UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date


Document Type

Article Postprint


Prenatal Injuries; Maternal Exposure; Maternal-Fetal Exchange; Smoking; Tobacco Smoke Pollution; Fuzzy Logic; Data Interpretation, Statistical


Biostatistics | Epidemiology | Female Urogenital Diseases and Pregnancy Complications | Maternal and Child Health


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.


UMCCTS funding, Prenatal tobacco exposure, Fuzzy clustering, Multiple imputation, Exposure pattern, Irritable reactivity

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DOI of Published Version



Neurotoxicol Teratol. 2011 Jan-Feb;33(1):155-65. doi: 10.1016/ PubMed PMID: 21256430; PubMed Central PMCID: PMC3052936. Link to article on publisher's website

Journal/Book/Conference Title

Neurotoxicology and teratology

PubMed ID



This is the authors' accepted manuscript which was formally published as: Fang H, Johnson C, Stopp C, Espy KA. A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering.

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Link to article in PubMed

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.