Introduction: The effects of tobacco exposure are typically examined by comparing groups based on a cut-score of selfreported number of cigarettes or bioassays collected in crosssectional studies. This study introduces a new fuzzy clustering method that facilitates detection of subtle exposure effects by objectively deriving subgroups from modeling multidimensional exposure measures. We test the new method on a known exposure effect (fetal growth) and report on the graded exposure effect detected in a pregnancy cohort. Methods: A total of 978 pregnant women were enrolled from 1986 to 1992 in the Maternal Infant Smoking Study of East Boston (MISSEB). Four kinds of exposure data were used to generate exposure groups: self-reported smoking, cotinine levels, nicotine levels, and nicotine dependence scores. Subgroups were identifi ed via a comprehensive validation procedure. The results from MISSEB (number of exposure clusters, exposure effects on birth weight, body length, and head circumference) were compared with those obtained in a separate cohort. Results: Using our new method in MISSEB, the same number of clusters was generated as previously, and graded exposure effects were again detected. Neonates with heavier exposure weighed less at birth relative to nonexposed neonates, with no difference between lighter-exposed and nonexposed neonates. Conclusions: The same graded prenatal exposure effect emerges for known exposure-related outcomes across 2 different studies, about 2 decades apart. Our new method characterizes the degree of prenatal exposure, with the potential to help detect subtler effects on developmental outcomes, such as defi cits in growth or development, neonatal temperament and behavior, and psychological functioning.
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health