Melody Goodman
Melody Goodman
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Interim Dean, School of Global Public Health
Professor of Biostatistics
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Professional overview
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Dr. Melody S. Goodman is a biostatistician and research methodologist. Her research interest is identifying the origins of health inequities and developing, as necessary, evidence-informed primary prevention strategies to reduce these health inequities. Dr. Goodman’s research efforts seek to develop a more rigorous understanding of the social risk factors contributing to urban health inequities to develop culturally competent, region-specific solutions through collaborative activities with community members, community-based organizations, faith-based organizations, and other community health stakeholders. Her work aims to develop solutions for improving health in minoritized and medically underserved communities.
Dr. Goodman led the team that developed and comprehensively evaluated the Research Engagement Survey Tool, a quantitative survey measure to assess the level of community engagement in research studies from the community partner perspective. She is the Founding Director of the Center for Antiracism, Social Justice, & Public Health. With numerous funders supporting her work (e.g., National Institutes of Health, Robert Wood Johnson Foundation, Verizon Foundation, Long Island Community Foundation, Patient-Centered Outcomes Research Institute, and Susan G. Komen), she has published over 100 peer-reviewed journal articles and two books (2018 Routledge/Taylor & Francis Group): 1) Public Health Research Methods for Partnerships and Practice and 2) Biostatistics for Clinical and Public Health Research. She is a Fellow of the American Statistical Association and the inaugural recipient of the Societal Impact Award from the Caucus for Women in Statistics (2021).
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Education
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BS, Economics and Applied Mathematics & Statistics, State University of New York at Stony Brook, Stony Brook, NYMS, Biostatistics, Harvard University, Cambridge, MAPhD, Biostatistics (Minors: Social Determinants of Health Disparities and Theoretical Statistics), Harvard University, Cambridge, MA
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Honors and awards
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Fellow, American Statistical Association (2021)Societal Impact Award, Caucus for Women in Statistics (2021)Network Builder Award, Robert Wood Johnson Foundation New Connections (2019)Siteman Cancer Center “Rock Doc” (2013)Satcher Health Leadership Institute - Morehouse School of Medicine, Community Health Leadership Institute Intensive Cohort II (2013)Women of the Year - Health, National Coalition of 100 Black Women, Inc. - Suffolk Chapter (2010)President’s Award for Teaching Excellence - Stony Brook University (2009)President’s Award for Excellence in Team Achievement - Stony Brook University (2008)
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Areas of research and study
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BiostatisticsCommunity HealthCommunity-based Participatory ResearchDissemination and Implementation of Evidence-based ProgramsHealth DisparitiesHealth EquityMinoritiesMinority HealthQuantitative Research
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Publications
Publications
Age-specific trends in pregnancy-associated suicide and homicide rates by race/ethnicity, 2005–2021
Bather, J. R., Mautner Wizentier, M., Cowan, S. K., Peipert, J. F., Furr-Holden, D., & Goodman, M. S. (n.d.).Publication year
2025Journal title
American Journal of Obstetrics and Gynecology MFMVolume
7Issue
2Increasing Interest in Data Literacy: The Quantitative Public Health Data Literacy Training Program
AbstractShah, J., Bather, J. R., Chen, Y., Kaul, S., Dias, J. J., & Goodman, M. S. (n.d.).Publication year
2025Journal title
Journal of Statistics and Data Science EducationVolume
33Issue
2Page(s)
210-222AbstractDue to the COVID-19 pandemic, the presentation of public health data to lay audiences has increased without most people having the knowledge to understand what these statistics mean. Recognizing that minoritized populations are deeply impacted by the pandemic and wanting to improve the racial representation in biostatistics we developed a training program aimed at increasing the data literacy of high school and college students from minoritized groups. The program introduced the basics of public health, data literacy, statistical software, descriptive statistics, and data ethics. The instructors taught eight synchronous sessions consisting of lectures and experiential group exercises. Five of the sessions were also offered asynchronously. Of the 209 students, 76% were college students; 90% identified as Black, Asian, or Latino/a/x; and the average age was 21 years. In synchronous sessions, 56% of students attended all sessions. All course sessions were rated as good/excellent by most ((Formula presented.)) students. The program recruited, engaged, and retained a large cohort ((Formula presented.)) of underrepresented students in biostatistics/data science for a virtual data literacy training. The program demonstrates the feasibility of developing and implementing public health training programs designed to increase racial and gender diversity in the field.Neighborhood characteristics and health literacy: Evidence from the survey of racism and public health
AbstractOdumegwu, J., Bather, J. R., Harris, A., Wizentier, M. M., Kaphingst, K. A., & Goodman, M. S. (n.d.).Publication year
2025Journal title
Public HealthVolume
242Page(s)
206-213AbstractObjectives: Limited research exists on the relationship between neighborhood characteristics and health literacy. We investigated the potential impacts of neighborhood characteristics on health literacy and explored whether these associations varied across racial/ethnic groups. Study design: The study was a web-based cross-sectional study of adults (18+ years old) residing in areas within the US Health and Human Services Regions 1, 2, or 3. Methods: The 2023 Survey of Racism and Public Health dataset was linked with the 2017–2021 American Community Survey dataset using the respondents' zip codes to obtain measures of residential segregation, neighborhood deprivation, racial and economic polarization, and racial and educational isolation. The Brief Health Literacy Screen was used to assess participants' health literacy. Unadjusted and adjusted models were employed to explore the associations between neighborhood characteristics and limited health literacy. Results: Of 4948 participants, the mean age was 47 (SD = 17) years, 48 % were White, and 42 % had college degrees or higher. Greater neighborhood deprivation was associated with an increased likelihood of limited health literacy (aOR = 1.12, 95 % CI: 1.01, 1.24). Higher racial and economic polarization was associated with decreased odds of limited health literacy (aOR = 0.83, 95 % CI: 0.73, 0.93). Increased racial isolation is associated with increased odds of limited health literacy (aOR = 1.31, 95 % CI:1.14,1.50). These associations did not significantly vary by racialized/ethnic groups. Conclusions: Incorporating neighborhood characteristics in health literacy research helps reveal a possible critical key risk factor; higher neighborhood deprivation increases the likelihood of limited health literacy with no variation across the racial/ethnic groups within the same neighborhood. The findings point policymakers toward the direction for intervention in policy changes that will reduce the maldistribution of health—and economic-promoting resources and risky life-course exposures in communities to improve public health literacy.Social vulnerability and genetic service utilization among unaffected BRIDGE trial patients with inherited cancer susceptibility
Failed generating bibliography.AbstractPublication year
2025Journal title
BMC CancerVolume
25Issue
1AbstractBackground: Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility. Methods: We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met. Results: Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66–0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63–0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition. Conclusions: These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists. Trial registration: BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov.Sociodemographic variation in experiences with medication shortages among US adults
AbstractFang, J., Goodman, M. S., Kaphingst, K. A., Parikh, N. S., Bae, J. Y., Silver, D., & Bather, J. R. (n.d.).Publication year
2025Journal title
Preventive Medicine ReportsVolume
51AbstractObjective: To investigate sociodemographic factors associated with prescribed and over-the-counter medication shortage experiences. Methods: We analyzed repeated cross-sectional data from the 2023 US Census Household Pulse Survey, a nationwide survey of US adults. Outcomes were based on the following question: “In the past month, have you or a member of your household been directly affected by the following?” We created binary indicators based on the following response options: (1) “Shortage of prescription medications, which includes any medicine required or provided by a healthcare provider, pharmacist, or hospital” and (2) “Shortage of over-the-counter medications, encompassing any medication available without a prescription.” Sociodemographic factors included age, gender identity, race/ethnicity, marital status, educational attainment, household income, number of children, employment status, health insurance coverage, at risk for depression/anxiety, disability status, and region. Weighted multivariable models accounted for the complex survey design and estimated adjusted odds ratios with 95 % confidence intervals. Results: We found that more experiences with prescribed and over-the-counter medication shortages were associated with middle age, transgender/other gender identity, non-Hispanic Other race/ethnicity, higher educational attainment, having at least one child, at risk for depression or anxiety, and being disabled. In contrast, fewer experiences with prescribed and over-the-counter medication shortages were associated with higher household income. Conclusions: Sociodemographic variation exist in experiences with medication shortages among US adults. These findings underscore the need to bolster the pharmaceutical supply chain to mitigate inequities in medication access.Structural Determinants of Health Literacy Among Formerly Incarcerated Individuals: Insights From the Survey of Racism and Public Health
AbstractBather, J. R., Goodman, M. S., & Kaphingst, K. A. (n.d.).Publication year
2025Journal title
Health literacy research and practiceVolume
9Issue
1Page(s)
e8-e18AbstractBACKGROUND: Formerly incarcerated individuals (FIIs) encounter difficulties with covering the cost of dental and medical care, adhering to medication regimens, and receiving fair treatment from health care providers. Yet, no published research has examined modifiable pathways to increase FIIs' health literacy (HL), which is essential for addressing the health needs of this vulnerable population. OBJECTIVE: The aim of this article is to examine neighborhood characteristics (neighborhood deprivation, racial and economic polarization, and residential segregation) and public assistance program enrollment as structural determinants of limited health literacy (LHL) among FIIs. METHODS: Using a socioecological framework, we analyzed a subsample of 578 FIIs from the 2023 Survey of Racism and Public Health, an online cross-sectional survey spanning U.S. Department of Health & Human Services Regions 1, 2, and 3. HL was assessed using the Brief Health Literacy Screen. Logistic regression models estimated unadjusted and adjusted associations of LHL with neighborhood characteristics and public assistance program enrollment. Adjusted models controlled for age, race and ethnicity, gender identity, educational attainment, marital and employment status, number of children, chronic health conditions, and incarceration length. KEY RESULTS: The 578 FIIs had an average age of 46, with 42% having LHL. We observed a statistically significant association between public assistance program enrollment and LHL (unadjusted odds ratio [OR] = 2.72, 95% confidence interval [CI]: 1.87, 4.01; adjusted OR = 2.50, 95% CI: 1.62, 3.88). We found no statistically significant associations of LHL with neighborhood deprivation, racial and economic polarization, and residential segregation. CONCLUSIONS: Our findings suggest that there may be an opportunity to develop tailored interventions for increasing HL among FIIs through public assistance programs. Dissemination of HL resources among this marginalized group can improve their self-management of chronic diseases. This is of paramount importance because FIIs must simultaneously navigate other challenges after incarceration (e.g., unstable housing). [HLRP: Health Literacy Research and Practice. 2025;9(1):e8-e18.].A training protocol compliance of 13% was observed in a research study of clinical research professionals
AbstractSolomon, E. D., Mozersky, J., Parsons, M. V., Baldwin, K., Goodman, M., & DuBois, J. M. (n.d.).Publication year
2024Journal title
BMC research notesVolume
17Issue
1AbstractObjective: We attempted to conduct a randomized controlled trial of three different informed consent training formats to evaluate their effectiveness. We recruited 503 clinical research professionals, who received $50 for participation. Incidental findings showed unexpectedly low rates of compliance with completing the study training protocols, resulting in insufficient statistical power to test our original hypotheses. In this report, we conducted a secondary analysis of the data in which we characterize and evaluate the observed low compliance. This involved using literature on average reading times, speed-reading times, and video play speeds to calculate the timeframes required to complete the three training formats. Results: Only 13% of participants completed the training in a reasonable timeframe. Furthermore, only 46% of participants completed the training in the minimum possible timeframe. These findings lead us to ask whether online research training is effective, since no training can be effective if participants do not actually complete the training. Given extensive requirements for educational training among clinical research professionals, we feel the burden of proof is on training programs to demonstrate that they have positive effects.An Immediate but Fleeting Interest in MPH Programs After the Onset of COVID-19: An Interrupted Time-Series Analysis
AbstractBather, J. R., Burke, E. M., Plepys, C. M., Rajbhandari-Thapa, J., Furr-Holden, D., & Goodman, M. S. (n.d.).Publication year
2024Journal title
Public Health ReportsAbstractObjectives: The relationship between the onset of the COVID-19 pandemic and interest in master of public health (MPH) programs is unknown. We examined trends in MPH application rates for 31 MPH concentrations and specifically for the MPH concentration in epidemiology and differences by race and ethnicity before and after the onset of the COVID-19 pandemic. Methods: We constructed a quasi-experimental design to examine trends in MPH application rates from academic years 2015-2016 through 2022-2023 by using Centralized Application Service for Schools and Programs of Public Health data. We used an interrupted time-series analysis to test whether application rates surged after the pandemic’s onset (academic years 2019-2020 through 2020-2021) and whether this increase persisted during the pandemic (academic years 2020-2021 through 2022-2023). We fit models for the overall sample, a combined racially and ethnically minoritized sample, each racial and ethnic group separately, and a non–US citizen sample. Results: The pandemic’s onset correlated with an immediate increase in application rates across most samples: overall (38%) and among American Indian/Alaska Native/Native Hawaiian/Pacific Islander (91%), Asian (35%), Black (42%), Hispanic (60%), multiracial (30%), racially and ethnically minoritized (44%), and White (53%) samples. However, this trend was not sustained; application rate trends during the pandemic were significantly lower than prepandemic trends. Application rate trends for all MPH concentrations and the MPH in epidemiology concentration among non–US citizens were significantly higher during the pandemic than prepandemic. Conclusions: Our results highlight the need for innovative strategies to sustain MPH degree interest and a diverse applicant pool.Application of a Heuristic Framework for Multilevel Interventions to Eliminate the Impact of Unjust Social Processes and Other Harmful Social Determinants of Health
AbstractGuilamo-Ramos, V., Thimm-Kaiser, M., Benzekri, A., Johnson, C., Williams, D., Wilhelm-Hilkey, N., Goodman, M., & Hagan, H. (n.d.).Publication year
2024Journal title
Prevention ScienceVolume
25Page(s)
446-458AbstractThere is consensus about the importance of developing a strong cadre of effective multilevel interventions to eliminate the impacts of unjust social processes, such as structural racism and other harmful social determinants of health (SDOH), on health inequities in the USA. However, the available cadre of rigorously evaluated evidence-based interventions for SDOH mitigation remains underdeveloped relative to the magnitude of historic and current health inequities. The proposed manuscript addresses this gap in two ways: first, by introducing a heuristic framework to inform decisions in multilevel intervention development, study design, and selection of analytic methods and, second, by providing a roadmap for future applications of the framework in multilevel intervention research through an exemplar application using the ongoing NIH-funded evaluation study of the Nurse-Community-Family Partnership (NCFP) intervention. NCFP leverages individual, family, institutional, and system factors to shape COVID-19 mitigation outcomes at the individual and household levels. NCFP takes an approach informed by the heuristic framework to addressing and mitigating unjust social processes and other harmful SDOH. We discuss the application of a two-arm parallel explanatory group randomized trial to evaluate the efficacy of NCFP in improving the primary (COVID-19 testing uptake) and secondary (adoption of COVID-19 control measures, COVID-19 vaccine uptake, mutual aid capacity, etc.) outcomes at the individual and household levels. The analysis approach relies on random-intercept models, and we calculate the variance partitioning coefficient to estimate the extent to which household- and individual-level variables contribute to the outcome, allowing examination of NCFP effects at multiple levels.Associations between perceived discrimination over the life course, subjective social status, and health literacy: A racial/ethnic stratification analysis
AbstractBather, J. R., Cuevas, A. G., Harris, A., Kaphingst, K. A., & Goodman, M. S. (n.d.).Publication year
2024Journal title
PEC InnovationVolume
5AbstractObjective: To analyze the relationship between perceived discrimination over the life course, social status, and limited health literacy (HL). Methods: 5040 adults who participated in the 2023 Survey of Racism and Public Health. We applied stratified multilevel models adjusted for sociodemographic characteristics. Results: The average age was 47 years, 48% identified as White, 20% as Latinx, and 17% as Black. In the overall sample, we observed associations of perceived discrimination (b = 0.05, 95% CI: 0.01, 0.09), subjective social status (b = −0.16, 95% CI: −0.23, −0.10), and their interaction (b = 0.02, 95% CI: 0.01, 0.03). More perceived discrimination was associated with lower HL in the White and Multiracial participants. Higher subjective social status was associated with higher HL in the White and Latinx participants. There was a statistically significant interaction between perceived discrimination and subjective social status on HL among the White, Latinx, and Multiracial participants. Conclusion: This analysis has implications for public health practice, indicating that multi-level interventions are needed to address limited HL. Innovation: Our findings provide novel insights for identifying key SDOH indicators to assess in clinical settings to provide health literate care.Associations between subjective social status and predictors of interest in genetic testing among women diagnosed with breast cancer at a young age
AbstractOdumegwu, J. N., Chavez-Yenter, D., Goodman, M. S., & Kaphingst, K. A. (n.d.).Publication year
2024Journal title
Cancer Causes and ControlVolume
35Issue
8Page(s)
1201-1212AbstractPurpose: Genetic testing for gene mutations which elevate risk for breast cancer is particularly important for women diagnosed at a young age. Differences remain in access and utilization to testing across social groups, and research on the predictors of interest in genetic testing for women diagnosed at a young age is limited. Methods: We examined the relationships between subjective social status (SSS) and variables previously identified as possible predictors of genetic testing, including genome sequencing knowledge, genetic worry, cancer worry, health consciousness, decision-making preferences, genetic self-efficacy, genetic-related beliefs, and subjective numeracy, among a cohort of women who were diagnosed with breast cancer at a young age. Results: In this sample (n = 1,076), those who had higher SSS had significantly higher knowledge about the limitations of genome sequencing (Odds Ratio (OR) = 1.11; 95% CI = 1.01−1.21) and significantly higher informational norms (OR = 1.93; 95% CI = 1.19−3.14) than those with lower SSS. Similarly, education (OR = 2.75; 95% CI = 1.79−4.22), health status (OR = 2.18; 95% CI = 1.44−3.31) were significant predictors among higher SSS women compared to lower SSS women in our multivariate analysis. Lower SSS women with low self-reported income (OR = 0.13; 95% CI = 0.08−0.20) had lower odds of genetic testing interest. Our results are consistent with some prior research utilizing proxy indicators for socioeconomic status, but our research adds the importance of using a multidimensional indicator such as SSS to examine cancer and genetic testing predictor outcomes. Conclusion: To develop interventions to improve genetic knowledge, researchers should consider the social status and contexts of women diagnosed with breast cancer at a young age (or before 40 years old) to ensure equity in the distribution of genetic testing benefits.Bayesian Kernel Machine Regression for Social Epidemiologic Research
AbstractBather, J. R., Robinson, T. J., & Goodman, M. S. (n.d.).Publication year
2024Journal title
EpidemiologyVolume
35Issue
6Page(s)
735-747AbstractBackground: Little attention has been devoted to framing multiple continuous social variables as a "mixture"for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects. Methods: Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable. Results: We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31). Conclusion: With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities: Rigorous Methods Are Available
AbstractMurray, D. M., & Goodman, M. S. (n.d.).Publication year
2024Journal title
Prevention ScienceVolume
25Page(s)
343-347AbstractIn June 2022, the NIH Office of Disease Prevention (ODP) issued a Call for Papers for a Supplemental Issue to Prevention Science on Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities. ODP sought to bring together current thinking and new ideas about design and analytic methods for studies aimed at reducing health disparities, including strategies for balancing methodological rigor with design feasibility, acceptability, and ethical considerations. ODP was particularly interested in papers on design and analytic methods for parallel group- or cluster-randomized trials (GRTs), stepped-wedge GRTs, group-level regression discontinuity trials, and other methods appropriate for evaluating multilevel interventions. In this issue, we include 12 papers that report new methods, provide examples of strong applications of existing methods, or provide guidance on developing multilevel interventions to reduce health disparities. These papers provide examples showing that rigorous methods are available for the design and analysis of multilevel interventions to reduce health disparities.Designing for data sharing: Considerations for advancing health equity in data management and dissemination
AbstractRabin, B. A., Smith, J. D., Dressler, E. V., Cohen, D. J., Lee, R. M., Goodman, M. S., D’angelo, H., Norton, W. E., & Oh, A. Y. (n.d.).Publication year
2024Journal title
Translational Behavioral MedicineVolume
14Issue
11Page(s)
637-642AbstractData sharing, the act of making scientific research data available to others, can accelerate innovation and discoveries, and ultimately enhance public health. The National Cancer Institute Implementation Science Centers in Cancer Control convened a diverse group of research scientists, practitioners, and community partners in three interactive workshops (May-June 2022) to identify and discuss factors that must be considered when designing research for equitable data sharing with a specific emphasis on implementation science and social, behavioral, and population health research. This group identified and operationalized a set of seven key considerations for equitable data sharing - conceptualized as an inclusive process that fairly includes the perspectives and priorities of all partners involved in and impacted by data sharing, with consideration of ethics, history, and benefits - that were integrated into a framework. Key data-sharing components particularly important for health equity included: elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and adverse impact of data sharing on diverse beneficiaries of health data and research.Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression
AbstractBather, J. R., Han, L., Bennett, A. S., Elliott, L., & Goodman, M. S. (n.d.).Publication year
2024Journal title
American Journal of Drug and Alcohol AbuseVolume
50Issue
5Page(s)
623-630AbstractBackground: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research. Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes. Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect. Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains. Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.Determinants of Breast Cancer Screening Adherence During the COVID-19 Pandemic in a Cohort at Increased Inherited Cancer Risk in the United States
AbstractHarris, A., Bather, J. R., Kawamoto, K., Fiol, G. D., Bradshaw, R. L., Kaiser-Jackson, L., Monahan, R., Kohlmann, W., Liu, F., Ginsburg, O., Goodman, M. S., & Kaphingst, K. A. (n.d.).Publication year
2024Journal title
Cancer ControlVolume
31AbstractBackground: We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. Methods: We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. Results: Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. Conclusion: Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer.Distal Factors Associated With Proximal Overdose Risk Behaviors and Recent Non-Fatal Overdose Among a Sample of People Who Use Illicit Opioids in New York City
AbstractElliott, L., Chen, Y., Goodman, M., & Bennett, A. S. (n.d.).Publication year
2024Journal title
Journal of Drug IssuesVolume
54Issue
3Page(s)
457-475AbstractThis analysis identifies factors associated with overdose risk behaviors and non-fatal overdose among a sample of 577 adult-age people who use illicit opioids and live in NYC. Survey data--which included outcome measures assessing (1) past 30-day non-fatal overdose and past 30-day overdose-related risk behaviors and (2) predictors representing potential risk and protective factors—were analyzed using bivariate and Poisson regression techniques. Results indicate being 41–56 years in age, being widowed, using cannabis, injecting, and having greater pain severity and mental health challenges were associated with greater risks. Current employment, homelessness, and prescription of medications for opioid use disorder were associated with fewer risks. Being 57+ was negatively associated with past-month overdose; higher pain severity and opioid related withdrawal were positively related, as were employment, cannabis use, and injection. Findings suggest the importance of expanding access to MOUD and tailoring OD prevention interventions for mental health and pain management services.Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own
AbstractBradshaw, R. L., Kawamoto, K., Bather, J. R., Goodman, M. S., Kohlmann, W. K., Chavez-Yenter, D., Volkmar, M., Monahan, R., Kaphingst, K. A., & Del Fiol, G. (n.d.).Publication year
2024Journal title
Journal of Biomedical InformaticsVolume
149AbstractObjective: This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs); and 2) assess their impact on relative differences across sex, race, ethnicity, and language preference. Materials and Methods: The study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data; SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language preference. Results: Among 120,007 patients aged 25–60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates; NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is English. Conclusion: Algorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.Evaluating ApoL1 Genetic Testing Policy Options for Transplant Centers: A Delphi Consensus Panel Project with Stakeholders
AbstractMcIntosh, T., Walsh, H., Baldwin, K., Iltis, A., Mohan, S., Sawinski, D., Goodman, M., & Dubois, J. M. (n.d.).Publication year
2024Journal title
Clinical Journal of the American Society of NephrologyVolume
19Issue
4Page(s)
494-502AbstractBackgroundApolipoprotein L1 (ApoL1) variants G1 and G2 are associated with a higher risk of kidney disease. ApoL1 risk variants are predominantly seen in individuals with sub-Saharan African ancestry. In most transplant centers, potential organ donors are being selectively genetically tested for ApoL1 risk variants. Transplant programs have highly variable ApoL1 testing practices and need guidance on essential ApoL1 clinical policy questions.MethodsWe conducted a Delphi consensus panel focused on ApoL1 clinical policy questions, including who gets tested, who decides whether testing occurs, how test results are shared, who receives test results, and how test results are used. A total of 27 panelists across seven stakeholder groups participated: living kidney donors (n=4), deceased donor family members (n=3), recipients of a deceased donor kidney (n=4), recipients of a living donor kidney (n=4), nephrologists (n=4), transplant surgeons (n=4), and genetic counselors (n=4). Nineteen panelists (70%) identified as Black. The Delphi panel process involved two rounds of educational webinars and three rounds of surveys administered to panelists, who were asked to indicate whether they support, could live with, or oppose each policy option.ResultsThe panel reached consensus on one or more acceptable policy options for each clinical policy question; panelists supported 18 policy options and opposed 15. Key elements of consensus include the following: ask potential donors about African ancestry rather than race; make testing decisions only after discussion with donors; encourage disclosure of test results to blood relatives and organ recipients but do not require it; use test results to inform decision making, but never for unilateral decisions by transplant programs.ConclusionsThe panel generally supported policy options involving discussion and shared decision making among patients, donors, and family stakeholders. There was general opposition to unilateral decision making and prohibiting donation altogether.Love after lockup: examining the role of marriage, social status, and financial stress among formerly incarcerated individuals
AbstractBather, J. R., McSorley, A. M. M., Rhodes-Bratton, B., Cuevas, A., Rouhani, S., Nafiu, R. T., Harris, A., & Goodman, M. (n.d.).Publication year
2024Journal title
Health and JusticeVolume
12Issue
1AbstractBackground: Upon reintegration into society, formerly incarcerated individuals (FIIs) experience chronic financial stress due to prolonged unemployment, strained social relationships, and financial obligations. This study examined whether marriage and perceived social status can mitigate financial stress, which is deleterious to the well-being of FIIs. We also assessed whether sociodemographic factors influenced financial stress across marital status. We used cross-sectional data from 588 FIIs, collected in the 2023 Survey of Racism and Public Health. The financial stress outcome (Cronbach’s α = 0.86) comprised of five constructs: psychological distress, financial anxiety, job insecurity, life satisfaction, and financial well-being. Independent variables included marital and social status, age, race/ethnicity, gender identity, educational attainment, employment status, and number of dependents. Multivariable models tested whether financial stress levels differed by marital and perceived social status (individual and interaction effects). Stratified multivariable models assessed whether social status and sociodemographic associations varied by marital status. Results: We found that being married/living with a partner (M/LWP, b = -5.2) or having higher social status (b = -2.4) were protective against financial stress. Additionally, the social status effect was more protective among divorced, separated, or widowed participants (b = -2.5) compared to never married (NM, b = -2.2) and M/LWP (b = -1.7) participants. Lower financial stress correlated with Black race and older age, with the age effect being more pronounced among M/LWP participants (b = -9.7) compared to NM participants (b = -7.3). Higher financial stress was associated with woman gender identity (overall sample b = 2.9, NM sample b = 5.1), higher education (M/LWP sample b = 4.4), and having two or more dependents (overall sample b = 2.3, M/LWP sample b = 3.4). Conclusions: We provide novel insights into the interrelationship between marriage, perceived social status, and financial stress among FIIs. Our findings indicate the need for policies and programs which may target the family unit, and not only the individual, to help alleviate the financial burden of FIIs. Finally, programs that offer legal aid to assist in expungement or sealing of criminal records or those offering opportunities for community volunteer work in exchange for vouchers specific to legal debt among FIIs could serve to reduce financial stress and improve social standing.Lower social vulnerability is associated with a higher prevalence of social media-involved violent crimes in Prince George’s County, Maryland, 2018–2023
AbstractBather, J. R., Silver, D., Gill, B. P., Harris, A., Bae, J. Y., Parikh, N. S., & Goodman, M. S. (n.d.).Publication year
2024Journal title
Injury EpidemiologyVolume
11Issue
1AbstractBackground: Social vulnerability may play a role in social media-involved crime, but few studies have investigated this issue. We investigated associations between social vulnerability and social media-involved violent crimes. Methods: We analyzed 22,801 violent crimes occurring between 2018 and 2023 in Prince George’s County, Maryland. Social media involvement was obtained from crime reports at the Prince George’s County Police Department. Social media application types included social networking, advertising/selling, ridesharing, dating, image/video hosting, mobile payment, instant messaging/Voice over Internet Protocol, and other. We used the Centers for Disease Control and Prevention’s Social Vulnerability Index to assess socioeconomic status (SES), household characteristics, racial and ethnic minority status, housing type and transportation, and overall vulnerability. Modified Poisson models estimated adjusted prevalence ratios (aPRs) among the overall sample and stratified by crime type (assault and homicide, robbery, and sexual offense). Covariates included year and crime type. Results: Relative to high tertile areas, we observed a higher prevalence of social media-involved violent crimes in areas with low SES vulnerability (aPR: 1.82, 95% CI: 1.37-2.43), low housing type and transportation vulnerability (aPR: 1.53, 95% CI: 1.17-2.02), and low overall vulnerability (aPR: 1.63, 95% CI: 1.23-2.17). Low SES vulnerability areas were significantly associated with higher prevalences of social media-involved assaults and homicides (aPR: 1.64, 95% CI: 1.02-2.62), robberies (aPR: 2.00, 95% CI: 1.28-3.12), and sexual offenses (aPR: 2.07, 95% CI: 1.02-4.19) compared to high SES vulnerability areas. Low housing type and transportation vulnerability (vs. high) was significantly associated with a higher prevalence of social media-involved robberies (aPR: 1.54, 95% CI:1.01-2.37). Modified Poisson models also indicated that low overall vulnerability areas had higher prevalences of social media-involved robberies (aPR: 1.71, 95% CI: 1.10-2.67) and sexual offenses (aPR: 2.14, 95% CI: 1.05-4.39) than high overall vulnerability areas. Conclusions: We quantified the prevalence of social media-involved violent crimes across social vulnerability levels. These insights underscore the need for collecting incident-based social media involvement in crime reports among law enforcement agencies across the United States and internationally. Comprehensive data collection at the national and international levels provides the capacity to elucidate the relationships between neighborhoods, social media, and population health.National and Regional Trends in Police Pursuit Fatalities in the US
Bather, J. R., & Goodman, M. S. (n.d.).Publication year
2024Journal title
JAMA network openVolume
7Issue
11Racial and Ethnic Composition of Departments of Health Policy & Management and Health Education & Behavioral Sciences
AbstractBather, J. R., Furr-Holden, D., Burke, E. M., Plepys, C. M., Gilbert, K. L., & Goodman, M. S. (n.d.).Publication year
2024Journal title
Health Education and BehaviorVolume
51Issue
6Page(s)
861-875AbstractThe diversity of racial/ethnic representation in the health services and policy research (HSPR) workforce plays a crucial role in addressing the health needs of underserved populations. We assessed changes (between 2012 and 2022) in the racial/ethnic composition of students and faculty from departments of Health Policy & Management (HPM) and Health Education & Behavioral Sciences (HEBS) among the Association of Schools and Programs of Public Health member institutions. We analyzed annual data from over 40 institutions that reported student and faculty data in 2012 and 2022 within each department. Racial/ethnic populations included American Indian/Alaska Native (AI/AN), Asian, Hispanic, Native Hawaiian/Pacific Islander (NH/PI), Black, White, Unknown, and Multiracial. We conducted analyses by department and examined racial/ethnic composition by student status, degree level, faculty rank, and tenure status. We found statistically significant increases in Black assistant professors (HPM and HEBS) and tenured faculty (HPM), Hispanic graduates and tenure-track faculty (HPM), Asian professors (HPM: full and tenured, HEBS: associate and tenured), and Multiracial students and graduates (HPM and HEBS). Statistically significant decreases were observed in White professors (HPM: assistant and full, HEBS: all ranks) and tenure-track faculty (HPM and HEBS), AI/AN associate professors and tenured faculty (HEBS), Hispanic associate professors (HPM), Asian assistant professors (HEBS), and NH/PI students (HPM and HEBS). Our findings highlight the importance of increasing racial/ethnic representation. Strategies to achieve this include facilitating workshops to raise awareness about the structural barriers encountered by Hispanic faculty, providing research support, evaluating promotion processes, establishing more pathway programs, and fostering interdisciplinary academic environments studying AI/AN or NH/PI populations.Racial Composition of Past and Current Social Environments and Health Literacy
AbstractBather, J. R., Liu, F., Goodman, M. S., & Kaphingst, K. A. (n.d.).Publication year
2024Journal title
Health literacy research and practiceVolume
8Issue
3Page(s)
e130-e139AbstractBACKGROUND: Research is needed to understand the impact of social determinants of health on health literacy throughout the life course. This study examined how racial composition of multiple past and current social environments was related to adults' health literacy. METHODS: In this study, 546 adult patients at a primary care clinic in St. Louis, Missouri, completed a self-administered written questionnaire that assessed demographic characteristics and a verbally administered component that assessed health literacy with the Rapid Estimate of Adult Literacy in Medicine - Revised (REALM-R) and Newest Vital Sign (NVS), and self-reported racial composition of six past and four current social environments. Multilevel logistic regression models were built to examine the relationships between racial composition of past and current social environments and health literacy. RESULTS: Most participants identified as Black or multiracial (61%), had a high school diploma or less (54%), and household income <$20,000 (72%). About 56% had adequate health literacy based on REALM-R and 38% based on NVS. In regression models, participants with multiple past white environments (e.g., locations/conditions in which most of the people who live, go to school, work, and have leisure time are White) and (vs. 0 or 1) were more likely to have adequate health literacy based on REALM-R (adjusted odds ratio [aOR] = 1.79; 95% confidence interval [CI]: 1.04-3.07). Similarly, participants who had multiple past white social environments were more likely (aOR = 1.94, 95% CI: 1.15-3.27) to have adequate health literacy based on NVS than those who had not. The racial composition of current social environments was not significantly associated with health literacy in either model. CONCLUSIONS: Racial composition of past, but not current, educational and residential social environments was significantly associated with adult health literacy. The results highlight the importance of examining the impact of social determinants over the life course on health literacy. The findings suggest that policies ensuring equitable access to educational resources in school and community contexts is critical to improving equitable health literacy. [HLRP: Health Literacy Research and Practice. 2024;8(3):e130-e139.].Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study
AbstractBather, J. R., Kaphingst, K. A., & Goodman, M. S. (n.d.).Publication year
2024Journal title
JMIR Public Health and SurveillanceVolume
10AbstractBackground: Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. Objective: This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). Methods: The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. Results: For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. Conclusions: These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.