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. 2025 Sep 9;42(11):e70133. doi: 10.1111/dme.70133

A longitudinal cohort study describing childhood type 1 diabetes incidence and prevalence rates in British Columbia, Canada over 27 years (1997–2023)

Shazhan Amed 1,2,✉, Jeffrey N Bone 2, Shreya B Kishore 3, Qian Zhang 2, Joseph Leung 4
PMCID: PMC12535355  PMID: 40925679

Abstract

Aims

Our study described incidence and prevalence trends of type 1 diabetes in children and youth under 20 years of age from 1997 to 2023 in the Canadian province of British Columbia (BC) and assessed for a 4‐, 5‐, or 6‐year cyclicity or increase in incidence during the COVID‐19 pandemic.

Methods

Using linked population‐level databases and a validated case‐finding and diabetes differentiating algorithm, we identified children with type 1 diabetes diagnosed between 1997 and 2023. Data sources included hospital admissions, outpatient physician visits, and dispensed prescriptions. Population denominators were based on annual age‐ and sex‐stratified population estimates. We calculated incidence per 100,000 and prevalence per 100 cases, using JoinPoint regression to identify trend changes. Cyclical patterns were tested using sine and cosine terms.

Results

From 1997 to 2023, the incidence of paediatric type 1 diabetes increased markedly until 2008 and then declined very gradually, with similar patterns across age and sex. Incidence was higher in boys, highest among children aged 5 to 14 years, and lowest in those aged 15 to 19 years. After 2012, prevalent cases stabilized in those under 10 and decreased in those over 10 years old. There was no evidence of cyclical trends or changes in incidence or prevalence during or after the COVID‐19 pandemic.

Conclusions/Interpretation

We report a stabilization of type 1 diabetes incidence and prevalence, along with the absence of pandemic‐related increases. These trends may in part reflect demographic changes in British Columbia's paediatric population, including a growing proportion of children from immigrant backgrounds with historically lower type 1 diabetes risk. Unfortunately, our data sources do not include ethnicity, limiting our ability to explore these patterns directly. This study will support the optimization of resource allocation and inform healthcare improvement and long‐term management of childhood type 1 diabetes.

Keywords: children, incidence, paediatric, prevalence, type 1 diabetes, youth

What's new?

What is already known?

  • The incidence of type 1 diabetes in children and youth in British Columbia (BC), Canada, increased between 2002 and 2013, like other countries, including the United States.

  • A stabilization of incidence rates has been reported in recent years in a few high‐income European population countries like Finland and Norway.

What did this study find?

  • Incidence and prevalence of type 1 diabetes in BC increased markedly until 2008 and then declined very gradually.

  • There was no increase in incidence rates of type 1 diabetes in BC during the COVID‐19 pandemic.

What are the implications of the study?

  • A recent gradual decline in incidence rates confirms that a focus on optimizing current care models, resource allocation, and long‐term management strategies for type 1 diabetes is critical, rather than addressing a continued rise in demand.

1. INTRODUCTION

Type 1 diabetes is an immune‐mediated disease that affects over 100,000 children under 15 years of age every year worldwide. 1 There are limited longitudinal studies on the incidence and prevalence of type 1 diabetes in children and youth in Canada. In an earlier report, we demonstrated an increase in the incidence of type 1 diabetes in children and youth in British Columbia, Canada, between 2002 and 2013. 2 This increase was consistent with rising rates of childhood diabetes reported in other parts of the world; however, there has been some evidence of a stabilization of incidence rates in recent years, after sharp increases dating from the early 1990s. 3 This levelling off has been particularly evident in countries with European‐origin populations, such as Finland and the United Kingdom. 4 , 5 , 6

Data on incidence trends of childhood diabetes also largely precedes the global pandemic beginning in March 2020 and officially ending in May 2023, leaving gaps in our knowledge regarding the impact of COVID‐19 on type 1 diabetes incidence in children. A few studies describing incidence rates of childhood type 1 diabetes during the global pandemic indicate the possibility of a higher incidence rate compared to pre‐pandemic. 7 , 8 A meta‐analysis of 42 studies by D'Souza et al. 8 reported a significant rise in childhood type 1 diabetes incidence during the COVID‐19 pandemic, with a 16% increase in the first year and a 28% increase in the second year compared to pre‐pandemic rates.

The objective of our study was to describe the trends in incidence and prevalence of type 1 diabetes in children and youth under 20 years of age from 1997 to 2023 in the Canadian province of British Columbia (BC) and to assess whether these patterns followed any cyclical variation or whether an increase in incidence rates during the COVID‐19 pandemic occurred. We also determined the overall mortality rate from 1997 to 2023, as well as the mortality rate with diabetes as the underlying cause of death (UCOD) or when diabetes was listed as any cause related to death.

2. METHODS

2.1. Data source

Using population‐level databases available from Population Data BC, we constructed a cohort of individuals living in BC, Canada, with childhood‐onset type 1 diabetes. Details of the methodology for cohort creation have been published elsewhere. 9 Briefly, we used linked databases from Population Data BC together with an established case‐finding 10 and diabetes differentiating algorithm 11 to capture presumed type 1 diabetes cases among children and youth aged ≥1 and <20 years, with diagnoses between April 1st, 1992, and March 31st, 2024. Between 1992 and 1997, the number of new type 1 diabetes cases appeared to stabilize, suggesting that a five‐year run‐in period was sufficient to differentiate incident from prevalent cases. Therefore, we included only type 1 diabetes cases diagnosed between January 1, 1997, and December 31, 2023, for incidence and prevalence reporting.

Access to data provided by the Data Stewards is subject to approval but can be requested for research projects through the Data Stewards or their designated service providers. The following data sets were used in this study: (1) Discharge Abstract Database (DAD), which provides information including diagnostic ICD‐9 and ICD‐10 codes on all hospital admissions within BC, (2) Medical Services Plan (MSP) data, which corresponds to any outpatient visit with a physician billing code, (3) Pharmanet data, which captures all dispensed drug prescriptions in BC, and (4) Vital Statistics – Death data, which includes all deaths registered in BC. These datasets all exist as part of BC's government‐funded universal healthcare system, where all citizens of BC are enrolled. You can find further information regarding these data sets by visiting the PopData project webpage at: https://my.popdata.bc.ca/project_listings/23‐084/. All inferences, opinions, and conclusions drawn in this publication are those of the author(s) and do not reflect the opinions or policies of the Data Steward(s). For population denominators, we used annual age‐ and sex‐stratified population estimates from Statistics Canada. 12

2.2. Population for prevalence and incidence

For each calendar year, incidence was defined as the number of children who met the type 1 diabetes diagnosis definition for the first time in that calendar year. The year that they were diagnosed with type 1 diabetes is referred to as their index year. Prevalence in a calendar year was defined as the number of identified cases in prior years plus the incident cases in the current year minus participants who (1) had reached 20 years old, (2) had died, or (3) had moved out of the province. For the latter, we censored participants if they had no data in any of the data sources in the current year and any subsequent year. The denominator for the prevalence estimate was the total <20‐year‐old population (stratified by age and sex, where appropriate) in BC during that calendar year; the denominator for incidence was the same, other than excluding prevalent participants in the previous year. Incidence and prevalence rates are reported between 1997 and 2023.

2.3. Mortality

Mortality rates were calculated as the number of deaths among children with T1D type 1 diabetes between 1997 and 2023, divided by the total person‐years of follow‐up, and expressed per 1000 person‐years. For each patient, person‐years were accrued from the index date until the earliest of the following: date of death, date of leaving the province, or the day before the patient's 20th birthday. Specifically, the mortality rates were reported for all causes, for deaths with diabetes as the UCOD, and for deaths where diabetes was listed as any cause related to death (UCOD and/or contributing cause). Due to the small number of deaths, only the overall mortality rates for the entire period were reported.

2.4. Statistical analysis

We calculated the incidence rate (per 100,000) and the prevalence rate (per 100) by calendar year. We used the JoinPoint regression analysis program (V5.3.0) 13 to examine whether there were specific years in which the trend in incidence and prevalence changed. The JoinPoint analyses used Poisson log‐linear models, and the model Weighted BIC was used to determine the best‐fitting piecewise continuous model, that is, to determine the year(s) (if any) at which a change in trend occurred. We plotted the fitted values from these models together with the raw incidence and prevalence rates. We used the resulting models to estimate the annual percent change (APC) and corresponding 95% confidence intervals in incidence and prevalence (1) overall (based on the assumption of no change in trend) and (2) before and after the change in trend (if identified via Joinpoint models). We further used these models to estimate future (e.g., 5‐year) incidence and prevalence rates. All results are presented graphically overall and by age and sex strata, where age was stratified into the following a priori groups: 1–4, 5–9, 10–14, and 15–19 years old. Finally, we reported the overall mortality rate, the mortality rate with diabetes as the primary cause, and the mortality rate with diabetes as any cause per 1000.

As there is some prior evidence that trends in type 1 diabetes are cyclical, 4 we examined whether there was evidence that the observed incidence or prevalence rates followed 4‐, 5‐ or 6‐year cycles. To do this, we used previously published methods by adding sine and cosine terms to the above models corresponding to these possible cycles (4, 5, or 6 years). We compared models including and excluding cyclical variation with the Likelihood Ratio Test, with p < 0.05, and graphical assessment considered possible evidence of cyclicity. All analyses were conducted using SAS (version 9.4).

3. RESULTS

A total of 8136 individuals with type 1 diabetes were included in the study period, among whom 6719 were incident cases from 1997 to 2023. Of these, 4409 (54.2%) were boys.

3.1. Incidence

At the start of the study period (1997), 199 new cases of type 1 diabetes in individuals <20 years of age were identified with an age‐adjusted incidence rate (IR) of 20.5 cases per 100,000 (95% CI: 17.6, 23.3). Rates peaked in 2008 with the highest number of new cases (277) and an IR of 30.75 per 100,000 (95% CI: 27.1, 34.4) (Table S1, Figure 1). Overall, APC was 0.6% (95% CI: 0.2%, 1.0%) with little variation between sexes. Younger ages had lower APCs than older ages (Figure 2a). This increase in incidence varied over the 27‐year period (Table S2). From 1997 to 2004, APC was 4.0% (95% CI: 1.9%, 9.0%), followed by a stable trend from 2004 to 2023 (APC = −0.1%; 95% CI: −0.8%, 0.4%). Based on these trends, we project an incidence rate of 27.2 cases per 100,000 in 2033 (95% CI = 24.8, 29.8) (Table S3). We found no evidence of meaningful changes in incidence during the COVID‐19 pandemic from 2020 to 2023 (Table S1).

FIGURE 1.

FIGURE 1

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Age standardized incidence rates per 100,000 overall and by sex.

FIGURE 2.

FIGURE 2

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Estimated annual percent change (95% CI) in incidence (a) and prevalence (b).

Results disaggregated by sex showed a stable trend for boys over most of the study period (2000–2023), while for girls, the APC was 3.6% (95% CI: 1.5%, 10.4%) from 1997 to 2007 and −1.1% (95% CI: −3.3%, −0.1%) from 2007 to 2023 (Table S2). Overall incidence was slightly higher in boys than in girls (Figure 1).

Incidence rates were stable in all age groups other than those in the 5–9 years group, where the incidence increased sharply (APC = 11.8%; 95% CI: 4.7%, 28.4%) up to 2002 and decreased slightly after. This pattern of sharp increase and then stabilization was driven by both sexes, but with different yearly ranges (Figure 3, Table S2).

FIGURE 3.

FIGURE 3

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Age and sex‐specific incidence rates per 100,000.

3.2. Prevalence

There were 1576 prevalent cases of type 1 diabetes in 1997, peaking at 2503 cases in 2013 and remaining consistent thereafter (Table S4). From 1997 to 2013, the corresponding age‐standardized prevalence increased from 0.16 (95% CI: 0.15, 0.17) to 0.27 (95% CI: 0.26, 0.28) per 100 (Figure 3) and then slightly declined to 0.26 (95% CI = 0.25, 0.27) in 2023. The overall APC was 1.3% (95% CI: 0.9%, 1.7%), with the fastest increases between 1997 and 2001 (5.3%) and 2001 and 2009 (3.1%). From 2013, the prevalence slightly declined annually (APC = −0.9%) (Table S5). Based on these trends, we project a prevalence of 0.22 cases per 100 in 2033 (95% CI = 0.22, 0.23) (Table S6).

When analysed by sex, there were multiple Joinpoint changes in prevalence rates in both boys and girls, but overall patterns were similar (increases up to 2013, followed by decreases) (Figure 4). From 1997 to the early 2000s, the relative increase in prevalence was highest in those <9 years of age, followed by those 15–19 years, and lowest in those 10–14 years of age (Figure 5, Table S5).

FIGURE 4.

FIGURE 4

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Age‐standardized prevalence rates per 100 overall and by sex.

FIGURE 5.

FIGURE 5

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Age and sex specific prevalence rates per 100.

3.3. Mortality

Between 1997 and 2023, a total of 42 deaths were identified among 8136 type 1 diabetes cases, resulting in an overall mortality rate of 0.7 per 1000 person‐years. Of these, 21 deaths were associated with any cause related to diabetes (0.3 per 1000 person‐years), and 13 deaths were attributed to diabetes as the UCOD (0.2 per 1000 person‐years).

3.4. Cyclical variation

For both incidence and prevalence, the fitted values from the cyclical models for 4‐, 5‐, and 6‐year cycles were similar to those with no cyclical variation. The likelihood ratio test comparing models with cyclical variation did not indicate improved fit after accounting for cyclicity (Table S7).

4. DISCUSSION

In this population cohort from 1997 to 2023 from BC, Canada, we found that the incidence of pediatric type 1 diabetes diagnoses increased until 2008 and decreased afterwards. These results were largely similar across age and sex categories. Incidence was slightly higher in boys than girls and was highest in those 5 to 14 years old and lowest in those 15 to 19 years old. The number of prevalent cases increased by roughly 50% from 1997 to 2023, driven by increases from 1997 to 2011. From 2012 onwards, the trend in prevalent cases was stable or decreasing. In contrast to some existing reports, we found no evidence of cyclical variation in incidence or prevalence rates or sharp changes in incidence during the COVID‐19 pandemic. Eighty percent of deaths over the study period were directly or indirectly attributed to diabetes.

4.1. Comparison with literature

We have previously examined trends in incidence and prevalence of type 1 diabetes in BC using similar data from 2002 to 2013, which showed slight average increases in incidence and prevalence during that period. 2 The data in this paper extend these findings both backward and forward and demonstrate that those increases appear to have stabilized in recent years.

In the United States, the SEARCH for Diabetes in Youth study demonstrated that between 2002 and 2011, there was a significant upward trend in the incidence of type 1 diabetes in youth under 20 years of age, from 19.5 cases per 100,000 youths to 21.7 cases per 100,000 youths, with an APC of 1.4%. 14 After accounting for age, sex, and race or ethnicity, the adjusted annual relative increase in type 1 diabetes incidence was 1.8%. The incidence also increased in non‐Hispanic White, non‐Hispanic Black, and Hispanic youth, but not Asian, Pacific Islander, or Native American youth. In Europe, Patterson et al. 15 demonstrated that there was an overall annual rate of increase of 3.4% (95% CI: 2.8%, 3.9%) per year in incidence of childhood type 1 diabetes across 26 European centres over 1989–2013. Cohen et al., 16 using data from the Quebec Integrated Chronic Disease Surveillance System, also reported increases in diabetes incidence in children aged 1 to 19 years between 2002 and 2010 from 16.3 to 27.8 per 100,000. While this study reported the overall rates for type 1 and type 2 diabetes together, it specified that type 1 diabetes incidence went up by 5.4% per year. 16

Our data during the overlapping study periods (1997–2013) support these findings of an overall increase in incidence; however, as noted above, in the latter part of our study from 2004, the incidence rates began to plateau and then decrease. Similarly, in Nova Scotia, Cummings et al. reported a 47% increase in incidence among children aged 0 to 14 years between 1994 and 1998 and 2004 and 2008, followed by a plateau through 2018. The highest rates were observed among boys aged 10 to 14 years, whose incidence nearly doubled over the study period. In contrast, incidence among youth aged 15 to 19 years remained stable. 17 These findings suggest that while the burden of type 1 diabetes in Canadian children has risen substantially, recent trends may indicate a levelling off in some regions and age groups. The initial increase is consistent with global trends and may reflect greater exposure to environmental risk factors, improved case detection, and a shift towards earlier onset in genetically susceptible individuals. The subsequent stabilization likely reflects consistent diagnostic practices and demographic changes, including increased ethnic diversity with lower baseline type 1 diabetes risk.

According to recent International Diabetes Federation data, Canada ranks 8th among the highest incidence countries in children under 15 years of age, while Finland ranks 1st. 1 Interestingly, in high‐incidence countries like Finland and Norway, there have been reports of a slowing of incidence rate increases. For example, Parviainen et al. reported a decline in Finland's type 1 diabetes incidence, from 57.9/100,000 person‐years in 2003 to 2006 to 52.2/100,000 in 2015 to 2018, with an incidence rate ratio of 0.90 (95% CI, 0.85–0.96). This decline was mainly attributable to children 0 to 4 years and girls aged 5 to 9 years. 5

Indeed, the changing ethnic composition of the paediatric population in British Columbia may be influencing incidence trends. International data, including from the SEARCH study in the United States 14 and from New Zealand, 18 have shown substantial ethnic differences in type 1 diabetes risk, with children of European descent generally having higher incidence rates than children of South Asian, East Asian, Pacific Islander, or sub‐Saharan African origin. Census data indicate that British Columbia has undergone significant demographic shifts since 1997, with a growing proportion of children from immigrant backgrounds who may initially have a lower risk of type 1 diabetes. For example, according to Statistics Canada, the proportion of British Columbia's population identifying as visible minorities increased from 25% in 2001 to over 34% by 2021, with particularly large increases in Chinese, South Asian, and Filipino populations. 19 It is possible that this evolving population mix may be contributing to the plateau or decline observed in incidence in recent years, particularly if incidence is stable or rising within European‐origin populations but offset by lower rates in more recently immigrated groups. Unfortunately, our data sources do not include ethnicity, limiting our ability to explore these patterns directly. Given the potential importance of ethnicity in shaping incidence trends, this should be a focus of future work and is acknowledged as a key limitation of our study.

Cyclicity in the incidence of diabetes was originally shown in Yorkshire, England, between 1978 and 1990 (four‐year cycles) 6 and further observed in an Australian cohort study from 2000 to 2011. 4 In Patterson et al.'s 15 European study, four centres out of the 26 studied demonstrated 4‐year cyclical patterns. There have been no clear explanations to date for why some areas have exhibited this pattern while others have not. Despite a relatively large sample size, we found no evidence (either visual or statistical) of cyclicity in our cohort.

Interestingly, several countries demonstrated an increase in the incidence of type 1 diabetes during the COVID‐19 pandemic. A German study demonstrated that the observed incidence of type 1 diabetes in children in 2020–2021 was significantly higher than the expected incidence, 24.4 vs. 21.2 cases in 100,000, with an IR ratio of 1.15 [1.10–1.20]; p < 0.001. 7 A meta‐analysis by D'Souza et al., 8 looking at 42 studies around the world, corroborated this and found that over the first 12 months of the pandemic, the incidence rate of childhood type 1 diabetes was 16% higher (95% CI, 10%–23%) compared to the year before the COVID‐19 pandemic. In the following 12 months, the incidence rate was 28% higher (95% CI, 18%–39%). These increases significantly exceeded the pre‐pandemic annual rise in type 1 diabetes incidence, which ranged from 2% to 3%. 8

Kamrath et al. 20 suggested that, theoretically, delayed diagnoses early in the pandemic may have caused a “catch‐up” increase in type 1 diabetes incidence. However, D'Souza et al.'s 8 study, which included at least 12 months of pre‐ and pandemic data to reduce seasonal bias, found a significant rise in incidence even in the second year of the pandemic. Other theories for this increased incidence during the pandemic include COVID‐19‐induced autoimmunity and direct toxicity of beta cells in children. 21 Recent data from Finland showed a 16% increase in childhood type 1 diabetes incidence (IRR 1.16, 95% CI, 1.06–1.25) during the first 18 months of the pandemic, with only 0.9% (5 of 583) of new cases testing positive for SARS‐CoV‐2 antibodies. 22 Similarly, a study of 51,970 children from Colorado and Bavaria found no significant association between SARS‐CoV‐2 antibodies and the development of type 1 diabetes‐related autoantibodies. 21 Yet another theory is that environmental changes during the COVID‐19 pandemic may have contributed to the rise in type 1 diabetes in children. Reduced biodiversity of exposures and fewer infections, which may protect against autoimmune diseases per the hygiene hypothesis, could shift environmental factors away from inducing islet autoimmunity. 21 Pandemic‐related weight gain in young children could also contribute to the increased risk of islet autoimmunity. 23

Notably, in our study in BC, an increase in the incidence of type 1 diabetes in children was not seen during the COVID‐19 pandemic. However, our data represents a single Canadian province, and COVID‐19 policy around isolation was provincially regulated in Canada. For example, BC was among the first provinces in Canada that had an early‐stage return to school for children in June 2020 after schools closed in March 2020. In contrast, in the Canadian province of Ontario, where children mostly did not go to school in person during the pandemic, there was a reported higher (but non‐significant) 1.18‐fold increase in the incidence of childhood diabetes during the COVID‐19 pandemic. 24 More research in this area is necessary to understand geographical variation in results and to assess how differences in the severity of the pandemic and public health policies may have impacted the incidence of paediatric type 1 diabetes.

Our study is strengthened by using several population‐level linked data sources, providing a comprehensive picture of paediatric type 1 diabetes in BC, Canada. We also note several limitations. First, although our case‐finding algorithm has been validated and shown to perform well, as with any administrative data study, there is the possibility of misclassified cases. For example, misclassification of type 1 and type 2 diabetes, or individuals mislabelled as having diabetes. Second, we used a data‐driven approach to estimating periods of change in incidence and prevalence trends. These analyses allow for the description of population‐level changes, but to our knowledge, the change points do not correspond to specific population‐level changes or changes in clinical assessment of type 1 diabetes. Our data is from one province of Canada with its unique sociodemographic profile and may not be generalizable to the rest of the country or the world. Finally, our data lacked information on other important social determinants of health, such as ethnicity or socioeconomic status, which are known to be potentially important predictors of population‐level burdens of chronic diseases.

The increase in the incidence of type 1 diabetes in children and youth in BC, Canada, from 1997 to 2008 was partially reversed from 2008 to 2019, while age‐standardized prevalence rates have been relatively stable since approximately 2011. This stabilization should not distract ongoing efforts to improve the lives of children and youth living with diabetes; rather, these data should anchor a continued focus on optimizing current care models and ensuring equitable resource allocation (i.e., public funding policies for diabetes technology) for the over one million children worldwide under the age of 15 living with type 1 diabetes globally. Our findings also highlight the need for further research into the possible biological, environmental, and migratory factors contributing to the stabilizing incidence and prevalence of type 1 diabetes to inform ongoing research in the prevention of childhood type 1 diabetes.

AUTHOR CONTRIBUTIONS

JB and QZ acquired, analysed, and interpreted the data. JB and SK wrote the first version of the manuscript. JL and SA provided the conceptual framework, interpreted the data, edited, and revised the final manuscript. All authors edited and revised the final manuscript and provided approval for submission.

FUNDING INFORMATION

This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.

CONFLICT OF INTEREST STATEMENT

SA holds a BC Children's Hospital Research Institute Research Salary Award. SA has received speaker and advisory board fees from Dexcom, Abbott, Novo Nordisk, Eli Lilly, Sanofi, and Insulet unrelated to this work. None of the other authors has a conflict of interest to disclose.

Supporting information

Table S1

DME-42-e70133-s001.docx (46.9KB, docx)

Amed S, Bone JN, Kishore SB, Zhang Q, Leung J. A longitudinal cohort study describing childhood type 1 diabetes incidence and prevalence rates in British Columbia, Canada over 27 years (1997–2023). Diabet Med. 2025;42:e70133. doi: 10.1111/dme.70133

DATA AVAILABILITY STATEMENT

Access to data provided by the Data Steward(s) is subject to approval, but can be requested for research projects through the Data Steward(s) or their designated service providers. All inferences, opinions, and conclusions drawn in this publication are those of the author(s) and do not reflect the opinions or policies of the Data Steward(s).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

DME-42-e70133-s001.docx (46.9KB, docx)

Data Availability Statement

Access to data provided by the Data Steward(s) is subject to approval, but can be requested for research projects through the Data Steward(s) or their designated service providers. All inferences, opinions, and conclusions drawn in this publication are those of the author(s) and do not reflect the opinions or policies of the Data Steward(s).

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