Lagos State University

Background: Despite Nigeria’s early commitment to the WHO Framework Convention on Tobacco Control (FCTC), enforcement of Article 16, which prohibits the sale of tobacco to minors remains poorly documented. Since no Global Youth Tobacco Survey (GYTS) has been conducted in Nigeria since 2008, there is a critical evidence gap on how adolescents continue to access tobacco products in violation of legal restrictions. This study provides the first detailed assessment of illegal youth access to tobacco, examining associated demographic, behavioral, and policy awareness factors using nationally representative microdata. Methods: We analyzed the Nigeria GYTS 2008, a cross-sectional school-based survey employing a two-stage cluster sample design across five sentinel areas (Abuja, Cross River, Ibadan, Kano, Lagos). The analytic sample included 2,379 adolescents aged 13–15 years. Complex survey design corrections were applied using Primary Sampling Units (PSU) strata, and final sample weights. The outcome variable—illegal access to tobacco—was defined as purchasing cigarettes without refusal due to age in the past 30 days. Weighted descriptive analyses, chi-square tests, and survey-adjusted logistic regressions (Models 1–3) were performed to identify predictors, including sex, age, rural residence, parental and peer smoking, school type, and awareness of youth-sales laws. Results: Overall, 13.2% (95% CI: 11.6–14.8) of Nigerian adolescents reported successful purchase of cigarettes without being refused for age. Rural youth had higher illegal access (18.5% vs. 11.4%, p<0.001). Adolescents with smoking peers had markedly elevated odds of illegal purchase (aOR=3.48, 95% CI: 2.86–4.25), while those aware of tobacco-age restrictions were less likely to report successful purchase (aOR=0.56, 95% CI: 0.43–0.73). Media exposure to anti-smoking messages showed a modest protective effect (aOR=0.78, 95% CI: 0.64–0.94). Age and male sex were independently associated with higher odds of illegal access. Conclusions: Illegal tobacco access among Nigerian adolescents remains alarmingly prevalent, especially among males, rural youth, and those with smoking peers. Awareness of age restrictions and anti-tobacco media exposure reduces the likelihood of illegal purchase, suggesting that policy enforcement and communication gaps are major barriers to FCTC Article 16 compliance. Strengthening retail surveillance, implementing vendor licensing, and integrating youth-access enforcement into Nigeria’s National Tobacco Control Act (NTCA) 2015 monitoring systems are urgently needed. Keywords: Adolescent tobacco access; Sales restrictions; FCTC Article 16; Policy enforcement; Nigeria; GYTS.

Human decisions about the design, deployment, and governance of AI systems are increasingly complex, involving multiple desiderata such as accuracy, fairness, transparency, cost, and environmental impact. This paper proposes a novel and scalable framework for managing this complexity through application of resources from the field of Multi-Criteria Decision Analysis (MCDA). After an introduction to the “ n > 2 Criteria Problem” in decision-making about AI, an introduction to MCDA methods, and a review of prior applications of MCDA to decision support in decisions involving AI, we lay out our own novel and scalable framework for applying MCDA to such decisions. We illustrate the framework through application to four simulated scenarios involving a medical institution’s choice among AI-powered diagnostic tools with varying performance on accuracy, fairness, transparency, and cost. We apply five multi-criteria decision analysis (MCDA) methods — namely, the Weighted Sum Model (WSM), the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje), and the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) — to rank ten AI models under four distinct weight configurations reflecting realistic deployment contexts. Sensitivity analyses are then conducted to examine how variations in criterion weight assignments affect model rankings across methods and scenarios. In concluding sections we discuss the scalability of these techniques for design, deployment, and policy decisions involving AI, as well as the likely limitations of MCDA methods for decision support involving AI.

Background: Children with acyanotic congenital heart diseases are prone to micronutrient and macronutrient deficiencies that worsen morbidity and mortality in affected individuals. Nigeria has a high prevalence of children with congenital heart diseases. Despite lower food fortification levels than in developed countries, there is no information on the burden of iron-deficiency anaemia in children with heart disease. The current study evaluated the prevalence of iron deficiency among Nigerian children with acyanotic congenital heart disease. Methods: The study was cross-sectional and analytical, involving 59 subjects with acyanotic congenital heart disease and apparently healthy controls. Red cell indices and serum iron status, including serum iron, Total Iron Binding Capacity (TIBC), and ferritin, were evaluated. Iron deficiency is indicated by low serum ferritin (<12 ng/L for children under five, <15 ng/L for those five and older), often with transferrin saturation below 16%, and reduced MCV and MCHC for age. Iron deficiency anemia is diagnosed when hemoglobin is below the expected range for age, along with signs of iron deficiency. Latent iron deficiency (iron depletion) is defined by serum ferritin below 20 ng/L across all ages. Results: Latent iron deficiency was observed in 13.6% of cases and 15.3% of controls. A total of 2 (3.4%) cases and 3 (5.1%) controls had iron deficiency. No subject had iron deficiency anaemia (IDA). Conclusions: There is a risk of iron deficiency anaemia in both children with acyanotic congenital heart disease and apparently healthy children. Due to the morbidity effect of IDA in children with congenital heart diseases, there is a need for screening and nutritional assessment for IDA in affected children.

This study examines the dynamic relationship between financial inclusion, macroeconomic drivers, and sustainable development in Nigeria using annual data spanning 1990–2024. An integrated ARDL–ECM framework, complemented by Structural Vector Autoregression (SVAR) for shock analysis, is employed to capture long-run relationships, short-run dynamics, and adjustment processes. The results indicate that access to financial services and credit to the private sector are positively and significantly associated with sustainable development, while inflation exerts a negative influence. Gross capital formation contributes positively to long-run development outcomes. Moderation analysis reveals that GDP per capita strengthens the impact of financial inclusion on sustainable development, whereas population growth weakens the effectiveness of private credit. The error-correction term is negative and statistically significant, indicating stable long-run convergence following short-run shocks. SVAR impulse responses and variance decomposition further show that financial inclusion and its interaction with income levels account for a substantial share of development variability, while inflation shocks exhibit persistent adverse effects. Overall, the findings highlight the importance of aligning financial inclusion strategies with macroeconomic stability and demographic conditions to support sustainable development in Nigeria and similar emerging economies.

SummaryPatient navigation comprises person-centred activities focused on addressing barriers and facilitating timely access to health care. Despite demonstrated effectiveness, the scope of patient navigation remains unclear. To clarify the scope of patient navigation and support global implementation, the Global Initiative to Advance Cancer Navigation for Better Outcomes (GINO) aimed to develop a practice framework for patient navigation in cancer care. Phase 1 involved reviewing patient navigation literature and identifying key areas of practice. Phase 2 involved a modified Delphi process with international experts in navigation (July to December 2024) to establish consensus on practices to include in the framework. Patient navigation experts across regions and disciplines were invited. Two rounds of online surveys were conducted where participants rated the importance of each practice on a scale from 1 (“Not important”) to 5 (“Critically important”). Practices rated ≥4 by ≥ 75% of participants in Round 2 met consensus criteria. The remaining items were discussed in a consensus meeting. Eighty-one experts from 29 countries (n = 45, 56% high-income, n = 36, 44% low-middle-income) participated in Round 1. Of these, 60 also participated in Round 2, including healthcare practitioners (n = 30, 50%), navigators (n = 16, 27%), researchers (n = 24, 40%), and advocates (n = 10, 17%). After Round 2, 27/35 (77%) practices reached consensus for inclusion. After the consensus meeting, two items were reworded, and 32 items were included in the final framework. We reached consensus among international experts on the contents of the GINO practice framework for patient navigation in cancer care. By describing the scope of patient navigation, the framework can support the development and implementation of patient navigation programs globally.

Abstract This chapter explores the implementation of artificial intelligence (AI) for knowledge management (KM) in small and medium enterprises (SMEs). Compared with larger corporations, SMEs frequently encounter limits to their capacity to adopt comprehensive KM strategies due to limited access to resources. The chapter argues that the deficit in KM is an obstacle to operational efficiency and SMEs’ sustainability. It thus proposes a comprehensive framework for understanding, implementing and optimising the integration of AI in SMEs from the lens of an effective KM system. A desk review approach was adopted to scan the extant literature on AI applications for KM in SMEs, including the analysis of selected secondary case studies. The chapter concluded that AI applications for KM are fundamental for knowledge sharing capabilities in SMEs. Therefore, a continuous learning and adaptability culture should be cultivated to promote a collaborative learning environment.

Nigeria’s power distribution networks continue to experience some of the highest energy losses globally, severely undermining operational efficiency, grid reliability, and the financial viability of utilities. This paper presents a detailed, systematic review of technical and commercial energy losses in the Nigerian power sector. Overloaded feeders, ageing infrastructure, reactive power imbalance, and weak network design mainly drive technical losses. Commercial losses stem from inadequate metering, electricity theft, billing inefficiencies, and poor customer indexing. Therefore, drawing on verified academic sources, regulatory reports, and global benchmarks, this study highlights the severity of Aggregate Technical, Commercial, and Collection (ATC&C) losses across distribution companies (DisCos), which often exceed 40%. Traditional mitigation techniques, such as network reinforcement, capacitor placement, and customer auditing, show limited long-term effectiveness without regulatory compliance, adequate investment, and digital grid modernisation. Therefore, this paper concludes that the adoption of predictive models has strong potential to enhance predictive accuracy for highly volatile, irregular Nigerian electricity datasets, thereby empowering DISCOMs to forecast technical and commercial (ATC&C) losses more reliably, optimise smart-meter rollout strategies, prioritise feeder rehabilitation, and deploy targeted anti-theft interventions. Far-reaching suggestions were made to reduce energy loss in Nigeria’s power distribution networks.

Due to the increasing world population, the global challenge of food security has led to the extensive use of agrochemicals to boost agricultural productivity. This, in addition to mining and industrial activities, has been reported to enhance environmental radioactivity, thereby posing a significant threat to human health. The presence of radionuclides in environmental media and their migration to food crops through soil and irrigation water has contributed significantly to internal radiation dose exposure. In this study, the levels of radionuclides in food crops grown in Rustenburg, one of South Africa's mining cities, are measured using inductively coupled plasma-mass spectrometry (ICP-MS). The average activity concentrations of naturally occurring radionuclides are found to be 1.32 ± 0.76 Bq/kg, 0.04 ± 0.02 Bq/kg and 14.06 ± 3.65 Bq/kg in wheat grain; 0.52 ± 0.11 Bq/kg, 0.17 ± 0.08 Bq/kg and 20.44 ± 2.48 Bq/kg in onion bulb; 0.48 ± 0.31 Bq/kg, 0.26 ± 0.18 Bq/kg and 22.37 ± 12.63 Bq/kg, in onion shoot; 1.43 ± 0.62 Bq/kg, 0.60 ± 0.10 Bq/kg and 29.82 ± 3.69 Bq/kg in beetroot bulb; 0.69 ± 0.77 Bq/kg, 0.24 ± 0.09 Bq/kg and 37.82 ± 2.36 Bq/kg in beetroot leaf; 0.25 ± 0.15 Bq/kg, 0.18 ± 0.11 Bq/kg and 28.84 ± 5.80 Bq/kg in leek shoot; 0.13 ± 0.18 Bq/kg, 0.14 ± 0.10 Bq/kg and 36.13 ± 3.65 Bq/kg in parsley leaf; 0.26 ± 0.09 Bq/kg, 0.07 ± 0.03 Bq/kg and 30.71 ± 5.07 Bq/kg in mint leaf and 0.08 ± 0.05 Bq/kg, 0.04 ± 0.02 Bq/kg and 32.97 ± 4.50 Bq/kg mint shoot, for 238U, 232Th and 40 K respectively. The mean committed effective dose in edible plants is found to be below the reported levels of 290 µSv/yr for total dose, 120 µSv/yr for 238U and 232Th, and 170 µSv/yr for 40 K, as reported by the United Nations Scientific Committee on the Effects of Atomic Radiation. The lifetime cancer risks estimated from the consumption of edible plants are also found to be less than the International Commission on Radiological Protection reference value of 2.5 × 10–3 for a public limit of 1 mSv/yr. These findings indicate that cultivated edible plants in the study area are safe for human consumption.