Proportion of Gestational Diabetes Mellitus Attributable to Overweight and Obesity Among Non-Hispanic Black, Non-Hispanic White, and Hispanic Women in South Carolina
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Objective was to estimate race-specific proportions of gestational diabetes mellitus (GDM) attributable to overweight and obesity in South Carolina. South Carolina birth certificate and hospital discharge data were obtained from 2004 to 2006. Women who did not have type 2 diabetes mellitus before pregnancy were classified with GDM if a diagnosis was reported in at least one data source. Relative risks (RR) and 95 % confidence intervals were calculated using the log-binomial model. The modified Mokdad equation was used to calculate population attributable fractions for overweight body mass index (BMI: 25.0–29.9 kg/m2), obese (30.0–34.9 kg/m2), and extremely obese (≥35 kg/m2) women after adjusting for age, gestational weight gain, education, marital status, parity, tobacco use, pre-pregnancy hypertension, and pregnancy hypertension. Overall, the adjusted RR of GDM was 1.6, 2.3, and 2.9 times higher among the overweight, obese, and extremely obese women compared to normal-weight women in South Carolina. RR of GDM for extremely obese women was higher among White (3.1) and Hispanic (3.4) women than that for Black women (2.6). The fraction of GDM cases attributable to extreme obesity was 14.0 % among White, 18.1 % among Black, and 9.6 % among Hispanic women. The fraction of GDM cases attributable to obesity was about 12 % for all racial groups. Being overweight (BMI: 25.0–29.9) explained 8.8, 7.8, and 14.4 % of GDM cases among White, Black, and Hispanic women, respectively. Results indicate a significantly increased risk of GDM among overweight, obese, and extremely obese women. The strength of the association and the proportion of GDM cases explained by excessive weight categories vary by racial/ethnic group.
KeywordsHealth disparity Racial difference Population attributable fraction Epidemiology
Body mass index
Gestational diabetes mellitus
Institute of medicine
National Hospital Discharge Survey
Population attributable fraction
Authors would like to acknowledge the initial data management efforts made by Kamala Swayampakala. We are thankful to the staff at the South Carolina (SC) Office of Research and Statistics at the SC Budget and Control Board for linkage data bases and releasing de-identified databases to our team. We are also grateful to the Josephine Abney Faculty Fellowship awarded to Dr. Liu by the Women’s Studies Program at the University of South Carolina. Dr. Hébert was supported by an Established Investigator Award in Cancer Prevention and Control from the Cancer Training Branch of the National Cancer Institute (K05 CA136975).
Conflicts of interest
The authors declare that they have no conflict of interest.
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