Persons with mental illnesses may experience stigma from their immediate family members in addition to other forms of stigma. The researchers used semi-structured interviews to investigate the experiences of familial mental illness stigma among 15 people diagnosed with mental illnesses in a mid-sized city in Canada. Five themes that spoke to participants’ experiences of familial mental illness stigma and ways to reduce it were identified by the investigators. The themes included: diagnosis as a ‘double-edged sword,’ potential familial isolation, familial stigma as societal stigma localized, stories of acceptance and support, and confronting potential familial mental illness stigma. Participants’ narratives indicate that familial mental illness stigma is rooted in the broader social or public stigma, which sees its way into familial relations as well. This stigma takes various forms, including relationship bias or unfair treatment, breakdown in romantic relationships, loss of status, verbal and emotional abuse, exclusion from decision-making, and alienation within their immediate and extended families. Familial mental illness stigma experiences negatively impact participant’s psychological well-being and personal empowerment. However, participants also shared ways that family members create supportive environments or actively confront or prevent stigma. Overall, this study has contributed to knowledge on mental illness stigma, particularly familial mental illness stigma from the perspective of participants living with a mental illness in a high-income country. Suggestions for future research include a focus on strategies to prevent ongoing familial mental illness stigma, and large-scale studies to explore familial mental illness stigma to understand why families might themselves at times perpetrate stigma.

Self-rated health (SRH) is a validated epidemiological measure that captures an individual’s overall health perception and predicts morbidity and mortality. Despite extensive research on SRH among older adults in India, evidence on its transition across the life course remains limited. Using data from 70,595 individuals aged 45 years and above from the Longitudinal Aging Study in India (LASI) 2017–2018, this study examined transitions in SRH from childhood to older adulthood. An adverse SRH trajectory was defined as a shift from good childhood health to poor or fair health in later life. Descriptive, bivariate, and multivariable logistic regression analyses were conducted. Overall, 51% of older adults experienced an adverse SRH trajectory. Higher odds were observed among women (AOR: 1.30), individuals with substance consumption (AOR:1.24), chronic multimorbidity (AOR: 3.37), functional limitations (AOR: 2.03), and depression (AOR: 1.51). Early-life disadvantages – child labour, child marriage, and persistent household poverty – were also significant risk factors. In contrast, higher education and participation in social and physical activities were protective. These findings indicate that an adverse subjective health trajectory is shaped by cumulative life-course exposures rather than ageing alone. Strengthening early-life social investments and community-based wellness initiatives is vital to promote healthy and equitable ageing in India.

In November 2025, Ethiopia confirmed its first outbreak of Marburg virus disease in South Omo Zone, marking a critical public health emergency in a previously unaffected country. The outbreak was detected following reports of suspected viral hemorrhagic fever in Jinka town, with laboratory confirmation achieved within days through national reference laboratory testing. By mid-December 2025, 14 laboratory-confirmed cases had been identified, including 9 deaths and 5 recoveries, following nearly 2 thousand investigations nationwide. This report describes the detection of the outbreak, early epidemiological characteristics, and key response actions implemented by national and regional authorities. It highlights operational challenges related to surveillance, diagnostic access, and response coordination in remote settings, as well as the importance of community engagement and multisectoral collaboration. Ethiopia’s first experience with Marburg virus disease provides timely lessons for strengthening preparedness, early detection, and response capacity for high-consequence pathogens in sub-Saharan Africa.

About 10 million people worldwide chew khat daily. Chronic khat use is a public health problem associated with physical and psychological impacts, such as mental health disorders and social consequences. The use of khat is increasing dramatically in Ethiopia among the young population. There are controversies over the legal status and health effects related to the consumption of khat. Despite the existing controversies, the problem is increasing with its detrimental impact. Policymakers need to enforce education campaigns aimed at awareness creation on the impact of khat use. It is important to consider legally restricting the availability and use of khat in vulnerable populations, such as children under 18 years, individuals with mental disorders and pregnant women, as well as in settings such as hospitals and schools.

Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While this metric has been widely used, an improved algorithm is still needed. In this work, we detail a greedy, label-switching algorithm to identify CP structure that is both fast and accurate. By leveraging a mathematical reformulation of the CP metric, our proposed heuristic offers an order-of-magnitude improvement on the number of operations compared to a naive implementation. We prove that the algorithm monotonically ascends to a local maximum while consistently yielding solutions within 90% of the global optimum on small toy networks. On synthetic networks, our algorithm exhibits superior classification accuracies and run-times compared to a popular competing method, and on one-real- world network, it is 340 times faster.