Characterizing the intersection of Co-occurring risk factors for illicit drug abuse and dependence in a U.S. nationally representative sample

Abstract

Few studies have attempted to characterize how co-occurring risk factors for substance use disorders intersect. A recent study examined this question regarding cigarette smoking and demonstrated that co-occurring risk factors generally act independently. The present study examines whether that same pattern of independent intersection of risk factors extends to illicit drug abuse/dependence using a U.S. nationally representative sample (National Survey on Drug Use and Health, 2011-2013). Logistic regression and classification and regression tree (CART) modeling were used to examine risk of past-year drug abuse/dependence associated with a well-established set of risk factors for substance use (age, gender, race/ethnicity, education, poverty, smoking status, alcohol abuse/dependence, mental illness). Each of these risk factors was associated with significant increases in the odds of drug abuse/dependence in univariate logistic regressions. Each remained significant in a multivariate model examining all eight risk factors simultaneously. CART modeling of these 8 risk factors identified subpopulation risk profiles wherein drug abuse/dependence prevalence varied from <1% to >80% corresponding to differing combinations of risk factors present. Alcohol abuse/dependence and cigarette smoking had the strongest associations with drug abuse/dependence risk. These results demonstrate that co-occurring risk factors for illicit drug/abuse dependence generally intersect in the same independent manner as risk factors for cigarette smoking, underscoring further fundamental commonalities across these different types of substance use disorders. These results also underscore the fundamental importance of differences in the presence of co-occurring risk factors when considering the often strikingly different prevalence rates of illicit drug abuse/dependence in U.S. population subgroups.

Keywords: Adults; Classification and regression tree (CART); Co-occurring risk factors; Illicit drug abuse and dependence; Multiple logistic regression; Risk factors; Substance use disorders; U.S. nationally representative sample.

Figure 1

Outcomes of testing all possible two-way interactions among significant risk factors for drug abuse/dependence in the multivariable logistic regression analysis; X and - symbols indicate risk-factor combinations where there was and was not a significant interaction, respectively.

Figure 2

Significant two-way interactions of risk factors for drug abuse/dependence; data points represent prevalence of illicit drug abuse/dependence within each subgroup along with associated odds ratios.

Figure 3

A pruned, weighted classification and regression tree (CART) model of associations between past year illicit drug abuse/dependence and the following eight risk factors in the U.S. adult (≥ 18 years of age) population: educational attainment, age, race/ethnicity, current cigarette smoking, past year alcohol abuse/dependence, annual income below federal poverty level, gender, and past year mental illness. Results from a saturated model were “pruned” using CART analytic software to reduce complexity (R Development Core Team, 2008). Rectangles (nodes) represent drug abuse/dependence 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, 2.53% of the population met criteria for drug abuse/dependence (97.47% did not), 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 absent moving leftward and downward and those in whom it is present 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). Terminal nodes contain the same information as the other nodes plus the percent of all adults with drug abuse/dependence represented by that node and inclusion of 95% CIs for each. Percent of adults with drug abuse/dependence represented is calculated by the following equation: % total population represented by a node X drug abuse/dependence prevalence in that node/drug abuse/dependence prevalence in the entire study sample X 100. Tallying % adults with drug abuse/dependence represented across all terminal nodes should = 100% of adults with drug abuse/dependence in the U.S population save possible rounding error.

Figure 4

A pruned, weighted classification and regression tree (CART) model of associations between past-year illicit drug abuse/dependence and seven of the eight risk factors in U.S. adults (≥ 18 years of age) without past-year alcohol abuse/dependence (all risk factors in Figure 3 minus past-year alcohol abuse/dependence).