Type 1 diabetes incidence increased during the COVID-19 pandemic years 2020-2021 in Czechia: Results from a large population-based pediatric register.

BACKGROUND: To explore type 1 diabetes incidence patterns during the pandemic years 2020 and 2021 in Czechia, to compare them to the trends from the previous decade, and to test its association with indicators of containment measures and of pandemic severity (school closing and the all-cause excess mortality). METHODS: The Czech Childhood Diabetes Register is a population-based incidence register recording patients age 0-14.99 years at diabetes onset. Type 1 diabetes incidence in the pandemic period (April 2020-end of observation Dec 2021) was compared by Poisson regression models to the incidence patterns over the past decade 2010-2019. RESULTS: During the pandemic years 2020-2021, 956 children 0-14.99 years old manifested with type 1 diabetes in Czechia. The observed incidence (27.2/100,000/year) was significantly higher than what was expected from the trends over 2010-2019 (incidence rate ratio, IRR = 1.16, 95%CI 1.06-1.28, p = 0.0022). The incidence had a trough during the first lockdown (March-May 2020), then it rose above expected values with no usual summer decrease. The assessed pandemic indicators (school closing and all-cause excess mortality) were not associated with the incidence levels. CONCLUSIONS: The COVID-19 pandemic was associated with a notable upward inflection of the type 1 diabetes incidence curve; the early months of the first lockdown were however hallmarked by a significant dip in new diabetes diagnoses. Long-term observation will show whether the increased incidence originated only from accelerating an advanced preclinical Stage 2 to overt diabetes, or whether the pandemic triggered new cases of islet autoimmunity.

Glycemic Control by Treatment Modalities: National Registry-Based Population Data in Children and Adolescents with Type 1 Diabetes.

INTRODUCTION: The aim of the study was to assess the differences in key parameters of type 1 diabetes (T1D) control associated with treatment and monitoring modalities including newly introduced hybrid closed-loop (HCL) algorithm in children and adolescents with T1D (CwD) using the data from the population-wide pediatric diabetes registry CENDA. METHODS: CwD younger than 19 years with T1D duration >1 year were included and divided according to the treatment modality and type of CGM used: multiple daily injection (MDI), insulin pump without (CSII) and with HCL function, intermittently scanned continuous glucose monitoring (isCGM), real-time CGM (rtCGM), and intermittent or no CGM (noCGM). HbA1c, times in glycemic ranges, and glucose risk index (GRI) were compared between the groups. RESULTS: Data of a total of 3,251 children (mean age 13.4 ± 3.8 years) were analyzed. 2,187 (67.3%) were treated with MDI, 1,064 (32.7%) with insulin pump, 585/1,064 (55%) with HCL. The HCL users achieved the highest median TIR 75.4% (IQR 6.3) and lowest GRI 29.1 (7.8), both p < 0.001 compared to other groups, followed by MDI rtCGM and CSII groups with TIR 68.8% (IQR 9.0) and 69.0% (7.5), GRI 38.8 (12.5) and 40.1 (8.5), respectively (nonsignificant to each other). These three groups did not significantly differ in their HbA1c medians (51.8 [IQR 4.5], 50.7 [4.5], and 52.7 [5.7] mmol/mol, respectively). NoCGM groups had the highest HbA1c and GRI and lowest TIR regardless of the treatment modality. CONCLUSION: This population-based study shows that the HCL technology is superior to other treatment modalities in CGM-derived parameters and should be considered as a treatment of choice in all CwD fulfilling the indication criteria.

Are all HCL systems the same? long term outcomes of three HCL systems in children with type 1 diabetes: real-life registry-based study.

Objective: To compare parameters of glycemic control among three types of hybrid closed loop (HCL) systems in children with T1D (CwD) using population-wide data from the national pediatric diabetes registry CENDA. Methods: CwD aged <19 years treated with Medtronic MiniMed 780G (780G), Tandem t:slim X2 (Control-IQ) or do-it-yourself AndroidAPS (AAPS) systems for >12 months and monitored by CGM >70% of the time were included. HbA1c, times in glycemic ranges, and Glycemia Risk Index (GRI) were used for cross-sectional comparison between the HCL systems. Results: Data from 512 CwD were analyzed. 780G, Control-IQ and AAPS were used by 217 (42.4%), 211 (41.2%), and 84 (16.4%) CwD, respectively. The lowest HbA1c value was observed in the AAPS group (44 mmol/mol; IQR 8.0, p<0.0001 vs any other group), followed by Control-IQ and 780G groups (48 (IQR 11) and 52 (IQR 10) mmol/mol, respectively). All of the systems met the recommended criteria for time in range (78% in AAPS, 76% in 780G, and 75% in Control-IQ users). CwD using AAPS spent significantly more time in hypoglycemia (5% vs 2% in 780G and 3% in Control-IQ) and scored the highest GRI (32, IQR 17). The lowest GRI (27, IQR 15) was seen in 780G users. Conclusion: Although all HCL systems proved effective in maintaining recommended long-term glycemic control, we observed differences that illustrate strengths and weaknesses of particular systems. Our findings could help in individualizing the choice of HCL systems.