About

Nandita Mitra, Ph.D., is a Professor of Biostatistics in the Department of Biostatistics and Epidemiology at the Hospital of the University of Pennsylvania, Perelman School of Medicine. She holds the position of Senior Scholar at the Center for Clinical Epidemiology and Biostatistics and is an Associate Member of the Abramson Cancer Center at the University of Pennsylvania. Additionally, she is a Senior Fellow at the Leonard Davis Institute in Health Economics. Dr. Mitra serves as the Director of Statistics for the Masters in Health Policy Research Graduate Program and the Director of Statistics Didactics in the Department of Radiation Oncology at the University of Pennsylvania. She is also the Chair of the Graduate Group in Epidemiology and Biostatistics and the Vice Chair for Faculty Professional Development in the Department of Biostatistics, Epidemiology and Informatics. Her educational background includes an AB in Mathematics from Brown University, an MA in Biostatistics from the University of California at Berkeley, and a PhD in Biostatistics from Columbia University. Her research focuses on statistical methods in medical research, including causal inference, cost-effectiveness analysis, and treatment effect estimation, as evidenced by her numerous publications in these areas.

Research topics

• Medicine
• Business
• Computer Science
• Environmental health
• Economics
• Internal medicine
• Nursing
• Mathematics
• Demography
• Agricultural economics
• Family medicine
• Data Mining
• Artificial Intelligence
• Food science
• Political Science
• Chemistry
• Statistics
• Public economics
• Actuarial science
• Animal science
• Internet privacy
• Econometrics
• Toxicology
• Marketing

Selected publications

• Thirty‐Day Readmission After Hospitalisation for Diabetic Foot Ulcer: A Nationwide Analysis

  Wound Repair and Regeneration · 2026-04-29

  article

  Diabetic foot ulcers are a frequent cause of hospitalisation among adults with diabetes mellitus and are associated with substantial morbidity and mortality. Hospital readmissions following diabetic foot ulcer hospitalisation remain common, yet nationally representative evidence describing readmission patterns and drivers is limited. This retrospective cohort study used the 2022 Nationwide Readmissions Database to characterise 30-day all-cause readmission rates, identify factors associated with readmission and compare index and readmission hospitalisations among adults hospitalised with diabetic foot ulcers in the United States. Adult hospitalisations with a diagnosis of diabetic foot ulcer were identified using diagnosis codes, and survey-weighted analyses were performed to generate national estimates. Among an estimated 234,797 index hospitalisations, 13.3% of patients were readmitted within 30 days, exceeding the national inpatient average of 8.2%. Readmitted patients demonstrated a higher prevalence of kidney disease, cardiovascular disease and mental health conditions. In multivariable analyses, kidney disease, cardiovascular comorbidities, tobacco use, drug use disorder and bipolar disorder were associated with increased odds of readmission. In contrast, infection-related diagnoses and procedures during the index hospitalisation, including bone infection, surgical debridement and lower-extremity amputation, were associated with lower odds of readmission. Readmission encounters were characterised by longer hospital stays and higher prevalence of acute systemic conditions. These findings indicate that early readmission after hospitalisation for diabetic foot ulcer is driven primarily by systemic medical instability rather than recurrent local infection, underscoring the need for multidisciplinary inpatient and postdischarge care strategies.

  PublisherDOI

• Differential Impact of a Digital Mental Health Engagement Platform on Black and Female Health Care Workers: A Secondary Analysis of a Randomized Trial

  Journal of General Internal Medicine · 2026-04-17

  articleOpen access

  IMPORTANCE: Health care workers (HCWs), particularly those identifying as female or Black, face disproportionate mental health strain. Digital mental health platforms have grown in popularity and, for health systems, may offer scalable solutions, but their differential impact across demographic groups remains understudied. DESIGN, SETTING, AND PARTICIPANTS: This secondary analysis of a randomized controlled trial enrolled 1275 HCWs from an urban academic health system between January and May 2022. Participants were randomized to usual care or proactive digital engagement via the Cobalt platform. Female and Black HCWs were oversampled to assess subgroup effects. INTERVENTION: Monthly digital outreach, including mental health symptom screening and linkage to resources via the Cobalt platform, compared with usual care. MAIN OUTCOMES AND MEASURES: Primary outcomes were changes in depression (PHQ-9) and anxiety (GAD-7) scores at 6 and 9 months. Secondary outcomes included well-being (WHO-5, WBI-9) and work productivity (LEAPS). Generalized linear models assessed HTE by gender and race. RESULTS: Of 1275 randomized participants (mean age 38.6 years; 83.4% female; 25.1% Black), both intervention and control groups showed significant reductions in anxiety and depression scores over time. No significant HTE was observed by gender or race for primary outcomes. Female HCWs receiving the intervention reported significantly greater improvement in work productivity at 6 months (LEAPS score difference: 1.70; p = 0.03). Black HCWs in the intervention arm showed a sustained improvement in depression scores at 9 months (- 2.21; p < 0.001), though adjusted models did not confirm statistical significance. CONCLUSIONS AND RELEVANCE: A proactive digital mental health strategy coupled with a well-being platform improved mental health outcomes across HCWs, with modest differential effects in productivity and depression among female and Black participants. These findings support the scalability of digital interventions and highlight the need for culturally tailored approaches to enhance equity and impact.

  PublisherOA PDFDOI

• Perspectives of female and under-represented physicians on well-being in medicine: a qualitative study from an academic medical centre in the USA

  BMJ Open · 2026-03-01

  articleOpen access

  BACKGROUND: Stress and burnout are pervasive among physicians. Academic physicians who are female and physicians who are under-represented in medicine (URM) face inequities in the workplace and beyond. Understanding their experiences is crucial for workforce sustainability and diversity, especially given the disproportionate effects on these individuals and overall workforce capacity. OBJECTIVE: To qualitatively explore the perspectives of academic female and URM physicians and identify key themes affecting their careers and well-being. DESIGN: Semi-structured interviews were conducted with 30 physicians at an urban academic health system. Interviews were audio-recorded, transcribed and thematically analysed using a general inductive approach. Interview guides were informed by prior literature and constructs. INTERVENTION: None. SETTING AND PARTICIPANTS: Female and URM physicians from a large, academic medical centre were recruited via email. Participants self-reported demographic information, including sex, race, ethnicity and tenure. OUTCOMES AND MEASURES: The primary outcomes encompassed the main themes identified through the analysis of interviews with female and URM physicians regarding their perspectives on well-being, mental health and academic medicine. RESULTS: 30 female or URM physicians were interviewed (27 (90%) female; 14 (47%) black, Asian or multi-racial). Thematic analysis revealed four key themes: physician identity (URM, female, family), well-being in the workplace (emotional health, staffing burden, non-clinical responsibilities), barriers to accessing well-being resources (workplace environment, culture, overgeneralisation) and facilitators to well-being (physician camaraderie, leadership support). Physicians discussed how their identities influenced their experiences of well-being. They highlighted emotional health challenges, staffing burdens and administrative tasks contributing to stress. Barriers to accessing resources included workplace culture and broad-based interventions, while supportive leadership and camaraderie were identified as facilitators of access. CONCLUSION: Female and URM physicians face systemic challenges impacting their well-being and careers. These findings underscore the need to address systemic changes and specifically design programmes focused on promoting the well-being and inclusivity of female and URM physicians. Tailored interventions to these individuals, supportive leadership structures and collaborative working cultures are crucial for addressing these issues and sustaining a diverse physician workforce.

  PublisherOA PDFDOI

• Longitudinal causal effects of serum bicarbonate treatment on kidney function: A G-computation analysis in the Chronic Renal Insufficiency Cohort (CRIC) Study

  Annals of Epidemiology · 2025-09-01

  article
  PublisherDOI

• Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures

  Biometrics · 2025-01-07 · 4 citations

  articleSenior author

  Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. In this work, we propose new estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of intervention, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the heterogeneous effects of a nutritional excise tax under different levels of accessibility to cross-border shopping.

  PublisherDOI

• Proactively Delivered Digital Mental Health Support for Health Care Workers: Usability and Acceptability Evaluation

  JMIR Formative Research · 2025-12-09

  articleOpen access

  BACKGROUND: Health systems are investing in mental health and well-being support tools and resources for health care workers (HCW). Considering the mental health strain facing HCWs, there is a need to optimize the current mental health delivery model. OBJECTIVE: This study aimed to evaluate the usability and acceptability of a proactive digital mental health approach (Cobalt+;Penn Medicine), which included services proactively sent to HCWs via text messaging, including (1) monthly automated text messaging reminders and links to Cobalt, and (2) bimonthly text-message-based measures of depression and anxiety. METHODS: This study used the System Usability Scale (SUS), Net Promoter Score (NPS), and open-ended questions to capture Cobalt+ participants who received proactive digital mental health tools and resources. Descriptive summary statistics were used for SUS and NPS outcome measures, and a chi-square test was used to detect group differences. Open-ended questions were analyzed using a qualitative open coding process by 2 coders. Research team members calculated interrater agreement (Cohen κ above 0.80). RESULTS: A total of 162 of 642 HCWs randomized to Cobalt+ (25.2%) visited Cobalt due to a proactive text message and completed usability and acceptability measures. The mean age was 38.9 years, most were female (90.7%), 56.8% White, 53.1% married or partnered, and 34.6% engaged in shift work. The mean SUS score was 74.43 (median score 72.5). Participants said they mostly "browsed" the online mental health platform. Cobalt+ received an NPS of 13.7. When asked to elaborate on their experience, 2 categories (eg, positive and negative experiences) with 13 subcategories were identified. Most participants noted the brief process that helped prioritize mental health: "Forget otherwise. Puts in forefront of my mind," and "Your texts do remind me to take stock of my current feelings." CONCLUSIONS: A proactive digital mental health approach may help overcome barriers in the uptake of services that are otherwise passively available to HCWs. This study demonstrated that the proactive approach is generally usable, modestly acceptable, and further supplemented by HCW feedback. These findings suggest the approach's viability and the need for additional research toward improvement and broader implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05028075; https://clinicaltrials.gov/study/NCT05028075.

  PublisherDOI

• Weighting methods for truncation by death in cluster-randomized trials

  Statistical Methods in Medical Research · 2025-01-31 · 1 citations

  articleOpen access

  Patient-centered outcomes, such as quality of life and length of hospital stay, are the focus in a wide array of clinical studies. However, participants in randomized trials for elderly or critically and severely ill patient populations may have truncated or undefined non-mortality outcomes if they do not survive through the measurement time point. To address truncation by death, the survivor average causal effect has been proposed as a causally interpretable subgroup treatment effect defined under the principal stratification framework. However, the majority of methods for estimating the survivor average causal effect have been developed in the context of individually randomized trials. Only limited discussions have been centered around cluster-randomized trials, where methods typically involve strong distributional assumptions for outcome modeling. In this article, we propose two weighting methods to estimate the survivor average causal effect in cluster-randomized trials that obviate the need for potentially complicated outcome distribution modeling. We establish the requisite assumptions that address latent clustering effects to enable point identification of the survivor average causal effect, and we provide computationally efficient asymptotic variance estimators for each weighting estimator. In simulations, we evaluate our weighting estimators, demonstrating their finite-sample operating characteristics and robustness to certain departures from the identification assumptions. We illustrate our methods using data from a cluster-randomized trial to assess the impact of a sedation protocol on mechanical ventilation among children with acute respiratory failure.

  PublisherDOI

• Mendelian Randomization for Dermatology Research

  JAMA Dermatology · 2025-02-05 · 4 citations

  articleSenior author

  This JAMA Network Insight describes the use of mendelian randomization, including key assumptions that must be met, in dermatology research.

  PublisherDOI

• A causal framework for evaluating drivers of policy effect heterogeneity using difference-in-differences

  Health Services and Outcomes Research Methodology · 2025-10-25 · 3 citations

  articleOpen accessSenior author

  Policymakers and researchers often seek to understand how a policy differentially affects a population and the pathways driving this heterogeneity. For example, when studying an excise tax on sweetened beverages, researchers might assess the roles of cross-border shopping, economic competition, and store-level price changes on beverage sales trends. However, traditional policy evaluation tools, like the difference-in-differences (DiD) approach, primarily target average effects of the observed intervention rather than the underlying drivers of effect heterogeneity. Common approaches to evaluate sources of heterogeneity often lack a causal framework, making it difficult to determine whether observed outcome differences are truly driven by the proposed source of heterogeneity or by other confounding factors. In this paper, we present a framework for evaluating such policy drivers by representing questions of effect heterogeneity under hypothetical interventions and use it to evaluate drivers of the Philadelphia sweetened beverage tax policy effects. Building on recent advancements in estimating causal effect curves under DiD designs, we provide tools to assess policy effect heterogeneity while addressing practical challenges including confounding and neighborhood dynamics. Supplementary Information: The online version contains supplementary material available at 10.1007/s10742-025-00358-5.

  PublisherOA PDFDOI

• Policy effect evaluation under counterfactual neighbourhood intervention in the presence of spillover

  Journal of the Royal Statistical Society Series A (Statistics in Society) · 2025-02-12 · 2 citations

  articleOpen accessSenior author

  Abstract Policy interventions can spill over to units of a population that is not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighbouring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under counterfactual exposure settings. When we only observe treated unit(s) surrounded by controls—as is common when a policy intervention is implemented in a single city or state—this effect inquires about the policy effects under a counterfactual neighbourhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighbourhood statuses. We develop several estimators that have desirable properties. We provide an illustrative data application to the Philadelphia beverage tax study.

  PublisherOA PDFDOI

Recent grants

• Novel Approaches to Estimating the Causal Effect of Policy Interventions in the Presence of Spillovers

  NSF · $360k · 2022–2025

Frequent coauthors

• Timothy R. Rebbeck

  174 shared

• Irene Orlow

  Memorial Sloan Kettering Cancer Center

  124 shared

• Klaus J. Busam

  Memorial Sloan Kettering Cancer Center

  124 shared

• Colin B. Begg

  Memorial Sloan Kettering Cancer Center

  124 shared

• Marianne Berwick

  University of New Mexico

  123 shared

• Bruce K. Armstrong

  University of Sydney

  123 shared

• Roberto Zanetti

  122 shared

• Anne Kricker

  122 shared