You are here

  • Home
  • Archive
  • Volume 5, Issue 3
  • Psychosocial deprivation in women with gestational diabetes mellitus is associated with poor fetomaternal prognoses: an observational study

Article Text

Article menu
XML
Diabetes and endocrinology
Research
Psychosocial deprivation in women with gestational diabetes mellitus is associated with poor fetomaternal prognoses: an observational study
  1. Emmanuel Cosson1,2,
  2. Hélène Bihan2,3,
  3. Gérard Reach3,
  4. Laurence Vittaz4,
  5. Lionel Carbillon5,
  6. Paul Valensi1
  1. 1Department of Endocrinology-Diabetology-Nutrition, AP-HP, Jean Verdier Hospital, Paris 13 University, Sorbonne Paris Cité, CRNH-IdF, CINFO, Bondy, France
  2. 2Sorbonne Paris Cité, UMR U1153 Inserm/U1125 Inra/Cnam/Université Paris 13, Bobigny, France
  3. 3Department of Diabetology, Metabolic Diseases, AP-HP, Avicenne Hospital, Paris 13 University, Sorbonne Paris Cité, CRNH-IdF, Bobigny, France
  4. 4Department of Endocrinology-Diabetology, Ballanger Hospital, Aulnay-Sous-Bois, France
  5. 5Department of Obstetrics and Gynecology, AP-HP, Jean Verdier Hospital, Paris 13 University, Sorbonne Paris Cité, Bondy, France
  1. Correspondence to Dr Emmanuel Cosson; emmanuel.cosson{at}jvr.aphp.fr

Abstract

Objective To evaluate the prognoses associated with psychosocial deprivation in women with gestational diabetes mellitus (GDM).

Design Observational study considering the 1498 multiethnic women with GDM who gave birth between January 2009 and February 2012.

Setting Four largest maternity units in the northeastern suburban area of Paris.

Participants The 994 women who completed the Evaluation of Precarity and Inequalities in Health Examination Centers (EPICES) questionnaire.

Main outcome measure Main complications of GDM (large infant for gestational age (LGA), shoulder dystocia, caesarean section, pre-eclampsia).

Results Psychosocial deprivation (EPICES score ≥30.17) affected 577 women (56%) and was positively associated with overweight/obesity, parity and non-European origin, and negatively associated with family history of diabetes, fruit and vegetable consumption and working status. The psychosocially deprived women were diagnosed with GDM earlier, received insulin treatment during pregnancy more often and were more likely to have LGA infants (15.1% vs 10.6%, OR=1.5 (95% CI 1.02 to 2.2), p<0.05) and shoulder dystocia (3.1% vs 1.2%, OR=2.7 (0.97 to 7.2), p<0.05). In addition to psychosocial deprivation, LGA was associated with greater parity, obesity, history of GDM, ethnicity, excessive gestational weight gain and insulin therapy. A multivariate analysis using these covariates revealed that the EPICES score was independently associated with LGA infants (per 10 units, OR=1.12 (1.03 to 1.20), p<0.01).

Conclusions In our area, psychosocial deprivation is common in women with GDM and is associated with earlier GDM diagnoses and greater insulin treatment, an increased likelihood of shoulder dystocia and, independently of obesity, gestational weight gain and other confounders with LGA infants.

  • OBSTETRICS
  • SOCIAL MEDICINE

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/bmjopen-2014-007120

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Strengths and limitations of this study

    • It is known that psychosocial deprivation is associated with more gestational diabetes and we report for the first time that, among women with gestational diabetes mellitus, psychosocial deprivation is associated with a poor prognosis.

    • Our large multicentre and diverse cohort and the adjustments for the relevant confounding factors, such as body mass index and gestational weight gain, ensure the robustness of our findings.

    • We defined psychosocial vulnerability using the Evaluation of Precarity and Inequalities in Health Examination Centers (EPICES) questionnaire, which has been validated during pregnancy or not. This tool evaluates several domains at an individual level, including material goods, money, friendship and family networks, healthcare and leisure.

    • However, the EPICES questionnaire was retrospectively fulfilled (6–24 months after pregnancy).

    Introduction

    Socioeconomic status reflects access to resources to prosper, and psychosocial deprivation is associated, across countries and over time,1 ,2 with higher mortality and morbidity, including type 2 diabetes.3 The main drivers in more incident type 2 diabetes appear to be higher body mass index (BMI) and impaired health behaviours.4 The American Diabetes Association recommends the inclusion of assessments of patients’ psychological and social situations as an ongoing part of the medical management of diabetes.5 Indeed, psychosocial deprivation in patients with diabetes has been reported to be associated with increased obesity,6 worse glycaemic control,7 poorer adherence,8 ,9 more diabetic complications6 ,7 ,10 ,11 and perhaps greater mortality.12–14 During pregnancy, psychosocial deprivation is also associated with poor outcomes that include increased rates of maternal15 and neonatal15 ,16 hospitalisation, stillbirth,17 postnatal death,18 preterm delivery17 ,19 and small for gestational age infants.17–20

    Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy and is now very common, with a prevalence ranging from 9.3% in Israel to 25.5% in California, USA.21 Although GDM is also more frequent in cases of psychosocial deprivation,18 ,19 ,22 ,23 its prognosis in case of poor psychological and social conditions is currently unknown. We hypothesised that psychosocial deprivation might be associated with poor prognoses in women with GDM when confounding factors, such as obesity,19 gestational weight gain (GWG)16 and smoking habits,20 are considered.

    The four largest maternity units in the Northeastern suburban area of Paris, France, participated in the IMPACT initiative, which aimed to improve postpartum screening for dysglycaemia after GDM.24 ,25 During this study, the women who attended these maternity units responded to the Evaluation of Precarity and Inequalities in Health Examination Centers (EPICES) questionnaire, a questionnaire which evaluates psychosocial deprivation.6 ,7 ,19 ,24 ,26 Therefore, for the first time, we had the opportunity to investigate the fetomaternal prognoses of these women with GDM according to their individual psychosocial statuses.

    Methods

    Patients

    This study is a secondary analysis of the IMPACT study.24 ,25 Briefly, the IMPACT initiative began in March 2011 and was a mobilisation campaign for women with GDM and their community caregivers that sought to increase postpartum screening for dysglycaemia. We aimed to evaluate the effect of this initiative by comparing the postpartum screening rates between the women who delivered before (between January 2009 and December 2010) and after this initiative (between April 2011 and February 2012). We systematically included women who were at least 18 years of age, free of known pregestational diabetes, had GDM and were followed during pregnancy in one of the four largest maternity units of the Seine-Saint Denis area of France during these periods of time. GDM was detected by oral glucose tolerance test and was defined by fasting blood glucose values ≥5.3 mmol/L and/or a 2 h blood glucose value ≥7.8 mmol/L between January 2009 and December 2010,24 ,25 and thereafter according to the International Association of Diabetes and Pregnancy Study Groups criteria, adopted in France in 2010.27 GDM screening was universal in the four centres. In the primary analyses, we included the women who could be contacted by telephone and provided self-reports that indicated whether they had undergone postpartum screening tests during the 6 months following their deliveries.24 ,25 For the current analysis, we included all of the women who delivered between January 2009 and February 2012 and who retrospectively completed the EPICES questionnaire by phone regardless of their report concerning the postpartum screening.

    Data collection and assessment of outcomes

    One single investigator extracted the following data from hospital records: age at conception, origin/ethnicity, family history of diabetes, history of previous GDM, gestational age at the time of GDM diagnosis (three classes: <24 weeks of gestation, between 24 and 28 weeks of gestation and ≥28 weeks of gestation), insulin treatment during pregnancy and GWG. Excessive GWG was defined according to the recommendations of the Institute of Medicine; that is, GWG ≥16 kg in women with pregravid BMIs <25 kg/m2, ≥11.5 kg in overweight women (BMIs between 25 and 29.9 kg/m2) and ≥9 kg in obese women (BMI ≥30 kg/m2). We also collected obstetrical and neonatal outcomes, including offspring birth weight in comparison to the standard French population (large for gestational age (LGA) was defined by a birth weight exceeding the 90th centile),28 pre-eclampsia (blood pressure ≥140/90 mm Hg on two recordings 4 h apart and proteinuria of at least 300 mg/24 h or 3+ or higher on dipstick testing of a random urine sample), shoulder dystocia (defined as the use of obstetrical manoeuvres, ie, McRoberts, episiotomy after delivery of the fetal head, suprapubic pressure, posterior arm rotation to an oblique angle, rotation of the infant by 180°, delivery of the posterior arm and acute or elective caesarean section).

    The investigator conducted semistructured interviews by phone between January and November 2011 for the women who delivered before the IMPACT campaign (maximum delay of time since delivery 24 months) and at least 6 months after delivery for the women who delivered after the IMPACT initiative. The investigator requested information about the participants’ current weights, heights, waist circumferences, professional statuses, smoking statuses, number of children, antihypertensive and lipid-lowering treatments, family histories of diabetes and daily consumptions of fruits and vegetables. All these data were therefore declarative. Waist circumference was deducted from the current waist size of trousers (waist circumference <80 cm: 6–14 (UK) or 34–42 (France), 80–88 cm: 16–20 (UK) or 44–48 (France); >88 cm: 22 (UK) or 48 (France) or more).

    The investigator also conducted interviews to assess psychosocial deprivation using the EPICES score, which is a French deprivation score that is calculated based on responses to 11 questions that consider both socioeconomic conditions and family environment (see online supplementary appendix 1).7 ,26 ,29 It evaluates several domains at an individual level, including material goods, money, friendship and family networks, healthcare and leisure. As previously reported, the EPICES score is a continuous variable, and increasing quintiles are associated with increased risks for poor health conditions such as obesity, diabetes in women, higher rates of smoking, poorer access to dental and gynaecological care, and poorer perceived health statuses.24 ,26 However, psychosocial deprivation can be defined by a score ≥30.17,29 which was the threshold used here.

    Statistical analyses

    Sample size calculations were based on the main criterion of the IMPACT study, that is, a postpartum screening test performed 6 months following delivery.24 ,25 Results reported in this manuscript were prespecified, exploratory end points. Continuous variables are expressed as means±SD, and normality was assessed with the Kolmogorov-Smirnov tests. There were no missing data concerning psychosocial deprivation and main outcomes. Comparisons of two independent groups were performed using the Student t test if the variable was normally distributed; otherwise, the Wilcoxon Mann-Whitney test was used. The significance of differences in proportions (ie, qualitative variables) were tested with the χ2 test, and the ORs and 95% CIs were calculated in cases of statistical significance (p<0.05). We defined EPICES score tertiles in our cohort: first tertile: EPICES score <23.71 (mean 11.7±6.2; n=296); second tertile: score between 23.71 and 51.5 (mean 35.0±8.5; n=355); and third tertile: score ≥51.5 (mean 69.9±12.6; n=343). The factors associated with having an LGA infant were assessed with a univariate logistic regression method. For multivariate analyses, we included all factors that were associated with LGA infants with p≤0.05 in the univariate analyses. SAS Statistics (V.9.2; Cary, USA) was used to conduct all statistical analyses.

    Results

    Characteristics of the women

    A total of 1498 women gave birth following GDM between January 2009 and February 2012 in our maternity units. Of these women, 994 responded retrospectively by phone to the EPICES questionnaire. Table 1 illustrates the characteristics of these women. The characteristics of the 994 women who responded to the EPICES questionnaire were similar to those of the 504 who did not respond, with the exception of greater daily consumptions of fruit and vegetables (66.1% vs 59.0%, respectively, p<0.01) and a trend towards being older (33.3±5.2 vs 32.7±5.5 years, respectively, p=0.06). The EPICES questionnaire could not be completed by the women who could not be reached by phone and those with French language proficiencies that were insufficient for answering the questions.

    View this table:
    Table 1

    Characteristics of the total cohort of women by psychosocial status

    Psychosocial deprivation affected 577 women (56%) and was positively associated with parity, overweight and obesity, greater waist circumference and non-European origin. Psychosocial deprivation was negatively associated with daily fruit and vegetable consumption, reduced family history of diabetes and working status (table 1).

    Pregnancy outcomes

    Table 2 shows that the psychosocially deprived women were not only more likely to have been diagnosed with GDM prior to 24 weeks of gestation but also more likely to have been treated with insulin during pregnancy than the non-psychosocially deprived women. The psychosocially deprived women were more likely to have LGA infants and infants with shoulder dystocia, but no differences in caesarean section or pre-eclampsia were found. Figure 1A–D shows that the prevalences of insulin treatment during pregnancy (figure 1A), LGA infants (figure 1C) and shoulder dystocia (figure 1D) increased with increasing EPICES score tertiles.

    View this table:
    Table 2

    Events during pregnancy by psychosocial status

    Figure 1
    Figure 1

    Prevalence of events according to the EPICES score tertiles. (A) Insulin therapy during pregnancy (%); (B) GDM diagnosis before 24 weeks gestation (%); (C) large for gestational age infants (%); (D) shoulder dystocia (%). *p<0.05 versus the first tertile. Tertile 1: EPICES score <23.71 (mean 11.7±6.2); tertile 2: EPICES score between 23.71 and 51.5 (mean 35.0±8.5) and tertile 3: EPICES score ≥51.5 (mean 69.9±12.6). EPICES, Evaluation of Precarity and Inequalities in Health Examination Centers; GDM, gestational diabetes mellitus; NS, not significant.

    Table 3 shows that, in addition to psychosocial deprivation (OR=1.5 (95% CI 1.02 to 2.22)), LGA was associated with higher parity, greater BMI and obesity of the mother (OR=2.1 (1.4 to 3.1)), increased incidence of GDM history (OR=2.0 (1.4 to 3.1)), ethnicity/origin, greater EPICES score (per 10 units: OR=1.50 (1.22 to 2.22)), greater GWG and excessive GWG (OR=2.8 (1.9 to 4.1)) and insulin treatment during pregnancy (OR=1.6 (1.1 to 2.4)). A multivariate analysis that considered parity, obesity, personal history of GDM, ethnicity, EPICES score, excessive GWG and insulin therapy during pregnancy revealed that the EPICES score remained independently associated with LGA infants (table 3). In a model that was identical to the aforementioned model with the exception that weight and height were used in the place of obesity, an association between psychosocial deprivation and LGA infants remained (per 10 units: OR=1.11 (1.02 to 1.20, p<0.05). In another model that was identical to the aforementioned model with the exception that psychosocial deprivation (ie, EPICES score ≥30.17) was used in place of the EPICES score, a trend towards an association between psychosocial deprivation and LGA infants remained (OR=1.53 (0.98 to 2.39), p=0.06). The prevalence of shoulder dystocia was too low to allow multivariate analyses.

    View this table:
    Table 3

    Factors associated with LGA infants

    Discussion

    In this study, psychosocial deprivation in women with GDM was associated with earlier GDM diagnoses and more extensive insulin treatment. Moreover, we show for the first time that, independent of confounding factors, psychosocial deprivation was associated with increases in adverse outcomes, particularly in LGA infants. We report that psychosocial deprivation (ie, an EPICES score above 30.17) affected 56% of the women with GDM in our study; another study reported a prevalence of 48% (11/23 women with GDM) in another area of France using the same definition of deprivation.19 This high prevalence is due not only to the prevalence of precarity30 and multiethnicity31 in the Northeastern suburban area of Paris, but also to the roles played by these conditions in the rate of GDM. Indeed, the prevalence of GDM has been reported to be 1.7-fold to 2.9-fold higher among patients with high EPICES scores,19 low educational statuses22 ,23 or low family incomes18 ,23 compared with their counterparts without these conditions. Notably, 23% of pregnant women in France have been reported to have high EPICES scores regardless of GDM status,19 and 17.5% have been coded as psychosocially deprived by social workers15 in two other areas in France. Together, our results advocate for screening for deprivation among pregnant women with GDM.

    As previously reported for women with and without GDM,19 ,32 we found that psychosocially deprived women with GDM were more likely to be obese. These women were also more likely to be unemployed and less likely to be daily consumers of fruits or vegetables; the latter association is most likely due to the cost of these foods. An association between socioeconomic status and healthy eating status, including fruit and vegetable consumption, has previously been reported.33 ,34 We also observed a link between ethnicity/origin and deprivation; similar links have previously been described as complex relationships.35–38 The women with and without psychosocial deprivation reported similar prevalences of pre-pregnancy antihypertensive and lipid-lowering treatments, although metabolic disorders are often associated with elevated EPICES scores39 ,40 and stress.41 The lack of association observed in our study might be specific to women of reproductive age or might be attributable to reduced numbers of medical visits prior to pregnancy due to precarity.16 The latter supposition would also result in undiagnosed metabolic syndrome prior to pregnancy, which would be in accordance with the greater prevalence of GDM diagnoses prior to 24 weeks of gestation among psychosocially deprived women. These findings suggest the possibility that these women might actually have had undiagnosed pregravid type 2 diabetes. Indeed, precarity is a risk factor for undiagnosed type 2 diabetes even in women of reproductive age.42 However, we do not have access to the results of postpartum glycaemic assessments that would be needed to confirm this hypothesis.

    We also studied the association between psychosocial deprivation and adverse pregnancy outcomes in women with GDM for the first time. Compared with women without precarity, those with precarity were more likely to have LGA infants and infants with shoulder dystocia. The association between EPICES scores and LGA infants was independent of obesity, which suggests that this relationship was only partially driven by the increased prevalence of overweight/obesity among the deprived women.43 ,44 GWG and the prevalence of excessive GWG, which are other confounding factors regarding LGA infants,43 ,44 were comparable between the women with and without precarity. We have recently shown that, compared with women from sub-Saharan Africa, European women experience more GDM-related events.31 Furthermore, racial/ethnic differences in the clinical outcomes of GDM, including macrosomia, are commonly reported (for review).45 The association of LGA with precarity remained significant after adjusting for origin/ethnicity while we did not find any association between precarity and offspring with birth weights greater than 4000 or 4250 g. The association between psychosocial deprivation and shoulder dystocia, which was not adjusted for confounding factors because the rate of dystocia events was low, was most likely driven by the prevalence of LGA infants. In a population-based study, the risk for shoulder dystocia increased significantly with BMI category in an unadjusted analysis, but this significance disappeared after adjusting for GDM.35 As previously reported for pregnant women regardless of GDM status,15 we found that the women in our cohort with GDM underwent caesarean section at similar rates regardless of the presence of psychosocial deprivation. Vulnerable women were diagnosed earlier with GDM, which suggests that unknown pregravid dysglycaemia might partially explain the increased rate of LGA infants.46 In a recent German study, the groups that were found to be at high risk for GDM were women of low socioeconomic status, migrants and obese women. An elevated risk of fetal malformations was found among the women who had been diagnosed with GDM, which suggests that many of these women might have had high glucose levels by the first trimester.47

    The present study has limits and strengths. The public hospital recruitment and the area we cover probably included a higher proportion of women living with vulnerable conditions, precluding a generalisation of our results. On the other hand, we could only include women who could fulfil the EPICES instrument and this may have underestimated the prevalence of psychosocial deprivation. Our large multicentre and diverse cohort and the adjustments for the relevant confounding factors ensure the robustness of our findings. However, we did not have access to data about glycaemic control, diet, physical activity or the numbers of visits during pregnancy. Thus, the adverse outcomes observed for the women with precarity might have been due to these factors based on the following arguments: (1) poor glycaemic control has been reported in vulnerable patients with diabetes7 and was most likely present in our population with GDM and psychosocial insecurity because insulin treatment was more often necessary during GDM among this population; (2) fruit and vegetable consumption was lower among the vulnerable women following pregnancy, which might be indicative of poorer nutritional habits during pregnancy;33 ,34 ,48 (3) exercise during late pregnancy has been reported to vary with the education level of the mother;33 ,34 ,49 and (4) access to healthcare might depend on socioeconomic status,19 but it is unlikely that access to healthcare influenced our results because healthcare is free of charge within the French healthcare system. Compliance may also differ according to psychosocial vulnerability status. Some data were self-reported, such as current weight, height, waist circumference class, professional status, smoking status, number of children, antihypertensive and lipid-lowering treatments, family history of diabetes and daily consumption of fruit and vegetables. We used the EPICES score, which is an individual index that has been validated during pregnancy19 and appears to be more strongly linked to the risk of adverse maternofetal outcomes than neighbourhood-level socioeconomic status.17 However, the EPICES questionnaire was retrospectively fulfilled (6–24 months after pregnancy).

    Conclusions

    To conclude, our results from a large multiethnic multicentre European cohort from an area in which precarity is common demonstrate that psychosocial deprivation affected more than half of the women with GDM. Psychosocial deprivation was associated with higher BMIs and earlier GDM diagnoses among the vulnerable women, which suggests that GDM most likely corresponded to unknown type 2 diabetes mellitus in these women and that prenatal diagnosis of type 2 diabetes should be reinforced in them, with weight control intervention and adherence to a healthy lifestyle before pregnancy.50 The vulnerable women were also more likely to be treated with insulin, but they gained as much weight during pregnancy as did the non-vulnerable women. Independent of the gestational age at GDM diagnosis, insulin use, overweight/obesity, GWG and other confounders, these women were also more likely to have LGA infants. This finding suggests that the routine screening of women with GDM for psychosocial vulnerability may be an important tool for improving the prognoses of these women and their children. For example, specific follow-up and psychosocial support might be beneficial in these women.

    Acknowledgments

    The authors acknowledge the statistical assistance of Bénédicte Borsik, Delphine Dubois and Anne Ourliac (Umanis, Paris, France—funding source: Novo Nordisk). The authors also thank Dr Faranaz Faghfouri (AP-HP, Jean Verdier Hospital, Paris 13 University, Sorbonne Paris Cité, Department of Obstetrics and Gynecology, Bondy, France) who was the study investigator (funding source: Novo Nordisk); and Dr H Dauphin (Ballanger Hospital, Department of Obstetrics and Gynecology, Aulnay-Sous-Bois, France); Dr Chafika Khiter (De La Fontaine Hospital, Department of Diabetology, Saint Denis, France), Dr Dominique Leboeuf (Seine-Saint-Denis Private Hospital, Department of Obstetrics and Gynecology, Le Blanc Mesnil, France) and Dr Astrid Lepagnol (De La Fontaine Hospital, Department of Obstetrics and Gynecology, Saint Denis, France) who recruited the women. This programme was sponsored by the Société Francophone de Diabétologie, the réseau pour la prise en charge et la prévention de l'obésité en pédiatrie 93-Seine-Saint-Denis (REPOP 93), L'Assurance Maladie Seine-Saint-Denis, l'Ordre National Des Pharmaciens and Université Paris 13.

    References

    1. ↵
      1. Clarke R,
      2. Emberson J,
      3. Fletcher A, et al
      . Life expectancy in relation to cardiovascular risk factors: 38 year follow-up of 19,000 men in the Whitehall study. BMJ 2009;339:b3513. doi:10.1136/bmj.b3513
      OpenUrlAbstract/FREE Full Text
    2. ↵
      1. Chandola T,
      2. Ferrie J,
      3. Sacker A, et al
      . Social inequalities in self reported health in early old age: follow-up of prospective cohort study. BMJ 2007;334:990. doi:10.1136/bmj.39167.439792.55
      OpenUrlAbstract/FREE Full Text
    3. ↵
      1. Kivimaki M,
      2. Virtanen M,
      3. Kawachi I, et al
      . Long working hours, socioeconomic status, and the risk of incident type 2 diabetes: a meta-analysis of published and unpublished data from 222 120 individuals. Lancet Diabetes Endocrinol 2015;3:27–34. doi:10.1016/S2213-8587(14)70178-0
      OpenUrl
    4. ↵
      1. Stringhini S,
      2. Tabak AG,
      3. Akbaraly TN, et al
      . Contribution of modifiable risk factors to social inequalities in type 2 diabetes: prospective Whitehall II cohort study. BMJ 2012;345:e5452. doi:10.1136/bmj.e5452
      OpenUrlAbstract/FREE Full Text
    5. ↵
      American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care 2014;37(Suppl 1):S14–80. doi:10.2337/dc14-S014
      OpenUrlFREE Full Text
    6. ↵
      1. Bihan H,
      2. Ramentol M,
      3. Fysekidis M, et al
      . Screening for deprivation using the EPICES score: a tool for detecting patients at high risk of diabetic complications and poor quality of life. Diabetes Metab 2012;38:82–5. doi:10.1016/j.diabet.2011.10.004
      OpenUrlPubMed
    7. ↵
      1. Bihan H,
      2. Laurent S,
      3. Sass C, et al
      . Association among individual deprivation, glycemic control, and diabetes complications: the EPICES score. Diabetes Care 2005;28:2680–5. doi:10.2337/diacare.28.11.2680
      OpenUrlAbstract/FREE Full Text
    8. ↵
      1. Brown AF,
      2. Ettner SL,
      3. Piette J, et al
      . Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev 2004;26:63–77. doi:10.1093/epirev/mxh002
      OpenUrlFREE Full Text
    9. ↵
      1. Wamala S,
      2. Merlo J,
      3. Bostrom G, et al
      . Socioeconomic disadvantage and primary non-adherence with medication in Sweden. Int J Qual Health Care 2007;19:134–40. doi:10.1093/intqhc/mzm011
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Chaturvedi N,
      2. Jarrett J,
      3. Shipley MJ, et al
      . Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall Study and the WHO Multinational Study of Vascular Disease in Diabetes. BMJ 1998;316:100–5. doi:10.1136/bmj.316.7125.100
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Nicolucci A,
      2. Carinci F,
      3. Ciampi A
      . Stratifying patients at risk of diabetic complications: an integrated look at clinical, socioeconomic, and care-related factors. SID-AMD Italian Study Group for the Implementation of the St. Vincent Declaration. Diabetes Care 1998;21:1439–44. doi:10.2337/diacare.21.9.1439
      OpenUrlAbstract/FREE Full Text
    12. ↵
      1. Saydah SH,
      2. Imperatore G,
      3. Beckles GL
      . Socioeconomic status and mortality: contribution of health care access and psychological distress among U.S. adults with diagnosed diabetes. Diabetes Care 2013;36:49–55. doi:10.2337/dc11-1864
      OpenUrlAbstract/FREE Full Text
    13. ↵
      1. Booth GL,
      2. Bishara P,
      3. Lipscombe LL, et al
      . Universal drug coverage and socioeconomic disparities in major diabetes outcomes. Diabetes Care 2012;35:2257–64. doi:10.2337/dc12-0364
      OpenUrlAbstract/FREE Full Text
    14. ↵
      1. Gnavi R,
      2. Petrelli A,
      3. Demaria M, et al
      . Mortality and educational level among diabetic and non-diabetic population in the Turin Longitudinal Study: a 9-year follow-up. Int J Epidemiol 2004;33:864–71. doi:10.1093/ije/dyh089
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Gayral-Taminh M,
      2. Daubisse-Marliac L,
      3. Baron M, et al
      . [Social and demographic characteristics and perinatal risks for highly deprived mothers]. J Gynecol Obstet Biol Reprod (Paris) 2005;34(1 Pt 1):23–32. doi:10.1016/S0368-2315(05)82667-5
      OpenUrlPubMed
    16. ↵
      1. Lejeune VN,
      2. Chaplet VM,
      3. Carbonne B, et al
      . Precarity and pregnancy in Paris. Eur J Obstet Gynecol Reprod Biol 1999;83:27–30. doi:10.1016/S0301-2115(98)00241-3
      OpenUrlPubMed
    17. ↵
      1. Luo ZC,
      2. Wilkins R,
      3. Kramer MS
      . Effect of neighbourhood income and maternal education on birth outcomes: a population-based study. CMAJ 2006;174:1415–20. doi:10.1503/cmaj.051096
      OpenUrlAbstract/FREE Full Text
    18. ↵
      1. Joseph KS,
      2. Liston RM,
      3. Dodds L, et al
      . Socioeconomic status and perinatal outcomes in a setting with universal access to essential health care services. CMAJ 2007;177:583–90. doi:10.1503/cmaj.061198
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Convers M,
      2. Langeron A,
      3. Sass C, et al
      . [Is the socioeconomic deprivation EPICES score useful in obstetrics?]. Gynecol Obstet Fertil 2012;40:208–12. doi:10.1016/j.gyobfe.2011.10.014
      OpenUrlPubMed
    20. ↵
      1. Brooke OG,
      2. Anderson HR,
      3. Bland JM, et al
      . Effects on birth weight of smoking, alcohol, caffeine, socioeconomic factors, and psychosocial stress. BMJ 1989;298:795–801. doi:10.1136/bmj.298.6676.795
      OpenUrlAbstract/FREE Full Text
    21. ↵
      1. Sacks DA,
      2. Hadden DR,
      3. Maresh M, et al
      . Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Diabetes Care 2012;35:526–8. doi:10.2337/dc11-1641
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Bo S,
      2. Menato G,
      3. Bardelli C, et al
      . Low socioeconomic status as a risk factor for gestational diabetes. Diabetes Metab 2002;28:139–40.
      OpenUrlPubMedWeb of Science
    23. ↵
      1. Hunsberger M,
      2. Rosenberg KD,
      3. Donatelle RJ
      . Racial/ethnic disparities in gestational diabetes mellitus: findings from a population-based survey. Womens Health Issues 2010;20:323–8. doi:10.1016/j.whi.2010.06.003
      OpenUrlCrossRefPubMed
    24. ↵
      1. Bihan H,
      2. Cosson E,
      3. Khiter C, et al
      . Factors associated with screening for glucose abnormalities after gestational diabetes mellitus: baseline cohort of the interventional IMPACT study. Diabetes Metab 2014;40:151–7. doi:10.1016/j.diabet.2013.12.002
      OpenUrl
    25. ↵
      1. Cosson E,
      2. Bihan H,
      3. Vittaz L, et al
      . Improving postpartum glucose screening following gestational diabetes mellitus: the IMPACT multicenter initiative. A cohort study. Diabet Med 2015;32:189–97. doi:10.1111/dme.12631
      OpenUrl
    26. ↵
      1. Sass C,
      2. Gueguen R,
      3. Moulin JJ, et al
      . [Comparison of the individual deprivation index of the French Health Examination Centres and the administrative definition of deprivation]. Sante Publique 2006;18:513–22. doi:10.3917/spub.064.0513
      OpenUrlCrossRefPubMed
    27. ↵
      [No authors listed]. Expert consensus on gestational diabetes mellitus. Summary of expert consensus. Diabetes Metab 2010;36(6 Pt 2):695–9. doi:10.1016/j.diabet.2010.11.019
      OpenUrlCrossRefPubMed
    28. ↵
      1. Leroy B,
      2. Lefort F
      . [The weight and size of newborn infants at birth]. Rev Fr Gynecol Obstet 1971;66:391–6.
      OpenUrlPubMed
    29. ↵
      1. Sass C,
      2. Belin S,
      3. Chatain C, et al
      . [Social vulnerability is more frequent in victims of interpersonal violence: value of the EPICES score]. Presse Med 2009;38:881–92. doi:10.1016/j.lpm.2008.10.016
      OpenUrlPubMed
    30. ↵
      Fiche 6.1 Pauvreté, précarité, Observatoire Régional de la Santé, 2011.
    31. ↵
      1. Cosson E,
      2. Cussac-Pillegand C,
      3. Benbara A, et al
      . The diagnostic and prognostic performance of a selective screening strategy for gestational diabetes mellitus according to ethnicity in Europe. J Clin Endocrinol Metab 2014;99:996–1005.
      OpenUrl
    32. ↵
      1. Park JH,
      2. Lee BE,
      3. Park HS, et al
      . Association between pre-pregnancy body mass index and socioeconomic status and impact on pregnancy outcomes in Korea. J Obstet Gynaecol Res 2011;37:138–45. doi:10.1111/j.1447-0756.2010.01332.x
      OpenUrlPubMed
    33. ↵
      1. Braveman PA,
      2. Cubbin C,
      3. Egerter S, et al
      . Socioeconomic disparities in health in the United States: what the patterns tell us. Am J Public Health 2010;100(Suppl 1):S186–96. doi:10.2105/AJPH.2009.166082
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. Bleich SN,
      2. Jarlenski MP,
      3. Bell CN, et al
      . Health inequalities: trends, progress, and policy. Annu Rev Public Health 2012;33:7–40. doi:10.1146/annurev-publhealth-031811-124658
      OpenUrlCrossRefPubMedWeb of Science
    35. ↵
      1. Ovesen P,
      2. Rasmussen S,
      3. Kesmodel U
      . Effect of prepregnancy maternal overweight and obesity on pregnancy outcome. Obstet Gynecol 2011;118(2 Pt 1):305–12. doi:10.1097/AOG.0b013e3182245d49
      OpenUrlCrossRefPubMedWeb of Science
    36. ↵
      1. Nazroo JY
      . The structuring of ethnic inequalities in health: economic position, racial discrimination, and racism. Am J Public Health 2003;93:277–84. doi:10.2105/AJPH.93.2.277
      OpenUrlCrossRefPubMedWeb of Science
    37. ↵
      1. Tanaka M,
      2. Jaamaa G,
      3. Kaiser M, et al
      . Racial disparity in hypertensive disorders of pregnancy in New York State: a 10-year longitudinal population-based study. Am J Public Health 2007;97:163–70. doi:10.2105/AJPH.2005.068577
      OpenUrlCrossRefPubMedWeb of Science
    38. ↵
      1. Smith GD
      . Learning to live with complexity: ethnicity, socioeconomic position, and health in Britain and the United States. Am J Public Health 2000;90:1694–8. doi:10.2105/AJPH.90.11.1694
      OpenUrlCrossRefPubMedWeb of Science
    39. ↵
      1. Jaffiol C,
      2. Thomas F,
      3. Bean K, et al
      . Impact of socioeconomic status on diabetes and cardiovascular risk factors: results of a large French survey. Diabetes Metab 2013;39:56–62. doi:10.1016/j.diabet.2012.09.002
      OpenUrl
    40. ↵
      1. La Rosa E,
      2. Le Clesiau H,
      3. Valensi P
      . Metabolic syndrome and psychosocial deprivation. Data collected from a Paris suburb. Diabetes Metab 2008;34:155–61. doi:10.1016/j.diabet.2007.12.005
      OpenUrlCrossRefPubMedWeb of Science
    41. ↵
      1. Rosmond R
      . Role of stress in the pathogenesis of the metabolic syndrome. Psychoneuroendocrinology 2005;30: 1–10. doi:10.1016/j.psyneuen.2004.05.007
      OpenUrlCrossRefPubMedWeb of Science
    42. ↵
      1. Paulweber B,
      2. Valensi P,
      3. Lindstrom J, et al
      . A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res 2010;42(Suppl 1):S3–36. doi:10.1055/s-0029-1240928
      OpenUrlCrossRefPubMedWeb of Science
    43. ↵
      1. Black MH,
      2. Sacks DA,
      3. Xiang AH, et al
      . The relative contribution of prepregnancy overweight and obesity, gestational weight gain, and IADPSG-defined gestational diabetes mellitus to fetal overgrowth. Diabetes Care 2013;36:56–62. doi:10.2337/dc12-0741
      OpenUrlAbstract/FREE Full Text
    44. ↵
      1. Bowers K,
      2. Laughon SK,
      3. Kiely M, et al
      . Gestational diabetes, pre-pregnancy obesity and pregnancy weight gain in relation to excess fetal growth: variations by race/ethnicity. Diabetologia 2013;56:1263–71. doi:10.1007/s00125-013-2881-5
      OpenUrl
    45. ↵
      1. Golden SH,
      2. Brown A,
      3. Cauley JA, et al
      . Health disparities in endocrine disorders: biological, clinical, and nonclinical factors––an Endocrine Society scientific statement. J Clin Endocrinol Metab 2012;97:E1579–639. doi:10.1210/jc.2012-2043
      OpenUrlCrossRefPubMedWeb of Science
    46. ↵
      1. Boulot P,
      2. Chabbert-Buffet N,
      3. d'Ercole C, et al
      . French multicentric survey of outcome of pregnancy in women with pregestational diabetes. Diabetes Care 2003;26:2990–3. doi:10.2337/diacare.26.11.2990
      OpenUrlAbstract/FREE Full Text
    47. ↵
      1. Schneider S,
      2. Hoeft B,
      3. Freerksen N, et al
      . Neonatal complications and risk factors among women with gestational diabetes mellitus. Acta Obstet Gynecol Scand 2011;90:231–7.
      OpenUrlPubMed
    48. ↵
      1. Bihan H,
      2. Castetbon K,
      3. Mejean C, et al
      . Sociodemographic factors and attitudes toward food affordability and health are associated with fruit and vegetable consumption in a low-income French population. J Nutr 2010;140:823–30. doi:10.3945/jn.109.118273
      OpenUrlAbstract/FREE Full Text
    49. ↵
      1. Foxcroft KF,
      2. Rowlands IJ,
      3. Byrne NM, et al
      . Exercise in obese pregnant women: the role of social factors, lifestyle and pregnancy symptoms. BMC Pregnancy Childbirth 2011;11:4. doi:10.1186/1471-2393-11-4
      OpenUrlPubMed
    50. ↵
      1. Zhang C,
      2. Tobias DK,
      3. Chavarro JE, et al
      . Adherence to healthy lifestyle and risk of gestational diabetes mellitus: prospective cohort study. BMJ 2014;349:g5450. doi:10.1136/bmj.g5450
      OpenUrlAbstract/FREE Full Text
    View Abstract

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      Files in this Data Supplement:

    Footnotes

    • Contributors EC researched the data, directed the research and wrote the manuscript. HB researched the data and wrote the manuscript. GR directed the research and reviewed/edited the manuscript. LV researched the data and contributed to the discussion. LC researched the data and reviewed/edited the manuscript. PV directed the research and reviewed/edited the manuscript. All authors contributed to the interpretation of the results and the revision of the manuscript for intellectual content and approved the final version of the manuscript.

    • Funding This research was supported by a grant from Novo Nordisk France. It provided money to pay the investigator, to perform statistical analyses and for the English language editing service.

    • Competing interests EC reports grants from Novo Nordisk France during the conduct of the study; personal fees from Novo Nordisk France outside the submitted work. HB reports personal fees from Novonordisk during the conduct of the study. LC reports personal fees from Novonordisk during the conduct of the study. PV reports grants from Novo Nordisk France during the conduct of the study; personal fees from Novo Nordisk France outside the submitted work.

    • Ethics approval The study protocol was approved by the National Ethics Committee (CCTIRS: Comité Consultatif sur le Traitement de l'Information en Matière de Recherche; advisory committee on research information processing).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement No additional data are available.