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Global estimates of the prevalence of hyperglycaemia in pregnancy

Published:December 03, 2013DOI:https://doi.org/10.1016/j.diabres.2013.11.003

      Abstract

      Aims

      We estimated the number of live births worldwide and by IDF Region who developed hyperglycaemia in pregnancy in 2013, including total diabetes in pregnancy (known and previously undiagnosed diabetes) and gestational diabetes.

      Methods

      Studies reporting prevalence of hyperglycaemia first-detected in pregnancy (formerly termed gestational diabetes) were identified using PubMed and through a review of cited literature. A simple scoring system was developed to characterise studies on diagnostic criteria, year study was conducted, study design, and representation. The highest scoring studies by country with sufficient detail on methodology for characterisation and reporting at least three age-groups were selected for inclusion. Forty-seven studies from 34 countries were used to calculate age-specific prevalence of hyperglycaemia first-detected in pregnancy in women 20–49 years. Adjustments were then made to account for heterogeneity in screening method and blood glucose diagnostic threshold in studies and also to align with recently published diagnostic criteria as defined by the WHO for hyperglycaemia first detected in pregnancy. Prevalence rates were applied to fertility and population estimates to determine regional and global prevalence of hyperglycaemia in pregnancy for 2013. An estimate of the proportion of cases of hyperglycaemia in pregnancy due to total diabetes in pregnancy was calculated using age- and sex-specific estimates of diabetes from the IDF Diabetes Atlas and applied to age-specific fertility rates.

      Results

      The global prevalence of hyperglycaemia in pregnancy in women (20–49 years) is 16.9%, or 21.4 million live births in 2013. An estimated 16.0% of those cases may be due to total diabetes in pregnancy. The highest prevalence was found in the South-East Asia Region at 25.0% compared with 10.4% in the North America and Caribbean Region. More than 90% of cases of hyperglycaemia in pregnancy are estimated to occur in low- and middle-income countries.

      Conclusion

      These are the first global estimates of hyperglycaemia in pregnancy and conform to the new WHO recommendations regarding diagnosis and also include estimates of live births in women with known diabetes. They indicate the importance of the disease from a public health and maternal and child health perspective, particularly in developing countries.

      Keywords

      1. Introduction

      Until recently, any hyperglycaemia first detected during pregnancy was termed gestational diabetes [
      Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus.
      ]. However, this definition did not differentiate between different severity of hyperglycaemia. The World Health Organization recently proposed new criteria for the diagnosis and definition of hyperglycaemia first detected in pregnancy which distinguishes the more serious diabetes in pregnancy (DIP), which is more likely to persist beyond the birth, from gestational diabetes (GDM), a milder degree of hyperglycaemia [
      Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy.
      ]. The new definition calls for an understanding of the burden of hyperglycaemia in pregnancy and its relationship with the growing epidemic of type 2 diabetes and distinguishes DIP from GDM based on the degree of hyperglycaemia; a reflection that the risk of serious complications is much higher in diabetes than in the milder GDM. Where studies previously reported the prevalence of GDM, under the new definition, these figures would also include the more severe hyperglycaemia classified as diabetes in pregnancy (DIP) under the broad title of hyperglycaemia first-detected in pregnancy (HFDP). Adding to this definition pregnancy in women with known diabetes, we use the term hyperglycaemia in pregnancy (HIP) to describe the burden of any glucose intolerance in pregnancy. In previous studies, any level of glucose intolerance in pregnancy was termed GDM. A description of the terminology used in this paper and its relation to the estimates proposed is presented in Fig. 1.
      Figure thumbnail gr1
      Fig. 1Terminology and classification for prevalence estimates of hyperglycaemia in pregnancy for 2013.
      HIP, including gestational diabetes mellitus (GDM) and total diabetes in pregnancy (TDP) (comprising both known diabetes in pregnant women, and previously undiagnosed diabetes in pregnancy (DIP)), is a common metabolic disorder during pregnancy and has been associated with serious perinatal complications for both mother and child. In the short-term, infants born to mothers with HFDP are at increased risk of foetal macrosomia (also known as large-for-gestational-age), hypoglycaemia and hyperinsulinemia at birth, and risks of shoulder dystocia associated with obstructed labour [
      Hyperglycemia and adverse pregnancy outcomes.
      ,
      • Simmons D.
      Diabetes and obesity in pregnancy.
      ]. Mothers with the condition are at increased risk of pre-eclampsia, gestational hypertension, caesarean section, and hydramnios [
      Hyperglycemia and adverse pregnancy outcomes.
      ,
      • Simmons D.
      Diabetes and obesity in pregnancy.
      ]. Moreover, TDP adds to these complications an increased risk of foetal malformations, foetal loss, perinatal and neonatal mortality, as well as an increased risk of maternal mortality [
      • Balsells M.
      • García-Patterson A.
      • Gich I.
      • Corcoy R.
      Maternal and fetal outcome in women with type 2 versus type 1 diabetes mellitus: a systematic review and metaanalysis.
      ,
      • Simmons D.
      • Thompson C.F.
      • Engelgau M.M.
      Controlling the diabetes epidemic: how should we screen for undiagnosed diabetes and dysglycaemia?.
      ]. The growing numbers of younger adults with type 2 diabetes mellitus (T2DM) [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ] may be contributing to rising trends in HIP.
      Studies describing the risk factors and risk markers of gestational diabetes used the previous definition of the disease and there is some overlap with risk factors for T2DM. The presence of previously undiagnosed T2DM may play a role in the similarities. These risk factors and risk markers include: advancing age; obesity; excessive weight gain during pregnancy; a family history of diabetes; gestational diabetes during a previous pregnancy; a history of stillbirth or infant with congenital abnormality; and glycosuria during pregnancy [
      • Ramos-Leví A.M.
      • Pérez-Ferre N.
      • Fernández M.D.
      • Del Valle L.
      • Bordiu E.
      • Bedia A.R.
      • et al.
      Risk factors for gestational diabetes mellitus in a large population of women living in Spain: implications for preventative strategies.
      ,
      • Yang H.
      • Wei Y.
      • Gao X.
      • Xu X.
      • Fan L.
      • He J.
      • et al.
      Risk factors for gestational diabetes mellitus in Chinese women: a prospective study of 16,286 pregnant women in China.
      ]. Similarly, certain ethnic groups found to have a higher prevalence of GDM have also been found to have a higher prevalence of T2DM [
      • Anna V.
      • van der Ploeg H.P.
      • Cheung N.W.
      • Huxley R.R.
      • Bauman A.E.
      Sociodemographic correlates of the increasing trend in prevalence of gestational diabetes mellitus in a large population of women between 1995 and 2005.
      ,
      • Ferrara A.
      • Hedderson M.M.
      • Quesenberry C.P.
      • Selby J.V.
      Prevalence of gestational diabetes mellitus detected by the national diabetes data group or the carpenter and coustan plasma glucose thresholds.
      ,
      • Hedderson M.M.
      • Darbinian J.A.
      • Ferrara A.
      Disparities in the risk of gestational diabetes by race-ethnicity and country of birth.
      ]. GDM poses a long-term risk of developing T2DM for both mother [
      • Anna V.
      • van der Ploeg H.P.
      • Cheung N.W.
      • Huxley R.R.
      • Bauman A.E.
      Sociodemographic correlates of the increasing trend in prevalence of gestational diabetes mellitus in a large population of women between 1995 and 2005.
      ] and possibly for the child as well [
      • Dabelea D.
      • Knowler W.C.
      • Pettitt D.J.
      Effect of diabetes in pregnancy on offspring: follow-up research in the Pima Indians.
      ] and may be contributing to the increasing global epidemic of T2DM. Despite this, a substantial proportion of women who develop GDM do not have a high-risk profile and some women who may be considered high-risk never develop the condition [
      • Cypryk K.
      • Szymczak W.
      • Czupryniak L.
      • Sobczak M.
      • Lewiński A.
      Gestational diabetes mellitus – an analysis of risk factors.
      ,
      • Shirazian N.
      • Emdadi R.
      • Mahboubi M.
      • Motevallian A.
      • Fazel-Sarjuei Z.
      • Sedighpour N.
      • et al.
      Screening for gestational diabetes: usefulness of clinical risk factors.
      ].
      Despite the serious public health implications of HIP, there has been is no universal definition and no universal standards for screening and a wide variety of methods are applied. Screening methods currently in use rely on variations of the oral glucose tolerance test whereby blood glucose is measured in the fasting state and again after an oral glucose challenge (Table 1). Depending on the criteria used the resulting prevalence can vary widely. A recent survey on GDM prevalence and practice administered among diabetologists and obstetricians in 173 countries found country-specific prevalence estimates ranging from < 1% in Germany up to 28% for a study in Nepal using a variety of criteria [
      • 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.
      ].
      Table 1Comparison of diagnostic criteria and screening protocols used for gestational diabetes.
      CriteriaFasting1-h2-h3-h
      mg/dLmmol/Lmg/dLmmol/Lmg/dLmmol/Lmg/dLmmol/L
      ADA/NDDG
      American Diabetes Association. Clinical practice recommendations 1999.
      ,
      Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. National Diabetes Data Group.
      1055.819010.51658.61457.8
      ADA
      American Diabetes Association. Clinical practice recommendations 2001.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      ,
      American Diabetes Association. Clinical Practice Recommendations 2005.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      ,
      • Association A.D.
      Diagnosis and classification of diabetes mellitus.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      955.3180101558.6Not measured
      ADIPS
      • McElduff A.
      • Cheung N.W.
      • McIntyre H.D.
      • Lagström J.A.
      • Oats J.J.N.
      • Ross G.P.
      • et al.
      The Australasian Diabetes in Pregnancy Society consensus guidelines for the management of type 1 and type 2 diabetes in relation to pregnancy.
      995.5Not measured1448
      CDA
      • Canadian Diabetes Association Clinical Practice Guidelines Expert Committee
      Canadian Diabetes Association 2008 clinical practice guidelines for the prevention and management of diabetes in Canada.
      955.319110.61608.9
      WHO
      • World Health Organisation
      Diabetes mellitus – report of a WHO study group.
      1407.8Not measured1407.8
      WHO
      Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus.
      12671407.8
      IADPSG
      • Lapolla A.
      • Dalfrà M.G.
      • Ragazzi E.
      • De Cata A.P.
      • Fedele D.
      New International Association of the Diabetes and Pregnancy Study Groups (IADPSG) recommendations for diagnosing gestational diabetes compared with former criteria: a retrospective study on pregnancy outcome.
      925.2180101538.5
      The lack of a uniform approach to estimate the prevalence of HIP, as well as the new definition from WHO, intensifies the need to develop a systematic means of estimating the prevalence of HIP using existing data. This paper presents the first estimates of the prevalence of HIP, including an estimated proportion of the figure which may be due to total (known and previously undiagnosed) diabetes in pregnancy (TDP), for the year 2013.

      2. Methods

      A detailed description of the methods used and the rationale for adjustments applied is available from Linnenkamp et al. [
      • Linnenkamp U.
      • Guariguata L.
      • Whiting D.R.
      • Cho N.H.
      IDF Diabetes Atlas methodology for estimating prevalence of hyperglycaemia in pregnancy.
      ]. Briefly, we conducted a systematic literature review of studies reporting the prevalence of gestational diabetes using the search terms: ‘gestational diabetes mellitus’, ‘GDM’, ‘prevalence’, ‘incidence’ and ‘screening’ and or. All studies reporting prevalence of GDM and conducted since 1980 were gathered and entered into a database for data cleaning, assessment, and characterisation. Studies were characterised based on a set of four criteria: diagnostic/screening method; year the study was conducted; study design; and study sample representation. Studies were then scored using a simple scoring method designed to favour studies that were recent (since 2005), nationally representative, population-based, and used a blood test based diagnostic method. The highest scoring study or studies were selected for each country.
      Studies were excluded that were conducted in a population considered not representative of the general population (e.g. single ethnic group, migrants, etc.); studies that assess prevalence exclusively on multi-parity pregnancies; where screening was conducted before 24 weeks of gestation; or where the methods were not sufficiently clear for assessment. Where studies compared more than one screening method in the same population, the data from a single method was entered with preference given to IADPSG [
      • Lapolla A.
      • Dalfrà M.G.
      • Ragazzi E.
      • De Cata A.P.
      • Fedele D.
      New International Association of the Diabetes and Pregnancy Study Groups (IADPSG) recommendations for diagnosing gestational diabetes compared with former criteria: a retrospective study on pregnancy outcome.
      ], followed by ADA [
      American Diabetes Association. Clinical practice recommendations 1999.
      ,
      American Diabetes Association. Clinical practice recommendations 2001.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      ,
      American Diabetes Association. Clinical Practice Recommendations 2005.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      ,
      • Association A.D.
      Diagnosis and classification of diabetes mellitus.
      ,
      American Diabetes Association. Diagnosis and classification of diabetes mellitus.
      ], NDDG [
      Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. National Diabetes Data Group.
      ], and WHO 1985/1999 [
      Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus.
      ,
      • World Health Organisation
      Diabetes mellitus – report of a WHO study group.
      ], respectively. Studies reporting prevalence based on aggregation of diagnosis by more than one method were considered to be a high risk for overestimation and therefore excluded. A total of 199 studies with information on the prevalence of HFDP were identified and 47 were selected for use the estimates.
      For countries without available study data, studies from other countries matched for geographic proximity, ethnic similarity, and socioeconomic development were used. Transformed country-level data were aggregated to produce regional estimates based on IDF Region (Africa (AFR); Europe (EUR); Middle East and North Africa (MENA); North America and the Caribbean (NAC); South and Central America (SACA); South-East Asia (SEA); and the Western Pacific (WP)) and global estimates.

      3. Statistical methods

      Data management and analysis were done using the R statistical programme (version 2.15.2) [
      • R Core Team
      R: a language and environment for statistical computing.
      ] and a MySQL database to store study information and transformed estimates. Age-specific prevalence estimates of HFDP were calculated for women from 20 to 49 years in 5-year age-groups. Data from selected studies were smoothed using logistic regression models with a midpoint of each age-group as the independent variable and a weighted number of cases with and without HFDP as the dependent variable. Only age groups starting at 18 years were included in the model.
      Adjustments were made to account for differences in glucose cut-off values reported across different studies. We used the criteria developed by the IADPSG as a reference for adjustment [
      • Lapolla A.
      • Dalfrà M.G.
      • Ragazzi E.
      • De Cata A.P.
      • Fedele D.
      New International Association of the Diabetes and Pregnancy Study Groups (IADPSG) recommendations for diagnosing gestational diabetes compared with former criteria: a retrospective study on pregnancy outcome.
      ]. Where the age-specific prevalence from a study was based on two abnormal blood glucose values, as with the NDDG criteria, the prevalence was assumed to be double of the reported age-specific prevalence in line with published comparison studies [
      • Benhalima K.
      • Hanssens M.
      • Devlieger R.
      • Verhaeghe J.
      • Mathieu C.
      Analysis of pregnancy outcomes using the new IADPSG recommendation compared with the carpenter and coustan criteria in an area with a low prevalence of gestational diabetes.
      ,
      • Santos-Ayarzagoitia M.
      • Salinas-Martínez A.M.
      • Villarreal-Pérez J.Z.
      Gestational diabetes: validity of ADA and WHO diagnostic criteria using NDDG as the reference test.
      ]. Where the prevalence was based on ADA criteria (2001–2006), which has a higher glucose cut-off for diagnosis (Table 1), the prevalence was further increased by 30% in keeping with published comparison studies [
      • 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.
      ]. Accounting for further differences in glucose thresholds within variations of the same criteria or between criteria was not done as there was not enough evidence of a gain in precision [
      • Hollander M.H.
      • Paarlberg K.M.
      • Huisjes A.J.M.
      Gestational diabetes: a review of the current literature and guidelines.
      ]. Prevalence data based on self-reported or abstraction of medical records were not scaled as there were no comparison studies found to support an adjustment.
      Calculation of the number of cases HFDP was done by applying the derived age-specific prevalence to the estimated number of live births for a country using the UN Population Division 2012 Revision estimates for country-, age- and sex-specific population in 2013 and the country- and age-specific fertility estimates from the UN Population Division for 2012 [
      • United Nations
      • Department of Economic and Social Affairs
      • Population Division
      World population prospects: the 2012 revision. New York.
      ]. Global and regional estimates were standardised to the world population derived from the UN Population Division estimates for 2013.

      4. Estimates of hyperglycaemia in pregnancy and total diabetes in pregnancy

      To align with the new WHO definition of HFDP which distinguishes DIP from GDM, we estimated the proportion of the HFDP prevalence that may be attributable to total diabetes in pregnancy (TDP) which would include DIP (previously undiagnosed diabetes in pregnancy) and estimates of the number of live births in women with known diabetes. The proportions were derived using age- and sex-specific prevalence of diabetes from the IDF Diabetes Atlas for women 20–49 for the year 2013 [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ]. Using the same fertility rates for women with diabetes as those for the general population, we multiplied the age-specific fertility rate estimates by age-specific estimated number of women with diabetes to get the expected number of live births affected by pregnancy in women with known diabetes. To estimate overall HIP, we added the estimated cases of pregnancy among women with known diabetes to the estimated cases of HFDP derived above (Fig. 1).
      We used a similar approach to estimate the proportion of HIP which may be due to TDP. Using estimates of the age-specific number of women with undiagnosed T2DM [
      • Beagley J.
      • Guariguata L.
      • Weil C.
      • Motala A.A.
      Global estimates of undiagnosed diabetes in adults.
      ] from the IDF Diabetes Atlas multiplied by the age-specific fertility rate estimates, we calculated the estimated number of live births to women with undiagnosed T2DM. We added this figure to the estimated live births among women with known diabetes to get the number of cases of TDP. We then divided this number by the estimate of HIP to get the proportion that may due to TDP.

      5. Sensitivity analyses

      Evidence from a single population-based study in women with type 1 diabetes in Sweden showed a standard fertility ratio (SFR) of 0.8 compared with women without diabetes [
      • Jonasson J.M.
      • Brismar K.
      • Sparén P.
      • Lambe M.
      • Nyrén O.
      • Östenson C.-G.
      • et al.
      Fertility in women with type 1 diabetes A population-based cohort study in Sweden.
      ]. We conducted a sensitivity analyses using this SFR and applying it to fertility rates when calculating the number of expected live births among women with diabetes to estimate the effect of reduced fertility in the population.

      6. Results

      We estimate that in 2013, 21.4 million out of an estimated 127.1 million live births to women aged 20–49 years (crude prevalence 16.9%; age-standardised prevalence 14.8%) were affected by hyperglycaemia in pregnancy of which 16% may be due to TDP including both previously undiagnosed diabetes in pregnancy, and live births in women with known diabetes. The South-East Asia Region had the highest crude prevalence of HIP at 23.1% of live births, followed closely by the Middle East and North Africa Region with 22.3%. Age-standardised estimates of HIP by region range from 10.4% in the North America and Caribbean Region to 25.0% in South-East Asia Region (Table 2). The Region with the highest number of live births affected by HIP is the South-East Asia Region with over 6.0 million cases, followed by the Africa Region with 4.3 million cases, and the Western Pacific Region with 3.5 million cases. The North America and Caribbean Region has the largest proportion which may be due to TDP at 24.9%, while the lowest proportion was found to be in the South-East Asia Region (9.5%).
      Table 2Global and regional estimates of hyperglycaemia in pregnancy for 2013.
      Number of live birthsCases of hyperglycaemia in pregnancyCrude prevalenceAge-standardised prevalenceProportion of cases that may be due to total diabetes in pregnancy
      MillionsMillions%%%
      IDF Region
      AFR28.74.616.014.419.6
      EUR10.71.715.212.610.9
      MENA15.33.422.317.517.7
      NAC6.50.913.210.424.9
      SACA6.90.913.211.417.3
      SEA27.50.623.125.09.5
      WP31.43.711.811.914.1
      Income group
      High-income12.02.117.513.816.4
      Upper-middle income16.62.414.812.817.1
      Lower-middle income71.212.717.817.613.4
      Low-income27.34.215.514.516.7
      World127.121.416.914.816.0
      *Age-standardised to population estimates from the World Population Prospects 2012 Revision
      • United Nations
      • Department of Economic and Social Affairs
      • Population Division
      World population prospects: the 2012 revision. New York.
      .
      Age-specific estimates show an increase in the prevalence of HIP as women age starting at 10.3% of women 20–24 and increasing to 47.9% in women 45–49. While the high prevalence of HIP by percentage is found in older women, the largest numbers of cases occur in women under 30 years. An estimated 14.9 million or 69.9% of cases occur in women under the age of 30 despite these women having a lower risk of developing HIP.
      Over 91.6% of cases of HIP occur in low- and middle-income countries. After dividing middle-income countries in to lower-middle and upper-middle income [

      World Bank. Country and lending groups. Available from: http://data.worldbank.org/about/country-classifications/country-and-lending-groups [cited 17.09.13] [Internet].

      ], the highest age-standardised prevalence of HIP occurs in lower middle-income countries at 17.6% followed by low-income countries at 14.5%. Low-income countries have the greatest proportion of cases of TDP that may be attributable to undiagnosed T2DM at 71.5% compared with 34.1% in high-income countries. Lower-middle income countries had by far the highest number of cases of HIP with 12.7 million and also the highest number of live births (Table 2). The lowest number of cases of HIP was in high-income countries (2.1 million).
      Country-specific estimates are reported for countries where source data were available and not based on extrapolation from other countries. Country-specific estimates are presented in Table 3. From countries with available data, the United Arab Emirates had the highest age-standardised prevalence of HIP at 36.8% (crude prevalence 40.4%) of women 20–49 years, followed by Norway (31.1%), India (27.5%), and the Netherlands (27.2%). Japan had the lowest age-standardised prevalence at 4.0%. India had the highest number of women affected by HIP with an estimated 5.7 million cases in 2013 followed by China with 1.2 million, Nigeria (950,000), and the United States of America (350,000).
      Table 3Country-level estimates of hyperglycaemia in pregnancy for countries with selected data sources.
      IDF RegionCountry/territoryLive births in women (20–49 years) (1000s)Cases (1000s)Crude prevalence (%)Age-standardised prevalence (%)Proportion of cases that may be due to total diabetes in pregnancy (%)
      AFRNigeria6156.31057.417.214.419.4
      EURBelgium127.38.06.35.021.4
      France779.5105.713.611.66.1
      Hungary94.413.714.512.26.1
      Ireland70.89.513.511.09.3
      Israel155.819.612.610.54.1
      Netherlands175.054.631.227.23.3
      Norway61.019.732.331.14.3
      Poland401.342.710.79.18.3
      Spain478.4175.736.732.12.5
      Turkey1172.6160.213.710.831.7
      United Kingdom719.5164.322.819.85.1
      MENAIslamic Republic of Iran1387.6264.819.116.916.0
      Qatar24.28.033.225.431.3
      United Arab Emirates134.454.340.436.816.2
      NACBarbados3.20.620.315.415.9
      Canada380.167.517.815.411.5
      Trinidad and Tobago18.01.79.26.931.7
      United States of America3894.3464.211.98.532.7
      SACAArgentina607.656.89.47.917.6
      Brazil2429.9280.711.69.721.7
      Cuba92.222.924.823.87.9
      SEABangladesh2544.3248.29.89.815.6
      India24,055.95995.324.927.59.1
      Sri Lanka369.847.912.99.927.7
      WPAustralia299.824.78.26.75.3
      China18,495.21301.37.07.721.4
      Hong Kong SAR67.112.017.812.88.4
      Japan1037.353.45.24.16.7
      Malaysia523.1115.622.117.620.7
      Singapore52.414.127.023.17.6
      Thailand597.4136.922.921.04.1
      Viet Nam1335.0263.719.820.63.4
      Sensitivity analyses using a lower fertility ratio found previously in a population-based study among women with type 1 diabetes [
      • Jonasson J.M.
      • Brismar K.
      • Sparén P.
      • Lambe M.
      • Nyrén O.
      • Östenson C.-G.
      • et al.
      Fertility in women with type 1 diabetes A population-based cohort study in Sweden.
      ] yielded a global estimate of 21.2 million live births or a 0.9% difference in the estimate. The greatest differences in the regional figures were seen where TDP contributed the largest proportion to the overall estimate of HIP (Table 4).
      Table 4Sensitivity analyses of estimates of hyperglycaemia in pregnancy using a standard fertility ratio (SFR) of 0.8 for women with diabetes.
      Same fertility rate for all womenReduced fertility rate in women with DM
      Cases of hyperglycaemia in pregnancyProportion that may be due to total diabetes in pregnancyCases of hyperglycaemia in pregnancyProportion that may be due to total diabetes in pregnancy
      Millions%Millions%
      IDF Region
      AFR4.619.64.515.9
      EUR1.710.91.68.6
      MENA3.417.73.314.3
      NAC0.924.90.820.6
      SACA0.917.30.914.2
      SEA6.39.56.37.7
      WP3.714.13.711.4
      Income group
      High-income2.116.42.113.4
      Upper-middle income2.417.12.314
      Lower-middle income12.713.412.510.8
      Low-income4.216.74.213.5
      World21.416.921.116.6
      Details of data sources used for generating the estimates are discussed in detail in Linnenkamp et al. [
      • Linnenkamp U.
      • Guariguata L.
      • Whiting D.R.
      • Cho N.H.
      IDF Diabetes Atlas methodology for estimating prevalence of hyperglycaemia in pregnancy.
      ]. Briefly, 199 studies were identified matching the search terms and eligibility criteria. Of these, 92 studies met the inclusion criteria and provided sufficient data for characterisation and scoring and 46 studies representing 34 countries were selected. The majority of studies selected were from high-income countries (60.8%, n = 28), although low- and middle-income countries were also represented (39.1%, n = 18). Few studies were selected for the Africa Region (n = 2) while several studies were selected for Europe (n = 13) and for the North America and Caribbean Region (n = 11) and, in particular, for the USA (n = 3) and Canada (n = 6).

      7. Discussion

      The estimates presented here indicate that with 21.4 million live births affected by hyperglycaemia in pregnancy in 2013, the condition poses a threat to global maternal health. The burden of HIP described in this paper, an estimated 170 cases per 1000 live births in 2013, puts it on par with other maternal conditions [
      • Wulf S.K.
      • Johns N.
      • Lozano R.
      Non-fatal burden of maternal conditions: country-level results from the GBD 2010 Study.
      ]. The prevalence of HIP increases sharply with age with a prevalence of 39.2% in women 40–44 and up to 47.9% in women 45–59 years, although fertility patterns have a profound effect in the number of women with hyperglycaemia in pregnancy as seen in Fig. 2. Patterns of fertility change with development, and women in high-income and upper-middle income countries are having children later in life, which in turn contributes to a greater proportion of live births in those countries affected by HIP. Nonetheless, the overwhelming majority of cases of HIP occur in low- and middle-income countries (91.6%). This is of concern especially in lower-middle income countries where, despite progress towards the Millennium Development Goal to reduce the maternal mortality ratio, only half of women receive the recommended healthcare during pregnancy [

      United Nations Millennium Development Goals. Available from: http://www.un.org/millenniumgoals/maternal.shtml [cited 20.09.13] [Internet].

      ]. As countries progress towards development and fertility patterns towards more children in older ages, coupled with an increasing prevalence of obesity [
      Global status report on noncommunicable diseases 2010.
      ] and type 2 diabetes in younger age groups [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ], we can expect to see increases in the burden of HIP around the world.
      Figure thumbnail gr2
      Fig. 2Number and prevalence of live births affected by hyperglycaemia in pregnancy by age (20–49 years) and income group, 2013.
      *World Bank Income groups as reported for April, 2013 [

      World Bank. Country and lending groups. Available from: http://data.worldbank.org/about/country-classifications/country-and-lending-groups [cited 17.09.13] [Internet].

      ]; number of live births estimated from the UN Population Division World Population Prospects for 2012 [
      • United Nations
      • Department of Economic and Social Affairs
      • Population Division
      World population prospects: the 2012 revision. New York.
      ].
      Indeed, the type 2 diabetes epidemic and the burgeoning number of cases of HIP are driven by similar determinants. Risk factors driven by changes in lifestyle and development, such as abdominal obesity, poor diet, low physical activity, and advancing age are shared by both the type 2 diabetes epidemic and HFDP [
      • Cypryk K.
      • Szymczak W.
      • Czupryniak L.
      • Sobczak M.
      • Lewiński A.
      Gestational diabetes mellitus – an analysis of risk factors.
      ,
      • Bener A.
      • Zirie M.
      • Janahi I.M.
      • Al-Hamaq A.O.A.A.
      • Musallam M.
      • Wareham N.J.
      Prevalence of diagnosed and undiagnosed diabetes mellitus and its risk factors in a population-based study of Qatar.
      ]. Similarly, socioeconomic determinants of health that can put disadvantaged or low-resource communities at greater risk of poor outcomes have been linked to both conditions. Some of these shared factors can be explained by the overlap in the conditions, where TDP is a result of previously-undiagnosed type 2 diabetes. However, a substantial proportion of women who develop HIP have no traditional risk factors and this is one of the reasons that many organisations have advocated for universal screening of pregnant women [
      • Arora D.
      • Arora R.
      • Sangthong S.
      • Leelaporn W.
      • Sangratanathongchai J.
      Universal screening of gestational diabetes mellitus: prevalence and diagnostic value of clinical risk factors.
      ]. More research is needed to illuminate the subtleties of risk factors and determinants at play in the development of gestational diabetes compared to diabetes in pregnancy.
      While any hyperglycaemia in pregnancy poses a threat to women and their infants, diabetes in pregnancy is associated with a higher risk of serious complications and thus may require more intensive management [
      • 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.
      ]. The new WHO definition of hyperglycaemia first detected in pregnancy makes the distinction between degrees of hyperglycaemia in reflection of the increased risk to mothers with diabetes in pregnancy [
      Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy.
      ,
      Hyperglycemia and adverse pregnancy outcomes.
      ]. Understanding and applying these new criteria will be essential to the appropriate allocation of resources, training of healthcare professionals, and pre- and post-natal education of pregnant mothers. Already, barriers to care for women with gestational diabetes are numerous, and are especially serious in low-resource settings [
      • Nielsen K.K.
      • de Courten M.
      • Kapur A.
      Health system and societal barriers for gestational diabetes mellitus (GDM) services – lessons from World Diabetes Foundation supported GDM projects.
      ] which potentially increases rates of complications. For example, women of low socioeconomic status in Australia undergoing management for gestational diabetes were found to have more problems understanding and adhering to self-management requirements than their high economic status counterparts [
      • Carolan M.
      • Gill G.K.
      • Steele C.
      Women's experiences of factors that facilitate or inhibit gestational diabetes self-management.
      ] which highlights the need for close monitoring and appropriate education programmes regardless of setting or level of development. However, awareness of gestational diabetes and its consequences has been found to be relatively low for women in general [
      • Shriraam V.
      • Rani M.A.
      • Sathiyasekaran B.W.C.
      • Mahadevan S.
      Awareness of gestational diabetes mellitus among antenatal women in a primary health center in South India.
      ,
      • Morrison M.K.
      • Lowe J.M.
      • Collins C.E.
      Perceived risk of type 2 diabetes in Australian women with a recent history of gestational diabetes mellitus.
      ].
      Regional variations in the figures partially reflect differences in the prevalence of diabetes in the adult population [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ] but also differences in fertility patterns and the characteristics of the underlying studies. For instance, the high number of cases in South-East Asia is partially explained by the high number of births predicted for the Region but also by studies that have reported prevalences in India ranging from 7.0% [
      • Wahi P.
      • Dogra V.
      • Jandial K.
      • Bhagat R.
      • Gupta R.
      • Gupta S.
      • et al.
      Prevalence of gestational diabetes mellitus (GDM) and its outcomes in Jammu region.
      ] to 18.9% [
      • Seshiah V.
      • Balaji V.
      • Balaji M.S.
      • Sanjeevi C.B.
      • Green A.
      Gestational diabetes mellitus in India.
      ] using different criteria. There is an exceptionally high estimated prevalence of HIP in India (27.5%), which is the main driver of the high prevalence in the Region, compared with 9.9% in Sri Lanka and 9.8% in Bangladesh, Similarly, estimates of the number of cases in the Africa Region are more a reflection of the high number of estimated live births (28.7 million) in the Region than prevalence (14.4%). The wide variability in the country-level estimates is largely due to differences in the reported prevalence in the underlying studies. For instance, neighbouring Netherlands and Belgium have widely different estimates (age-standardised prevalences of 27.2 and 5.0%, respectively), but the difference is similar in magnitude to that reported by the studies the estimates are based on (crude prevalences of 24.2% and 3.3%, respectively) [
      • Benhalima K.
      • Hanssens M.
      • Devlieger R.
      • Verhaeghe J.
      • Mathieu C.
      Analysis of pregnancy outcomes using the new IADPSG recommendation compared with the carpenter and coustan criteria in an area with a low prevalence of gestational diabetes.
      ,
      • Arendz I.J.
      • Oomen P.H.N.
      • Wolthuis A.
      • van der Velde N.M.
      • Kroese J.A.
      • van der Veen I.
      • et al.
      Prevalence of gestational diabetes in high-risk pregnancies: screened using an oral glucose tolerance test.
      ]. The country-level proportions of the HIP estimates that may be due to TDP reflect the age-specific patterns of the prevalence of diabetes in women for those countries. European countries which have a relatively low proportion of women of reproductive age with diabetes have a lower proportion of the HIP estimate that may be due to TDP. Conversely, countries in the Middle East and North Africa Region show a high proportion of TDP, reflecting the overall high prevalence of diabetes in women 20–49 years in those countries [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence for 2013 and projections for 2035.
      ].

      8. Limitations

      The estimates presented in this paper must be interpreted with caution as they are based largely on extrapolation and a number of assumptions. The most substantial limitation to the estimates is the heterogeneity of methods used by the underlying studies to estimate the prevalence of HFDP. However, it is important to note that none of the screening criteria currently in use (Table 1) is lower than the values for diagnosis for the new WHO definition of HFDP (Fig. 1) and thus using these studies based on various criteria as the underlying data for the estimates will not produce an overestimate of HIP.
      Significantly, figures on the proportion of HIP due to TDP presented here are very likely to be an underestimate as some women will present at screening with hyperglycaemia severe enough to be classified as DIP but will not necessarily have had pre-existing undiagnosed T2DM. In addition, the prevalence estimates of diabetes in women derived from the IDF Diabetes Atlas is taken from population-based studies that systematically exclude pregnant women. Thus, the true proportions of DIP and subsequently TDP in HIP are most likely higher. More research is needed in applying the new definition of HFDP to understand the true proportions of this burden and allocate appropriate resources.
      While the method applied for these estimates takes the first evidence-based approach at accounting for the differences, a limited set of data are available to understand and quantify each set of screening methods used against the other. As new studies using the definition recently put forth by the WHO become available, the comparability of the estimates will improve. In addition, the majority of studies are derived from hospital-based cohorts and unlikely to be representative of the whole population. Adjusting for fertility and age goes some way at improving the estimate as well as selecting studies including the largest catchment area available, but local and regional variability in the underlying studies compromise the reliability of the estimates.
      In addition, regional and global estimates are based on extrapolation from a few studies which are applied to large groupings of countries matched for region and World Bank Income group. Where there are more data sources available of reasonable quality, the estimate is likely more precise. However, vast regions have relatively few data sources available. For example, the estimates for HIP in the Africa Region are based entirely on a single study from Nigeria. More studies would improve the precision and reliability of the estimates. Changes in the underlying data currently have a large influence on country-level figures and the estimates would benefit from sensitivity analyses to test the system by which studies are selected.
      Relatively few assumptions are applied to generate the estimates. However, they have a large impact on the final estimates. The first assumptions relate to the scaling of data from some sources based on the screening method used. The scaling is described in detail in Linnenkamp et al. [
      • Linnenkamp U.
      • Guariguata L.
      • Whiting D.R.
      • Cho N.H.
      IDF Diabetes Atlas methodology for estimating prevalence of hyperglycaemia in pregnancy.
      ], but is based on relatively few data. The second assumption is that the fertility rate of women with diabetes is not substantially different from that of the general population. While there is evidence of a reduced fertility rate in women with type 1 diabetes [
      • Jonasson J.M.
      • Brismar K.
      • Sparén P.
      • Lambe M.
      • Nyrén O.
      • Östenson C.-G.
      • et al.
      Fertility in women with type 1 diabetes A population-based cohort study in Sweden.
      ] there is no information available for type 2 diabetes. There is evidence of a disruption in fertility in women with diabetes [
      • Danielson K.K.
      • Palta M.
      • Allen C.
      • D’Alessio D.J.
      The association of increased total glycosylated hemoglobin levels with delayed age at menarche in young women with type 1 diabetes.
      ] and concomitant conditions, like polycystic ovarian syndrome [
      • Conn J.J.
      • Jacobs H.S.
      • Conway G.S.
      The prevalence of polycystic ovaries in women with type 2 diabetes mellitus.
      ]. In addition, the increased risk of malformations in the development of the foetus for women with diabetes in pregnancy lead to early termination of the pregnancy and a decreased fertility [
      • Simmons D.
      Diabetes and obesity in pregnancy.
      ]. The sensitivity analyses presented in this paper using the reported standardised fertility ratio from the Swedish cohort showed a relatively small impact on the global figures and prevalence (Table 4); however, more data are needed in order to integrate these figures into the methodology for global estimates. The overall effect of reducing the fertility rate in women with diabetes was relatively low using the available information. The effect may be greater than what that single study reports. Population-based studies examining at the fertility of women with diabetes would greatly improve the estimates.
      Furthermore, the population and diabetes prevalence estimates used in the methods to derive the estimates of HIP are extrapolations and estimations in their own rights and subject to many of the same biases and limitations presented here.

      9. Conclusion

      Hyperglycaemia in pregnancy is a serious and growing global health threat to women. Integration of strategies for screening and managing women with the condition into public policy and health systems is essential. The growing numbers of women developing HIP will have implications not only for health systems, but will contribute to increases in the global diabetes epidemic.

      Conflict of interest statement

      The authors have no conflicts to disclose.

      Acknowledgements

      The authors wish to thank the IDF Diabetes Atlas Committee for reviewing and contributing to the methods used to generate these estimates. In addition, the authors wish to thank Prof. Boyd Metzger, Dr. David J. Pettitt, and Prof. Lois Jovanovič for independently reviewing the methodology. We also thank Dr. Lydia Makaroff for reading and commenting on the manuscript. The authors also wish to thank Dr. Gojka Roglic for her advice and contribution regarding the methodology and the estimates.
      The 6th edition of the IDF Diabetes Atlas was supported by the following sponsors: Lilly Diabetes, Merck and Co, Inc., Novo Nordisk A/S supported through an unrestricted grant by the Novo Nordisk Changing Diabetes® initiative, Pfizer, Inc., and Sanofi Diabetes.

      Appendix A. Supplementary data

      The following are the supplementary data to this article:

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