Kenneth S Kendler1,2, Sara L Lönn3, Jan Sundquist3,4,5, Kristina Sundquist3,4,5. 1. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia. 2. Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia. 3. Center for Primary Health Care Research, Lund University, Malmö, Sweden. 4. Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York. 5. Center for Community-based Healthcare Research and Education (CoHRE), Department of Functional Pathology, School of Medicine, Shimane University, Japan.
Abstract
OBJECTIVE: Given the public health importance of opioid use disorder (OUD), we sought to understand better its risk predictors in the Swedish general population. METHOD: We examined the Swedish population, born 1950-1970 (n = 2,092,359), and followed through 2018. Using Cox, logistic, and co-sibling models, we explored associations between a wide range of putative risk factors and a first onset of OUD--assessed through medical, criminal, and pharmacy registers--in the entire cohort and in the cohort wherein prior cases of drug use disorder (DUD) were censored. RESULTS: OUD was predicted by the following four risk factor domains: (a) externalizing syndromes, especially prior non-opioid DUD; (b) psychopathology; (c) psychosocial factors, including social class and immigrant and marital status; and (d) serious injuries and pain diagnoses. When predicting OUD as the first form of DUD, the importance of pain diagnoses as a predictor increased. Co-sibling analyses suggested that the association of some of these risk factors with OUD onset was likely largely causal, whereas others were a mixture of causal effects and familial confounding. An aggregate risk score from these individual risk factors had reasonable receiver operating characteristic (ROC) curve performance. CONCLUSIONS: OUD is a multifactorial syndrome for which risk can be meaningfully predicted by prior externalizing syndromes, internalizing and psychotic psychopathology, indicators of psychosocial status, and predictors of pain diagnoses. Some important differences were seen in the prediction of any OUD onset versus OUD onset as the first form of DUD. Much of the effect of these predictors appear, in co-sibling analyses, to likely reflect causal influences.
OBJECTIVE: Given the public health importance of opioid use disorder (OUD), we sought to understand better its risk predictors in the Swedish general population. METHOD: We examined the Swedish population, born 1950-1970 (n = 2,092,359), and followed through 2018. Using Cox, logistic, and co-sibling models, we explored associations between a wide range of putative risk factors and a first onset of OUD--assessed through medical, criminal, and pharmacy registers--in the entire cohort and in the cohort wherein prior cases of drug use disorder (DUD) were censored. RESULTS: OUD was predicted by the following four risk factor domains: (a) externalizing syndromes, especially prior non-opioid DUD; (b) psychopathology; (c) psychosocial factors, including social class and immigrant and marital status; and (d) serious injuries and pain diagnoses. When predicting OUD as the first form of DUD, the importance of pain diagnoses as a predictor increased. Co-sibling analyses suggested that the association of some of these risk factors with OUD onset was likely largely causal, whereas others were a mixture of causal effects and familial confounding. An aggregate risk score from these individual risk factors had reasonable receiver operating characteristic (ROC) curve performance. CONCLUSIONS: OUD is a multifactorial syndrome for which risk can be meaningfully predicted by prior externalizing syndromes, internalizing and psychotic psychopathology, indicators of psychosocial status, and predictors of pain diagnoses. Some important differences were seen in the prediction of any OUD onset versus OUD onset as the first form of DUD. Much of the effect of these predictors appear, in co-sibling analyses, to likely reflect causal influences.
Authors: M T Tsuang; M J Lyons; S A Eisen; J Goldberg; W True; N Lin; J M Meyer; R Toomey; S V Faraone; L Eaves Journal: Am J Med Genet Date: 1996-09-20
Authors: Anna Fugelstad; Ingmar Thiblin; Lars Age Johansson; Gunnar Ågren; Anna Sidorchuk Journal: Drug Alcohol Depend Date: 2019-06-25 Impact factor: 4.492