Download Seasonally in the Clinical Onset of Insulin

American Journal of Epidemiology
Copynght © 1996 by The Johns Hopkins University School of Hygiene and Public Health
All rights reserved
Vol. 143, No. 2
Printed in U.S.A.
Seasonally in the Clinical Onset of Insulin-dependent Diabetes Mellitus in
Finnish Children
Marjatta Karvonen,1 Jaakko Tuomilehto,1 Esa Virtala,1 Janne Pitkaniemi,1 Antti Reunanen,1
Eva Tuomilehto-Wolf,1 and Hans K. Akerblom2 for the Childhood Diabetes in Finland (DiMe) Study Group
Seasonal patterns in the incidence of insulin-dependent diabetes based on 2,062 cases diagnosed at age
14 years or under in Finland are described for the years 1987-1992. Seasonal patterns were estimated
presenting the data as short Fourier series up to three harmonics together with a possible linear trend. This
method allows an arbitrary shape for the seasonal effect. Likelihood ratio tests and Akaike's information
criterion were used to determine the number of harmonics necessary to model the seasonal pattern and to test
differences among age- and sex-specific subgroups in the population. Seasonal patterns in incidence were
compared between sexes and between the three 5-year age groups with each controlling for the other's effect.
A significant seasonal pattern in the incidence of insulin-dependent diabetes was found for the sexes
combined and for two age groups (0-9 and 10-14 years). A statistically significant seasonal pattern could be
confirmed for males, but not for females. During a calendar year, one cycle with a decreased incidence of
insulin-dependent diabetes in June was found among younger boys. Among older boys, there were two
distinct cycles with a decreased incidence, the first in June and the second during November-December. The
most visible seasonal pattern was a lower number of cases diagnosed in June, while during the rest of the year
the incidence remained relatively stable and high. The average annual incidence was 35.6 per 100,000 persons
without any upward peaks. Am J Epidemiol 1996;143:167-76.
diabetes mellitus, insulin-dependent; incidence data; seasons
Seasonal variation in incidence of insulin-dependent
diabetes mellitus in children was reported in the 1920s
when higher rates of "acute diabetes" were found
during the late autumn, winter, and early spring (1).
Peaks in incidence, with one peak in winter months
and the other during the late summer, were detected in
northern Sweden among children aged 0-14 years
registered from 1938 to 1977 (2).
Several other epidemiologic studies have described
seasonal patterns in the onset (or, better, at diagnosis)
of new cases of insulin-dependent diabetes in children.
Most studies have reported higher occurrence of insulin-dependent diabetes during the cold autumn and
winter months than during the wanner spring and
summer months (3-22), but these findings are difficult
to compare because of differences in methodology.
Seasonal variation in the diagnosis of insulin-dependent diabetes has been considered as an indirect evidence for environmental exposure in the development
of insulin-dependent diabetes. Recent studies have
provided more indirect evidence for an association
between viral infections and the pathogenesis of insulin-dependent diabetes, but the final evidence for viruses causing insulin-dependent diabetes is still missing (23). Some communicable diseases occur more
frequently during the cold winter months in areas
where the climate changes during the year. Therefore,
infectious diseases could play a role, at least as a
triggering factor in the onset of clinical symptoms of
insulin-dependent diabetes.
Some limited data about the seasonality of the incidence of insulin-dependent diabetes exist in Finland,
where the incidence of insulin-dependent diabetes in
children is the highest in the world and has risen
steeply over the last 4 decades (24). During the 1950s,
the onset of insulin-dependent diabetes seemed to be
distributed rather evenly in time, although the number
of incident cases was highest in September (25). Over
the period from 1968 to 1979, a seasonal variation in
incidence of insulin-dependent diabetes (defined as
Received for publication December 9, 1994, and in final form
October 30, 1995.
Abbreviations: AIC, Akaike's information criterion; IDDM, insulindependent diabetes mellitus.
1
National Public Health Institute, Department of Epidemiology
and Health Promotion, Helsinki, Finland.
2
Children's Hospital, II Department of Pediatrics, Stenbackinkatu 11, FIN-00290 Helsinki, Finland.
Reprint requests to Dr. Marjatta Karvonen, National Public Health
Institute, Department of Epidemiology and Health Promotion, Diabetes and Genetic Epidemiology Unit, Mannerheimintie 166, FIN00300 Helsinki, Finland.
167
168
Karvonen et al.
onset of first symptoms) was reported in children in
northern Finland aged 0-14 years, with peaks in incidence in April and September (26). The aim of this
paper was to analyze seasonal patterns in incidence of
insulin-dependent diabetes in children in Finland during the most recent years.
MATERIALS AND METHODS
Since 1987, all hospitals in Finland treating diabetic
children have participated in the prospective nationwide registration of childhood insulin-dependent diabetes. Details of the procedures in case ascertainment
have previously been described elsewhere (27). Currently, the collection of incidence data continues as a
part of the World Health Organization DIAMOND
project (DIAbetes MONDiale) (28). In Finland, all
children with insulin-dependent diabetes are treated in
a hospital at the time of diagnosis (27). The diabetes
nurses in the pediatric wards of the hospitals record
necessary information on standardized forms and send
them to the National Public Health Institute in Helsinki. It has been shown that the case ascertainment is
virtually 100 percent complete (27). During 1987—
1992, 2,062 children aged 14 years or under were
diagnosed with insulin-dependent diabetes.
Population data were obtained from the National
Population Registry, which is updated continuously.
The Finnish population aged 14 years or under varied
from 952,943 to 968,280 during 1987-1992.
Statistical methods
The average annual incidence rates were calculated
per 100,000 population per year. The midyear populations aged 14 years or under were used as the denominator. The 95 percent confidence intervals were
estimated assuming the Poisson distribution of the
cases. Age adjustment of the rates was performed
using year intervals (0-4, 5-9, and 10-14 years) with
the proportions 1/3, 1/3, and 1/3, respectively, as the
standard according to the previous approach by the
Diabetes Epidemiology Research International Group
(29). The average annual incidence of insulin-dependent diabetes was 35.6 per 100,000 population per
year (95 percent confidence interval 34.1-37.2) in
Finland during 1987-1992. Seasonal patterns in incidence of insulin-dependent diabetes were evaluated by
using the method described by Jones et al. (30). The
method fits sine waves to the incidence data with a
fundamental period of one cycle per year, and when
necessary higher harmonics can be used. It also allows
for different lengths of time intervals and different
sizes of populations at risk.The incidence model for
group i at time t can be written as
A,(r) = a,
a,h cos
1
2TTht\
+ /3asin
where
a, = a parameter that allows the size of each group
to be arbitrary;
«i* = cii,cos((j)ih) and j3/A = -c,7lsin(^,7l);
a, = a parameter that allows the size of each group
to be arbitrary;
b; = (t - 772), the time trend;
clh = the amplitude of the cyclic curve;
$ih = the phase angle;
P = the fundamental period of the seasonal model;
h = the harmonic of the fundamental frequency;
p = the number of terms in the sum.
In this case, the 6-year data starting on January 1,
1987, have been divided into 72 intervals, at the time
points f, = 31, t2 = 31 + 28, t3 = 31 + 28 + 31, etc.,
with a day as the unit of time and the fundamental
period of seasonal model P = 365.
This method allows an arbitrary shape for the seasonal effect by presenting the data as a short Fourier
series with up to three harmonics together with a
possible linear trend. Likelihood ratio tests and
Akaike's information criterion (AIC) (31) were used
to determine the number of harmonics necessary to
model the seasonal pattern adequately and to test differences among subgroups in the population. Maximum likelihood estimation based on Poisson distribution was used to estimate the parameters of the model.
Seasonal patterns in incidence were also compared
between males and females and between the three
5-year age groups, each controlling for the other's
effect.
RESULTS
The observed numbers of cases each month are
shown in table 1 and the 3-month moving averages of
the insulin-dependent diabetes mellitus (IDDM) incidence are shown in figure 1. The month-to-month
variation was more pronounced in males than in females, and it did not follow any obvious pattern in
either sex.
The values of -2(log-likelihood), the number of
estimated parameters, and AIC from the analysis of
seasonal variation in incidence fitting different coefficients to each sex (two) and age groups (three) with
and without time trend are shown in tables 2-6. The
Am J Epidemiol
Vol. 143, No. 2, 1996
CD
CO
CO
O
05
191
All
184
25
167
31
29
31
30
32
42
24
81
28
91
11
17
12
15
12
14
27
33
74
All
13
15
15
15
17
16
103
28
24
29
29
10
10
10
15
13
16
Females
1987
1988
1989
1990
1991
1992
76
19
15
30
9
19
11
March
37
32
31
117
All
15
9
14
14
11
13
February
Total
1987
1988
1989
1990
1991
1992
27
22
21
16
14
17
January
Males
1987
1988
1989
1990
1991
1992
Year
156
26
160
33
35
27
20
27
23
66
16
8
12
12
11
7
90
11
12
15
11
22
19
May
24
14
35
29
23
85
10
20
15
12
16
12
75
4
15
14
11
19
12
April
116
21
15
23
28
19
10
63
14
14
10
6
9
10
53
9
14
9
4
6
11
June
Month
164
18
23
29
28
34
32
71
11
9
10
13
17
11
93
7
14
19
15
17
21
July
200
27
40
32
38
30
33
87
18
18
13
13
16
9
113
22
14
25
17
17
18
August
,
189
32
31
40
24
27
35
82
13
14
12
17
17
9
107
18
26
12
10
18
23
September
186
26
31
23
32
42
32
84
15
9
12
23
13
12
102
16
14
20
19
19
14
October
TABLE 1. Cases of insulin-dependent diabetes meliitus in Finland per month from January 1,1987 to December 31,1992 (72 months)
168
19
19
26
42
32
30
74
8
7
9
17
15
18
94
11
12
17
25
17
12
November
181
37
20
26
28
41
29
92
19
7
15
14
17
20
89
18
13
11
14
24
9
December
2,062
335
328
352
337
376
334
950
158
148
145
172
173
154
1,112
177
180
207
165
203
180
Total
lUual
170
Karvonen et al.
MONTH
FIGURE 1. The number of cases of insulin-dependent diabetes diagnosed each month in Finland, 1987-1991. Data are presented as
3-month moving averages for males (•), females (A), and both sexes (•).
TABLE 2. Summary of model selection for three age
groups* and for both sexes, Finland, January 1,1987 to
December 31,1992
No.
of
harmonics
-2
loglikelihood
No. of
estimated
parameters
AlCt
Without trend
0
1
2
3
0.00
-24.20
-47.41
-58.90
0
1
2
3
-2.27
-25.74
-49.28
-61.07
0
12
24
36
0.00
-0.20
0.59
13.10
6
18
30
42
9.73
10.26
10.72
22.93
With trend
* Age groups: 0-4, 5-9, and 10-14 years,
t AIC, Akaike's information criterion.
best models can be conveniently selected based on
AIC, with the minimum AIC being the best. The
change in — 2(log-likelihood) for models can be tested
as chi-square with degrees of freedom equal to the
difference in the number of parameters.
In the first analysis (table 2), different coefficients
were fit to each of the three age groups (0-4, 5-9, and
10-14 years) and both sexes for up to three harmonics
with and without trend. The best fit in this test seemed
to be the model with one harmonic without trend and
with minimum AIC (—0.20). However, the difference
in the AIC values between models with null, one, and
two harmonics were small, and changes in the — 2(log
likelihood) terms tested by chi-square test showed that
these three models had an equal fit.
The next analysis was done by assuming that the
two youngest age groups (0-4 and 5-9 years) have
similar coefficients. Therefore, the two youngest age
groups were pooled, and the analysis was performed
for males and females in the age groups 0-9 and
10-14 years. The likelihood ratio test for this pooling
was -47.41 - (-44.22) = 3.19, and the chi-square
test with 8 df was nonsignificant, indicating that pooling was acceptable. The best fit was the model without
trend, with two harmonics and the minimum AIC
TABLE 3. Summary of model selection for two age groups*
and both sexes, Finland, January 1,1987 to December 31,
1992
No.
of
harmonics
-2
loglikelihood
No. of
estimated
parameters
AlCt
Without trend
0
1
2
3
0.00
-22.91
-44.22
-51.28
0
8
16
24
0.00
-6.91
-12.22
-3.28
4
12
20
28
6.89
0.63
-5.03
3.97
With trend
0
1
2
3
-1.11
-23.37
-45.03
-52.03
* Age groups: 0-9 and 10-14 years,
t AIC, Akaike's information criterion.
Am J Epidemiol
Vol. 143, No. 2, 1996
Seasonally in the Diagnosis of IDDM
171
TABLE 4. Summary for fitting the model (without trend) for age groups 0-9 and 10-14 years for males and females, Finland,
January 1,1987 to December 31,1992
Age group 0-9 years
No.
of
harmonics
0
1
2
3
-2
loglikelihood
0.00
-15.31
-15.65
-17.17
Age group 10-14 years
Females
Males
AIC*
-2
loglikelihood
0.00
-11.31
-7.65
-5.17
0.00
-6.09
-7.16
-8.04
Males
AIC
-2
loglikelihood
0.00
-2.09
0.84
3.96
0.00
-0.05
-15.91
-19.26
Females
AIC
-2
loglikelihood
AIC
0.00
-3.95
-7.91
-7.26
0.00
-1.47
-5.49
-6.81
0.00
2.53
2.51
5.19
* AIC, Akaike's information criterion.
-12.22 (table 3). For younger males (age 0-9 years),
the model with one harmonic and the minimum AIC
— 11.31 was the best fit, and for older males (10-14
years), the model with two harmonics and the AIC
—7.91 was the best fit, indicating a significant
monthly variation in incidence among males in both
age groups (table 4). Among younger females, the
minimum AIC was —2.09 for the model with one
harmonic, showing a small seasonal variation. The
observed and estimated seasonal pattern for males in
age groups 0-9 and 10-14 years with an overall 95
percent confidence interval for the curve is shown in
figure 2.
Of several poolings, the model with two harmonics
without trend for males and females together and for
two age groups (0-9 years and 10-14 years) was the
best fit with the minimum AIC of -24.36 (table 5).
The likelihood ratio test (3.86, 8 df) showed that
pooling was acceptable. The stepwise fitting of this
model for 0-3 harmonics is shown in table 6. The
younger age group (0-9 years) had a significant seasonal pattern with one harmonic (minimum AIC,
-15.90), whereas in the older age group (10-14
years), we found the pattern with two harmonics with
minimum AIC of — 11.15. The observed and estimated
seasonal pattern for sexes pooled in the two age groups
(0-9 and 10-14 years) with an overall 95 percent
confidence interval for the curve is shown in figure 3.
In both age groups, with pooled sexes and in males
alone (but not in females alone), a seasonal pattern in
incidence was seen.
Among all study subjects and also in males alone,
the number of cases diagnosed in June decreased {p <
0.001). In the older age group, we found also another
low-incidence period during the cold season, in
November-December. However, June was the only
month for which the incidence was significantly different (lower) from the overall incidence level. There
was no single month with a significantly higher observed incidence than expected.
Am J Epidemiol
Vol. 143, No. 2, 1996
DISCUSSION
Our results showed that in Finland the incidence of
insulin-dependent diabetes mellitus in children fluctuates to some extent within a calendar year and also
within a longer period of time. In this analysis, we
applied a recently developed statistical method previously used to analyze seasonal patterns in incidence in
the Denver insulin-dependent diabetes register (30).
This method allows us to test whether a seasonal
pattern exists for sex and age groups, and it can also be
used to test whether two or more groups have a same
seasonal pattern in incidence.
Although there was a significant seasonal pattern in
the incidence of insulin-dependent diabetes for sexes
pooled and for two age groups (0-9 and 10-14 years),
we could confirm a statistically significant seasonal
pattern only for males. Among younger boys, we
found one distinct cycle during a calendar year with a
decreased incidence in June. Among older boys, there
were two distinct cycles with a decreased number of
cases in June and a smaller one during NovemberDecember. The lower number of cases diagnosed in
June was the most visible seasonal pattern, while during the rest of the year the incidence remained relatively stable and high. A seasonal variation in the
onset of insulin-dependent diabetes with higher rates
during cooler seasons, autumn and winter, has been
described previously by several investigators (3-22).
However, our results show that there were no high
peaks in incidence during the cool months that would
have significantly differed from the overall incidence.
With the same method to determine the seasonal pattern in the incidence of insulin-dependent diabetes in
Denver a seasonal pattern was found only for children
aged 10-17 years (30). In several other studies, a
seasonal variation in incidence of insulin-dependent
diabetes has been proposed for both sexes only among
children aged 5 years or older (3-6, 8-22). The only
exception is a report from Canada (7) in which a high
incidence seasonal peak was found among children
172
Karvonen et al.
MALE / 0-9 YEARS
NO TREND/ 1 HARMONIC
1.75
i
N
c
i
o
E
N
C
E
V
A
R
0.75
A
I
0
N
0.50
0.25
<•
MALE/ 10-14 YEARS
NO TREND / 2 HARMONICS
1.75
I
N
C
I
0
E
N
C
E
1.50
1.25
1.00
V
A
R
I
A
T
0
N
0.75
0.50
0.251
2
3
4
5
6
7
9
10
11
10
11
MONTH
MALE / 0-14 YEARS
NO TREND/ 2 HARMONICS
I
N
C
I
D
E
N
C
E
V
A
R
I
A
T
I
O
N
0.75
0.50
0.25
2
3
4
5
6
7
8
12
FIGURE 2. Estimated and observed (solid line) seasonal pattern in the diagnosis of insulin-dependent diabetes in males in Finland,
1987-1991. The value 1 on y-axis denotes the mean of the incidence during the entire study period for the particular age group. The inner
interval of two confidence intervals shown is the pointwise 95 percent confidence interval, and the outer interval is a 95 percent confidence
interval for the entire curve. The equations for the seasonal pattern (where tis in days) are: males age 0-9 years—X(t) = 1 + 0.140073
cos(27rf/365) - 0.166026 sin(27rt/365); males age 10-14 years—\(t) = 1 - 0.002083 cos(2irf/365) + 0.004831 sin(2irt/365) - 0.108747
cos(47rt/365) + 0.261600 sin(47rt/365); males age 0-14 years—\(t) = 1 + 0.081288 cos(27rt/365) - 0.100043 sin(2Trt/365) - 0.063334
cos(47rt/365) + 0.110573 sin(47rt/365).
Am J Epidemiol
Vol. 143, No. 2, 1996
Seasonally in the Diagnosis of IDDM
TABLE 5. Summary of model selection for two age groups*
and pooled sexes, Finland, January 1,1987 to December 31,
1992
-2
loglikelihood
No.
Of
harmonics
No. of
estimated
parameters
Without trend
0
4
8
12
0.00
-12.35
-24.36
-20.29
2
6
10
14
3.55
-8.41
-20.58
-16.47
0.00
-20.35
-40.36
-44.29
0
1
2
3
AlCt
With trend
-0.45
-20.41
-40.58
-44.47
0
1
2
3
* Age groups: 0-9 and 10-14 years,
t AIC, Akaike's information criterion.
TABLE 6. Summary for fitting the model for age groups 0-9
and 10-14 years with pooled males and females, Finland,
January 1,1987 to December 31,1992
No.
of
harmonics
Age group
0-9 years
Age group
10-14 years
-2
loglikelihood
AIC*
0.00
-19.90
-21.21
-23.52
0.00
-15.90
-13.21
-11.52
-2
loglikelihood
0.00
-0.46
-19.15
-20.77
AIC
0.00
-3.54
-11.15
-8.77
* AIC, Akaike's information criterion.
aged 0-9 years, but in the Canadian study the majority
of children in this age group were age 5 years or older.
We also found a seasonal pattern in incidence for
males in the younger age group.
Since the 1920s, there has been increasing interest in
the possibility that a virus infection may play a role in
the etiology of insulin-dependent diabetes (23, 32, 33).
However, virologic studies on patients have not yet
produced convincing evidence to prove this hypothesis, although the possibility of numerous viruses in the
etiology has been suggested. Our results, in Finland,
the country with the highest incidence of insulindependent diabetes in the world, showed that there
was a different seasonal pattern for females and males
with one nadir among younger males and two nadirs
among older males. This does not fit the assumption
that the seasonal variation in incidence were caused by
a virus infection. The occurrence of diseases caused by
viruses, such as mumps, measles, and Coxsackie B,
that have been proposed to trigger insulin-dependent
diabetes show an epidemic pattern, and the duration of
the high-incidence period is often short. However, the
Am J Epidemiol
Vol. 143, No. 2, 1996
173
very high and relatively stable incidence of insulindependent diabetes in Finland supports the assumption
that if a virus infection were involved in the etiology
of diabetes, such a virus should be endemic in the
country. The incidence of common virus infections in
children is at the highest during the cold seasons—late
autumn, winter, and early spring. Therefore, the seasonal pattern in incidence of insulin-dependent diabetes and the nadir around June can support the assumption of a high endemic level of infective agent. The
incidence of insulin-dependent diabetes in children
increases with age. In our study, nearly 60 percent of
the children had been cared outside the home before
the school age of 7 years and thus were probably
exposed to various infections before school age.
Therefore, if an infective agent were be a pathogen
causing insulin-dependent diabetes, it must have had a
low degree of infectivity. On the other hand, the high
and stable incidence of insulin-dependent diabetes
suggests chronic or recurrent infections and thus a
high endemic level of infections in the country. Almost all Finnish children aged 14 years or under have
been vaccinated against several common viruses that
previously caused occasional epidemics. However, in
spite of such a vaccination program, the incidence of
insulin-dependent diabetes is still high and is increasing in Finland, especially among the youngest age
groups. Our results suggest that for a virus infection to
cause the beta cell damage, the behavior of such a
pathogen would have to be very atypical for viruses
that cause usually short epidemics in a susceptible
population.
It is very likely that the seasonality in diagnosis of
insulin-dependent diabetes, if found, is not related to
causal factors that trigger insulin-dependent diabetes,
but it may be associated with the expression of symptoms in subjects who already are at an advanced stage
in developing the disease that may take months or
years. Many physiologic parameters (for instance,
blood glucose, blood lipids, blood pressure, and body
weight) and health habits (diet and physical activity)
have a seasonal pattern. These may be sufficient to
explain the seasonal effect that we observed in the
diagnosis of insulin-dependent diabetes. In addition,
the beginning of the school holidays in June may play
a role in the low rate of newly diagnosed children in
Finland. Interestingly, Somersalo et al. (25), who reviewed a series of 359 diabetic children in Finland
diagnosed during the 1940s and early 1950s, found a
peak in onset in September (12.5 percent of cases
diagnosed in that month). They pointed out that the
school term began on September 1 and that a peak in
newly diagnosed cases could mean that either infec-
174
Karvonen et al.
MALES FEMALE/0-9 YEARS
NO THEM) M HARMONIC
MONTH
MALE & FEMALE / 10-14 YEARS
NO TREND / 2 HARMONICS
10
11
12
MONTH
MALE & FEMALE / 0-14 YEARS
NO T R E N D / 2 HARMONICS
1.75
i
N
C
I
1.50
D
E
N
C
E
V
A
R
I
A
T
I
0.75
0.50
O
N
0.25
2
3
4
5
6
7
12
FIGURE 3. Estimated and observed (solid line) seasonal pattern in the diagnosis of insulin-dependent diabetes in both sexes in Finland,
1987-1991. The value 1 on y-axis denotes the mean of the incidence during the entire study period for the particular age group. The inner
interval of two confidence intervals shown is the pointwise 95 percent confidence interval, and the outer interval is a 95 percent confidence
interval for the entire curve. The equations for the seasonal pattern are: males and females age 0-9 years—\(t) = 1 + 0.133758 cos(27rt/365)
- 0.119919 sin(2irt/365); males and females age 10-14 years—A(() = 1 + 0.008431 cos(2irt/365) + 0.038453 sin(27rf/365) - 0.100288
cos(4irt/365) + 0.206264 sin(47rf/365); males and females age 0-14 years—A(f) = 1 + 0.083940 cos(27rt/365) - 0.059583 sin(2Trf/365) 0.063365 cos(4-rt/365) + 0.094250 sin(47rt/365).
Am J Epidemiol
Vol. 143, No. 2, 1996
Seasonally in the Diagnosis of IDDM
tious or psychologic factors are related to the onset of
childhood diabetes.
Many earlier papers have been based on data in
which a considerable number of children newly diagnosed with IDDM had ketoacidosis at diagnosis. In
other words, it means that the time between the onset
of clinical manifestation to the actual date bf diagnosis
may have been rather long. In our present series, only
about one fifth of newly diagnosed cases of IDDM had
signs of ketoacidosis, which is one of the lowest rates
reported. This indicates that the delay prior to diagnosis has been short.
Most of the previous studies of seasonality in the
risk of insulin-dependent diabetes have been based on
relatively few cases. As we have demonstrated in this
study, to analyze the month-to-month variation with a
sufficient power, it is necessary to have several hundred or maybe over 1,000 cases of insulin-dependent
diabetes.
8.
9.
10.
11.
12.
13.
14.
15.
ACKNOWLEDGMENTS
The Childhood Diabetes in Finland (DiMe) Study Group:
Principal Investigators—H. K. Akerblom and J. Tuomilehto; Coordinators—R. Lounamaa and L. Toivanen; Local
Investigators—A. Fagerlund, M. Flittner, B. Gustafsson, C.
Haggqvist, A. Hakulinen, L. Herva, P. Hiltunen, T. Huhtamaki, N-P. Huttunen, T. Huupponen, M. Hyttinen, T. Joki,
R. Jokisalo, M-L. Kaar, S. Kallio, E. A. Kaprio, U. Kaski,
M. Knip, L. Laine, J. Lappalainen, J. Maenpaa, A-L.
Makela, K. Niemi, A. Niiranen, A. Nuuja, P. Ojajarvi, T.
Otonkoski, K. Pihlajamaki, S. Pontynen, J. Rajantie, J.
Sankala, J. Schumacher, M. Sillanpaa, M-R Stahlberg, C-H.
Strahlmann, T. Uotila, M. Vare, P. Varimo, and G. Wetterstrand.
16.
17.
18.
19.
20.
21.
22.
23.
REFERENCES
1. Adams F. The seasonal variation in the onset of acute diabetes.
Arch Intern Med 1926;37:861-4.
2. Hagglof B, Holmgren G, Wall S. Incidence of insulin-dependent diabetes mellitus among children in a North-Swedish
population 1938-1977. Hum Hered 1982;32:408-17.
3. Christau B, Kromann H, Ortved-Andersen O, et al. Incidence,
seasonal and geographical patterns of juvenile-onset insulindependent diabetes mellitus in Denmark. Diabetologia 1977;
13:281-4.
4. Durruty P, Ruiz F, Carcia de los Rios M. Age at diagnosis and
seasonal variation in the onset of insulin-dependent diabetes in
Chile (southern hemisphere). Diabetologia 1979; 17:357-60.
5. Dahlquist G, Blom L, Holmgren G, et al. The epidemiology of
diabetes in Swedish children 0-14 years—a six-year prospective study. Diabetologia 1985;28:802-8.
6. Tajima N, LaPorte RE, Hibi I, et al. A comparison of the
epidemiology of youth-onset insulin-dependent diabetes mellitus between Japan and the United States (Allegheny County,
Pennsylvania). Diabetes Care 1985;8 (Suppl. 1): 17-23.
7. Allen C, Palta M, Alessio DJD. Incidence and differences in
Am J Epidemiol
Vol. 143, No. 2, 1996
24.
25.
26.
27.
28.
29.
175
urban-rural seasonal variation of type 1 (insulin-dependent)
diabetes in Wisconsin. Diabetologia 1986;29:629-33.
Siemiatycki J, Colle E, Aubert D, et al. The distribution of
type 1 (insulin-dependent) diabetes mellitus by age, sex, secular trend, seasonality, time clusters, and space-time clusters:
evidence from Montreal, 1971-1983. Am J Epidemiol 1986;
124:545-60.
Joner G, Sovik O. Increasing incidence of diabetes mellitus in
Norwegian children 0-14 years of age 1973-1982. Diabetologia 1989;32:79-83.
Ludvigsson J, Afoke AO. Seasonality of type 1 (insulindependent) diabetes mellitus: values of C-peptide, insulin antibodies and haemoglobin A l c show evidence of a more rapid
loss of insulin secretion in epidemic patients. Diabetologia
1989;32:84-91.
Bingley PJ, Gale EAM. Incidence of insulin dependent diabetes in England: a study in the Oxford region, 1985-6. BMJ
1989;298:558-60.
Schranz AG, Prikatsky V. Type 1 diabetes in the Maltese
Islands. Diabet Med 1989;6:228-31.
Hamman R, Gay E, Cruickshanks K, et al. Colorado IDDM
Registry. Incidence and validation of IDDM in children aged
0-17 yr. Diabetes Care 1990;13:499-506.
Serrano Rios M, Moy CS, Martin Serrano R, et al. Incidence
of type 1 (insulin-dependent) diabetes mellitus in subjects
0-14 years of age in the Comunidad of Madrid, Spain. Diabetologia 1990;33:422-4.
Metcalfe MA, Baum JD. Incidence of insulin-dependent diabetes in children aged under 15 years in the British Isles
during 1988. BMJ 1991;302:443-7.
Songini M. Incidence of type I diabetes mellitus in Sardinia.
Medicographia 1991;13 (Suppl. l):10-13.
Green A, Andersen PK, Svendsen AJ, et al. Increasing incidence of early onset type 1 (insulin-dependent) diabetes
mellitus: a study of Danish male birth cohorts. Diabetologia
1992;35:178-82.
Elamin A, Omar MIA, Zein K, et al. Epidemiology of childhood type I diabetes in Sudan 1987-1990. Diabetes Care
1992,15:1556-9.
Goday A, Castell C, Tresserras R, et al. Sex differences in
seasonality at onset of type 1 (insulin-dependent) diabetes
mellitus in Catalonia. Diabetologia 1992;35 (Suppl. 1):A131.
Kostraba JN, Gay EC, Cai Y. Incidence of insulin-dependent
diabetes mellitus in Colorado. Epidemiology 1992;3:232-8.
Podar T, Tuomilehto-Wolf E, Tuomilehto J, et al. Insulindependent diabetes mellitus in native Estonians and immigrants to Estonia. Am J Epidemiol 1992;135:1231-6.
Lipman TH. The epidemiology of type I diabetes in children
0-14 yr of age in Philadelphia. Diabetes Care 1993; 16:922-5.
Szopa TM, Titchener PA, Portwood ND, et al. Diabetes mellitus due to viruses—some recent developments. Diabetologia
1993;36:687-95.
Tuomilehto J, Rewers M, Reunanen A, et al. Increasing trend
in type 1 (insulin-dependent) diabetes mellitus in childhood in
Finland. Analysis of age, calendar time and birth cohort effects during 1965 to 1984. Diabetologia 1991;34:282-7.
Somersalo O, Hiekkala H, Rantakallio P, et al. Studies in
childhood diabetes. Review of 359 cases. I. Onset and physical development. Ann Paediatr Fenn 1960;6:253-74.
Reunanen A, Akerblom HK, Kaar ML. Prevalence and tenyear (1970-1979) incidence of insulin-dependent diabetes in
children and adolescents in Finland. Acta Paediatr Scand
1982;71:893-9.
Tuomilehto J, Lounamaa R, Tuomilehto-Wolf E, et al. Epidemiology of childhood diabetes mellitus in Finland—
background of a nationwide study of type 1 (insulin-dependent) diabetes mellitus. Diabetologia 1992;35:70-6.
WHO Multinational Project for Childhood Diabetes, WHO
DIAMOND Project Group. Diabetes Care 1990;13:1062-8.
Diabetes Epidemiology Research International Group. Geographic patterns of childhood insulin-dependent diabetes mellitus. Diabetes 1988;37:1113-19.
176
Karvonen et al.
30. Jones R, Ford P, Hamman R. Seasonality comparisons among
groups using incidence data. Biometrics 1988;44:1131-44.
31. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, eds. Second
international symposium on information theory. Budapest,
Hungary: Akatemia Kaido, 1973:267-81.
32. Gundersen E. Is diabetes infectious origin? J Infect Dis 1927;
4:197-202.
33. Gamble DR. A possible virus etiology for juvenile diabetes,
In: Creutzfeld W, Kobberling J, Neel V, eds. The genetics of
diabetes mellitus. New York, NY: Springer-Verlag, 1976:
95-105.
Am J Epidemiol
Vol. 143, No. 2, 1996