Mustafa H. Khazali

Background: Psychosocial stress has been recognized as a potential risk factor for stroke; however, the strength and consistency of its association with first-ever stroke remain uncertain. This systematic review and meta-analysis aimed to determine the overall association between psychosocial stress and the risk of first stroke in a stroke-free population. Methods: We conducted a systematic review and meta-analysis in accordance with the PRISMA and MOOSE guidelines. PubMed, Scopus, Web of Science, and Ovid were searched from inception to 1 January 2026 for cohort or case–control studies. Eligible studies enrolled participants without a prior history of stroke and reported adjusted risk estimates with 95% confidence intervals. Psychosocial stressors were categorized into psychological, vocational, interpersonal, and behavioral domains. Stroke outcomes included ischemic stroke, hemorrhagic stroke, subarachnoid hemorrhage, transient ischemic attack, and unspecified stroke. Results: Eighty-four studies were included (72 prospective cohort and 12 case–control), comprising 4,128,167 participants. In cohort studies, psychological stress was associated with a higher risk of first-ever stroke (hazard ratio [HR] 1.27, 95% confidence interval 1.19–1.36). Vocational stressors were also associated with increased stroke risk (HR 1.32, 95% CI 1.23–1.43). Interpersonal stressors showed a smaller but statistically significant association (HR 1.11, 95% CI 1.03–1.20). Behavioral stress-related traits showed the largest effect estimate (HR 1.51, 95% CI 1.05–2.15), although this finding should be interpreted cautiously because it was based on only three cohort studies. In sex-stratified analyses, the association was significant among men (HR 1.19, 95% CI 1.07–1.32) but not among women (HR 0.86, 95% CI 0.58–1.28); however, the subgroup interaction was not statistically significant and should be interpreted as inconclusive rather than evidence of a definite sex-specific difference. Conclusion: Psychosocial stress is associated with an increased risk of first-ever stroke, particularly psychological and vocational stressors, supporting psychosocial factors as potentially modifiable components of stroke prevention.

Background: The COVID-19 pandemic accelerated adoption of telemedicine and digital decision systems, including artificial intelligence (AI), in primary care. However, quantitative evidence on the clinical effectiveness of AI-integrated telemedicine across outcome domains remains fragmented. We conducted a systematic review and meta-analysis to evaluate the effectiveness of AI-supported telemedicine and hybrid care models in primary care settings after the onset of the pandemic.Methods: We performed a systematic review and meta-analysis in accordance with PRISMA 2020 guidance. PubMed and Scopus were searched for studies published from March 2020 onward evaluating AI-supported telemedicine interventions in primary care or closely related front-line care settings. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using RoB 2. Random-effects meta-analysis was applied using harmonized effect measures. Heterogeneity was quantified using I² and Tau². Prespecified subgroup analyses and meta-regression were conducted to explore variability. Publication bias was evaluated using Egger’s test and Trim-and-Fill methods.Findings: Of 146 eligible studies, 82 provided extractable quantitative effect estimates. The pooled standardized effect suggested an overall positive association between AI-integrated telemedicine and primary care outcomes (10·31, 95% CI 4·86–15·77), although between-study heterogeneity was very high (I²≈100%). Subgroup analyses showed larger effects in broader clinical outcome domains and more modest effects in mental health, infectious disease, and clinical control outcomes. Meta-regression did not identify study year, specialty, or design as significant moderators. Egger’s test indicated possible small-study effects, whereas Trim-and- Fill adjustment did not materially change pooled estimates.Interpretation: AI-supported telemedicine interventions are associated with improved outcomes across multiple primary care domains, although effect magnitude varies by context and outcome type. Very high heterogeneity limits interpretation of a single pooled estimate and supports outcome-specific interpretation. Further standardized and domain-focused studies are needed to refine effectiveness estimates and guide implementation.