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Sociodemographic Correlates of the Increasing Trend in Prevalence of Gestational Diabetes Mellitus in a Large Population of Women Between 1995 and 2005

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Sociodemographic Correlates of the Increasing Trend in Prevalence of Gestational Diabetes Mellitus in a Large Population of Women Between 1995 and 2005 Epidemiology/Health Services Research ORIGINAL ARTICLE Sociodemographic Correlates of the Increasing Trend in Prevalence of Gestational Diabetes Mellitus in a Large Population of Women Between 1995 and 2005 1 1 VIBEKE ANNA, MIPH RACHEL R. HUXLEY, DPHIL dictor of type 2 diabetes. Women with 2 2 HIDDE P. VAN DER PLOEG, PHD ADRIAN E. BAUMAN, PHD GDM are up to six times more likely to 3 N. WAH CHEUNG, PHD develop type 2 diabetes than women with normal glucose tolerance in pregnancy (3). The incidence of GDM varies among OBJECTIVE — Gestational diabetes mellitus (GDM) is an increasingly prevalent risk factor populations, similar to the variation of for the development of type 2 diabetes in the mother and is responsible for morbidity in the child. type 2 diabetes, with recent prevalence To better identify women at risk of developing GDM we examined sociodemographic correlates estimates ranging from 2.8% of pregnant and changes in the prevalence of GDM among all births between 1995 and 2005 in Australia’s women in Washington, DC, to 18.9% in largest state. India and 22% in Sardinia, Italy (4). The RESEARCH DESIGN AND METHODS — A computerized database of all births (n ϭ risk for GDM increases with age, and in- 956,738) between 1995 and 2005 in New South Wales, Australia, was used in a multivariate cidence rates vary by ethnicity within a logistic regression that examined the association between sociodemographic characteristics and population, again similar to the risk for the occurrence of GDM. type 2 diabetes (4,5). There is also evi- dence that obesity, parity, smoking, and RESULTS — Between 1995 and 2005, the prevalence of GDM increased by 45%, from 3.0 to family history are risk factors for GDM 4.4%. Women born in South Asia had the highest adjusted odds ratio (OR) of any region (4.33 (5). However, less is known regarding [95% CI 4.12–4.55]) relative to women born in Australia. Women living in the three lowest socioeconomic quartiles had higher adjusted ORs for GDM relative to women in the highest the sociodemographic distribution of quartile (1.54 [1.50–1.59], 1.74 [1.69–1.8], and 1.65 [1.60–1.70] for decreasing socioeco- GDM. Given the strong link between nomic status quartiles). Increasing age was strongly associated with GDM, with women aged GDM and the subsequent risk of diabetes Ͼ40 years having an adjusted OR of 6.13 (95% CI 5.79–6.49) relative to women in their early for the mother and the perinatal morbid- 20s. Parity was associated with a small reduced risk. There was no association between smoking ity for mother and child—an association and GDM. recently updated with findings of a con- tinuous association of maternal glucose CONCLUSIONS — Maternal age, socioeconomic position, and ethnicity are important levels and adverse perinatal outcomes correlates of GDM. Future culturally specific interventions should target prevention of GDM in these high-risk groups. by the Hyperglycemia and Adverse Preg- nancy Outcomes Study Cooperative Diabetes Care 31:2288–2293, 2008 Research Group (6)—a better under- standing of the sociodemographic deter- ype 2 diabetes affects an estimated risk of having a stroke or heart attack. minants of GDM may provide novel 246 million individuals world- Moreover, diabetes appears to be particu- opportunities to reduce the incidence and T wide—a figure that is predicted to larly hazardous in women, as there is a to prevent the onset of type 2 diabetes in increase to 380 million by 2025, with a 50% greater risk of dying from coronary later life. disproportionate number of affected indi- heart disease compared with that of men Most studies that have examined the viduals living in lower- and middle- with the same condition (2). etiology of GDM have been hospital based income countries of the Asia-Pacific Gestational diabetes mellitus (GDM), or have been based on samples of births in region (1). Diabetes is a major cardiovas- defined as glucose intolerance first de- a particular region (4,5). There are cur- cular risk factor, more than doubling the tected during pregnancy, is a strong pre- rently no large, comprehensive popula- ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● tion-wide urban and rural datasets that From the 1The George Institute for International Health and School of Public Health, University of Sydney, have been collected in an attempt to Sydney, Australia; the 2Centre for Physical Activity and Health, School of Public Health, University of examine multiple risk factors for GDM Sydney, Sydney, Australia; and the 3Centre for Diabetes and Endocrinology Research, Westmead Hospital over a number of years and no popula- and University of Sydney, Sydney, Australia. tion-based studies outside the U.S. The Corresponding author: Hidde P. van der Ploeg, [email protected]. Received 6 June 2008 and accepted 28 August 2008. New South Wales (NSW) Midwives Data- Published ahead of print at http://care.diabetesjournals.org on 22 September 2008. DOI: 10.2337/dc08-1038. set has information on nearly 1 million © 2008 by the American Diabetes Association. Readers may use this article as long as the work is properly births in the state of NSW during the pe- cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons. riod from 1995 to 2005 in a health system org/licenses/by-nc-nd/3.0/ for details. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby in which there is almost universal screen- marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. ing for GDM. This dataset was used to 2288 DIABETES CARE, VOLUME 31, NUMBER 12, DECEMBER 2008 Anna and Associates study the current and changing popula- Maternal age was categorized as Ͻ20, GDM (P Ͻ 0.001). Overall, the age- and tion rates of GDM and its associated so- 20–24, 25–29, 30–34, 35–39, and Ն40 ethnicity-adjusted incidence of GDM in- ciodemographic risk factors in a large, years. The NSW MDC has been validated creased by 45% from 3.0 to 4.4% between ethnically diverse population of women. by comparison with hospital medical 1995 and 2005 (Fig. 1). records in 1993 and 1998. In 1993, it was RESEARCH DESIGN AND found that reporting of GDM could be Risk factors for GDM METHODS — This study was under- improved (10) and the MDC form was The associations of different risk factors taken using data from the NSW Depart- subsequently redesigned and first used in with GDM adjusted for all other recorded ment of Health Midwives Data Collection 1998. A further validation study was un- risk factors are shown in Table 1. Age was (MDC) dataset collected between 1995 dertaken in 1998, at which time there strongly and positively associated with and 2005 (inclusive). The dataset records were excellent levels of agreement be- risk of GDM and increased with each information collected at all births of Ͼ20 tween MDC data and information ob- successive age-group. Compared with weeks’ gestation in NSW by attending tained directly from medical records women aged 20–24 years, women aged midwives at public and private hospitals (99%), with high specificity and sensitiv- 35–39 years had an approximately four and home births. ity (11). times higher risk of GDM. In women aged Ͼ40 years, the risk of GDM was more Study design and assessment of Statistical analysis than six times that of those aged 20–24 GDM Variables were compared using Student’s years. Information on infant’s date of birth, birth t test for means and ␹2 tests for propor- Region of country of birth, as a proxy weight, maternal age, maternal country of tions between women who had GDM and for ethnicity, was associated with an in- origin, number of previous pregnancies of the whole population and between those creased risk for GDM for all regions com- Ͼ20 weeks, maternal postcode, maternal who had GDM and those who did not pared with women born in Australia and smoking status (including average num- have GDM. Women with identified New Zealand (Table 1). Women born in ber of cigarettes smoked per day), pres- preexisting type 1 or type 2 diabetes were South Asia had the greatest risk, with ence of previously existing type 1 or type excluded from the analysis. The relation- odds of developing GDM Ͼ4 times 2 diabetes, and presence of GDM was ship between each of the potential explan- greater than that of those born in Australia available. GDM status was assigned ac- atory variables with GDM was tested and New Zealand. cording to information recorded on the using binary logistic regression, and the Socioeconomic status was inversely NSW MDC form that was collected at the odds ratios (ORs) and 95% CIs were re- associated with risk of GDM. The risk of time of birth. In Australia, the Australa- ported. Variables that were significantly GDM was approximately two-thirds sian Diabetes in Pregnancy Society criteria associated with GDM were subsequently higher in women living in the lowest so- for the diagnosis of GDM are usually ap- tested in multivariate logistic regression cioeconomic postcodes compared with plied. A 75-g oral glucose tolerance test is models. Hosmer-Lemeshow goodness-of women in the highest group. The inverse performed, with a fasting plasma glucose fit tests and residual and influence analy- relationship between socioeconomic sta- level of Ͼ5.5 mmol/l or a 2-h level Ͼ8.0 ses were performed. The explanatory tus and risk of GDM was apparent across mmol/l being diagnostic for GDM.
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