In:
Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 135, No. suppl_1 ( 2017-03-07)
Abstract:
Background: The prevalence of gestational diabetes mellitus (GDM) in the US and the state of Ohio is approximately 9.0%. GDM is associated with increased risks for mother and child, including macrosomia, preterm birth, preeclampsia, and development of type 2 diabetes. In addition to known risk factors, the role of exposure to environmental pollutants in development of GDM warrants further investigation. Because exposure to traffic-related pollutants has been shown to influence the development of type 2 diabetes, we assessed the contribution of exposure to high traffic to the development of GDM during pregnancy among women without prior diabetes history. Methods: A population of 275 pregnant women in Ohio reported perceived exposure to high traffic areas and health behaviors. Clinical information and addresses were obtained through their electronic health records. Using ArcMap TM 10.2.2 (ESRI), addresses were geocoded to assess individual exposures, and linked with area exposures and demographic indicators at the level of the census block group from EJScreen (EPA). A woman was classified as “near” a major roadway if one fell within a 500m buffer of her home. Distance to nearest major roadway was also calculated. Logistic regression was used to examine the association between quintiles of traffic exposure at the census block group level, self-reported proximity, individual-level proximity, health behaviors, and demographic factors with development of GDM. Because assessment of individual-level exposures may be difficult to use in clinical and large scale population settings, a model was also fit using only data publically available from EJScreen and self-report. Results: The prevalence of GDM was 8.0% (22/275) and distribution of demographics factors were similar between those with and without GDM. After adjustment for potential confounders, quintile of traffic exposure was significantly associated with development of GDM (p=.036). Compared to those residing in block groups in the lowest quintile, the odds of GDM for those in the second quintile were 8.1 times greater [95% CI 1.2, 56.3] and for those in fourth quintile were 10.4 times greater [95% CI 1.6, 67.6] . Addition of individual-level proximity factors did not significantly improve the model (p=.08). Conclusions: This study suggests that living in an area with high traffic density contributes to the risk of developing GDM. For both the clinical practitioner and public health researcher it is difficult or impractical to obtain individual level environmental exposure data. From our analysis, the individually calculated exposure proxies did not significantly improve the fit of the model. We suggest examining ways to combine self-report measures with existing environmental data, such as EJScreen, to identify populations at elevated risk.
Type of Medium:
Online Resource
ISSN:
0009-7322
,
1524-4539
DOI:
10.1161/circ.135.suppl_1.p317
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2017
detail.hit.zdb_id:
1466401-X
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