38 research outputs found

    Drinking patterns and harm of unrecorded alcohol in Russia: a qualitative interview study

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    <p><b>Background:</b> Consumption of unrecorded alcohol (alcohol, consumed as a beverage, but not reflected in official statistics) has been linked to heavy drinking and alcohol-related mortality in Russia, with different studies looking for possible toxic components or other explanations. This study explores self-reported drinking behaviors of people diagnosed with alcohol dependence to elicit the perspectives of consumers of unrecorded alcohol.</p> <p><b>Methods:</b> Semi-structured in-depth expert interviews were conducted with patients (<i>n</i> = 25) of state-run addiction treatment centers of two Russian cities. Interviews were analyzed using thematic content analysis.</p> <p><b>Results:</b> A strict hierarchy between different types of unrecorded alcohol products, their ascribed quality, and the subjective harm caused by their consumption was found, with home-made spirits for own consumption at the top and technical fluids at the bottom. The ranking order correlated with product price, social status of associated consumers, and severity of their alcohol dependence. Binge drinking was the prevailing drinking pattern and shifts from recorded to unrecorded consumption within a single binge or a zapoi (continuous drinking for at least two days) were typical. Consumption of low-quality unrecorded alcohol was associated with stronger hang-overs, zapois, alcohol psychoses and poisonings, and other indicators of alcohol attributable harm, while no such connection was found for spirits for own consumption.</p> <p><b>Conclusions:</b> In the dominant explanation patterns of the consumers, the experienced alcohol-induced harm is attributed to alcohol quality, while a thorough analysis of their reported drinking behaviors cannot exclude specific drinking patterns linked to the severity of alcohol dependence as the main determinants of the described health detriments.</p

    Behavioural data, illustrated separately for each experimental condition (H50, H100, T50, T100).

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    <p>Upper half: Subjective ratings on the dimensions arousal, valence, anxiety, and authenticity. Higher values indicate higher arousal, but positive valence. Lower half: Skin conductance data (range-corrected values). Error bars indicate the standard error of mean (SEM). #NS.SCR: mean number of non-stimulus specific skin conductance reactions; AMP.NS.SCR: mean amplitude of non-stimulus specific skin conductance reactions. * p < 0.05. ** p < 0.01</p

    PPI results for the contrast heart > tone; seed region: right insula (x = 39, y = 15, z = 3).

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    <p><i>Note</i>. Significance threshold: p < 0.01 (uncorrected); minimum cluster size: 10 voxels.</p><p>PPI results for the contrast heart > tone; seed region: right insula (x = 39, y = 15, z = 3).</p

    Correlation analyses.

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    <p>Scatterplots display correlations between the estimated beta values in the right anterior insular cortex (MNI coordinates (x = 39, y = 15, z = 3)) and AMP.NS.SCR (range-corrected) in response to H100 (a), subjective arousal ratings for H100 (b), and the Anxiety Sensitivity Index (c). Pearson correlation coefficients (R) are given in the plots. Estimated ß-values for the four regressors of interest (H50, H100, T50, T100) were extracted and added so that the t-contrast H50+H100 > T50+T100 was replicated, i.e. difference values were entered into correlation analyses. ** p < 0.01 (Bonferroni-corrected).</p

    Design of the heartbeat paradigm.

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    <p>Upper half: Illustration of the block design structure (four conditions with four blocks of stimulation each, separated by 30 s baseline periods). Order of blocks was randomized across subjects. Lower half: Temporal sequence within one condition, using the example of the heartbeat condition. Hearts illustrate the heartbeat condition; clefs illustrate the sinus tone condition.</p

    Behavioural data, illustrated separately for each experimental condition (H50, H100, T50, T100).

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    <p>Upper half: Subjective ratings on the dimensions arousal, valence, anxiety, and authenticity. Higher values indicate higher arousal, but positive valence. Lower half: Skin conductance data (range-corrected values). Error bars indicate the standard error of mean (SEM). #NS.SCR: mean number of non-stimulus specific skin conductance reactions; AMP.NS.SCR: mean amplitude of non-stimulus specific skin conductance reactions. * p < 0.05. ** p < 0.01</p

    Demographic characteristics and questionnaire scores of the sample.

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    <p><i>Note</i>. Means and Standard Deviations are reported, except for the variables “Female gender” and “Smoking”. ASI = Anxiety Sensitivity Index; BDI II = Beck Depression Inventory II.</p><p>Means (SD) except where noted.</p

    CH02 Adversity and psychosis: a 10‐year prospective study investigating synergism between early and recent adversity in psychosis

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    <div><b>Objective:</b>  Recent studies have suggested that early adverse events, such as childhood trauma, may promote enduring liability for psychosis whereas more recent adverse events may act as precipitants. Examination of these environmental dynamics, however, requires prospective studies in large samples. This study examines whether the association between recent adverse events and psychosis is moderated by exposure to early adversity.</div><div><br></div><div><b>Method:</b>  A random regional representative population sample of 3021 adolescents and young adults in Munich, Germany, was assessed three times over a period of up to 10 years, collecting information on sociodemographic factors, environmental exposures, and measures of psychopathology and associated clinical relevance. Evidence of statistical non-additivity between early adversity (two levels) and more recent adversity (four levels) was assessed in models of psychotic symptoms. Analyses were a priori corrected for age, gender, cannabis use, and urbanicity.</div><div><br></div><div><b>Results:</b>  Early and recent adversity were associated with each other (RR = 1.32, 95% CI 1.06–1.66; P = 0.014) and displayed statistical non-additivity at the highest level of exposure to recent adversity (χ2 = 4.59; P = 0.032).</div><div><br></div><div><b>Conclusion:</b>  The findings suggest that early adversity may impact on later expression of psychosis either by increasing exposure to later adversity and/or by rendering individuals more sensitive to later adversity if it is severe.</div><div><br></div><div>see also:</div><div>Lataster, J., Myin‐Germeys, I., Lieb, R., Wittchen, H. U., & van Os, J. (2012). Adversity and psychosis: a 10‐year prospective study investigating synergism between early and recent adversity in psychosis. <i>Acta psychiatrica scandinavica</i>, <i>125</i>(5), 388-399.<br></div><div><br></div><div><b>DOI: 10.1111/j.1600-0447.2011.01805.x</b><br></div

    Schematic view of the target detection task.

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    <p>The example illustrates the brightening of the inner circle (cue) indicating a short cue target interval (CTI). After the cue stimulus a CTI of either 600 ms (expected) or 1400 ms (unexpected) followed, than the target appeared (large cross). Long CTIs’ were cued by brightening of the outer circle.</p
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