Diabetes distress and self-reported health in a sample of Alabama Medicaid-covered adults before and during the COVID-19 pandemic

Temporary closures of outpatient health facilities and transitions to virtual care during the COVID-19 pandemic interrupted the care of millions of patients with diabetes contributing to worsening psychosocial factors and enhanced difficulty in managing type 2 diabetes mellitus. We explored associations between COVID time period and self-reported diabetes distress on self-reported health among a sample of Alabama Medicaid-covered adults with diabetes pre-COVID (2017–2019) and during-COVID (2020–2021). Method: In this cross-sectional study, we surveyed a population-based sample of adults with type 2 diabetes covered by the Alabama Medicaid Agency. Participants were dichotomized into pre-COVID (March 2017 to October 2019) vs during-COVID (October 2020 to May 2021) groups. Participants with missing data were removed from analyses. We assessed diabetes related stress by the Diabetes Distress Scale. We measured self-reported health using a single item with a 5-point Likert scale. We ran logistic regressions modeling COVID time period on self-reported poor health controlling for demographics, severity of diabetes, and diabetes distress. Results: In this sample of 1822 individuals, median age was 54, 74.5% were female and 59.4% were Black. Compared to pre-COVID participants, participants surveyed during COVID were younger, more likely to be Black (64.1% VS 58.2%, p=0.01) and female (81.8% VS 72.5%, p<0.001). This group also had fewer individuals from rural areas (29.2% VS 38.4%, p<0.001), and shorter diabetes duration (7 years VS 9 years, p<0.001). During COVID individuals reported modestly lower levels of diabetes distress (1.2 VS 1.4, p<0.001) when compared to the pre-COVID group. After adjusting for demographic differences, diabetes severity, and diabetes distress, participants responding during COVID had increased odds of reporting poor health (Odds ratio [OR] 1.41, 95% Confidence Interval [CI] 1.11–1.80). Discussion: We found respondents were more likely to report poorer health during COVID compared to pre-COVID. These results suggest that increased outreach may be needed to address diabetes management for vulnerable groups, many of whom were already at high risk for poor outcomes prior to the pandemic.

Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction.

INTRODUCTION: Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization. METHODS: Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance. RESULTS: A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821. CONCLUSIONS: Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.

Associations between HbA1c, Area Level Social Vulnerability, and Poor COVID-Outcomes

Background: HbA1c has been associated with COVID-poor outcomes in diabetic and non-diabetic populations, while patients living in census tracts with high levels of social vulnerability [measured using the CDC's Social Vulnerability Index (SVI) ] have experienced poorer outcomes. Objective: To examine associations between HbA1c, area level social vulnerability and poor COVID outcomes in patients who tested positive for COVID and were captured in the electronic medical record (EMR) at a large academic institution in the Southeastern United States. Methods: HbA1c and SVI, collected up to 3 years prior to a positive COVID test, were extracted from the EMR. HbA1c and SVI were compared by poor outcome status [hospitalization, intensive care unit (ICU) admission, and mortality]. Bayesian logistic regression was used to examine associations between HbA1c [≤5.7%;5.7%-<6.5%;and ≥6.5%], SVI [living in high SVI census tract vs low SVI tract] and each COVID outcome separately. Multivariable models were adjusted for sex, age, race, body mass index and diabetes status. Results presented as odds ratios (OR) and 95% confidence intervals (95% CI) . Results: N=3,7patients were identified [mean age: 54 years (SD 16.3) , 60% female, 47% Black]. Patients with HbA1c ≥ 6.5% and those living in a high SVI census tract were more likely to experience a poor COVID outcome (p's<0.0001) . In multivariable models, patients with HbA1c ≥ 6.5% had higher odds of hospitalization (OR 1.79, 95% CI 1.44-2.22) ;ICU admission (OR 2.13, 95% CI 1.78-2.55) ;and mortality (OR 1.60, 95% CI 1.12-2.28) . Patients living in a high SVI census tract had higher odds of hospitalization (OR 2.47, 95% CI 1.94-3.15) ;ICU admission (OR 2.58, 95% CI 2.12-3.14) ;and mortality (OR 2.07, 95% CI 1.39-3.09) . Conclusion: HbA1c ≥6.5% and living in a census tract with high social vulnerability were independently associated with poor COVID-outcomes. Findings highlight the need to assess HbA1c and area level social determinants in the context of COVID.

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.

OBJECTIVE: This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes. METHODS: Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared. RESULTS: A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83). CONCLUSIONS: Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

The COVID-19 pandemic and weight management: Effective behaviors and pandemic-specific risk factors.

BACKGROUND: Obesity and comorbid conditions are associated with worse outcomes related to COVID-19. Moreover, social distancing adherence during the COVID-19 pandemic may predict weight gain due to decreased physical activity, increased emotional eating, and social isolation. While early studies suggest that many individuals struggled with weight management during the pandemic, less is known about healthy eating and weight control behaviors among those enrolled in weight loss programs. METHODS: The present study evaluated weight management efforts among weight loss program participants during the COVID-19 pandemic. Participants' (N = 55, 90.9% female, 36% white, Mage = 49.8) demographics and body mass index were collected two months prior to the COVID-19 statewide shutdown. During the lockdown, an online survey assessed health behaviors, coping, COVID-19 experiences (e.g., social distancing, loneliness), and weight gain. Logistic regressions examined demographics, health behaviors, and COVID-19 factors as predictors of weight gain. RESULTS: Most participants (58%) reported gaining weight during COVID-19. Weight gain was predicted by challenges with the following health behaviors: physical activity, monitoring food intake, choosing healthy foods, and emotional eating. Loneliness and working remotely significantly related to emotional eating, physical activity, and choosing healthy foods. CONCLUSIONS: Loneliness and working remotely increased the difficulty of weight management behaviors during COVID-19 among weight loss program participants. However, staying active, planning and tracking food consumption, choosing healthy foods, and reducing emotional eating protected against weight gain. Thus, these factors may be key areas for weight management efforts during the pandemic.