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. 2016 Feb;41(3):869-76.
doi: 10.1038/npp.2015.213. Epub 2015 Jul 15.

Transitioning from First Drug Use to Dependence Onset: Illustration of a Multiparametric Approach for Comparative Epidemiology

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Transitioning from First Drug Use to Dependence Onset: Illustration of a Multiparametric Approach for Comparative Epidemiology

Olga A Vsevolozhskaya et al. Neuropsychopharmacology. 2016 Feb.

Abstract

Studying transitions from first drug use (DU) to drug dependence (DD) onset, we estimate a parsimonious set of parameters based on epidemiological data, with plans for future longitudinal research on newly incident drug users and with tracking of self-administration frequencies and DD clinical features. Our expectation is a distinctive sigmoid pattern with one asymptote for lower DD probability (when DU is insubstantial), upturning slopes of DD risk beyond a middle value (PD50), and eventual higher DD risk asymptotes at higher DU frequencies. We illustrate this novel approach using cross-sectional data from the United States National Surveys on Drug Use and Health, 2002-2011. Empirical DD probabilities observed soon after newly incident use are estimated across DU frequency values, using parametric Hill functions and four governing parameters for differential comparison across drugs and DU subgroups. Among drug subtypes considered, cocaine shows larger estimates, especially among females (estimated P(min)=7% for females vs 3% for males; p<0.001), for whom PD50 is shorter by 8 days of use (p=0.027), conditional on the same rate of use in the past 30 days. Clear alcohol male-female differences also are observed (eg, female PD50 < male PD50; p=0.002). Although based on cross-sectional snapshots soon after DU onset, this novel multiparametric statistical approach for comparative epidemiological DD research creates new opportunities in planned studies with prospectively gathered longitudinal data. The cross-sectional estimates provide starting values needed to plan future longitudinal research programs on transitions from initial DU until formation of a DD syndrome.

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Figures

Figure 1
Figure 1
The ‘S'-shaped curve with the count of days of drug use on the x axis and estimated risk of developing drug dependence on the y axis. The four parameters that determine the shape of the curve are Pmin, Pmax, PD50 and k. Data from newly incident drug users found in the United States National Surveys on Drug Use and Health, 2002–2011.
Figure 2
Figure 2
Dots and vertical bars are observed empirical estimates for risk of drug dependence (DD) with the corresponding 95% confidence intervals (95% CI). Lines show estimated probability of having developed DD across drug-frequency strata, as estimated via nonlinear regression, and shaded regions show corresponding 95% bootstrap CIs. Data from newly incident drug users found in the United States National Surveys on Drug Use and Health, 2002–2011.
Figure 3
Figure 3
Female–male contrasts in estimated probability of alcohol dependence across levels of recent drinking. Data from newly incident drug users found in the United States National Surveys on Drug Use and Health, 2002–2011.
Figure 4
Figure 4
Female–male contrasts in estimated probability of cocaine dependence across levels of recent cocaine use. Data from newly incident drug users found in the United States National Surveys on Drug Use and Health, 2002–2011.

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