**3. The incidence of type 1 diabetes mellitus**

This section provides a comprehensive description of type 1 diabetes incidence, its changes over the last years, and its variability in populations and patient subgroups.

### **3.1. Geographic differences**

**2. Estimating the epidemiology of type 1 diabetes mellitus**

or cross-sectional studies or different-sized registries.

4 Type 1 Diabetes

be representative for the epidemiology of type 1 diabetes.

al or multinational.

The epidemiology of type 1 diabetes can be estimated in different ways. In principle, there is the possibility of estimating epidemiologic data by self-report of the patients, longitudinal-

Data gained from self-reporting of diabetic patients have been shown to underestimate the true burden of diabetes (Forouhi, Merrick et al. 2006). Another possibility, but with similar limitations, is to assess data retrospectively (Mooney, Helms et al. 2004). Generally, longitu‐ dinal or cross-sectional studies are often locally or regionally performed. This limits the op‐ portunity to get generalizable results because the epidemiology of type 1 diabetes is known to be heterogeneous regarding geography and ethnicity. Cross-sectional studies do not pro‐ vide information on the time-dependent changes of the epidemiology. Additionally, many studies are limited to special settings, e.g. a general practice setting (Frese, Sandholzer et al. 2008), and although providing useful and necessary information, the reported data may not

Especially when estimating the incidence of type 1 diabetes, the latency of onset until diag‐ nosis is important and influences the quality of estimated data. Also the validity of the chos‐ en diagnosis should be critically reviewed. In a recent German investigation, 60 (10.3%) of 580 patients were reclassified at mean 2.4 years after the diagnosis of type 1 diabetes: 23 (38.3%) as type 1 diabetes; 9 (15%) as maturity onset diabetes of the young; 20 (33.3%) as "other specific diabetes forms", and 8 (13.3%) as "remission" of type 2 diabetes (Awa, Schob‐ er et al. 2011). The validity of the chosen diagnosis may differ depending on the data source that affords a correct differential diagnosis, e.g. between type 1 diabetes and malnutrition diabetes in developing countries or type 1 diabetes, type 2 diabetes and maturity onset dia‐ betes of the young in industrial countries, as well as a correct encoding of diagnosis. This is because usual classification systems such as the International Classification of Primary Care or International Classification of Diseases cannot be assumed to be sufficiently complete and valid (Gray, Orr et al. 2003; Wockenfuss, Frese et al. 2009; Frese, Herrmann et al. 2012).

It is conclusive that reliable and valid – and thereby comparable – data on type 1 diabetes epidemiology have to be based on a complete and detailed assessment. Disease registries can be assumed to be probably the best method to estimate and manage standardized data. However, the availability, completeness, quality and accuracy of diabetes registers are again very variable (Forouhi, Merrick et al. 2006). Type 1 diabetes registries were established on different levels: local (Howitt and Cheales 1993), regional (Galler, Stange et al. 2010), nation‐

Much of our knowledge of the epidemiology of type 1 diabetes in young people has been generated by large collaborative efforts based on standardized registry data: the EURODIAB study in Europe and the DIAMOND project worldwide (Dabelea, Mayer-Davis et al. 2010). In order to provide reliable information about the incidence and geographical variation of type 1 diabetes throughout Europe, EURODIAB was established as a collaborative research project (Fuller 1989; Green, Gale et al. 1992). During a 15-year period, 1989 to 2003, 20 popu‐ Mean incidence rates of type 1 diabetes vary considerably depending on the geographic region (Galler, Stange et al. 2010). The worldwide incidence of type 1 diabetes is described to vary by at least 100- to 350-fold among different countries (Karvonen, Viik-Kajander et al. 2000). The high‐ est incidence rates are found in Finland and Sardinia (Italy) and the lowest in South American countries, e.g. Venezuela and Brazil, and Asian countries, e.g. China or Thailand (Karvonen, Viik-Kajander et al. 2000; Borchers, Uibo et al. 2010; Panamonta, Thamjaroen et al. 2011). Apart from regions with low to intermediate incidence rates ranging between 5 and 20 per 100,000 chil‐ dren or adolescents per year, there are areas with incidence rates as high as 27 to 43 per 100,000 children or adolescents per year. Canada and Northern European countries, such as Finland and Sweden, have the highest incidence rates ranging between 30 and 40 per 100,000 children/ adolescents per year. Incidence rates of countries in Central Europe (with the exception of Sardi‐ nia) vary from 8 to 18 per 100,000 children/adolescents per year. The incidence for type 1 diabetes in German children aged 0 to 14 years was estimated at 13 per 100,000 per year for 1987–1998 and at 15.5 per 100,000 per year for 1999–2003. The registry of the former German Democratic Repub‐ lic, which was kept from 1960 until 1989, reported incidence rates between 7 and 14 per 100,000 children/adolescents per year (Galler, Stange et al. 2010). In Mediterranean countries, the inci‐ dence rates of type 1 diabetes also show wide variations, although for some of them, there are still no relevant and reliable data (Muntoni 1999). Summarizing the data on type 1 diabetes incidence, the polar-equatorial gradient does not seem to be as strong as previously assumed. The incidence of type 1 diabetes among different countries is presented in Table 1 and Table 2. When comparing the incidence of type 1 diabetes between countries, it is important to keep the size of the sample and the area of sampling in mind. This is because the incidence of type 1 diabetes may show strong variations among different regions from many countries as United States or Italy. Also a Romanian study revealed a wide geographic variation (6.71-fold) between the highest and the lowest incidence rates in different districts of the country (Ionescu-Tirgoviste, Guja et al. 2004).

**Country Sample 1st period 2nd period AI** Austria whole nation 9.0 13.3 4.3 (3.3 to 5.3) Belgium Antwerp 10.9 15.4 3.1 (0.5 to 5.8) Bosniaa Tuzla canton 8.9 - 15 (6.0 to 25) Croatiab two sources 6.9 - 9.0 (5.8 to 12.2) Czech Republic whole nation 8.7 17.2 6.7 (5.9 to 7.5) Denmarkc whole nation 22.0 - 3.4 (1.9 to 5.0) Estoniad whole nation 10.1 16.9 3.3 (n.s.) Finland two regions 39.9 52.6 2.7 (1.4 to 4.0) Germany BadenWürttemberg 13.0 15.5 3.7 (2.9 to 4.5) Germany Düsseldorf 12.5 18.3 4.7 (3.1 to 6.3) Hungary 18 counties 8.8 11.5 2.9 (1.9 to 3.9) Italye Sardinia 37.7 49.3 2.8 (1.0 to 4.7) Lithuana whole nation 7.3 10.3 3.8 (2.2 to 5.3) Luxembourg whole nation 11.4 15.5 2.4 (-1.4 to 6.3) Maltaf n.s. 14.7 - 0.5 (-2.1 to 3.2) Montenegrog whole nation 10.8 16.3 4.6 (0.4 to 9.6) Norway eight counties 21.1 24.6 1.3 (0.1 to 2.6) Poland Katowice 5.2 13.3 9.3 (7.8 to 10.8) Romania Bucharest 4.7 11.3 8.4 (5.8 to 11.0) Slovakia whole nation 8.2 13.6 5.1 (4.0 to 6.3) Slovenia whole nation 7.9 11.1 3.6 (1.6 to 5.7) Spain Catalonia 12.4 13.0 0.6 (-0.4 to 0.6) Sweden Stockholm county 25.8 34.6 3.3 (2.0 to 4.6) United Kingdom Northern Ireland 20.0 29.8 4.2 (3.0 to 5.5) United Kingdom Yorkshire 17.1 22.4 2.2 (1.1 to 3.4) United Kingdom Oxford 16.0 23.3 3.6 (2.6 to 4.6)

The Epidemiology of Type 1 Diabetes Mellitus

http://dx.doi.org/10.5772/52893

7

a

c

e

f

n.s.: not sepcified

Stipancic, La Grasta Sabolic et al. 2008, 1995-2003 bTahirovic and Toromanovic 2007, 1995-2004

Svensson, Lyngaae-Jorgensen et al. 2009, 1996-2005 dTeeaar, Liivak et al. 2010, 1983-1990 vs. 1999-2006

Casu, Pascutto et al. 2004, 1989-1994 vs. 1995-1999

gSamardzic, Marinkovic et al. 2010, 1997-2001 vs. 2002-2006

**Table 2.** The incidence (per 100,000 per year) of type 1 diabetes and its annual increase (AI; with 95% confidence interval) in different European regions. If not otherwise indicated, data were adopted from Patterson, Dahlquist et al.

(2009) and were estimated during the periods 1989-1993 and 1999-2003, respectively.

Schranz and Prikatsky 1989, 1980-1987

While genetic factors are thought to explain some of the geographic variability in type 1 diabetes occurrence, they cannot account for its rapidly increasing frequency. Instead, the declining pro‐ portion of newly diagnosed children with high-risk genotypes suggests that environmental pressures are now able to trigger type 1 diabetes in genotypes that previously would not have de‐ veloped the disease during childhood (Borchers, Uibo et al. 2010). The importance of environ‐ mental factors towards manifestation of type 1 diabetes is also supported by migration studies: For example a recently published study revealed that being born in Sweden, a country with high type 1 diabetes incidence, increases the risk for type 1 diabetes in children with a genetic origin in low-incidence countries (Soderstrom, Aman et al. 2012).


bPanamonta, Thamjaroen et al. 2011

c Vehik, Hamman et al. 2007

n.s.: not sepcified

**Table 1.** The incidence (per 100,000 per year) of type 1 diabetes and its annual increase (AI; with 95% confidence interval) in different non-European countries. If not otherwise indicated, data were adopted from the review of Onkamo, Vaananen et al. (1999). The analyzed time period differed from country to country.


a Stipancic, La Grasta Sabolic et al. 2008, 1995-2003

bTahirovic and Toromanovic 2007, 1995-2004

c Svensson, Lyngaae-Jorgensen et al. 2009, 1996-2005

dTeeaar, Liivak et al. 2010, 1983-1990 vs. 1999-2006

e Casu, Pascutto et al. 2004, 1989-1994 vs. 1995-1999

f Schranz and Prikatsky 1989, 1980-1987

gSamardzic, Marinkovic et al. 2010, 1997-2001 vs. 2002-2006

n.s.: not sepcified

strong variations among different regions from many countries as United States or Italy. Also a Romanian study revealed a wide geographic variation (6.71-fold) between the highest and the lowest incidence rates in different districts of the country (Ionescu-Tirgoviste, Guja et al. 2004).

While genetic factors are thought to explain some of the geographic variability in type 1 diabetes occurrence, they cannot account for its rapidly increasing frequency. Instead, the declining pro‐ portion of newly diagnosed children with high-risk genotypes suggests that environmental pressures are now able to trigger type 1 diabetes in genotypes that previously would not have de‐ veloped the disease during childhood (Borchers, Uibo et al. 2010). The importance of environ‐ mental factors towards manifestation of type 1 diabetes is also supported by migration studies: For example a recently published study revealed that being born in Sweden, a country with high type 1 diabetes incidence, increases the risk for type 1 diabetes in children with a genetic origin in

**Country Sampling Region Incidence AI of Incidence** Algeria Oran 4.7 7.9 (1.85 to 14.00) Australia West 14.9 6.3 (2.11 to 10.50) Australiaa New South Wales 19.4 2.8 (1.9 to 3.8) Canada Prince Edward Island 23.5 3.2 (-0.33 to 6.38) Canada Montreal 9.3 1.6 (-0.67 to 3.82) China Shanghai 0.7 7.4 (2.3 to 12.5) Iceland n.s. 9.0 2.3 (-2.38 to 6.96) Israel Yemenite Jews 5.0 3.2 (2.51 to 3.88) Japan Hokkaido 1.7 5.9 (4.14 to 7.63) Libya n.s. 8.7 6.3 (0.69 to 11.8) New Zealand Auckland 10.1 6.4 (4.20 to 8.52) New Zealand Canterbury 12.7 2.7 (.0.05 to 10.50) Peru Lima 0.5 7.7 (-1.0 to 16.4)

Thailandb Northeast Thailand 0.6 n.s. United Statesc Colorado 19.4 2.3 (1.6 to 3.1) United States Hawaii 7.8 7.8 (1.8 to 14.9) United States Allegheny County 14.7 1.5 (0.21 to 2.83) United States Colorado 12.3 -0.2 (-2.52 to 2.19)

**Table 1.** The incidence (per 100,000 per year) of type 1 diabetes and its annual increase (AI; with 95% confidence interval) in different non-European countries. If not otherwise indicated, data were adopted from the review of

Onkamo, Vaananen et al. (1999). The analyzed time period differed from country to country.

low-incidence countries (Soderstrom, Aman et al. 2012).

a

6 Type 1 Diabetes

c

Taplin, Craig et al. 2005

Vehik, Hamman et al. 2007

n.s.: not sepcified

bPanamonta, Thamjaroen et al. 2011

**Table 2.** The incidence (per 100,000 per year) of type 1 diabetes and its annual increase (AI; with 95% confidence interval) in different European regions. If not otherwise indicated, data were adopted from Patterson, Dahlquist et al. (2009) and were estimated during the periods 1989-1993 and 1999-2003, respectively.
