Trends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989–2013: a multicentre prospective registration study

Abstract

Aims/hypothesis:
Against a background of a near-universally increasing incidence of childhood type 1 diabetes, recent reports from some countries suggest a slowing in this increase. Occasional reports also describe cyclical variations in incidence, with periodicities of between 4 and 6 years.

Methods:
Age/sex-standardised incidence rates for the 0- to 14-year-old age group are reported for 26 European centres (representing 22 countries) that have registered newly diagnosed individuals in geographically defined regions for up to 25 years during the period 1989-2013. Poisson regression was used to estimate rates of increase and test for cyclical patterns. Joinpoint regression software was used to fit segmented log-linear relationships to incidence trends.

Results:
Significant increases in incidence were noted in all but two small centres, with a maximum rate of increase of 6.6% per annum in a Polish centre. Several centres in high-incidence countries showed reducing rates of increase in more recent years. Despite this, a pooled analysis across all centres revealed a 3.4% (95% CI 2.8%, 3.9%) per annum increase in incidence rate, although there was some suggestion of a reduced rate of increase in the 2004-2008 period. Rates of increase were similar in boys and girls in the 0- to 4-year-old age group (3.7% and 3.7% per annum, respectively) and in the 5- to 9-year-old age group (3.4% and 3.7% per annum, respectively), but were higher in boys than girls in the 10- to 14-year-old age group (3.3% and 2.6% per annum, respectively). Significant 4 year periodicity was detected in four centres, with three centres showing that the most recent peak in fitted rates occurred in 2012.

Conclusions/interpretation:
Despite reductions in the rate of increase in some high-risk countries, the pooled estimate across centres continues to show a 3.4% increase per annum in incidence rate, suggesting a doubling in incidence rate within approximately 20 years in Europe. Although four centres showed support for a cyclical pattern of incidence with a 4 year periodicity, no plausible explanation for this can be given.

Full text

ARTICLE
Trends and cyclical variation in the incidence of childhood type 1
diabetes in 26 European centres in the 25 year period 1989–2013:
a multicentre prospective registration study
Christopher C. Patterson
1
&Valma Harjutsalo
2
&Joachim Rosenbauer
3
&Andreas Neu
4
&Ondrej Cinek
5
&
Torild Skrivarhaug
6
&Birgit Rami-Merhar
7
&Gyula Soltesz
8
&Jannet Svensson
9
&Roger C. Parslow
10
&Conxa Castell
11
&
Eugen J. Schoenle
12
&Polly J. Bingley
13
&Gisela Dahlquist
14
&Przemysława K. Jarosz-Chobot
15
&
DalėMarčiulionytė
16,17
&Edna F. Roche
18
&Ulrike Rothe
19
&Natasa Bratina
20
&Constantin Ionescu-Tirgoviste
21
&
Ilse Weets
22
&Mirjana Kocova
23
&Valentino Cherubini
24
&Natasa Rojnic Putarek
25
&Carine E. deBeaufort
26
&
Mira Samardzic
27
&Anders Green
28
Received: 14 August 2018 /Accepted: 28 September 2018 /Pu blished online: 28 November 2018
#The Author(s) 2018
Abstract
Aims/hypothesis Against a background of a near-universally increasing incidence of childhood type 1 diabetes, recent reports
from some countries suggest a slowing in this increase. Occasional reports also describe cyclical variations in incidence, with
periodicities of between 4 and 6 years.
Methods Age/sex-standardised incidence rates for the 0- to 14-year-old age group are reported for 26 European centres
(representing 22 countries) that have registered newly diagnosed individuals in geographically defined regions for up to 25 years
during the period 1989–2013. Poisson regression was used to estimate rates of increase and test for cyclical patterns. Joinpoint
regression software was used to fit segmented log-linear relationships to incidence trends.
Results Significant increases in incidence were noted in all but two small centres, with a maximum rate of increase of 6.6% per
annum in a Polish centre. Several centres in high-incidence countries showed reducing rates of increase in more recent years.
Despite this, a pooled analysis across all centres revealed a 3.4% (95% CI 2.8%, 3.9%) per annum increase in incidence rate,
although there was some suggestion of a reduced rate of increase in the 2004–2008 period. Rates of increase were similar in boys
and girls in the 0- to 4-year-old age group (3.7% and 3.7% per annum, respectively) and in the 5- to 9-year-old age group (3.4%
and 3.7% per annum, respectively), but were higher in boys than girls in the 10- to 14-year-old age group (3.3% and 2.6% per
annum, respectively). Significant 4 year periodicity was detected in four centres, with three centres showing that the most recent
peak in fitted rates occurred in 2012.
Conclusions/interpretation Despite reductions in the rate of increase in some high-risk countries, the pooled estimate across
centres continues to show a 3.4% increase per annum in incidence rate, suggesting a doubling in incidence rate within approx-
imately 20 years in Europe. Although four centres showed support for a cyclical pattern of incidence with a 4 year periodicity, no
plausible explanation for this can be given.
Keywords Cyclical variation .Epidemiology .Incidence .Temporal change .Type 1 diabetes mellitus
Introduction
The increasing incidence of childhood type 1 diabetes has
been well documented both in Europe, with an estimated an-
nual increase of 3.9% (95% CI 3.6%, 4.2%) during the period
1989–2003 [1], and worldwide, with an estimated annual in-
crease of 2.8% (95% CI 2.4%, 3.2%) in the period 1990–1999
[2]. Recent reports have, however, suggested a slowing or
stabilisation in the rate of increase.
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00125-018-4763-3) contains peer-reviewed but
unedited supplementary material, which is available to authorised users.
*Christopher C. Patterson
c.patterson@qub.ac.uk
Extended author information available on the last page of the article
Diabetologia (2019) 62:408–417
https://doi.org/10.1007/s00125-018-4763-3
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In the USA, pooled data from five centres for children and
adolescents under 20 years of age indicated a 1.8% (95% CI
1.0%, 2.6%) annual increase during 2002–2012 after adjust-
ment for age, sex and race or ethnic group [3], and a similar
rate of increase of 1.3% (95% CI 0.0%, 2.5%) has been re-
ported for the Canadian province of British Columbia in the
period 2002–2013 [4]. In Australia, a non-significant annual
increase of 0.4% (95% CI −0.1%, 0.9%) was reported in the
under-15-year-old population during the period 2000–2011,
although a significant increase of 1.2% (95% CI 0.4%,
2.1%) was observed in the 10- to 14-year-old age group [5].
WithinEurope,noincreasewasfoundinSwedenduringthe
period 2005–2007 despite a prolonged period of uniform in-
crease during the previous 15 years [6]. Very similar levelling
incidence rates, beginning at about the same time and with lon-
ger periods of observation, were subsequently reported in two
other high-incidence Scandinavian countries, Finland [7]and
Norway [8]. In contrast, a report from Zhejiang province in the
low-incidence region of China described a very rapid 12.0%
(95% CI 7.6%, 16.6%) increase in annual incidence rate among
those aged under 20 years during the period 2007–2013 [9].
There have also been reports in the literature of a cyclical
variation in year-to-year incidence rates. The earliest report
was from the Yorkshire regional registry in England during
the period 1978–1990, which described a marked epidemic
pattern with peaks at 4 year intervals [10]. A subsequent brief
report from a neighbouring area of north-east England in the
period 1990–2007 described a 6 year cyclical pattern with an
amplitude of ±25% [11], but there is no established register in
the region and no support for the claim of high ascertainment.
A sinusoidal cyclical pattern with peaks observed every
5 years and an amplitude of ±14% has also been reported from
Western Australia for the period 1985–2010 [12], and was
subsequently replicated in an Australia-wide analysis during
the period 2000–2011 [5]. A report from five regions of
Poland during the period 1989–2012 using Fourier series
methods found a 5.33 year periodicity in rates, with an ampli-
tude of ±8% [13].
To help clarify the recent trends in European incidence
rates, an analysis of EURODIAB registry data from over
84,000 children in 26 European centres representing 22 coun-
tries is presented for the 25 year period 1989–2013, with sep-
arate estimates of incidence rate increases derived in each of
five subperiods. This dataset also provides an excellent oppor-
tunity to investigate the claims of cyclical variation in inci-
dence rates.
Methods
The establishment of the registries and case definition used
has previously been described [14]. Type 1 diabetes was de-
fined on the basis of a clinical diagnosis made by a physician,
omitting cases that were secondary to other conditions (e.g.
cystic fibrosis or high-dose corticosteroid treatment).
Registries attempt to capture prospectively all newly diag-
nosed individuals in a geographically defined region.
Primary and secondary sources of ascertainment were record-
ed for each child, and these were used to estimate complete-
ness by capture–recapture methodology. The completeness
Diabetologia (2019) 62:408–417 409
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findings for 1989–2008 have previously been reported as be-
ing considerably in excess of 90% in most of the registries (as
reported by ESM Table 2from the 20 year report) [15]. The
geographical coverage of the 26 registries is shown in Fig. 1
and represents 23% of the estimated European childhood pop-
ulation in 2011 (excluding Belarus, Ukraine and the Russian
Federation). Ethics approval was obtained by individual cen-
tres where required.
Incidence rates were obtained by dividing the numbers of
registered children by annual population estimates.
Standardisation of rates was obtained by the direct method
with a standard population comprising equal numbers in each
of six subgroups defined by age group (0–4years,5–9years
and 10–14 years) and sex. Standard errors for the directly
standardised rates were also calculated [16]. Trends in annual
incidence rates in each country were investigated in the 25 year
period using Poisson regression incorporating an adjustment
for age group and sex. Comparisons of trends between age
groups and sexes were obtained within each country by incor-
porating interactions into the Poisson regression model. The
Joinpoint regression analysis program version 4.2 (Statistical
Methodology and Applications Branch and Data Modeling
Branch, National Cancer Institute, Bethesda, MD, USA) was
used to fit segmented regression lines to the logarithmically
transformed directly standardised incidence rates, taking ac-
count of their standard errors. Pooled estimates of rates of
increase across all 26 centres were obtained using a mixed
effects Poisson regression model with centre treated as a ran-
dom effect and age and sex as fixed effects.
Motivated by reports in the literature of 4, 5 and 6 year
cycles in incidence rate, sine and cosine terms representing
such cycles were added to Poisson regression models for an-
nual age-/sex-specific incidence rates, along with terms for
age group and sex as well as the segmented log-linear trends
with year as identified by the Joinpoint analysis. The sine and
cosine terms are similar to those described for the study of
seasonal variation in month-to-month counts [17] but were
adapted for the detection of cyclical variation in yearly rates.
Statistical analyses were performed in SPSS version 24
(IBM Corp, Armonk, NY, USA) and Stata release 14
(StataCorp, College Station, TX, USA). Unless otherwise stat-
ed, hypothesis testing was performed at the 5% significance
level (p<0.05).
Results
Ascertainment rates remained in excess of 90% for most cen-
tres, although data were not available for all of these (see
electronic supplementary material [ESM] Table 1). Table 1
shows the total numbers of children registered during the
25 year period 1989–2013 in each of the 26 centres, and the
age- and sex-standardised incidence rates (with standard er-
rors) in the 5 year subperiods 1989–1993, 1994–1998, 1999–
Spain–Catalonia
FYR of Macedonia
Montenegro
UK–Northern
Ireland
Ireland
UK–Yorkshire
UK–Oxford
Norway
Sweden–Stockholm
County
Lithuania
Germany–Saxony
Belgium–Antwerp
Luxembourg
Germany–Baden-
Württemberg
Germany–North Rhine-
Westphalia
Switzerland Austria
Slovenia
Czechia
Denmark
Croatia–Zagreb
Poland–Katowice
Romania–Bucharest
Hungary–except Budapes
t
Finland
Italy–Marche
Fig. 1 Map of 26 participating EURODIAB centres (whole nations unless a region is specified). Administrative boundaries: ©EuroGeographics 2018;
adapted with permission. FYR, Former Yugoslav Republic
410 Diabetologia (2019) 62:408–417
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2003, 2004–2008 and 2009–2013. The age- and sex-specific
incidence rates for each period used in the calculations are
shown in ESM Table 2.
As illustrated in Fig. 2, the trends in age-standardised rates
differed little between the sexes. Two of the centres (Denmark
and Germany–North Rhine-Westphalia) expanded their geo-
graphical coverage in 1999, and the lines for these centres are
therefore shown with a break at that point, although in both
cases the degree of any discontinuity appears to be minimal. In
a preliminary analysis, incidence rate increases wereestimated
using Poisson regression analysis assuming a constant rate of
increase throughout the period.
Figure 3shows that the rate of increase was highest in the
Poland–Katowice centre (6.6% per annum) and the lowest in the
Spain–Catalonia centre (0.5% per annum). Except for the
Ireland and Italy–Marche centres, all rates of increase were sig-
nificantly greater than zero. A significant inverse relationship
was found between the rate of increase in each centre and its
directly standardised rate during the entire period (Spearman’s
rank correlation coefficient, r
s
=−0.45, p= 0.02), indicating that
the percentage increase in rate tended to be lower in centres with
higher rates. A comparison of rates of increase between the
sexes within each centre revealed differences in three centres,
each showing a significantly higher rate of increase in boys than
girls. A comparison of rates of increase between age groups
within each centre revealed differences in nine centres, and in
six of the nine the highest rate of increase was found in the 0- to
4-year-old age group. Full details are available in ESM Table 3.
Table 1 Incidence rates per 100,000 person-years (with standard errors) standardised for age group and sex in 5 year periods for 26 EURODIAB
centres
Centre Period
Years Cases 1989–1993 1994–1998 1999–2003 2004–2008 2009–2013
Rate SE Rate SE Rate SE Rate SE Rate SE
Austria–whole nation 1989–2013 4594 9.0 0.4 9.8 0.4 13.2 0.4 17.3 0.5 19.7 0.6
Belgium–Antwerp 1989–2013 610 10.9 1.2 13.0 1.3 16.4 1.4 16.3 1.4 18.1 1.5
Croatia–Zagreb 1995–2013 364 –11.1 1.5 14.8 1.6 19.4 1.8 18.8 1.8
Czechia–whole nation 1989–2013 6528 8.5 0.3 11.5 0.4 17.1 0.5 19.3 0.5 21.8 0.5
Denmark–whole nation
a
1989–2013 4148 17.0 1.2 16.3 1.2 22.5 0.7 25.2 0.7 27.0 0.7
Finland–whole nation 1989–2011 11,132 38.2 0.9 46.7 1.0 54.5 1.1 63.3 1.2 60.9 1.5
Germany–Baden-Württemberg 1989–2013 6931 11.0 0.4 13.0 0.4 15.4 0.4 22.0 0.5 23.1 0.6
Germany–North Rhine-Westphalia
b
1989–2013 10,202 13.3 0.8 16.2 0.9 20.9 0.4 24.0 0.4 26.9 0.5
Germany–Saxony 1998–2013 1463 –11.6 1.5 15.6 0.8 20.1 1.0 22.8 1.0
Hungary–except Budapest 1989–2013 4283 9.0 0.3 10.7 0.4 12.4 0.4 18.3 0.6 20.0 0.6
Ireland–whole nation 2008–2013 1566 –––27.5 1.7 27.5 0.8
Italy–Marche 1989–2013 579 9.6 1.0 11.9 1.1 12.6 1.2 12.6 1.1 11.6 1.1
Lithuania–whole nation 1989–2013 1862 7.3 0.4 8.2 0.5 10.3 0.6 14.2 0.7 19.9 0.9
Luxembourg–whole nation 1989–2013 312 11.4 1.9 12.3 1.8 15.7 2.0 18.4 2.1 18.6 2.0
Macedonia (FYR)–whole nation 1989–2013 584 3.2 0.4 3.9 0.4 6.0 0.5 7.2 0.6 7.7 0.7
Montenegro–whole nation 1996–2013 362 –10.1 1.5 14.7 1.5 17.4 1.7 18.5 1.8
Norway–whole nation 1989–2013 6348 22.7 0.8 24.6 0.8 29.6 0.8 33.1 0.9 33.6 0.9
Poland–Katowice 1989–2013 2360 5.2 0.3 7.9 0.4 13.0 0.6 16.5 0.7 20.1 0.8
Romania–Bucharest 1989–2013 714 4.7 0.5 5.6 0.5 9.7 0.8 12.3 1.0 13.4 1.0
Slovenia–whole nation 1989–2013 949 7.8 0.6 9.1 0.7 11.1 0.8 14.9 1.0 16.3 1.1
Spain–Catalonia 1989–2013 3447 12.4 0.5 13.6 0.5 13.1 0.5 12.1 0.5 16.1 0.5
Sweden–Stockholm County 1989–2013 2704 25.8 1.3 25.6 1.3 34.5 1.4 35.1 1.4 39.6 1.5
Switzerland–whole nation 1991–2013 3020 7.9 0.5 8.3 0.4 11.0 0.4 13.1 0.5 13.4 0.5
UK–Northern Ireland 1989–2013 2652 20.0 1.0 24.7 1.1 29.9 1.3 34.2 1.4 34.4 1.4
UK–Oxford 1989–2013 2920 17.2 0.8 21.7 0.9 24.0 1.0 25.2 1.0 22.8 0.9
UK–Yor k s h i r e 1 9 8 9–2011 3660 16.1 0.7 19.7 0.7 23.5 0.8 25.5 0.9 31.0 1.2
a
Four counties only for 1989–1998
b
Düsseldorf region only for 1989–1998
FYR, Former Yugoslav Republic
Diabetologia (2019) 62:408–417 411
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Mixed effects Poisson regression provided estimated rates
of increase in the pooled data from the 26 centres, as shown in
Tab le 2. Overall, the annual rate of increase was estimated to
be 3.4% (95% CI 2.8%, 3.9%). Rates of increase were similar
in boys and girls in the 0- to 4-year-old age group (3.7% and
3.7% per annum, respectively) and in the 5- to 9-year-old age
group (3.4% and 3.7% per annum, respectively), but were
higher in boys than girls in the 10- to 14-year-old age group
(3.3% and 2.6% per annum, respectively). The estimates of
overall rate of increase by period suggested a slowing in
2004–2008, but the rate of increase appeared to have almost
returned to previous levels in the 2009–2013 period.
6.6% (6.0%, 7.2%)
5.6% (4.6%, 6.7%)
5.5% (4.8%, 6.2%)
4.8% (3.6%, 6.0%)
4.7% (4.3%, 5.0%)
4.4% (4.0%, 4.8%)
4.4% (3.2%, 5.5%)
4.3% (3.9%, 4.7%)
4.2% (2.1%, 6.3%)
4.1% (3.8%, 4.5%)
3.9% (3.0%, 4.8%)
3.9% (1.9%, 5.8%)
3.2% (2.7%, 3.7%)
3.2% (2.6%, 3.7%)
2.8% (2.3%, 3.4%)
2.7% (1.1%, 4.4%)
2.7% (2.4%, 3.0%)
2.6% (2.1%, 3.0%)
2.3% (1.2%, 3.5%)
2.3% (1.8%, 2.9%)
2.1% (1.7%, 2.4%)
1.8% (-1.1%, 4.8%)
1.6% (0.8%, 2.3%)
1.2% (0.7%, 1.7%)
0.8% (-0.3%, 2.0%)
0.5% (0.0%, 0.9%)
3.4% (2.8%. 3.9%)
Estimate (95% CI)
Poland
−
Katowice
Romania
−
Bucharest
Lithuania
Macedonia (FYR)
Czechia
Austr ia
Germany
−
Saxony
Hungary
−
except Budapest
Montenegro
Germany
−
Baden Württemberg
Slovenia
Croatia
−
Zagreb
UK
−
Yorkshir e
Switzerland
UK
−
Northern Ireland
Luxembourg
Finland
Germany
−
N. Rhine-Westphalia
Belgium
−
Antwerp
Sweden
−
Stockholm County
Norway
Ireland
Denmark
UK
−
Oxfor d
Italy
−
Marche
Spain
−
Catalonia
Overall pooled (random effe cts)
-20 2 4 6 8
Rate of increase (%)
Fig. 3 Estimated rates of annual
increase in type 1 diabetes in 26
European centres. Rates of
increase in individual centres
were derived from Poisson
regression analyses with
adjustment for age, sex, age × sex
interaction and inclusion of a log-
linear term for year in the model.
The overall pooled estimate was
derived from a Poisson regression
with centre as a random effect.
FYR, Former Yugoslav Republic;
N., North
Incidence rate (per 100,000)
100
25
5
1
100
25
5
1
100
25
5
1
100
25
5
1
100
25
5
1
Austria Belgium (Antwerp) Croatia (Zagreb) Czechia Denmark Finland
Germany
(Baden Württemberg)
Germany (North-
Rhine Westphalia) Germany (Saxony)
Hungary
(except Budapest) Ireland Italy (Marche)
Lithuania
(Northern Ireland)
Poland (Katowice)
Norway
Montenegro
Switzerland
(Stockholm County)
Macedonia (FYR)
Spain (Catalonia)
Slovenia
Luxembourg
(Bucharest)
(Yorkshire)
(Oxford)
UK
UK UK
Romania Sweden
1989
1993
1997
2001
2005
2009
2013
1989
1993
1997
2001
2005
2009
1989
1993
1997
2001
2005
2009
2013
1989
1993
1997
2001
2005
2009
2013
1989
1993
1997
2001
2005
2009
2013
1989
1993
1997
2001
2005
2009
2013
2013
Fig. 2 Trends in age-standardised incidence rates, plotted on a logarith-
mic scale, by sex for type 1 diabetes in 26 European centres during 1989–
2013. Blue lines, boys; red lines, girls. Breaks are shown for Denmark
and Germany (North Rhine-Westphalia) between 1998 and 1999 because
of increased coverage of these registers, but any discontinuities appear to
be very minor. Macedonia (FYR), Former Yugoslav Republic of
Macedonia
412 Diabetologia (2019) 62:408–417
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The fitted Joinpoint segmented regression analyses for each
centre are presented in ESM Fig. 1. The best fit for 18 of the 26
centres throughout the period was a log-linear increase in the age-
standardised rate. Six centres showed more rapid increases in an
early period followed by lower rates of increase in a later period.
In two Central European centres (Czechia and Poland–
Katowice), the change took place in 2002, at roughly the same
time as in two UK centres (UK–Oxford in 2000 and UK–
Northern Ireland in 2003). In two Scandinavian centres
(Finland and Norway), the levelling off took place a little later,
in the years 2005 and 2007, respectively. Only in a single centre
(Lithuania) was an initially low rate of increase followed by a
period after 1996 with a higher rate of increase. The final centre
(Germany–Baden-Württemberg) showed a more complex pat-
tern, with steady rates of increase in the early and late part of
the 25 year period separated by a short period of more rapid
increase in 2001–2004.
Poisson regression results provided most support for a 4 year
periodicity, with four centres giving likelihood ratio tests that
attained significance at the reduced p<0.01level(toallowfor
multiple testing) compared with none for a 5 year periodicity and
two for a 6 year periodicity (tests of significance summarised in
ESM Table 4). Plots of the observed age-standardised annual
incidence rates and the fitted rates for 4 year cycles are shown
in Fig. 4for these four centres. One of the four centres showed its
most recent peak in fitted incidence rate in 2011 (Switzerland,
with an amplitude of ±10% superimposed on the log-linear in-
creasing trend), while the three remaining centres showed the
most recent peaks in 2012 (Germany–North Rhine-Westphalia
with an amplitude of ±5%, Germany–Saxony with an amplitude
of ±15% and UK–Oxford with an amplitude of ±9%).
Discussion
Our analyses of individual centre results confirmed the recent
slowing of incidence rate increases in some high-incidence
Standardised incidence rate
(per 100,000 person-years)
Standardised incidence rate
(per 100,000 person-years)
Standardised incidence rate
(per 100,000 person-years)
Standardised incidence rate
(per 100,000 person-years)
Yea r
Year
Yea r
Year
b
a
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
cd
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Fig. 4 Observed (continuous blue
line) and fitted (red dashed line)
standardised incidence rates (per
100,000 person-years) obtained
by Poisson regression in four
centres that showed significant
(p< 0.01) 4 year periodicity when
superimposed on long-term
Joinpoint segmented regression
trends: (a) Germany–North
Rhine-Westphalia;
(b)Germany–Saxony; (c)
Switzerland; (d)UK–Oxford
Table 2 Annual increases in incidence rate over a 25 year period pooled
over centres as estimated by mixed effects Poisson regression in sub-
groups defined by age group, sex and time period
Subgroup Annual increase (%) (95% CI)
Boys
0–4 years old 3.7 (3.0, 4.4)
5–9 years old 3.4 (2.7, 4.0)
10–14 years old 3.3 (2.8, 3.8)
0–14 years old 3.5 (2.9, 4.0)
Girls
0–4 years old 3.7 (2.9, 4.5)
5–9 years old 3.7 (3.2, 4.2)
10–14 years old 2.6 (2.2, 3.0)
0–14 years old 3.3 (2.8, 3.8)
Period
1989–1993 4.1 (2.7, 5.6)
1994–1998 3.2 (1.9, 4.5)
1999–2003 4.1 (3.1, 5.2)
2004–2008 1.1 (0.2, 2.1)
2009–2013 2.7 (1.7, 3.7)
1989–2013
a
3.4 (2.8, 3.9)
a
Overall time period
Diabetologia (2019) 62:408–417 413
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areas such as Finland [7] and Norway [8], but using only data
from Stockholm County we were unable to detect the same
pattern that had previously been reported from Sweden [6].
Two of the three centres from the UK, another country with
high rates, also showed reducing rates of increase, although
these seemed to have begun a few years earlier than in
Scandinavia.
Our pooled estimates suggest that, despite some high-risk
countries showing some slowing in the rate of increase in
recent years, the overall pattern is still one of an approximately
3% per annum increase, although with a possible temporary
slowing in the 2004–2008 period. As previously noted in our
15 year analysis, the rate of increase in girls aged 10–14 years
is less marked than in other age/sex subgroups [1].
Our analysis shows that, in the majority of centres, a steady
log-linear increase in rates with time provided a good descrip-
tion of the temporal changes, with only a few (mainly high-
incidence) areas showing some evidence of non-uniformity.
The cyclical pattern in incidence observed in four of our 26
centres is consistent with the earliest report of a 4 year cyclical
incidence pattern [10], but subsequent reports have described
5 year or 6 year periodicities [11–13], for which we found
little support in our data.
No clear rationale for periodicity has yet been proposed
and, to the authors’knowledge, no climatological factor
[18], viral infection [19] or other environmental exposure
has yet been firmly established that exhibits such a cyclical
pattern. Since autoimmunity and progressive beta cell destruc-
tion typically start long before the clinical diagnosis of type 1
diabetes, the periodicity in diagnosis could be indicative of
cycles of infectious disease that accelerate the diagnosis rather
than initiate the disease. Regular cycles of infectious diseases
are well known from classic work done before population-
wide vaccination for measles, an extremely contagious viral
disease of childhood; this research showed that, in an other-
wise stable population, epidemic cyclicity depends on com-
munity size [20].
It is also unclear why only a small proportion of the 26
centres showed this periodicity and, although we acknowl-
edge that power may be limited in smaller centres, it was not
apparent in many of the largest centres that might be expected
to have had a high power to detect it. This could perhaps
suggest that it may have more localised origins. What deter-
mines this localisation remains enigmatic, as cyclical patterns
were absent in Austria, Czechia and Germany–Baden-
Württemberg, three large registers each with neighbouring
areas where pronounced cyclical patterns were noted. It is
possible that not only the size of the population, but also its
spatial structure (i.e. the size of the communities, and their
mutual links) may play an important role in the ability of the
hypothetical infectious accelerator to be transmitted [21].
To our knowledge, among autoimmune conditions, only
incidence cycles in juvenile idiopathic arthritis have been
correlated to cycles of serologically confirmed microbial
agents—in a Canadian study, peak incidences of arthritis were
concurrent with peaks of Mycoplasma pneumoniae,whereas
no such phenomenon was noted for the incidence of seroneg-
ative (i.e. non-immune mediated) spondyloarthropathies [22].
The recent reportof a twofoldrisk of type 1 diabetesdiagnosed
by the age of 30 years among those with laboratory-confirmed
pandemic influenza A (H1N1) [23] may stimulate interest in
less consistent patterns of incidence peaks in type 1 diabetes
since localised seasonal influenza epidemics (as opposed to
much rarer pandemics) can occur at irregular intervals [24].
Most of the participating registers have maintained their
completeness of coverage at levels in excess of 90% in the
most recent 5 year period, but these estimates of completeness
rely on an assumption of independence in the primary and
secondary sources that is very difficult to verify. As more
sophisticated information systems for drug prescribing and
clinical management become available, it seems likely that
the traditional approach based on notification of individual
new diagnoses will give way to more automated approaches
that take advantage of these information systems.
Although it could be argued that the diagnosis of type 1
diabetes should ideally be confirmed by the presence of one
or more specific autoimmune markers [25], this is seldom done
in clinical practice, and we have therefore continued to use a
pragmatic definition of type 1 diabetes based on clinical judge-
ment. A UK study found that all but 8 (3%) of 256 clinically
diagnosed cases of type 1 diabetes in individuals aged 20 years
or younger were positive for one or more of four antibodies
[26], but the case for routine antibody testing at diagnosis is not
compelling [27]. Individuals diagnosed before 6 months of age
nowtendtoberoutinelyinvestigated for monogenic forms of
the disease [28], but the number of such cases is very small.
Findings in the literature on whether or not type 2 diabetes is
becoming more common in children and adolescents are incon-
sistent [29–31], but the distinction between the two types of
diabetes is generally not difficult in the paediatric age group.
Furthermore, European studies [30–33]confirmthattherateof
type 2 diabetes is a small fraction of that of type 1 diabetes, and
we do not therefore feel that misclassification of type 2 diabetes
represents a serious challenge to the validity of our findings.
The use of mixed effects Poisson regression, in which age
group and sex are considered as fixed effects but centre is
treated as a random effect, gives similar estimates of the in-
crease in incidence rate to the more conventional fixed effects
analysis that we have used in previous analyses; however,
confidence limits for the mixed effects model tend to be rather
wider and should give a fairer reflection of uncertainty in the
estimates of incidence rate increase. Taking into account the
uncertainty associated with our overall incidence rate increase
of 3.4% (95% CI 2.8%, 3.9%), we may expect to see a dou-
bling in European incidence in between 18 and 25 years if the
trends evident in the last 25 years are maintained.
414 Diabetologia (2019) 62:408–417
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

The steadily increasing number of children being diag-
nosed with this chronic disease, which is associated with
well-documented, life-long increases in morbidity and mortal-
ity, has important implications for those planning and deliver-
ing healthcare. The limited success in identifying either envi-
ronmental causes or gene–environment interactions that could
eventually lead to disease prevention means that efforts must
continue to improve quality of care to help reduce long-term
complications and diabetes-related deaths. Key to this is the
improvement in glycaemic control that will be achieved not
only by more sophisticated methods of insulin delivery, but
also by an increased investment in services to support well-
trained and dedicated care teams in sufficient numbers to meet
the growing needs of this group of children and their families.
The EURODIAB childhood type 1 diabetes registers, with
their wide, population-based coverage of European regions of
differing incidence, and their high levels of case ascertain-
ment, will continue to provide a valuable source of data for
monitoring the future incidence of childhood type 1 diabetes.
Acknowledgements The authors acknowledge the Austrian Diabetes
Incidence Study Group, the Belgian Diabetes Registry, the Czech
Childhood Diabetes Register and the Czech Association of Parents and
Friends of Diabetic Children, the Danish Study Group of Diabetes in
Childhood and contributors to DanDiabKids, the Chronic Disease
Prevention Unit of the Finnish National Institute for Health and
Welfare, the Baden-Württemberg Diabetes Incidence Registry
(DIARY), the German Paediatric Surveillance Unit, DPV Science
Initiative, Ulm University, the German Competence Network Diabetes
Mellitus and the German Center for Diabetes Research (DZD), the
Saxonian Childhood Diabetes Register Group, the Hungarian
Childhood Diabetes Epidemiology Group, the Irish Childhood Diabetes
National Register, the Registry for Incidence of Diabetes in Italy (RIDI),
the Lithuanian Childhood Diabetes Registry Study Group, the Norwegian
Childhood Diabetes Study Group, the Catalan Epidemiology Type 1
Diabetes Study Group, the Swedish Childhood Diabetes Study Group,
the Managed Clinical Network for Paediatric Diabetes in Northern
Ireland, the Bart’s–Oxford Study Group and the Yorkshire Register of
Diabetes in Children and Young People.
Data availability Much of the data generated or analysed during this
study are included in this article and its accompanying electronic supple-
mentary material (ESM) files. Requests for further data should be sent to
the corresponding author.
Funding The following centres received support as listed: Austria centre
from Sanofi and Novo-Nordisk, Czechia centre from the Czech Ministry of
Health (grant 00064203), Germany–North Rhine-Westphalia centre from
the German Federal Ministry of Education and Research, Ireland centre
from the National Children’s Hospital Foundation, Norway centre from
the South-Eastern Norway Regional Health Authority, Poland–Katowice
centre from the Medical University of Silesia (grants SUM KNW-1-083/K/
6/K and SUM KNW-1-063/N/7/K, Sweden–Stockholm centre from the
Swedish Research Council (project number 07531), UK–Oxford centre
from Diabetes UK, UK–Yorkshire centre from the UK Department of
Health and the Yorkshire and Humber Children and Young People’s
Diabetes Network. IW was in receipt of a fellowship from the Belgian
Fund for Scientific Research. Funding to establish the collaboration was
supplied 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 AG set up the collaboration and coordinated the
group until 1998 and together with GD established the registration meth-
odology. GS coordinated the group from 1998 to 2009. CCP has coordinated
the group since 2010, undertook the statistical analysis and wrote a first draft
of the report. JR also contributed to the statistical analysis. The remaining
authors established and/or maintained the registration process in the different
centres and validated the ascertainment level. All authors commented on a
draft of the report and approved the final manuscript. CCP is the guarantor of
this work.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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Affiliations
Christopher C. Patterson
1
&Valma Harjutsalo
2
&Joachim Rosenbauer
3
&Andreas Neu
4
&Ondrej Cinek
5
&
Torild Skrivarhaug
6
&Birgit Rami-Merhar
7
&Gyula Soltesz
8
&Jannet Svensson
9
&Roger C. Parslow
10
&Conxa Castell
11
&
Eugen J. Schoenle
12
&Polly J. Bingley
13
&Gisela Dahlquist
14
&Przemysława K. Jarosz-Chobot
15
&
DalėMarčiulionytė
16,17
&Edna F. Roche
18
&Ulrike Rothe
19
&Natasa Bratina
20
&Constantin Ionescu-Tirgoviste
21
&
Ilse Weets
22
&Mirjana Kocova
23
&Valentino Cherubini
24
&Natasa Rojnic Putarek
25
&Carine E. deBeaufort
26
&
Mira Samardzic
27
&Anders Green
28
1
Centre for Public Health and UKCRC Centre of Excellence for
Public Health Northern Ireland, Queen’s University Belfast,
Grosvenor Road, Belfast BT12 6BJ, UK
2
Folkhälsan Institute of Genetics, Folkhälsan Research Center,
Helsinki, Finland
3
German Diabetes Center, Institute of Biometrics and Epidemiology,
Leibniz Center for Diabetes Research at Heinrich Heine University
Düsseldorf, Düsseldorf, Germany
4
University Children’s Hospital, Tübingen, Germany
416 Diabetologia (2019) 62:408–417
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

5
Department of Pediatrics, Second Faculty of Medicine, Charles
University and University Hospital Motol, Prague, Czechia
6
Division of Adolescent and Paediatric Medicine, Institute of Clinical
Medicine, University of Oslo, Oslo University Hospital,
Oslo, Norway
7
Department of Pediatric and Adolescent Medicine, Medical
University of Vienna, Vienna, Austria
8
Department of Paediatrics, Medical School, University of Pécs,
Pécs, Hungary
9
Department of Paediatrics and Adolescent Medicine, Copenhagen
University Hospital, Herlev and Gentofte, Denmark
10
Leeds Institute of Cardiovascular and Metabolic Medicine,
University of Leeds, Leeds, UK
11
Department of Health, Government of Catalonia, Barcelona, Spain
12
Department of Endocrinology and Diabetology, University
Children’s Hospital, Zurich, Switzerland
13
Diabetes and Metabolism, Bristol Medical School, University of
Bristol, Bristol, UK
14
Department of Clinical Sciences, Paediatrics, University of Umeå,
Umeå, Sweden
15
Department of Children’s Diabetology, Medical University of
Silesia, Katowice, Poland
16
Institute of Endocrinology, Lithuanian University of Health
Sciences, Kaunas, Lithuania
17
Institute of Microbiology and Virology, Lithuanian University of
Health Sciences, Kaunas, Lithuania
18
Department of Paediatrics, Tallaght University Hospital, Trinity
College Dublin, The University of Dublin, Dublin, Ireland
19
Health Sciences/Public Health, Faculty of Medicine Carl Gustav
Carus, Technische Universität Dresden, Dresden, Germany
20
Diabetes and Metabolic Diseases, University Children’sHospital,
Department of Endocrinology, Ljubljana, Slovenia
21
National Institute of Diabetes Nutrition and Metabolic Diseases, NC
Paulescu, Bucharest, Romania
22
Diabetes Research Center and Laboratory of Clinical Biology,
Brussels Free University–Vrije Universiteit Brussel, University
Hospital Brussels–Universitair Ziekenhuis Brussel,
Brussels, Belgium
23
Department of Endocrinology and Genetics, University Children’s
Hospital, Skopje, Republic of Macedonia
24
Division of Paediatric Diabetes, Azienda University Hospital,
Ancona, Italy
25
Department of Pediatric Endocrinology and Diabetes, University
Hospital Zagreb, Zagreb, Croatia
26
Department of Paediatric Diabetes and Endocrinology, University
of Luxembourg, Luxembourg, Luxembourg
27
Department of Endocrinology, Institute for Sick Children,
Podgorica, Montenegro
28
Odense Patient data Exploratory Network (OPEN), Odense
University Hospital/Department of Clinical Research, University of
Southern Denmark, Odense, Denmark
Diabetologia (2019) 62:408–417 417
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
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