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Diabetologia

, Volume 56, Issue 7, pp 1471–1488 | Cite as

Global trends in the incidence and prevalence of type 2 diabetes in children and adolescents: a systematic review and evaluation of methodological approaches

  • S. Fazeli Farsani
  • M. P. van der Aa
  • M. M. J. van der Vorst
  • C. A. J. Knibbe
  • A. de Boer
Systematic Review

Abstract

Aims/hypothesis

This study aimed to systematically review what has been reported on the incidence and prevalence of type 2 diabetes in children and adolescents, to scrutinise the methodological issues observed in the included studies and to prepare recommendations for future research and surveillances.

Methods

PubMed, the Cochrane Database of Systematic Reviews, Scopus, EMBASE and Web of Science were searched from inception to February 2013. Population-based studies on incidence and prevalence of type 2 diabetes in children and adolescents were summarised and methodologically evaluated. Owing to substantial methodological heterogeneity and considerable differences in study populations a quantitative meta-analysis was not performed.

Results

Among 145 potentially relevant studies, 37 population-based studies met the inclusion criteria. Variations in the incidence and prevalence rates of type 2 diabetes in children and adolescents were mainly related to age of the study population, calendar time, geographical regions and ethnicity, resulting in a range of 0–330 per 100,000 person-years for incidence rates, and 0–5,300 per 100,000 population for prevalence rates. Furthermore, a substantial variation in the methodological characteristics was observed for response rates (60–96%), ascertainment rates (53–99%), diagnostic tests and criteria used to diagnose type 2 diabetes.

Conclusions/interpretation

Worldwide incidence and prevalence of type 2 diabetes in children and adolescents vary substantially among countries, age categories and ethnic groups and this can be explained by variations in population characteristics and methodological dissimilarities between studies.

Keywords

Adolescents Children Global trends Incidence Methodology Prevalence Systematic review Type 2 diabetes 

Abbreviations

AIHW

Australian Institute of Health And Welfare

BPSU

British Paediatric Surveillance Unit

CCDR

Chicago Childhood Diabetes Registry

CFPC-NaReS

College of Family Physicians of Canada National Research System

CPSP

Canadian Paediatric Surveillance Program

DIARY

Diabetes Registry

FPG

Fasting plasma glucose

IHS

Indian Health Service

NIH

National Institute of Health

NZHIS

New Zealand Health Information Service

NZPSU

New Zealand Paediatric Surveillance Unit

RLDR

Richmond/Lexington County Childhood and Adolescents Diabetes Registry

US PHS

United States Public Health Service

WACDD

Western Australian Children’s Diabetes Database

Introduction

Global prevalence of diabetes is increasing rapidly [1]. With an estimated 285 million patients (aged 20–79 years) in 2010, diabetes is becoming one of the main threats to human health in the 21st century in both developed and developing countries [1, 2]. Globally, type 2 diabetes accounts for more than 90% of the cases of diabetes [2]. Until recently, type 2 diabetes was barely diagnosed in children, since it was considered to be a disease of adulthood. Over the past two decades, an increase in prevalence of type 2 diabetes in children and adolescents has been reported in several countries [1, 3]. Early onset of type 2 diabetes is associated with increased risk of morbidity and mortality [4, 5]. Type 2 diabetes has a disrupting effect on young individuals during their most productive years and leads to increased healthcare costs. In addition, (severe) complications of type 2 diabetes, such as nephropathy, retinopathy, neuropathy, dyslipidaemia and cardiovascular disease are an ongoing threat [4, 5, 6, 7].

An overview of global trends in the incidence and prevalence of type 2 diabetes in children and adolescents is important, since type 2 diabetes has significant effects on health and quality of life, use of medical services and reduced employability resulting in a huge economic burden [6, 7, 8, 9]. To support appropriate healthcare policies, population-based epidemiological studies are required to show the actual burden of the disease and its secular trends [7]. We have performed a systematic search to detect systematic reviews or meta-analyses summarising the existing data on incidence and prevalence of type 2 diabetes in young age groups and the different methodologies used to estimate these population data. However, such studies have not been published. Therefore, the aims of our study are to review what is known about the incidence and prevalence of type 2 diabetes in children and adolescents, to scrutinise the methodological issues observed in the included studies and to prepare recommendations for future research and surveillances.

Methods

Data sources and searches

This systematic review follows the guidelines of ‘Meta-analysis of Observational Studies in Epidemiology (MOOSE)’ [10]. After defining the research question and consulting a medical librarian in the library of Utrecht University, a search was performed in PubMed, the Cochrane Database of Systematic Reviews, Scopus, EMBASE and Web of Science. Our search covered all publications in the above databases, from the inception of each database up to the third week of February 2013. The search terms and search strategy are listed in Table 1. All search results were imported to a Refworks file (www.refworks.com).
Table 1

Search terms and search strategy

Search terms and search strategy

Search terms

Population

(‘Infant’ OR ‘Infants’ OR ‘Toddler’ OR ‘Toddlers’ OR ‘Child’ OR ‘Children’ OR ‘Adolescent’ OR ‘Adolescents’ OR ‘Teens’ OR ‘Teen’ OR ‘Teenagers’ OR ‘Teenager’ OR ‘Youth’ OR ‘Youths’ OR ‘Adolescence’)

AND

Outcome

(‘Incidence’ OR ‘Incidences’ OR ‘Prevalence’ OR ‘Prevalences’) AND (‘Diabetes Mellitus, Non Insulin Dependent’ OR ‘Diabetes Mellitus, Non-Insulin-Dependent’ OR ‘Non-Insulin-Dependent Diabetes Mellitus’ OR ‘Type 2 Diabetes Mellitus’ OR ‘Diabetes Mellitus, Slow-Onset’ OR ‘Diabetes Mellitus, Slow Onset’ OR ‘Slow-Onset Diabetes Mellitus’ OR ‘Diabetes Mellitus, Stable’ OR ‘Stable Diabetes Mellitus’ OR ‘Diabetes Mellitus, Type II’ OR ‘NIDDM’ OR ‘Diabetes Mellitus, Adult-Onset’ OR ‘Adult-Onset Diabetes Mellitus’ OR ‘Diabetes Mellitus, Adult Onset’ OR ‘Diabetes Mellitus, Noninsulin Dependent’ OR ‘T2DM’ OR ‘T2D’)

AND

Limitations (if possible)

Language English, Dutch, German

NIDDM, non-insulin-dependent diabetes mellitus

Study selection

After removing duplicates, the title and abstract of each article were screened by two of the authors (S. Fazeli Farsani and M. P. van der Aa) to exclude irrelevant studies. Articles were selected based on inclusion and exclusion criteria (Table 2). Reference lists of included articles were assessed to find additional articles. Full texts of all included articles were retrieved via the library of Utrecht University or through contact with the authors. Abstracts of conferences were not included in this review. Studies were excluded based on title and abstract if they were not population-based (e.g. studies were performed in specific populations like obese children and adolescents) or if they described prevalence or incidence of concurrent diseases in patients with type 2 diabetes.
Table 2

Inclusion/exclusion criteria for screening studies

Inclusion/exclusion criterion

Study attributes/characteristics of groups studied

Inclusion criteria

  Study design

Population-based observational studies

  Age

Children (0–9 yearsa) and/or adolescents (10–19 yearsa)

  Population

General population

  Outcome

Incidence/prevalence of type 2 diabetes

Exclusion criteria

  Language

Language other than English, German and Dutch

  Age

Study population of adults only

 Restricted groups

Study population restricted to a group of people (e.g. obese children)

aBased on WHO definition

Full text screening was performed when it was not clear whether the study population involved children and adolescents, or when the abstract was unclear about the type of diabetes. In case of disagreement the study was checked by a third author (A. de Boer or M. M. J. van der Vorst).

Data extraction and quality assessment

Once consensus on the included articles was achieved, data was extracted from the full text articles. The following data—author, country of the study, calendar time, study design and denominator used, diagnostic criteria, characteristics of the study population (age, sex and ethnicity), excluded cases, incidence (per 100,000 person-years) and prevalence (per 100,000 population)—were separately extracted, entered into a table by two of the authors (S. Fazeli Farsani and M. P. van der Aa) and then compared for similarity (see Table 3 and electronic supplementary material [ESM] Table 1).
Table 3

Studies investigating the incidence and prevalence of type 2 diabetes in children and adolescents

First author, year (by region)

Country

Calendar time

Study design and denominator used

Diagnosis of type 2 diabetes

Limitations

Asia

 Kitagawa, 1994 [31]

Japan

1975–1990

• Screening of school children

• Measurement of diabetes: urine glucose screening (using glucose oxidase tapes) and performing OGTT in cases with two positive urine tests

Case ascertainment: not applicable

• Denominator: not clearly stated

• Differentiation between type 1 and 2: based on insulin secretion ability, auto-antibodies, beta cell function. Type of diabetes was reported by healthcare provider

Response rate: approximately 60%

• Guidelines: WHO/US PHS

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (not excluding other types [e.g. MODY])

Representativeness: of Tokyo school children

Denominator: not clearly stated

 Urakami, 2005, 2006, 2007 [11, 12, 13]

Japan, Tokyo metropolitan area

1974–2004

• Population-based screening of school children

• Measurement of diabetes: urine glucose screening (using glucose oxidase tapes) and performing OGTT in cases with two positive urine tests

Case ascertainment: not applicable

• Denominator: total number of children examined in each period

• Differentiation between type 1 and 2: based on insulin secretion ability, checking for obesity, auto-antibodies, beta cell function. Type of diabetes was reported by healthcare provider

Response rate: not specified

• Guidelines: WHO/US PHS

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (not excluding other types [e.g. MODY])

Representativeness: of Tokyo school children

Denominator: not optimal

 Wei, 2003 [32]

Taiwan

1992–1999

• Nationwide screening of school children

• Measurement of diabetes: urine glucose screening (using Hemscomistix IV urine strip) and using FPG test in cases with 2 positive urine test, physical examination confirmed by a follow-up telephone survey

Case ascertainment: not applicable

• Denominator: not specified

• Differentiation between type 1 and 2: based on retrospective questionnaire interview with parents and reports from family physicians, history of using oral glucose-lowering agents (type 2) or insulin (type 1), not having diabetic acidosis, checking for obesity. Type of diabetes was reported by parents or family physician

Response rate: not complete

• Guidelines: ADA

Test of diabetes: not optimal

• Excluded cases: cases in which type of DM could not be confirmed

Misclassification of type 2 diabetes: yes (not excluding other types [e.g. MODY])

Representativeness: Taiwanese school children

Denominator: not specified

Australia and New Zealand

 Craig, 2007 [46]

Australia, NSW

2001–2006

• Prospective population-based incidence study (primary ascertainment was from Australian Paediatric Endocrine Group New South Wales Diabetes Registera, with secondary ascertainment from the national diabetes registryb [AIHW])

• Measurement of diabetes: physical examination and blood tests (fasting insulin and C-peptide)

Case ascertainment: overall more than 99%

• Denominator: from Australian Bureau of Statistics

• Differentiation between type 1 and 2: based on the presence of acanthosis nigricans, elevated fasting insulin and C-peptide auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: cases with secondary diabetes (e.g. cystic fibrosis or hyperinsulinism), genetic beta cell defects (MODY), not NSW residents

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, older adolescents or children from remote areas)

Representativeness: of NSW children and adolescents

Denominator: no limitation

 McMahon, 2004 [42]

Australia, WA

1990–2002

• Review of prospectively recorded diabetes on the WACDD databasec

• Measurement of diabetes: demographic and anthropometric data, blood tests (OGTT, fasting insulin, C-peptide, HbA1c)

Case ascertainment: not specified

• Denominator: from Australian Bureau of Statisticsd

• Differentiation between type 1 and 2: based on physical examination, anthropometry (weight, BMI), presence of acanthosis nigricans, family history, C-peptide and auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, not excluding other types [e.g. MODY])

Representativeness: not clear

Denominator: not specified

 Campbell-Stokes, 2005 [34]

New Zealand

1999–2000

• Monthly reporting of type 2 diabetes cases by paediatricians to the NZPSU and using hospital discharge data from NZHIS

• Measurement of diabetes: at least one of the following tests (random blood glucose, FPG, OGTT)

Case ascertainment: 95.2%

• Denominator: from 1996 census data (includes <15-year-old residents of New Zealand)

• Differentiation between type 1 and 2: based on demographics, ethnicity, family history, presence of acanthosis nigricans, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: optimal

• Excluded cases: cases secondary to drugs or stress (e.g. sepsis); cases >15 years old; not New Zealand residents

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of New Zealand children (<15 years)

Denominator: no limitation

 Jefferies, 2012 [40]

New Zealand, Auckland region

1995–2007

• Retrospective analysis of prospectively recorded data of the referred cases by Starship Paediatric diabetes service (regional care)

• Measurement of diabetes: Anthropometry criteria, laboratory tests (HbA1c), history of treatments

Case ascertainment: over 95%

• Denominator: National census data from New Zealand statistics

• Differentiation between type 1 and 2: based on obesity, family history, ethnicity, insulin resistance, presence of acanthosis nigricans, polycystic ovarian syndrome, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: not optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, levels of C-peptide and insulin were not measured)

Representativeness: of Auckland children (<15 years)

Denominator: no limitation

Caribbean

 Perez-Perdomo, 2005 [35]

Puerto Rico

1995–2003

• Cases were reported by 11 paediatric endocrinologists

• Measurement of diabetes: demographic, clinical and biochemical data, BMI, blood tests (C-peptide, fasting blood sugar)

Case ascertainment: 84.6% (of paediatric endocrinologists) and 85.3% (of defined cases)

• Denominator: age-specific population data obtained from municipalities

• Differentiation between type 1 and 2: based on obesity, family history, presence of acanthosis nigricans and insulin resistance. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: cases were classified according to national standards

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases and cases of type 2 diabetes who were visited by primary healthcare physicians)

Representativeness: of Puerto Rico children and adolescents

Denominator: no limitation

Europe

 Rami, 2003; Schober, 2009 [14, 15]

Austria

1999–2007

• Prospective population-based epidemiological study and nationwide registratione

• Measurement of diabetes: clinical and laboratory findings (C-peptide, insulin levels)

Case ascertainment: >93%

• Denominator: from the National Population Registry (Statistics Austria)

• Differentiation between type 1 and 2: based on obesity, family history, insulin resistance, presence of acanthosis nigricans, elevated levels of C-peptide, lack of ketonuria and insulin dependence, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: cases >15 years old

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of Austrian children ≤15 years old

Denominator: no limitation

 Neu, 2009 [36]

Germany, Baden-Württemberg

2004–2005

• Using diabetes registry (DIARY)f

• Measurement of diabetes: clinical examination, blood tests (C-peptide, HbA1c), information regarding therapy modalities

Case ascertainment: 97.2%

• Denominator: from the national census of 1987 and the subsequent annual updates

• Differentiation between type 1 and 2: based on BMI, HbA1c, family history, C-peptide levels, presence of acanthosis nigricans, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: duplicate entries

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of Baden-Württemberg area in Germany

Denominator: no limitation

 Ubink-Veltmaat, 2003 [37]

the Netherlands

1998–2000

• Prospective population-based studyg

• Measurement of diabetes: blood tests (glucose measurement, HbA1c), using glucose-lowering medications

Case ascertainment: not specified

• Denominator: from the Central Bureau for Statistics

• Differentiation between type 1 and 2: based on age. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: national guidelines of the Dutch college of general practitioners 1989 and 1999 (based on 1985 WHO and 1997 ADA criteria )

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, revising guidelines during the study period may have led to some under- or overestimation, lack of appropriate tests for classification, not excluding other types [e.g. MODY])

Representativeness: not clear

Denominator: no limitation

 Thunander, 2008 [43]

Sweden, Kronoberg

1998–2001

• Data obtained retrospectively from paediatric departments

• Measurement of diabetes: blood tests (FPG, C-peptide)

Case ascertainment: not specified

• Denominator: from regional and national authorities

• Differentiation between type 1 and 2: based on levels of C-peptide, auto-antibodies (not all children were checked). Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: WHO/ADA

Test of diabetes: optimal

• Excluded cases: cases with unconfirmed diagnosis of diabetes after extra testing

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, lack of appropriate tests for classification of all cases)

Representativeness: not clear

Denominator: no limitation

 Uckun-Kitapci, 2004 [18]

Turkey, Ankara

Not specified

• Cross-sectional population-based study in six randomly selected schools

• Measurement of diabetes: Standardised physical examination, blood tests (FPG, OGTT, and HbA1c)

Case ascertainment: not applicable

• Denominator: not specified

• Differentiation between type 1 and 2: not specified. Type of diabetes was reported by healthcare provider

Response rate: 86.9%

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: three cases as they were over 19 years of age, other ethnicities than white

Misclassification of type 2 diabetes: yes (not excluding other types [e.g. MODY])

Representativeness: of Ankara school children (12–18 years old)

Denominator: not specified

 Ehtisham, 2004 [27]

UK

2000

• Cross-sectional postal questionnaire survey of paediatric diabetes centres in the UKh

• Measurement of diabetes: physical examination, blood tests (insulin, C-peptide, lipid profile)

Case ascertainment: 75%

• Denominator: from the Office of National Statistics for mid-year 2000

• Differentiation between type 1 and 2: based on BMI, family history, presence of acanthosis nigricans, raised insulin and C-peptide, abnormal lipid profile, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: WHO

Test of diabetes: optimal

• Excluded cases: Not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of UK children

Denominator: no limitation

 Ehtisham, 2001 [28]

UK, Birmingham

2000

• Hospital-based study by the Paediatric Diabetes Subgroup of the Pan-Birmingham Diabetes Advisory Group

• Measurement of diabetes: blood tests (C-peptide, insulin)

Case ascertainment: not specified

• Denominator: not specified

• Differentiation between type 1 and 2: not clearly specified but they checked for the presence of acanthosis nigricans and elevated levels of insulin and C-peptide. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: not clearly specified

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: not clear

Denominator: not specified

 Feltbower, 2003 [29]

UK, Leeds

2000

• Hospital-based cross-sectional studyi

• Measurement of diabetes: demographic detail and clinical information

Case ascertainment: not specified

• Denominator: mid-year (2000) population estimates for the district of Leeds were used for each group (which is co-terminus with Leeds Health Authority); for South Asians and non-South Asians data derived from the 1991 Census

• Differentiation between type 1 and 2: based on clinical expert opinion, auto-antibodies (a few children). Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: WHO/ADA

Test of diabetes: optimal

• Excluded cases: patients with other forms of diabetes (other than type 1 diabetes, type 2 diabetes and MODY), uncertain type 1 diabetes or type 2 diabetes, secondary diabetes (e.g. secondary to pancreatectomy, haematoma or lipodystrophy), insulin-treated ‘J’ type (West Indian), and unclassified diabetes

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of children in Leeds

Denominator: no limitation

 Haines, 2007 [30]

UK and Republic of Ireland

2004–2005

• Prospective monthly surveillance through the BPSU frameworkj

• Measurement of diabetes: anthropometric data, physical examination and blood tests (fasting insulin, C-peptide)

Case ascertainment: 93% for BPSU; 91% for nurses and 94% of clinical questionnaires were received

• Denominator: from the Office of National Statistics

• Differentiation between type 1 and 2: based on insulin resistance, presence of acanthosis nigricans, increased fasting insulin or C-peptide, family history, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: duplicate cases, cases >17 years and cases who were diagnosed outside the study period or whose diagnosis was not non-type 1 diabetes

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of the UK and Republic of Ireland children

Denominator: no limitation

 Hsia, 2009 [41]

UK

January 1998 to December 2005

• Retrospective cohort study using UK IMS disease analyser databasek

• Measurement of diabetes: using prescriptions for glucose-lowering therapy as a proxy for detecting cases with diabetes

Case ascertainment: not specified

• Denominator: total number of children registered in the database in the same year

• Differentiation between type 1 and 2: using prescription of insulin and oral glucose-lowering drugs as a proxy to classify the type of diabetes

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: not optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases and cases treated with diet only, not excluding other types [e.g. MODY] and classification was based on prescription data)

Representativeness: not clear

Denominator: not optimal

North America

 Dean, 1992 [38]

Canada, North-Eastern Manitoba

1984–1990

• Referred cases to the diabetes clinicsl

• Measurement of diabetes: BMI, blood tests (HbA1c, serum insulin level), family history of type 2 diabetes

Case ascertainment: not specified

• Denominator: 5- to 14-year-old children in mid-1987 in Manitoba (Manitoba Health Service Commission)

• Differentiation between type 1 and 2: based on symptoms of polyuria and nocturia with ketonuria and severe hyperglycaemia may mimic type 1. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: National Diabetes Data Group (1979) guidelines

Test of diabetes: optimal

• Excluded cases: cases with the history of diabetic ketoacidosis

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of native Indian children in Manitoba

Denominator: no limitation

 Dean, 1998 [33]

Canada, St Theresa point First Nation

1996–1997

• Cross-sectional survey of school children

• Measurement of diabetes: blood tests (fasting serum glucose and insulin), anthropometric data

Case ascertainment: not applicable

• Denominator: not specified

• Differentiation between type 1 and 2: not specified. Type of diabetes was reported by healthcare provider

Response rate: 82%

• Guidelines: ADA

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (not excluding other types [e.g. MODY])

Representativeness: of remote northern Ojibwa-Cree community of St Theresa

Denominator: not specified

 Amed, 2010 [26]

Canada

2006–2008

• Prospective national surveillance (with collaboration of CPSP and CFPC-NaReS)m

• Measurement of diabetes: clinical presentation and laboratory investigations

Case ascertainment: 83%

• Denominator: from 2006 Canadian Census estimates; for specific ethnic groups population estimated from 2001 Canadian Censusn

• Differentiation between type 1 and 2: based on obesity, insulin resistance, family history, auto-antibodies (varied across Canada). Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: Canadian Diabetes Association 2003 clinical practice guidelines

Test of diabetes: optimal

• Excluded cases: duplicate cases

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of Canadian children

Denominator: no limitation

 Pavkov, 2007 [17]

USA, Gila River Indian Community, AZ

1965–2003

• Systematic population screeningo

• Measurement of diabetes: OGTT, BMI, self-reported treatment with glucose-lowering medicines (insulin or oral medicines)

Case ascertainment: not applicable

• Denominator: not specified

• Differentiation between type 1 and 2: not specified. Type of diabetes was reported by healthcare provider

Response rate: not specified

• Guidelines: not specified

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (revising guidelines during the study period may lead to some under or overestimation)

Representativeness: of Gila River Indians

Denominator: not specified

 Dabelea, 1998 [16]

USA, Gila River Indian Community, AZ

1967–1996

• Systematic population screeningo

• Measurement of diabetes: OGTT, BMI, medical history, family history

Case ascertainment: not applicable

• Denominator: not specified

• Differentiation between type 1 and 2: not Specified. Type of diabetes was reported by healthcare provider

Response rate: 53.2% to 96.3%

• Guidelines: WHO

Test of diabetes: optimal

• Excluded cases: not specified

Misclassification of type 2 diabetes: yes (revising guidelines during the study period may have led to some under or overestimation)

Representativeness: of Gila River Indians

Denominator: not specified

 Fagot-Campagna, 1999 [7]

USA, Gila River Community, AZ

1988–1997

• Systematic population screening of the Gila River community by NIH and also reported cases by the IHS from clinics in south-western USAp

NIH study:

NIH study:

• Denominator: for NIH was the exact number of adolescents examined at the NIH clinics; and for IHS was from the US census

• Measurement of diabetes: OGTT

Case ascertainment: not applicable

• Differentiation between type 1 and 2: not specified.

Response rate: not specified

Type of diabetes was reported by healthcare provider

Test of diabetes: optimal

• Guidelines: not specified

Misclassification of type 2 diabetes: yes (not excluding other types)

• Excluded cases: not specified

Representativeness: of Gila River Indians

IHS study:

Denominator: not optimal

•Measurement of diabetes: Medical records

IHS study:

• Differentiation between type 1 and 2: not specified. Type of diabetes was reported by healthcare provider

Case ascertainment: not specified

• Guidelines: not specified

Response rate: not applicable

• Excluded cases: not specified

Test of diabetes: not specified

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases, and Indians who did not use IHS services, and not excluding other types)

Representativeness: not clear

Denominator: no limitation

 Oeltmann, 2003 [39]

USA, SC

1999

• Population-based surveillance RLDRq

• Measurement of diabetes: using billing data to identify all potential patients who were billed with a diabetes related code

Case ascertainment: ∼98%

• Denominator: From the USA 2000 census data for the two-county region

• Differentiation between type 1 and 2: based on the latest clinical impression of the paediatric endocrinologist, auto-antibodies (not available for patients who were diagnosed years ago). Type of diabetes was reported by healthcare provider, parents or children

Response rate: not applicable

• Guidelines: ADA

Test of diabetes: not specified

• Excluded cases: 71 records were excluded because the status of diabetes could not be validated to type 1 diabetes or type 2 diabetes

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of African-American and non-Hispanic whites in SC

Denominator: no limitation

 Moore, 2003 [44]

USA, MT and WY

1999–2001

• Annually reviewing the medical records of all diabetic patients at six IHS facilities

• Measurement of diabetes: Demographic, clinical information, documentation of diagnostic blood tests (C-peptide, insulin), history of using glucose-lowering treatments, and other laboratory information

Case ascertainment: not specified

• Denominator: in this study the most currently available IHS user populations for youths ≤19 years were usedr

• Differentiation between type 1 and 2: based on BMI, presence of acanthosis nigricans, age, insulin resistance, elevated C-peptide and insulin, family history, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

– Prevalence: sum of the population estimates for 1995–1997, divided by 3

• Guidelines: ADA

Test of diabetes: optimal

– Incidence: 1997 population estimate minus the prevalent cases in 2001

• Excluded cases: American Indian youths who received services solely from non-IHS providers

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of American Indians in MT and WY who used IHS services

Denominator: no limitation

 Smith, 2007 [45]

USA, Chicago

1 January 1994 to 31 December 2003

• Population-based incidence study (CCDR)s

• Measurement of diabetes: from medical records and interviews

Case ascertainment: 85%

• Denominator: from census data

• Differentiation between type 1 and 2: physician diagnosis based on clinical judgement, and history of using glucose-lowering medications, BMI, signs of polycystic ovary syndrome or acanthosis nigricans. Type of diabetes was reported by healthcare provider

Response rate: not applicable

• Guidelines: not specified

Test of diabetes: not optimal

• Excluded cases: secondary cases of diabetes (e.g. due to cystic fibrosis, Prader-Willi syndrome or steroid use)

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of Chicago children and adolescents

Denominator: not optimal (inaccurate because of undercount of minorities in the used US census)

 SEARCH study (Bell 2009, Dabelea 2009, Lawrence 2009, Liu 2009, Mayer-Davis 2009, Liese 2006, Dabelea 2007) [19, 20, 21, 22, 23, 24, 25]

USA

2001 (prevalence), 2002–2005 (incidence)

• Observational multicentre population-based studyt

• Measurement of diabetes: diagnosed by physicians, or self-report of parents or children about the physician diagnosis of diabetes, questionnaire on medical history, a brief physical examination, blood tests (C-peptide, insulin, etc.)

Case ascertainment: 93% (for incidence study), 92% (for prevalence study)

• Denominator:

• Differentiation between type 1 and 2: based on anthropometry criteria, presence of acanthosis nigricans, C-peptide, auto-antibodies. Type of diabetes was reported by healthcare provider

Response rate: not applicable

– 2001: four geographically based sitesu which used non-military non-institutionalised 2000 census.

• Guidelines: based on recommendation of ADA expert committee

Test of diabetes: optimal

– 2002 and beyond: geographically based centres were using projections of population changes based on 2000 census to estimate denominator of incidence

• Excluded cases: cases of gestational diabetes, participants older than 20 years in the index year, non-residents of the study area, active duty military or institutionalised members, cases with maturity-onset of the young, hybrid, other types, or missing type were excluded

Misclassification of type 2 diabetes: yes (underestimation of asymptomatic cases)

Representativeness: of US children and adolescents

Denominator: no limitation

aCases of type 2 diabetes were reported by paediatricians, physicians, paediatric endocrinologists, endocrinologist, diabetes educators and nurses

bCases of insulin-treated diabetes obtained from the National Diabetes Supply Scheme

cCases of type 2 diabetes were reported from Princess Margaret Hospital Diabetes Unit (the only tertiary referral hospital for diabetic children in WA)

dEstimates of the indigenous population were made by the Epidemiology Branch of the WA Department of Health based on 2001 census

eNewly diagnosed type 2 diabetes cases were registered by the Austrian Diabetes Incidence Study Group based on clinical definition by a network covering all paediatric hospitals, wards and dialectologists

fCases of type 2 diabetes were reported and classified by healthcare providers in institutions belonging to the DIARY network including children hospitals (n = 31) and one diabetes centre in Baden-Württemberg (BW). In addition, diabetologists in private practice (n = 122), internal medicine units (n = 164) and a few other institutions (n = 6) in BW participated. Questionnaires were used and missing data were obtained by telephone calls

gCases of type 2 diabetes were reported by 61 general practitioners participating in the Zwolle Outpatient Diabetes project Integrating Available Care (ZODIAC) study

hCases of type 2 diabetes were reported by healthcare givers by a cross-sectional postal questionnaire survey. All paediatric diabetes centres and all consultants involved in the care of children with diabetes (281 paediatricians) participated in the survey, which was undertaken by the British Society for Paediatric Endocrinology and Diabetes Clinical Trials/Audit Group

iCases of type 2 diabetes were identified from diabetes clinic lists of the following hospitals: the General Infirmary at Leeds, Wharfedale General Hospital, St James’s University Hospital and Seacroft Hospital

jActive monthly reporting of type 2 diabetes cases by 2,665 consultant paediatricians in the UK and the Republic of Ireland through the BPSU of the Royal College of Paediatrics and Child Health, with additional (every 2 months) reports from 157 specialist diabetes nurses to identify adolescents directly referred to adult services. A questionnaire was sent to the clinicians who reported new cases to complete the information about cases

kGeneral practitioners contribute data to the IMS DA database

lCases of type 2 diabetes were obtained from referred patients to the diabetes clinic at the Children’s Hospital of Winnipeg and also family practitioners who provide on-site care or consultation to all northern Manitoba Indian reserves and chronic disease registry of the Medical Services Branch, Manitoba Region Department of National Health and Welfare was searched

mA network of paediatricians (n = 2,567), paediatric endocrinologists, family physicians (n = 98) and adult endocrinologists (n = 49) was established for a surveillance study. New cases of physician-diagnosed type 2 diabetes were reported monthly and followed by a detailed questionnaire on clinical presentation and other information. Physician classifications were reviewed by clinician investigators

nPopulation estimates for children belonging to Hispanic, Middle Eastern or mixed ethnicity were not available and therefore were not included

oEvery 2 years, OGTT and anthropometric measurements were carried out in Pima Indians (≥5 years) regardless of their health situation and new cases of type 2 diabetes were obtained

pStudy population of NIH was limited to Pima Indians and their close relatives while in the IHS study all other south-western tribes were included

qPhysician diagnosed cases of type 2 diabetes residing in a two-county region were ascertained from hospitals, the sole office of paediatric endocrinologists, two large-area paediatric outpatient clinics, adult endocrinology clinics and several smaller sources

rIHS user populations were somewhat larger than the census estimates of American Indian youth living in counties or near these reservations

sCases of type 2 diabetes for CCDR were ascertained primarily from medical records of 29 hospitals and general paediatric services located in Chicago and its suburbs. The Illinois Department of Public Aid payment database provided a third source of cases through 2001

tPhysician-diagnosed cases of type 2 diabetes were obtained in this study

uFour geographically based sites were Cincinnati, Colorado, Seattle and South Carolina

AIHW, Australian Institute of Health and Welfare; BPSU, British Paediatric Surveillance Unit; CCDR, Chicago Childhood Diabetes Registry; CFPC-NaReS, College of Family Physicians of Canada National Research System; CPSP, Canadian Paediatric Surveillance Program; DIARY, Diabetes Registry; FPG, fasting plasma glucose; IHS, Indian Health Service; NIH, National Institute of Health; NZHIS, New Zealand Health Information Service; NZPSU, New Zealand Paediatric Surveillance Unit; RLDR, Richmond/Lexington County Childhood and Adolescents Diabetes Registry; US PHS, United States Public Health Service; WACDD, Western Australian Children’s Diabetes Database

Data synthesis and analysis

Two authors (S. Fazeli Farsani and M. P. van der Aa) checked the extracted data. For the methodological evaluation the following aspects were assessed: response rates of population-based screening studies (the percentage of the invited population that consented to participate), ascertainment rate of studies in which children diagnosed by doctors were captured (the percentage of children with type 2 diabetes identified and registered by healthcare providers of all diagnosed children with type 2 diabetes), the performed diagnostic tests (e.g. measurement of glucose concentration in urine or in plasma), diagnostic guidelines, classification and differentiation between type 1 and 2 diabetes, quality of denominators used to calculate incidence and prevalence rates, sample size of the population on which the rate estimates were based and representativeness of the evaluated population for the whole population in the defined geographic area.

Due to substantial methodological heterogeneity and considerable variations in study populations a quantitative meta-analysis of the data was not performed.

Results

The search of the five electronic databases yielded 5,920 articles, of which 145 were potentially relevant after screening title and abstract. Out of these papers, 37 articles met the inclusion criteria. Manual reference screening did not result in additional articles (Fig. 1). All included articles were written in English. Of the total of 37 included articles, 12 described prevalence studies, 16 described incidence studies and 9 contained both incidence and prevalence data. As shown in Table 3, the included studies originated from four continents (13 different countries). Most of the studies were conducted in the USA and North European countries, while there were no studies from African or South American countries or from a major part of Asia. Three Japanese [11, 12, 13], two Austrian [14, 15] and two American studies [16, 17] were based on the same data source, albeit the data were analysed differently and comprised other study periods. There was a substantial variation in population characteristics and methodological aspects in the identified studies (Table 3), as described below.
Fig. 1

Flowchart of search results. DSR, Database of Systematic Reviews

Study population and ethnicities

The number of children in the populations in which type 2 diabetes was detected ranged from 1,647 [18] to several millions [11, 12, 13, 19, 20, 21, 22, 23, 24, 25] and covered different age categories. The 37 included studies described data about many different ethnicities—Japanese, Taiwanese, multi-ethnic, New Zealand-Maori, New Zealand-European, Turkish, South Asian, Black, Aboriginal, African-American, American Indian, Pima Indian, Ojibwa-Cree, non-Hispanic whites, Hispanic, etc. (ESM Table 1).

Calendar time and duration of the study

Calendar time of the 37 studies varied widely. The first population-based study that reported on the epidemiology of type 2 diabetes in children and adolescents was started in 1965 in Pima Indians in the USA [17], while the most recent study was a Canadian study performed between 2006 and 2008 [26]. Pavkov et al reported the longest follow-up of 39 years [17], followed by a Japanese study with a follow-up of 30 years [11, 12, 13]. The shortest follow-up was 1 year [27, 28, 29, 30].

Study design and case ascertainment methods

Different methods were used in the studies for investigating incidence and prevalence of type 2 diabetes. In nine studies, cases of type 2 diabetes were found through population screening [11, 12, 13, 16, 17, 18, 31, 32, 33]. The population that was invited for screening also varied between studies. The complete Pima Indian population was screened regardless of their health situation [16, 17], in Japan and Taiwan school children from all schools were screened [11, 12, 13, 31, 32] and in Ankara (Turkey) only children in six randomly selected schools participated in the screening programme [18]. In a Canadian study, all school children (aged 4–19 years) in the remote northern Ojibwa-Cree community of St Theresa Point First Nation were screened [33].

The second method, which was the one most commonly applied (in 20 studies), was to capture all cases of type 2 diabetes in children and adolescents that had been diagnosed by the healthcare provider. In these 20 studies, healthcare providers with different backgrounds (e.g. general practitioners, paediatric endocrinologists, adult endocrinologists, diabetes nurses, etc.) participated in a surveillance system [14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 34, 35, 36, 37, 38, 39, 40]. The numbers of diagnosed cases were related to denominators of numbers of children in the relevant geographical areas (Table 3).

A third method, applied in six studies, involved the use of administrative databases (e.g. using prescriptions for glucose-lowering treatment listed in a database as a proxy for finding cases of diabetes [41]) or medical chart reviews in hospitals or medical centres [29, 42, 43, 44, 45].

Two of the included studies (Fagot-Campagna et al and Craig et al) used a combination of methods for identifying cases of type 2 diabetes [7, 46]. Fagot-Campagna et al used both population screening and medical chart reviews while Craig et al used administrative databases and a surveillance system to find cases of type 2 diabetes.

In the population-based screening studies, when reported, response rates varied from 60% to 96%. In the studies that used healthcare provider diagnosis or administrative databases, case ascertainment ranged from 53% to 99% (Table 3).

Diagnosis of type 2 diabetes and classification

Another source of variation in the methodology of the included studies was the use of different diagnostic guidelines for type 2 diabetes. In most studies, the ADA or WHO guidelines were used to identify cases. In a few studies, national guidelines were used to diagnose type 2 diabetes [26, 37, 38].

Different diagnostic tests were used to identify type 2 diabetes cases (e.g. in two screening programmes, urine strips were used to diagnose glycosuria and children with two positive results were checked with additional diagnostic tests) [11, 12, 13, 31, 32]. In most studies diabetes was diagnosed based on clinical presentation in combination with diagnostic tests (e.g. OGTT, fasting plasma glucose (FPG), fasting plasma insulin and/or C-peptide level, etc.). Classification of the type of diabetes was another major issue. Differentiation between type 2 and type 1 diabetes was difficult, especially in the earliest and retrospective studies and in studies in which questionnaires (or telephone surveys) were used. In the 37 included studies, different criteria were used to differentiate between type 1 and type 2 diabetes (Table 3) (e.g. 26 of the included studies reported that they used tests for detecting auto-antibodies or beta cell function [11, 12, 13, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 34, 36, 39, 40, 42, 43, 44, 45, 46]).

In almost all studies, the healthcare providers or investigators confirmed the type of diabetes, except in two studies in which the type of diabetes was reported by parents or children themselves via questionnaire or telephone survey [32, 39].

Incidence and prevalence estimates

Data on incidence and prevalence from all studies are summarised in Figs 2 and 3. Incidence and prevalence rates were found to vary widely depending on age, sex and ethnicity of the study population and geographical region, resulting in a range of 0–330 per 100,000 person-years for incidence and 0–5,300 per 100,000 children and adolescents for prevalence [7, 17, 18, 37]. Detailed information from all studies is presented in ESM Table 1.
Fig. 2

Overview of the reported incidences of type 2 diabetes per 100,000 person-years (because of considerable variations in the observed rates, incidence data are graphed on a base 10 logarithmic scale). The incidence rates were calculated for male/female populations. White bars represent children (0–9 years), grey bars represent adolescents (10–19 years) and black bars represent children and adolescents (0–19 years)

Fig. 3

Overview of the reported prevalences of type 2 diabetes per 100,000 children and/or adolescents (because of considerable variations in the observed rates, prevalence data are graphed on a base 10 logarithmic scale). The prevalence rates were calculated for male/female populations, except for the prevalence rate of 3,600 per 100,000 which was for Ojibwa-Cree females. White bars represent children (0–9 years), grey bars represent adolescents (10–19 years) and black bars represent children and adolescents (0–19 years)

The lowest incidence rates were observed in European countries. The Netherlands, with no new cases of type 2 diabetes during the 1998–2000 period, and Austria, with an incidence rate of 0.29 per 100,000 person-years for the 1999–2007 period, had the lowest reported incidences [14, 15, 37].

The highest incidence of type 2 diabetes in youth was observed in 4- to 15-year-old Pima Indians in the USA (330 per 100,000 person-years) [17]. This was followed by the Navajo population (USA), American Indians (USA) and African-Americans (USA) (27.7, 23.3 and 19, respectively, per 100,000 person-years) [20, 23, 44]. The Aboriginal population in Canada also had a high incidence rate (23.3 per 100,000 person-years) [26] (Fig. 2).

Among European countries, a study from the UK showed incidence rates of type 2 diabetes stratified by ethnicity: black individuals had 3.9 cases per 100,000 person-years, while South Asian and white individuals had rates of 1.25 and 0.35 per 100,000 person-years, respectively [30].

In Europe, the UK had the highest number of prevalence studies [27, 28, 29, 41]. Of four prevalence studies in the UK, two covered the whole country but produced different results. In one study, the prevalence for the population 0–16 years of age in 2000 was reported to be 0.21 per 100,000 children/adolescents, while in another study in the same year the reported prevalence rate was 1.9 per 100,000 for the 0- to 18-year-old study population [27, 41]. Among the European countries, the highest prevalence rate of type 2 diabetes was reported in 15- to 19-year-old adolescents in Leeds (UK) with a prevalence of 9 per 100,000 adolescents in 2000 [29].

The highest prevalence of type 2 diabetes in youth was observed in female Pima Indians (USA) aged 15–19 years (5,300 per 100,000 in 1987–1996), while for male Pima Indians the rate was lower (3,800 per 100,000) [16, 17]. For 10- to 14-year-old Pima Indians in the same time period, the respective prevalence for female and male subjects was 2,900 and 1,400 per 100,000 [7]. The Ojibwa-Cree population in Canada had the second highest prevalence rate with 3,600 per 100,000 for 10- to 19-year old females and 1,100 per 100,000 for 4- to 19-year-old male and female children and adolescents in 1996–1997 [33] (Fig. 3). High prevalence rates were also reported in American Indians, Navajo population and African-American children and adolescents [20, 23, 24, 25]. Among the different ethnicities in the USA, non-Hispanic white individuals had the lowest prevalence of type 2 diabetes [24, 25] (ESM Table 1).

Discussion

According to the findings of this systematic review, the epidemiological studies of type 2 diabetes in children and adolescents differed widely in population characteristics and study methods, which resulted in a substantial variation in incidence and prevalence estimates. While these differences in the rates of type 2 diabetes could in part be explained by differences in age, ethnicity, country and calendar time, methodological dissimilarities contributed as well. Referring to two studies performed in the UK in 2000, Hsia et al reported a prevalence rate for type 2 diabetes which was almost ten times higher than that reported by Ehtisham et al [27, 41]. Although there was only a slight difference in age between the two study populations, the different methods used (Ehtisham et al used a survey to find cases of type 2 diabetes while Hsia et al used an administrative database) and the lower case ascertainment in the study by Ehtisham et al probably led to an underestimation of the type 2 diabetes cases. Another example of the impact of methodological differences on the rate estimates is the study of Fagot-Campagna et al, which compared the prevalence of type 2 diabetes in adolescents aged 15–19 years, obtained from two different sources using different methods of data collection and analysis. The prevalence estimate of 5.1% vs 0.46% shows the remarkable effect of methodological differences on rate estimation [7].

In most of the studies included in this review, cases of type 2 diabetes were reported by healthcare providers who participated in a surveillance system. There are several drawbacks of using this method to estimate the occurrence of type 2 diabetes. First, children and adolescents with type 2 diabetes may remain asymptomatic and undiagnosed for a long period of time [47, 48] leading to underestimation of the number of type 2 diabetes cases [14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 34, 37, 39, 43, 44, 46]. Although the Princeton study recently showed that undiagnosed type 2 diabetes is very uncommon in adolescents and that this has not changed over the last decades [49], the findings from the Princeton study probably cannot be extrapolated to countries in which patients have only limited access to healthcare and/or the healthcare system has a suboptimal quality. Second, the quality of detection and classification of type 2 diabetes might differ between healthcare providers [29, 32, 35, 36]. This may be related to the different backgrounds of the healthcare providers (e.g. general practitioners, paediatricians and endocrinologists have different test strategies) and also to the lack of an internationally agreed diagnostic method and classification system for identifying cases of type 2 diabetes in children and adolescents. Third, case ascertainment rates in surveillance systems showed substantial differences, again leading to different rates and underestimation of incidence and prevalence rates [27, 41].

Administrative databases (e.g. prescription databases) are sometimes used for incidence and prevalence estimation, with the advantage that this approach for some aspects is less susceptible to ascertainment and selection biases. For instance when a prescription record database of pharmacies is used, the registration of prescriptions is often similar and complete [50]. However, the problem of missing undiagnosed cases of type 2 diabetes and differences in testing and treatments of diabetes between healthcare providers remain important limitations [41].

Retrospectively reviewing the medical charts and patient files in medical centres to find cases of diabetes, and to classify them based on the retrieved data, is often suboptimal because of missing information and differences by which healthcare providers test for diabetes and record medical information [44].

For all three methods mentioned above (capturing of cases by participation of healthcare providers in surveillance systems, administrative databases and reviewing of medical charts) an important difficulty is that of retrieving a valid denominator. Often the denominator originates from organisations registering vital statistics. A valid demarcation of the area from which the cases derive is a challenge.

Although population-based screening was not often applied among our 37 studies, it has a number of important advantages when compared with the other methods discussed. The starting point is that all people, regardless of their health situation, are screened and the same valid screening method and diagnostic criteria can be used. Thereby, the chance of finding all (and even asymptomatic undiagnosed) type 2 diabetes cases will increase. Furthermore, the denominator is directly available, being the total number of screened children and adolescents. However, in these population-based screening programmes some important issues should also be taken into account. For instance, non-participation of invited children (suboptimal response rate) can lead to biased estimations. Also, just as in the other methods, the sensitivity and specificity of the diagnostic tests, with the potential risks of under- or over-diagnosis, respectively, can be a problem. Wei et al reported that the sensitivity of the glycosuria test used in the screening for diabetes ranged from 20% to more than 80% [32]. Finally, the performance of a population-based screening is probably more costly than the other methods. As far as we know the different methods have not been compared with respect to cost-effectiveness. Recently Wu et al conducted a cost-effectiveness analysis of screening strategies for identifying type 2 diabetes and dysglycaemia (prediabetes) in children and adolescents (aged 10–17 years) [51]. Although they found that the cost of screening per case was high for diabetes, they mentioned that screening for diabetes in youth could be more cost-effective if dysglycaemia was explicitly considered as a screening outcome. However, Wu et al did not address the benefits and harms of early detection of type 2 diabetes and prediabetes in children and adolescents.

Even if case ascertainment, classification and definition of type 2 diabetes and response rates are satisfactory, the researchers involved in all methods face the challenge of choosing the right sample(s) of the population to be really representative for the intended population [7]. Regional differences may be expected in vital statistics, reflecting geographical, ethnic, genetic, environmental and socioeconomic factors. For this reason, the demographic characteristics of the population sample should be well described and the analysis should take into account such differences.

After considering all methodological differences, we can state in general that ethnic minorities have higher rates of type 2 diabetes than white individuals in almost all countries. There is also a great difference among type 2 diabetes rates in ethnic minorities in the USA and Canada compared with ethnic groups in other locations (e.g. in Europe and Australia) [19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 34, 46]. Although genetics plays an important role in the development of diabetes, environmental and lifestyle factors, such as dietary habits and having a sedentary lifestyle, are also relevant. For instance, Pima Indians in Arizona have the highest reported prevalence of type 2 diabetes in the world [7] while Pima Indians in Mexico with the same ethnicity have a much lower prevalence [7]. Pima Indians in Mexico have a healthier and more active lifestyle than Arizona Pima Indians, which indicates the importance of environmental factors in the development of type 2 diabetes [7].

Both incidence and prevalence data were considerably higher in female vs male subjects and, in addition, there was a great difference between adolescents (aged 10–19 years) and children (aged 0–9 years) [24, 25].

Concerning changes in the occurrence of type 2 diabetes over time, existing data are limited for most countries. Only a few studies (e.g. Urakami et al in Japan and Pavkov et al in the USA) evaluated trends in incidence/prevalence of type 2 diabetes over a prolonged period of time [11, 12, 13, 17]. Pavkov’s study demonstrated a significant increase in type 2 diabetes rates while the Japanese study showed a decrease in incidence of type 2 diabetes in Japanese school children [11, 12, 13, 17].

The lack of studies performed in some parts of the world, mostly developing countries, and the absence of data on the socioeconomic status of the study populations are limitations of our review. In addition, the heterogeneity of study designs and population characteristics did not allow for a more rigorous quantitative analysis (such as a meta-analysis).

In conclusion, the incidence and prevalence of type 2 diabetes in children and adolescents show important variations among countries and ethnic groups worldwide, caused by both population characteristics and methodological differences. Based on our findings it is important to continue to follow global trends in the incidence and prevalence of type 2 diabetes in the young population and to use a valid study design, appropriate diagnostic tools and the same diagnostic criteria. Population-based screening programmes appear to fulfil most of these requirements although the cost-effectiveness of such programmes is not clear yet.

Notes

Acknowledgements

The authors would like to thank N. Zuiverloon (librarian at the library of Utrecht University) for her helpful suggestions for searching databases.

Funding

No funding source had any role in the design and conduct of the study, the collection, management, analysis and interpretation of the data or the preparation, review or approval of the manuscript.

The Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, has received unrestricted funding from the Netherlands Organisation for Health Research and Development (ZonMW), the Dutch Health Care Insurance Board (CVZ), the Royal Dutch Pharmacists Association (KNMP), the private-public funded Top Institute Pharma (www.tipharma.nl, includes co-funding from universities, government, and industry), the EU Innovative Medicines Initiative (IMI), the EU 7th Framework Program (FP7) and the Dutch Ministry of Health and industry (including GlaxoSmithKline, Pfizer, and others).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript, no support from any organisation for the submitted work, no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work.

Contribution statement

SFF and MPA searched the literature and extracted data. In case of disagreement AB and MMJV checked the study. All authors discussed the data and interpreted the results. SFF and MPA wrote the manuscript. MMJV, CAJK and AB reviewed and edited the manuscript. All authors approved the final version and take full responsibility for the contents of the manuscript.

Supplementary material

125_2013_2915_MOESM1_ESM.xlsx (208 kb)
ESM Table 1 (XLSX 207 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • S. Fazeli Farsani
    • 1
  • M. P. van der Aa
    • 2
  • M. M. J. van der Vorst
    • 2
  • C. A. J. Knibbe
    • 3
    • 4
  • A. de Boer
    • 1
  1. 1.Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS)Utrecht UniversityUtrechtthe Netherlands
  2. 2.Department of PaediatricsSt Antonius HospitalNieuwegeinthe Netherlands
  3. 3.Department of Clinical PharmacySt Antonius HospitalNieuwegeinthe Netherlands
  4. 4.Division of Pharmacology, Leiden/Amsterdam Centre for Drug ResearchLeiden UniversityLeidenthe Netherlands

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