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Projections of the prevalence of hyperglycaemia in pregnancy in 2019 and beyond: Results from the International Diabetes Federation Diabetes Atlas, 9th edition

  • Lili Yuen
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    School of Medicine, Western Sydney University, Campbelltown, Australia
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  • Pouya Saeedi
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    International Diabetes Federation, Brussels, Belgium
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  • Musarrat Riaz
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    Baqai Medical University, Karachi, Pakistan
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  • Suvi Karuranga
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    International Diabetes Federation, Brussels, Belgium
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  • Hema Divakar
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    Divakar’s Specialty Hospital, Bengaluru, India

    Federation of Obstetric and Gynaecological Societies of India (FOGSI), India
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  • Naomi Levitt
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    University of Cape Town, South Africa
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  • Xilin Yang
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    Tianjin Medical University, Tianjin, China
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  • David Simmons
    Correspondence
    Corresponding author at: School of Medicine, Western Sydney University, Campbelltown, New South Wales, Australia.
    Affiliations
    International Diabetes Federation Atlas 9th Edition Hyperglycaemia in Pregnancy Special Interest Group (HIP-SIG), Belgium

    School of Medicine, Western Sydney University, Campbelltown, Australia
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Published:September 10, 2019DOI:https://doi.org/10.1016/j.diabres.2019.107841

      Abstract

      Aim

      Hyperglycaemia in pregnancy (HIP) is one of the most common complications of pregnancy. This study aims to examine the projected HIP prevalence in 2030 and 2045 using multiple methods.

      Methods

      The International Diabetes Federation Diabetes Atlas 2019 prevalence was projected to 2030 and 2045 by: (1) carrying forward the 2019 age-adjusted prevalence rates; (2) applying a linear regression of the past four editions of the IDF Diabetes Atlas; (3) applying a regression of the previous editions with the most consistent trend, followed by extrapolation from the 9th edition HIP estimate.

      Results

      Respectively, for 2030 and 2045, Method 1 projected a declining HIP rate with prevalences of 14.0% and 13.3%, Method 2 projected an increasing HIP prevalence at 16.5% and 18.3%, Method 3 predicted stabilisation of the rate from 16.0% to 15.8%.

      Conclusion

      Assuming other factors remain unchanged, our best estimation of age-adjusted HIP will show stabilisation between 2019 and 2045 of 15.8% to 16.0%. However, this estimate is confounded by the heterogeneity of studies and the influence of different gestational diabetes mellitus diagnostic criteria. To provide accurate future comparisons we recommend standardising the diagnostic criteria to the International Association of Diabetes in Pregnancy Study Groups.

      1. Introduction

      Hyperglycaemia in pregnancy (HIP) is described as the most common metabolic abnormality in pregnancy but the burden globally and the extent of a rise over time is confounded by a variety of factors including increasing population, maternal age and obesity [
      • Ferrara A.
      Increasing prevalence of gestational diabetes mellitus: a public health perspective.
      ,
      • Jiwani A.
      • Marseille E.
      • Lohse N.
      • Damm P.
      • Hod M.
      • Kahn J.G.
      Gestational diabetes mellitus: results from a survey of country prevalence and practices.
      ,
      • Kampmann U.
      • Madsen L.R.
      • Skajaa G.O.
      • Iversen D.S.
      • Moeller N.
      • Ovesen P.
      Gestational diabetes: a clinical update.
      ]. In 2014, the World Health Organization (WHO) has defined HIP as diabetes first detected at any time during pregnancy, along with pre-existing diabetes, and are further sub-classified as diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM) [

      World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes research and clinical practice. 2014;103: pp. 341–363.

      ]. Prior to this it had been defined as “any degree of glucose intolerance with onset or first recognition during pregnancy” [
      • Metzger B.E.
      • Coustan D.R.
      Summary and recommendations of the fourth international workshop-conference on gestational diabetes mellitus. The organizing committee.
      ].
      There is a documented global rise in the prevalence of type 2 diabetes (T2D) [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ,

      International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.

      ,

      International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels, Belgium: International Diabetes Federation; 2017. p. http://www.diabetesatlas.org/.

      ,

      International Diabetes Federation. IDF Diabetes Atlas. 7th ed. Brussels, Belgium: International Diabetes Federation; 2015. p. http://www.diabetesatlas.org/.

      ]. We would similarly expect that prevalence of HIP would be steadily increasing, which has been reported in the past [
      • Ferrara A.
      Increasing prevalence of gestational diabetes mellitus: a public health perspective.
      ,
      • Colagiuri S.
      • Falavigna M.
      • Agarwal M.M.
      • Boulvain M.
      • Coetzee E.
      • Hod M.
      • et al.
      Strategies for implementing the WHO diagnostic criteria and classification of hyperglycaemia first detected in pregnancy.
      ]. However, recent factors such as access to universal gestational diabetes screening and changes in diagnostic criteria have confounded these estimates and caused a substantial change in the reported prevalence rates. [

      HAPO Study Cooperative Research Group, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. The New England journal of medicine. 2008; 358 :pp. 1991–2002.

      ,
      • Trujillo J.
      • Vigo A.
      • Duncan B.B.
      • Falavigna M.
      • Wendland E.M.
      • Campos M.A.
      • et al.
      Impact of the international association of diabetes and pregnancy study groups criteria for gestational diabetes.
      ,
      • Dabelea D.
      • Snell-Bergeon J.K.
      • Hartsfield C.L.
      • Bischoff K.J.
      • Hamman R.F.
      • McDuffie R.S.
      • et al.
      Increasing prevalence of gestational diabetes mellitus (GDM) over time and by birth cohort: Kaiser permanente of Colorado GDM screening program.
      ].
      While the diagnosis of DIP is often clear cut, there has been controversy around the diagnosis of GDM without universal consensus as to the diagnostic method to best diagnose GDM. The International Association of Diabetes and Pregnancy Study Group (IADPSG) proposed a standardised strategy for the detection and diagnosis of GDM during the first antenatal visit with a fasting glucose level <5.1 mmol/L then following up with 75-g oral glucose tolerance test (OGTT) at 24 to 28 weeks’ gestation with diagnostic criteria as detailed in Fig. 1. Many countries have adopted the IADPSG’s interpretation of the guidelines drawn from the Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study, and has been ratified by the World Health Organization [

      World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes research and clinical practice. 2014;103: pp. 341–363.

      ] along with many other national groups [

      Nankervis A, McIntyre HD, Moses R, Ross GP, Callaway L, Porter C, et al. ADIPS Consensus Guidelines for the Testing and Diagnosis of Hyperglycaemia in Pregnancy in Australia and New Zealand. In: Society ADiP, editor.2014.

      ,

      American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes care. 2018; 41: pp. S13–S27.

      ,
      • Hod M.
      • Kapur A.
      • Sacks D.A.
      • Hadar E.
      • Agarwal M.
      • Di Renzo G.C.
      • et al.
      The international federation of gynecology and obstetrics (FIGO) initiative on gestational diabetes mellitus: a pragmatic guide for diagnosis, management, and care.
      ]. However, there are national groups slow to adopt an alternative diagnosis of GDM due to resource, socio-economic, cultural and logistic constraints [
      • Yuen L.
      • Wong V.W.
      • Simmons D.
      Ethnic disparities in gestational diabetes.
      ,
      • Macaulay S.
      • Dunger D.B.
      • Norris S.A.
      Gestational diabetes mellitus in Africa: a systematic review.
      ], such as Canadian Diabetes Association (CDA) [

      Diabetes Canada Clinical Practice Guidelines Expert C, Feig DS, Berger H, Donovan L, Godbout A, Kader T, et al. Diabetes and Pregnancy. Canadian journal of diabetes. 2018; 42 Suppl 1:pp. S255–S82.

      ], Diabetes in Pregnancy Study Group of India (DIPSI) [
      • Seshiah V.
      • Banerjee S.
      • Balaji V.
      • Muruganathan A.
      • Das A.K.
      • Diabetes Consensus G.
      Consensus evidence-based guidelines for management of gestational diabetes mellitus in India.
      ], Finnish Medical Society Duodecin [

      Finnish Medical Society Duodecim. Gestational Diabetes: Current Care Summary. 2015 http://www.nfog.org/files/guidelines/150519 Finland GDM.pdf.

      ], New Zealand Ministry of Health [

      New Zealand Ministry of Health. Diabetes in Pregnancy: Quick reference guide for health professionals on the screening, diagnosis and treatment of gestational diabetes in New Zealand. 2014 https://www.health.govt.nz/system/files/documents/publications/diabetes-in-pregnancy-quick-reference-guide-dec14-v4.pdf.

      ] and National Institute for Health Care and Excellence (NICE) [

      National Institute for Health and Care Excellence. Diabetes in pregnancy: management of diabetes and its complications from preconception to the postnatal period. (NG3). 2015.

      ]. DIPSI recommends the pragmatic use of a non-fasting OGTT while other national groups recommend a higher fasting, and differing 2-hour glucose level thresholds. Table 1 lists the HIP diagnostic criteria currently used in the published literature to date. These differences are likely to have a significant impact on the estimation of GDM prevalence.
      Figure thumbnail gr1
      Fig. 1Flow diagram of studies selected for inclusion in determining hyperglycaemia in pregnancy prevalence estimates.
      Table 1Diagnostic and screening methods currently in use for estimating gestational diabetes and hyperglycaemia first detected in pregnancy. Gestational Diabetes Mellitus. The table lists below the most commonly used screening methods for estimating gestational diabetes around the world in publications to date, based on universal screening using a fasting 75-gram oral glucose tolerance test (OGTT) with serum glucose levels measured at 0, 1, and 2 h. A 3 h 100-gram OGTT is also described but uncommonly used.
      CriteriaYear & ReferenceFasting1-hour2-hour3-hourNotes
      mg/dLmmol/Lmg/dLmmol/Lmg/dLmmol/Lmg/dLmmol/L
      American Diabetes Association (ADA)/ American College of Obstetricians and Gynaecologists (ACOG)2003, 2018

      American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes care. 2018; 41: pp. S13–S27.

      ,

      American Diabetes Association. Gestational diabetes mellitus. Diabetes care. 2003;26 Suppl 1:pp. S103–5.

      955.3180
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      10.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      1558.61407.8Recommends either the IADPSG one step or two-step approach; initial screening by measuring plasma or serum glucose concentration after 1-h 50-g oral glucose load (GCT). Those exceeding the cut-off perform either a 100 g OGTT or 75 g OGTT, requiring two or more venous plasma concentrations to be met or exceed the threshold.
      Australian Diabetes in Pregnancy Society (ADIPS)2014

      Nankervis A, McIntyre HD, Moses R, Ross GP, Callaway L, Porter C, et al. ADIPS Consensus Guidelines for the Testing and Diagnosis of Hyperglycaemia in Pregnancy in Australia and New Zealand. In: Society ADiP, editor.2014.

      925.1180
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      10.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      1538.5
      Diabetes Canada Clinical Practice Guidelines2018

      Diabetes Canada Clinical Practice Guidelines Expert C, Feig DS, Berger H, Donovan L, Godbout A, Kader T, et al. Diabetes and Pregnancy. Canadian journal of diabetes. 2018; 42 Suppl 1:pp. S255–S82.

      955.310.69.0Listed is the preferred approach, the alternate approach is the IADPSG
      Diabetes in Pregnancy Study Group of India (DIPSI)2014
      • Seshiah V.
      • Banerjee S.
      • Balaji V.
      • Muruganathan A.
      • Das A.K.
      • Diabetes Consensus G.
      Consensus evidence-based guidelines for management of gestational diabetes mellitus in India.
      1407.8Uses a non-fasting 75 g OGTT
      European Association for the Study of Diabetes (EASD)1991
      • Lind T.
      • Phillips P.R.
      Influence of pregnancy on the 75-g OGTT. A prospective multicenter study. The diabetic pregnancy study group of the European association for the study of diabetes.
      110
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      /126
      6.1
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      /7.0
      162
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      /180
      9.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      /10.0
      International Federation of Gynaecology and Obstetrics (FIGO)2015
      • Hod M.
      • Kapur A.
      • Sacks D.A.
      • Hadar E.
      • Agarwal M.
      • Di Renzo G.C.
      • et al.
      The international federation of gynecology and obstetrics (FIGO) initiative on gestational diabetes mellitus: a pragmatic guide for diagnosis, management, and care.
      925.1180
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      10.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      1538.5
      International Association of Diabetes and Pregnancy Society Groups (IADPSG)2010

      International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International Association of Diabetes and Pregnancy Study Groups Recommendations on the Diagnosis and Classification of Hyperglycemia in Pregnancy. Diabetes care. 2010;33:pp. 676–82.

      925.1180
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      10.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      1538.5Recommends exclusion of GDM with a fasting plasma glucose level of less than 5.1 mmol/L during the first prenatal visit on all or high risk women.
      National Institute for Health and Care Excellence (NICE)2015

      National Institute for Health and Care Excellence. Diabetes in pregnancy: management of diabetes and its complications from preconception to the postnatal period. (NG3). 2015.

      5.67.8
      World Health Organization (WHO)1999
      • Alberti K.G.
      • Zimmet P.Z.
      Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.
      110
      Refers to whole blood glucose level.
      /126
      6.1
      Refers to whole blood glucose level.
      /7.0
      120
      Refers to whole blood glucose level.
      /140
      6.7
      Refers to whole blood glucose level.
      /7.8
      World Health Organization (WHO)2013

      World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes research and clinical practice. 2014;103: pp. 341–363.

      925.1180
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      10.0
      There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      1538.5
      Diabetes in Pregnancy
      Diabetes in pregnancy should be diagnosed by the 2006 WHO criteria for diabetes if one or more of the following criteria are met:
      • fasting plasma glucose ≥ 7.0 mmol/L (126 mg/dL).
      • 2-hour plasma glucose ≥ 11.1 mmol/L (200 mg/dL) following a 75 g oral glucose load.
      • random plasma glucose ≥ 11.1 mmol/L (200 mg/ dL) in the presence of diabetes symptoms

        World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes research and clinical practice. 2014;103: pp. 341–363.

        .
      OR
      • Haemoglobin A1c (HbA1c) level ≥ 6.5% (48 mmol/mol)#

        American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes care. 2018; 41: pp. S13–S27.

        .
      #The A1C test should be performed using a method that is certified by the NGSP and standardized to the Diabetes Control and Complications Trial (DCCT) assay.
      * There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on the 1-h post-load value
      ^ Refers to whole blood glucose level.
      The International Diabetes Federation (IDF) Diabetes estimates the prevalence of diabetes worldwide and across seven Regions – Africa (AFR), Europe (EUR), Middle East and North Africa (MENA), North America and Caribbean (NAC), South and Central America (SACA), South-East Asia (SEA), Western Pacific (WP). Within the past three editions of the IDF Diabetes Atlas, the estimates of prevalence of HIP have also been published, showing the rates have been steadily increasing [

      International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels, Belgium: International Diabetes Federation; 2017. p. http://www.diabetesatlas.org/.

      ,

      International Diabetes Federation. IDF Diabetes Atlas. 7th ed. Brussels, Belgium: International Diabetes Federation; 2015. p. http://www.diabetesatlas.org/.

      ,

      International Diabetes Federation. IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013. p. http://www.diabetesatlas.org/.

      ].
      The 8th edition of the IDF Diabetes Atlas released in 2017 estimated that the prevalence of DIP to be 2.2% and GDM 14.0% with total prevalence of HIP at 16.2% [

      International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels, Belgium: International Diabetes Federation; 2017. p. http://www.diabetesatlas.org/.

      ]. The 9th edition released in 2019 showed a slight reduction in the overall HIP prevalence to 15.8%, with GDM at 12.8% and DIP at 2.6%, made up of hyperglycaemia first detected in pregnancy of and pre-existing diabetes both respectively at 1.3% [

      International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.

      ]. This increase from the 8th edition may be due to a change in the study criteria scoring since the introduction of the IADPSG and may not reflect an actual increase in HIP prevalence.
      The IDF Diabetes Atlas has projected the prevalence of type 2 diabetes (T2D) in previous editions [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ,
      • Ogurtsova K.
      • da Rocha Fernandes J.D.
      • Huang Y.
      • Linnenkamp U.
      • Guariguata L.
      • Cho N.H.
      • et al.
      IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040.
      ,
      • Guariguata L.
      • Whiting D.R.
      • Hambleton I.
      • Beagley J.
      • Linnenkamp U.
      • Shaw J.E.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ]. What has yet to be published in previous editions is a projection of HIP. In the current edition of the IDF Diabetes Atlas, we calculated a projection of HIP prevalence to the years 2030 and 2045. Taking these factors into consideration, the aim of this article is to estimate the prevalence of HIP in 2019 and project its prevalence to 2030 and 2045.

      2. Methods

      The methods used for the collection of HIP prevalence have been previously described in Linnenkamp et al. [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ]. In brief, studies were identified by a literature search using PubMed, Google Scholar and relevant citations from within papers. The search was conducted looking for studies between January 1990 to December 2018 using search terms: “gestational diabetes mellitus”, “GDM”, “prevalence”, “incidence” and “screening” and < country name > or < region/continent > .
      Overall, studies were excluded if they were cohort studies, used populations screened at less than 24 weeks’ gestation, or if the prevalence results were not categorised into three or more age ranges [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ]. To increase availability of data, authors from studies which would have qualified except for the inclusion of three age-ranges were individually emailed to ascertain whether this information could be made available.
      Studies that were selected underwent logistic regression to calculate an age-specific prevalence and were adjusted for diagnostic criteria if the IADPSG was not use as described in Linnenkamp [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ].
      There has been a revision to the scoring system used to grade the papers since 2013 [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ], refer to Table 2 for detail. The domain of diagnostic criteria was now valued up to 2 points, and 2 points was weighted towards the WHO/IADPSG criteria for universal screening, which reflects the IDF model of care for GDM [

      International Diabetes Federation. IDF GDM Model of Care: Implementation Protocol, Guidelines for Healthcare Professionals. Brussels, Belgium 2015.

      ]. A domain for screening approach was added, with universal screening scoring 2 points, selective and/or 2-step scoring 1 point each. Further, the cut-off point was raised from 3 to 4 marks to account for these additions.
      Table 2Updated scoring system for hyperglycaemia in pregnancy studies for the 9th Edition of IDF Diabetes Atlas (* denotes updated criteria).
      DomainValueRationale for the update
      Threshold of Diagnostic Criteria*This has been thoroughly updated to reflect the wide range of diagnostic criteria used. In 2014 there were only two items – “record based/self-reported” or “criteria based”. We have added in multiple items to this domain to reflect the breadth of criteria considered. The IADPSG is the IDF model of care for GDM

      International Diabetes Federation. IDF GDM Model of Care: Implementation Protocol, Guidelines for Healthcare Professionals. Brussels, Belgium 2015.

      , hence studies using this approach were scored higher than other diagnostic criteria.
      IADPSG*2
      WHO1
      EASD1
      ADIPS1
      ADA1
      NDDG1
      CDA1
      DPSG1
      ICD-categorization0
      Self-reported0
      DIPSI0*
      Other e.g. Scandinavian0
      Not recorded0
      Screening approach*ScoreThe domain for screening approach was added to preference studies which used a universal screening approach, in line with the IADPSG recommendations. Accordingly, studies with a selective or 2-step or more approach (e.g. risk factors, use of glucose challenge test prior to the oral glucose tolerance test) were scored lower.
      Universal one step2
      Selective1
      2 + steps1
      Selective 2 + steps0
      Study yearScore
      Later than 20062
      Between 2000 and 20051
      Before 20000
      No study year mentioned0
      Study designScore
      Population based1
      Modelling0
      Not mentioned0
      Sample representationScore
      National3
      Regional2
      Multi-city2
      Community1
      Single/tertiary level hospital0
      Not mentioned0
      Studies were excluded if they were cohort studies, or had data sources with the objective focused on women being screened at less than 24 weeks’ gestation. Only studies scoring greater than 4 were selected.
      Three methods were used to project HIP: Method 1 carried forward the age-adjusted 2019 estimate, which is in line with the methods used to predict the prevalence of other diabetes sub-groups throughout the Atlas [
      • Ogurtsova K.
      • da Rocha Fernandes J.D.
      • Huang Y.
      • Linnenkamp U.
      • Guariguata L.
      • Cho N.H.
      • et al.
      IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040.
      ,
      • Guariguata L.
      • Whiting D.R.
      • Hambleton I.
      • Beagley J.
      • Linnenkamp U.
      • Shaw J.E.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ], this was consistent with the method used in other IDF Diabetes Atlas projections [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ,
      • Ogurtsova K.
      • da Rocha Fernandes J.D.
      • Huang Y.
      • Linnenkamp U.
      • Guariguata L.
      • Cho N.H.
      • et al.
      IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040.
      ]. Method 2 involved performing a linear regression based on calculated age-adjusted prevalence for editions six to nine, spanning the years 2013, 2015, 2017 and 2019. This method was devised to determine whether linear regression would provide a suitable projection. However, we expected there could be large distortions in the data due to the change in our scoring method and the known increase in prevalence when using the IADPSG diagnostic criteria compared with older criteria [
      • Feldman R.K.
      • Tieu R.S.
      • Yasumura L.
      Gestational diabetes screening: the international association of the diabetes and pregnancy study groups compared with carpenter-coustan screening.
      ,
      • Duran A.
      • Saenz S.
      • Torrejon M.J.
      • Bordiu E.
      • Del Valle L.
      • Galindo M.
      • et al.
      Introduction of IADPSG criteria for the screening and diagnosis of gestational diabetes mellitus results in improved pregnancy outcomes at a lower cost in a large cohort of pregnant women: the St. Carlos gestational diabetes study.
      ,
      • Costa E.
      • Kirckpartick C.
      • Gerday C.
      • De Kempeneer A.
      • Derisbourg S.
      • Vercoutere A.
      • et al.
      Change in prevalence of gestational diabetes and obstetric complications when applying IADPSG screening criteria in a Belgian French speaking university hospital. A retrospective cohort study.
      ,
      • O'Sullivan E.P.
      • Avalos G.
      • O'Reilly M.
      • Dennedy M.C.
      • Gaffney G.
      • Dunne F.
      Atlantic diabetes in pregnancy (DIP): the prevalence and outcomes of gestational diabetes mellitus using new diagnostic criteria.
      ]. Method 3 involved linear regression of only the previous IDF Diabetes Atlas HIP edition estimates that showed a consistent trend, followed by extrapolation from the age-adjusted 2019 HIP estimate. This method was devised to account for the distortions inherent in the change in scoring system between the most recent 9th edition and the prior editions’ prevalence estimates, but also aimed to take factor in the projected general growth in diabetes prevalence and population.
      For all three methods, the projected prevalence was then multiplied to the birth estimate for the corresponding years. This was obtained from the United Nations (UN) population estimates for women age 20–49 years multiplied by the UN fertility rate per woman for the corresponding years [

      United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects. The 2017 Revision. New York, USA. 2017. p. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf https://population.un.org/wpp/.

      ].
      Countries without HIP prevalence data in 2019 were matched to the ‘data region’ which is a combination of IDF Region, World Bank country income group and matched by ethnicity, or the most relevant data region with available data [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ]. Projected HIP cases are unadjusted for pre-existing (known or unknown) Type 1, Type 2, monogenic forms of diabetes or diabetes from other causes.

      3. Results

      Overall, 141 prevalence studies were identified, of which 51 fulfilled the criteria for inclusion in the analysis, representing 41 countries. Fig. 1 is a flow chart of the study selection. There were 170 countries without studies represented in the analysis. Table 3 lists the studies selected for inclusion in the calculation of the HIP for the 9th edition.
      Table 3Data sources for estimates of hyperglycaemia in pregnancy for the 51 countries which met selection criteria for inclusion.
      IDF Region & CountryYearSample SizeStudy DesignSampling FrameDiagnostic Criteria
      Africa
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      • Washington S.
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      The prevalence of gestational diabetes mellitus amongst black South African women is a public health concern.
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      Prevalence of gestational diabetes mellitus in urban and rural Tanzania.
      2011910Population-basedNationalWHO 1999
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      2012796,346Population-basedNationalWHO 1999
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      The prevalence and predictors of gestational diabetes mellitus in Hungary.
      20001835Population-basedRegionalWHO 1999
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      Gestational diabetes mellitus - an analysis of risk factors.
      20062130Population-basedSingle hospitalWHO 1999
      Slovakia

      National Health Information Centre. Ambulatory diabetes care annual report 2013. In: Center NHI, editor. Annual Reports. Slovakia 2014.

      20131,353,608Population-basedNationalICD-categorisation
      Spain
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      • et al.
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      20101454Population-basedSingle hospitalIADPSG
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      • et al.
      Introduction of IADPSG criteria for the screening and diagnosis of gestational diabetes mellitus results in improved pregnancy outcomes at a lower cost in a large cohort of pregnant women: the St. Carlos gestational diabetes study.
      20111750Population-basedSingle hospitalIADPSG
      Sweden
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      Trends in the prevalence of gestational diabetes mellitus in southern Sweden, 2003–2012.
      201016,907Population-basedRegionalEASD
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      Prevalence of gestational diabetes mellitus evaluated by universal screening with a 75-g, 2-hour oral glucose tolerance test and IADPSG criteria.
      20131434Population-basedNationalIADPSG
      United Kingdom
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      20102376Population-basedLocalIADPSG
      United Kingdom
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      20101671Population-basedLocalIADPSG
      Middle East and North Africa
      Islamic Republic of Iran
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      Estimation of gestational diabetes mellitus and dose-response association of BMI with the occurrence of diabetes mellitus in pregnant women of the west of Iran.
      20151010Population-basedLocalIADPSG
      Pakistan
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      • Madhani S.
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      • Khan T.A.
      Gestational diabetes mellitus and the predisposing factors.
      20141210Population-basedLocalIADPSG
      Qatar
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      Prevalence of newly detected diabetes in pregnancy in Qatar, using universal screening.
      20162000Population-basedSingle hospitalIADPSG
      United Arab Emirates
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      • Shah S.M.
      Gestational diabetes mellitus: simplifying the international association of diabetes and pregnancy diagnostic algorithm using fasting plasma glucose.
      200810,283Population-basedLocalIADPSG
      North America and Caribbean
      Barbados
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      20102093Population-basedSingle hospitalIADPSG
      Canada
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      20102028Population-basedSingle hospitalIADPSG
      United States of America
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      2010797Population-basedSingle hospitalIADPSG
      United States of America
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      2010757Population-basedSingle hospitalIADPSG
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      2010753Population-basedSingle hospitalIADPSG
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
      20101981Population-basedSingle hospitalIADPSG
      South and Central America
      Argentina
      • McCarthy A.D.
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      • Arnol V.
      • et al.
      Universal versus selective screening for the detection, control and prognosis of gestational diabetes mellitus in Argentina.
      20081702Population-basedLocalWHO 1999
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      • Forti A.C.
      • Schmidt M.I.
      Evaluation of a 1-h 75-g oral glucose tolerance test in the diagnosis of gestational diabetes.
      19954998Population-basedMulti-cityWHO 1999
      Cuba
      • Gomez H.L.
      • Martinez M.L.
      • Rodriguez Z.M.
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      20081003Population-basedRegionalWHO 1999
      South-East Asia
      Bangladesh
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      2002147Population-basedRegionalWHO 1999
      Bangladesh
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      • et al.
      Screening for gestational diabetes mellitus and its prevalence in Bangladesh.
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      200612,056Population-basedRegionalWHO 1999
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      Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and adverse pregnancy outcome (HAPO) study.
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      We used three methods to project regional estimates in HIP. The 9th edition of the IDF Diabetes Atlas reports on Method 1, which shows a decline from the 2019 prevalence from 15.8% to 14.0% in 2030 and 13.3% in 2045. In Method 2, we projected HIP prevalence to rise steadily to 16.5% in 2030 and again to 18.3% in 2045. In Method 3, there was an initial increase in the HIP prevalence to 16.1% in 2030 which then reverts to 15.8% in 2045. Refer to Table 4, Table 5, Table 6 for detailed projections of regional HIP as well as an overall prevalence. Fig. 2, Fig. 3, Fig. 4 shows the change in HIP prevalence between 2030 and 2045 by Method and Region.
      Table 4Method 1 – projection of HIP prevalence in women aged 20–49 years for 2030 and 2045 by IDF region by carrying forward the 2019 estimates.
      REGIONTOTAL BIRTHS 2030TOTAL PROJECTED HIP 2030CALCULATED AGE-ADJUSTED PREVALENCE 2030 %TOTAL BIRTHS 2045TOTAL PROJECTED HIP 2045CALCULATED AGE-ADJUSTED PREVALENCE 2045 %
      AFRICA (AFR)39,341,8424,049,76710.29%47,723,2784,892,66810.39%
      EUROPE (EUR)9,438,8851,181,37112.52%9,855,635975,2289.90%
      MIDDLE EAST AND NORTH AFRICA (MENA)16,245,2121,005,1276.19%17,272,4931,067,5006.18%
      NORTH AMERICA AND CARRIBEAN BARBADOS (NACB)6,831,8601,464,44121.44%6,713,8111,435,80721.39%
      SOUTH AND CENTRAL AMERICA (SACA)6,439,990673,68110.46%6,063,395634,39310.46%
      SOUTH-EAST ASIA (SEA)26,547,3837,284,29627.44%23,321,8996,399,40827.44%
      WESTERN PACIFIC (WP)25,752,0702,624,65710.19%24,798,5822,524,61510.18%
      OVERALL PREVALENCE130,597,24218.283,34014.00%

      [95% Confidence Interval (CI): 13.998, 14.000]
      135,750,09417,993,46413.25%

      [95% CI: 13.254,13.256]
      Table 5Method 2 – regression of IDF HIP prevalence estimates between 2013 and 2019 to provide projection of HIP prevalence for women aged 20–49 years for 2030 and 2045.
      REGIONTOTAL BIRTHS 2030TOTAL PROJECTED HIP 2030CALCULATED PREVALENCE 2030 %TOTAL BIRTHS 2045TOTAL PROJECTED HIP 2045CALCULATED PREVALENCE 2045 %
      AFRICA (AFR)39,341,8424,687,03411.91%47,723,2786,850,85714.36
      EUROPE (EUR)9,438,8851,099,50611.65%9,855,6351,065,47010.81%
      MIDDLE EAST AND NORTH AFRICA (MENA)16,245,212828,1265.10%17,272,493413,6742.39%
      NORTH AMERICA AND CARRIBEAN BARBADOS (NACB)6,831,8602,738,02240.08%6,713,8114,590,91168.38%
      SOUTH AND CENTRAL AMERICA (SACA)6,439,990817,89612.70%6,063,395828,72613.67%
      SOUTH-EAST ASIA (SEA)26,547,3838,281,39531.19%23,321,8997,559,31232.41%
      WESTERN PACIFIC (WP)25,752,0703,107,92312.07%24,798,5823,490,11414.07%
      OVERALL PREVALENCE130,597,24221,559,90316.51%

      [95% CI: 16.507, 16.511]
      135,750,09424,799,06418.27%

      [95% CI: 18.266, 18.271]
      Table 6Method 3 – regression of previous IDF HIP prevalence estimates with a consistent trend and extrapolated from 2019 to provide HIP projection in women aged 20–49 years between 2030 and 2045.
      REGIONTOTAL BIRTHS 2030TOTAL PROJECTED HIP 2030CALCULATED PREVALENCE 2030 %TOTAL BIRTHS 2045TOTAL PROJECTED HIP 2045CALCULATED PREVALENCE 2045 %
      AFRICA (AFR)39,341,8424,924,70712.52%47,723,2786,781,24514.21%
      EUROPE (EUR)9,438,8851,321,06014.00%9,855,6351,373,88813.94%
      MIDDLE EAST AND NORTH AFRICA (MENA)16,245,2121,112,5346.85%17,272,4931,106,4406.41%
      NORTH AMERICA AND CARRIBEAN BARBADOS (NACB)6,831,8601,704,22724.95%6,713,8111,945,73428.98%
      SOUTH AND CENTRAL AMERICA (SACA)6,439,990769,05511.94%6,063,395712,59611.75%
      SOUTH-EAST ASIA (SEA)26,547,3838,288,85631.22%23,321,8996,864,15929.43%
      WESTERN PACIFIC (WP)25,752,0702,788,69710.83%24,798,5822,696,78810.87%
      OVERALL PREVALENCE130,597,24221,050,49716.01%

      [95% CI: 16.01, 16.012]
      135,750,09421,650,07715.82%

      [95% CI: 15.822, 15.825]
      Figure thumbnail gr2
      Fig. 2Method 1 – Hyperglycaemia in pregnancy prevalence by region between 2019 and 2045 (millions).
      Figure thumbnail gr3
      Fig. 3Method 2 – Hyperglycaemia in pregnancy prevalence by region between 2019 and 2045 (millions).
      Figure thumbnail gr4
      Fig. 4Method 3 – Hyperglycaemia in pregnancy prevalence by region between 2019 and 2045 (millions).

      4. Discussion

      Overall the HIP prevalence is expected to decline in Method 1, increase in Method 2, and stabilise in Method 3. The projected HIP prevalence is rising between 2019 and 2030 and 2045 for the Regions of AFR and SEA, but there are reciprocal declines in prevalence in the EUR, MENA, and WP Regions (WP includes China, the country with the largest number of HIP). The projected HIP prevalence are a reflection of variation in the regional growth in population and a decline in birth rates for AFR and SEA and WP Regions, with a mild increase in Europe but stabilising in the other Regions [

      United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects. The 2017 Revision. New York, USA. 2017. p. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf https://population.un.org/wpp/.

      ]. Depending on the degree with which this is expected to influence HIP prevalence, these factors combine to either decrease, increase or stabilise the HIP prevalence.
      Recent large systematic reviews in the AFR, SEA and WP Regions have published prevalence data with mixed results. For example Gao et al. published the first systematic review GDM prevalence in China, estimated to be 14.8% [
      • Gao C.
      • Sun X.
      • Lu L.
      • Liu F.
      • Yuan J.
      Prevalence of gestational diabetes mellitus in mainland China: a systematic review and meta-analysis.
      ]. While Nguyen et al. published an extensive systematic review of 48 studies from SEA and WP Region countries of China, Japan, Korea, Malaysia, Singapore, Taiwan, Thailand and Vietnam which reported a rate of 10.1% [
      • Nguyen C.L.
      • Pham N.M.
      • Binns C.W.
      • Van Duong D.
      • Lee A.H.
      Prevalence of gestational diabetes mellitus in eastern and southeastern Asia: a systematic review and meta-analysis.
      ]. Interestingly, Li et al. found a large variation of Indian GDM estimates (0.0–41.9%) depending on the diagnostic criteria use. They conducted a meta-analysis of 64 studies reporting 90 prevalence estimates in India, and found using the IADPSG criteria resulted in significantly higher prevalence of 19.2% compared with WHO 1999 of 10.13% or DIPSI of 7.4% [
      • Li K.T.
      • Naik S.
      • Alexander M.
      • Mathad J.S.
      Screening and diagnosis of gestational diabetes in India: a systematic review and meta-analysis.
      ]. Similarly, Macaulay’s systematic review published in 2014 of 6 countries in Africa estimated that the prevalence varied between 0% for Tanzania to 13.9% in Nigeria [
      • Macaulay S.
      • Dunger D.B.
      • Norris S.A.
      Gestational diabetes mellitus in Africa: a systematic review.
      ].
      Regionally, the prevalence of HIP is highest in the SEA Region and lowest in the AFR Region in 2019, however this is expected to change with Africa’s population rate rapidly rising. Nigeria, Africa’s most populace country, has a higher than regional average HIP prevalence (18.0%) in 2019 and this has increased the overall prevalence for AFR Region in 2030 and 2045. For MENA, there is heterogeneity of data, with Pakistan’s 2019 HIP age-adjusted prevalence estimated at 4.1% (and crude prevalence at 8.7%), compared with age-adjusted prevalence of 37.5% (crude 41.0%) in the United Arab Emirates. The projections in the MENA Region are skewed towards Pakistan, where it has lower than regional average prevalence and is the most populous country in the Region, resulting in decline in prevalence between 2019 and 2030. China has a steadily declining birth rate and lower than regional average HIP prevalence of 8.3% (crude 8.6%) which has resulted in a sharp decline in 2030 and 2045 projections in the WP Region (Table 2, Table 3, Table 4).
      Looking at our projections, we had varying results across all three methods. For Method 1, we found a steady decline in the HIP prevalence in the SACA and WP Regions, likely reflecting a sharp decline in the fertility rates of the Regions [

      United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects. The 2017 Revision. New York, USA. 2017. p. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf https://population.un.org/wpp/.

      ]. There is also a trend of a gradual decline in the population levels of women aged 20–49 years for most Regions except the AFR. AFR has the lowest and a below average HIP prevalence in 2019 of 10.8%, but is projected to increase steadily in line with the rise in population levels.
      In Method 2, there was a linear increase in HIP prevalence, however the change in scoring criteria resulted in a much higher HIP prevalence in the NACB Region in the 9th compared to the 8th edition. There were adjustments in the studies included from the 8th to the 9th editions, due to the new scoring criteria applied for study inclusion, which distorted the HIP prevalence, predominantly in the MENA and NAC Regions. This resulted in a higher than expected prevalence for NACB from 23.4% in 2019 to 40.0% in 2030 and 68.4% in 2045. There was also a sharp decline in MENA HIP prevalence from 11.3% in 2019 to 2.39% in 2045.
      Overall we felt the best projection is that of Method 3, which predicts stabilising of the rate around 16.0%. There is initially a mild increase of the prevalence of HIP from 15.8% in 2019 to 16.0% in 2030, but then stabilises to 15.8% in 2045. This method aims to eliminate the distortions created by the change in scoring criteria and factors in the aforementioned population and fertility changes. Although there is a general trend of increasing diabetes prevalence projected by Cho et al. [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ], this method has predicted a stabilising HIP trend. This may be related to a stabilisation of the factors of average maternal body mass index, particularly in Western Countries [
      • Sperrin M.
      • Marshall A.D.
      • Higgins V.
      • Buchan I.E.
      • Renehan A.G.
      Slowing down of adult body mass index trend increases in England: a latent class analysis of cross-sectional surveys (1992–2010).
      ,
      • Olds T.
      • Maher C.
      • Zumin S.
      • Peneau S.
      • Lioret S.
      • Castetbon K.
      • et al.
      Evidence that the prevalence of childhood overweight is plateauing: data from nine countries.
      ,
      • Francaite-Daugeliene M.
      • Petrenko V.
      • Baliutaviciene D.
      • Velickiene D.
      Retrospective analysis of age-adjusted body mass index among pre-pregnant women in the Lithuanian urban area during three decades.
      ]. However, this is partially offset by the expected rise in HIP prevalence of AFR Regions.
      There were limitations in our approach. The first limitation is the number of small studies overall that qualified for inclusion, and the absence of high quality HIP studies in some of the low to middle income Regions, even though globally around 79% of people living with diabetes live in these Regions [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ]. While we found increasing numbers of high quality population-based studies reporting on the prevalence of HIP, the scoring criteria for study inclusion into the IDF Diabetes Atlas meant some of these studies were excluded from age-specific HIP estimation due to not reporting three age groups. Although effort was made to contact the authors for this information, many studies did not or were not able to provide this data. This has inevitably meant a loss of these studies into the estimation of HIP, and makes it unclear whether there is inherent bias in the data used, or whether we are over or understating the estimate, further research in this area is needed. We would encourage future HIP prevalence studies, where possible, to report on age-stratified data.
      There appears to be few high-quality HIP prevalence studies from low-income countries (LIC) in AFR, MENA and SACA, and middle-income countries (MIC) in AFR, MENA, EUR, NACB, SACA, and SEA. This has led to an approximation of the estimates from aggregation of data from nearby Regions of similar, but are often higher-income countries. This method can skew the estimates, as within a Region, there can be great variation in prevalence. For instance, in 2019, the AFR-LIC prevalence was estimated at 7.4%, but almost doubled to 14.5% for the AFR-MIC Region; high income countries (HIC) in MENA had an average prevalence of 33.5% in 2019, but this declined sharply to 4.8% for the MIC. There were no LIC studies that qualified in the MENA, SACA, SEA and WP Regions. In time, as more high quality data become available, we hope to attain more specific HIP estimates within each Region.
      Furthermore, clear data on the prevalence of pre-existing DIP versus GDM are not always available. In the calculation of age-adjusted HIP, we predicted DIP from the age- and sex-specific diabetes prevalence rates for women aged 20–49 from the IDF Diabetes Atlas 9th edition [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ,

      International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.

      ]. Many studies used in our GDM calculation reported solely on GDM prevalence rates, without estimation of undiagnosed DIP, there could be a component of undiagnosed pre-existing DIP which is under-represented by our data. We know from our IDF diabetes atlas estimates that AFR has the highest rates of undiagnosed diabetes at 69.2%, but SEA and WP Regions also have over 50% of diabetes undiagnosed [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ].
      As discussed in Linnenkamp, there are limited data on the fertility rates of women affected by HIP, and we have assumed these are in line with the country’s fertility rates [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ].
      In addition, the reporting of HIP in the age range of 15–19 years was not accounted for, and looking at the UN’s fertility rates, there is a significant portion of women from the less developed and low- to middle-income regions that give birth at this age bracket [

      United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects. The 2017 Revision. New York, USA. 2017. p. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf https://population.un.org/wpp/.

      ]. There is undoubtedly a much smaller proportion of GDM and DIP diagnosed in the 15–19 years’ age bracket than in the older age ranges. What is not known is the rate of impaired glucose tolerance and fasting glucose levels in the women aged 15–19 years.
      Moreover, the UN predicts that much of the population change will be a result of migration to nations like Europe, North America and Oceania [

      United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects. The 2017 Revision. New York, USA. 2017. p. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf https://population.un.org/wpp/.

      ]. The rates of net migration will be heavily influenced by political, economic and social factors within these regions. It will be interesting to see the effects these will have on the population levels and future rates of HIP.
      Lastly, there is inherent uncertainty around prediction of a prevalence based on different diagnostic criteria. It is our view that standardisation of the diagnostic criteria to IADPSG as ratified by the IDF, WHO, International Federation of Gynaecology and Obstetrics, American Diabetes Association, Australian Diabetes in Pregnancy Society and many other countries and regional bodies will allow for better understanding and comparison of HIP worldwide. Describing the potential worldwide public health burden caused by HIP can inform policy to create interventions that can prevent the poor maternal and foetal outcomes caused by HIP [
      • Crowther C.A.
      • Hiller J.E.
      • Moss J.R.
      • McPhee A.J.
      • Jeffries W.S.
      • Robinson J.S.
      • et al.
      Effect of treatment of gestational diabetes mellitus on pregnancy outcomes.
      ,
      • Scholtens D.M.
      • Kuang A.
      • Lowe L.P.
      • Hamilton J.
      • Lawrence J.M.
      • Lebenthal Y.
      • et al.
      Hyperglycemia and adverse pregnancy outcome follow-up study (HAPO FUS): maternal glycemia and childhood glucose metabolism.
      ].

      5. Conclusion

      Using our best estimation of HIP per the IDF Diabetes Atlas method, and assuming factors stay the same, we expect the worldwide prevalence of HIP to stabilise around 15.8% to 16.0% between 2019 and 2045. However, the results are largely confounded by the heterogeneity of data, and the use of different GDM diagnostic criteria and approaches. It would be beneficial to have a uniform criterion for GDM screening and diagnosis across all countries and Regions, similar to that which currently exists for DIP.

      Funding

      The authors received no funding from an external source.

      Declaration of Competing Interest

      The authors declare no conflict of interest.

      Appendix A. Supplementary material

      The following are the Supplementary data to this article:

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