change in hemoglobin A1c levels in patients with type 2 diabetes. Diabetes Care 2018; 41: 461-8.
- Wood GC, Horwitz D, Still CD, Mirshahi T, Benotti P, Parikh M, Hirsch AG. Performance of the DiaRem score for predicting diabetes remission in two health systems following bariatric surgery procedures in Hispanic and non-Hispanic white patients. Obesity Surgery 2018; 28: 61-68.
- Lent MR, Benotti PN, Mirshahi T, Gerhard G, Strodel WE, Petrick AT, Gabrielsen JD, Rolston DD, Still CD, Hirsch AG, Zubair F, Cook A, Carey DJ, Wood GC. All-cause and specific-cause mortality risk following Roux-en-Y gastric bypass in patients with and without diabetes. Diabetes Care 2017; 40: 1379-1385.
- Wood GC, Mirshahi T, Still CD, Hirsch AG. Association of DiaRem score with cure of type 2 diabetes following bariatric surgery. JAMA Surgery 2016; 15: 779-781.
- Liu AY, Curriero FC, Glass TA, Stewart WF, Schwartz BS. The contextual influence of coal abandoned mine lands in communities and type 2 diabetes in Pennsylvania. Health & Place 2013; 22: 115-122.
- Schoenthaler AM, Schwartz BS, Wood C, Stewart WF. Patient and physician factors associated with adherence to diabetes medications. Diabetes Educator 2012; 38: 397-408.