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Estimating mental health impacts of alcohol's harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States.

ABSTRACT: BACKGROUND AND AIMS:Alcohol's harms to others (AHTO) may cause substantial distress, particularly when harms are perpetrated by close others. One challenge to identifying causal impacts is that people harmed by drinkers differ in many ways from those not so harmed, so our aim was to assess mental health in relation to two serious types of AHTO, financial harm and assault by someone who had been drinking, using propensity score (PS) weighting to adjust for potentially confounding differences. DESIGN:Cross-sectional, nationally representative, random sample of adults. SETTING:United States. PARTICIPANTS:Seventy-six respondents reporting financial harm compared with 4625 with no past-year AHTO; 192 respondents reporting assault compared with 4623 with no past-year AHTO. MEASUREMENTS:Predictors were reported exposure to financial problems due to someone's drinking and assault by someone who had been drinking. Mental health outcomes were quality of life, distress and positive affect. Confounders included family history of alcohol problems, child physical/sexual abuse, substance use/dependence, impacts of recent economic recession, racial/ethnic discrimination, poverty and other demographics. RESULTS:In double-robust PS weighted models for financial harm, there were associations with reduced quality of life (B = -0.28, P = 0.02) and increased distress [adjusted odds ratio (aOR) = 4.69, P < 0.001], and for assault by a partner or family member, there were associations with increased distress (aOR = 2.23, P = 0.09). For assault by a friend or stranger, none of the associations are statistically significant after PS weighting (all P > 0.10). CONCLUSIONS:Financial troubles due to someone else's drinking and assaults perpetrated by drinking intimates (spouses, other partners or family members) are associated with reduced mental health.

SUBMITTER: Karriker-Jaffe KJ

PROVIDER: S-EPMC6158083 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Estimating mental health impacts of alcohol's harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States.

Karriker-Jaffe Katherine J KJ Li Libo L Greenfield Thomas K TK

Addiction (Abingdon, England) 20180625 10

<h4>Background and aims</h4>Alcohol's harms to others (AHTO) may cause substantial distress, particularly when harms are perpetrated by close others. One challenge to identifying causal impacts is that people harmed by drinkers differ in many ways from those not so harmed, so our aim was to assess mental health in relation to two serious types of AHTO, financial harm and assault by someone who had been drinking, using propensity score (PS) weighting to adjust for potentially confounding differen ...[more]

PMID: 29893009

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Project description:Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

Dataset Information

Estimating mental health impacts of alcohol's harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States.

Publications

Estimating mental health impacts of alcohol's harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States.

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