Using the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) to Predict Gambling Disorder Among U.S. College Students

The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) screening tool has not previously been used to evaluate risk for gambling disorder (GD). We aimed to assess the level at which each specific substance involvement score (SSIS), measured by ASSIST, most optimally predicted GD among U.S. college students. Data were analyzed for 141,769 students from the National College Health Assessment (fall 2019-spring 2021) utilizing multivariable logistic regression models. Sensitivities and specificities were utilized to find optimal cutoffs that best identified those with GD, overall and by biological sex and age group. Lower threshold of substance risk related to prescription opioids, cocaine, and hallucinogens (all with SSIS cutoffs of 4) predicts gambling disorder compared to sedatives (SSIS cutoff of 19). Younger students had lower thresholds of substance risk predicting GD than older students for heroin, but for all other substance classifications students 25 years and older had lower thresholds of SSIS predicting GD than students 18-24 years old. This study aids in the understanding that substance use behavior may put students at risk for other addictive behaviors such as GD. This study is the first to utilize the ASSIST tool to predict GD among U.S. college students, extending its application beyond substance use disorders. The identification of optimal cutoffs for each SSIS provides a novel approach to concurrently screen for GD and substance use disorders. This unique contribution could enhance early detection and intervention strategies for GD in the college student population.

Benchmarking MicrobIEM - a user-friendly tool for decontamination of microbiome sequencing data

BACKGROUND

Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.

RESULTS

We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108-103 cells, 0.4-80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden's index. In the skin dataset, the Decontam prevalence filter and MicrobIEM's ratio filter effectively reduced common contaminants while keeping skin-associated genera.

CONCLUSIONS

MicrobIEM's ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Screening of coexisting depression or anxiety disorders among Danish patients with alcohol use disorder in outpatient treatment

BACKGROUND

Depression and anxiety are highly prevalent among patients seeking outpatient treatment for alcohol use disorders (AUD) and if depression and anxiety are addressed the prognosis is improved. Screening instruments for depression and anxiety have been validated in populations suffering from drug use disorders, but not in populations suffering from AUD. The aim of this study was to validate four self-administrated screening instruments (PHQ-9, GAD-7, Kessler-6, and SRQ) and calculate the optimal cut-off value for identifying depression and anxiety.

METHODS

The study included 73 patients with self-reported depression or anxiety during AUD treatment. Each patient filled out the above-mentioned instruments and was subsequently interviewed by trained clinicians blinded to the results of the instruments with the Present State Examination to establish a diagnosis of depression or anxiety according to ICD-10. ROC curves were constructed for each instrument and the area under the curve (AUC) was calculated using patients with no depression or anxiety as reference. Youden's index was calculated to assess the optimal cut-off for each instrument.

RESULTS

A total of 33 (45.2%) were diagnosed with depression or anxiety. The AUC for PHQ-9, GAD-7, Kessler-6, and SRQ were 0.767, 0.630, 0.793, and 0.698 respectively. Kessler-6, the instruments performing best based on the AUC, identified 27 (82%) of the 33 patients using a cut-off of 10 points.

CONCLUSION

Kessler-6 seems to be valid and reliable in identifying patients requiring treatment for depression or anxiety among patients seeking treatment for AUD who are reporting depression or anxiety.

A simple framework to identify optimal cost-effective risk thresholds for a single screen: Comparison to Decision Curve Analysis

Decision Curve Analysis (DCA) is a popular approach for assessing biomarkers and risk models, but does not require costs and thus cannot identify optimal risk thresholds for actions. Full decision analyses can identify optimal thresholds, but typically used methods are complex and often difficult to understand. We develop a simple framework to calculate the Incremental Net Benefit for a single-time screen as a function of costs (for tests and treatments) and effectiveness (life-years gained). We provide simple expressions for the optimal cost-effective risk-threshold and, equally importantly, for the monetary value of life-years gained associated with the risk-threshold. We consider the controversy over the risk-threshold to screen women for mutations in BRCA1/2. Importantly, most, and sometimes even all, of the thresholds identified by DCA are infeasible based on their associated dollars per life-year gained. Our simple framework facilitates sensitivity analyses to cost and effectiveness parameters. The proposed approach estimates optimal risk thresholds in a simple and transparent manner, provides intuition about which quantities are critical, and may serve as a bridge between DCA and a full decision analysis.

Performance evaluation of existing immunoassays for Clonorchis sinensis infection in China

BACKGROUND

Clonorchiasis ranks among the most important food-borne parasitic diseases in China. However, due to low compliance to traditional fecal examination techniques in the general population and medical personnel, immunodiagnosis is expected. This study evaluated, in parallel, the performance of four immunodiagnostic kits detecting clonorchiasis in China.

RESULTS

A bank with 475 sera was established in this study. Except for the low performance of the kit detecting IgM, the other three kits detecting IgG showed sensitivities ranging from 81.51% (194/238) to 99.16% (236/238). Higher sensitivity was presented in heavy infection intensity [89.47% (68/76) to 100% (76/76)]. Among the four kits, the overall specificity varied from 73.42% (174/237) to 87.34% (207/237). It was observed that the specificity was lower in the sera of the participants living in clonorchiasis-endemic areas but without any parasite infection [67.5% (81/120) to 90% (108/120)], as compared to those from the non-endemic area [94% (47/50) to 98% (49/50)]. The cross-reaction rate varied from 14.93% (10/67) to 31.34% (21/67). Youden's index was -0.022, 0.689, 0.726, and 0.802 for kits T1, T2, T3 and T4, respectively. Repeatability was high in all four kits.

CONCLUSIONS

Three immunodiagnosis kits targeting IgG antibody had high performance on detecting chronic Clonorchis sinensis infection, but that detecting IgM antibody had not. The kits detecting IgG antibody also showed high sensitivity in heavy infection intensity. Research on immunological diagnosis of clonorchiasis is expected to be strengthened to improve the sensitivity in light infection and specificity.

The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation

Introduction The choice of criteria in determining optimal cut-off value is a matter of concern in quantitative diagnostic tests. Several indexes such as Youden's index, Euclidean index, product of sensitivity and specificity in receiver operator characteristic space and diagnostic odds ratio have been used in clinical practices but their advantages and limitations are not well understood by clinicians. This study aimed to compare these methods in determining optimal cut-off values for quantitative diagnostic test. Methods The different configurations of binormal and bilogistic distributions with equal and unequal variances for nondiseased and diseased subjects were examined. The cut-off values with increment of 0.1 in Z-scale were varied. Then, the Youden's index, Euclidean index, product of sensitivity and specificity, and diagnostic odds ratio were calculated over various cut-off values under distributional assumptions with confirmed parameters. Results According to the obtained data from binormal model and equal variances, the optimal cut-off values derived from Youden's index, Euclidean index, and product method were similar but the diagnostic odds ratio yielded either extremely low or extremely high optimal cut-off value. For bilogistic pair distributions with equal variances, the Youden's, Euclidean indexes and product method resulted in an identical cut-off value but the diagnostic odds ratio was constant over various cut-points. By both binormal and bilogistic data with more variations in nondiseased population, the Youden's index produced a higher sensitive optimal cut-off value; but with more variation for diseased distribution, the Euclidean index showed a more sensitive optimal cut-off. For bilogistic data with unequal variance, the log(diagnostic odds ratio) had a straight line relationship over cut-off values with either positive or negative slope. Conclusion As a measure of association, diagnostic odds ratio cannot be informative in determining an optimal cut-off value. The advantage of receiver operator characteristic analysis to obtain the optimal cut-off value is to use Youden's index, Euclidean index, or product index which is recommended. The choice between them depends on variability of test results in diseased and nondiseased subjects and the desired sensitivity.

Youden's Index and the Weight of Evidence Revisited

A correspondence between Youden's index for rating diagnostic tests and the log-likelihood ratio of a positive test outcome is illustrated by data calculated from two previously-published studies of binary tests.

Youden's index and the weight of evidence

By means of Shannon's relationship between information and probability, Youden's index for rating diagnostic tests is shown to be a probability-scale analogue of the log-likelihood ratio of a positive test outcome.

Specific cut-off points for waist circumference and waist-to-height ratio as predictors of cardiometabolic risk in Black subjects: a cross-sectional study in Benin and Haiti

PURPOSE

Waist circumference (WC) and waist-to-height ratio (WHtR) are widely used as indicators of abdominal adiposity and the cut-off values have been validated primarily in Caucasians. In this study we identified the WC and WHtR cut-off points that best predicted cardiometabolic risk (CMR) in groups of African (Benin) and African ancestry (Haiti) Black subjects.

METHODS

This cross-sectional study included 452 apparently healthy subjects from Cotonou (Benin) and Port-au-Prince (Haiti), 217 women and 235 men from 25 to 60 years. CMR biomarkers were the metabolic syndrome components. Additional CMR biomarkers were a high atherogenicity index (total serum cholesterol/high density lipoprotein cholesterol ≥4 in women and ≥5 in men); insulin resistance set at the 75th percentile of the calculated Homeostasis Model Assessment index (HOMA-IR); and inflammation defined as high-sensitivity C-reactive protein (hsCRP) concentrations between 3 and 10 mg/L. WC and WHtR were tested as predictors of two out of the three most prevalent CMR biomarkers. Receiver operating characteristic (ROC) curves, Youden's index, and likelihood ratios were used to assess the performance of specific WC and WHtR cut-offs.

RESULTS

High atherogenicity index (59.5%), high blood pressure (23.2%), and insulin resistance (25% by definition) were the most prevalent CMR biomarkers in the study groups. WC and WHtR were equally valid as predictors of CMR. Optimal WC cut-offs were 80 cm and 94 cm in men and women, respectively, which is exactly the reverse of the generic cut-offs. The standard 0.50 cut-off of WHtR appeared valid for men, but it had to be increased to 0.59 in women.

CONCLUSION

CMR was widespread in these population groups. The present study suggests that in order to identify Africans with high CMR, WC thresholds will have to be increased in women and lowered in men. Data on larger samples are needed.