Abstract
Objective:
The German/Austrian Diabetes Patient Follow-up Registry (Diabetes-Patienten-Verlaufsdokumentation or DPV), England/Wales National Pediatric Diabetes Audit (NPDA), and Type 1 Diabetes Exchange (T1DX) in the United States investigated changes in hemoglobin A1c (HbA1c) and diabetes technology use from 2010 to 2018.
Methods:
Registry/audit data from 2010 to 2018 were analyzed in annual cohorts using linear regression for those <18 years of age with type 1 diabetes diagnosed at age >6 months. Time trends in HbA1c, pump, and continuous glucose monitoring (CGM) use were studied using repeated measurements linear and logistic regression models with an autoregressive covariance structure and with year and data source as independent variables.
Results:
A total of 1,172,980 visits among 114,264 (54,119 DPV, 43,550 NPDA, 16,595 T1DX) patients were identified. HbA1c remained clinically stable in DPV (7.7% [61 mmol/mol] to 7.6% [60 mmol/mol]), decreased in the NPDA (8.7% [72 mmol/mol] to 7.9% [63 mmol/mol]), and increased in T1DX (8.0% [64 mmol/mol] to 8.5% [69 mmol/mol] from 2010 to 2018). In all registries/audits, insulin pump and CGM use increased over time with greatest pump use in T1DX and lowest uptake reported in NPDA.
Conclusions:
These data reveal three different longitudinal patterns of change in registry/audit HbA1c from 2010 to 2018. Diabetes technology use increased throughout, at different rates. Quality improvement (QI) programs in DPV have been ongoing for 25 years, began in NPDA in 2009 and T1DX in 2016. We speculate that in England/Wales, development of networks, peer review, and implementation of QI measures contributed to reductions in population HbA1c. Many of these interventions had been implemented in DPV before 2010. Further efforts to understand this improvement, including the role of QI, and continued success within standardized documentation and benchmarking could inform T1DX programs to reduce HbA1c.
Keywords: Audit, Diabetes technology, International, Registry, Quality improvement
Introduction
Comparison of longitudinal data for pediatric type 1 diabetes from different countries provides the opportunity to identify possible interventions to improve outcomes.1 The German and Austrian Diabetes Patient Follow-up (Diabetes-Patienten-Verlaufsdokumentation or DPV) includes centers using freely available standardized computer-based documentation system originally developed in 1992 at the department of pediatrics in Ulm.2 The DPV registry includes >90% of German and >80% of Austrian children with diabetes from 442 centers.3
The National Pediatric Diabetes Audit (NPDA) collects data on individuals with diabetes seen at all 175 pediatric diabetes units in England and Wales.4 It started in 2003, with a 100% coverage of units providing care reached by 2011. Beginning in 2010, the Type 1 Diabetes Exchange (T1DX) clinic registry included 81 U.S. pediatric and adult diabetes clinics in 35 states and ended data collection in 2018.3,5 This clinic-based registry was largely made up of expert diabetes centers and encompasses a smaller fraction of the pediatric population living with type 1 diabetes than DPV or NPDA.
The longitudinal data provided by these registries/audit allow us to compare international trends in diabetes care and outcomes, as well as the use of diabetes technology. These observations can drive hypothesis generation promoting potential health care changes. Diabetes technology has seen tremendous advances over the period that these registries/audit have collected data.6 One might predict that these changes in treatment should lead to improved glycemic control with a reduction in hemoglobin A1c (HbA1c), but this has not been universally confirmed. Recent network meta-analysis reveals particular technology usage to have graded impact on glycemic control.7
In particular, the advantage of insulin pump use over multiple daily injections has been highly variable in the literature and likely requires proper education and training.8 Therefore, pump use alone may be insufficient for reducing HbA1c and must be paired with appropriate structured education supported by time, funding, and staff. In addition, quality improvement (QI) programs on local and national levels have been implemented in these three registries/audit with the goal of improving outcomes. The aim of this study was to explore longitudinal trends in international pediatric diabetes management by describing change in HbA1c and diabetes technology use in Germany/Austria (DPV), England/Wales (NPDA), and the United States (T1DX) between 2010 and 2018.
Methods
In this observational study, all pediatric registry/audit data from visits during 2010 to 2018 were analyzed for those with type 1 diabetes diagnosed at >6 months old who were <18 years of age. Those without insulin therapy data were excluded from the analysis. Kruskal–Wallis test for continuous and chi-square test for binary variables were used for unadjusted comparisons. We controlled for multiple comparison with the false discovery method. Time trends in HbA1c, pump, and continuous glucose monitoring (CGM) use were studied using repeated measurements linear and logistic regression models with an autoregressive covariance structure and with year and data source as independent variables.
Moreover, repeated linear and logistic regression models were adjusted for gender, age group (<6, 6 to <10, 10 to <14, and ≥14 years), and diabetes duration groups (<2, 2 to <5, and ≥5 years). Statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC). A two-sided P-value <0.05 was considered as statistically significant. We consider an HbA1c difference of 0.5% [5.5 mmol/mol] to be clinically significant.
Diabetes-Patienten-Verlaufsdokumentation
Every 6 months, anonymized data are sent from DPV centers to Ulm University for validation and analysis. For each year, individual participant data were aggregated with median for HbA1c and body mass index (BMI) and related to age and diabetes duration at the last visit during the respective year. Data collection was approved by the ethics committee at Ulm University and by the institutional review boards of the participating centers. Each participating center receives a benchmarking report every 6 months.
There is a yearly national meeting of the DPV initiative dedicated to pediatric diabetes care and outcome, together with 2 to 4 yearly meetings of regional pediatric quality circles, where center results are discussed openly among participating centers. As a joint effort, the DPV group publishes results of pediatric diabetes care (for full list, see http://d-p-v.eu).
National Pediatric Diabetes Audit
The NPDA was established to compare the care and outcomes of all children and young people with diabetes receiving care from pediatric diabetes units in England and Wales. The audit is commissioned by the Health Quality Improvement Partnership, funded by National Health Service (NHS) England and the NHS in Wales, and is managed by the Royal College of Pediatrics and Child Health (RCPCH).
Participating pediatric diabetes units submit data on outcomes annually to a core audit. The RCPCH has U.K. approval to collect and store patient data without written consent. Individuals and their parents are informed of the data submission by the individual diabetes center.
During the data collection period of this report, there have been progressive audit-wide QI efforts implemented, including the establishment of clinical networks (2009/10 in England, 2014/15 in Wales), more explicit NPDA reporting providing benchmarking of centers since 2011, tariff payments for clinical care (England only 2010/11), quality assurance “peer review” activities (established 2011/12 in England and 2013/14 in Wales), strategic diabetes delivery plans (from 2012/13), and culminating in the development of the National Diabetes QI Program in 2017/18.9
Type 1 Diabetes Exchange
Each clinic receives approval from local institutional review boards and informed consent is obtained based on local requirements. Data from the participant's medical record and questionnaires are collected for inclusion in the registry. Metrics collected at individual centers is anonymized and stored at the Jaeb Center for Health Research. For each year, participant data were taken from the last visit. A formal QI collaborative was launched in 2016 among a limited subset of seven pediatric and three adult diabetes centers operating within T1DX.10 Participating clinics share data and best practices to improve care delivery.
Results
A total of 114,264 individuals (54,119 DPV, 43,550 NPDA, 16,595 T1DX) were included in the analysis from 2010 to 2018, providing data from 1,172,980 visits. In total, 858 individuals were excluded from the DPV cohort due to missing data on insulin delivery. Characteristics of all participants at their clinic visit in the middle of the study period and those in individual registries/audit are presented in Table 1. At the median visit for each participant, age in years was 13.5 (10.0, 15.4) overall (13.3 [9.7, 15.6] in DPV, 14.0 [10.5, 15.9] in NPDA, and 12.5 [10.0, 14.0] in T1DX) with diabetes duration of 3.1 (1.4, 6.4) (2.8 [1.1, 6.0] DPV, 3.3 [1.5, 6.5] NPDA, and 4.0 [8.0, 13.0] T1DX) years.
Table 1.
Demographic Data from for All Participants Between 2010 and 2018
| All registries | DPV | NPDA | T1DX | |
|---|---|---|---|---|
| n | 114,264 | 54,119 | 43,550 | 16,595 |
| Age (years) at median visit | 13.5 (10.0, 15.4) | 13.3 (9.7, 15.6) | 14.0 (10.5, 15.9) | 12.5 (10.0, 14.0) |
| Diabetes duration (years) at median visit | 3.1 (1.4,6.4) | 2.8 (1.1, 6.0) | 3.3 (1.5, 6.5) | 4.0 (8.0, 13.0) |
| Gender, % | ||||
| Male | 53 | 53 | 54 | 51 |
| Female | 47 | 47 | 46 | 49 |
| Migrant identification, % | — | 23 | — | — |
| Ethnic minority identification, % | — | — | 18 | 23 |
| BMI | ||||
| (kg/m2) | 20.2 (17.7, 23.1) | 19.7 (17.3, 22.6) | 20.8 (18.2, 23.7) | 20.0 (17.6, 22.9) |
| (Z-score) | 0.7 (0.0, 1.4) | 0.6 (−0.1, 1.3) | 0.8 (0.1, 1.5) | 0.9 (0.2, 1.5) |
| Visits per patient | 5 (3, 13) | 14 (5, 26) | 4 (2, 6) | 3 (2, 5) |
| Pump use, % | 47 | 48 | 39 | 64 |
| CGM use, % | 31 | 41 | 14 | 24 |
| HbA1c | ||||
| (%) | 8.0 (7.3, 9.0) | 7.7 (7.0, 8.6) | 8.3 (7.5, 9.2) | 8.4 (7.7, 9.2) |
| (mmol/mol) | 64 (56, 75) | 60 (53, 70) | 67 (59, 78) | 68 (60, 77) |
Normally distributed quantities are expressed as mean ± standard deviation, skewed distributions as median (Q1, Q3).
BMI, body mass index; CGM, continuous glucose monitoring; DPV, Diabetes-Patienten-Verlaufsdokumentation; HbA1c, hemoglobin A1c; NPDA, National Pediatric Diabetes Audit; T1DX, Type 1 Diabetes Exchange.
In total, 53.2% identified as male (53.2% DPV, 53.9% NPDA, and 51.3% T1DX) and 23.2% identified as migrants in DPV, whereas 17.5% in NPDA and 22.6% in T1DX identified as minorities. There were 5 (3, 13) visits per participant (14 [5, 26] DPV, 4 [2, 6] NPDA, and 3 [2, 5] T1DX) over 3.0 (1.0, 5.0) (3.0 [1.0, 5.6] DPV, 3.0 [0.8, 4.8] NPDA, and 2.0 [1.0, 5.0] T1DX) years. Demographics between the registries/audit were significantly different for age at median visit (P < 0.01), diabetes duration at median visit (P < 0.01), gender (P < 0.01), BMI (P < 0.01 and P < 0.01 for Z-score), and clinic visits per patient (P < 0.01).
To better encapsulate longitudinal data, a subset of annual data is presented in Table 2 for the years 2010, 2012, 2014, 2016, and 2018. Longitudinal data on insulin pump use, CGM use, and HbA1c are presented in Figure 1A (unadjusted) and Figure 1B (adjusted for gender, age group, and diabetes duration). For all registries/cohorts, there was a statistically significant change in pump use (P < 0.01), CGM use (P < 0.01), and HbA1c (P < 0.01) by year. Among all cohorts, there was no consistent relationship between population HbA1c and the use of insulin pump or CGM.
Table 2.
Demographic Data for 2010, 2012, 2014, 2016, and 2018
| Registry | 2010 |
2012 |
2014 |
2016 |
2018 |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DPV | NPDA | T1DX | DPV | NPDA | T1DX | DPV | NPDA | T1DX | DPV | NPDA | T1DX | DPV | NPDA | T1DX | |
| n | 21,282 | 20,113 | 953 | 23,206 | 20,257 | 7879 | 24,985 | 25,562 | 6573 | 26,601 | 23,158 | 7315 | 27,592 | 23,569 | 1107 |
| Age (years) | 12.0 ± 4.0 | 12.9 ± 3.7 | 10.6 ± 3.2 | 12.1 ± 4.0 | 12.3 ± 3.7 | 10.8 ± 3.3 | 12.2 ± 4.0 | 11.9 ± 4.0 | 11.6 ± 2.8 | 12.3 ± 4.0 | 12.4 ± 3.8 | 11.1 ± 3.3 | 12.4 ± 4.0 | 12.4 ± 3.8 | 11.7 ± 2.9 |
| Diabetes duration (years) | 4.5 ± 3.6 | 4.5 ± 3.6 | 3.6 ± 3.1 | 7.5 ± 4.0 | 4.8 ± 3.6 | 4.2 ± 3.3 | 4.7 ± 3.8 | 4.5 ± 3.8 | 5.6 ± 2.9 | 4.8 ± 3.8 | 4.5 ± 3.7 | 5.1 ± 3.3 | 4.8 ± 3.9 | 4.9 ± 3.7 | 6.0 ± 3.1 |
| Gender, % | |||||||||||||||
| Male | 52.3 | 54.1 | 49.0 | 52.7 | 53.9 | 51.6 | 52.6 | 54.0 | 51.7 | 52.7 | 53.6 | 51.6 | 52.8 | 53.0 | 51.0 |
| Female | 47.7 | 45.9 | 51.0 | 47.3 | 46.1 | 48.4 | 47.4 | 46.0 | 48.3 | 47.3 | 46.4 | 48.4 | 47.2 | 47.0 | 49.0 |
| Migrant identification, % | 19.1 | — | — | 20.6 | — | — | 22.1 | — | — | 23.8 | — | — | 25.0 | — | — |
| Ethnic minority identification, % | — | 12.7 | 16.5 | — | 19.2 | 21.0 | — | — | 21.6 | — | 14.4 | 22.2 | — | 15.3 | 21.5 |
| BMI | |||||||||||||||
| (kg/m2) | 19.4 (17.1, 22.4) | 20.1 (17.8, 23) | 19.1 (17.0, 22.1) | 19.4 (17.1, 22.4) | — | 19.2 (17.0, 22.2) | 19.5 (17.1, 22.5) | 20.2 (17.6, 23.3) | 19.9 (17.5, 22.9) | 19.6 (17.2, 22.6) | 20.3 (17.7, 23.5) | 19.5 (17.2, 22.7) | 19.7 (17.2, 22.8) | 20.7 (18.1, 24.0) | 20.0 (17.6, 23.1) |
| (Z-score) | 0.6 (0.0, 1.3) | 0.5 (−0.2, 1.2) | 0.9 (0.2, 1.6) | 0.6 (−0.1, 1.3) | — | 0.8 (0.2, 1.5) | 0.6 (−0.1, 1.3) | 0.9 (0.2, 1.6) | 0.9 (0.2, 1.6) | 0.6 (−0.1, 1.3) | 0.8 (0.1, 1.6) | 0.9 (0.2, 1.6) | 0.6 (−0.1, 1.3) | 0.9 (0.2, 1.7) | 0.9 (0.2, 1.7) |
| Pump use, % | 36 | — | 52 | 41 | 19 | 56 | 47 | 25 | 63 | 51 | 36 | 62 | 55 | 41 | 73 |
| CGM use, % | 6 | — | 4 | 4 | — | 3 | 4 | — | 8 | 21 | — | 24 | 64 | 13 | 40 |
| HbA1c | |||||||||||||||
| (%) | 7.7 (7.0, 8.7) | 8.7 (7.9, 9.7) | 8.0 (7.3, 8.9) | 7.7 (7.0, 8.6) | 8.5 (7.8, 9.5) | 8.2 (7.5, 9.1) | 7.6 (6.9, 8.5) | 8.3 (7.6, 9.3) | 8.3 (7.6, 9.3) | 7.6 (7.0, 8.5) | 7.4 (8.1, 9.0) | 8.4 (7.6, 9.3) | 7.6 (6.9, 8.5) | 7.9 (7.2, 8.8) | 8.5 (7.7, 9.6) |
| (mmol/mol) | 61 (53, 71) | 72 (63, 83) | 64 (56, 74) | 60 (53, 70) | 69 (62, 80) | 66 (58, 76) | 60 (52, 69) | 67 (60, 78) | 67 (60, 78) | 60 (53, 69) | 57 (65, 75) | 68 (60, 78) | 60 (52, 69) | 63 (55, 73) | 69 (61, 81) |
Normally distributed quantities are expressed as mean ± standard deviation, skewed distributions as median (Q1, Q3).
FIG. 1.
(A) Unadjusted longitudinal insulin pump use, CGM use, and mean HbA1c with SEM for each registry/audit. (B) Adjusted longitudinal insulin pump use, CGM use, and mean HbA1c with SEM for each registry/audit. CGM, continuous glucose monitoring; DPV, Diabetes-Patienten-Verlaufsdokumentation; HbA1c, hemoglobin A1c; NPDA, National Pediatric Diabetes Audit; SEM, Standard Error of the Mean; T1DX, Type 1 Diabetes Exchange.
In all registries/audit, insulin pump use increased over time to 55% in DPV, 41% in NPDA, and 73% in T1DX by 2018. CGM use also generally increased over time to 64% in DPV, 13% in NPDA, and 40% in T1DX in 2018.
Over the study period, HbA1c remained relatively stable in DPV (7.7% [61 mmol/mol] to 7.6% [60 mmol/mol]; P < 0.01), decreased in a clinically significant manner in NPDA (8.7% [72 mmol/mol] to 7.9% [63 mmol/mol]; P < 0.01), and increased in a clinically significant manner in T1DX (8.0% [64 mmol/mol] to 8.5% [69 mmol/mol]; P < 0.01).
In 2014, both NPDA and T1DX reported similar average pediatric HbA1c. At the same time, there was 25% reported pump use in the NPDA group compared with a much higher level of 63% in T1DX. However, the T1DX cohort experienced an increase in HbA1c thereafter, whereas those in NPDA saw a decrease, despite significantly less insulin pump utilization in NPDA.
Discussion
These data indicate three different longitudinal patterns in HbA1c levels from 2010 to 2018 at the registry/audit level. Each region has its own culture, resources, and social policies with England/Wales and Germany/Austria offering nationalized universal health care that the United States lacks. Although low in magnitude, the demographic differences may be associated with differences in clinical outcomes, such as HbA1c. There were substantially more visits per participant in DPV. However, visits were similar for NPDA and T1DX, with each having different longitudinal trends in HbA1c.
The differences in health care system including private and partially or fully funded public options can have a significant impact on care. A detailed comparison of the different health care systems is beyond the scope of this article, but beyond the question of coverage for diabetes technology, we also have to look at availability and coverage of appropriate patient education by diabetes teams familiar with these technologies, patient-to-provider ratios, access to emergency intervention, psychological support, and many more facets relevant for pediatric diabetes care.
The 2021 American Diabetes Association guidelines for medical care in diabetes recommend an HbA1c goal of <7% [53 mmol/mol] for children and adolescents or <7.5% in certain situations [58 mmol/mol].11 In 2018, those reaching a goal of <7.5% [58 mmol/mol] was 47.4% in DPV, 34.9% in NPDA, and 17.0% in T1DX. These results indicate the need for improvement internationally, particularly in the T1DX cohort. All cohorts demonstrated an increased use of diabetes technology including insulin pumps and CGM. QI efforts were initiated at different times and varied in the number of included centers. The effect of such efforts is not instantaneous and serve a greater fraction of the population as more centers get involved.
The strengths of this design are the large sample encompassing the vast majority of the pediatric age groups with type 1 diabetes in DPV and NPDA. T1DX is clinic based and may not be reflective of the entire pediatric type 1 diabetes population in the United States. Indeed, the adult T1DX cohort had a significantly lower HbA1c than a large cohort of adults with diabetes in an insurance claims database that includes many people with type 1 diabetes not seen at specialized diabetes centers.12 We would expect that the proportion meeting glycemic targets from the T1DX registry may be higher than the entire U.S. type 1 diabetes population. However, given the longitudinal nature of the data, some bias would have likely occurred at each year, so is less likely to affect the interpretation of trends over time.
The study has several important limitations. Although data are nationally representative in England/Wales and Germany/Austria, pediatric patients in the T1DX cohort are a subset of each participating center's population who see a subset of all pediatric individuals with type 1 diabetes. Given the observational nature of the study, we can only generate hypotheses for the potential contributors to longitudinal patterns in glycemic control for each registry/audit, we cannot identify causality. Many of the statistically significant demographic differences between registries were the result of the large sample sizes.
There were missing data concerning diabetes technology including utilization, type of system, and CGM glycemic outcomes. As CGM use increases, it will be crucial to start collecting outcomes to overcome the limits of HbA1c. New measures for metabolic control must be included in the registries in a standardized way to allow direct comparison in the future.
The longitudinal international registry and audit work presented here are required to understand the effect of our care delivery, where we are now, where we have been, and where we are going. It is impossible to assess change without a baseline benchmark. Prior study has highlighted reductions in HbA1c between 2011 and 2017 for children, adolescents, and young adults with type 1 diabetes participating in the Swedish Pediatric Diabetes Quality Registry and the Swedish National Diabetes Register, representing >95% of the pediatric population.1
Sweden has a regulated universal health care system and well-established QI collaborative that has been shown to reduce HbA1c and severe hypoglycemia.13,14 The prior findings suggest an important effect of QI initiatives in improving population-level metabolic control.
The DPV cohort has maintained a longstanding coordinated QI effort with very consistent pediatric HbA1c and gradual increases in pump use over time. Although both NPDA and T1DX had similar average HbA1c in 2014, there were significant changes thereafter. One might hypothesize that this may have been attributable to several QI projects within the NPDA community. Before 2014, the NPDA cohort had the highest HbA1c of the three registries.
In 2009, the National Clinical Director for Children, Young People, and Maternity Services commented on the poor outcome in the NPDA and wrote in the foreword to the national report, “this disappointing situation cannot be allowed to continue.” Subsequently, various high-level strategies based on successful work from the Swedish pediatric diabetes quality registry13 were introduced to drive QI. These began with the development of networks to aid shared experience, guidelines, protocols, and more latterly the rolling out of a national QI program.9
Thus far, there has not been the same coordinated efforts among pediatric T1DX centers. A formal QI collaborative was formed among 10 centers operating within T1DX, but further engagement from all centers will be crucial for establishing change.10 Ideally, all centers and all members of each diabetes team are involved in QI efforts.
Pump and CGM use in T1DX exceeded that of NPDA and DPV. In fact, NPDA demonstrated a clinically significant decrease in average HbA1c while having the lowest diabetes technology use among the registries. During the period reported, most insulin pumps did not have predictive low glucose suspend or closed-loop technology, and we did not have data on the use of this technology. Although evidence for the glycemic effect of predictive low glucose suspend and closed-loop technology is strong,7 the benefit of insulin pumps without automation is more dependent upon teaching by the health system.8
Indeed, despite significant increases in pump use in all registries, the HbA1c trajectory moved in three distinct directions. Thus, the data suggest that insulin pump therapy alone is insufficient to reduce HbA1c. A common goal is to understand how children and young people with diabetes can be best trained and supported to realize the potential advantages of using diabetes technology. A coordinated and systematic effort as demonstrated by the NPDA and DPV pediatric centers is likely required.
Insulin pumps and CGM are valuable tools in the management of type 1 diabetes. The NPDA data suggest that reduction of population HbA1c can occur even in the face of comparatively low technology use, whereas the T1DX data reveal increases in population HbA1c despite significant insulin pump uptake. Further efforts should focus on how NPDA improved their population HbA1c, how DPV maintained and slightly decreased an even lower HbA1c, and how T1DX should implement QI programs to reverse HbA1c increases.
Acknowledgment
The findings were presented at the 46th Annual ISPAD Conference, held virtually in October 2020. We thank Julia Grimsmann, Ulm University, for help with the initial data analysis.
Authors' Contributions
R.A.L. wrote the article and generated figures. H.R., S.P.P., F.C., and J.W. revised the article, provided NPDA data, and analyzed data. S.L., R.K., A.N., and R.W.H. revised the article, provided DPV data, and analyzed data. K.M.M., P.C., and D.M.M. revised the article, provided T1DX data, and analyzed data. J.W., R.W.H., and D.M.M. are the guarantors of the study. DPV—German Federal Ministry for Education and Research. T1DX—Leona M. and Harry B. Helmsley Charitable Trust. NPDA—Healthcare Quality Improvement Partnership.
Author Disclosure Statement
R.A.L. has consulted for Abbott Diabetes Care, Biolinq, Capillary Biomedical, Deep Valley Labs, Morgan Stanley, Gluroo, and Tidepool. D.M.M. has consulted for Abbott, the Helmsley Charitable Trust, Sanofi, Eli Lilly, Novo Nordisk, and has served on an advisory board for Insulet. H.R., S.L., S.P.P, R.K., P.C., F.C., A.N., R.W.H., and J.W. have no conflicts of interest or disclosures.
Funding Information
R.A.L. is supported by a Diabetes, Endocrinology and Metabolism Career Development Grant (1K23DK122017) from NIDDK and had additional research support from the Stanford Maternal and Child Health Research Institute. K.M.M. was supported by the Helmsley Charitable Trust. D.M.M. has research support from the NIH (including P30 DK116074 and 1K12DK122550) and the Helmsley Charitable Trust.
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