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

Abstract

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 (≥18years) 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.

Keywords: Adults; Cigarette smoking; Classification and regression tree (CART); Co-occurring risk factors; Current smokers; Educational attainment; Multiple logistic regression; Risk factors; U.S. nationally representative sample.

Fig. 1

Outcomes of two-way interaction testing among significant risk factors for current smoker status in the multiple logistic regression analysis; x and − symbols indicate risk-factor combinations where there was and was not a significant interaction, respectively.

Fig. 2

Three illustrative examples of significant two-way interactions of risk factors for current smoker status; data points represent odds ratios.

Fig. 3

A pruned, weighted classification and regression tree (CART) model of associations between current (past 30 days) smoking status and the following eight risk factors in the U.S. adult (≥18 years of age) population: educational attainment, age, race/ethnicity, past year drug abuse/dependence, past year alcohol abuse/dependence, annual income below federal poverty level, and past year mental illness. Results from a saturated model were “pruned” using CART analytic software to reduce complexity (R Core Team, 2013). Rectangles (nodes) represent smoking prevalence rates for the entire population (top-most node) or population subgroups (all others nodes). Nodes also list the proportion of the adult population represented. Using the root node as an example, 78% of the population are non-smokers, 22% smokers, and this node represents 100 of the U.S. non-institutionalized adult population. Lines below nodes represent the binary “yes”–”no” branching around particular risk factors and risk-factor levels, with subgroups in whom the risk factor/level is present moving leftward and downward and those in whom it is absent moving rightward and downward for further potential partitioning based on additional risk factors/levels. The bottom row comprises terminal nodes (i.e., final partitioning for a particular subgroup). Note that minimal terminal node size was set to ≥1000 individuals. Terminal nodes contain the same information as the other nodes plus the percent of all adult current smokers represented by that node. Percent of current smokers represented is calculated by the following equation: % total population represented by a node × smoking prevalence in that node/smoking prevalence in the entire study sample × 100. Tallying % current smokers represented across all terminal nodes should = 100% of smokers in the U.S adult population save possible rounding error.