Global Development Network

ART adherence, essential to preventing disease progression for people living with HIV (PLHIV), remains challenged by factors like poor mental health condition, insufficient social support, and substance use. This study investigates the roles of social support and substance use as mediators in the relationship between mental health and antiretroviral therapy (ART) adherence among PLHIV in a multisite, international context. Cross-sectional data from N = 1598 PLHIV across eight countries were analyzed using parallel mediation analyses in R. Results indicated that both social support and substance use independently mediated the relationship between mental health and ART adherence. Hallucinogens mediated the mental health-adherence link. Cocaine, Amphetamine-type stimulants, inhalants, and opioids directly influenced adherence but did not mediate this relationship. In contrast, no significant effects were observed for tobacco, alcohol, cannabis or sedatives. This large-scale, nurse-led, international study offers critical insights into the roles of social support and substance use as psychosocial influencers in the cascade of care for PLHIV. Clinical implications include prioritizing educative, preventative, and harm-reductive approaches to specific substances that impact ART adherence. Findings may also inform interventions that emphasize strengthening social support to improve adherence in ART and care while addressing substance use as a barrier to care and well-being.

This paper is part of the KAMC2025 Conference Proceedings (View) Full Paper View / Download the full paper in a new tab/window

The purpose of this study was to understand the impact of the coronavirus disease 2019 (COVID-19) pandemic and mitigation efforts on health and social outcomes for people with HIV at the individual, social, and structural levels of the Social Ecological Model. The International Nursing Network for HIV collected data for a cross-sectional survey of people with HIV in Botswana, Canada, Colombia, Hong Kong, Kenya, Nigeria, South Africa, Thailand, and the United States from August 2021 through June 2023. Among 1,400 participants, 47.5% experienced decreased quality of life, 40.9% experienced increased anxiety, 33.0% had reduced connection with friends, and 38.8% had reduced access to resources. Participants’ reported impacts of COVID-19 varied by socioeconomic factors. Among these people with HIV, changes in quality of life, anxiety, social connectedness, and access to resources due to the COVID-19 pandemic were significantly associated with individual, social, and structural level factors using the Social Ecological Framework.

Coverage-guided fuzzing has proven highly effective in uncovering software vulnerabilities. However, many industrial systems cannot be instrumented or inspected due to security, legal, or operational constraints. In such settings, blackbox test generation remains the only viable testing strategy. This paper presents a case study in collaboration with our industrial partner, Mettle Networks, where we were tasked with testing the protocol-handling subsystem of a virtualized packet processing engine without access to the source code or internal documentation. To overcome this constraint, we applied blackbox grammar inference techniques to reverse-engineer the input protocol accepted by the system, achieving an F1 score of 0.94 for the inferred protocol grammar. Our blackbox test campaign resulted in 33.6% code coverage, as measured by Mettle Networks after the test campaign concluded. We discuss our testing methodology and the practical challenges encountered. This case study highlights current limitations of automatic testing frameworks when deployed in blackbox industrial environments, and offers actionable insights for improving their effectiveness in such settings.

Abstract Background Measles remains a major public health issue, especially in regions with low vaccination rates and limited healthcare access. Timely outbreak prediction is crucial for effective intervention and resource allocation. Traditional epidemiological models struggle with real-time predictions due to reliance on historical data and limited analysis. Artificial intelligence (AI) offers a data-driven approach to capturing complex outbreak patterns and improving prediction accuracy. Methods This study developed and evaluated machine learning (ML) models to predict measles outbreaks at the community level, using real-world data collected by community volunteers in Ethiopia. The dataset includes demographic, vaccination status, location, clinical, and image data, providing a comprehensive assessment of outbreak risk factors. Five ML models-Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)-were assessed using metrics such as area under the receiver operating curve (AUC), precision, recall, and F1-score. Results The results demonstrate that machine learning models can effectively predict measles outbreaks, with the top-performing model, K-Nearest Neighbors (KNN), achieving an AUC of 0.87 (95% CI: 0.84-0.90). Among the five models tested, KNN outperformed the others, showing the highest predictive accuracy, and the difference in AUC was statistically significant (p < 0.05). Conclusions The model's ability to capture complex epidemiological factors underscores its potential to enhance surveillance and early warning systems. This research highlights the effectiveness of ML-based measles prediction in resource-limited settings. Integrating these models into public health strategies can improve early detection and reduce measles impact. Future work will focus on refining the model, incorporating real-time data, and expanding evaluation metrics for practical deployment. Key messages • Machine learning enables accurate early measles detection fusing different data sources, supporting faster response and better health outcomes in communities. • Machine learning offers a powerful tool for predicting outbreaks early, filling critical gaps in low-resource health systems.

This review is an abridged translation of selected chapters of the report “Prevention and control of iodine deficiency in the WHO European Region: adapting to changes in diet and lifestyle”, published by the WHO Regional Office for Europe and the Iodine Global Network (IGN) in 2024. Iodine deficiency, especially mild iodine deficiency, remains a widespread problem in the WHO European Region. Since the last WHO report on iodine deficiency in the Region 15 years ago, much new data on iodine status has become available, especially for vulnerable populations. This review presents data on the iodine status of the population in 53 WHO European Member States (and Kosovo), the adverse effects of mild iodine deficiency and the effectiveness of salt iodization in preventing iodine deficiency. Mainly due to progress in salt iodization, the number of countries with iodine deficiency has decreased from 23 in 2003 to 2 in 2023. Mandatory salt iodization ensures adequate iodine intake in all population groups, with the exception of a few countries where these programs are poorly implemented. The positive cost-benefit ratio for preventing mild iodine deficiency in the European Region is plausible given the high prevalence of thyroid disease and the low cost of salt fortification programs.

Abstract Background: Clinical and health systems research is essential for advancing global healthcare. Engaging and training medical students in research will foster lifelong evidence-based practice, particularly for the emerging healthcare workforce. This study investigates the involvement of Nigerian medical students in research, identifying key opportunities and barriers to enhance their engagement. Methods: This cross-sectional, descriptive, mixed-methods study used an online survey targeting recent graduates from 42 accredited Nigerian medical schools. A purposive sampling approach selected one federal, state, and private institution from each of Nigeria’s six geopolitical zones. Data were collected between February and June 2024 using a face-validated online questionnaire distributed via WhatsApp groups. Descriptive statistics and thematic analysis were employed to explore trends in the data. Ethical approval was obtained by the Jos University Teaching Hospital ethics review board (JUTH/DCS/IREC/127/XXXI/871), and participants provided informed consent. Results: Of the 429 respondents (270 men, 159 women) from 15 universities across all six zones, 92% participated in research during medical school. Men engaged in more research projects than women (p=0.03). Most respondents participated in two projects, usually group-based, with limited individual projects. Few had presented (20%) or published (17%) their research. Proficiency was highest in developing appropriate research methodologies (54%), while data analysis was the most challenging aspect of research (42%). Participation in research was supported by adequate mentorship for most participants (64%), whereas time constraints(74%), lack of research funding (55%), and inadequate research training (31%) were the main barriers to involvement. Regional differences were noted, with the highest participation by students in the South-South geo-political zone (98%) and the lowest in the South-West (85%). Learners from the South-West had more opportunities to present research at conferences, while those from the South-East had the fewest. Participants advocated for integrating more research opportunities into the medical curriculum, emphasising early skill development. Conclusion: There is a pressing need to enhance structured research opportunities in Nigerian medical education, especially for women. This includes expanding access to training in data analysis, increasing publication and presentation opportunities, and integrating research skills early in the curriculum while addressing barriers like time constraints. Clinical trial number: not applicable.