1 Department of Allied Health Sciences, Grand Valley State University, Grand Rapids, MI, USA.
2 Health Data Research, Analysis and Mapping Center at Western Michigan University, Kalamazoo, MI, USA.
Public Health Rep. 2019 Jan/Feb;134(1):27-35. doi: 10.1177/0033354918815183. Epub 2018 Dec 6.
The incidence of gestational diabetes mellitus (GDM) in the United States has increased during the past several decades. The objective of this study was to use birth records and a combination of statistical and geographic information system (GIS) analyses to evaluate GDM rates among subgroups of pregnant women in Michigan.
We obtained data on maternal demographic and health-related characteristics and regions of residence from 2013 Michigan birth records. We geocoded (ie, matched to maternal residence) the birth data, calculated proportions of births to women with GDM, and used logistic regression models to determine predictors of GDM. We calculated odds ratios (ORs) from the exponentiated beta statistic of the logistic regression test. We also used kernel density estimations and local indicators of spatial association (LISA) analyses to determine GDM rates in regions in the state and identify GDM hot spots (ie, areas with a high GDM rate surrounded by areas with a high GDM rate).
We successfully geocoded 104 419 of 109 168 (95.6%) births in Michigan in 2013. Of the geocoded births, 5185 (5.0%) were to mothers diagnosed with GDM. LISA maps showed a hot spot of 8 adjacent counties with high GDM rates in southwest Michigan. Of 11 064 births in the Southwest region, 829 (7.5%) were to mothers diagnosed with GDM, the highest rate in the state and a result confirmed by geospatial analyses.
Birth data and GIS analyses may be used to measure statewide pregnancy-associated disease risk and identify populations and geographic regions in need of targeted public health and maternal-child health interventions.
在过去几十年中，美国的妊娠糖尿病（GDM）发病率有所增加。本研究旨在使用出生记录以及统计和地理信息系统（GIS）分析相结合的方法，评估密歇根州孕妇亚组的 GDM 发生率。
我们从 2013 年密歇根州出生记录中获取了产妇人口统计学和与健康相关的特征以及居住地区的数据。我们对出生数据进行地理编码（即将其与产妇居住地相匹配），计算患有 GDM 的产妇的分娩比例，并使用逻辑回归模型确定 GDM 的预测因素。我们从逻辑回归检验的指数β统计量中计算出优势比（OR）。我们还使用核密度估计和局部空间关联（LISA）分析来确定该州各地区的 GDM 发生率，并确定 GDM 热点（即，高 GDM 率地区周围有高 GDM 率地区）。
我们成功地对 2013 年密歇根州的 109168 次分娩中的 104419 次进行了地理编码（95.6％）。在地理编码的分娩中，有 5185 次（5.0％）是患有 GDM 的母亲分娩的。LISA 地图显示，密歇根州西南部有 8 个相邻的县存在 GDM 高发热点。在西南部地区的 11064 次分娩中，有 829 次（7.5％）是患有 GDM 的母亲分娩的，这是全州最高的比率，这一结果也得到了地理空间分析的证实。
出生数据和 GIS 分析可用于衡量全州范围内与妊娠相关的疾病风险，并确定需要有针对性的公共卫生和母婴健康干预的人群和地理区域。
