Ten Years of Improving Glycemic Control in Pediatric Diabetes Care: Data From the Norwegian Childhood Diabetes Registry

In 2018, the International Society for Pediatric and Adolescent Diabetes (ISPAD) lowered the target HbA_1c from <58 mmol/mol (7.5%) recommended in 2014 (1) to <53 mmol/mol (7.0%), with the aim of avoiding long-term vascular complications (2). The degree to which this glycemic target has been achieved varies considerably among different countries, and many children and adolescents with type 1 diabetes do not meet it (3–7). Some pediatric diabetes registries have reported decreasing HbA_1c values, while others have reported increases in recent years (8). Differences in the achievement of the 53 mmol/mol (7.0%) HbA_1c target in different populations have been attributed to variations in national HbA_1c targets (5,9), differences in the use of diabetes technology (10–15), quality improvement projects (16–21), and carbohydrate counting for meal bolus calculation (21–23). Sweden has drawn international attention by consistently reducing its national mean HbA_1c over many years in the pediatric age-group through quality improvement collaborations. Altered concepts resulting from these collaborations include improved local guidelines, improved teamwork through frequent meetings, the active use of registry data, a lower HbA_1c target, carbohydrate counting from the onset of diabetes, increased insulin pump use, and regular tracking of patient glucose meters (18).

Motivated by Sweden, the Norwegian Childhood Diabetes Registry (NCDR) in 2017 initiated a national quality improvement project (Improvement Quality [IQ] Norway) to facilitate improvements in the quality of care by pediatric diabetes teams. The main aims of IQ Norway were improved mean HbA_1c levels and a higher proportion of children and adolescents achieving ISPAD’s glycemic target at participating centers. Norway has one of the world’s highest incidence rates of type 1 diabetes, at 49.1 per 100,000 person-years in the 0–14 age-group (24). Over the past decade, Norwegian pediatric departments have implemented several measures to improve diabetes care, such as education in carbohydrate counting for meal bolus calculations from the onset of diabetes, increased use of diabetes technology, and systematic quality improvement work. In 2022, a national HbA_1c target of ≤48 mmol/mol (6.5%) was established for pediatric type 1 diabetes in Norway.

We recently published cross-sectional data from 2017 from NCDR regarding Norwegian pediatric diabetes care (14,25). As considerable effort has gone into improving glycemic control at the national level since the NCDR was founded in 2006, we present here national data showing the development of diabetes care from 2013 to 2022. Comprehensive data from the large national pediatric cohort of children and adolescents with type 1 diabetes in NCDR provide an opportunity to review differences in diabetes care over time in a real-world national sample, identify factors that contribute positively to glycemic control, and learn from the Norwegian experiences of the past decade. The aims of the current study were to evaluate during the 10-year period 1) the extent to which HbA_1c, the incidence of acute complications (e.g., diabetic ketoacidosis [DKA] and severe hypoglycemia [SH]), and the use of diabetes technology have changed at the national level and 2) how glycemic control is associated with the use of diabetes technology, carbohydrate counting, and participation in a quality improvement project.

In Norway, all children with type 1 diabetes up to 18 years of age receive diabetes care in a pediatric department, all of which report standardized data to the NCDR at diabetes onset and annually thereafter. The NCDR has collected data from children and adolescents with diabetes since 2001, and from 2013 to 2022, it captured nearly all children and adolescents with type 1 diabetes, with a completeness of 95–98%. The proportion of Nordic ethnicity in the participants was 93% (24). In our study, we included all children and adolescents with type 1 diabetes who participated in the annual registrations from 2013 to 2022. We excluded individuals aged ≥18 years at the time of data registration and individuals without a valid HbA_1c measurement.

The annual registrations for the NCDR comprise anamnestic variables reported by patients and parents, such as the use of carbohydrate counting for meal bolus calculations; variables reported by the diabetes team, such as the diabetes technology in use; measured variables, such as laboratory tests; and data collected from diabetes technology, such as the number of daily boluses or basal rate. The primary outcome measure for our study was HbA_1c. All HbA_1c samples were analyzed at a single national Diabetes Control and Complications Trial (DCCT)–standardized laboratory, and one HbA_1c measurement was performed per child per year. The primary data set consisted of 28,813 annual registrations. After excluding types of diabetes other than type 1 (n = 710), participants age ≥18 years (n = 149), and registrations without an HbA_1c measurement (n = 740), there were 27,214 annual registrations from 6,775 children and adolescents in the final data set (Fig. 1). Missing responses regarding DKA (n = 287 [1.1%]) and SH (n = 305 [1.1%]) were handled by listwise deletion.

The predictor variables for the inferential analyses were 1) the use of diabetes technology, i.e., insulin pumps (776 missing [2.9%]), and continuous glucose monitoring (CGM) (data recorded from 2016 to 2022, 375 missing [1.4%]), 2) the patient’s and family’s use of carbohydrate counting (recorded from 2016 to 2022 [n = 19,836], 4,219 missing [21.3%]), and 3) whether the patient’s diabetes team participated in the IQ Norway quality improvement project (no missing data). Missing data on the predictor variables were handled by listwise deletion.

All 24 Norwegian pediatric departments were invited to participate in the IQ Norway quality improvement project. The applications from each department had to include permission from the head of the department for the whole interdisciplinary diabetes team to participate in the scheduled whole-day meetings. Fifteen departments applied, and 9 departments of dissimilar sizes were randomly selected to participate. The project was designed with inspiration from Sweden (18) and the Breakthrough series (26,27). It included five learning sessions and one follow-up meeting and focused on learning about and working with systematic improvement methods, including the value compass, microsystem analysis, flow charts, fishbone diagrams, and the plan-do-study-act wheel (26,28,29). In the intervals between the learning sessions, each diabetes team worked on identifying problems and improvement areas at their clinics, creating action plans, and closely monitoring their glycemic results. Some major measures that the participating clinics tried out were reported as strongly contributing to better glycemic control by the end of the project, including written protocols that resulted in better-structured outpatient clinics due to clear goal setting with specific deadlines for reaching those goals, frequent team meetings, and individualized follow-ups for patients in need. A complete and continuously updated overview of the clinic’s patients and their results was also an important success factor.

Continuous variables (HbA_1c, age, diabetes duration) were described as means with SDs. HbA_1c was also described as percentage of achieved ISPAD target. Categorical variables (sex, incidence of acute diabetes complications, use of diabetes technology, carbohydrate counting) were presented as valid numbers and percentages and were compared using Pearson χ² test. Continuous variables were compared using Mann-Whitney U test.

Associations between the predictors and HbA_1c were analyzed using linear mixed-effects (LME) regression analysis. Patient identities and hospitals were included as the random intercepts, and the models were adjusted for age and duration of diabetes at the time of inclusion in the registry. Years since the beginning of the observation period and age (as a continuous variable) were modeled using cubic polynomial regression terms because of their nonlinear relationships with HbA_1c. Differences in the associations between groups were assessed by including the group indicators as a main effect and the interaction effect between the group and the year. If the interaction between the higher-order polynomial term and the group was not significant, it was removed from the model. P values from Wald tests were also reported.

Data for boys and girls were analyzed in separate models because sex may moderate the associations being studied. The directions of associations were reported as predicted marginal means of the outcomes with 95% CIs for each year and group, with all covariates at their respective mean values. All statistical analyses were performed using R version 4.2.3 software (R Project for Statistical Computing); mixed model analyses were fitted using the lme4 package version 1.1-31 (30). This study was approved by the Regional Committee for Medical and Health Research Ethics (reg. no. 2016/1613/REC West).

The registry data used to support the findings of this study have not been made available due to the ethics rules of the NCDR and for reasons of patient privacy.

Of the 6,775 children and adolescents with type 1 diabetes registered from 2013 to 2022, 4,111 were recorded as experiencing diabetes onset during this period. The total number recorded per year increased from 2,424 in 2013 to 3,128 in 2022 (29% increase); further characteristics of the study population (27,214 records) are shown in Table 1.

From 2013 to 2022, there was a steady decrease of the mean and SD of HbA_1c values (Table 1). In addition to the overall study population, we evaluated the progress of children and adolescents with new diagnoses during the study period. The mean HbA_1c values for children and adolescents newly diagnosed each year, i.e., diagnosed the previous year and first recorded in the year being analyzed, steadily decreased. Supplementary Fig. 1A shows the first-measured HbA_1c of each annual cohort and each cohort’s HbA_1c development over the following years.

The percentage of children and adolescents reaching ISPAD’s 2014 HbA_1c target of <58 mmol/mol (7.5%) and the 2018 target of <53 mmol/mol (7.0%) increased between 2013 and 2022 from 29 to 65% (<58 mmol/mol [7.5%]) and from 13 to 43% (<53 mmol/mol [7.0%]). The percentage of children and adolescents with HbA_1c values of ≥75 mol/mol (9.0%) decreased from 22% in 2013 to 4% in 2022. Regarding HbA_1c results in different age-groups, there was a decrease in mean HbA_1c from 2013 to 2022 in all age-groups from 3 to 18 years (Supplementary Fig. 1B).

Unadjusted comparison revealed that boys had lower mean (SD) HbA_1c levels (65.9 [13.7] mmol/mol, 8.2% [1.3%]) than girls (67.1 [14.0] mmol/mol, 8.3% [1.3%]) in 2013 (P = 0.049) (Supplementary Fig. 1C). Also in 2022, boys had lower mean (SD) HbA_1c levels (54.8 [10.2] mmol/mol, 7.2% [0.9%]) compared with girls (56.1 [10.8] mmol/mol, 7.3% [1.0%], P < 0.001). Furthermore, boys reached the HbA_1c targets more often than girls. For the HbA_1c target of <58 mmol/mol (7.5%), the difference between boys and girls was significant in both 2013 (P = 0.028) and 2022 (P = 0.002) and was similar for the HbA_1c target of <53 mmol/mol (7.0%) (P = 0.227 for 2013, P = 0.001 for 2022).

The incidence of reported DKA leading to hospitalization decreased from 4.8% in 2013 to 1.0% in 2022. The incidence of SH decreased from 4.9% in 2013 to 2.1% over the same period (P < 0.001) (Table 1).

The associations between the predictor variables and HbA_1c, examined using LME in a regression analysis, are summarized in Table 2 (HbA_1c in %) and Supplementary Table 1 (HbA_1c in mmol/mol). Figure 2 shows the predicted marginal means and sex differences in the estimated trends.

There was an increase of 26 percentage points in the use of insulin pumps during the observation period from 65% in 2013 to 91% in 2022. As shown in Supplementary Fig. 1D, there was no descriptive difference in HbA_1c trends between insulin pump users and pen users from 2013 to 2018, but pump users had increasingly lower HbA_1c levels than pen users from 2018 to 2022. Pump use without the use of CGM was associated with significantly higher HbA_1c levels than pump or pen use in combination with CGM (Supplementary Fig. 1F). The LME analysis revealed a significant decrease in HbA_1c levels for all patient groups, including both users and nonusers of pumps (Table 2 and Fig. 2A). For boys, pump use was found not to be beneficial for HbA_1c values in 2013 (β = 1.38 [95% CI 0.49, 2.26], P = 0.002) but with a gradual change in trend (P < 0.001). For girls, pump use was associated with lower HbA_1c values in 2013 (β = −1.52 [95% CI −2.30, −0.74], P < 0.001), and the effect was constant over time (P = 0.050).

CGM use was not recorded in the NCDR until 2016, but from 2016 to 2022, there was an increase in CGM use from 34 to 97%. Supplementary Fig. 1E shows the increasing descriptive difference in the mean HbA_1c values of children and adolescents using CGM, with consistently lower HbA_1c values for CGM users than nonusers. In 2022, CGM users reached ISPAD’s HbA_1c targets of <58 mmol/mol (7.5%) and <53 mmol/mol (7.0%) more often than those not using CGM (66 vs. 40% for the former target, 44 vs. 28% for the latter, P = 0.007).

The LME analysis revealed a significant decrease in HbA_1c for all children and adolescents, regardless of CGM use (Table 2 and Fig. 2B). For boys, the effect of CGM was not significant in 2016 (β = 0.25 [95% CI −0.47, −0.97], P = 0.494), but the adjusted difference in HbA_1c between the CGM and non-CGM groups increased in absolute value each year (β = −0.80 [95% CI −1.08, −0.52], P < 0.001, the interaction term). For girls, use of CGM was found to produce lower HbA_1c values (β = −1.11 [95% CI −2.12, −0.10], P = 0.031) and a trend toward improved HbA_1c values (interaction term P < 0.001). Because of very few observations in the non-CGM group in the period 2020–2022, the estimated means for this subgroup in Fig. 2B may not describe the general trend.

There was a slight increase from 2016 to 2022 in the percentage of children and adolescents using carbohydrate counting for calculating meal boluses from 79 to 92%; however, this meant that 8% of families were still not using carbohydrate counting for meal bolusing in 2022. Children and adolescents who did not count carbohydrates in 2022 were significantly older (mean age 15.5 vs. 12.3 years, P < 0.001), had longer mean diabetes duration (7.3 vs. 5.1 years, P < 0.001), and were equally likely to be female (43 vs. 46%, P = 0.458) compared with those who counted carbohydrates.

The children and adolescents and their families who counted carbohydrates had descriptively lower mean HbA_1c levels throughout the recorded period (2016–2022) than those who did not (Supplementary Fig. 1G). There was also a higher percentage of children and adolescents with HbA_1c levels <58 mmol/mol (7.5%) and <53 mmol/mol (7.0%) among those who counted carbohydrates than those who did not throughout the same period (55 vs. 32% for the former HbA_1c target and 33 vs. 18% for the latter, P < 0.001).

For boys, the LME analysis revealed that the relationship of HbA_1c levels with carbohydrate counting was already significant in 2016 (β = −3.96 [95% CI −5.21, −2.71], P < 0.001), with further improvements thereafter (interaction term P = 0.008). For girls, carbohydrate counting was associated with an improvement in HbA_1c values (P = 0.001), which becomes apparent after 2019 (Table 2 and Fig. 2C).

Before the start of the quality improvement project in 2017, there was no difference in mean HbA_1c values between the pediatric departments that later participated (62.1 mmol/mol [7.8%]) and those that did not (62.4 mmol/mol [7.9%]) (mixed-model analysis P = 0.536). LME analysis with project participation as a predictor variable and HbA_1c as the outcome variable showed an immediate effect of the quality improvement project on mean HbA_1c at the end of the active project phase in 2019 for both boys (β = −1.69 [95% CI −2.45, −0.93], P < 0.001) and girls (β = −1.56 [95% CI −2.43, −0.69], P < 0.001) (Table 2 and Fig. 2D). For both sexes, this effect remained consistent for the next 3 years (P = 0.360 for boys, P = 0.502 for girls). The percentage of children and adolescents reaching ISPAD’s HbA_1c targets was not descriptively different between the participating and nonparticipating clinics before the IQ Norway project, but there was a higher overall proportion reaching the HbA_1c targets in the participating hospitals after the quality improvement project compared with the nonparticipating clinics (62 vs. 57% for the 58 mmol/mol [7.5%] target, 42 vs. 34% for the 53 mmol/mol [7.0%] target).

This study is the first to present the progress made in pediatric diabetes care in Norway since 2013 (31). We found a steady decrease in mean HbA_1c levels between 2013 and 2022, from 66.6 mmol/mol (8.2%) to 55.4 mmol/mol (7.2%). The use of diabetes technology had increased by the end of the study period in 2022 to 91% for insulin pumps and 97% for CGM. The use of carbohydrate counting to calculate meal boluses had increased to 92% of children and adolescents in 2022. Use of insulin pump, CGM, and carbohydrate counting were associated with better mean HbA_1c levels and the achievement of ISPAD’s glycemic targets. Pediatric departments that participated in IQ Norway reduced their mean HbA_1c values further than the nonparticipating clinics. Mean HbA_1c values were lower in clinics that participated in the quality improvement project both at the end of the project and for the next 3 years. Females still had higher mean HbA_1c levels than males at the end of the study period in 2022.

Several important improvements in diabetes care were implemented during the study period. CGM was introduced, became calibration free, and as of 2022, was the treatment standard for the pediatric population. Insulin pumps became more advanced, with patch pumps, stop-at-hypoglycemia and stop-before-hypoglycemia functions, and since 2019, hybrid closed-loop systems. Educating children and adolescents with type 1 diabetes and their caregivers in carbohydrate counting from diagnosis has become standard. The continuous improvements in HbA_1c levels led to a new national HbA_1c target of ≤48 mmol/mol (6.5%) in Norway in 2022. Regardless of the observed improvements in glycemic control in this study, the example of Sweden demonstrates that an even better glycemic control is possible on a national level (7,32).

Our results add to the understanding of the mechanisms of optimal glycemic control and diabetes care. Regarding the use of diabetes technology, we found an association between lower HbA_1c levels and the use of CGM, and while insulin pump use was not associated with lower HbA_1c levels in 2017 (14), there were increasingly better glycemic results among those who combined CGM with an insulin pump. By 2022, nearly all children and adolescents with type 1 diabetes in Norway were using either CGM (97%), an insulin pump (91%), or a combination of the two (85%). Notably, the glycemic results reported in studies of the newest hybrid closed-loop systems are also good (10,15,33,34) but still do not match the results from the current study or Swedish national cohorts (35). The group in our study not using CGM in 2022 was very small (3%), and the group not using CGM or a combination of a pump with CGM and automated insulin delivery might be biased by other factors.

Diabetes technology is fully reimbursed by the Norwegian health care system, avoiding a possible selection bias based on the family’s economy. Furthermore, national guidelines do not limit the use of insulin pumps and CGM in children and adolescents. Diabetes technology is supplied by the regional hospitals, with no costs for the user. All diabetes teams are well trained in all available systems and will, based on medical needs and the family’s preference, aim to find the best possible treatment modality and educate the families in its use. This approach leads to a high adoption of new diabetes technology, which also might have added to the continuous improvement in glycemic control in Norway.

Quality improvement work may be one of the important factors contributing to the positive development in glycemic control in addition to the effect of technology use. Our results support this benefit, consistent with several other studies on the effect of quality improvement projects (18,36–38). Such quality improvement work may, in addition to improving patient care and glycemic outcomes, reduce frustration and inertia among patients and health care professionals “from care processes that just do not work” (17), thus having an effect over a longer period. However, maintaining quality improvement after a project period has ended is commonly described as “the trickiest part of any [quality improvement] project” (39), yet the HbA_1c results during the 4 years following the project in our study show that sustained effects from quality improvement work are possible.

During the IQ Norway project period, even the centers that did not participate in the project showed an improvement of HbA_1c. This improvement may be due to a spillover effect, as reported in the Swedish study (18). The center size of the pediatric departments was not associated with a higher or lower mean HbA_1c in the annual cohorts, and there was no difference in HbA_1c change over time associated with the center size.

Since measuring results is an integral part of quality improvement work, the publication and, thus, public availability of outcome measures for individual hospitals might also be a major motivational factor for improvements in diabetes care. Norwegian pediatric diabetes benchmarking reports were anonymous until 2012, but from 2013, each Norwegian pediatric diabetes clinic could be identified by name in the annual registry reports. From 2005 to 2011, HbA_1c levels actually increased slightly from 66 mmol/mol (8.2%) to 67 mmol/mol (8.3%) (31), but they decreased after 2013. This supports the positive effects of open benchmarking and of the measurement of effects in quality improvement work.

Carbohydrate counting was widely used by children and adolescents with diabetes in our cohort and was clearly associated with lower HbA_1c levels. In the small proportion of families not using carbohydrate counting, the children and adolescents were older and had had longer diabetes duration, which suggests that learning to count carbohydrates at the time of diagnosis may be an important factor. We could not determine the reason why carbohydrate counting was not implemented in these children’s daily routines from the registry data, but we suggest that implementing tools for use in everyday settings and systematic efforts to teach children and adolescents and their caregivers about carbohydrate counting could lead to increased use of this simple and cost-efficient approach.

Studies comparing different pediatric diabetes departments are often adjusted for sex, age, and diabetes duration, and differences in glycemic control by sex are therefore usually not reported (5). The higher HbA_1c levels of girls in our cohort are in line with previous publications regarding glycemic control (7,33,36), yet there are also publications showing no differences between males and females regarding HbA_1c (37,38). Rawshani et al. (39) described female sex as the highest risk for excess mortality among young adults with type 1 diabetes, and future diabetes research should therefore include glycemic outcome differences between females and males and consider the possible need for a differentiated care approach. We can only speculate regarding the differences in HbA_1c between boys and girls in the current study cohort (Supplementary Fig. 1C). There was no difference in the proportion of CGM and insulin pump use between boys and girls. The main HbA_1c difference between boys and girls was evident in adolescence, mainly in the age-group of 13–14 years. The earlier puberty in girls might have contributed to the higher HbA_1c in the female group. Puberty is often followed by emotional and motivational changes that can lead to behavioral challenges regarding diabetes control.

During the study period, the coronavirus 2019 pandemic hit Norway in spring 2020. We could not see any effect of the pandemic on the glycemic results (HbA_1c, SH, or DKA). The number of missing HbA_1c measurements, i.e., missed annual follow-up appointments, was very low during the complete observation period. The number of missing HbA_1c measurements per year varied between 46 and 105 but was not different in the pandemic years (2020, 70 measurements; 2021, 84 measurements). We have no data regarding the number of follow-up appointments during the pandemic.

The current study has limitations. Because it is based on observational data, we could only assess the associations of possible predictor variables with glycemic control, as operationalized by HbA_1c levels, not causality. Another limitation is the potential bias-by-indication effect, i.e., some of the evaluated groups may change over time, such as the group not using CGM changing as individuals start or stop using CGM. The NCDR also did not record additional data on glycemic control, such as time in range or mean sensor glucose, during the observation period, which would have supplemented HbA_1c as proxies for glycemic control. Data on the use of automated insulin delivery systems were not collected during the study period. Data about carbohydrate counting were registered in NCDR from 2016. From 2016 to 2018, data registered in NCDR on carbohydrate counting were incomplete. This issue was addressed by the registry and discussed in national network meetings, leading to much better data completeness from 2019 onward (87–94% data completeness). There was no unexpected change in carbohydrate counting from 2015 (48% missing) to 2019 (6% missing), indicating that the earlier years should be representative for the complete annual cohort. This is also in line with our clinical experience. Finally, there may be unknown or uncontrolled confounding factors. Notably, NCDR did not collect data on the social or educational backgrounds of the participants, which may have affected the associations analyzed.

Despite these limitations, the study has several strengths. The findings have a high degree of generalizability because of the participation rate, which represents 98% of the pediatric population, and a high degree of data completeness. This national cohort provides a contemporary picture of an entire pediatric population with type 1 diabetes over a 10-year period, and the findings are likely generalizable to pediatric diabetes populations in other western countries that give liberal access to diabetes technology. Since diabetes technology is fully reimbursed by the Norwegian health care system, there should be no selection bias regarding the use of diabetes technology because of economic factors. Further strengths include the accuracy of the HbA_1c measurements, which were analyzed centrally at a single DCCT-standardized laboratory. Finally, the analyses are presented separately for boys and girls where appropriate, revealing important sex differences.

In conclusion, we found that children and adolescents with type 1 diabetes in Norway steadily achieved better glycemic control between 2013 and 2022, with lower HbA_1c levels and fewer acute complications, such as DKA and SH. Children and adolescents using diabetes technology and carbohydrate counting had better glycemic outcomes than those who did not. Systematic quality improvements in pediatric departments led to improvements in glycemic control. Further research should consider sex differences when studying the effects of clinical- and system-level efforts to further improve diabetes care.

Acknowledgments. The authors thank the laboratory staff at the Department of Medical Biochemistry Aker, Oslo University Hospital, for the HbA_1c analysis and the Norwegian Study Group for Childhood and Adolescent Diabetes, representing the pediatric departments in Norway and their patients and parents, for participating in the NCDR. The authors also acknowledge the contributions and efforts of administrative and research staff at the NCDR, Osman Gani and Nicolai Andre Lund-Blix, for data management and analyses.

Funding. This work is part of a PhD scholarship, with funding from Helse Vest grant 912283 (H.B.). The NCDR is funded by the South-Eastern Norway Regional Health Authority. P.R.N. was supported by H2020 European Research Council grant AdG #293574, Trond Mohn Research Foundation, University of Bergen, Haukeland University Hospital, Norges Forskningsråd Researcher Project for Young Talents grant 240413, Helse Vest, and Novo Nordisk Foundation Distinguished Research Award (#54741).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. H.B. wrote the first draft of the manuscript. H.B., E.B., H.D.M., and T.S. conceptualized the study. H.B. and A.U. performed the statistical analyses. E.B., P.R.N., and T.S. supervised the study. S.J.K. coordinated the data collection for the study. S.J.K. and T.S. initiated the IQ Norway project. T.S. was responsible for the collection of the data in the NCDR. All authors interpreted the study data and edited, reviewed, and approved the final version of the manuscript. H.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in poster form at the 49th Annual ISPAD Conference, Rotterdam, the Netherlands, 18–21 October 2023.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were John B. Buse and Emily K. Sims.