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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. 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