Protecting Children From Smoke Exposure in Disadvantaged Homes

Rowa-Dewar, Neneh; Lumsdaine, Colin; Amos, Amanda

doi:10.1093/ntr/ntu217

Cited by 25 publications

(19 citation statements)

References 21 publications

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“…While not explicitly examined in this study, smokers with these profiles are more likely to be nicotine dependent and are at increased risk for adverse health impacts of smoking (Higgins and Chilcoat, 2009; Hiscock et al, 2012). The lower educational attainment levels and higher rates of alcohol and drug use disorders represented in those nodes also make it likely that other medical co-morbidities will be present further increasing vulnerability to the adverse health impacts of smoking (Cutler and Lleras-Muney, 2010; Gaalema et al, 2015; Hser et al, 2001; Niaura et al, 2012; Rowa-Dewar et al, 2015; Schroeder, 2007). These are patterns that contribute directly to the unsettling problem of health disparities (Higgins, 2014; Schroeder, 2007).…”

Section: Discussionmentioning

confidence: 99%

Co-occurring risk factors for current cigarette smoking in a U.S. nationally representative sample

Higgins

Kurti

Redner

et al. 2016

Preventive Medicine

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Introduction Relatively little has been reported characterizing cumulative risk associated with co-occurring risk factors for cigarette smoking. The purpose of the present study was to address that knowledge gap in a U.S. nationally representative sample. Methods Data were obtained from 114,426 adults (≥ 18 years) in the U.S. National Survey on Drug Use and Health (years 2011–13). Multiple logistic regression and classification and regression tree (CART) modeling were used to examine risk of current smoking associated with eight co-occurring risk factors (age, gender, race/ethnicity, educational attainment, poverty, drug abuse/dependence, alcohol abuse/dependence, mental illness). Results Each of these eight risk factors was independently associated with significant increases in the odds of smoking when concurrently present in a multiple logistic regression model. Effects of risk-factor combinations were typically summative. Exceptions to that pattern were in the direction of less-than-summative effects when one of the combined risk factors was associated with generally high or low rates of smoking (e.g., drug abuse/dependence, age ≥65). CART modeling identified subpopulation risk profiles wherein smoking prevalence varied from a low of 11% to a high of 74% depending on particular risk factor combinations. Being a college graduate was the strongest independent predictor of smoking status, classifying 30% of the adult population. Conclusions These results offer strong evidence that the effects associated with common risk factors for cigarette smoking are independent, cumulative, and generally summative. The results also offer potentially useful insights into national population risk profiles around which U.S. tobacco policies can be developed or refined.

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Section: Discussionmentioning

confidence: 99%

Co-occurring risk factors for current cigarette smoking in a U.S. nationally representative sample

Higgins

Kurti

Redner

et al. 2016

Preventive Medicine

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“…However this finding was not statistically significant when sex, age, education, smoking restrictions at home and living with a non-smoker were taken into account, suggesting that the decision to expose children to SHS in cars may not differ across household income. Most parents, including those of low SES, want to protect their children from SHS exposure and many report efforts to change their smoking behaviour for the sake of their children 47 48. Yet it may be that low SES parents experience greater barriers in having smoke-free private spaces49 and difficulty in establishing rules when it comes to the family car, with bans reported to be less likely in disadvantaged families 50…”

Section: Discussionmentioning

confidence: 99%

Social disparities in children's exposure to secondhand smoke in privately owned vehicles

et al. 2016

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“…An effect of partial, i.e., temporary quitting, may be particularly important for low-income women who are much more likely to continue to smoke during pregnancy [8] and be heavier smokers. This group of smokers is also more likely to be exposed to SHS particularly in their household environment [25,26]. …”

Section: Introductionmentioning

confidence: 99%

Patterns of Smoking Behaviour in Low-Income Pregnant Women: A Cohort Study of Differential Effects on Infant Birth Weight

Hayes

Kearney

O'Carroll

et al. 2016

IJERPH

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Abstract:Maternal smoking accounts for 20%-30% of low birth weight (BW). Second-Hand Smoke (SHS) also negatively affects BW. This cohort study explored the differential effect of smoking patterns during pregnancy on infant BW. Smoking status for 652 self-reported smokers attending public ante-natal clinics was assessed at baseline (V1 first ante-natal visit), 28-32 weeks (V2) and one week after birth (V3). Multivariable generalised linear regression models tested smoking patterns (continuing to smoke, sustained quitting, partial quitting) on BW adjusting for household smoking and other co-variates. Total quitting showed a median increase of 288 g in BW (95% CI (confidence intervals): 153.1-423 g, p < 0.001), compared to partial quitting (147 g, (95% CI: 50-244 g), p < 0.003). In partial quitters, increased BW was observed only in females 218 g, (95% CI: 81-355 g), p = 0.002). Household SHS showed a specific negative influence on pre-term but not term BW. This study suggests that, for low-income women, quitting or partial quitting during pregnancy both have a positive influence on infant BW. Whether others in the household smoke is also important.

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Protecting Children From Smoke Exposure in Disadvantaged Homes

Cited by 25 publications

References 21 publications

Co-occurring risk factors for current cigarette smoking in a U.S. nationally representative sample

Co-occurring risk factors for current cigarette smoking in a U.S. nationally representative sample

Social disparities in children's exposure to secondhand smoke in privately owned vehicles

Patterns of Smoking Behaviour in Low-Income Pregnant Women: A Cohort Study of Differential Effects on Infant Birth Weight

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