Prior research, largely focused on US male veterans, indicates an increased risk of cardiovascular disease among individuals with post-traumatic stress disorder (PTSD). Data from other settings and populations are scarce. The objective of this study is to examine PTSD as a risk factor for incident major adverse cardiovascular events (MACEs) in South Africa.

We analysed reimbursement claims (2011–2020) of a cohort of South African medical insurance scheme beneficiaries aged 18 years or older. We calculated adjusted hazard ratios (aHRs) for associations between PTSD and MACEs using Cox proportional hazard models and calculated the effect of PTSD on MACEs using longitudinal targeted maximum likelihood estimation.

We followed 1,009,113 beneficiaries over a median of 3.0 years (IQR 1.1–6.0). During follow-up, 12,662 (1.3%) persons were diagnosed with PTSD and 39,255 (3.9%) had a MACE. After adjustment for sex, HIV status, age, population group, substance use disorders, psychotic disorders, major depressive disorder, sleep disorders and the use of antipsychotic medication, PTSD was associated with a 16% increase in the risk of MACEs (aHR 1.16, 95% confidence interval (CI) 1.05–1.28). The risk ratio for the effect of PTSD on MACEs decreased from 1.59 (95% CI 1.49–1.68) after 1 year of follow-up to 1.14 (95% CI 1.11–1.16) after 8 years of follow-up.

Our study provides empirical support for an increased risk of MACEs in males and females with PTSD from a general population sample in South Africa. These findings highlight the importance of monitoring cardiovascular risk among individuals diagnosed with PTSD.

Nearly 85% of adults on the autism spectrum are unemployed, although nearly 70% of those who are unemployed express a desire and willingness to work. The job interview has been identified as a significant obstacle to obtaining employment by young adults on the spectrum. A growing field of research has been focused on evaluating innovative training tools to improve interview skills. Our previous work shows that a virtual reality job interview training (VR-JIT) tool improves certain job interview skills (such as sounding professional, establishing rapport), but does not improve the ability to speak about personal strengths and abilities. The current study combined VR-JIT with a new training tool: Kessler Foundation Strength Identification and Expression (KF-STRIDE), an intervention grounded in principles of positive psychology. KF-STRIDE targets identification of personal character strengths and expressing those strengths to employers in a socially appropriate way.

The current study evaluated data from 20 autistic youth, randomized to an experimental group (n=10) and a services-as-usual (SAU) control group (n=10). Those in the experimental group participated in a 12 session intervention (9 sessions using VR-JIT and 3 sessions in KF-STRIDE). Each session was roughly one hour. Job interview performance was assessed by video-recorded mock job interviews rated by blinded assessors pre- and post- the intervention. Paired samples t-tests were conducted to examine differences in job interview skills from baseline to follow up in both groups.

The intervention group showed a significant improvement from baseline to follow-up in job interview skills in general (p = .004), and specifically sharing strengths about themselves to a future employer (p = .004). No significant differences were seen from baseline to follow-up in the SAU group. Conclusions: Individuals on the autism spectrum are significantly underemployed, which negatively impacts one’s ability to lead an independent life. Two innovative tools: VR-JIT and KF-STRIDE successfully improved job interview skills, including the ability to identify and express personal strengths. These findings indicate that these combined tools may help to improve employment skills for individuals on the autism spectrum.

Individuals diagnosed with autism spectrum disorder (ASD) experience negative self-evaluation, indicated by low levels of self-esteem and describing themselves more negatively to others. Variations in reading comprehension, difficulty identifying emotions, and masking (camouflaging of autistic traits) make it difficult to accurately measure self-evaluation of individuals with ASD using subjective self-report scales such as the Rosenburg Self-Esteem Scale. Therefore, it is important to explore more objective methods of measuring self-evaluation in ASD. Sentiment analysis is a popular Natural Language Processing (NLP) technique used to quantify the emotional content of language programmatically by automatically transforming text into a data frame of words represented as individual values or tokens. Each token can then be categorized as positive or negative with a sentiment dictionary. The current study aims to investigate an automated sentiment analysis approach to evaluate self-evaluation by quantifying implicit linguistic affective valence of ASD participants' verbal self-describing statements in a naturalistic setting. Specifically, we evaluated the frequency of positive or negative words used during a mock job interview in which individuals with ASD were asked to describe themselves. We then examined the relationship between positive and negative word usage and standard self-report measures of self-evaluation.

Twenty-four young adults with ASD were included in this study with an age range of 15-24 and a mean age of 19.2 years. Participants completed a battery of assessments including a mock job interview in which they were asked to describe themselves as a measure of implicit self-esteem. Self-esteem and knowledge of personal strengths was assessed using the Rosenberg Self-Esteem Scale and Strengths Knowledge Scale, respectively. Interview transcripts were automatically transformed into word token data frames using the tidytext package in Rstudio. Frequencies of positive and negative words were calculated and their ratio to total word count was used to measure the implicit positivity and negativity of transcripts.

There was a significant negative correlation between the frequency of negative sentiment in transcripts and measure on the Rosenburg Self-Esteem Scale (r = -.376, p = .035) and the Strengths Knowledge Scale (r = -.387, p = .031) indicating that individuals with higher self-esteem and knowledge of their strengths used fewer negative words when talking during a mock interview.

While our results are preliminary, this pilot study represents the first to use automated sentiment analysis to study self-evaluation in individuals with ASD. The use of this technique on natural linguistic data collected through a mock job interview allows researchers to quantitatively analyze the emotionality of transcriptions and create insights that would otherwise be unavailable using more subjective qualitative techniques. Limited research into self-evaluation in this population has yielded inconsistent results, relying too heavily upon qualitative or self-report measures. The ability to programmatically quantify affective valence in transcripts is a time and cost-effective technique for improving validity of future measures of self-evaluation.