Vol. 7 (2026)

Abstract  Background: Malawi continues to record recurring Cholera outbreaks in Priotized Areas for Multi-sectoral Interventions (PAMI), owing to a lack of access to clean water and sanitation facilities. In November 2024, a fresh outbreak began in Mzimba North and expanded to a neighboring district. This study sought to determine the geographical distribution of cases, environmental risks and drug susceptibility patterns.
Methods: A cross-sectional descriptive study was carried out in two phases. The initial phase involved analysis of laboratory test reports as well as the clinical and social demographic data contained in the line list. Field case mapping of the affected households were subsequently conducted using Geographical Positioning System (GPS) device. Descriptive statistics, statistical t-tests, and geospatial maps were used to explore demographic characteristics, spatial distribution of cases, determination of risks and drug resistance patterns.
Results: Out of 75 cholera suspects, 32 (43%) were confirmed positive. The majority of cases were male tenants working in farms (n=22; 68.8%, Median age=26). The epidemic curve indicated a progressive source outbreak, with a significant drop in incidence following implementation of Cholera Area Targeted Interventions (66.7% reduction; p=0.005). Most cases did not appear clustered, reflecting local settlement patterns as well as hydrological networks and population mobility. Environmental risk assessment revealed shallow wells, salty borehole water, and poor housing structures. Most antibiotics showed moderate to high effectiveness.
Conclusion: The Cholera outbreak in Mzimba North reflect persistent underlying environmental risks as well as occupation-related exposure among male tenants. Investing in piped-water and sanitation infrastructures is essential for preventing recurring outbreaks.

Abstract  Background: Previous studies assessed the quality of life and fear of COVID-19 in various categories of people, but no study has been conducted among families of nurses working in Corona hospitals. The purpose of this study was to look into people's fear of COVID-19 and how it relates to their quality of life.
Methods: This cross-sectional study was carried out in Iran in 2021. The study population consisted of family members of nurses working in hospitals that admitted COVID-19 patients. A convenience sampling method was used for sampling. Data was collected using a three-part technique that included one checklist and two questionnaires.
Results: This study included 244 participants. The participants' mean age was 38.3 years. The mean terror score was 19.4 ± 5.7. It was significantly greater in women (p=0.02) and those with underlying conditions (p=0.007). The average score for quality of life was 21.1±5.3. It was substantially greater in those with underlying diseases than in those without underlying diseases, indicating a lower quality of life (p = 0.003). Pearson's correlation coefficient revealed a significant link between quality of life and fear of COVID-19 (p<0.001, r=0.508), indicating that fear of COVID-19 leads to a deterioration in quality of life.
Conclusion: The current study found that individuals had a somewhat low quality of life during the epidemic, indicating that their quality of life was influenced by their fear of the pandemic. As a result, in some cases, efforts must be taken to alleviate fear and improve the quality of life for this population.

Abstract  Background: In order to make accurate predictions for decision-making bodies, it is crucial to establish an accurate model. The Weekly Cycle (WC) of daily case counts on global data for countries during the COVID-19 pandemic is regarded as an artifact of daily test numbers and is not included in models. WC's persistence throughout time suggests that it might not. As a result, the nature of WC is thoroughly examined in this retrospective study.
Methods: A model Back-Tracing (BTrac) is created utilizing the Rayleigh distribution and features of the SARS-CoV-2 virus to compute daily infection numbers from daily case numbers after examining the relationship between daily case and test numbers. Additionally, BTrac is adjusted to use both constant and variable daily reproduction numbers (R0) in order to simulate and compare the amount of new infections.
Results: WC has a minimum on weekdays according to the daily infection numbers computed with BTrac. The characteristics of the virus and socioeconomic variables, such as national holidays, govern this weekly minimum. It travels toward Monday and Friday, respectively, throughout the pandemic's increasing and decreasing stages, and it recognizes two distinct settings as working and socializing for the virus's spread. Furthermore, simulation using BTrac predicts less infection than the model with constant R0, and the data indicates a variable rather than constant R0.
Conclusions: A model developed here that incorporates the features of the virus demonstrates the existence of WC and effectively reveals the impact of socioeconomic factors on the pandemic's destiny. Therefore, greater pandemic control may result from decision-making bodies adopting BTrac.

Abstract  Background: This study aimed to investigate self-reported lifestyle and nutritional practices linked to lifestyle illnesses among people in the Dhaka metropolitan area, Bangladesh.
Methods: A cross-sectional survey of 312 individuals in Dhaka, Bangladesh, was conducted from September to December 2021, with an online questionnaire distributed via social media channels.
Results: The majority of the participants were young (70.8% between the ages of 18 and 25) and highly educated (72.4% with a graduate or higher degree). 64.8% had a normal BMI, however 32.7% were overweight or obese. In terms of lifestyle behaviors, high smartphone usage (68.6%), sedentary behavior (46.5%), and widespread environmental exposure (58.3%) were considered. While 54.5% reported eating nutritious foods all the time, harmful eating habits were common: 87.5% consumed deep-fried meals at least once a month, 77.3% fast food, 86.9% red meat monthly, and 79.5% carbonated beverages on a regular basis. Despite 63.8% of people trying to smoke, just 15.1% became habitual smokers. Alcohol usage was low (77.6% never consumed), indicating cultural norms. Lifestyle diseases were prevalent: 41% had at least one of the following conditions: hypertension (24.0%), unexplained pain (15.7%), hyperlipidemia (12.5%), hypothyroidism (12.5%), or diabetes (12.2%). BMI was found to be a strong predictor of lifestyle diseases, including hyperlipidemia, osteoarthritis, hypothyroidism, and diabetes (p<0.01). Consumption of red meat (p=0.02) and carbonated beverages (p=0.03) was associated with hyperlipidemia (p=0.02), as well as smoking with COPD (p<0.01). Screen time was substantially linked to diabetes, renal issues, and COPD (p<0.01), with higher duration associated with lower risk. Approximately 59% had limited to no understanding of the links between lifestyle and disease.
Conclusions: This study emphasizes the importance of raising knowledge about better lives in order to prevent lifestyle-related disorders among Dhaka's adult population.

Abstract  Background: Artificial intelligence (AI) is transforming the way people think, teach, and learn around the world. The use of artificial intelligence (AI) in Nigerian higher education, particularly in nursing education, is gradually increasing. This paper investigates the revolutionary role of AI technologies in nursing education, with a focus on automated content generation, adaptive learning platforms, virtual simulations, intelligent assessments, and inclusive tools for students with special needs.
Methods: The study uses a qualitative-descriptive technique to evaluate the benefits, challenges, and repercussions of AI deployment. It is based on a critical literature review and an examination of current advancements in Nigerian tertiary education institutions. The research examines the incorporation of artificial intelligence in nursing education using academic literature, international frameworks, and regional initiatives. 
Results: The findings suggest that AI improves teaching effectiveness, personalizes learning for each student, improves clinical decision-making skills, and assists students in remote or resource-limited places. Nonetheless, considerable hurdles remain, including increased implementation costs, ethical quandaries, data security dangers, and decreased interpersonal connection. Numerous studies have shown how AI technology in Nigerian classrooms can be tailored to the context of nursing education. 
Conclusion: The report concludes with advice for those with a stake in the issue, such as legislators, nurse educators, and curriculum developers. It advocates for national policies that prioritize AI infrastructure in nursing education, assist teachers in developing their abilities, and establish guidelines for responsible AI use. Finally, it suggests future research on how well AI works to improve clinical abilities, educate empathy, and change the curriculum in Nigerian nursing schools.

Abstract  The post-COVID-19 respiratory era is defined by the ongoing transmission of SARS-CoV-2 alongside seasonal influenza. Severe illness primarily affects high-risk groups, such as the elderly, those with chronic diseases, immunocompromised individuals, pregnant women, and those residing in long-term care facilities. This brief communication suggests a seasonal, tiered strategy that prioritizes annual COVID-19 vaccination for high-risk groups, maintains universal flu shots beginning at the age of six months, and encourages co-administration of both vaccines before winter to reduce missed doses. Targeted use of RSV and pneumococcal vaccines, particularly among the elderly and those with heart or lung difficulties, helps to prevent lower respiratory infections while also boosting COVID-19 and flu prevention efforts. Health officials advise against delaying immunization during formulation transitions, as high-risk individuals benefit from any available dose rather than waiting for variant-updated products. Equity-focused ways to increasing coverage include pre-scheduled appointments, the incorporation of digital reminders into care systems, and on-site vaccines.

Abstract  Background: Urinary tract infections (UTIs) are still a major public health problem due to changing uropathogen patterns and rising antibiotic resistance. Understanding pathogen-specific relationships with host demographic, genetic, and clinical features is critical for developing effective clinical care and preventative measures.
Methods: A cross-sectional study was conducted on 250 individuals with laboratory-confirmed UTIs who visited a tertiary healthcare center in Birnin Kebbi, Nigeria. Structured questionnaires and laboratory records were used to collect information about sociodemographic variables, ABO blood groups, infection severity, and urine culture results. Descriptive statistics and multinomial logistic regression analysis were performed to determine characteristics associated with uropathogen type, with Escherichia coli (E. coli) serving as the reference category. Statistical significance was determined at p < 0.05.
Results: The majority of participants (40.0%) were women aged 25-34 years old (61.6%). E. coli was the most common uropathogen (42.0%), followed by Klebsiella spp. (22.0%), Candida spp. (21.2%), and Staphylococcus spp. (14.8%). A multinomial regression study found that male sex and unemployment were substantially linked with Candida spp. infection. Older age, ABO blood group B, and severe infection were all significant predictors of Klebsiella spp. infection. In contrast, no examined factor had a significant connection with Staphylococcus spp. infection compared to E. coli (adjusted odds ratio [AOR] = 0.89; 95% confidence interval [CI]: 0.43-1.86).
Conclusion: The distribution of uropathogens among UTI patients differs based on ABO blood group genotypes, as well as demographic and clinical variables, indicating pathogen risk profiles. These findings highlight the necessity of tailored diagnostic and therapeutic techniques for improving UTI care, especially in resource-constrained settings.

Abstract  Background: Occupational injuries remain a serious public health concern worldwide, notably in the construction industry. This study investigated the prevalence, trends, and correlates of occupational injuries among construction workers in Ilorin, Nigeria.
Methods: A cross-sectional study was done with construction workers recruited from key daily labor assembly hubs in the Ilorin metropolis. Data were gathered using an interviewer-administered questionnaire derived from the International Labour Organization's Occupational Safety and Health Toolkit. Information on socio-demographic variables, job history, safety training, personal protective equipment (PPE) use, and occupational injuries over the previous 12 months was acquired. The data was examined using descriptive statistics, chi-square tests, and multivariable logistic regression, with statistical significance defined at p < 0.05.
Results: The incidence of occupational injuries was 61.2%. The most commonly reported injury categories were lacerations and falls. Lack of safety training, inconsistent usage of personal protective equipment (PPE), and longer work experience were all strongly associated with occupational injuries.
Conclusion: Occupational injuries were common among construction workers in Ilorin, with various sociodemographic and work-related characteristics indicating strong relationships. These findings indicate key areas for occupational safety initiatives, emphasizing the importance of longitudinal research to uncover causal pathways.

Abstract  Background: Healthcare-Associated Infections (HAIs), particularly Surgical Site Infections (SSIs), are a significant concern in trauma departments due to invasive operations and open wounds.
Methods: A convergent parallel mixed-methods design was implemented. The observational arm used a standardized checklist (based on WHO/CDC guidelines) to track 15 healthcare workers (total population sampling) across ten important hygiene categories. Simultaneously, a retrospective case-control investigation examined 50 patient records (25 infected cases and 25 non-infected controls) obtained through consecutive sampling over a 45-day period.
Results: Observational data revealed a clear dichotomy in compliance: whereas hand cleanliness and catheter care had good adherence, there were significant gaps in the provision of specified Personal Protective Equipment (PPE), particularly masks and goggles. The most common self-reported impediments were material resource limitations (40%) and work overload (26.7%). Notably, 53.3% of respondents saw medical staff (physicians) as the "weakest link" in the aseptic chain. In the clinical study, individuals with open fractures had an Odds Ratio (OR) of 2.38 (95% CI: 0.70–8.07) for infection compared to closed fractures. However, due to the small sample size, this association was not statistically significant (p=0.135).
Conclusion: Compliance is hampered mostly by logistical shortcomings rather than a lack of theoretical knowledge. The findings imply that written protocols are insufficient in the absence of a secure supply chain for protective equipment. To improve patient safety, a multi-modal strategy that focuses on logistical security, required physician interaction, and active post-operative care surveillance is advised.

Abstract  Background: Domestic violence (DV) is a pervasive problem in Nigeria, affecting millions of women physically, emotionally, and financially. Spousal communication, which promotes understanding, conflict resolution, and emotional support, is an important feature that may help to reduce DV. This study looks at the link between spousal communication and domestic violence, specifically how effective communication affects dispute resolution, family stability, and marital harmony.
Methods: Data from 518 married women were acquired using a mixed-method cross-sectional methodology, which included structured surveys, in-depth interviews (IDIs), and key informant interviews (KIIs). Chi-square and logistic regression were employed to evaluate quantitative data, while qualitative data was analyzed thematically.
Results: The findings found that rural areas had a greater rate of domestic violence (57.5%) than urban areas (45.9%). Poor marital communication was substantially associated with greater domestic violence (p=0.001).
Conclusion: Effective spousal communication is essential for preventing domestic violence. Policies promoting marital therapy, conflict resolution training, and economic empowerment may serve to strengthen relationships and lower the prevalence of domestic violence.

Abstract  Bloodborne microbes remains one of the leading causes of morbidity and mortality globally, especially in countries with scarce resources. Health care workers (HCWs) face a significant risk of occupational blood exposures (OBEs) and the spread of blood-borne microbes such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV) and Treponema pallidum (T. pallidum), which are responsible for an important percentage of infectious diseases worldwide. This review offers insights into the epidemiology and disease burden of bloodborne microbes, in addition to the potential risk of bloodborne microbes among HCWs and infection control. Data from electronic databases: PubMed, Google Scholar, and public health reports from the World Health Organization and United Nations Programme on HIV/AIDS were used. In the health care environments, bloodborne pathogen transmission occurs largely by percutaneous or mucosal exposure of personnel to the blood or bodily fluids of infected patients. Exposure to bloodborne microbes affect the HCWs safety, there are millions HCWs with bloodborne infection worldwide. Healthcare-associated infections (HAIs) have increased in prevalence due to the growing complexity of medical care and the intricacy of patient conditions. High rates of HAIs caused by bloodborne microbes represent a high burden of disease that leads to lengthy hospital admissions. Monitoring of HAIs constitutes a fundamental component of infection prevention and control programs. Detection of bloodborne microbes and postexposure prophylaxis (PEP) may assist to prevent infections.

Abstract  Background: One and a half years after the official end of the COVID-19 pandemic, assessing disease patterns and immunization outcomes is still critical. The purpose of this study is to look into the epidemiology of COVID-19 in Malaysia, as well as to evaluate the effectiveness and side effects of the three locally available vaccines.
Methods: The data for this study were gathered from publicly available databases on the Malaysian Ministry of Health's official website. The principal statistical tests used were the Kruskal-Wallis test and one-way ANOVA. The results showed that the COVID-19 booster dose had the greatest influence on lowering mortality rates.
Results: While minor side effects were recorded in just 2.2% of instances, the AstraZeneca vaccination had much greater rates of muscular discomfort, joint pain, and chills than the Pfizer and Sinovac vaccines. Adults aged 40 and up faced much lower risks, with age-specific studies revealing lower vulnerability in children and protective effects in older adults.
Conclusion: The booster dose is an important public health strategy that contributes significantly to the reduction of COVID-19 deaths. It is extremely advised to continue giving booster doses, especially to high-risk populations.

Abstract  Background: Neurodevelopmental disorders (NDDs), which include autism spectrum disorders, attention deficit/hyperactivity disorder, intellectual disabilities, and cerebral palsy, are a major global public health concern. In Sub-Saharan Africa (SSA), however, the economic consequences of NDDs are little researched and rarely addressed in health and social policy frameworks. This review looks at the available literature on the economic burden of NDDs in SSA, with an emphasis on direct healthcare expenses, indirect productivity losses, caregiver difficulties, and larger societal implications.
Methods: A narrative assessment of published literature was conducted to summarize the available knowledge on the economic costs of NDDs in SSA. The review included studies on healthcare costs, productivity losses, educational exclusion, employment constraints, and policy gaps in neurodevelopmental care.
Results: Despite sparse data, the findings indicate that the total economic impact of NDDs in SSA is significant and largely underestimated due to underdiagnosis, stigma, and inadequate health systems. Families regularly face catastrophic health-care costs and long-term financial strain, which contribute to cycles of poverty and health disparities. The lack of established support structures, early intervention programs, inclusive education policies, and specialized services exacerbates socioeconomic disparities. Furthermore, limited investment in research and service delivery has led to significant policy and resource allocation gaps.
Conclusion: NDDs have a huge economic impact in Sub-Saharan Africa, yet it is often overlooked. Multisectoral efforts are urgently required to increase early detection, access to care, and social inclusion. Investing in neurodevelopmental services fulfills both a human rights requirement and a cost-effective strategy for lowering long-term societal costs and promoting sustainable development.

Abstract  Abstract

Background: This review article compares traditional sampling techniques in epidemiology, particularly non-probability methods, with emerging artificial intelligence (AI)-enhanced recruitment strategies. Classical methods such as convenience, purposive, snowball, and quota sampling remain widely used due to their feasibility in resource-constrained and hard-to-reach settings, despite inherent risks of selection bias and limited generalizability.

Methods: By synthesizing evidence from recent methodological and epidemiological studies, this review examines the strengths, limitations, ethical implications, and practical applications of both traditional non-probability approaches and AI-driven strategies.

Results: AI-driven approaches leverage machine learning, natural language processing (NLP), predictive analytics, and digital data sources to optimize participant identification, reduce recruitment inefficiencies, and potentially mitigate certain forms of bias under appropriate data and governance conditions. The findings suggest that AI is transforming epidemiological recruitment not by replacing classical methods, but by augmenting them through adaptive, data-driven hybrid frameworks.

Conclusion: Responsible integration of AI, guided by epidemiological principles and ethical safeguards, is essential to maximize public health impact. Future research should focus on hybrid models that balance the feasibility of classical methods with the precision of AI.

Abstract  Background: The high prevalence of infectious diseases in chickens poses a significant danger to their health and production. In Bangladesh's commercial chicken farms, antibiotics are frequently utilized as both a prophylactic and therapeutic strategy. Infected hens frequently shown medication resistance. Our understanding of the prevalence of antimicrobial resistance (AMR) for Escherichia coli (E. coli), Salmonella spp., and Klebsiella spp. in healthy hens remains poor.
Methods: A cross-sectional study was conducted to evaluate the AMR pattern of E. coli, Salmonella spp., and Klebsiella spp. in apparently healthy chicken. Freshly laid feces were collected from nine different chickens of varying ages (3 days, 25 days, and more than 18 weeks). In addition, we obtained a wastewater sample from the farm's effluent. All samples were tested for E. coli, Salmonella spp., and Klebsiella spp. using the total viable count (TVC) method. The disk diffusion method was used to determine antimicrobial resistance (AMR) in bacterial isolates.
Results: E. coli was detected in the majority of the ten samples tested, followed by Salmonella spp. (30%) and Klebsiella spp. All sixteen isolates were resistant to cotrimoxazole (100%), amoxycillin (100%), erythromycin (100%), doxycycline (100%), ciprofloxacin (87.5%), levofloxacin (75%), azithromycin (68.75%), and cephalexin (37.5%). No isolate shown complete susceptibility to all tested antibiotics.
Conclusion: The presence of AMR E. coli, Salmonella spp., and Klebsiella spp. in both healthy chickens and farm surroundings suggests that they may operate as a reservoir for clinically relevant resistant bacteria, with public health implications.

Abstract  Objective

To develop and propose a pragmatic, evidence-based framework for selecting machine learning (ML) models in low- and middle-income countries (LMICs), balancing predictive performance, interpretability, resource constraints, and fairness to enable ethical and feasible adoption in global health research.

Methods and Analysis

Evidence was synthesized from systematic reviews on ML performance, TRIPOD+AI reporting standards, and clinical AI fairness gap analyses. This informed a six-step framework: (1) dataset characterization, (2) interpretability assessment, (3) resource evaluation, (4) performance benchmarking, (5) fairness evaluation, and (6) transparent reporting. The framework operationalizes decision thresholds grounded in the literature, including a 5,000-observation complexity threshold and a 0.05 fairness gap benchmark. Decision tools—including a standardized table and a logically branched flowchart—guide context-sensitive choices to prioritize clinical utility and local feasibility.

Results

The six-step framework elucidates trade-offs among model complexity, interpretability, and resource needs (categorized as "LMIC Fit"). Application of the framework suggests that for tabular health data, parsimonious models often achieve comparable performance to complex ensembles while ensuring superior transparency. The framework mandates 10-fold cross-validation and 95% confidence intervals to ensure reproducibility, alongside mandatory adherence to global reporting standards.

Conclusion

Model selection in global health is not merely a technical task but a balance of performance, equity, and resources. The best choice of methodology is the most parsimonious model that meets performance requirements without compromising fairness or local feasibility. This framework provides researchers with a defensible, evidence-based tool to navigate these trade-offs, fostering the adoption of AI that is transparent, reproducible, and contextually appropriate for LMIC settings.

Abstract  Emerging infectious diseases continue to pose a significant public health issue, particularly in resource-constrained settings such as Nigeria, where surveillance infrastructure is frequently fragmented and underfunded. Weak data integration, delayed reporting, and inadequate predictive capacity have all contributed to ineffective epidemic response, inappropriate resource allocation, and avoidable morbidity and mortality. Improving national health security requires strengthening surveillance mechanisms.
This essay looks at the potential role of artificial intelligence (AI) in improving disease surveillance and epidemic preparedness in Nigeria, based on worldwide experiences and digital epidemic intelligence models.
AI-powered tools like BlueDot and HealthMap show how machine learning, natural language processing, and predictive analytics may help with early outbreak detection and forecasting. While these tools hold great promise for improving Nigeria's Integrated Disease Surveillance and Response system, their application is hampered by data quality gaps, infrastructure restrictions, algorithmic bias, staff capacity shortfalls, and poor regulatory governance.
Integrating AI-driven predictive analytics into Nigeria's surveillance system has the potential to significantly improve early detection, resource allocation, and epidemic preparedness. However, successful adoption necessitates progressive implementation, investment in digital infrastructure, strong data governance frameworks, and ongoing capacity building to ensure ethical and equitable deployment

Abstract  Backgrounds: Diseases and illnesses in pets pose a huge global health, welfare, and economic burden, with high mortality rates. A cross-sectional study examined the prevalence of health cases in Kohat's pets by month, season, and species. Methods: This is a retrospective research. The information was obtained from the record at the Civil Veterinary Research Hospital in Kohat, Pakistan. Results: Chronic respiratory disease and coryza were the most common conditions in 1296 pets, with 12.2% each, followed by parvovirus 9.2%, vomiting 8.4%, external/internal wounds 8.3%, coccidiosis 6.4%, and diarrhea 6.0%, worms and ticks 4.4%, feline leukemia 4.1%, fracture 3.8%, mange 3.0%, dehydration 2.9%, fever 2.6%, and weakness 2.3%. The remaining diseases and disorders have a prevalence of less than 1% of diagnosed cases. Diarrhea and vomiting accounted for 59.0% of mixed cases. The largest occurrence occurred throughout the summer (39.4%), peaking in March (12.4%), and the lowest cases occurred in June (4.8%). Dogs (42.0%) and birds (38.3%) were more common than cats (19.7%). Cats, like dogs and birds, demonstrated the lowest rates in the spring and fall, respectively. Dogs had the highest prevalence from March to August (excluding July) and November, whereas cats had the lowest frequencies, except in June. Birds had a higher occurrence in January and February, July, September, and October, as well as December. Conclusion: Kohat experiences significant monthly, seasonal, and species-based changes in pet diseases and disorders, with summer being the peak season and dogs and birds having the most overall cases.These discoveries necessitate timely veterinary care in these species.

Abstract  Background: The relatively high level of item non-response continues to pique researchers' interest in Demographic and Health Surveys (DHS), particularly in areas involving the gathering of sensitive data and data for new noncommunicable disease (NCD) modules.
Methods: For this study, the 2024 Nigeria DHS Individual Women's Recode dataset was used, and data from 39,050 women aged 15 to 49 were examined. A comparison between four imputation methods: Multiple Imputation by Chained Equations (MICE) with predictive mean matching, K Nearest Neighbors (KNN), missForest, and XGBoost-based methods with key variables, such as sociodemographic characteristics, a derived indicator for skilled prenatal care, women's BMI, and NCD indicators for hypertension awareness, diagnosis, and treatment. We were able to evaluate the effectiveness of the missing data imputation technique by examining prevalence estimates, time to finality, and the impact on logistic regression models for hypertension diagnosis. A sensitivity comparison study was also undertaken.
Results: The pooled prevalence of hypertension ever identified was 7.62%, with skilled prenatal care accounting for 22.46% overall. Sensitivity analysis using survey weights produced results similar to unweighted models. MissForest had the quickest computation time of 2.1 seconds, whereas KNN took 447.8 seconds. The missingness pattern was Missing at Random (MAR), which resulted from structural skip logic. The logistic regression coefficients and significance levels remained constant regardless of the imputation method utilized.
Conclusion: The findings show that when dealing with structural missingness in survey data, researchers should add strong auxiliary variables and do sensitivity analysis.

Abstract  Objectives: Artificial intelligence (AI) and machine learning are increasingly used in epidemiology for predictive modeling and risk stratification. However, non-representative datasets can lead to biased predictions that exacerbate health disparities. This review identifies methodological pitfalls threatening representativeness in AI-driven epidemiology and proposes mitigation strategies across the AI lifecycle, emphasizing epidemiological rigor, fairness-aware design, and applications in low- and middle-income countries (LMICs).



Methods: We synthesized evidence on bias sources in public health AI, including selection, proxy, measurement, and optimization biases. Examples are drawn from LMIC contexts, such as fragmented records in Nigeria and pulse oximetry disparities. A lifecycle framework is proposed, integrating epidemiological principles, fairness-aware machine learning techniques, and continuous monitoring.



Results: Key pitfalls include data-level issues like urban over-representation and model-level issues such as spurious correlations and optimization paradoxes. The proposed lifecycle framework outlines stage-specific mitigations, including personalized federated learning, robust aggregation, and transfer learning for fragmented systems. A case study applying fairness-aware constraints to sub-Saharan African COVID-19 survey data demonstrates the measurable impact of these techniques on improving subgroup equity.



Conclusions: Maintaining representativeness requires intentional, lifecycle-aligned interventions. By prioritizing diverse data, rigorous validation, and equity-focused design—supported by hybrid machine learning architectures and pragmatic guidance on accuracy-equity trade-offs—AI systems can advance equitable public health outcomes.

Abstract  Background: Street begging creates huge challenges for public health, urban management, and social sustainability, particularly in low- and middle-income countries like Nigeria. Homelessness, poverty, disability, and inadequate social welfare systems are all major contributors to the problem. Despite their visibility, local drivers and consequences are usually ignored. This study looked at the perceived causes, effects, and mitigation methods for street begging among adults in the Sango community of Ilorin, Nigeria, with a focus on long-term, community-based interventions.
Methods: A descriptive cross-sectional study involving 460 people was conducted utilizing multistage sampling and a structured interviewer-administered questionnaire. Of the 460 respondents contacted, 455 completed surveys, yielding a response rate of approximately 99% and a non-response rate of 1%. The data was examined by descriptive statistics and chi-square tests at a significance level of 0.05 alpha.
Results: Poverty (93.6%), physical disability (79.5%), family breakup (66.0%), and homelessness were identified as risk factors for street begging. Typical results included sexual violence, accidents, and physical assaults. Interventions recommended included the construction of homeless shelters, financial help for disabled individuals, and the enhancement of social welfare programs.
Conclusion: Street begging in Ilorin is caused by a combination of structural, cultural, and social vulnerabilities that require long-term, diverse policy responses.

Abstract  Background: Mosquitoes are the most important vector population in tropical and subtropical countries. It is extremely difficult to reduce the mosquito population in an overcrowded community. Recurrent rainfall also promotes their proliferation. As a result, mosquito-borne viral infections spread through the enormous population during the rainy season. This study sought to explore the current state of vector-borne viral infections in a specific area of Dhaka, Bangladesh. Methods: Blood samples were taken from symptomatic patients. Blood sera were tested using real-time RT-PCR. The detection kit is intended for the qualitative detection of Chikungunya, Dengue, and Zika viruses. Results: The findings revealed that the younger age group (21-30 years) had more Dengue cases, while the middle-aged group (41-50 years) had Chikungunya sickness. There were no cases of Zika among the 118 patients. All of the patients had fevers and headaches. Males had a 10% lower risk of Dengue virus infection than females. On the other hand, the male group was 50% more likely to have Chikungunya fever than the female group. A few patients tested positive for both Dengue and Chikungunya. Conclusion: Mosquitoes can transmit viral infections. Forest regions, pet waste, tyres, and lengthy wet seasons all contribute to mosquito proliferation. In this study, we investigated the current state of vector-borne diseases. Mosquitoes transmitted the Dengue and Chikungunya viruses across the population. Mosquito control is crucial because patients lack access to treatment. This project aims to raise stakeholders’ awareness of mosquito-borne diseases.

Abstract  Background: Phylodynamic systems combine epidemiological dynamics and pathogen evolution, and they can display complicated nonlinear characteristics such as oscillations and instability. Hopf bifurcations are particularly interesting because they represent the shift from stable disease dynamics to long-term epidemic cycles.
Methods: In MATCONT, a continuous phylodynamic susceptible-infected-recovered model with adaptive pathogen evolution was examined through numerical continuation. Computing the maximum real part of the Jacobian eigenvalues yielded a stability dataset from equilibrium branches. A feedforward neural network was trained to approximate this stability measure and then integrated into a Pyomo-based optimum control formulation. The dynamic optimization problem was handled utilizing orthogonal collocation and nonlinear programming.
Results: Bifurcation analysis revealed a Hopf bifurcation point at (0.126448, 1.543549, 7.016131, 0.085120, 0.842161). The first Lyapunov coefficient was -3.41 × 10⁻², indicating a supercritical Hopf bifurcation and the formation of a stable limit cycle. The neural network surrogate successfully approximated the stability boundary, allowing stability information to be included in the optimization problem without requiring explicit eigenvalue calculations. The stability-constrained optimum control formulation yielded an objective value of around 0.0787, which represents the cost of maintaining stability while meeting disease-control objectives.
Conclusion: The proposed framework combines bifurcation analysis, artificial intelligence, and optimum control to create a coherent technique for phylodynamic systems. By punishing trajectories that approach instability, the strategy produces dynamically stable control policies that restrict oscillatory behavior while lowering infection levels. The framework offers a practical way to managing changing infectious illnesses while maintaining stability.

Abstract  Background: In Nigeria, alternative medicine remains deeply rooted in culture, tradition, and religion, forming vital component of healthcare for a significant portion of the population. This study aims to explore the reasons behind the widespread use of alternative medicine in Nigeria, examine its potential side effects, and evaluate its role within the broader health system.

Method: The study adopted a quantitative research approach. Total of 322 responses were gotten through structured questionnaires (Google Forms). Frequencies, percentages, means, and standard deviations were used to summarize the socio-demographic characteristics of the respondents. Inferential statistics was also applied where necessary to test associations and relationships between selected variables. The level of statistical significance was set at p < 0.05

Results: The results indicate a high prevalence of AM use among respondents, with 63.4% being male and 64.9% aged between 18 and 30 years. Notably, 38.8% of participants were from the medical profession. Commonly used forms of AM included herbal remedies (46.9%), prayer/fasting (25.4%), and bone-setting (23.3%). Key motivations for AM use were financial challenges (34.9%), strong beliefs (30.5%), and lack of accessibility to medical facilities (21.7%). While 33.2% of respondents affirmed that they were very satisfied with AM. 18.2% reported experiencing side effects such as pain (44.7%) and headaches (29.8%), which were often dismissed by traditional healers as signs of healing.

Conclusion: The findings underscore the widespread reliance on AM and highlight the need for regulatory frameworks, public education, and possible integration of AM into formal healthcare systems to ensure safety and efficacy.

Abstract  Background: Youth unemployment in Northern Nigeria is a serious public health issue, aggravated by insecurity, poverty, and educational disparities. While economic measures track labor participation, the psychological burden is under-reported. This review compiles research on the socioeconomic causes of young unemployment and their influence on mental health in the region. Methods: A narrative review aggregated literature spanning 2016 to 2026, including peer-reviewed research and technical reports from the National Bureau of Statistics (NBS), World Bank, and WHO. Thematic synthesis was augmented with anonymize case archetypes to highlight clinical processes. Results: While the official youth unemployment rate in Q2 2024 is 4.3%, the national NEET (Not in Education, Employment, or Training) rate of 12.5% (14.3% for females) indicates a more serious issue in the Northwest and Northeast. The findings demonstrate a robust link between unemployment and prevalent mental diseases, with depression rates approaching 30%. A local study found a 60.9% screen-positive incidence for substance abuse as a maladaptive coping method. The qualitative study revealed two key pathways: "Educational Scarring" in urban areas and "Insecurity-Trauma" among displaced groups, with significant gender variations in outcomes. Conclusion: The link between unemployment and mental illness in Northern Nigeria is a "vicious cycle" that undermines productivity and stability. Current policies frequently disregard psycho-social support in occupational training. To close the treatment gap and promote economic resilience, multidisciplinary techniques, such as predictive analytic and AI-driven digital interventions, are required.

Abstract  Background: Dengue fever has emerged as a serious public health hazard in Bangladesh, with the 2023 epidemic exceeding the previous 23-year total of 321,179 confirmed cases and 1,705 deaths. However, despite the expanding use of machine learning in dengue prediction, substantial problems remain in post-2023 model validation, replication using publically available data, and model output interpretation.
Methods: An explainable multi linear (ML) framework was created using Bangladesh Meteorological Department climate data and Directorate General of Health Services (DGHS)/Institute of Epidemiology, Disease Control and Research (IEDCR) surveillance data (2010-2023), with a strict temporal holdout (2022-2023), and lagged meteorological predictors to compare eXtreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting, and Linear Regression, followed by SHapley Additive exPlanations (SHAP)-based explainability analysis.
Results: XGBoost outscored other models, including linear regression (R² = 0.412), with the best predictive performance (R² = 0.912, Root Mean Squared Error (RMSE) = 1,247 cases/month, Mean Absolute Error (MAE) = 876, Mean Absolute Percentage Error (MAPE) = 18.3%, and Area Under Curve (AUC) = 0.89). SHAP analysis identified two-month delay rainfall and one-month lagged relative humidity (with a ~80% RH threshold) as key predictors of dengue incidence. The mean temperature demonstrated a nonlinear relationship, with maximal risk in the 25-28°C region, which is Aedes aegypti's thermal niche. The model performed well overall; it overestimated the August-October 2023 peak due to its atypical character, but the 0-100 risk scoring system detected High and Very High risk periods with 95.8% accuracy and 100% sensitivity.
Conclusion: This study presents Bangladesh's first rigorously reproducible, SHAP-integrated early warning technique, which serves as a portable template for dengue surveillance in South and Southeast Asia based on the 2023 epidemic perspective.

Abstract  Background: Human Immunodeficiency Virus (HIV) is a serious global public health concern, especially in Sub-Saharan Africa, where a large majority of HIV patients live. Community-based statistics are critical for identifying local variations in HIV burden and developing focused therapies. This study looked at the prevalence of HIV and its relationship to selected sociodemographic characteristics among adults in two rural communities in Udi Local Government Area, Enugu State, Nigeria.
Methods: A community-based cross-sectional study of adults aged 18 and up was done in the Ngwo Uno and Abor areas. Participants were chosen using a multi-stage sampling method, followed by simple random sampling. Data were gathered by standardized interviewer-administered questions, and consenting participants were tested for HIV using a fast test kit. Data was processed with IBM SPSS version 26. Descriptive statistics and Chi-square tests were employed to analyze the relationship between HIV status and sociodemographic factors at a p-value of < 0.05.
Results: The survey had a total of 425 respondents. The overall HIV prevalence was 7.8%. HIV prevalence was substantially related with geography (p<0.001) and age (p=0.005), with the highest prevalence observed among those over 65 years old.There were no significant relationships discovered between HIV status and gender or marital status.
Conclusion: HIV remains a public health concern in rural Enugu, with clear sociodemographic differences. Targeted, context-sensitive treatments are required to overcome these disparities and support ongoing HIV control efforts.

Abstract  Background: Functional water, sanitation, and hygiene (WASH) services are essential for preventing cholera and other waterborne diseases, yet infrastructure availability does not always ensure reliable service delivery. Evidence on WASH functionality in Nigerian public institutions remains limited. This study assessed availability, functionality, and vulnerability of WASH facilities in selected Local Government Areas (LGAs) of Nasarawa State to inform interventions.

Methods: A mixed-methods cross-sectional study was conducted in 24 public facilities across six LGAs, including markets, motor parks, Primary Health Care (PHC) facilities, and government secondary schools. Structured observational assessments, based on WHO/UNICEF Joint Monitoring Programme-aligned indicators, covered water supply, sanitation, hygiene, and environmental sanitation. Twenty-four Key Informant Interviews with institutional stakeholders were thematically analysed. A Composite WASH Vulnerability Index classified facilities as low, moderate, or high vulnerability.

Results: Boreholes were the predominant water source (75.0%); however, only 45.8% were fully functional, while 54.2% were partially functional or non-functional. Although flush toilets were available in 75.0% of facilities, 25.0% had no toilet facility. Hygiene infrastructure was inadequate, with 75.0% lacking handwashing stations and none providing soap and water. Overall, 45.8% of facilities were highly vulnerable. Motor parks had the highest mean vulnerability score (3.3/4), while PHC facilities had the lowest (0.3/4). Doma and Kokona LGAs showed the highest vulnerability profiles. Key barriers included unreliable electricity, inadequate maintenance funding, seasonal flooding, and weak policy implementation.

Conclusion: WASH functionality, not infrastructure availability alone, determines environmental health vulnerability in public facilities, highlighting the need for functionality-focused interventions, particularly in high-risk motor parks and markets.