Diabetologia

, Volume 55, Issue 8, pp 2142–2147

Trends in childhood type 1 diabetes incidence in Europe during 1989–2008: evidence of non-uniformity over time in rates of increase

  • C. C. Patterson
  • E. Gyürüs
  • J. Rosenbauer
  • O. Cinek
  • A. Neu
  • E. Schober
  • R. C. Parslow
  • G. Joner
  • J. Svensson
  • C. Castell
  • P. J. Bingley
  • E. Schoenle
  • P. Jarosz-Chobot
  • B. Urbonaité
  • U. Rothe
  • C. Krzisnik
  • C. Ionescu-Tirgoviste
  • I. Weets
  • M. Kocova
  • G. Stipancic
  • M. Samardzic
  • C. E. de Beaufort
  • A Green
  • G. G. Dahlquist
  • G. Soltész
Short Communication

Abstract

Aims/hypothesis

The aim of the study was to describe 20-year incidence trends for childhood type 1 diabetes in 23 EURODIAB centres and compare rates of increase in the first (1989–1998) and second (1999–2008) halves of the period.

Methods

All registers operate in geographically defined regions and are based on a clinical diagnosis. Completeness of registration is assessed by capture–recapture methodology. Twenty-three centres in 19 countries registered 49,969 new cases of type 1 diabetes in individuals diagnosed before their 15th birthday during the period studied.

Results

Ascertainment exceeded 90% in most registers. During the 20-year period, all but one register showed statistically significant changes in incidence, with rates universally increasing. When estimated separately for the first and second halves of the period, the median rates of increase were similar: 3.4% per annum and 3.3% per annum, respectively. However, rates of increase differed significantly between the first half and the second half for nine of the 21 registers with adequate coverage of both periods; five registers showed significantly higher rates of increase in the first half, and four significantly higher rates in the second half.

Conclusions/interpretation

The incidence rate of childhood type 1 diabetes continues to rise across Europe by an average of approximately 3–4% per annum, but the increase is not necessarily uniform, showing periods of less rapid and more rapid increase in incidence in some registers. This pattern of change suggests that important risk exposures differ over time in different European countries. Further time trend analysis and comparison of the patterns in defined regions is warranted.

Keywords

Epidemiology Incidence Temporal change Trends Type 1 diabetes 

Introduction

Recent incidence rate trends in childhood type 1 diabetes have been well characterised in publications by the EURODIAB registries in Europe [1] and by the DIAMOND (Diabetes Mondiale) Project Group worldwide [2]. The DIAMOND report described increasing trends in nearly every continent in the 1990s, whereas in Europe there was clear evidence that relative increases were highest in central and eastern European countries and in the under-5-year age group during the period 1989–2003.

More recent analyses from Norway [3] and Finland [4] suggest that rates of increase were lower in the 1980s, with a subsequent acceleration in the 1990s. The same pattern is evident in data from Sweden [5], and that analysis additionally raises the possibility that the rapid increase in the 1990s may soon be reversed, a reduction in rates having been observed beginning with the 2000 birth cohort.

The EURODIAB group has maintained registers of childhood diabetes in a range of European countries since 1989 using standardised methodology and with validation of completeness of ascertainment, and these observations from Scandinavia led us to compare incidence rates in the first half of the 20-year registration period (1989–1998) with those in the second half (1999–2008).

Methods

Case inclusion criteria were as previously described for the EURODIAB registers [6]: new diagnoses of type 1 (insulin-dependent) diabetes mellitus among children aged under 15 years resident in the geographically defined region. The completeness of registration was estimated separately in each of the 10-year periods using capture–recapture methodology [7], which requires that independent primary and secondary sources of ascertainment are available. In most centres, the primary source of ascertainment was through hospital records or notifications by paediatricians and family doctors, whereas secondary sources varied depending on local circumstances and included social insurance schemes, diabetes associations and prescription data.

Annual estimates of the population resident in each centre’s geographically defined area were used as denominators for the calculation of directly standardised incidence rates using a standard population consisting of equal numbers of children in each of six subgroups defined by age group (0–4, 5–9 and 10–14 years) and sex.

Poisson regression was used to estimate the trends in incidence rate within centres. For each centre, a model with terms for age group, sex and an age group × sex interaction was first fitted. Then either a categorical variable representing the 5-year subperiods or a linear term testing for trend across individual years was added to the model to provide comparisons of incidence rates over time that took account of changes in the age structure of the population. For three centres whose registers changed coverage during the period, separate estimates of trend were fitted for each 10-year subperiod, and the two estimates were compared by likelihood ratio test. For the remaining centres, a similar model was fitted but with the added constraint that the fitted lines should meet between 1998 and 1999. Models were fitted using Stata Release 11 (Stata, College Station, TX, USA).

The Joinpoint regression program (Version 3.5 – April 2011; Statistical Methodology and Applications Branch and Data Modeling Branch, Surveillance Research Program National Cancer Institute, Bethesda, MD, USA) specifically designed for surveillance of trends in cancer incidence, was also used to see how sensitive conclusions were to the arbitrary division of the period into two 10-year subperiods. Joinpoint provides greater flexibility by accommodating the fitting of two or more linear segments that join at time points that are estimated from the data. The program provides a permutation test to assess the number of linear segments and the times at which they join, while taking into account the multiple testing issues inherent in the approach. A less conservative Bayesian information criterion for model selection was also employed. In order that Joinpoint should mimic as closely as possible the Poisson regression approach, the log-linear model option was chosen, and heteroscedasticity was taken into account by using the standard error of the annual standardised rates.

Further details are provided in the electronic supplementary material [ESM] Statistical methods.

Results

Table 1 shows the total numbers of cases registered during the 20-year period 1989–2008 in each of the 23 centres, and the age- and sex-standardised incidence rates in the four 5-year subperiods 1989–1993, 1994–1998, 1999–2003 and 2004–2008. The age- and sex-specific incidence rates used in the calculations are available in ESM Table 1. Investigation of changes in incidence during the period was complicated in Denmark and Norway because the registers changed from regional to national coverage, and in Germany because of the extension of the Düsseldorf register to cover the whole North Rhine–Westphalia region. Poisson regression analysis confirmed that there were significant differences in the age-standardised rates between the four periods in all remaining centres with the exception of Catalonia (Spain), and incidence rates were universally observed to increase.
Table 1

Age and sex standardised rates of type 1 diabetes diagnosed before 15 years of age in four five year periods for 23 EURODIAB centres from 19 countries in Europe

Centre

Region

Period

Number of cases

Standardised incidence rate per 100,000a

P1

P2

P3

P4b

Austria

Whole nation

1989–2008

3372

9.0

9.9

13.3

17.5

Belgium

Antwerp

1989–2008

448

10.9

12.9

15.5

15.9

Croatia

Zagreb

1989–2008

339

6.7

6.4

8.2

10.4

Czech Republic

Whole nation

1989–2008

4883

8.5

11.5

17.0

19.3

Denmarkc

1) Four counties

1989–1998

385

17.0

16.3

2) Whole nation

1999–2008

2402

22.6

25.1

Germany

Baden Württemberg

1989–2007

4804

11.0

13.0

15.4

21.8d

Germanyc

1) Düsseldorf (seven districts)

1989–1998

595

13.3

16.8

2) North Rhine-Westphalia

1999–2008

6331

21.3

23.7

Germany

Saxony

1998–2008

921

11.6d

15.6

20.1

Hungary

18 counties

1989–2008

3239

9.0

10.7

12.4

18.3

Lithuania

Whole nation

1989–2008

1396

7.3

8.2

10.3

14.2

Luxembourg

Whole nation

1989–2008

229

11.4

12.3

15.5

19.0

Macedonia

Whole nation

1989–2008

447

3.2

3.9

5.6

5.8

Montenegro

Whole nation

1996–2008

252

10.1d

14.0

17.5

Norwayc

1) Eight counties

1989–2003

1380

21.1

20.5

24.6

2) Whole nation

2004–2008

1504

32.8

Poland

Katowice

1989–2008

1719

5.2

7.9

12.9

16.5

Romania

Bucharest

1989–2008

534

4.7

6.1

11.3

14.5

Slovenia

Whole nation

1989–2008

715

7.9

9.2

11.1

14.6

Spain

Catalonia

1989–2008

2527

12.4

13.6

12.9

12.1

Sweden

Stockholm county

1989–2008

1978

25.8

25.6

34.5

36.6

Switzerland

Whole nation

1991–2008

2220

8.0d

8.3

11.0

13.1

United Kingdom

Northern Ireland

1989–2008

2043

20.0

24.7

29.8

33.9

United Kingdom

Oxford

1989–2008

2288

17.2

21.7

24.0

25.1

United Kingdom

Yorkshire

1989–2008

3018

16.1

19.7

23.5

25.5

aStandard population with six age-sex subgroups of equal size

bPeriods denoted P1: 1989–1993, P2: 1994–1998, P3: 1999–2003, P4: 2004–2008

cIn three registries, period 2) has extended geographic coverage compared with period 1)

dRate based on registration data for only part of the period

In Table 2 estimates of completeness of ascertainment obtained by the capture–recapture method are presented in the two 10-year periods 1989–1998 and 1999–2008 (for further details, see ESM Table 2). In most centres, greater than 90% ascertainment was maintained in both periods, with many achieving in excess of 95% ascertainment. The estimated annual increases (with 95% confidence intervals) in the first and second 10-year periods are given in the final columns. Comparison of these rates of increase showed a significant acceleration in the rate of increase in four centres (Austria, Germany [Baden Württemberg], Hungary and Lithuania) and a significant deceleration in the rate of increase in five centres (Czech Republic, Germany [Düsseldorf/North Rhine–Westphalia], Poland [Katowice] and the UK [Oxford and Yorkshire]). The median rates of increase over all the centres changed little, from 3.4% per annum in 1989–1998 to 3.3% per annum in 1999–2008.
Table 2

Completeness of ascertainment and estimated annual rates of increase compared in the two 10-year periods 1989–1998 and 1999–2008 for 23 EURODIAB centres

Centre

Region

Completeness of ascertainment, %

Rates of increase per annum, %a (95 % CI)

1989–1998:1999–2008

1989–1998 : 1999–2008

p value

Austria

Whole nation

99.8 : 97.2

3.3 (1.8,4.8) : 6.1 (4.8,7.4)

0.03*

Belgium

Antwerp

98.·6 : 94.·9

3.3 (–0.6,7.4) : 1.9 (–1.6,5.5)

0.68

Croatia

Zagreb

99.7 : 100.0

0.1 (–4.3,4.7) : 6.8 (2.7,11.0)

0.09

Czech Republic

Whole nation

99.9 : 97.4

7.6 (6.3,8.8) : 3.9 (2.9,5.0)

0.001*

Denmarkb

1) Four counties

99.1 : ―

0.5 (–2.9,4.1)c : ―

0.56c

2) Whole nation

― : 99.2d

― : 1.7 (0.2,3.1)c

 

Germany

Baden Württemberg

97.2 : 100.0

2.9 (1.7,4.2) : 6.5 (5.3,7.7)e

0.002*

Germanyb

1) Düsseldorf (seven districts)

94.0 : ―

6.1 (3.2,9.2)c : ―

0.01*c

2) North Rhine–Westphalia

― : 98.6

― : 2.1 (1.3,3.0)c

 

Germany

Saxony

― : 93.6d

― : 4.6 (2.2,7.1)

Hungary

18 counties

97.1 : 98.7

3.2 (1.8,4.7) : 5.8 (4.5,7.2)

0.04*

Lithuania

Whole nation

100.0 : n/a

2.1 (–0.1,4.3) : 7.2 (5.1,9.3)

0.009*

Luxembourg

Whole nation

100.0 : 100.0

0.9 (–4.7,6.8) : 5.8 (0.9,10.8)

0.32

Macedonia

Whole nation

94.9 : 100.0

6.4 (2.2,10.7) : 2.9 (–0.5,6.5)

0.33

Montenegro

Whole nation

100.0 : 100.0

― : 6.5 (1.6,11.7)

Norwayb

1) Eight counties

100.0 : ―

–1.2(–3.5,1.1)c : ―

0.42c

2) Whole nation

― : 92.0d

― : 0.5 (–3.0,4.2)c,e

 

Poland

Katowice

99.9 : n/a

10.7 (8.3,13.1) : 5.5 (3.7,7.3)

0.006*

Romania

Bucharest

100.0 : 100.0

8.0 (4.3,12.0) : 7.8 (4.4,11.2)

0.93

Slovenia

Whole nation

100.0 : 100.0

4.1 (1.0,7.2) : 3.9 (1.1,6.8)

0.96

Spain

Catalonia

89.4 : 97.6

0.9 (–0.5,2.4) : –1.4 (–2.9,0.1)

0.09

Sweden

Stockholm county

100.0 : n/a

2.5 (0.6,4.5) : 2.5 (0.9,4.2)

0.66

Switzerland

Whole nation

91.7d: 91.3d

2.1 (–0.3,4.5)e : 5.4 (3.8,7.0)

0.07

UK

Northern Ireland

99.5 : 99.5

4.6 (2.7,6.5) : 2.8 (1.2,4.5)

0.27

UK

Oxford

n/a : n/a

4.0 (2.3,5.8) : 0.4 (–1.1,2.0)

0.02*

UK

Yorkshire

99.0 : 99.6

4.7 (3.1,6.3) : 1.6 (0.3,2.9)

0.02*

aDerived from Poisson regression model estimates of log-linear trends constraining lines to meet between 1998 and 1999 (see ESM Statistical methods)

bIn three registries, period 2) has extended geographic coverage compared with period 1)

cRates of increase in periods 1) and 2) were estimated and compared without constraining the fitted log-linear trends to meet between 1998 and 1999

dEstimate was obtained independently of the EURODIAB study

eRate of increase was based on registration data for only part of the period

*p < 0.05

n/a, not available

Using the default permutation test settings, the Joinpoint program detected significant departures from a uniform log-linear trend with time for two of the largest centres that our analyses had identified – the Czech Republic, with a join between two log-linear segments in 2000, and Germany (Baden Württemberg), with a join in 2002. Using the alternative and less conservative Bayesian Information Criterion settings, the program detected departures from uniform log-linear trend with time in a further five of the centres identified by our analysis – Austria, Hungary, Lithuania, Poland (Katowice) and the UK (Yorkshire).

Discussion

The incidence rate of childhood type 1 diabetes continues to rise across Europe by approximately 3–4% per annum, but the increase is not necessarily uniform, with periods of less rapid and more rapid increase in incidence occurring in many registers. Although such patterns of change in incidence rate have previously been described in Scandinavian countries, our analysis suggests that the same phenomenon is also occurring in other parts of Europe.

Although our analysis may be criticised for comparing two arbitrarily selected 10-year periods, the finding of significant differences in the rates of increase in nine of the 21 centres for which the comparison was possible does suggest that these changes in rate are not an artefact, particularly since the completeness of registration was uniformly high in most of these centres. The Joinpoint program provided broad confirmation of our findings, but only when the less conservative Bayesian Information Criterion was used for model selection.

The possibility that changes in trends are affecting different age groups at different times cannot be ruled out. Use of age–period–cohort models has the potential to address this issue, but long-term data over broad age ranges are necessary for this technique to provide useful conclusions [8], and there are well-recognised difficulties in separating period and cohort effects when the predominant pattern of change is one of a log-linear increase [9].

Given the changing trends over time observed within individual centres in our analysis, forecasting future numbers of cases of type 1 diabetes in children by extrapolating past trends in a single centre or country could be misleading, and a better general strategy may be to derive trend estimates from groups of geographically adjacent countries with broadly similar incidence rates when attempting such extrapolations [1].

Our findings suggest that important environmental risk exposures are changing over time in different ways across European countries. Further time trend analysis and comparison of the patterns in different regions within Europe are warranted.

Acknowledgements

The authors acknowledge the Austrian Diabetes Incidence Study Group, the Belgian Diabetes Registry (fellowship for I. Weets from the Belgian Fund for Scientific Research), the Danish Study Group of Diabetes in Childhood, the Baden-Württemberg Diabetes Incidence Registry (DIARY), the German Paediatric Surveillance Unit, Düsseldorf University and DPV Science Initiative, Ulm University, the German Competence Network Diabetes Mellitus (Federal Ministry of Education and Research, support codes 01GI0802, 01GI0859), the Saxonian Childhood Diabetes Register Group, the Hungarian Childhood Diabetes Epidemiology Group, the Norwegian Childhood Diabetes Study Group (supported by the South-Eastern Norway Regional Health Authority), the Catalan Epidemiology Type 1 Diabetes Study Group, the Swedish Childhood Diabetes Study Group (supported by the Swedish Research Council, Project number 07531), the Northern Ireland Childhood Diabetes Group, the Bart’s-Oxford Study Group (supported by Diabetes UK) and the Yorkshire Register of Diabetes in Children and Young People (supported by the UK Healthcare Quality Improvement Partnership). The collaboration was supported in part by European Community Concerted Action Program grants (BMH1-CT92-0043, BMH4-CT96-0577 and IC20-CT96-0070).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

CCP has coordinated the group since 2010, undertook the statistical analysis and wrote a first draft of the report. EG maintained contact with the study centres and assembled and validated the data for analysis. AG set up the collaboration and coordinated the group until 1998 and together with GGD established the registration methodology. GS coordinated the group from 1998 to 2009. Remaining authors established and/or maintained the registration process in the different centres, and were thereby responsible for data collection. They also validated the ascertainment level. All authors commented on a draft of the report and approved the final manuscript.

Supplementary material

125_2012_2571_MOESM1_ESM.pdf (167 kb)
ESM Table 1(PDF 166 kb)
125_2012_2571_MOESM2_ESM.pdf (180 kb)
ESM Table 2(PDF 179 kb)
125_2012_2571_MOESM3_ESM.pdf (155 kb)
ESM Statistical methods(PDF 155 kb)

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • C. C. Patterson
    • 1
  • E. Gyürüs
    • 2
  • J. Rosenbauer
    • 3
  • O. Cinek
    • 4
  • A. Neu
    • 5
  • E. Schober
    • 6
  • R. C. Parslow
    • 7
  • G. Joner
    • 8
  • J. Svensson
    • 9
  • C. Castell
    • 10
  • P. J. Bingley
    • 11
  • E. Schoenle
    • 12
  • P. Jarosz-Chobot
    • 13
  • B. Urbonaité
    • 14
  • U. Rothe
    • 15
  • C. Krzisnik
    • 16
  • C. Ionescu-Tirgoviste
    • 17
  • I. Weets
    • 18
  • M. Kocova
    • 19
  • G. Stipancic
    • 20
  • M. Samardzic
    • 21
  • C. E. de Beaufort
    • 22
  • A Green
    • 23
  • G. G. Dahlquist
    • 24
  • G. Soltész
    • 2
  1. 1.Centre for Public HealthQueen’s University BelfastBelfastUK
  2. 2.Department of PaediatricsPécs UniversityPécsHungary
  3. 3.Institute of Biometrics and Epidemiology, German Diabetes CentreLeibniz Institute for Diabetes Research at Heinrich Heine UniversityDüsseldorfGermany
  4. 4.Department of Pediatrics, Second Faculty of MedicineCharles University in Prague and University Hospital MotolPragueCzech Republic
  5. 5.University Children’s HospitalTübingenGermany
  6. 6.Department of Pediatric and Adolescent MedicineMedical University of ViennaViennaAustria
  7. 7.Leeds Institute of Genetics, Health and TherapeuticsUniversity of LeedsLeedsUK
  8. 8.Department of PediatricsUllevål University HospitalOsloNorway
  9. 9.Department of PaediatricsUniversity of CopenhagenHerlevDenmark
  10. 10.Department of HealthGovernment of CataloniaBarcelonaSpain
  11. 11.School of Clinical SciencesUniversity of BristolBristolUK
  12. 12.Department of Endocrinology and DiabetologyUniversity Children’s HospitalZurichSwitzerland
  13. 13.Department of Pediatrics, Endocrinology and DiabetesMedical University of SilesiaKatowicePoland
  14. 14.Institute of EndocrinologyLithuanian University of Health ScienceKaunusLithuania
  15. 15.Department for Epidemiology and Health Care ResearchTechnical University of DresdenDresdenGermany
  16. 16.Department of PediatricsUniversity Children’s HospitalLjubljanaSlovenia
  17. 17.Nutrition and Metabolic Diseases ClinicN. Paulescu Institute of Diabetes and Metabolic DiseasesBucharestRomania
  18. 18.Diabetes Research CenterBrussels Free UniversityBrusselsBelgium
  19. 19.Department of Endocrinology and GeneticsUniversity Children’s HospitalSkopjeMacedonia
  20. 20.Department of PaediatricsUniversity Hospital Sestre MilosrdniceZagrebCroatia
  21. 21.Department of Endocrinology and DiabetesUniversity Children’s HospitalPodgoricaMontenegro
  22. 22.Department of Paediatric Diabetes and EndocrinologyPaediatric ClinicLuxembourgLuxembourg
  23. 23.Odense Patient data Exploratory NetworkUniversity of Southern DenmarkOdenseDenmark
  24. 24.Department of Clinical ScienceUniversity of UmeåUmeåSweden

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