TY - JOUR
T1 - Uncovering exposures responsible for birth season - disease effects
T2 - A global study
AU - Boland, Mary Regina
AU - Parhi, Pradipta
AU - Li, Li
AU - Miotto, Riccardo
AU - Carro, Robert
AU - Iqba, Usman
AU - Nguyen, Phung Anh
AU - Schuemie, Martijn
AU - You, Seng Chan
AU - Smith, Donahue
AU - Mooney, Sean
AU - Ryan, Patrick
AU - Li, Yu Chuan
AU - Park, Rae Woong
AU - Denny, Josh
AU - Dudley, Joel T.
AU - Hripcsak, George
AU - Gentine, Pierre
AU - Tatonetti, Nicholas P.
N1 - Funding Information:
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA, 2Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA, 3Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, USA, 4Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA, 5Department of Biomedical Informatics, Columbia University, New York, NY, USA, 6Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA, 7Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA, 8Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 9Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA, 11Masters Program in Global Health and Development Department, College of Public Health, Taipei Medical University, Taiwan, 12College of Medical Science and Technology, Taipei Medical University, Taiwan, 13International Center for Health Information Technology, Taipei Medical University, Taiwan, 14Janssen Research and Development, Raritan, NJ, USA, 15Department of Biomedical Informatics, Ajou University School of Medicine, Republic of Korea, 16Department of Biomedical Informatics, University of Washington, Seattle, Washington, USA and 17Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA Corresponding Author: Mary Regina Boland, 423 Guardian Drive, 421 Blockley Hall, Philadelphia, PA 19104, USA. E-mail: [email protected]. Phone: 1-215-573-7394, Nicholas P Tatonetti, 622 West 168th Street, PH-20, New York, NY 10032, USA. E-mail: [email protected]. Phone: 1-480-467-7456
Funding Information:
We would like to thank Dr Andrew Gelman, Department of Statistics, Columbia University, for his tremendous help, support, and guidance during this project. Support for this research was provided through the following mechanisms: MRB is supported by generous funding by the Perelman School of Medicine, University of Pennsylvania; was supported by the National Library of Medicine training grant T15 LM00707 from July 2014 to June 2016; and was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through TL1 TR000082, formerly the NCRR, TL1 RR024158, from July 2016 to June 2017. MRB and NPT were both supported by R01 GM107145. DS was supported by a National Library of Medicine Medicine training grant at the University of Washington, T15 LM007442. SM was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UL1 TR000423. SCY and RWP were supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI16C0992).
Funding Information:
We would like to thank Dr Andrew Gelman, Department of Statistics, Columbia University, for his tremendous help, support, and guidance during this project. Support for this research was provided through the following mechanisms: MRB is supported by generous funding by the Perelman School of Medicine, University of Pennsylvania; was supported by the National Library of Medicine training grant T15 LM00707 from July 2014 to June 2016; and was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through TL1 TR000082, formerly the NCRR, TL1 RR024158, from July 2016 to June 2017. MRB and NPT were both supported by R01 GM107145. DS was supported by a National Library of Medi- cine training grant at the University of Washington, T15 LM007442. SM was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UL1 TR000423. SCY and RWP were supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI16C0992).
Publisher Copyright:
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Objective: Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season. Material and Methods: This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month-disease risk curves from each site in a case-control manner. Next, we correlated each birth month-disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month-exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures. Results: Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R=0.725, confidence interval [95% CI], 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R=0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R=-0.816, 95% CI, -0.5767, -0.929). Conclusion: A global study of birth month-disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.
AB - Objective: Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season. Material and Methods: This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month-disease risk curves from each site in a case-control manner. Next, we correlated each birth month-disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month-exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures. Results: Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R=0.725, confidence interval [95% CI], 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R=0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R=-0.816, 95% CI, -0.5767, -0.929). Conclusion: A global study of birth month-disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.
KW - Attention deficit hyperactivity disorder
KW - Electronic health records
KW - Environmental exposure
KW - Pregnancy
KW - Seasons
UR - http://www.scopus.com/inward/record.url?scp=85043317259&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043317259&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocx105
DO - 10.1093/jamia/ocx105
M3 - Article
C2 - 29036387
AN - SCOPUS:85043317259
SN - 1067-5027
VL - 25
SP - 275
EP - 288
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 3
ER -
