TY - GEN
T1 - Terminology enabled spatio-temporal analysis and visualization for preterm birth data in the US
AU - Wang, Kui
AU - Yao, Lixia
N1 - Publisher Copyright:
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Preterm birth can lead to many health problems in infants, including brain damage, neurologic disorders, asthma, intestinal problems and vision problems, but the exact cause of preterm birth is unclear. In this study, we investigated if geographic location or the environment can contribute to preterm birth by building a customized data model based on multiple controlled terminologies. We then performed a large-scale quantitative analysis to understand the relationships between the prevalence of preterm birth, the biological mothers’ demographic information and the Metropolitan Statistical Areas (MSAs) of their primary residency from 2010 to 2014. More specifically we considered education, income, race and marital status information of 388 MSAs from the US Census Bureau. The results demonstrated that the overall preterm birth rate for the United States decreased during 2010 to 2014, with Chicago-Naperville-Elgin (Illinois) Metro Area, Houston-Sugar Land (Texas) Metro Area and Billings (Montana) Metro Area observing the most visible improvement. There are statistically significant correlations between race distribution, education level and preterm birth. But median income, marital status and insurance coverage ratio are found irrelevant to preterm birth. This study demonstrated the power of controlled terminologies in integrating medical claims data and geographic data to study preterm birth for first time. The customized common data model and the interactive tool for online visualizing a large preterm dataset from both the temporal and spatial perspectives can be used for future public health studies of many other diseases and conditions.
AB - Preterm birth can lead to many health problems in infants, including brain damage, neurologic disorders, asthma, intestinal problems and vision problems, but the exact cause of preterm birth is unclear. In this study, we investigated if geographic location or the environment can contribute to preterm birth by building a customized data model based on multiple controlled terminologies. We then performed a large-scale quantitative analysis to understand the relationships between the prevalence of preterm birth, the biological mothers’ demographic information and the Metropolitan Statistical Areas (MSAs) of their primary residency from 2010 to 2014. More specifically we considered education, income, race and marital status information of 388 MSAs from the US Census Bureau. The results demonstrated that the overall preterm birth rate for the United States decreased during 2010 to 2014, with Chicago-Naperville-Elgin (Illinois) Metro Area, Houston-Sugar Land (Texas) Metro Area and Billings (Montana) Metro Area observing the most visible improvement. There are statistically significant correlations between race distribution, education level and preterm birth. But median income, marital status and insurance coverage ratio are found irrelevant to preterm birth. This study demonstrated the power of controlled terminologies in integrating medical claims data and geographic data to study preterm birth for first time. The customized common data model and the interactive tool for online visualizing a large preterm dataset from both the temporal and spatial perspectives can be used for future public health studies of many other diseases and conditions.
KW - Claims Database
KW - Controlled Terminology
KW - Metropolitan Statistical Areas
KW - Preterm Birth
UR - http://www.scopus.com/inward/record.url?scp=85051733756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051733756&partnerID=8YFLogxK
U2 - 10.5220/0006647505100518
DO - 10.5220/0006647505100518
M3 - Conference contribution
AN - SCOPUS:85051733756
T3 - HEALTHINF 2018 - 11th International Conference on Health Informatics, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
SP - 510
EP - 518
BT - HEALTHINF 2018 - 11th International Conference on Health Informatics, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
A2 - Zwiggelaar, Reyer
A2 - Gamboa, Hugo
A2 - Fred, Ana
A2 - Bermudez i Badia, Sergi
PB - SciTePress
T2 - 11th International Conference on Health Informatics, HEALTHINF 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
Y2 - 19 January 2018 through 21 January 2018
ER -