Elizabeth MacQuillan1, Amy Curtis2, Kathleen Baker2, Rajib Paul2. 1. 1 Department of Allied Health Sciences, Grand Valley State University, Grand Rapids, MI, USA. 2. 2 Health Data Research, Analysis and Mapping Center at Western Michigan University, Kalamazoo, MI, USA.
Abstract
OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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. PRACTICE APPLICATIONS: 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.
OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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. PRACTICE APPLICATIONS: 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.
Entities:
Keywords:
GDM; diabetes; geographic information systems; mapping; pregnancy
Authors: Catherine L Kothari; Ruth Zielinski; Arthur James; Remitha M Charoth; Luz del Carmen Sweezy Journal: Am J Public Health Date: 2013-12-19 Impact factor: 9.308
Authors: Girish Rayanagoudar; Amal A Hashi; Javier Zamora; Khalid S Khan; Graham A Hitman; Shakila Thangaratinam Journal: Diabetologia Date: 2016-04-13 Impact factor: 10.122