Tobacco Retail Outlets, Neighborhood Deprivation and the Risk of Prenatal Smoke Exposure

Abstract

Introduction Smoking and smoke exposure among pregnant women remain persistent public health issues. Recent estimates suggest that approximately one out of four nonsmokers have measurable levels of cotinine, a marker indicating regular exposure to secondhand smoke. Epidemiological research has attempted to pinpoint individual-level and neighborhood-level factors for smoking during pregnancy. However, most of these studies have relied upon self-reported measures of smoking. Aims and Methods To more accurately assess smoke exposure resulting from both smoking and secondhand exposure in mothers during pregnancy, we used Bayesian regression models to estimate the association of cotinine levels with tobacco retail outlet (TRO) exposure and a neighborhood deprivation index (NDI) in six counties in North Carolina centered on Durham County. Results Results showed a significant positive association between TRO exposure (β = 0.008, 95% credible interval (CI) = [0.003, 0.013]) and log cotinine after adjusting for individual covariates (eg, age, race/ethnicity, education, marital status). TRO exposure was not significant after including the NDI, which was significantly associated with log cotinine (β = 0.143, 95% CI = [0.030, 0.267]). However, in a low cotinine stratum (indicating secondhand smoke exposure), TRO exposure was significantly associated with log cotinine (β = 0.005, 95% CI = [0.001, 0.009]), while in a high cotinine stratum (indicating active smoking), the NDI was significantly associated with log cotinine (β = 0.176, 95% CI = [0.005, 0.372]). Conclusions In summary, our findings add to the evidence that contextual factors are important for active smoking during pregnancy. Implications In this study, we found several significant associations that suggest a more nuanced understanding of the potential influence of environmental- and individual-level factors for levels of prenatal smoke exposure. Results suggested a significant positive association between TRO exposure and cotinine levels, after adjusting for the individual factors such as race, education, and marital status. Individually, NDI was similarly positively associated with cotinine levels as well. However, when combining TRO exposure alongside NDI in the same model, TROs were no longer significantly associated with overall cotinine levels.

Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

“…Such an approach is preferable to a single‐exposure analysis because it acknowledges that not one but many exposures act upon individuals concurrently and adjusts for this in the estimation of exposure effects. In mixture analysis, the mixture components are typically environmental chemicals, 5‐9 owing to their ubiquity in consumer products 10 and presence as airborne pollutants or in agricultural pesticides, but they could also consist of socio‐demographic variables 11‐13 . Owing to the similarities in structure of some chemicals, and the use of multiple chemicals in certain products, there may be high correlations between many chemicals in a mixture, and this lack of independence requires additional care in modeling.…”

“…In mixture analysis, the mixture components are typically environmental chemicals, 5 , 6 , 7 , 8 , 9 owing to their ubiquity in consumer products 10 and presence as airborne pollutants or in agricultural pesticides, but they could also consist of socio‐demographic variables. 11 , 12 , 13 Owing to the similarities in structure of some chemicals, and the use of multiple chemicals in certain products, there may be high correlations between many chemicals in a mixture, and this lack of independence requires additional care in modeling. Several approaches have been developed to assess the effects of correlated exposures in a mixture analysis.…”