About

Dr. Melody S. Goodman is a biostatistician and research methodologist with a focus on developing solutions through partner-engaged research approaches. Her work aims to understand social risk factors contributing to urban health outcomes and to develop interventions for high-risk populations. She conducts translational research that bridges research and practice, emphasizing rigorous study design, measurement, and advanced statistical analysis methods. Her contributions span prevention, treatment, intervention, and policy, including the development and evaluation of tools such as the Research Engagement Survey Tool to assess partner engagement in research studies. Dr. Goodman has published over 150 peer-reviewed journal articles and authored two books on public health research methods and biostatistics. Her research has been supported by numerous funders, including the National Institutes of Health and the Robert Wood Johnson Foundation. She has received several honors, such as being a Fellow of the American Statistical Association and the New York Academy of Medicine, and has been recognized for her societal impact and leadership in community health and biostatistics.

Research topics

• Political Science
• Sociology
• Computer Science
• Psychology
• Data science
• Public relations
• Law
• Pathology
• Medicine
• Social psychology
• Knowledge management

Selected publications

• Neighborhood Opportunity and Genetic Literacy in a Representative Sample of US Adults

  Prevention Science · 2026-03-30

  article
  PublisherDOI

• Health Literacy and Awareness of Family Health History in the All of Us Research Program

  Public Health Genomics · 2026-01-19

  articleOpen access

  INTRODUCTION: A 2016 study showed that limited health literacy was associated with lower awareness of family health history. However, this analysis was conducted among adult patients in St. Louis, Missouri, thereby warranting broader replication. METHODS: We quantified the association between health literacy and awareness of family health history using a nationwide cross-sectional study of 286,293 All of Us Research Program participants. Modified Poisson regression models estimated PRs (PRs): model 1 (unadjusted), model 2 (demographic factors), model 3 (socioeconomic status), model 4 (health insurance), model 5 (self-rated health status), and model 6 (number of chronic health conditions). RESULTS: The average age was 53 years (SD = 17), with 4% who self-reported no awareness of family health history and 17% who had limited health literacy. Without controlling for confounders (model 1), participants with limited health literacy were 3.06 (95% confidence interval [CI]: 2.95-3.17) times more likely than those with adequate health literacy to report no awareness of family health history. This significant association persisted but attenuated in models 2 (adjusted PR [aPR]: 2.04, 95% CI: 1.96-2.12) and 3 (aPR: 1.43, 95% CI: 1.37-1.49). The association remained stable in models 4-6 with the sequential addition of health insurance coverage (aPR: 1.42, 95% CI: 1.37-1.48), self-rated health status (aPR: 1.42, 95% CI: 1.36-1.47), and number of chronic health conditions (aPR: 1.42, 95% CI: 1.36-1.48). CONCLUSION: These findings indicate that increasing health literacy may increase awareness of family health history, which is vital for delivery of personalized healthcare and active patient participation in precision medicine.

  PublisherDOI

• Police Pursuit Fatality Rates in the US and Directions for Future Research

  JAMA Network Open · 2026-04-01

  articleOpen accessSenior author
  PublisherDOI

• Facing the pandemic amidst mental health challenges: A longitudinal study of Black and Latino public housing residents

  Journal of Affective Disorders · 2026-05-12

  article
  PublisherDOI

• Ghost gun recovery and firearm deaths in California, 2014–2023

  Journal of Epidemiology & Community Health · 2026-01-13 · 1 citations

  articleSenior author

  BACKGROUND: We investigated whether ghost gun recovery rates are significantly associated with firearm mortality rates in the following year across California's 58 counties from 2014 to 2023. METHODS: We obtained yearly county-level data on ghost guns recovered in California from The Trace's Gun Violence Data Hub. County-level firearm death counts (total, suicide and homicide) were pulled from the Centers for Disease Control and Prevention's Restricted-Use Vital Statistics Data. Spatiotemporal models quantified the covariate-adjusted associations between ghost gun recoveries per capita and firearm death rates (total, suicide and homicide) in the following year. Secondary analyses examined suicide and homicide models stratified by sex and race/ethnicity. RESULTS : For every 20 ghost guns recovered per 100 000 population, there was an associated 6.4% increase in firearm suicide rate (adjusted incidence rate ratio (aIRR): 1.064, 95% credible interval (CrI) 1.019 to 1.111) in the following year. We found no evidence of a significant ghost gun recovery association with total firearm death rate (aIRR: 1.036, 95% CrI 0.999 to 1.075) and firearm homicide rates (aIRR: 1.002, 95% CrI 0.946 to 1.064). Stratified models for firearm suicide rates suggested variations across sex and racial/ethnic groups, with significant positive associations observed for male (6.5% increase; aIRR: 1.065, 95% CrI 1.017 to 1.115), non-Hispanic white (6.2% increase; aIRR: 1.062, 95% CrI 1.005 to 1.122) and Hispanic (12.6% increase; aIRR: 1.126, 95% CrI 1.031 to 1.230) individuals. A different pattern emerged for firearm homicide death rates, where associations across demographic groups were not statistically significant. CONCLUSIONS: Practitioners concentrating on suicide prevention efforts should be advised about the threat that ghost guns may present.

  PublisherDOI

• A community case study of pedestrian environment features of neighborhood walkability and built environment-related 311 service requests surrounding public housing developments serving low-income Boston residents

  Frontiers in Public Health · 2026-05-12

  articleOpen access

  Physical activity is a modifiable health behavior influenced by neighborhood walkability. In-person field audits of pedestrian environment features of neighborhood walkability were conducted at the start of an ongoing multilevel, multicomponent physical activity promotion intervention called Community Walks, taking place in 12 low-income public housing developments (PHDs) in Boston, MA, USA. Following these audits, we then explored alternative, remote methods of walkability assessment. One option is the publicly available database of service requests submitted by individuals in municipalities across the U.S. by calling ‘311’ to report problems that can present obstacles to physical activity (e.g., potholes, broken crosswalk signals). In this Community Case Study, we examined the cross-sectional relationship between pedestrian environment features and neighborhood walkability scores in the 12 low-income PHDs in Community Walks, and the frequency of 311 service requests within a 0.1-mile radius of each PHD’s address. In adjusted analyses, a one-unit increase in the overall walkability score was associated with a 12% (95% CI: 1.03–1.22) increase in the frequency of 311 service requests. We then describe possible explanations of these findings and implications for community-based physical activity interventions. For example, pedestrian advocacy training is an intervention component that could raise residents’ awareness of the 311 system and encourage its use to increase reporting of walkability concerns. This study represents an exploratory starting point for future multi-level physical activity interventions in which 311 data could inform intervention programming aimed at increasing health-promoting physical activity behaviors.

  PublisherOA PDFDOI

• "The Agenda of the People": A Multisector Partnership for COVID-19 Mitigation in New York City.

  PubMed · 2026-04-01

  articleOpen access

  2026;116(4):431-436. https://doi.org/10.2105/AJPH.2025.308358).

  PublisherOA PDFDOI

• Remote work and loneliness: Evidence from a nationally representative sample of employed U.S. adults

  Journal of Affective Disorders · 2025-10-13 · 3 citations

  article
  PublisherDOI

• Incomplete Family History and Meeting Algorithmic Criteria for Genetic Evaluation of Hereditary Cancer

  JAMA Network Open · 2025-10-28 · 1 citations

  articleOpen access

  Importance: Incomplete electronic health record (EHR) documentation may limit the effectiveness of clinical decision support (CDS) algorithms designed to identify patients eligible for hereditary cancer genetic evaluation. Objectives: To determine whether a CDS algorithm can identify patients who meet criteria for hereditary cancer genetic evaluation when family history data are incompletely documented in the EHR, and to examine whether data missingness is associated with identification patterns across patient subgroups. Design, Setting, and Participants: This cross-sectional study analyzed EHR data extracted in December 2020 from 2 large US health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Eligible patients were adults aged 25 to 60 years who visited a primary care clinic within the previous 3 years and had some EHR documentation of cancer family history. Data analysis was conducted in August 2024. Exposures: Patient demographic factors (age, sex, race and ethnicity, and language preference) and cancer family history characteristics (number of cancer history records, number of affected first- and second-degree relatives, relatives with rising mortality cancers, presence of hereditary cancer-related terms in comments, and completeness of documentation). Main Outcomes and Measures: The primary outcome was meeting at least 1 CDS algorithm criterion for genetic evaluation of hereditary cancer risk based on National Comprehensive Cancer Network guidelines. Missing data patterns were assessed using the Little missing completely at random test, with analyses conducted using complete case analysis and multiple imputation. Results: This study included 157 207 patients: 55 918 from UHealth and 101 289 from NYULH. Their mean (SD) age was 43.5 (9.8) years, and most (65.7%) were female. A total of 5607 UHealth patients (10.0%) and 10 375 NYULH patients (10.2%) met CDS criteria for genetic evaluation. At UHealth, data appeared to be missing completely at random (χ239 = 39.09; P = .47), and complete case compared with multiple imputation analyses yielded similar results. At NYULH, data were not missing completely at random (χ255 = 914.89; P < .001). Compared with multiple imputation, complete case analysis produced different association magnitudes for older age and having relatives with rising mortality cancers, suggesting bias when excluding incomplete records. Conclusions and Relevance: In this cross-sectional study, the magnitude of the association between incomplete family history documentation and identification of patients eligible for hereditary cancer genetic evaluation depended on whether data were missing randomly or systematically. These findings suggest that health care organizations implementing CDS algorithms should assess their specific missing data patterns and consider tailored approaches to handling incomplete family history information to ensure equitable identification of all patients who could benefit from genetic evaluation services.

  PublisherOA PDFDOI

• Bridging Technology and Pretest Genetic Services: Quantitative Study of Chatbot Interaction Patterns, User Characteristics, and Genetic Testing Decisions

  Journal of Medical Internet Research · 2025-09-17 · 3 citations

  articleOpen access

  BACKGROUND: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. OBJECTIVE: We examined users' interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users' clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. METHODS: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. RESULTS: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. CONCLUSIONS: Findings highlight the chatbot's effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot's scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

  PublisherDOI

Recent grants

• NIH Grant R01CA168608

  NIH · $826k · 2015

• NIH Grant F31HD043695

  NIH · $108k · 2007

• NIH Grant R03HD061220

  NIH · $63k · 2011

Frequent coauthors

• Kimberly A. Kaphingst

  114 shared

• Melvin Blanchard

  University of Baltimore

  29 shared

• Jemar R. Bather

  New York University

  27 shared

• Vetta L. Sanders Thompson

  Barnes-Jewish Hospital

  25 shared

• Graham A. Colditz

  Washington University in St. Louis

  24 shared

• Richard T. Griffey

  Washington University in St. Louis

  23 shared

• Bettina F. Drake

  Washington University in St. Louis

  21 shared

• Christopher R. Carpenter

  Mayo Clinic

  20 shared

Education

• PhD, Biostatistics

  Harvard University

  2006

• SM, Biostatistics

  Harvard School of Public Health

  2003

• BS, Applied Mathematics and Statistics/Economics

  Stony Brook University

  1999

Awards & honors

• Fellow, American Statistical Association (2021)
• Societal Impact Award, Caucus for Women in Statistics (2021)
• Network Builder Award, Robert Wood Johnson Foundation New Co…
• Siteman Cancer Center “Rock Doc” (2013)
• Satcher Health Leadership Institute - Morehouse School of Me…