TY - JOUR
T1 - A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering
AU - Fang, Hua
AU - Johnson, Craig
AU - Stopp, Christian
AU - Espy, Kimberly Andrews
N1 - Funding Information:
This research was supported in part by the National Institutes of Health grants R01 DA023653 , DA014661 , DA015223 , MH065668 , and HD050309 . We recognize the tireless efforts and support of Dr. Vincent Smeriglio who first encouraged us to pursue this line of investigation. Dr. Smeriglio was instrumental in developing a federal portfolio of systematic scientific investigations to respond to a national health crisis of substance use during pregnancy and potentially deleterious effects on children. His dedication, enthusiasm, and commitment to cutting edge science have resulted in a growing corpus of information that has helped to inform pregnant women on the potentials risks of substance use. His wise counsel regarding how to navigate successfully through the NIH system to our team as junior investigators is particularly appreciated. The authors gratefully acknowledge Vince, as well as the participating families, hospital staff, and project personnel who made this work possible.
PY - 2011/1
Y1 - 2011/1
N2 - Background: Prenatal tobacco exposure is a risk factor for the development of externalizing behaviors and is associated with several adverse health outcomes. Because pregnancy smoking is a complex behavior with both daily fluctuations and changes over the course of pregnancy, quantifying tobacco exposure is a significant challenge. To better measure the degree of tobacco exposure, costly biological specimens and repeated self-report measures of smoking typically are collected throughout pregnancy. With such designs, there are multiple, and substantially correlated, indices that can be integrated via new statistical methods to identify patterns of prenatal exposure. Method: A multiple-imputation-based fuzzy clustering technique was designed to characterize topography of prenatal exposure. This method leveraged all repeatedly measured maternal smoking variables in our sample data, including (a) cigarette brand; (b) Fagerstrom nicotine dependence item scores; (c) self-reported smoking; and (d) cotinine level in maternal urine and infant meconium samples. Identified exposure groups then were confirmed using a suite of clustering validation indices based on multiple imputed datasets. The classifications were validated against irritable reactivity in the first month of life and birth weight of 361 neonates (Male_n=185; Female_n=176; Gestational Age_Mean=39weeks). Results: This proposed approach identified three exposure groups, non-exposed, lighter-tobacco-exposed, and heavier-tobacco-exposed based on high-dimensional attributes. Unlike cut-off score derived groups, these groupings reflect complex smoking behavior and individual variation of nicotine metabolism across pregnancy. The identified groups predicted differences in birth weight and in the pattern of change in neonatal irritable reactivity, as well as resulted in increased predictive power. Multiple-imputation-based fuzzy clustering appears to be a useful method to categorize patterns of exposure and their impact on outcomes.
AB - Background: Prenatal tobacco exposure is a risk factor for the development of externalizing behaviors and is associated with several adverse health outcomes. Because pregnancy smoking is a complex behavior with both daily fluctuations and changes over the course of pregnancy, quantifying tobacco exposure is a significant challenge. To better measure the degree of tobacco exposure, costly biological specimens and repeated self-report measures of smoking typically are collected throughout pregnancy. With such designs, there are multiple, and substantially correlated, indices that can be integrated via new statistical methods to identify patterns of prenatal exposure. Method: A multiple-imputation-based fuzzy clustering technique was designed to characterize topography of prenatal exposure. This method leveraged all repeatedly measured maternal smoking variables in our sample data, including (a) cigarette brand; (b) Fagerstrom nicotine dependence item scores; (c) self-reported smoking; and (d) cotinine level in maternal urine and infant meconium samples. Identified exposure groups then were confirmed using a suite of clustering validation indices based on multiple imputed datasets. The classifications were validated against irritable reactivity in the first month of life and birth weight of 361 neonates (Male_n=185; Female_n=176; Gestational Age_Mean=39weeks). Results: This proposed approach identified three exposure groups, non-exposed, lighter-tobacco-exposed, and heavier-tobacco-exposed based on high-dimensional attributes. Unlike cut-off score derived groups, these groupings reflect complex smoking behavior and individual variation of nicotine metabolism across pregnancy. The identified groups predicted differences in birth weight and in the pattern of change in neonatal irritable reactivity, as well as resulted in increased predictive power. Multiple-imputation-based fuzzy clustering appears to be a useful method to categorize patterns of exposure and their impact on outcomes.
KW - Exposure pattern
KW - Fuzzy clustering
KW - Irritable reactivity
KW - Multiple imputation
KW - Prenatal tobacco exposure
UR - http://www.scopus.com/inward/record.url?scp=78751558523&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751558523&partnerID=8YFLogxK
U2 - 10.1016/j.ntt.2010.08.003
DO - 10.1016/j.ntt.2010.08.003
M3 - Article
C2 - 21256430
AN - SCOPUS:78751558523
SN - 0892-0362
VL - 33
SP - 155
EP - 165
JO - Neurotoxicology and Teratology
JF - Neurotoxicology and Teratology
IS - 1
ER -