Faculty Bibliography

OBJECTIVES/OBJECTIVE:Rural residents face unique challenges that contribute to poor diet quality and health. The objective of this study was to assess diet quality using the Alternative Healthy Eating Index (aHEI) among residents of a rural New York county with poor health outcomes, and identify geographic and individual-level factors associated with lower diet quality. DESIGN/METHODS:We performed a cross-sectional study using survey data and multivariable linear regression analyses, supplemented by geographic distribution assessment of aHEI scores. SETTING/METHODS:Sullivan County, New York, a rural county with poor health outcomes. PARTICIPANTS/METHODS:Households in Sullivan County who responded to a dietary and sociodemographic health survey in 2021-2022. OUTCOME MEASURES/METHODS:The primary outcome was the aHEI score and its dietary subscores. Secondary measures included sociodemographic characteristics, receipt of income assistance, financial hardship, food and housing insecurity, primary food store type, distance to food store and frequency of canned or plastic-wrapped food consumption. RESULTS:The overall survey response rate was 42.5%. The mean aHEI score was 56.5 (SD: 11.9), normally distributed across respondents. Lower aHEI scores were associated with not completing high school (10.0 points lower vs college graduates, p<0.01), reporting a disability (3.5 points lower, p<0.01), experiencing food insecurity (3.0 points lower, p<0.01) and frequent consumption of canned foods (6.2 points lower compared with those who never consumed canned foods, p<0.01). Geographic analysis revealed clustering of predictors but no substantial geographic clustering of aHEI scores. CONCLUSIONS:Poor diet quality in rural areas is associated with education level, disability, food insecurity and canned food consumption. These findings highlight potentially modifiable risk factors and support the need for targeted interventions to improve diet quality and reduce health disparities in rural populations.

INTRODUCTION/UNASSIGNED:The SARS-CoV-2 virus resulted in significant disability and diagnostic challenges among patients with Post-Acute Sequelae of COVID-19 (PASC). Here, we assessed microvascular perfusion, clotting, and autoimmune responses to lung targets in PASC patients compared to healthy controls with the aim of explaining the persistent respiratory symptoms of patients with PASC. METHODS/UNASSIGNED:We performed a blinded case-control study of 20 PASC patients with persistent respiratory symptoms versus 20 healthy controls previously infected with SARS-CoV-2 virus. We assessed lung perfusion using Technetium-99m macroaggregated albumin (MAA) SPECT-CT scans, clotting using coagulation and thromboelastrogram (TEG) tests, and autoimmunity to vascular and lung antigens using ELISA assays. RESULTS/UNASSIGNED:Subjective respiratory symptoms and quality-of-life measures were significantly worse among the PASC patients compared with healthy controls (p<0.001). Clinical symptoms among PASC patients were inversely correlated with plasma total IgA levels (coefficient: -0.61, p=0.004) and with autoimmune IgA recognizing pulmonary microvascular endothelial cell antigens (coefficient: -0.51, p=0.02). Additionally, levels of total IgA were directly correlated with fibrinogen and fibrin-related clot strength (coefficient: +0.52, p=0.02; coefficient: +0.63, p=0.003). SPECT-CT scans were positive only among 25% of PASC cases versus 10% of healthy controls (p=0.41). TEG tests showed no differences between the groups. CONCLUSIONS/UNASSIGNED:Our small study of PASC patients identified that circulating IgA antibodies may correlate inversely with clinical symptoms and directly with clotting parameters, suggesting a possible link between autoimmunity and coagulation. However, many of the study's findings were null, which may mean that tissue-level studies or alternative explanations of PASC need to be explored.

Prior studies suggest that diabetes is associated with severe outcomes following COVID-19. However, most research has focused on early phases of the COVID-19 pandemic, and less is known about changing diabetes-associated risks over time. We constructed a retrospective cohort of U.S. Veterans with documented COVID-19 between March 2020 and August 2023 (N = 426,170). We used Poisson regression models to estimate relative risks of 60-day mortality following COVID-19 among Veterans with and without diabetes, incorporating demographic and clinical covariates, as well as weights to address unequal probabilities of selection into the sample. We then incorporated interaction terms representing six-month time windows and plotted predicted mortality risks over time. To contextualize risk estimates, we repeated the analysis among a cohort of Veterans without documented COVID-19. Diabetes was associated with overall higher risk of 60-day mortality following COVID-19 (RR = 1.21, 95% CI = 1.17-1.26). Mortality risks attenuated over time and converged with risks observed among Veterans without COVID-19 by March-August 2022. Results suggest that post-COVID-19 mortality risks associated with diabetes may have attenuated over time. Mechanisms underlying the attenuation of mortality risks were beyond the scope of the paper, however, future studies can potentially shed light on the contributions of population immunity (driven by previous infection or vaccination status), changing treatment patterns, and other factors to time-varying mortality risks following COVID-19 among individuals with diabetes.

OBJECTIVE:To perform a detailed characterisation of diabetes burden and pre-diabetes risk in a rural county with previously documented poor health outcomes in order to understand the local within-county distribution of diabetes in rural areas of America. DESIGN, SETTING, AND PARTICIPANTS/METHODS:In 2021, we prospectively mailed health surveys to all households in Sullivan County, a rural county with the second-worst health outcomes of all counties in New York State. Our survey included questions on demographics, medical history and the American Diabetes Association's Pre-diabetes Risk Test. PRIMARY OUTCOME AND METHODS/UNASSIGNED:Our primary outcome was an assessment of diabetes burden within this rural county. To help mitigate non-response bias in our survey, raking adjustments were performed across strata of age, sex, race/ethnicity and health insurance. We analysed diabetes prevalence by demographic characteristics and used geospatial analysis to assess for clustering of diagnosed diabetes cases. RESULTS:After applying raking procedures for the 4725 survey responses, our adjusted diagnosed diabetes prevalence for Sullivan County was 12.9% compared with the 2019 Behavioural Risk Factor Surveillance System (BRFSS) estimate of 8.6%. In this rural area, diagnosed diabetes prevalence was notably higher among non-Hispanic Black (21%) and Hispanic (15%) residents compared with non-Hispanic White (12%) residents. 53% of respondents without a known history of pre-diabetes or diabetes scored as high risk for pre-diabetes. Nearest neighbour analyses revealed that hotspots of diagnosed diabetes were primarily located in the more densely populated areas of this rural county. CONCLUSIONS:Our mailed health survey to all residents in Sullivan County demonstrated higher diabetes prevalence compared with modelled BRFSS estimates that were based on small telephone samples. Our results suggest the need for better diabetes surveillance in rural communities, which may benefit from interventions specifically tailored for improving glycaemic control among rural residents.

OBJECTIVE/UNASSIGNED:To perform a geospatial analysis of food insecurity in a rural county known to have poor health outcomes and assess the effect of the COVID-19 pandemic. METHODS/UNASSIGNED:In 2020, we mailed a comprehensive cross-sectional survey to all households in Sullivan County, a rural county with the second-worst health outcomes among all counties in New York State. Surveys of households included validated food insecurity screening questions. Questions were asked in reference to 2019, prior to the pandemic, and for 2020, in the first year of the pandemic. Respondents also responded to demographic questions. Raking adjustments were performed using age, sex, race/ethnicity, and health insurance strata to mitigate non-response bias. To identify significant hotspots of food insecurity within the county, we also performed geospatial analysis. FINDINGS/UNASSIGNED:From the 28,284 households surveyed, 20% of households responded. Of 4725 survey respondents, 26% of households reported experiencing food insecurity in 2019, and in 2020, this proportion increased to 35%. In 2020, 58% of Black and Hispanic households reported experiencing food insecurity. Food insecurity in 2020 was also present in 58% of unmarried households with children and in 64% of households insured by Medicaid. The geospatial analyses revealed that hotspots of food insecurity were primarily located in or near more urban areas of the rural county. CONCLUSIONS/UNASSIGNED:Our countywide health survey in a high-risk rural county identified significant increases of food insecurity in the first year of the COVID-19 pandemic, despite national statistics reporting a stable rate. Responses to future crises should include targeted interventions to bolster food security among vulnerable rural populations.

OBJECTIVE:The aim of this study was to examine relationships between the food environment and obesity by community type. METHODS:Using electronic health record data from the US Veterans Administration Diabetes Risk (VADR) cohort, we examined associations between the percentage of supermarkets and fast-food restaurants with obesity prevalence from 2008 to 2018. We constructed multivariable logistic regression models with random effects and interaction terms for year and food environment variables. We stratified models by community type. RESULTS:Mean age at baseline was 59.8 (SD = 16.1) years; 93.3% identified as men; and 2,102,542 (41.8%) were classified as having obesity. The association between the percentage of fast-food restaurants and obesity was positive in high-density urban areas (odds ratio [OR] = 1.033; 95% CI: 1.028-1.037), with no interaction by time (p = 0.83). The interaction with year was significant in other community types (p < 0.001), with increasing odds of obesity in each follow-up year. The associations between the percentage of supermarkets and obesity were null in high-density and low-density urban areas and positive in suburban (OR = 1.033; 95% CI: 1.027-1.039) and rural (OR = 1.007; 95% CI: 1.002-1.012) areas, with no interactions by time. CONCLUSIONS:Many healthy eating policies have been passed in urban areas; our results suggest such policies might also mitigate obesity risk in nonurban areas.

INTRODUCTION:Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved. METHODS AND ANALYSIS:The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression. ETHICS AND DISSEMINATION:The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.

OBJECTIVE:Infants with high birthweight have increased risk for adverse outcomes at birth and across childhood. Prenatal risks to healthy food access may increase odds of high birthweight. We tested whether having a poor neighborhood food environment and/or food insecurity had associations with high birthweight. METHODS:We analyzed cross-sectional baseline data in Greenlight Plus, an obesity prevention trial across six US cities (n = 787), which included newborns with a gestational age greater than 34 weeks and a birthweight greater than 2500 g. We assessed neighborhood food environment using the Place-Based Survey and food insecurity using the US Household Food Security Module. We performed logistic regression analyses to assess the individual and additive effects of risk factors on high birthweight. We adjusted for potential confounders: infant sex, race, ethnicity, gestational age, birthing parent age, education, income, and study site. RESULTS:Thirty-four percent of birthing parents reported poor neighborhood food environment and/or food insecurity. Compared to those without food insecurity, food insecure families had greater odds of delivering an infant with high birthweight (adjusted odds ratios [aOR] 1.96, 95% confidence intervals [CI]: 1.01, 3.82) after adjusting for poor neighborhood food environment, which was not associated with high birthweight (aOR 1.35, 95% CI: 0.78, 2.34). Each additional risk to healthy food access was associated with a 56% (95% CI: 4%-132%) increase in high birthweight odds. CONCLUSIONS:Prenatal risks to healthy food access may increase high infant birthweight odds. Future studies designed to measure neighborhood factors should examine infant birthweight outcomes in the context of prenatal social determinants of health.

INTRODUCTION/UNASSIGNED:There is growing interest in using electronic health records (EHRs) for chronic disease surveillance. However, these data are convenience samples of in-care individuals, which are not representative of target populations for public health surveillance, generally defined, for the relevant period, as resident populations within city, state, or other jurisdictions. We focus on using EHR data for estimation of diabetes prevalence among young adults in New York City, as rising diabetes burden in younger ages call for better surveillance capacity. METHODS/UNASSIGNED:This article applies common nonprobability sampling methods, including raking, post-stratification, and multilevel regression with post-stratification, to real and simulated data for the cross-sectional estimation of diabetes prevalence among those aged 18-44 years. Within real data analyses, we externally validate city- and neighborhood-level EHR-based estimates to gold-standard estimates from a local health survey. Within data simulations, we probe the extent to which residual biases remain when selection into the EHR sample is non-ignorable. RESULTS/UNASSIGNED:Within the real data analyses, these methods reduced the impact of selection biases in the citywide prevalence estimate compared to gold standard. Residual biases remained at the neighborhood-level, where prevalence tended to be overestimated, especially in neighborhoods where a higher proportion of residents were captured in the sample. Simulation results demonstrated these methods may be sufficient, except when selection into the EHR is non-ignorable, depending on unmeasured factors or on diabetes status. CONCLUSIONS/UNASSIGNED:While EHRs offer potential to innovate on chronic disease surveillance, care is needed when estimating prevalence for small geographies or when selection is non-ignorable.

OBJECTIVE:Given the significant disparities in diabetes burden and access to care, this study uses qualitative interviews of Black men having HbA1c levels consistent with previously undiagnosed diabetes or prediabetes to understand their perceptions of the healthcare system. RESEARCH DESIGN AND METHODS/METHODS:We recruited Black men from Black-owned barbershops in Brooklyn, NY, who were screened using point-of-care HbA1c tests. Among those with HbA1c levels within prediabetes or diabetes thresholds, qualitative interviews were conducted to uncover prevalent themes related to their overall health status, health behaviors, utilization of healthcare services, and experiences with the healthcare system. We used a theoretical framework from the William and Mohammed medical mistrust model to guide our qualitative analysis. RESULTS:Fifty-two Black men without a prior history of diabetes and an HbA1c reading at or above 5.7% were interviewed. Many participants stated that their health was in good condition. Some participants expressed being surprised by their abnormal HbA1c reading because it was not previously mentioned by their healthcare providers. Furthermore, many of our participants shared recent examples of negative interactions with physicians when describing their experiences with the healthcare system. Finally, several participants cited a preference for incorporating non-pharmaceutical options in their diabetes management plans. CONCLUSION/CONCLUSIONS:To help alleviate the disparity in diabetes burden among Black men, healthcare providers should take a more active role in recognizing and addressing their own implicit biases, engage in understanding the specific healthcare needs and expectations of each patient, and consider emphasizing non-medication approaches to improve glycemic control.