Dataset Information

Investigation and estimation of the prevalence of drug addicts in Xichang, china.

ABSTRACT: To use a new method to estimate the prevalence of drug addicts in Xichang for the first time and to suggest that Chinese Center for Disease Control and Prevention should strengthen surveillance and intervention for them.The Capture-Mark-Recapture (CMR) method of 3 correlated sources under the simple random sampling and its formulas detailed in this paper were applied to a survey of issues relevant to drug addicts in Xichang, China.To view half a year as reacquisition time, the prevalence of drug users in Xichang could be 5264 in 2014, accounting for 0.7% of the resident population of the same year.This investigation method and its formulas studied in this paper have both high reliability and high validity, being worthy of promotion in terms of estimating the prevalence of drug addicts in other parts of the world.

SUBMITTER: Chen K

PROVIDER: S-EPMC6486045 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Investigation and estimation of the prevalence of drug addicts in Xichang, china.

Chen Kejin K Bian Chaorong C Song Benli B Gao Ge G

Medicine 20190101 1

To use a new method to estimate the prevalence of drug addicts in Xichang for the first time and to suggest that Chinese Center for Disease Control and Prevention should strengthen surveillance and intervention for them.The Capture-Mark-Recapture (CMR) method of 3 correlated sources under the simple random sampling and its formulas detailed in this paper were applied to a survey of issues relevant to drug addicts in Xichang, China.To view half a year as reacquisition time, the prevalence of drug ...[more]

PMID: 30608420

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Project description:Despite explicitly wanting to quit, long-term addicts find themselves powerless to resist drugs, despite knowing that drug-taking may be a harmful course of action. Such inconsistency between the explicit knowledge of negative consequences and the compulsive behavioral patterns represents a cognitive/behavioral conflict that is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. However, the functional mechanism that integrates these neuropharmacological observations with the above-mentioned cognitive/behavioral conflict is unknown. Here we provide a formal computational explanation for the drug-induced cognitive inconsistency that is apparent in the addicts' "self-described mistake". We show that addictive drugs gradually produce a motivational bias toward drug-seeking at low-level habitual decision processes, despite the low abstract cognitive valuation of this behavior. This pathology emerges within the hierarchical reinforcement learning framework when chronic exposure to the drug pharmacologically produces pathologicaly persistent phasic dopamine signals. Thereby the drug hijacks the dopaminergic spirals that cascade the reinforcement signals down the ventro-dorsal cortico-striatal hierarchy. Neurobiologically, our theory accounts for rapid development of drug cue-elicited dopamine efflux in the ventral striatum and a delayed response in the dorsal striatum. Our theory also shows how this response pattern depends critically on the dopamine spiraling circuitry. Behaviorally, our framework explains gradual insensitivity of drug-seeking to drug-associated punishments, the blocking phenomenon for drug outcomes, and the persistent preference for drugs over natural rewards by addicts. The model suggests testable predictions and beyond that, sets the stage for a view of addiction as a pathology of hierarchical decision-making processes. This view is complementary to the traditional interpretation of addiction as interaction between habitual and goal-directed decision systems.

Project description:Background and aimsCapture-recapture (CRC) analysis is recommended for estimating the prevalence of problem drug use or people who inject drugs (PWID). We aim to demonstrate how naive application of CRC can lead to highly misleading results, and to suggest how the problems might be overcome.MethodsWe present a case study of estimating the prevalence of PWID in Bristol, UK, applying CRC to lists in contact with three services. We assess: (i) sensitivity of results to different versions of the dominant (treatment) list: specifically, to inclusion of non-incident cases and of those who were referred directly from one of the other services; (ii) the impact of accounting for a novel covariate, housing instability; and (iii) consistency of CRC estimates with drug-related mortality data. We then incorporate formally the drug-related mortality data and lower bounds for prevalence alongside the CRC into a single coherent model.ResultsFive of 11 models fitted the full data equally well but generated widely varying prevalence estimates, from 2740 [95% confidence interval (CI) = 2670, 2840] to 6890 (95% CI = 3740, 17680). Results were highly sensitive to inclusion of non-incident cases, demonstrating the presence of considerable heterogeneity, and were sensitive to a lesser extent to inclusion of direct referrals. A reduced data set including only incident cases and excluding referrals could be fitted by simpler models, and led to much greater consistency in estimates. Accounting for housing stability improved model fit considerably more than did the standard covariates of age and gender. External data provided validation of results and aided model selection, generating a final estimate of the number of PWID in Bristol in 2011 of 2770 [95% credible interval (Cr-I) = 2570, 3110] or 0.9% (95% Cr-I = 0.9, 1.0%) of the population aged 15-64 years.ConclusionsSteps can be taken to reduce bias in capture-recapture analysis, including: careful consideration of data sources, reduction of lists to less heterogeneous subsamples, use of covariates and formal incorporation of external data.

Project description:The emergence of novel drugs and the continuous expansion of the scope of the types of drugs under control have greatly increased requests for screening of a range of drugs in hair. Here, a multi-analyte method for the detection and quantification of 88 psychotropic drugs in the hair of addicts in drug abstinence was developed and fully validated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Hair samples (25 mg) were washed, cut into pieces, cryogenically ground, and extracted in methanol. The extracted analytes were separated on an Allure PFPP column (100×2.1 mm, 5 mm i.d., Restek, USA) and analyzed by LC-MS/MS in multiple reaction monitoring mode. The limits of detection and limits of quantification ranged from 0.1 to 20 pg/mg and 0.2 to 50 pg/mg, respectively. The intra- and inter-assay precisions (RSD) of all analysis ranged from 0.9 to 14.9% and 1.9 to 15.9%, respectively. Accuracy values were 100±20%. The extraction recovery of quality control samples ranged from 50.9% to 99.6% for all analytes. The matrix effects for all analytes ranged from 46.8% to 99.7%. The method was successfully used to analyze 1,865 hair samples from addicts in drug rehabilitation at their own communities.. Among the samples, 129 cases were positive; the majority of positive cases were from males (78.29%), 92.25% of whom were over 35 years old. Traditional drugs, like methamphetamine and opioids, accounted for most positive cases, and 27 of the abstinence cases with a use history of methamphetamine were still positive. In addition to abused drugs, like methamphetamine, morphine, and cocaine, the sedative hypnotic and psychotherapeutic drugs, including clonazepam, alprazolam, estazolam, zolpidem, and quetiapine, were detected in 26% of the hair samples, suggesting that these addicts may have insomnia and mental problems such as depression and psychosis, probably due to the long-term effects of drugs and withdrawal reactions. Three synthetic cannabinoids were also detected in 4 (2.7%) cases. A total of 37 cases were positive for methadone, tramadol, and dextromethorphan, reflecting a new trend of alternative drug use when traditional drugs were not easy to obtain during the COVID-19 outbreak.

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Investigation and estimation of the prevalence of drug addicts in Xichang, china.

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Investigation and estimation of the prevalence of drug addicts in Xichang, china.

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