Faculty Bibliography

In the pediatric population, insulin pump therapy, or CSII, is often considered the gold standard for intensive diabetes management. Insulin pump technology offers families and caregivers many beneficial features including a calculator for insulin dosing and the ability to review diabetes management data to provide data-driven diabetes management. However, for those who find CSII challenging or choose to use multiple daily injections (MDI) there is an option that offers similar features called the Smart Insulin Pen (SIP). Even though SIP technology provides a safe and data-driven diabetes self-management tool for the pediatric population using MDI, there is limited pediatric specific literature. This article will describe current options, data-driven diabetes management, benefits, challenges and clinical use of SIP technology in the pediatric population.

BACKGROUND:Diabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies. METHODS:T1D Exchange coordinated a US type 1 diabetes COVID-19 registry. Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization. RESULTS:Data from 266 patients with previously established type 1 diabetes aged <19 years with COVID-19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID-19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34-1.84) after adjusting for age, gender, insurance, and race/ethnicity. CONCLUSIONS:Higher A1c remained the only predictor for hospitalization with COVID-19. Diabetic ketoacidosis is the primary concern among this group.

OBJECTIVE:We examined whether diabetic ketoacidosis (DKA), a serious complication of type 1 diabetes (T1D) was more prevalent among Non-Hispanic (NH) Black and Hispanic patients with T1D and laboratory-confirmed coronavirus disease 2019 (COVID-19) compared with NH Whites. METHOD:This is a cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 52 clinical sites in the United States, data were collected from April to August 2020. We examined the distribution of patient factors and DKA events across NH White, NH Black, and Hispanic race/ethnicity groups. Multivariable logistic regression analysis was performed to examine the odds of DKA among NH Black and Hispanic patients with T1D as compared with NH White patients, adjusting for potential confounders, such as age, sex, insurance, and last glycated hemoglobin A1c (HbA1c) level. RESULTS:We included 180 patients with T1D and laboratory-confirmed COVID-19 in the analysis. Forty-four percent (n = 79) were NH White, 31% (n = 55) NH Black, 26% (n = 46) Hispanic. NH Blacks and Hispanics had higher median HbA1c than Whites (%-points [IQR]: 11.7 [4.7], P < 0.001, and 9.7 [3.1] vs 8.3 [2.4], P = 0.01, respectively). We found that more NH Black and Hispanic presented with DKA compared to Whites (55% and 33% vs 13%, P < 0.001 and P = 0.008, respectively). After adjusting for potential confounders, NH Black patients continued to have greater odds of presenting with DKA compared with NH Whites (OR [95% CI]: 3.7 [1.4, 10.6]). CONCLUSION:We found that among T1D patients with COVID-19 infection, NH Black patients were more likely to present in DKA compared with NH White patients. Our findings demonstrate additional risk among NH Black patients with T1D and COVID-19.

Highlights Our multicenter study reports a higher proportion of diabetic ketoacidosis presentation of over 60% in newly diagnosed patients with type 1 diabetes with or without confirmed coronavirus disease 2019 (COVID-19) at diagnosis. This finding is suggestive of delays in seeking care during the COVID-19 pandemic.

OBJECTIVE:s rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these two conditions relate, and which subgroups of children are more likely to develop diabetes with and without obesity. METHODS:To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from healthcare claims data, from 2009-2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots. RESULTS:We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents. CONCLUSIONS:Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric type 2 diabetes. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these two populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

BACKGROUND:Studies demonstrate that children with type 1 diabetes may not be meeting exercise recommendations. This, coupled with the lack of data on the determinants of exercise promotion in youth, may indicate a need for additional focus on exercise guidelines and promotion in youth with type 1 diabetes. OBJECTIVE:The objective of this study is to understand provider perspectives regarding exercise promotion in children with type 1 diabetes. SUBJECTS AND METHODS/METHODS:An online survey regarding perspectives on exercise was emailed to Pediatric Endocrine Society members. RESULTS:Of the 84 respondents, 85.5% believe counseling regarding exercise recommendations is a priority. However, 87.8% did not identify Office of Disease Prevention and Health Promotion (ODPHP) guidelines correctly and 79.3% did not identify American Diabetes Association (ADA) guidelines correctly. Providers who exercised regularly (p = 0.009) and providers who identified ODPHP guidelines correctly (p = 0.004) were more likely to identify ADA guidelines correctly. Providers who identified ADA guidelines correctly were 4.21 times (OR 4.21; 95% CI 1.30-13.7) more likely to make good recommendations and those who discussed recommendations at diagnosis were 6.10 times (OR 6.10; 95% CI 1.76-21.2) more likely to make good recommendations. CONCLUSION/CONCLUSIONS:To our knowledge, this study is the first to investigate provider perspectives of exercise promotion in children with type 1 diabetes. We found provider recommendations were not consistent with ADA exercise guidelines and most providers were not fully aware of the recommendations. Future research should address increasing provider education regarding exercise guidelines and developing exercise promotion tools. This article is protected by copyright. All rights reserved.

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Background: Chronic illness in children has adverse effects on family members besides the patient and can impact the integrity and function of the family unit. Most previous studies have examined a single disease entity. However, there has been limited assessment comparing the effect of different illnesses on family function.
Method(s): Established patients treated in the pediatric ambulatory Nephrology or DM clinics were included in the study. Their parents were asked to complete the 2-page Pediatric Quality-of-Life Family Impact Module (PedsQL-FIM), version 2.0, a validated survey instrument. Clinical and laboratory data were retrieved from the electronic health record. Data were summarized as mean+/-SD. Disease group and child age were entered as predictors in linear regression analyses with FIM total and subscale scores as outcome variables. Comparisons between groups were assessed using paired t-tests.
Result(s): 96 patients (43 F: 53 M) were evaluated in the Nephrology Clinic and 55 (30 F: 25 M) in the DM Clinic. The mean age of the patients was 13.0+/-3.9 and 10.4+/- 6.3 yr, respectively. Within the KD sample, older age was significantly associated with lower scores on all FIM subscale scores. Gender was not a significant predicator for FIM scores in either disease group. Controlling for age, chronic illness group was a significant predictor of the FIM total and subscale scores. Parents of D patients endorsed significantly lower total FIM scores compared to the KD patients (D 58+/-16; KD 79+/-17 p <0.001) as well as on subscales of physical, emotional, social, and cognitive functioning, communication, worry, daily activities, family relationships, and reports of health-related quality of life (P<0.01).
Conclusion(s): Our findings confirm that chronic illness in childhood adversely affects a wide range of aspects of family function. The impact is greater in older children with KD and varies depending on the disease context. Families with children who have DM manifested greater disturbances than those with children who have isolated KD. Further study is warranted to assess the effects of the underlying renal disease and intensity of medical care and whether there are specific features can be used to identify vulnerable families