Evaluating the relationship between adult attention-deficit/hyperactivity disorder and riding behavior of motorcyclists

The Factor Structure and Generalizability of the Iranian Socioeconomic Status (SES) Questionnaire Administered in a Nationally Divergent Population

Background

Measuring socioeconomic status (SES) as an independent variable is challenging, especially in epidemiological and social studies. This issue is more critical in large-scale studies on the national level. The present study aimed to extensively evaluate the validity and reliability of the Iranian SES questionnaire.

Methods

This psychometric, cross-sectional study was conducted on 3000 households, selected via random cluster sampling from various areas in East Azerbaijan province and Tehran, Iran. Moreover, 250 students from Tabriz University of Medical Sciences were selected as interviewers to collect data from 40 districts in Iran. The construct validity and internal consistency of the SES questionnaire were assessed using exploratory and confirmatory factor analyses and the Cronbach's alpha. Data analysis was performed in SPSS and AMOS.

Results

The complete Iranian version of the SES questionnaire consists of 5 factors. The Cronbach's alpha was calculated to be 0.79, 0.94, 0.66, 0.69, and 0.48 for the occupation, self-evaluation of economic capacity, house and furniture, wealth, and health expenditure, respectively. In addition, the confirmatory factor analysis results indicated the data's compatibility with the 5-factor model (comparative fit index = 0.96; goodness of fit index = 0.95; incremental fit index = 0.96; root mean square error of approximation = 0.05).

Conclusion

According to the results, the confirmed validity and reliability of the tool indicated that the Iranian version of the SES questionnaire could be utilized with the same structure on an extensive level and could be applicable for measuring the SES in a broader range of populations.

Screening for Adulthood ADHD and Comorbidities in a Tertiary Mental Health Center Using EarlyDetect: A Machine Learning-Based Pilot Study

Screening for adult Attention-Deficit/Hyperactivity Disorder (ADHD) and differentiating ADHD from comorbid mental health disorders remains to be clinically challenging. A screening tool for ADHD and comorbid mental health disorders is essential, as most adult ADHD is comorbid with several mental health disorders. The current pilot study enrolled 955 consecutive patients attending a tertiary mental health center in Canada and who completed EarlyDetect assessment, with 45.2% of patients diagnosed with ADHD. The best ADHD classification model using composite scoring achieved a balanced accuracy of 0.788, showing a 2.1% increase compared to standalone ADHD screening, detecting four more patients with ADHD per 100 patients. The classification model including ADHD with comorbidity was also successful (balanced accuracy = 0.712). The results suggest the novel screening method can improve ADHD detection accuracy and inform the risk of ADHD with comorbidity, and may further inform specific comorbidity including MDD and BD.

Psychometric properties of the 12-item WHODAS applied through phone survey: an experience in PERSIAN Traffic Cohort

BACKGROUND

Due to limited capability to function in post-injury daily life injury, survivors need to be reliably assessed without need to commute more than necessary. The key action is to determine the level of functioning difficulties. Having the opportunity of conducting a national post-crash traffic safety and health cohort study, we aimed to translate into Persian and assess the psychometric properties of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) through phone surveys six month post injury.

METHODS

First, having World Health Organization permission, we tested the translation validity by forward translation, expert panel evaluation, back-translation, pre-testing and cognitive interviewing, and finalizing the Persian WHODAS. Then, through a psychometric study within a national cohort platform, the validity, reliability and applicability of the 12-item WHODAS was assessed through phone surveys. We included data of 255 road traffic injury patients enrolled from the cohort at six-month follow-up. The psychometric assessment (internal consistency reliability and stability reliability) was conducted on test-retest data of 50 patients with an average 7-day time span. An exploratory factor analysis tested the construct validity using extraction method of principal component factor and oblique rotation on data from 255 patients. Regarding the multiple criteria including an eigenvalue > 0.9, Cattell's scree test, cumulative variance, and the theoretical basis, the minimum number of factors were retained. Data were analyzed using STATA statistical software package.

RESULTS

The respondents were mostly male (81%), employed (71%), educated (87%), and with a mean age of 37.7(14.9). The Persian version had high internal consistency reliability (Cronbach's α = 0.93) and excellent stability reliability (ICC = 0.97, 95% CI: 0.92-0.98). An exploratory factor analysis retained four factors defining 86% of all the variance. Factors of Self-care, Mobility, and Cognition were completely retained.

CONCLUSIONS

The brief Pesrian WHODAS 2.0 was highly reliable and valid to be applied through phone interviews post injury.

Psychometric Properties of EQ-5D-3L Applied through Phone Follow-Ups: An Experience in PERSIAN Traffic Cohort

Objective

To assess psychometric properties of the European Quality of Life 5-Dimension 3-Level Version (EQ-5D-3L) commonly used tool for measuring road traffic injury (RTI) patients' quality of life.

Methods

The psychometric study assessed the reliability and applicability of EQ-5D-3L through phone surveys, based on a national cohort platform. Data of 150 RTI patients recruited from the cohort study were included as 50 patients per each follow-up phase (one, six, and twelve months after discharge). A 12- day-time span was between test and retest. We measured psychometric properties (internal consistency reliability and stability reliability) and agreement using Kappa coefficients and percentages of agreement and Bland-Altman method. Data were analysed using software STATA statistical package.

Results

The majority of patients were men (80%) with mean age (SD) of 41(14.7%), employed (78%) and educated (86%). The Persian version represented high internal consistency reliability at total level (Cronbach's α=0.81) and moderate to good reliability at phase levels (0.62-0.87). The stability reliability was excellent at total (ICC=0.98, 95% CI: 0.97, 0.98) and phase levels (0.97-0.98. The kappa agreement coefficients were valued moderate to perfect (0.6-0.8, p>0.0001). The Bland-altman plot illustrated high agreement between test and retest scores. No floor and ceiling effects were found.

Conclusion

The study revealed that EQ-5D-3L was highly reliable and responsive to be applied through phone interviews at three different times post injury and discharge, as no previous study considered its psychometric properties at various phone follow-ups after RTIs.

The association between the score of adult attention-deficit/hyperactivity traits and risky driving behaviors with alcohol intake and narcotics consumption among Iranian motorcyclists

BACKGROUND

Motorcycle drivers are among the most vulnerable road users, accounting for a large proportion of global traffic accidents. This study aimed to investigate the association between the score of adult attention-deficit/hyperactivity (ADHD) traits and risky driving behaviors (RDB) with alcohol intake (AI) and narcotics consumption (NC) among motorcyclists in Iran.

METHODS

This multi-center cross-sectional study encompassed 1747 motorcyclists from three cities in Iran. A random sampling method was applied in this study, and the required data was collected using three standard questionnaires on ADHD, substance abuse, and RDB. Independent sample t-test, covariance analysis, and quantile regression (QR) were used to analyze the data.

RESULTS

The results of t-test and analysis of covariance indicated that AI and NC were significantly associated with ADHD score and RDB among the motorcycle drivers. Additionally, the QR models showed that these effects were significant at all quantiles of ADHD and RDB, even for individuals who were at lower quantiles.

CONCLUSIONS

Considering the potential dangers of driving after AI and NC, appropriate measures should be adopted before certifying a driving license to screen ADHD as a predisposing factor for substance abuse and RDB. Furthermore, it is essential to equip traffic police with adequate diagnosis kits and establish heavy penalties for the offenders. In this regard, all interventions aimed to reduce traffic accidents among motorcycle drivers should be done considering the interrelationship between ADHD, RDB, and substance abuse.