Faculty Bibliography

Background A number of studies have shown an association between social determinants of health and the emergence of obesity and diabetes, but whether the relationship is causal is not clear. Objective To test whether social, environmental, and medical determinants directly or indirectly affect population-level diabetes prevalence after controlling for mediator-mediator interactions. Methods Data were obtained from the CDC and supplemented with nine other data sources for 3,109 US counties. The dependent variable was the prevalence of diabetes in 2017. Independent variables were a given county's 30 social, environmental, and medical characteristics in 2015 and 2016. A network multiple mediation analysis was conducted. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) regression to relate the 2017 diabetes rate in each county to 30 predictors measured in 2016, identifying statistically significant and robust predictors as the mediators within the network model and as direct determinants of 2017 diabetes. Second, each of the direct causes of diabetes was taken as a new response variable and LASSO-regressed on the same 30 independent variables measured in 2015, identifying the indirect (mediated) causes of diabetes. Subsequently, these direct and indirect predictors were used to construct a network model. The completed network was then employed to estimate the direct and mediated impact of variables on diabetes. Results For 2017 diabetes rates, 63% of the variation was explained by five variables measured in 2016: the percentage of residents who were (1) obese, (2) African American, (3) physically inactive, (4) in poor health condition, and (5) had a history of diabetes. These five direct predictors, measured in 2016, mediated the effect of indirect variables measured in 2015, including the percentage of residents who were (1) Hispanic, (2) physically distressed, (3) smokers, (4) living with children in poverty, (5) experiencing limited access to healthy foods, and (6) had low income. Conclusion All of the direct predictors of diabetes prevalence, except the percentage of residents who were African American, were medical conditions potentially influenced by lifestyles. Counties characterized by higher levels of obesity, inactivity, and poor health conditions exhibited increased diabetes rates in the following year. The impact of social determinants of illness, such as low income, children in poverty, and limited access to healthy foods, had an indirect effect on the health of residents and, consequently, increased the prevalence of diabetes.

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

BACKGROUND:While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. METHODS:We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018-2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. RESULTS:In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110-140) to 50 (95%CI,30-80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640-2040) to 810 (95%CI,710-930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020-2021 from 330 (95%CI,310-350) to 770 (95%CI,720-820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. CONCLUSIONS:Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity.

Introduction/UNASSIGNED:Geographic disparities in diabetes burden exist throughout the United States (US), with many risk factors for diabetes clustering at a community or neighborhood level. We hypothesized that the likelihood of new onset type 2 diabetes (T2D) would differ by community type in three large study samples covering the US. Research design and methods/UNASSIGNED:We evaluated the likelihood of new onset T2D by a census tract-level measure of community type, a modification of RUCA designations (higher density urban, lower density urban, suburban/small town, and rural) in three longitudinal US study samples (REGARDS [REasons for Geographic and Racial Differences in Stroke] cohort, VADR [Veterans Affairs Diabetes Risk] cohort, Geisinger electronic health records) representing the CDC Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network. Results/UNASSIGNED:In the REGARDS sample, residing in higher density urban community types was associated with the lowest odds of new onset T2D (OR [95% CI]: 0.80 [0.66, 0.97]) compared to rural community types; in the Geisinger sample, residing in higher density urban community types was associated with the highest odds of new onset T2D (OR [95% CI]: 1.20 [1.06, 1.35]) compared to rural community types. In the VADR sample, suburban/small town community types had the lowest hazard ratios of new onset T2D (HR [95% CI]: 0.99 [0.98, 1.00]). However, in a regional stratified analysis of the VADR sample, the likelihood of new onset T2D was consistent with findings in the REGARDS and Geisinger samples, with highest likelihood of T2D in the rural South and in the higher density urban communities of the Northeast and West regions; likelihood of T2D did not differ by community type in the Midwest. Conclusions/UNASSIGNED:The likelihood of new onset T2D by community type varied by region of the US. In the South, the likelihood of new onset T2D was higher among those residing in rural communities.

Importance/UNASSIGNED:Diabetes causes substantial morbidity and mortality among adults in the US, yet its incidence varies across the country, suggesting that neighborhood factors are associated with geographical disparities in diabetes. Objective/UNASSIGNED:To examine the association between neighborhood food environment and risk of incident type 2 diabetes across different community types (high-density urban, low-density urban, suburban, and rural). Design, Setting, and Participants/UNASSIGNED:This is a national cohort study of 4 100 650 US veterans without type 2 diabetes. Participants entered the cohort between 2008 and 2016 and were followed up through 2018. The median (IQR) duration of follow-up was 5.5 (2.6-9.8) person-years. Data were obtained from Veterans Affairs electronic health records. Incident type 2 diabetes was defined as 2 encounters with type 2 diabetes International Classification of Diseases, Ninth Revision or Tenth Revision codes, a prescription for diabetes medication other than metformin or acarbose alone, or 1 encounter with type 2 diabetes International Classification of Diseases Ninth Revision or Tenth Revision codes and 2 instances of elevated hemoglobin A1c (≥6.5%). Data analysis was performed from October 2020 to March 2021. Exposures/UNASSIGNED:Five-year mean counts of fast-food restaurants and supermarkets relative to other food outlets at baseline were used to generate neighborhood food environment measures. The association between food environment and time to incident diabetes was examined using piecewise exponential models with 2-year interval of person-time and county-level random effects stratifying by community types. Results/UNASSIGNED:The mean (SD) age of cohort participants was 59.4 (17.2) years. Most of the participants were non-Hispanic White (2 783 756 participants [76.3%]) and male (3 779 555 participants [92.2%]). The relative density of fast-food restaurants was positively associated with a modestly increased risk of type 2 diabetes in all community types. The adjusted hazard ratio (aHR) was 1.01 (95% CI, 1.00-1.02) in high-density urban communities, 1.01 (95% CI, 1.01-1.01) in low-density urban communities, 1.02 (95% CI, 1.01-1.03) in suburban communities, and 1.01 (95% CI, 1.01-1.02) in rural communities. The relative density of supermarkets was associated with lower type 2 diabetes risk only in suburban (aHR, 0.97; 95% CI, 0.96-0.99) and rural (aHR, 0.99; 95% CI, 0.98-0.99) communities. Conclusions and Relevance/UNASSIGNED:These findings suggest that neighborhood food environment measures are associated with type 2 diabetes among US veterans in multiple community types and that food environments are potential avenues for action to address the burden of diabetes. Tailored interventions targeting the availability of supermarkets may be associated with reduced diabetes risk, particularly in suburban and rural communities, whereas restrictions on fast-food restaurants may help in all community types.

INTRODUCTION/BACKGROUND:Emergency department (ED) patients have higher than average levels of food insecurity. We examined the association between multiple measures of food insecurity and frequent ED use in a random sample of ED patients. METHODS:We completed survey questionnaires with randomly sampled adult patients from an urban public hospital ED (n = 2,312). We assessed food insecurity using four questions from the United States Department of Agriculture Household Food Security Survey. The primary independent variable was any food insecurity, defined as an affirmative response to any of the four items. Frequent ED use was defined as self-report of ≥4 ED visits in the past year. We examined the relationship between patient food insecurity and frequent ED use using bivariate and multivariable analyses and examined possible mediation by anxiety/depression and overall health status. RESULTS:One-third (30.9%) of study participants reported frequent ED use, and half (50.8%) reported any food insecurity. Prevalence of food insecurity was higher among frequent vs. non-frequent ED users, 62.8% vs 45.4% (P <0.001). After controlling for potential confounders, food insecurity remained significantly associated with frequent ED use (adjusted odds ratio 1.48, 95% confidence interval, 1.20-1.83). This observed association was partially attenuated when anxiety/depression and overall health status were added to models. CONCLUSION/CONCLUSIONS:The high observed prevalence of food insecurity suggests that efforts to improve care of ED patients should assess and address this need. Further research is needed to assess whether addressing food insecurity may play an important role in efforts to reduce frequent ED use for some patients.

BACKGROUND:Telemedicine use rapidly increased during the COVID-19 pandemic. This study assessed quality aspects of rapid expansion of a virtual urgent care (VUC) telehealth system and the effects of a secondary telephonic screening initiative during the pandemic. METHODS:A retrospective cohort analysis was performed in a single health care network of VUC patients from March 1, 2020, through April 20, 2020. Researchers abstracted demographic data, comorbidities, VUC return visits, emergency department (ED) referrals and ED visits, dispositions, intubations, and deaths. The team also reviewed incomplete visits. For comparison, the study evaluated outcomes of non-admission dispositions from the ED: return visits with and without admission and deaths. We separately analyzed the effects of enhanced callback system targeting higher-risk patients with COVID-like illness during the last two weeks of the study period. RESULTS:A total of 18,278 unique adult patients completed 22,413 VUC visits. Separately, 718 patient-scheduled visits were incomplete; the majority were no-shows. The study found that 50.9% of all patients and 74.1% of patients aged 60 years or older had comorbidities. Of VUC visits, 6.8% had a subsequent VUC encounter within 72 hours; 1.8% had a subsequent ED visit. Of patients with enhanced follow-up, 4.3% were referred for ED evaluation. Mortality was 0.20% overall; 0.21% initially and 0.16% with enhanced follow-up (p = 0.59). Males and black patients were significantly overrepresented in decedents. CONCLUSION/CONCLUSIONS:Appropriately deployed VUC services can provide a pragmatic strategy to care for large numbers of patients. Ongoing surveillance of operational, technical, and clinical factors is critical for patient quality and safety with this modality.

BACKGROUND:The coronavirus disease (COVID)-19 pandemic quickly challenged New York City health care systems. Telemedicine has been suggested to manage acute complaints and divert patients from in-person care. OBJECTIVES/OBJECTIVE:The objective of this study was to describe and assess the impact of a rapidly scaled virtual urgent care platform during the COVID-19 pandemic. METHODS:This was a retrospective cohort study of all patients who presented to a virtual urgent care platform over 1 month during the COVID-19 pandemic surge. We described scaling our telemedicine urgent care capacity, described patient clinical characteristics, assessed for emergency department (ED) referrals, and analyzed postvisit surveys. RESULTS:During the study period, a total of 17,730 patients were seen via virtual urgent care; 454 (2.56%) were referred to an ED. The most frequent diagnoses were COVID-19 related or upper respiratory symptoms. Geospatial analysis indicated a wide catchment area. There were 251 providers onboarded to the platform; at peak, 62 providers supplied 364 h of coverage in 1 day. The average patient satisfaction score was 4.4/5. There were 2668 patients (15.05%) who responded to the postvisit survey; 1236 (49.35%) would have sought care in an ED (11.86%) or in-person urgent care (37.49%). CONCLUSIONS:A virtual urgent care platform was scaled to manage a volume of more than 800 patients a day across a large catchment area during the pandemic surge. About half of the patients would otherwise have presented to an ED or urgent care in person. Virtual urgent care is an option for appropriate patients while minimizing in-person visits during the COVID-19 pandemic.