Screening for Depression and Psychological Distress in a Currently Serving Military Population: The Diagnostic Accuracy of the K10 and the PHQ9

General population normative data from seven European countries for the K10 and K6 scales for psychological distress

The 10-item Kessler Psychological Distress scale (K10) and its 6-item short-form version (K6) measure psychological distress, particularly anxiety or depressive symptoms. While these questionnaire scales are widely used in various settings and populations, general population normative data are rarely available. To facilitate the interpretation of K10 and K6 scores, we provide normative general population data from seven European countries. We used an online survey to collect K10 data from general population samples in Austria, Italy, Germany, France, the Netherlands, Poland and Spain. We calculated the age- and sex-specific normative values separately for each country. For more specific estimates of K10 and K6 scores for individuals or groups, we also established a multivariable regression model based on socio-demographic and health data. In total, N = 7,087 adults participated in our study (51.6% women; mean age, 49.6 years). The mean K10 score in the total sample was 8.5 points (standard deviation, 7.3) on 0-40 points metric, with mean scores in individual countries ranging from 6.9 (the Netherlands) to 9.9 (Spain). Women showed higher scores than men and younger participants scored higher than older participants. Our study is the first to present normative K10 and K6 data from several European countries using a consistent sampling approach. These reference values will facilitate the interpretation of K10 and K6 scores in clinical research and practice and also highlight the variation in psychological distress levels across countries and groups according to their socio-demographic and health characteristics.

Psychometric properties of the Kessler psychological scales in a Swiss young-adult community sample indicate poor suitability for community screening for mental disorders

AIM

The Kessler psychological distress scales (K10 and K6) are used as screening tools to assess psychological distress related to the likely presence of a mental disorder. Thus, we studied the psychometric properties of their German versions in a Swiss community sample to evaluate their potential usefulness to screen for mental disorders or relevant mental problems in the community and, relatedly, for low threshold transdiagnostic German-speaking services.

METHODS

The sample consisted of 829 citizens of the Swiss canton Bern of age 19-43 years. K10/K6 were validated against mini-international neuropsychiatric interview (M.I.N.I.) diagnoses, questionnaires about health status and quality of life. Receiver operating characteristic (ROC) curve analyses were used to test for general discriminative ability and to select optimal cut-offs of the K10 and K6 for non-psychotic full-blown and subthreshold mental disorders.

RESULTS

Cronbach's alphas were 0.81 (K10) and 0.70 (K6). ROC analyses indicated much lower optimal thresholds than earlier suggested; 10 for K10 and 6 for K6. At these thresholds, against M.I.N.I. diagnoses, Cohen's κ (≤0.173) and correspondence rates (≤58.14%) were insufficient throughout. Values were higher at the earlier suggested threshold, yet, at the cost of sensitivity that was below 0.5 in all but three, and below 0.3 in all but six cases.

CONCLUSIONS

For the lack of sufficient validity and sensitivity, respectively, our findings suggest that both K10 and K6 would only be of limited use in a low-threshold transdiagnostic mental health service for young adults in Switzerland and likely other German-speaking countries.

Factors Influencing Gambling Behavior Among Employees in Macau Gambling Industry

Casino staff of the gambling industry seem to have a higher risk of pathological gambling than the general public because they share many previously identified risk factors, such as easy access to gambling activities. The studies in western countries reported casino staff have a higher prevalence of problematic gambling than the general public. However, some studies in Macau revealed gambling behavior among casino staff is not more common than among other residents there. A quantitative study was conducted with employees of Macau's casinos (N = 714) to understand gambling behavior among casino staff. Structural equation modeling was developed with time spent on gambling as a dependent variable, and awareness of responsible gambling, sensation-seeking tendency, psychological distress, demographic factors, peer/family pressure against gambling, and perception of expected rewards as independent variables. T-test, ANOVA, and correlations were computed for analyzing the obtained data. The structural equation modelling with the time spent on gambling as a dependent variable showed an acceptable model fit (The Chi-square = 5.567, Probability level = .696, CMIN/DF 0.696; NFI = 0.976; NNFI = 1.072; CFI = 1.000; RMSEA = 0.000). Psychological distress, sensation-seeking tendency, peer/family pressure against gambling all have significant effects on time spent on gambling. Perception of expected rewards showed the highest R square in the structural equation modeling and accounted for 77.9% cases of variance for the time spent on gambling. It is predicted that protective factors against pathological gambling include prohibition of gambling in the working venue, a satisfactory salary, and exposure to negative impacts of gambling via their job duties. Given that a high proportion of staff share most risk factors and less than 30% of respondents participated in gambling in the past year, further exploration of the protective factors from casino employees' point of view and further research on casino workers' "expected rewards" are recommended to generate ideas for improving the effectiveness of responsible gambling campaigns in future.

Treating postpartum depression in rural veterans using internet delivered CBT: program evaluation of MomMoodBooster

Depression in the postpartum period impacts approximately 13–26% of the general population. This number can be much higher for rural veteran women who face additional barriers to accessing specialized mental health services due to isolation and cultural factors. This study reports on a program evaluation of MomMoodBooster, a coach-supported internet-delivered CBT program for the treatment of maternal depression in veteran women. Repeated measures ANOVA, run with this sample of 326 women, demonstrated an overall positive effect size across outcome measures and engagement with no differences found between rural women and their urban counterparts. Some differences between urban and rural participants were found in total and average time spent with coaches as well as ratings of coach helpfulness, possibly indicating some cultural differences between coaches and rural women that need to be addressed. These results and the results of earlier trials suggest that MomMoodBooster can be a valid and efficacious option for reaching under-served veteran populations with specialized postpartum mental health support and is as effective with rural women as with urban women

Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study

PURPOSE

Traditional questionnaires assessing the severity of depression are limited and might not be appropriate for military personnel. We intend to explore the diagnostic ability of three machine learning methods for evaluating the depression status of Chinese recruits, using the Chinese version of Beck Depression Inventory-II (BDI-II) as the standard.

PATIENTS AND METHODS

Our diagnostic study was carried out in Luoyang City (Henan Province, China; 10/16/2018-12/10/2018) with a sample of 1000 Chinese male recruits selected using cluster convenient sampling. All participants completed the BDI and 3 questionnaires including the data of demographics, military careers and 18 factors. The participants were randomly selected as the training set and the testing at 2:1. The machine learning methods tested for assessing the presence or absence of depression status were neural network (NN), support vector machine (SVM), and decision tree (DT).

RESULTS

A total of 1000 participants completed the questionnaires, with 223 reporting depression status and 777 not. The highest sensitivity was observed for DT (94.1%), followed by SVM (93.4%) and NN (93.1%). The highest specificity was observed for NN (60.0%), followed by SVM (58.8%) and DT (43.3%). The area under the curve (AUC) of the SVM was the largest (0.862) compared with NN (0.860) and DT (0.734). The regression prediction error and error volatility of the SVM were the smallest.

CONCLUSION

The SVM has the smallest prediction error and error volatility, as well as the largest AUC compared with NN and DT for assessing the presence or absence of depression status in Chinese recruits.