Incidence of COVID-19 and Risk of Diabetic Ketoacidosis in New-Onset Type 1 Diabetes Clemens Kamrath, MD, Joachim Rosenbauer, MD, Alexander J

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Incidence of COVID-19 and Risk of Diabetic Ketoacidosis in New-Onset Type 1 Diabetes Clemens Kamrath, MD, Joachim Rosenbauer, MD, Alexander J. Eckert, MSc, Angeliki Pappa, MD, Felix Reschke, MD, Tilman R. Rohrer , Prof, Kirsten Mönkemöller, MD, Michael Wurm, MD, Kathrin Hake, MD, Klemens Raile, Prof, and Reinhard W. Holl, Prof

DOI: 10.1542/peds.2021-050856 Journal: Pediatrics Article Type: Regular Article

Citation: Kamrath C, Rosenbauer J, Eckert AJ, et al. Incidence of COVID-19 and risk of diabetic ketoacidosis in new-onset type 1 diabetes. Pediatrics. 2021; doi: 10.1542/peds.2021- 050856

This is a prepublication version of an article that has undergone peer review and been accepted for publication but is not the final version of record. This paper may be cited using the DOI and date of access. This paper may contain information that has errors in facts, figures, and statements, and will be corrected in the final published version. The journal is providing an early version of this article to expedite access to this information. The American Academy of Pediatrics, the editors, and authors are not responsible for inaccurate information and data described in this version.

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Incidence of COVID-19 and Risk of Diabetic Ketoacidosis in New-Onset Type 1 Diabetes Clemens Kamrath, MD*1, Joachim Rosenbauer, MD*2,3, Alexander J. Eckert, MSc3,4, Angeliki Pappa, MD5, Felix Reschke, MD6, Tilman R. Rohrer , Prof7, Kirsten Mönkemöller, MD8, Michael Wurm, MD9, Kathrin Hake, MD10, Klemens Raile, Prof11, and Reinhard W. Holl, Prof3,4

* Clemens Kamrath and Joachim Rosenbauer contributed equally to this study. Affiliations: 1 Division of Pediatric Endocrinology and Diabetology, Centre of Child and Adolescent Medicine, Justus Liebig University, Giessen, Germany; 2 Institute for Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre for Diabetes Research at Heinrich Heine University Dusseldorf, Dusseldorf, Germany; 3 German Centre for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4 Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany; 5 Department of Pediatrics, University Hospital RWTH Aachen, Aachen, Germany; 6 Diabetes Centre for Children and Adolescents, Children's Hospital Auf der Bult, Hannover, Germany; 7 Department of Pediatrics, Saarland University, Homburg/Saar, Germany; 8 Department of Pediatrics, Kinderkrankenhaus Amsterdamer Straße, Cologne, Germany; 9 Department of Pediatrics, Klinik St. Hedwig, University Hospital Regensburg, Krankenhaus Barmherzige Brüder, Regensburg, Germany; 10 Department of Pediatrics, Müritz Klinikum, Waren, Germany; 11 Department of Pediatric Endocrinology and Diabetology, Charité, University Medicine Berlin, Germany Corresponding author: Clemens Kamrath, MD, Division of Pediatric Endocrinology and Diabetology, Centre of Child and Adolescent Medicine, Justus Liebig University, Feulgenstr. 12, 35385 Giessen, Germany, phone: +49 641 985 43411, fax: +49 641 985 43459 email: [email protected] Declaration of interests: Klemens Raile is an advisory board member of Lilly Diabetes Care and Abbott Diabetes Care, and reports paid talks for Sanofi, Dexcom, NovoNordisk and Springer Health Care outside the submitted work. Kirsten Mönkemöller received educational fees from Medtronic outside the submitted work. Funding: The DPV is supported through the German Federal Ministry for Education and Research within the German Centre for Diabetes Research (DZD, grant number: 82DZD14A02). Further financial support was received by the German Robert Koch Institute (RKI) and the German Diabetes Association (DDG). This study was partly funded by the Deutsche Diabetes Stiftung (FP-0433-2020). Role of Funder: The funding organization had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Abbreviations: COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; RR, relative risk; IQR, interquartile range; DPV, Diabetes-Patienten- Verlaufsdokumentation Key words: Corona pandemic; SARS-CoV-2; manifestation; decompensation; pH; fear of infection; hospital admission; diagnostic delay

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Article Summary: The risk of diabetic ketoacidosis in children with new-onset type 1 diabetes was significantly associated with the regional incidence of COVID-19 cases and deaths during the first wave of the pandemic. What’s known on this Subject: Significant delays in diagnosis and treatment were reported during the COVID-19 pandemic, leading to increased rates of diabetic ketoacidosis in children. Temporal associations between delayed hospital presentations or treatment initiations and pandemic containment measures have been reported. What this Study adds: This study found that the regional 7-days incidence of COVID-19 cases and deaths, rather than nationwide pandemic containment measures such as social distancing, were associated with risk of ketoacidosis and in children with new-onset type 1 diabetes.

Contributors’ Statement Page Dr. Kamrath had the idea of this study, conceptualized the study, interpreted the analyses, drafted the initial manuscript, and reviewed and revised the manuscript. Dr. Rosenbauer conceptualized the study, analyzed the data, supervised the statistical analysis, and critically reviewed and revised the manuscript. Mr. Eckert carried out the initial analyses, and reviewed and revised the manuscript. Prof. Holl conceptualized the study, coordinated and supervised data collection, acquired funding for the study, and critically reviewed the manuscript for important intellectual content. Dr. Pappa, Dr. Reschke, Prof. Rohrer, Dr. Mönkemöller, Dr. Wurm, Dr. Hake, and Prof. Raile collected data, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. Dr. Kamrath and Dr. Rosenbauer contribute equally to this study.

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Abstract Objectives: The aim of this study was to quantify the relative risk of diabetic ketoacidosis at diagnosis of type 1 diabetes during the year 2020, and to assess whether it was associated with the regional incidence of COVID-19 cases and deaths. Methods: Multicenter cohort study based on data from the German Diabetes Prospective Follow- up Registry DPV. The monthly relative risk (RR) for ketoacidosis in 2020 was estimated from observed and expected rates in 3,238 children with new-onset type 1 diabetes. Expected rates were derived from data of 2000-2019 using a multivariable logistic trend regression model. The association between the regional incidence of COVID-19 and the rate of ketoacidosis was investigated by applying a log-binomial mixed effects model to weekly data with Germany divided into five regions. Results: The observed versus expected frequency of diabetic ketoacidosis was significantly higher from April to September and in December (mean adjusted RRs, 1.48–1.96). During the first half of 2020, each increase in the regional weekly incidence of COVID-19 by 50 cases or one death per 100,000 population was associated with an increase in the RR of diabetic ketoacidosis of 1.40 [95% CI, 1.10–1.77; p=0.006] and 1.23 [1.14–1.32; p<0.001], respectively. This association was no longer evident during the 2nd half of 2020. Conclusions: These findings suggest that the local severity of the pandemic rather than health policy measures appear to be the main reason for the increase in diabetic ketoacidosis, and therefore for the delayed use of health care during the pandemic.

Introduction The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory

syndrome coronavirus 2 (SARS-CoV-2) spread quickly across Europe and America during

March and April, 2020 (1). Within Germany, however, there were large regional differences in

the number of COVID-19 cases and deaths – the South, namely the federal states of Bavaria and

Baden-Württemberg, and the West, namely North Rhine-Westphalia, of Germany were

particularly affected at the beginning of the pandemic (2). In response to the pandemic,

nationwide health policy measures to contain the spread of SARS-CoV-2 were swiftly

implemented in Germany in order to achieve social distancing, such as restrictions on social

contacts, school closures, and the general recommendation to stay at home. While the rates of

new cases of COVID-19 increased remarkably during March and peaked in early April, and

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deaths related to COVID-19 peaked in mid-April in Germany, the rates declined thereafter and

stabilized at lower levels in May and June (1,2). In early October, however, COVID-19 cases showed another sharp increase in a second COVID-19 wave, with a peak in late December (1,2).

The pandemic has also resulted in harm to patients who were not affected by COVID-19.

Admissions for health care during the pandemic have markedly declined (3-6). As a result,

diagnoses were delayed, and diseases were identified at an advanced stage (6-9). This delay has

been quantified for instance by an increase in the frequency of diabetic ketoacidosis at onset of

type 1 diabetes in children and adolescents during the first two months of the COVID-19

pandemic in Germany (10). Ketoacidosis is an acute life-threatening complication of a delayed

diagnosis of new onset of type 1 diabetes and could serve as a measure of delayed access to

health care (11-14).

The aim of this study was to quantify the relative risk of diabetic ketoacidosis at diagnosis of

type 1 diabetes during the year of 2020 and to assess whether the increased risk was associated

with the regional incidence of COVID-19 cases and COVID-19-related deaths. Knowledge of

factors leading to a decrease in health care use and to a delay in diagnosis could help to prevent

future health risks from non-COVID-19 diseases by taking countermeasures and by improving

the resilience of outpatient and inpatient care to sudden massive challenges.

Patients and Methods

Data source and study population

This study used data from the German Diabetes Prospective Follow-up Registry DPV (Diabetes-

Patienten-Verlaufsdokumentation) of children and adolescents aged between six months and 18

years living in Germany, with the diagnosis of new-onset type 1 diabetes during the year 2020.

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The control group consisted of 42,417 children and adolescents living in Germany with new-

onset type 1 diabetes diagnosed during the years 2000 to 2019. The DPV registry has a

nationwide coverage of more than 90% of pediatric patients with type 1 diabetes in Germany and

comprises 257 pediatric diabetes centers (hospitals and practices) as of March 2021 (15). Twice a

year, locally collected longitudinal data are pseudonymized and transmitted for central

plausibility checks and analyses to Ulm University, Ulm, Germany. Inconsistent data are

reported back to participating centers for validation and/or correction. Data are then completely

anonymized for analysis. Verbal or written informed consent for participation in the DPV

registry was obtained from patients or their guardians. The ethics committee of Ulm University

approved the analysis of anonymized data from the DPV registry. This study followed the

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting

guideline for cohort studies (16).

The data of the pandemic were taken from the official statistics from the Robert Koch Institute,

Berlin, Germany (2). In accordance with the international standards of WHO, all laboratory confirmations of SARS-CoV-2, irrespective of the presence and severity of clinical symptoms, were considered as COVID-19 cases (1).

Variables

Demographic data included year, month (additionally week for 2020 data) and age at diabetes onset, sex, and immigrant background (patient or at least one parent born outside of Germany).

For regional analysis, Germany was divided into five geographical regions - the North, consisting of the federal states of Schleswig-Holstein, Mecklenburg-Western Pomerania,

Hamburg, Bremen, and Lower Saxony, the Middle, consisting of Saarland, Rhineland-Palatinate and Hesse, the West, consisting of North Rhine-Westphalia, the East, consisting of Thuringia,

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Saxony, Saxony-Anhalt, Berlin, and Brandenburg, and the South, consisting of Bavaria and

Baden-Württemberg (Figure 1A). Patients were assigned to regions based on their residence at diabetes onset if the information was available, and if this was not the case via the postal code of the first-care clinic.

Diabetic ketoacidosis was defined as a pH level less than 7.3 and/or a bicarbonate level less than

15 mmol/L, and severe ketoacidosis as a pH level less than 7.1 and/or bicarbonate level less than

5mmol/L (14).

The severity of the COVID-19 pandemic was measured as the weekly incidence rates of new

COVID-19 cases and COVID-19-related deaths per 100,000 population.

Statistical modelling

Median and interquartile range (IQR) are provided for the description of continuous variables, frequencies and percentages for the description of categorical variables. Continuous or categorical variables were compared between different groups via the Kruskal-Wallis test or the

χ2 test, respectively, adjusted for multiple testing using the Bonferroni Holm method.

For the selection of possibly confounding variables, we applied to control for covariates for which there is evidence for association with the outcome. Immigrant status, younger age, and female sex are known to increase the risk of ketoacidosis at diabetes onset (17).

Applying a multivariable logistic trend regression model that included year at diabetes onset (as continuous term), month of diabetes onset, and an interaction term of both, age group at diabetes onset (6 months - <6 years, 6-<12 years, 12-18 years), sex, and immigrant background (as independent variables), we estimated the expected monthly rate of ketoacidosis, and severe ketoacidosis for 2020 based on data from 2000 to 2019, standardized for age group, sex and

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immigrant background with the total population of all type 1 cases with onset between 2000 and

2020 as reference.

In the same way, we applied a multivariable logistic regression model that included month of

diabetes onset, age group, sex, and immigrant background as independent variables to

standardize the observed monthly rates in 2020 to the same reference distributions of the total

study population. A log-binomial regression model that included month of diabetes onset, a binary variable indicating observed and predicted data, and an interaction term of both as independent variables was used to compare the standardized observed with the standardized expected monthly rates of ketoacidosis, severe ketoacidosis, and impaired consciousness; the

results are presented as adjusted relative risk (adjusted RR) with 95% CI and corresponding p-

values of Wald tests.

To evaluate the association of the regional severity of the COVID-19 pandemic with the regional

rate of ketoacidosis in children and adolescents with new-onset type 1 diabetes in the year 2020,

we applied a multivariable log-binomial mixed effects model with ketoacidosis as dependent

variable and the regional incidence rate of the COVID-19 pandemic as exposure variable,

adjusting for sex, age group and immigrant background. Because data on the severity of the

COVID-19 pandemic were available on a region by week pattern, all patients with type 1

diabetes onset within a specific region by week “cluster” were assigned the same severity level.

Therefore, we included a region-by-week interaction as a normally distributed random intercept

term in the model. The severity of the COVID-19 pandemic was measured as the weekly

incidence rate of new COVID-19 cases and deaths related to COVID-19 per 100,000 population.

In order to capture changes in the course of the pandemic, we conducted the described analysis separately for the first and second half of the year 2020 (calendar weeks 1 to 26 and 27 to 52,

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Wald tests. In addition, the probability of a ketoacidosis at type 1 diabetes onset estimated from the log-binomial model is plotted dependent on the incidence of COVID-19 cases or COVID-19- related deaths.

A two-sided p-value < 0.05 was considered statistically significant. All analyses were performed with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Results

During the year 2020, data of 3,238 children and adolescents with new-onset type 1 diabetes were registered in DPV from 189 diabetes centers in Germany. The median age of our cohort was 9.8 years (interquartile range, 6.0–12.9 years). The proportion of males was 55.6 %.

Ketoacidosis was present in 1,094 patients (33.8%) of which 401 (12.4%) were severe. Table 1 provides a descriptive overview of the study population.

According to the applied multivariable logistic trend model, the standardized expected monthly proportions of ketoacidosis for the year 2020 ranged from 20.1% [95% CI, 16.1–25.0%] in

January to 25.3% [95% CI, 20.6–31.0%] in October (Table 2). In contrast, the standardized observed monthly rates of ketoacidosis during the year 2020 ranged from 22.6% [95% CI, 18.4–

27.8%] in January to 43.3% [95% CI, 37.5–50.1%] in August (Table 2). Compared with the expected monthly ketoacidosis rates for 2020, the observed frequencies of ketoacidosis in the year 2020 were significantly higher from April to September and again in December with mean adjusted RRs ranging from 1.48 to 1.96 in these months (Table 2 and Figure 2).

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The observed frequencies of severe ketoacidosis during the pandemic compared to the expected frequencies were also increased in April (adjusted RR, 2.10 [95% CI, 1.27–3.48], p=0.004), May

(adjusted RR, 2.50 [1.49–4.21], p<0.001), July (adjusted RR, 1.71 [1.06–2.75], p=0.03), August

(adjusted RR, 2.14 [1.33–3.43], p=0.002), and September (adjusted RR, 1.73 [1.05–2.85], p=0.03) (Table 2).

Subsequently, we compared the standardized monthly rates of ketoacidosis observed during the year 2020 with the corresponding standardized expected frequencies derived from 2000 to 2019 data for the five regions of Germany. The relative risk for ketoacidosis was significantly elevated in April, May, July, and August in the West and in the South of Germany, and in the West additionally in October (Figure 2 and Table S1 in the Supplement).

The applied multivariable mixed effects log-binomial model showed a significant association

between the regional weekly incidences of COVID-19 cases and COVID-19-related deaths and

the corresponding rates of ketoacidosis at diagnosis of type 1 diabetes during the first half of the

year 2020. The adjusted RR for the predicted rate of diabetic ketoacidosis was 1.40 (95% CI,

1.10–1.77; p=0.006) per increase in the 7-days incidence of COVID-19 cases by 50 units per

100,000 population, and 1.23 (95% CI, 1.14–1.32; p<0.001) per increase in the 7-days incidence

of COVID-19-related deaths by 1 unit per 100,000 population. To illustrate these associations,

Figure 3 shows the model-derived probability of a ketoacidosis at type 1 diabetes onset

dependent on the incidence of COVID-19 cases (Figure 3A) or COVID-19-related deaths (Figure

3B) during the first half of the year 2020. In contrast, during the second half of the year 2020,

there was an inverse association between the regional weekly incidences of COVID-19 cases and

the corresponding rates of ketoacidosis at diagnosis of type 1 diabetes. The adjusted RR for the

predicted rate of diabetic ketoacidosis was 0.92 (95% CI, 0.87–0.98; p=0.007) per increase in the

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7-days incidence of COVID-19 cases by 50 units per 100,000 population (Figure 3C). In

addition, the association between the 7-days incidence of COVID-19-related deaths and the

corresponding rates of ketoacidosis at diagnosis of type 1 diabetes was not significant during the

second half of the year 2020 (RR 0.98; 95% CI, 0.93–1.03; p=0.42) (Figure 3D).

Discussion

This study found a significant increase in the risks of diabetic ketoacidosis and severe diabetic ketoacidosis in children and adolescents with new-onset type 1 diabetes during the coronavirus

pandemic in Germany. Our finding of an increased risk of ketoacidosis during the COVID-19

pandemic is potentially indicative of a delayed admission to health care and is consistent with

reports from different parts of the world (10, 18,19).

At the time of the highest relative risk of ketoacidosis, the severity of the pandemic also reached

its peak with the highest incidence of new COVID-19 cases and deaths (1,2). During March,

however, nationwide measures were also taken to contain the pandemic, such as restrictions on

social contacts, school closures, and the general advice on staying at home (Table S2 in the

Supplement). Due to the temporal parallelism of both, the increase of the incidence of COVID-

19 and pandemic containment measures, it is important to determine which factors could have

affected the increase in diabetic ketoacidosis at diagnosis of diabetes type 1 and thus the delay in

admission to health care and diagnosis during the COVID-19 pandemic, as this would have implications for further measures during this pandemic, as well as for future pandemics or similar disasters. While general measures to contain the pandemic affected the whole country in a similar way, both the severity of the pandemic and its development over time varied considerably between different regions within Germany – the South and the West of Germany

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were particularly affected during March and April (Appendix 1 in the Supplement and Figure 2)

(2,20,21). It is therefore not surprising that our study demonstrated that these two regions

showed most pronounced increases in the rates of diabetic ketoacidosis in the following months.

Our analysis could demonstrate that the regional COVID-19 incidences of new cases and deaths during the first wave of the pandemic were associated with the risk of ketoacidosis in children and adolescents at diagnosis of type 1 diabetes. During the rapid spread of SARS-CoV-2, there was a significant decrease in the number of children presented in the emergency department, resulting in a diagnostic delay (22). Since the development of ketoacidosis is commonly caused by a delay in diagnosis in patients with type 1 diabetes (11-14), our study suggests that the

incidence level of the pandemic may be associated with a delayed use of health services. We

assume that a rapidly increasing number of COVID-19 cases and deaths could cause anxiety and insecurity among the population (25,26). As a result, contact with the health care system would

be avoided as far as possible for fear of possible infection. It has been reported that the obvious

concern about COVID-19 led to a decline in the use of life-saving evidence-based treatments

(27,28). Therefore, our study suggests that an early onset and a very rapid increase in COVID-19

cases and especially in COVID-19-related deaths might have led to a high level of uncertainty

and fear among the population, which could explain the increase in diabetic ketoacidosis.

This study shows that the increased risk of ketoacidosis outlasted the first pandemic wave and

also the first lockdown by several months. It can therefore be assumed that neither the lockdown

nor an overload of the health care system was responsible for the increase in the rate of

ketoacidosis observed in our study. From the findings that the higher observed versus expected

rate of ketoacidosis continued through the summer, when COVID-19 infection rates were stable

at a low level, it can be hypothesized that public concern and fear had persisted for quite

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considerable time. It was not until September and October that there was a marked decrease in

the observed compared with the expected rates of ketoacidosis. Thereafter, the risk of ketoacidosis then rose again while the incidence of COVID-19 cases and deaths increased during

the second wave of the pandemic.

Due to the low incidence of COVID-19 cases and deaths during the summer months but the

persistently high rates of ketoacidosis, the paradoxical result of an inverse association between

COVID-19 incidence and risk of ketoacidosis in the second half of the year emerged. This

indicates that the behavior of the population adapts to the changing environmental parameters

such as the dynamic or severity of the pandemic after a certain delay. After the summer, the

incidence of COVID-19 increased again from October onwards. In contrast to the first wave,

where there was a prompt increase in the rate of ketoacidosis, this increase was clearly delayed

in the second wave. During the 2nd wave of the pandemic, where a significantly higher incidence

of COVID-19 has been documented by the authorities, there was a smaller increase in the

observed to predicted rate of ketoacidosis compared to the first wave. Beside differences in

COVID-19 test capacities between the 1st and the 2nd wave, this may indicate that the behavior of

the population in relation to health care utilization could have changed during the pandemic in

form of a habituation effect.

A French registry study found a decrease in hospital admissions for myocardial infarction

following the lockdown that was irrespective of the regional prevalence of COVID-19 (27). We

have previously reported that the number of children and adolescents with new-onset of type 1

diabetes did not change during the lockdown from mid-March to mid-May in Germany (28). In

contrast to the association between the decrease in hospital admission and the regional

prevalence of COVID-19 in the French study, we investigated the association between the

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One strength of our study is that we analyzed data across the entire first year of the pandemic.

Another strengths of the present study include the large sample size of a population-based cohort. Furthermore, for comparisons with observed frequencies, the estimates of the expected frequencies of diabetic ketoacidosis in 2020 were derived from appropriate statistical methods, considering the observed slight but significant increase in the frequency of diabetic ketoacidosis in children with new-onset type 1 diabetes over past two decades (29). Limitations of our study include that the multivariable logistic regression models included only some potential confounders of the association between COVID-19 incidence and diabetic ketoacidosis. Thus, residual confounding due to both individual patient level and population level confounders cannot be excluded. Potentially confounding factors include socioeconomic status, distance to health facility, family members with COVID-19, or regional differences in health policy measures. In addition, we calculated associations without evidence of causality. Since we have not assessed the individual behavior of the patient’s families, we cannot prove whether our presumption that uncertainty and concern caused by the pandemic led to avoidance of health care and thus an increase in observed compared to expected rates of diabetic ketoacidosis, is true.

Further research is needed to understand the reasons for the increased rates of diabetic ketoacidosis during the COVID-19 pandemic.

Conclusion

This study found a significant increase in the frequency of ketoacidosis associated with the regional severity of the pandemic, i.e. the incidence of COVID-19 cases and deaths. The increased risk of ketoacidosis has outlasted the first wave of the COVID-19 pandemic by several

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months. The measures taken to contain the spread of the virus were implemented nationwide und

were limited in time, and do not explain the regional differences and the prolonged time in the

risk of ketoacidosis seen in our study. Therefore, these nationwide health policy measures do not appear to be the main reason for the significant increase in diabetic ketoacidosis at presentation, and therefore also not for the delayed use of health care. Instead, it could be that concern caused

by the pandemic itself may have been a reason for avoiding contact with health care. It is therefore important that people regain confidence in hospitals and medical care. Information and education campaigns must communicate that avoiding the use of healthcare can lead to significant harm, disproportionately higher than the negligible risk of contracting COVID-19.

Acknowledgments

Special thanks to A. Hungele and R. Ranz for support and the development of the DPV documentation software, K. Fink and E. Bollow for the DPV data management (all clinical data managers, Institute of Epidemiology and Medical Biometry (ZIBMT), Ulm University, Ulm, Germany), and Prof. O. Kuss (Institute for Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre for Diabetes Research at Heinrich Heine University Dusseldorf, Dusseldorf, Germany) for statistical advice. We thank the Robert Koch Institute very much for the public provision of the COVID-19 statistics. We wish to thank all centres participating in the DPV project (a list is available in the Appendix 3 in the Supplement).

Data availability AE and RWH had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Access to the data is possible by remote data processing upon reasonable request.

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References 1. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed December 1, 2020) 2. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Ges amt.html (accessed December 1, 2020) 3. Mafham MM, Spata E, Goldacre R, et al. COVID-19 pandemic and admission rates for and management of acute coronary syndromes in England. Lancet. 2020;396 (10248):381-389. 4. Solomon MD, McNulty EJ, Rana JS, et al. The Covid-19 Pandemic and the Incidence of Acute Myocardial Infarction. N Engl J Med. 2020;383(7):691-693. 5. Baum A, Schwartz MD. Admissions to Veterans Affairs Hospitals for Emergency Conditions during the COVID-19 Pandemic. JAMA. 2020;324(1):96-99. 6. Wu Y, Chen F, Wang Z, et al. Reductions in Hospital Admissions and Delays in Acute Stroke Care During the Pandemic of COVID-19. Front Neurol. 2020;11:584734. 7. Aldujeli A, Hamadeh A, Briedis K, et al. Delays in Presentation in Patients with Acute Myocardial Infarction during the COVID-19 Pandemic. Cardiol Res. 2020;11(6):386- 391. 8. Primessnig U, Pieske BM, Sherif M. Increased mortality and worse cardiac outcome of acute myocardial infarction during the early COVID-19 pandemic [published online ahead of print, 2020 Dec 6]. ESC Heart Fail. 2020;10.1002/ehf2.13075. 9. Kobo O, Efraim R, Saada M, et al. The impact of lockdown enforcement during the SARS-CoV-2 pandemic on the timing of presentation and early outcomes of patients with ST-elevation myocardial infarction. PLoS One. 2020;15(10):e0241149. 10. Kamrath C, Mönkemöller K, Biester T, et al. Ketoacidosis in Children and Adolescents With Newly Diagnosed Type 1 Diabetes During the COVID-19 Pandemic in Germany. JAMA. 2020;324(8):801-804. 11. White NH. Diabetic ketoacidosis in children. Endocrinol Metab Clin North Am. 2000;29(4):657-682 12. Dhatariya KK, Glaser NS, Codner E, Umpierrez GE. Diabetic ketoacidosis. Nat Rev Dis Primers. 2020;6(1):40. 13. Bui H, To T, Stein R, Fung K Daneman D. Is diabetic ketoacidosis at disease onset a result of missed diagnosis? J Pediatr. 2010;156(3):472-477. 14. Wolfsdorf JI, Glaser N, Agus M, et al. ISPAD Clinical Practice Consensus Guidelines 2018: Diabetic ketoacidosis and the hyperglycemic hyperosmolar state. Pediatr Diabetes 2018;19 Suppl 27:155–177. 15. Hofer SE, Schwandt A, Holl RW. Standardized documentation in pediatric diabetology: experience from Austria and Germany. J Diabetes Sci Technol 2016;10:1042–1049. 16. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. 17. Segerer H, Wurm M, Grimsmann JM, et al. Diabetic Ketoacidosis at Manifestation of Type 1 Diabetes in Childhood and Adolescence—Incidence and Risk Factors [published online ahead of print, 2021 Jun 4]. Dtsch Arztebl Int. 2021;118:arztebl.m2021.0133.

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18. Lawrence C, Seckold R, Smart C, et al. Increased paediatric presentations of severe diabetic ketoacidosis in an Australian tertiary centre during the COVID-19 pandemic [published online ahead of print, 2020 Oct 6]. Diabet Med. 2020;e14417. 19. Rabbone I, Schiaffini R, Cherubini V, Maffeis C, Scaramuzza A; Diabetes Study Group of the Italian Society for Pediatric Endocrinology and Diabetes. Has COVID-19 Delayed the Diagnosis and Worsened the Presentation of Type 1 Diabetes in Children? Diabetes Care. 2020;43(11):2870-2872. 20. Böhmer MM, Buchholz U, Corman VM, et al. Investigation of a COVID-19 outbreak in Germany resulting from a single travel-associated primary case: a case series. Lancet Infect Dis. 2020;20(8):920-928. 21. Walker A, Houwaart T, Wienemann T, et al. Genetic structure of SARS-CoV-2 reflects clonal superspreading and multiple independent introduction events, North-Rhine Westphalia, Germany, February and March 2020. Euro Surveill. 2020;25(22):2000746. 22. Lazzerini M, Barbi E, Apicella A, Marchetti F, Cardinale F, Trobia G. Delayed access or provision of care in Italy resulting from fear of COVID-19. Lancet Child Adolesc Health. 2020;4(5):e10-e11. 23. Mantica G, Riccardi N, Terrone C, Gratarola A. Non-COVID-19 visits to emergency departments during the pandemic: the impact of fear. Public Health. 2020; 183: 40-41. 24. Wessler BS, Kent DM, Konstam MA. Fear of Coronavirus Disease 2019-An Emerging Cardiac Risk. JAMA Cardiol. 2020;5(9):981-982. 25. Garcia S, Albaghdadi MS, Meraj PM, et al. Reduction in ST-Segment Elevation Cardiac Catheterization Laboratory Activations in the United States during COVID-19 Pandemic. J Am Coll Cardiol. 2020;75(22):2871-2872. 26. Lai PH, Lancet EA, Weiden MD, et al. Characteristics Associated with Out-of-Hospital Cardiac Arrests and Resuscitations during the Novel Coronavirus Disease 2019 Pandemic in New York City [published online ahead of print, 2020 Jun 19]. JAMA Cardiol. 2020;5(10):1154-1163. 27. Mesnier J, Cottin Y, Coste P, et al. Hospital admissions for acute myocardial infarction before and after lockdown according to regional prevalence of COVID-19 and patient profile in France: a registry study. Lancet Public Health. 2020;5(10):e536-e542. 28. Tittel SR, Rosenbauer J, Kamrath C, et al. Did the COVID-19 Lockdown Affect the Incidence of Pediatric Type 1 Diabetes in Germany? Diabetes Care. 2020;43(11):e172- e173. 29. Cherubini V, Grimsmann JM, Åkesson K, et al. Temporal trends in diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes between 2006 and 2016: results from 13 countries in three continents. Diabetologia. 2020;63(8):1530-1541.

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Table 1. Description of the study population.

All No diabetic Diabetic Severe ketoacidosis ketoacidosis ketoacidosis (all) All patients – No 3238 (100) 2144 (66.2) 1094 (33.8) 401 (12.4) (%)

Age – years, 9.8 (6.0–12.9) 9.9 (6.1–13.0) 9.8 (5.8–12.9) 9.4 (4.8–12.5)§ median (interquartile range) Male – No (%) 1799 (55.6) 1207 (56.3) 592 (54.1) 209 (52.1)

Immigrant 808 (25.0) 486 (22.7) 322 (29.4)# 134 (33.4)# background – No (%)

No (%*) of the geographical regions

North 657 (20.3) 441 (67.1) 216 (32.9) 90 (13.7)

Middle 436 (13.5) 305 (70.0) 131 (30.0) 47 (10.8)

West 807 (24.9) 524 (64.9) 283 (35.0) 98 (12.1)

East 503 (15.5) 330 (65.6) 173 (34.4) 60 (11.9)

South 835 (25.8) 544 (65.1) 291 (34.9) 106 (12.7)

§ p=0.01 and # p<0.001 vs. no diabetic ketoacidosis (Kruskal-Wallis test (for age) and χ2 test (for immigrant background) adjusted for multiple testing using the Bonferroni Holm method).

* the first column shows the percentage of patients in this region out of all patients; the following columns show the respective percentage based on the number of patients in the respective region.

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Table 2. Observed versus expected rates of diabetic ketoacidosis, severe ketoacidosis, and impaired consciousness at diagnosis of type 1 diabetes during the COVID-19 pandemic in

Germany from January 1, through June 30, 2020.

Observed rate in Expected rate for 2020 Observed vs. Month P-Value 2020 – % (95% CI)# based on data from 2000 expected rate – to 2019 – % (95% CI)§ adjusted RR (N=3,238) (95% CI)¶ (N=42,417)

Diabetic ketoacidosis January 22.6 (18.4–27.8) 20.1 (16.1–25.0) 1.13 (0.83–1.52) 0.44 February 30.5 (25.5–36.4) 22.9 (18.5–28.4) 1.33 (1.01–1.76) 0.05 March 28.1 (23.0–34.3) 24.8 (20.0–30.8) 1.13 (0.84–1.52) 0.41 April 41.1 (35.5–47.6) 20.9 (16.5–26.5) 1.96 (1.49–2.59) <0.001 May 39.0 (33.7–45.2) 21.3 (17.0–26.7) 1.83 (1.40–2.40) <0.001 June 31.6 (26.6–37.7) 21.3 (17.0–26.8) 1.48 (1.11–1.98) 0.007 July 39.7 (34.5–45.7) 21.9 (17.7–27.1) 1.82 (1.40–2.35) <0.001 August 43.3 (37.5–50.1) 24.2 (19.3–30.2) 1.79 (1.37–2.34) <0.001 September 36.0 (31.0–41.8) 24.5 (20.1–29.8) 1.47 (1.15–1.88) 0.002 October 29.7 (24.7–35.6) 25.3 (20.6–31.0) 1.17 (0.89–1.54) 0.25 November 27.8 (22.9–33.9) 20.8 (16.4–26.4) 1.34 (0.98–1.83) 0.07 December 33.5 (27.8–40.3) 22.6 (17.7–28.9) 1.48 (1.09–2.01) 0.01

Severe diabetic ketoacidosis January 7.3 (4.9–10.9) 5.7 (3.7–9.0) 1.28 (0.70–2.32) 0.42 February 9.7 (6.8–13.9) 8.2 (5.6–12.2) 1.17 (0.69–2.00) 0.55 March 11.8 (8.4–16.6) 10.1 (7.0–14.7) 1.16 (0.70–1.92) 0.56 April 16.3 (12.3–21.5) 7.8 (5.1–11.8) 2.10 (1.27–3.48) 0.004 May 16.3 (12.4–21.2) 6.5 (4.2–10.2) 2.50 (1.49–4.21) <0.001 June 9.6 (6.6–13.8) 7.0 (4.5–10.8) 1.37 (0.78–2.41) 0.28 July 13.8 (10.4–18.3) 8.1 (5.5–11.9) 1.70 (1.06–2.75) 0.03 August 19.6 (15.2–25.3) 9.2 (6.2–13.7) 2.14 (1.33–3.43) 0.002 September 12.4 (9.2–16.7) 7.2 (4.8–10.8) 1.73 (1.05–2.85) 0.03

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October 8.1 (5.4–12.0) 8.4 (5.7–12.5) 0.96 (0.55–1.67) 0.88 November 7.8 (5.2–12.0) 8.2 (5.5–12.4) 0.95 (0.53–1.71) 0.87 December 12.2 (8.6–17.4) 7.7 (4.9–12.2) 1.59 (0.89–2.83) 0.12

§ Estimated based on data from the first half of each year from 2000 to 2019 using a

multivariable logistic trend regression model that included year and month, an interaction term of

both, age group, sex, and immigrant background as independent variables, and standardized to

the reference population defined in #.

¶ Estimated from a log-binomial regression model that included month of diabetes onset, a binary

variable indicating observed and predicted data and an interaction term of both, applied to the

standardized observed and standardized expected monthly frequencies.

Diabetic ketoacidosis: pH <7.3 and/or serum bicarbonate <15 mmol/L; severe diabetic

ketoacidosis: pH <7.1 and or serum bicarbonate <5 mmol/L. Immigrant background was defined as patient or at least one parent born outside of Germany.

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Figure 1A. The five geographical regions of Germany with their corresponding

COVID-19 incidences. New cases of COVID-19 per 100,000 population in March 2020, color-coded to the five geographical regions of Germany.

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Figure 1B. The five geographical regions of Germany with their corresponding COVID-19 incidences. Weekly incidence of new COVID-19 cases per 100,000 population in the five geographical regions of Germany from the calendar weeks 9 to 20 in 2020 (2).

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Figure 2. Relative risks of diabetic ketoacidosis at diagnosis of type 1 diabetes during the

COVID-19 pandemic year 2020 in Germany and in five regions of Germany.

Adjusted relative risk (RR) for ketoacidosis in 2020 was estimated from observed and expected rates. Expected rates were derived from data of 2000–2019 using a multivariable logistic trend regression model. The dark line describes the mean RR, the vertical lines the corresponding 95% confidence interval.

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Figure 3. Predicted rate of diabetic ketoacidosis dependent on weekly incidence of COVID- 19 cases and deaths. The predicted rate of diabetic ketoacidosis dependent on the weekly incidence of new COVID- 19 cases (A, C) and COVID-19-related deaths (B, D) for the first half (A, B) and the second half (C, D) of the year 2020 are shown, based on a multivariable log-binomial mixed effects model. The dark line describes the predicted rate of diabetic ketoacidosis, the light area around it the corresponding 95% confidence interval.

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Online-only Supplements

Appendix 1. Early course of the spread of the COVID-19 pandemic in Germany during the first wave Table S1. Adjusted relative risk of diabetic ketoacidosis at diagnosis of type 1 diabetes during the COVID-19 pandemic in five regions of Germany. Table S2. Nationwide measures with associated date to contain the spread of the COVID 19 pandemic in Germany. Appendix 2. Contributing diabetes centers to this study

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Appendix 1. Early course of the spread of the COVID-19 pandemic in Germany during the first wave The first case of COVID-19 in Germany was discovered in Bavaria, Southern Germany, at the end of January 2020. By 11th February, a total of 16 cases of COVID-19 had been identified in this cluster (20). In addition, there was a significant increase in COVID-19 cases in the West of Germany (i.e. Heinsberg district in North-Rhine Westphalia) early in March after Carnival at the end of February (21), with about three times more new SARS-CoV-2 positive cases per 100,000 population in calendar week 10 (from March 2, through March 8, 2020) than in the South of Germany, and about six times as many new cases than in the other three regions of Germany.2 At the end of this week, 398 (44%) out of a total of all 902 proven cases of COVID-19 in Germany have been detected in the West, i.e. North- Rhine Westphalia. From calendar week 11 onwards, most SARS-CoV-2 infections and most COVID-19-related deaths were observed in Southern Germany. From calendar weeks 11 and 12 on, there was a significantly greater increase in incidence of COVID-19 with a higher maximum in the South and the West of Germany compared to the other regions (2).

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Table S1. Observed versus expected rates of diabetic ketoacidosis at diagnosis of type 1 diabetes during the COVID-19 pandemic in five regions of Germany.

Region/ Observed vs. P-Value Month Observed vs. P-Value Month expected rate – expected rate – adjusted RR (95% adjusted RR (95% ¶ ¶ CI) CI) North January 1.27 (0.63–2.57) 0.50 July 1.22 (0.72–2.06) 0.45 February 1.07 (0.57–2.01) 0.83 August 1.60 (0.95–2.71) 0.08 March 1.47 (0.86–2.50) 0.16 September 1.57 (0.89–2.78) 0.12 April 1.27 (0.71–2.25) 0.42 October 0.90 (0.40–2.00) 0.80 May 1.75 (0.96–3.19) 0.07 November 1.15 (0.61–2.17) 0.66 June 1.70 (0.86–3.36) 0.13 December 1.36 (0.71–2.61) 0.35 Middle January 0.83 (0.39–1.77) 0.64 July 1.80 (0.93–3.48) 0.08 February 1.00 (0.48–2.07) 1.00 August 1.17 (0.45–3.00) 0.75 March 0.78 (0.33–1.83) 0.56 September 1.11 (0.51–2.41) 0.79 April 1.67 (0.71–3.91) 0.24 October 1.00 (0.47–2.12) 1.00 May 1.33 (0.65–2.73) 0.43 November 0.80 (0.35–1.84) 0.60 June 1.25 (0.57–2.75) 0.58 December 1.88 (0.91–3.85) 0.09 West January 0.93 (0.48–1.80) 0.84 July 2.78 (1.42–5.42) 0.003 February 1.50 (0.83–2.72) 0.18 August 2.33 (1.32–4.12) 0.004 March 1.00 (0.52–1.92) 1.00 September 1.41 (0.90-2.20) 0.13 April 2.73 (1.51–4.93) <0.001 October 1.73 (1.02–2.94) 0.04 May 2.50 (1.39–4.48) 0.002 November 1.90 (0.96–3.77) 0.07 June 1.40 (0.78–2.50) 0.26 December 1.07 (0.57–2.03) 0.83 East January 1.22 (0.57–2.61) 0.60 July 1.00 (0.59–1.69) 1.00 February 1.33 (0.63–2.83) 0.45 August 1.63 (0.89–3.01) 0.11 March 0.80 (0.36–1.75) 0.58 September 1.53 (0.90–2.60) 0.11 April 1.70 (0.88–3.29) 0.12 October 0.63 (0.35–1.16) 0.14 May 1.08 (0.55–2.12) 0.81 November 1.38 (0.64–2.98) 0.42 June 1.36 (0.77–2.38) 0.29 December 1.30 (0.66–2.56) 0.45 South January 1.35 (0.78–2.34) 0.28 July 2.33 (1.39–3.93) 0.001 February 1.47 (0.87–2.48) 0.15 August 1.93 (1.12–3.31) 0.02 March 1.31 (0.69–2.47) 0.41 September 1.35 (0.79–2.33) 0.27

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April 2.23 (1.27–3.91) 0.005 October 1.47 (0.86–2.50) 0.16 May 2.06 (1.25–3.40) 0.005 November 1.50 (0.79–2.84) 0.21 June 1.38 (0.74–2.58) 0.31 December 1.88 (0.88–3.99) 0.10

# Multivariable logistic regression model that included month of diabetes onset (January to June), age group (6 months - <6 years, 6-<12 years, 12-18 years), sex, and immigrant background as independent variables, standardized for age group, sex, and immigrant background, with the total population of all type 1 cases with onset in the first half of each year between 2000 and 2020 as reference. § Estimated based on data from the first half of each year from 2000 to 2019 using a multivariable logistic trend regression model that included year and month, an interaction term of both, age group, sex, and immigrant background as independent variables, and standardized to the reference population defined in #. ¶ Estimated from a log-binomial regression model that included month of diabetes onset, a binary variable indicating observed and predicted data and an interaction term of both, applied to the standardized observed and standardized expected monthly frequencies. Diabetic ketoacidosis: pH <7.3 and/or serum bicarbonate <15 mmol/L; severe diabetic ketoacidosis: pH <7.1 and or serum bicarbonate <5 mmol/L. Immigrant background was defined as patient or at least one parent born outside of Germany.

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Table S2. Nationwide measures with associated date to contain the spread of the COVID 19 pandemic in Germany. Date Nationwide measures March 16, 2020 Germany closed its borders with Austria, Denmark, France, Luxembourg, and Switzerland March 16, 2020 All federal states implemented extensive measures to enforce social distancing as well as school and daycare closures or suspension of compulsory school attendance March 17, 2020 The risk assessment of COVID-19 by the Robert Koch Institute estimated the overall risk to the health of the German population to be ‘high’ March 23, 2020 Gatherings of more than 2 persons (with the exception of families and household members) are banned in all states. Restaurants and businesses concerned with body care were closed. In public spaces, all persons must maintain a distance of 1.5 meters to other individuals April 15, 2020 The German government and the federal states agreed to gradually reduce social distancing measures April 29, 2020 The wearing of (non-medical) facemasks in public transport and in shops is obligatory in all federal states in Germany Source: Robert Koch Institute (2).

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Appendix 3. Contributing diabetes centers to this study Augsburg Uni-Kinderklinik, Aachen - Uni-Kinderklinik RWTH, Ahlen St. Franziskus Kinderklinik, Aue Helios Kinderklink, Aurich Kinderklinik, Berlin Lichtenberg - Kinderklinik, Berlin Virchow-Kinderklinik, Bremerhaven Kinderklinik, Bielefeld Kinderklinik Gilead, Bonn Uni-Kinderklinik, Celle Klinik für Kinder- und Jugendmedizin, Chemnitz Kinderklinik, Coesfeld Kinderklinik, Düsseldorf Uni-Kinderklinik, Darmstadt Kinderklinik Prinz. Margaret, Deggendorf Medizinische Klinik II, Düren-Birkesdorf Kinderklinik, Delmenhorst Kinderklinik, Detmold Kinderklinik, Dortmund Kinderklinik, Dresden Uni-Kinderklinik, Datteln Vestische Kinderklinik, Essen Uni- Kinderklinik, Erlangen Uni-Kinderklinik, Erfurt Kinderklinik, Esslingen Klinik für Kinder und Jugendliche, Frankfurt Uni-Kinderklinik, Freiburg Uni-Kinderklinik, Fürth Kinderklinik, Fulda Kinderklinik, Garmisch- Partenkirchen Kinderklinik, Gießen Uni-Kinderklinik, Gelsenkirchen Kinderklinik Marienhospital, Göttingen Uni- Kinderklinik, Görlitz Städtische Kinderklinik, Hannover Kinderklinik auf der Bult, Halle Uni-Kinderklinik, Hamm Kinderklinik, Bremen Zentralkrankenhaus Kinderklinik, Bremen - Kinderklinik Nord, Heidelberg Uni-Kinderklinik, Heidenheim Kinderklinik, Herford Klinikum Kinder & Jugendliche, Bad Hersfeld Kinderklinik, Hagen Kinderklinik, Hamburg Altonaer Kinderklinik, Hamburg Kinderklinik Wilhelmstift, Hamburg-Nord Kinder-MVZ, Hildesheim Kinderklinik, Lübeck Uni-Kinderklinik, Homburg Uni-Kinderklinik Saarland, Hanau Kinderklinik, Itzehoe Kinderklinik, Jena Uni-Kinderklinik, Köln Uni-Kinderklinik, Karlsruhe Städtische Kinderklinik, Kaiserslautern-Westpfalzklinikum Kinderklinik, Karlsburg Klinik für Diabetes & Stoffwechsel, Kiel Städtische Kinderklinik, Koblenz Kinderklinik Kemperhof, Kassel Klinikum Kinder- und Jugendmedizin, Leipzig Uni- Kinderklinik, Ludwigsburg Kinderklinik, Landshut Kinderklink, Lingen Kinderklinik St. Bonifatius, Lippstadt Evangelische Kinderklinik, Ludwigshafen Kinderklinik St.Anna-Stift, Lüdenscheid Märkische Kliniken - Kinder & Jugendmedizin, München von Haunersche Kinderklinik, Mannheim Uni-Kinderklinik, Mechernich Kinderklinik, Minden Kinderklinik, Moers Kinderklinik, Münster Uni-Kinderklinik, Mutterstadt Kinderarztpraxis, Nürnberg Uniklinik Zentrum f Neugeb./Kinder & Jugendl., Neuwied Kinderklinik Elisabeth, Neunkirchen Marienhausklinik Kohlhof Kinderklinik, Nürnberg Cnopfsche Kinderklinik, Oberhausen Kinderklinik, Oldenburg Kinderklinik, Osnabrück Christliches Kinderhospital, Bad Oeynhausen Herz-und Diabeteszentrum NRW, Paderborn St. Vincenz Kinderklinik, Pforzheim Kinderklinik, Regensburg Kinderklinik St. Hedwig, Mönchengladbach Kinderklinik Rheydt Elisabethkrankenhaus, Rendsburg Kinderklinik, Rosenheim Kinderklinik, Ravensburg Kinderklink St. Nikolaus, Rotenburg/Wümme Agaplesion Diakonieklinikum Kinderabteilung, Stuttgart Olgahospital Kinderklinik, Saarbrücken Kinderklinik Winterberg, Schw. Gmünd Stauferklinik Kinderklinik, Suhl Kinderklinik, Siegen Kinderklinik, Singen - Hegauklinik Kinderklinik, Stade Kinderklinik, Trier Kinderklinik der Borromäerinnen, Ulm Uni-Kinderklinik, Vechta Kinderklinik, Viersen Kinderkrankenhaus St. Nikolaus, Weiden Kinderklinik, Wiesbaden Helios Horst-Schmidt-Kinderkliniken, Winnenden Rems-Murr Kinderklinik, Worms Kinderklinik, Wuppertal Universitäts-Kinderklinik, Magdeburg Uni-Kinderklinik, Schweinfurt Kinderklinik, München-Schwabing Kinderklinik, Passau Kinderklinik, Neuburg Kinderklinik, Memmingen Kinderklinik, Tübingen Uni-Kinderklinik, Gelnhausen Kinderklinik, Stolberg Kinderklinik, Münster St. Franziskus Kinderklinik, Leverkusen Kinderklinik, Offenburg Kinderklinik, Kiel Universitäts-Kinderklinik, Rostock Uni-Kinderklinik, Bocholt Kinderklinik, Schwerin Kinderklinik, Rheine Mathiasspital Kinderklinik, Essen Elisabeth Kinderklinik, Mainz Uni-Kinderklinik, Herford Kinderarztpraxis, München-Gauting Kinderarztzentrum, Wilhelmshaven Klinikum Kinderklinik, Bochum Universitätskinderklinik St. Josef, Köln Kinderklinik Amsterdamerstrasse, Heilbronn Kinderklinik, Krefeld Kinderklinik, Rosenheim Schwerpunktpraxis, Kirchen DRK Krankenhaus Kinderklinik, Berlin DRK-Kliniken Pädiatrie, München 3. Orden Kinderklinik, Bautzen Oberlausitz KK, St. Augustin Kinderklinik, Dresden Neustadt Kinderklinik, Haren Kinderarztpraxis, Konstanz Kinderklinik, Gera Kinderklinik, Reutlingen Kinderarztpraxis, Oldenburg Schwerpunktpraxis Pädiatrie, Hof Kinderklinik, Hameln Kinderklinik, Heide Kinderklinik, München Kinderarztpraxis diabet. SPP, Wittenberg Kinderklinik, Braunschweig Kinderarztpraxis, Böblingen Kinderklinik, Plauen Vogtlandklinikum, Reutlingen Kinderklinik, Frankfurt Diabeteszentrum Rhein-Main-pädiat. Diabetologie (Clementine-Hospital), Duisburg Sana Kinderklinik, Villingen-Schwenningen Schwarzwald Baar Klinikum Kinderklinik, Oberhausen St.Clemens Hospitale Sterkrade, Dessau Kinderklinik, Waren-Müritz Kinderklinik, Duisburg-St.Johannes Helios, Leer Klinikum - Klinik Kinder & Jugendmedizin, Singen Kinderarztpraxis, Freudenstadt Kinderklinik, Coburg Kinderklinik, Essen Kinderarztpraxis, Filderstadt Kinderklinik, Meissen Kinderklinik Elblandklinikum, Greifswald Uni-Kinderklinik, Traunstein Kinderklinik, Augsburg Josefinum Kinderklinik, Amberg Kinderklinik St. Marien, Flensburg Diakonissen Kinderklinik, Bielefeld Kinderarztpraxis,

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Speyer Diakonissen Stiftungskrankenhaus Pädiatrie, Wesel Marienhospital Kinderklinik, Neuss Lukas-Krankenhaus Kinderklinik, Ulm Endokrinologikum Amedes, Neunkirchen Gemeinschaftspraxis Kinderheilkunde, Jena Kinderarztpraxis, Gummersbach Oberbergklinikum, Witten Kinderarztpraxis, Magdeburg Ki-Klinik St. Marienstift, Bad Kreuznach Diakonie Kikli, Schleswig Heliosklinik Kinderklinik, Hildesheim Bernward Krks Kinderheilkunde, Altötting Kinderklinik Zentrum Inn-Salzach, Remscheid Kinderklinik, Garmisch-Partenkirchen Klinikum Pädiatrie.

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Appendix 4. STROBE Statement—checklist of items that should be included in reports of observational studies

Item Page No Recommendation number Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done 5 and what was found

Introduction Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 6

Objectives 3 State specific objectives, including any prespecified hypotheses 6-7

Methods Study design 4 Present key elements of study design early in the paper 8

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, 8-9 exposure, follow-up, and data collection

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of 8 selection of participants. Describe methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect 8-9 modifiers. Give diagnostic criteria, if applicable

Data sources/ 8* For each variable of interest, give sources of data and details of methods of 8-9 measurement assessment (measurement). Describe comparability of assessment methods if there is more than one group

Bias 9 Describe any efforts to address potential sources of bias 9-11

Study size 10 Explain how the study size was arrived at 8, 12

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, 9 describe which groupings were chosen and why

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 9-11

(b) Describe any methods used to examine subgroups and interactions 9-11

(d) Cohort study—If applicable, explain how loss to follow-up was addressed No loss

Case-control study—If applicable, explain how matching of cases and controls was addressed

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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

(e) Describe any sensitivity analyses 9-11

Results Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers 12 potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed

(b) Give reasons for non-participation at each stage

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, 12,Table 1 social) and information on exposures and potential confounders

(b) Indicate number of participants with missing data for each variable of No missing interest data

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures 12-13, over time Table 2+3

Case-control study—Report numbers in each exposure category, or summary measures of exposure

Cross-sectional study—Report numbers of outcome events or summary measures

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted 12-13, estimates and their precision (eg, 95% confidence interval). Make clear Table 2+3, which confounders were adjusted for and why they were included Fig. 2

(b) Report category boundaries when continuous variables were categorized

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and N.D. sensitivity analyses

Discussion Key results 18 Summarise key results with reference to study objectives 14-15

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias 16 or imprecision. Discuss both direction and magnitude of any potential bias

Interpretation 20 Give a cautious overall interpretation of results considering objectives, 15-17 limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

Generalisability 21 Discuss the generalisability (external validity) of the study results 16-17

Other information 32 Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based

Downloaded from©202 www.aappublications.org/news1 American Academy by of guest Pediatrics on September 25, 2021 Incidence of COVID-19 and Risk of Diabetic Ketoacidosis in New-Onset Type 1 Diabetes Clemens Kamrath, Joachim Rosenbauer, Alexander J. Eckert, Angeliki Pappa, Felix Reschke, Tilman R. Rohrer, Kirsten Mönkemöller, Michael Wurm, Kathrin Hake, Klemens Raile and Reinhard W. Holl Pediatrics originally published online May 19, 2021;

Updated Information & including high resolution figures, can be found at: Services http://pediatrics.aappublications.org/content/early/2021/05/18/peds.2021-05 0856.citation Permissions & Licensing Information about reproducing this article in parts (figures, tables) or in its entirety can be found online at: http://www.aappublications.org/site/misc/Permissions.xhtml Reprints Information about ordering reprints can be found online: http://www.aappublications.org/site/misc/reprints.xhtml

Downloaded from www.aappublications.org/news by guest on September 25, 2021 Incidence of COVID-19 and Risk of Diabetic Ketoacidosis in New-Onset Type 1 Diabetes Clemens Kamrath, Joachim Rosenbauer, Alexander J. Eckert, Angeliki Pappa, Felix Reschke, Tilman R. Rohrer, Kirsten Mönkemöller, Michael Wurm, Kathrin Hake, Klemens Raile and Reinhard W. Holl Pediatrics originally published online May 19, 2021;

The online version of this article, along with updated information and services, is located on the World Wide Web at: http://pediatrics.aappublications.org/content/early/2021/05/18/peds.2021-050856.citation

Pediatrics is the official journal of the American Academy of Pediatrics. A monthly publication, it has been published continuously since 1948. Pediatrics is owned, published, and trademarked by the American Academy of Pediatrics, 345 Park Avenue, Itasca, Illinois, 60143. Copyright © 2021 by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.

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