About

Elizabeth D Nesoff, PhD, MPH, is an Assistant Professor of Biostatistics and Epidemiology at the University of Pennsylvania Perelman School of Medicine. She is a senior fellow at the Leonard Davis Institute for Health Economics, a senior scholar at the Penn Injury Science Center, and a senior scholar at the Center for Clinical Epidemiology and Biostatistics, as well as the Penn Center for Public Health. Her research expertise focuses on substance use, built environment, injury prevention, and health disparities. She holds a BA from Wellesley College, an MPH from Emory University Rollins School of Public Health, and a PhD in Health, Behavior & Society from The Johns Hopkins University Bloomberg School of Public Health. Her work involves developing tools and conducting research on neighborhood characteristics related to drug overdoses, harm reduction, and public health interventions.

Research topics

• Medicine
• Demography
• Environmental health
• Psychology
• Geography

Selected publications

• Fatal Opioid Overdoses by Historical and Contemporary Neighborhood-Level Structural Racism

  JAMA Health Forum · 2025-11-07 · 2 citations

  articleOpen accessSenior author

  Importance: Black, Indigenous, and Latino communities are disproportionately affected by the US overdose epidemic. Structural inequalities, encompassing social, economic, and infrastructural dimensions, have been increasingly theorized as fundamental drivers of these disparities. Objective: To investigate whether there is an association between neighborhood-level structural racism and opioid-involved overdose deaths in an urban area. Design, Setting, and Participants: This ecological serial cross-sectional study of 796 census tracts (2017-2019) and 792 census tracts (2020-2022) in Chicago, Illinois, used a geospatial and intersectional analytic approach. A quasi-Poisson spatial regression was conducted to examine associations between neighborhood-level structural racism and census tract-level opioid-involved overdose deaths before the COVID-19 pandemic (2017-2019) and during the COVID-19 pandemic (2020-2022). Eigenvector spatial filtering was used to control for residual spatial autocorrelation. Population density was also accounted for in the regression model. Two structural racism indicators (historical redlining and contemporary racialized economic segregation) were combined to develop an index that captures 4 distinct neighborhood intersectional groups of racism over an 80-year period. Average marginal effect calculations were also performed to support the interpretability of the findings. Data were analyzed from February 19, 2024, to July 3, 2025. Exposure: A combined measure of 2 structural racism indicators (historical redlining and contemporary racialized economic segregation). Main Outcomes and Measures: Overdose deaths were aggregated to census tracts; the main outcome measure was the number of overdose deaths at the census tract-level. Results: The total sample sizes were 796 census tracts before the COVID-19 pandemic (2017-2019) and 792 census tracts during the COVID-19 pandemic (2020-2022). As defined by the study's combined measure of structural racism, census tracts with high levels of racism in the past and/or present showed statistically significantly higher number of fatal overdoses compared with tracts with low levels of racism both in the past and present. Just before the COVID-19 pandemic (ie, 2017-2019), tracts with high sustained levels of structural racism past and present had, on average, over 2 more fatal overdoses per tract compared with sustained advantaged tracts (average marginal effect, 2.60; 95% CI, 2.02-3.19; P < .001). During the COVID-19 pandemic (2020-2022), tracts that were advantaged in the past but experienced high present-day segregation had, on average, almost 4 more fatal overdoses per tract compared with sustained advantaged tracts (average marginal effect, 3.81; 95% CI, 1.94-5.68; P < .001). The overall burden of overdose death was higher for all neighborhood groups during the pandemic compared with before the pandemic. Conclusions and Relevance: These findings provide preliminary evidence that structural racism could be a root cause of opioid-involved overdose deaths. Future research is needed to identify mechanisms linking structural racism to overdose deaths and to develop effective policies and programs to reduce fatal overdose rates.

  PublisherDOI

• Stimulant Overdose Prediction Model for Medicaid-Insured Persons

  JAMA Health Forum · 2025-09-19

  articleOpen access

  Importance: Overdoses involving methamphetamines and cocaine have increased in recent years. Identification of individuals at highest risk could facilitate the implementation of evidence-based interventions to reduce overdose risk. Objective: To develop and internally validate a model that predicts hospitalization or emergency department (ED) treatment for stimulant-involved overdose among the Medicaid-insured population. Design, Setting, and Participants: This was a retrospective case-cohort study using Medicaid claims data from 2016 to 2019 (development) and 2020 (validation) for all Medicaid enrollees age 15 years or older with a cocaine- or other stimulant-involved overdose. A subcohort was created using a simple random sample of the full cohort of all cases. Within the full cohort, cases were identified as those having any inpatient or ED encounter for stimulant-involved overdose during the following year. A case-cohort sample was obtained for each calendar year from 2016 to 2020, each with a subcohort size of 100 000. Each individual contributed only 1 case event (for an individual with multiple overdoses, only the first eligible was selected). For each of the 4 overdose outcomes, a predictive weighted Cox model was first developed among enrollees of sampling years 2016 to 2019 (development set), and its performance was evaluated in our test set of 2020. The prediction models were first developed in November 2023, and the model fairness assessment was performed in April to May 2025. Interventions or Exposures: Individual-level candidate predictors were demographic characteristics, enrollment, health care utilization, and other clinical variables. Area-level variables included social, economic, housing, and demographic characteristics data from the American Community Survey, rural-urban classification, Social Deprivation Index, retail opioid dispensing rates, and health resources. Main Outcomes and Measures: Four types of stimulant-involved overdose associated with hospitalization or ED treatment: cocaine-involved overdose, (1) involving an opioid or (2) not involving an opioid; or methamphetamine-, ecstasy-, or other psychostimulant-involved overdose (hereafter, other stimulant), (3) involving an opioid or (4) not involving an opioid. Results: The analysis included 78 795 enrollees with cocaine- and other stimulant-involved overdose (mean [SD] age, 42.2 [13.7] years; 33 304 [42%] female and 45 491 [58%] male individuals). Weighted Cox regression prediction models showed good calibration and high discriminatory performance (Harrell C statistic): cocaine-involved overdose, with (0.923) or without (0.902) an opioid; other stimulant-involved overdose, with (0.909) or without (0.868) an opioid. For cocaine-involved overdose with opioids, previous individual opioid use disorder diagnosis or cocaine use disorder diagnosis played the largest role in overdose risk prediction. For cocaine-involved overdose without opioids, previous cocaine use disorder diagnosis and area-level income inequality and housing variables contributed most to prediction. For other stimulant-involved overdose with opioids, previous opioid use disorder diagnosis and area-level percentage of those living with a disability contributed most to prediction. For other stimulant-involved overdoses without opioids, previous stimulant-related disorder and area-level proportion of individuals receiving Supplemental Nutrition Assistance Program contributed most to prediction. Conclusions and Relevance: This case-cohort study found that readily available data can be used to identify those at high risk of hospitalization or ED visit for cocaine- or stimulant-involved overdose. These individuals would likely benefit most from evidence-based interventions and awareness of risk factors for overdose.

  PublisherOA PDFDOI

• The Continued Usefulness of Social Media Recruitment for Surveys of People who Use Opioids

  Drug and Alcohol Dependence · 2025-02-01

  article1st authorCorresponding
  PublisherDOI

• Addressing the critical need for long-term mental health data during the COVID-19 pandemic: Changes in mental health from April to September 2020

  UNC Libraries · 2025-06-04

  articleOpen access
  PublisherDOI

• An Intersectional and Spatial Approach to Assessing the Relationship Between Structural Racism and Opioid-Involved Overdose Deaths in Chicago, Illinois

  Drug and Alcohol Dependence · 2025-02-01

  articleSenior author
  PublisherDOI

• Challenging the Continued Usefulness of Social Media Recruitment for Surveys of Hidden Populations of People Who Use Opioids

  Journal of Medical Internet Research · 2025-04-30 · 1 citations

  articleOpen access1st authorCorresponding

  Historically, recruiting research participants through social media facilitated access to people who use opioids, capturing a range of drug use behaviors. The current rapidly changing online landscape, however, casts doubt on social media's continued usefulness for study recruitment. In this viewpoint paper, we assessed social media recruitment for people who use opioids and described challenges and potential solutions for effective recruitment. As part of a study on barriers to harm reduction health services, we recruited people who use opioids in New York City to complete a REDCap (Research Electronic Data Capture; Vanderbilt University) internet-based survey using Meta (Facebook and Instagram), X (formerly known as Twitter), Reddit, and Discord. Eligible participants must have reported using opioids (heroin, prescription opioids, or fentanyl) for nonprescription purposes in the past 90 days and live or work in New York City. Data collection took place from August 2023 to November 2023. Including study purpose, compensation, and inclusion criteria caused Meta's social media platforms and X to flag our ads as "discriminatory" and "spreading false information." Listing incentives increased bot traffic across all platforms despite bot prevention activities (eg, reCAPTCHA and counting items in an image). We instituted a rigorous post hoc data cleaning protocol (eg, investigating duplicate IP addresses, participants reporting use of a fictitious drug, invalid ZIP codes, and improbable drug use behaviors) to identify bot submissions and repeat participants. Participants received a US $20 gift card if still deemed eligible after post hoc data inspection. There were 2560 submissions, 93.2% (n=2387) of which were determined to be from bots or malicious responders. Of these, 23.9% (n=571) showed evidence of a duplicate IP or email address, 45.9% (n=1095) reported consuming a fictitious drug, 15.8% (n=378) provided an invalid ZIP code, and 9.4% (n=225) reported improbable drug use behaviors. The majority of responses deemed legitimate (n=173) were collected from Meta (n=79, 45.7%) and Reddit (n=48, 27.8%). X's ads were the most expensive (US $1.96/click) and yielded the fewest participants (3 completed surveys). Social media recruitment of hidden populations is challenging but not impossible. Rigorous data collection protocols and post hoc data inspection are necessary to ensure the validity of findings. These methods may counter previous best practices for researching stigmatized behaviors.

  PublisherDOI

• Exploring modifiable neighborhood risk factors for fatal opioid overdose: a case–control study in two US cities

  American Journal of Epidemiology · 2025-07-14

  articleOpen access1st authorCorresponding

  To explore associations between physical and social neighborhood factors and fatal opioid overdose, we remotely visited 2018-2019 fatal opioid overdose locations in New York City (n = 2867) and Chicago (n = 1677) via Google Street View and used a reliable and valid tool to assess 65 street block characteristics. We compared these locations to a proportional sample of blocks with no 2018-2019 overdoses (New York City n = 2093; Chicago n = 1148). We used logistic regression to explore associations between block characteristics and odds of an overdose event, controlling for neighborhood-level covariates (poverty, segregation). For both cities, blocks had significantly increased odds (P < .05) of being overdose case sites if they had apartment buildings, bus stops, street trash, traffic calming features, and warning signs. New York City blocks also had significantly increased overdose odds if they had multifamily homes, commercial businesses, poor sidewalk maintenance, and loitering, and significantly decreased odds if they had single family homes, row homes, and security alarm signs. Chicago blocks with significantly increased overdose odds had vacant lots, abandoned buildings, alleys, restaurants, and adults on the street and significantly decreased odds with landscaping. Findings support neighborhood social and physical characteristics as important risk factors for fatal opioid overdose over and above sociodemographics.

  PublisherOA PDFDOI

• Challenging the Continued Usefulness of Social Media Recruitment for Surveys of Hidden Populations of People Who Use Opioids (Preprint)

  2024-06-26

  preprint1st authorCorresponding

  <sec> <title>UNSTRUCTURED</title> Historically, recruiting research participants through social media facilitated access to people who use opioids, capturing a range of drug use behaviors. The current rapidly changing online landscape, however, casts doubt on social media’s continued usefulness for study recruitment. In this viewpoint paper, we assessed social media recruitment for people who use opioids and described challenges and potential solutions for effective recruitment. As part of a study on barriers to harm reduction health services, we recruited people who use opioids in New York City to complete a REDCap (Research Electronic Data Capture; Vanderbilt University) internet-based survey using Meta (Facebook and Instagram), X (formerly known as Twitter), Reddit, and Discord. Eligible participants must have reported using opioids (heroin, prescription opioids, or fentanyl) for nonprescription purposes in the past 90 days and live or work in New York City. Data collection took place from August 2023 to November 2023. Including study purpose, compensation, and inclusion criteria caused Meta’s social media platforms and X to flag our ads as “discriminatory” and “spreading false information.” Listing incentives increased bot traffic across all platforms despite bot prevention activities (eg, reCAPTCHA and counting items in an image). We instituted a rigorous post hoc data cleaning protocol (eg, investigating duplicate IP addresses, participants reporting use of a fictitious drug, invalid ZIP codes, and improbable drug use behaviors) to identify bot submissions and repeat participants. Participants received a US $20 gift card if still deemed eligible after post hoc data inspection. There were 2560 submissions, 93.2% (n=2387) of which were determined to be from bots or malicious responders. Of these, 23.9% (n=571) showed evidence of a duplicate IP or email address, 45.9% (n=1095) reported consuming a fictitious drug, 15.8% (n=378) provided an invalid ZIP code, and 9.4% (n=225) reported improbable drug use behaviors. The majority of responses deemed legitimate (n=173) were collected from Meta (n=79, 45.7%) and Reddit (n=48, 27.8%). X’s ads were the most expensive (US $1.96/click) and yielded the fewest participants (3 completed surveys). Social media recruitment of hidden populations is challenging but not impossible. Rigorous data collection protocols and post hoc data inspection are necessary to ensure the validity of findings. These methods may counter previous best practices for researching stigmatized behaviors. </sec>

  PublisherDOI

• Neighborhood and Individual Disparities in Community-Based Naloxone Access for Opioid Overdose Prevention

  Journal of Urban Health · 2024-01-09 · 5 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Neighborhood and Individual Disparities in Community-Based Naloxone Access

  Drug and Alcohol Dependence · 2024-07-01

  article1st authorCorresponding
  PublisherDOI

Recent grants

• Alcohol and Pedestrian Injury: Inquiry into Built and Social Environment Factors

  NIH · $85k · 2015–2017

Frequent coauthors

• Adam J. Milam

  Mayo Clinic in Florida

  26 shared

• C. Debra M. Furr‐Holden

  14 shared

• Sílvia S. Martins

  Columbia University

  12 shared

• Charles C. Branas

  University of Otago

  6 shared

• Amy R. Knowlton

  Cabot (United States)

  5 shared

• Debra M. Furr‐Holden

  Michigan State University

  5 shared

• Keshia M. Pollack

  Cleveland Clinic

  4 shared

• Andrea C. Gielen

  Johns Hopkins University

  4 shared

Education

• PhD, Health, Behavior & Society

  Johns Hopkins University Bloomberg School of Public Health

  2017

• MPH

  Emory University School of Public Health

  2010

• BA

  Wellesley College

  2005

Awards & honors

• Senior Fellow, Leonard Davis Institute for Health Economics
• Senior Scholar, Penn Injury Science Center
• Senior Scholar, Center for Clinical Epidemiology and Biostat…
• Senior Fellow, Penn Center for Public Health