Methods: The sample comprised 334 Korean immigrants (97 women and 237 men) who reported daily smoking for the past 6 months. Item-by-item responses and exploratory factor analyses (EFA) were compared by gender. Promax rotation was selected based on findings from previous studies suggesting correlated factors.

Results: Compared with men, women smoked fewer cigarettes per day, were more likely to smoke when ill in bed, and were less likely to smoke frequently in the morning. The entire sample and men within the sample had the same factor loading pattern, where three items (time to first cigarette, the cigarette most hate to give up, and smoke more frequently in the morning) were loaded on Factor 1 (morning smoking) and the remaining three items (difficult to refrain from smoking in public places, number of cigarettes smoked per day, and smoking even when ill in bed) on Factor 2 (daytime smoking). For women, however, neither the 1- nor 2-factor model fit the data well.

Conclusions: For Korean American male smokers, the psychometric properties of the FTND were similar to those seen in other populations, but this was not the case with Korean American women. Clinicians may need to modify their interpretation of nicotine dependence severity if basing only on the FTND with Korean women. The FTND assesses smoking patterns which has a cultural influence and other measures of nicotine dependence should be considered.

METHODS: 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 identified 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 deficits in growth or development, neonatal temperament and behavior, and psychological functioning.

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.

Citation: Wang, H., Fang, H., Espy, K. A., Peng, D, Sharif, H. (2007). A Bayesian Multilevel Modeling Approach for Data Query in Wireless Sensor Networks. Lecture Notes in Computer Science (LNCS),Y. Shi et al. (Eds.): Part III, LNCS 4489, pp. 859–866. DOI: 10.1007/978-3-540-72588-6_137

METHODS: Generalized boosted models were used to estimate propensity scores from 42 confounding variables for a sample of 361 neonates. Using emergent neonatal attention and orientation skills as an example developmental outcome, we examined the impact of tobacco exposure with and without accounting for selection bias. Weight at birth, an outcome related to tobacco exposure, also was used to examine the functionality of the propensity score approach.

RESULTS: Without inclusion of propensity scores, tobacco-exposed neonates did not differ from their nonexposed peers in attention skills over the first month or in weight at birth. When the propensity score was included as a covariate, exposed infants had marginally lower attention and a slower linear change rate at 4 weeks, with greater quadratic deceleration over the first month. Similarly, exposure-related differences in birth weight emerged when propensity scores were included as a covariate. CONCLUSIONS: The propensity score method captured the selection bias intrinsic to this observational study of prenatal tobacco exposure. Selection bias obscured the deleterious impact of tobacco exposure on the development of neonatal attention. The illustrated analytic strategy offers an example to better characterize the impact of prenatal tobacco exposure on important developmental outcomes by directly modeling and statistically accounting for the selection bias from the sampling process.