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Environmental Health

Title: Linking Electronic Health Records and In-depth Interviews to Uncover Barriers to Social Mobility and Health in a Declining Coal Mining Community

Site PI: Annemarie G. Hirsch, PhD, MPH (PI: Jennifer Silva, Indiana University)
Funder: Russell Sage Foundation
Dates: 01/01/20 – 03/31/2023

The Russell Sage Foundation is funding a collaboration among Geisinger, Indiana University, and Bucknell University to identify discrepancies between medical record and patient narrative data in terms of sources of poor health, diagnoses, and treatment among women living in rural, socioeconomically deprived communities in central Pennsylvania. Health systems are attempting to capture social determinants of health (SDoH) in electronic health records (EHR) and use these data to adjust care plans. To date, however, methods for identifying social needs, which are the SDoH prioritized by patients, have been underexplored, and there is little guidance as to how clinicians should act on SDoH data when caring for patients. Moreover, the unintended consequences of collecting and responding to SDoH are poorly understood. The objective of this study is to use two data sources, EHR data and patient interviews, to describe divergences between the EHR and patient experiences that could help identify gaps in the documentation of SDoH in the EHR; highlight potential missed opportunities for addressing social needs, and identify unintended consequences of efforts to integrate SDoH into clinical care.


  • Hirsch AG, Durden TE, Silva J. Linking electronic health records and in-depth interviews to inform efforts to integrate social determinants of health into health care delivery: Protocol for a qualitative research study. JMIR Research Protocols. 2022; 1(3):e36201
    doi: 10.2196/36201.
  • Title: Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network

    Co-PIs: Annemarie G. Hirsch, PhD, MPH and Brian S. Schwartz, MD, MS
    Funding Source: CDC
    Project Number: 1U01DP006296
    Dates: 09/2017 – 09/2022

    The Geisinger-Johns Hopkins Bloomberg School of Public Health Environmental Health Institute (EHI) is a site in the CDC-funded Network, a collaboration dedicated to identifying the contributions of community features and geography on type 2 diabetes risk and control. Diabetes prevalence and incidence in the US vary substantially by geography. The LEAD Network aims to guide policy decision-making to reduce the burden of type 2 diabetes across the US. LEAD sites conduct studies at their respective institutions as well as network-wide analyses. Using longitudinal electronic health record data and community data, the EHI is studying the role that community types and community features have in onset of type 2 diabetes and type 2 diabetes control (e.g., blood pressure and kidney function) in central and northeastern Pennsylvania. We are evaluating a range of community features, including greenness, blue space, community socioeconomic deprivation, the food environment, urbanicity, and leisure-time physical activity resources. Using questionnaire and salivary cortisol measures we are exploring the various pathways through which community features and community perceptions may impact type 2 diabetes control, including stress, food insecurity, diet, and physical activity. In collaboration with the other LEAD sites, the EHI has developed harmonized approaches to measurement and analysis, allowing for multi-site investigations of modifiable community features, including the food and physical activity environment, that may mediate the association between community socioeconomic deprivation and type 2 diabetes onset.


  • Hirsch AG, Nordberg CM, Bandeen-Roche K, Pollack 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. In press.
  • Thorpe LE, Adhikari A, Lopez P, Kanchi R, McClure LA, Hirsch AG, Howell CR, Zhu A, Alemi F, Rummo P, Ogburn EL, Algur Y, Nordberg CM, Poulsen MN, Long L, Carson AP, DeSilva SA, Meeker M, Schwartz BS, Lee DC, Siegel KR, Imperatore G, Elbel B. Neighborhood socioeconomic environment and risk of type 2 diabetes: Associations and mediation through food environment pathways in three independent study samples. Diabetes Care. 2022 Apr;45(4):798-810.
  • McAlexander TP, Algur Y, Schwartz BS, Rummo PE, Lee DC, Siegel KR, Ryan V, Lee NL, Malla G, McClure LA. Categorizing community type for epidemiologic evaluation of community factors and chronic disease across the United States. Social Sciences & Humanities Open. 2022;5(1):100250.
  • Hirsch AG, Nordberg CM, Chang A, Poulsen MN, Moon KA, Siegel KR, Rolka DB, Schwartz BS. Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis. Social Science & Medicine: Population Health. 2021 (15).
  • Poulsen MN, Schwartz BS, DeWalle J, Nordberg C, Pollak JS, Silva J, Mercado CI, Rolka DB, Siegel KR, Imperatore G, Hirsch AG. Proximity to freshwater blue space and type 2 diabetes: the importance of historical and economic context. Landscape and Urban Planning. 2021;209:104060.
  • Poulsen MN, Schwartz BS, Nordberg C, DeWalle J, Pollak J, Imperatore G, Mercado CI, Siegel KR, Hirsch AG. Association of greenness with blood pressure among individuals with type 2 diabetes across rural and urban community types in Pennsylvania, USA. International Journal of Environmental Research and Public Health. 2021;18:614
  • Schwartz BS, Pollak JS, Poulsen MN, Bandeen-Roche K, Moon KA, DeWalle J, Siegel KR, Mercado CI, Imperatore Giuseppina, Hirsch AG. Association of community types and features with risk of new onset type 2 diabetes across diverse geography in Pennsylvania. BMJ Open. 2021;11:e043528. doi:10.1136/bmjopen-2020-043528.
  • Hirsch AG, Carson AP, Lee NL, McAlexander T, Mercado C, Siegel K, Black NC, Elbel B, Long DL, Lopez P, McClure LA, Poulsen MN, Schwartz BS, Thorpe LE. The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics. JMIR Res Protoc. 2020;9(10):e21377. doi: 10.2196/21377.
  • Feldman J, Lee D, Lopez P, Rummo P, Hirsch A, Carson A, McClure L, Elbel B, Thorpe LE. Assessing county-level determinants of individual-level diabetes status in the United States (2003 – 2012): A random-effects within-between analysis of repeated cross-sectional data. Health & Place. 2020;63:102324.
  • Title: Understanding the role of community determinants in opioid use disorder and program implementation factors influencing patient adherence to opioid agonist therapy

    PI: Melissa N. Poulsen, PhD, MPH
    Funder: National Institute on Drug Abuse
    Grant number: K01 DA 049903-01A1
    Dates: 07/01/2020 – 06/30/2025

    To inform an effective response to the opioid epidemic, understanding the multilevel factors that influence risk of opioid use disorder (OUD) is critical. In addition, although the efficacy of medications for treating OUD is proven, long-term retention in treatment remains a challenge. Thus, efforts are needed to identify program implementation factors that can be strengthened to improve OUD treatment outcomes. This research seeks to generate evidence to understand novel, understudied risk factors for OUD, with implications for investment in community-level prevention interventions, and how health systems can improve treatment retention among individuals with OUD. The aims of this grant are twofold. The first aim centers on the social determinants that engender vulnerability to OUD, evaluating associations of community contextual factors related to socioeconomic, social, and physical conditions with OUD, and the role individual-level genetic risk factors, healthcare factors, and comorbid medical conditions play in these relations. We are utilizing electronic health record and genetic data from Geisinger patients coupled with secondary data characterizing community factors to conduct a nested case-control study. The second aim seeks to understand factors at the individual, interpersonal, and organizational levels that influence patient engagement in and retention to medication treatment for OUD. We are conducting a mixed methods study that includes interviews with patients and key informants to identify multilevel barriers and facilitators influencing treatment success followed by a survey of adult patients in Geisinger’s addiction medicine treatment clinics to quantify factors influencing treatment engagement and retention from a patient perspective.


  • Poulsen MNFreda PJ, Troiani V, Davoudi A, Mowery DL. Classifying characteristics of opioid use disorder from hospital discharge summaries using natural language processing. Frontiers in Public Health, 2022; 10