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Purpose Mental health challenges are highly prevalent in African migrants. However, understanding of mental health outcomes in first-generation voluntary African migrants is limited, despite the unique challenges faced by this migrant subgroup. This review aimed to synthesize the literature to understand the mental health challenges, help-seeking behavior, and the relationship between mental health and mental health help-seeking behavior in first-generation voluntary African migrants living outside Africa. Methods Medline Complete, EMBASE, CINAHL Complete, and APA PsychINFO were searched for studies published between January 2012 to December 2023. Retrieved articles were processed, data from selected articles were extracted and synthesized to address the study aims, and included studies were evaluated for risk of bias. Results Eight studies were included, including four quantitative and four qualitative studies, which focused on women with postnatal depression. Mental health challenges reported in the quantitative studies were depression, interpersonal disorders, and work-related stress. Risk (e.g., neglect from health professionals and lack of social/spousal support) and protective (e.g., sensitivity of community services and faith) factors associated with mental health were identified. Barriers (e.g., cultural beliefs about mental health and racial discrimination) and facilitators (sensitizing African women about mental health) of mental health help-seeking behavior were also identified. No significant relationship was reported between mental health and mental health help-seeking behavior, and the risk of bias results indicated some methodological flaws in the studies. Conclusion This review shows the dearth of research focusing on mental health and help-seeking behavior in this subgroup of African migrants. The findings highlight the importance of African migrants, especially mothers with newborns, examining cultural beliefs that may impact their mental health an

Background: Repeatability and validity are important components of precision in any measurement system. Aim: This study aimed to determine the effect of change in head and neck position and body posture on the repeatability of intraocular pressure (IOP) measurements with PT-100 noncontact tonometer and compare with Goldmann, PT-100 and Schi⊘tz tonometer readings. Setting: Optometry clinic, Saudi Arabia. Methods: The IOP was measured in one selected eye of 84 healthy participants (mean aged 21.9 ± 2.0 years) using PT-100 in three head and body positions, Goldmann tonometer and Schi⊘tz on two separate visits, in a randomised fashion. Central corneal thickness (CCT) was measured using an ultrasound pachymeter. The repeatability, agreement and correlations between CCT and IOP differentials were assessed. Results: The IOP measured in three head and body positions with the PT-100 were similar and comparable to Goldmann IOPs in sessions one and two with 74% and 86% of PT-100 measurements within ±3 mmHg of the Goldmann tonometry, respectively, for sessions one and two. The Schi⊘tz tonometer-measured IOP was higher than the Goldmann IOPs (p < 0.05) with 60% and 44% of Schi⊘tz IOPs within ±3 mmHg of the Goldmann tonometer IOP in sessions one and two, respectively. The limits of repeatability and reproducibility were best with the PT-100 and worst with the Schi⊘tz tonometer. The mean CCT (552±36 µm) was negatively correlated with differences between Goldmann and both PT-100 and Schi0tz-measured IOP. Conclusion: Postural changes did not affect the validity and repeatability of PT-100 readings. PT-100 measurements were interchangeable with Goldmann tonometer. Schi⊘tz overestimated Goldmann IOP in thicker corneas more than the PT-100.

Purpose: The multicentre, prospective South Western Eye and Diabetes Deep Learning Algorithm study has developed a new diabetes retinal screening service, incorporating a novel deep learning algorithm, for a low socio-economic, multi-ethnic, outer-metropolitan Sydney region. This preliminary report examines patient satisfaction with retinal imaging and correlation between diabetic retinopathy (DR) and quality of life (QOL). Methods: Supported by an Ophthalmic Research Institute of Australia grant, South Western Eye and Diabetes Deep Learning Algorithm participants were recruited from diabetes outpatient clinics and underwent two-field, table-top fundus photography and ocular coherence tomography. Questionnaires regarding patient satisfaction with retinal imaging and self-perceived QOL (EQ5D) were completed. Proportion of participants with high satisfaction levels were reported and mean self-perceivedQ OL (EQ-VAS) scores for those with and without DR were compared. Results: The study is ongoing and preliminary data (n=54) is presented. DR was detected in 50.0% of recruited participants. All participants found it very convenient to undergo photography on the same day as their diabetes appointment and 83.3% would undergo retinal imaging for DR screening again. This was despite 50.0% of participants requiring pharmacological pupil dilation. Preliminary QOL data showed that although DR did not correlate with greater disability, pain or anxiety/depression, average self-perceived health was lower (mean EQ-VAS scores of 68.3% vs 76.3%) in participants with DR than those without DR. Conclusions: The correlation between DR and lower self-perceived QOL, occurring independently of both visual acuity and knowledge of DR diagnosis, supports the need for efficient screening and early treatment. Retinal imaging was well-tolerated with an overwhelming preference for same-day outpatient DR screening.

Purpose: Diabetic retinopathy (DR) is highly prevalent in the multi-ethnic and low socioeconomic population of South Western Sydney. This is a significant public health burden, demanding efficient and cost-effective diabetes retinal screening. The multi-centre South Western Eye and Diabetes Deep Learning Algorithm study, supported by an Ophthalmic Research Institute of Australia grant, aims to develop and validate a novel deep learning algorithm, capable of grading DR, for a new diabetes retinal screening service. This abstract evaluates the performance of the Diabetic retinopathy OCT Open souRce Artificial Intelligence (Doctor AI©SK) program. Methods: Doctor AI analyses fundus photographs and optical coherence tomography (OCT) images simultaneously to grade diabetic retinopathy. It was trained with over 50,000 fundus photographs and 8000 OCT scans, using a combination of Australasian and publicly available datasets. As a screening tool, the algorithm's operating point was optimised for sensitivity and negative predictive value, and its performance reevaluated. Clinical validation is being undertaken in the recruited population at each study site. Results: For the detection of diabetic macula oedema from OCT images, Doctor AI achieved a 96.8% sensitivity, 98.1% specificity and 97.5% accuracy. Area under the receiver operating characteristic curve was 0.996. For the diagnosis of DR from fundus photographs, Doctor AI achieved a 90.4% sensitivity, 96.8% specificity and 88.9% accuracy. Preliminary clinical validation revealed an accuracy of 89% and 85% for the OCT and fundus photograph modules of the algorithm, respectively. Conclusion: Doctor AI is a unique deep learning algorithm capable of diagnosing DR and diabetic macula oedema with high sensitivity and accuracy.