Faculty Bibliography

Nationally, there is increased investment in interventions that address diet-related chronic diseases however few studies and interventions are developed to reflect the values and lifestyles of many communities, presenting a barrier to participation. This study aims to better understand the motivators and barriers for the Bangladeshi American community in Brooklyn, New York to participate in diet-related interventions. Formative qualitative interviews were conducted in English and Bangla with Bangladeshi adults (n = 12) to understand current shopping/cooking behaviors, access and usage of food programs, awareness and interest in food programs, and future program preferences. Participants reported three key themes: centrality of community behaviors for program acceptability, creating opportunities to leverage community and social motivations, and addressing logistical concerns during program development. Gardening emerged as a promising program offering to increase access to fresh produce, strengthen community bonds, and foster cross-cultural understanding. Using community feedback, Harvest Share Seedlings, a community-informed home gardening program, was co-developed and implemented with farming and community partners to increase access to fresh produce for the Bangladeshi community. The findings highlight the need to understand and center community-specific considerations when designing and implementing food programs and interventions. Adopting a community-informed approach increases uptake and acceptability from the community, and ensures sustainability in the long run.

BACKGROUND:New York City (NYC) Cancer Outreach Network in Neighborhoods for Equity and Community Translation (CONNECT) aims to address determinants of cancer screening and access disparities in low-income NYC neighborhoods with high cancer burden. OBJECTIVES/OBJECTIVE:NYC CONNECT community partners formed neighborhood action councils (NACs) and engaged in mixed-method formative research and an iterative consensus-building process to co-identify structural and social determinants of cancer screening. The NACs co-developed with health and academic partners community-level strategies to address structural determinants of health. The objective of this manuscript is to describe participatory processes to engage and support community partners in low-income and high poverty communities and the evaluation of their functioning within the context of a community-academic research partnership. METHODS:NYC CONNECT partnership development is informed by trauma-informed community building and community-based participatory research principles. Our mixed-methods partnership evaluation included surveys, qualitative interviews, and speaking time in meetings as a proxy for engagement. LESSONS LEARNED/CONCLUSIONS:Trauma-informed community building and community-based participatory research informed co-learning and consensus-building activities among the NACs. Identifying and prioritizing structural and social determinants of health strategies was a non-linear, iterative process that required multiple interactive activities (e.g., asset mapping, impact to effort matrix, and voting). CONCLUSION/CONCLUSIONS:Community engagement is a dynamic process that requires adapting to community partners' goals and sharing decision-making power.

AIMS/OBJECTIVE:Rural communities experience a higher prevalence of type 2 diabetes and diabetes-related mortality than urban populations. This study sought to disaggregate the influences of demographic and socioeconomic factors, food environment, diet quality, and dietary chemical exposures on diabetes risk in rural areas. MATERIALS AND METHODS/METHODS:We enrolled participants from rural Sullivan County in an observational cohort study involving surveys and biospecimen collection measuring bisphenols and phthalates. We measured these endocrine disrupting chemicals found in food packaging, as rural residents generally consume canned foods and other shelf-stable foods more frequently than their urban counterparts. We used LASSO regression to compare the relative influence of these factors had on rural diabetes risk. RESULTS:Based on values for LASSO regression coefficients among 276 participants, the strongest risk factors for diagnosed diabetes included: older age (+0.486), lower household income (+0.172), Hispanic ethnicity (+0.124), red meat intake (+0.093), proportion of fast food restaurants among nearby restaurants (+0.071), and two phthalates (+0.149 and + 0.107). Among study participants without a history of diabetes, high HbA1c levels were associated with older age (+0.106), being non-Hispanic Black (+0.064), more trans-fat and red meat intake (+0.044 and +0.028), higher BMI (+0.014), higher levels of total bisphenols (+0.005), and higher levels of high-molecular weight phthalates (+0.002). CONCLUSIONS:Demographic and socioeconomic factors were the strongest predictors of rural diabetes risk; however, diet quality, food environment, and dietary chemical exposures also each played a key role. Our study identified modifiable risk factors, which could help reduce the burden of rural diabetes.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Alzheimer's disease and related dementias (ADRD) are a leading cause of death, affecting up to 57 million globally. Up to 45% of dementia cases could be prevented or delayed by addressing non-medical drivers of health (NMDoH). Community engagement, health communication, and implementation science are core areas of public health and important to consider when researching ADRD. However, these fields are often siloed, limiting efficacy of ADRD prevention and intervention. This scoping review maps how researchers have incorporated models and theoretical frameworks from these fields specific to ADRD outcomes and with attention to NMDoH. RESEARCH DESIGN AND METHODS/METHODS:We searched five social science databases, and articles were included if they were empirical, written in English language, published 2010 forward, focused on ADRD or cognitive health, guided by or developed a framework, theory, or model, and addressed community engagement, health communication, or implementation science. RESULTS:We retrieved 2,428 articles which were reviewed in multiple stages by five co-authors, resulting in a final sample of 32 articles. Most articles utilized published frameworks, models, or theories, while five were guided by author-developed approaches. Nine articles integrated two core areas, and only one article integrated all three. DISCUSSION AND IMPLICATIONS/CONCLUSIONS:Increased integration of core areas and systematic application of theoretical frameworks are necessary to improve ADRD research with attention to NMDoH. Findings have the potential to inform training and mentorship opportunities for early-career researchers on best practices in interdisciplinary ADRD research, thereby improving community and population health outcomes.

BACKGROUND:Individuals with opioid use disorder (OUD) may experience fewer barriers to treatment following incarceration if offered in-jail medications for OUD (MOUD). We aimed to identify care trajectories of community OUD treatment after incarceration and examine the association between receiving in-jail MOUD and experiencing specific community treatment trajectories. METHODS:This retrospective cohort study using matched New York City (NYC) health care administrative data included adults with OUD incarcerated on or after May 2011 and discharged during 2014-2017. We defined states of community OUD treatment at the weekly level over one year following index jail discharge and performed state sequence analysis (SSA) to identify trajectories of treatment after jail and assessed the influence of receiving in-jail MOUD on treatment trajectories. RESULTS:Of 14,923 eligible individuals, 26.2% received in-jail MOUD. SSA identified eight clusters of community care trajectories: continuous methadone treatment (9.7%), methadone treatment discontinuation (3.7%), methadone treatment and reincarceration (6.7%), methadone treatment initiation (4.8%), continuous reincarceration (3.5%), short reincarceration with little community treatment (20.3%), long reincarceration with little community treatment (7.0%), and no community OUD treatment or reincarceration (44.5%). Receiving in-jail MOUD was associated with belonging to the continuous methadone treatment cluster compared to the no community OUD treatment or reincarceration cluster (adjusted OR: 12.5, 95% CI: 9.9-15.7). CONCLUSION/CONCLUSIONS:We identified eight unique patterns of community OUD treatment after jail release. Receipt of in-jail MOUD was associated with belonging to the continuous methadone treatment cluster. These findings suggest that provision of in-jail MOUD could improve methadone uptake in the community.

OBJECTIVE:We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS/METHODS:In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS:Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION/CONCLUSIONS:Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION/CONCLUSIONS:Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

We evaluated the effectiveness of a community research partnership focused on improving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing for New York City residents during the pandemic. We employed interviews, a focus group, and a survey to evaluate partnership characteristics, engagement, and future collaboration. Qualitative analysis revealed five core themes: committee identity, collective goals, information sharing, adaptability, and trust. The findings highlight the importance of flexibility, shared goals, diverse representation, open communication, and trust in effective multisector community health partnerships. (Am J Public Health. 2026;116(4):431-436. https://doi.org/10.2105/AJPH.2025.308358).

BACKGROUND:Co-occurring alcohol, substance, and mood-related (CASM) conditions are prevalent, mutually reinforcing, and under-diagnosed contributors to morbidity, mortality, and health disparities. OBJECTIVE:To evaluate screening strategies leveraging the predictive value arising from patterns of CASM co-occurrence in populations with high CASM prevalence. DESIGN/METHODS:Individual-based health risks model validated to predict US life expectancy and causes of death by sex and age decile, including CASM conditions of depression, anxiety, chronic pain, and unhealthy alcohol, tobacco, opioid and stimulant use. The model includes CASM co-occurrence patterns, mutual reinforcement across CASM conditions, and reduced engagement in other preventative care due to CASM. PARTICIPANTS/METHODS:Veterans Aging Cohort Study (VACS), a large longitudinal cohort of in-care US veterans. INTERVENTIONS/METHODS:(1) Screening alcohol, tobacco, and/or depression symptoms; (2) adding further screening of CASM conditions likely to co-occur with those screened positive, with variation in the minimum co-occurrence rate; (3) screening all CASM conditions (hypothetical maximum). MAIN MEASURES/METHODS:Estimated life expectancy (LE) and quality-adjusted life-years (QALYs). KEY RESULTS/RESULTS:The maximum strategy added 0.52 years to estimated LE (95% CI: 0.51 - 0.54) and 0.68 QALYs/person (95% CI: 0.67 - 0.69). Screening individual CASM conditions added a small fraction of this benefit, the largest LE gain from tobacco screening: 0.08 years (95% CI: 0.07 - 0.09). Screening for depression, alcohol, and tobacco provided 34.6% of the maximum strategy's LE gain (0.19 years, 95% CI: 0.17 - 0.20). Additionally screening conditions with moderate (≥ 20%) probability of co-occurring with those already screened positive provided 84.8% of the maximum strategy's LE gain. Screening all CASM conditions if depression, alcohol, and/or tobacco screened positive provided 86.6% of the maximum strategy's LE gain. CONCLUSIONS:Compared to common practice of screening one or few CASM conditions, large health benefits are possible by further assessing CASM conditions most likely to co-occur with those already screening positive, improving health without increasing up-front screening burden in populations with high CASM prevalence.

We characterize within-city life expectancy gaps and their correlation with social and environmental characteristics in 948 US cities. Life expectancy estimates were drawn from the US Life Expectancy Estimation Program. City life expectancy gaps were calculated by subtracting the lowest tract-level life expectancy estimate from the highest for each city. Correlations were established using Spearman's correlation coefficient. The average city-level life expectancy gap in our sample was 11.8 years. Life expectancy gaps were larger in cities with lower average life expectancy and were evident across the USA. Life expectancy gaps of a decade were seen even in smaller cities and in high life expectancy cities. Life expectancy gaps were most strongly correlated with racialized residential segregation, children in poverty, and household income. Significant between-neighborhood gaps in life expectancy exist across US cities. Life expectancy gaps present a compelling target for establishing robust health equity goals.