Journal of Medicine and Health Research

Energy Poverty and Gendered Inequities in African Communities: A Review of Health Impacts and Policy Gaps

2026-05-23T12:41:42+00:00

Energy poverty remains one of the most widespread structural barriers to human development across sub-Saharan and North Africa. Its impact is not experienced equally across society, but rather disproportionately borne by women and girls, influenced by deeply rooted gender inequalities and societal division of energy-related domestic tasks into women's domain in most African societies. This review focuses on the African continent and draws on peer-reviewed studies, multilateral agency reports, and policy documents, primarily from 2015 to 2026.

The aim is to critically review, from a gender perspective, the nature of energy poverty in Africa. Specific aims are to: (1) describe the burden and distribution of energy poverty by region in Africa; (2) capture health outcomes of gendered exposure to energy; (3) explore structural and governance drivers of the gender-energy nexus; (4) critically question policy actions and highlight persistent gaps in design and (5) identify gaps in evidence base.

A narrative review design was used. The literature was collected from four databases (PubMed, Scopus, Web of Science, and Google Scholar), as well as grey literature from the WHO, IEA, World Bank, and the Clean Cooking Alliance. The main publication period was 2015-2026, and studies conducted prior to that were retained only if their conclusions had not been replicated. Sources that specifically mention Africa were included. Sources that were not peer-reviewed, opinion pieces without empirical support, and studies relevant only to contexts outside Africa were excluded.

Women living in energy-poor households are exposed to long-term indoor air pollution from burning biomass fuels such as firewood, charcoal, or crop residues. This exposure increases their risk of respiratory illness, cardiovascular disease, and other long-term health problems. Gender-sensitivity in policy responses has been lacking, focusing on how to disseminate technology rather than the social and economic factors that affect women's access to and ability to sustain clean energy transitions.

In conclusion, energy poverty as a gender justice problem needs policy frameworks that recognize women as producers, not consumers. Integrating gender-responsive design into electrification and clean cooking programmes, and closing persistent gaps in sex-disaggregated data, are prerequisites for meaningful progress.

Predictive Modeling and Machine Learning Approaches in Agrochemical Exposure and Health Risk Assessment

2026-05-25T10:18:02+00:00

The increasing global reliance on agrochemicals, including pesticides, herbicides, and fertilizers, has substantially enhanced agricultural productivity while intensifying concerns regarding environmental contamination and human health risks. Human exposure occurs through occupational, environmental, and dietary pathways and is associated with acute toxicity as well as chronic conditions such as cancer, neurodegenerative disorders, and endocrine disruption. This narrative review systematically synthesized recent peer-reviewed literature on predictive modeling and machine learning (ML) applications in agrochemical exposure and health risk assessment, drawing from studies focused on environmental monitoring, biomonitoring, geospatial analysis, and health outcome prediction. Evidence was selected based on relevance to ML-driven risk assessment frameworks, model applicability, and comparative analytical value. Findings indicate that ML approaches, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, consistently outperform many conventional deterministic and probabilistic models in handling nonlinear interactions, integrating high-dimensional datasets, and improving predictive accuracy across spatial–temporal and biomonitoring contexts. Ensemble and deep learning models demonstrated particularly strong performance for exposure estimation and disease risk stratification, although challenges remain regarding data quality, interpretability, overfitting, and ethical governance. The review highlights emerging opportunities in explainable artificial intelligence, wearable sensor integration, and real-time surveillance systems to enhance model transparency and public health applicability. Overall, ML-driven predictive frameworks represent a transformative advancement in agrochemical risk assessment and offer significant potential to strengthen evidence-based regulatory policies, targeted interventions, and sustainable environmental health decision-making.

Medicinal Plant Diversity, Traditional Therapeutics and Conservation Perspectives in Odisha, India

2026-06-05T12:39:15+00:00

Medicinal plants constitute an essential component of traditional healthcare systems and continue to play a significant role in primary healthcare, pharmaceutical research, and biodiversity conservation. Odisha, located in eastern India, possesses rich floristic diversity owing to its varied ecological regions, including coastal ecosystems, central plateaus, forested landscapes, and the Eastern Ghats. The state is inhabited by numerous tribal communities that have preserved extensive ethnomedicinal knowledge through generations. This review synthesizes available information on medicinal plant diversity, traditional therapeutic applications, phytochemical constituents, ethnopharmacological significance, and conservation perspectives in Odisha. Information was compiled from published scientific literature, ethnobotanical studies, books, and government reports. Several medicinal plant species, including Tinospora cordifolia, Andrographis paniculata, Rauvolfia serpentina, Ocimum sanctum, Curcuma longa, and Azadirachta indica, are widely utilized for the treatment of infectious diseases, metabolic disorders, respiratory ailments, inflammatory conditions, and general health maintenance. The review further highlights the importance of bioactive phytochemicals such as alkaloids, flavonoids, phenolic compounds, terpenoids, and glycosides that contribute to the pharmacological properties of medicinal plants. Traditional medicinal knowledge preserved by indigenous communities remains an important resource for future drug discovery and sustainable healthcare development. However, habitat degradation, overexploitation, deforestation, and loss of traditional knowledge threaten medicinal plant resources in the region. Strengthening conservation strategies, scientific validation, sustainable utilization practices, and community participation is essential for safeguarding Odisha's medicinal plant heritage and promoting its contribution to healthcare and biodiversity conservation.

Mechanisms, Models, and Future Directions for Predicting Biochemical Pathways Underlying Antibiotic Resistance in Microbial Communities Using Artificial Intelligence

2026-06-10T12:25:29+00:00

Antimicrobial resistance (AMR) has emerged as one of the most pressing global public health challenges, reducing the effectiveness of existing antimicrobial treatments and increasing the burden of infectious diseases worldwide. The development of resistance is driven by complex ecological interactions, genetic transfer, and adaptive biochemical processes within microbial communities. However, current gene-based analyses alone are insufficient to fully explain these mechanisms, necessitating a broader investigation of biochemical pathways and molecular networks involved in resistance regulation. This study explores the potential of Artificial Intelligence (AI) in advancing the understanding of AMR through the analysis of large-scale microbiome and multi-omics datasets. Machine learning, deep learning, and network-based modeling approaches are considered for their ability to detect hidden biological patterns, predict resistance-associated pathways, and model microbial ecosystem behavior.AI-based approaches demonstrate significant potential in transforming AMR research from traditional descriptive analysis to predictive and interpretive modeling. These methods enhance the ability to identify resistance mechanisms, forecast evolutionary trends, and improve the understanding of microbial interactions at a systems level. Despite these advantages, challenges such as data quality limitations, lack of model interpretability, inadequate standardization, and unequal access to computational resources remain significant barriers. Ethical concerns related to data governance and clinical implementation must also be addressed. Future advancements in explainable AI, integrated multi-omics analysis, and robust computational frameworks are expected to improve predictive accuracy and support better diagnostic and treatment strategies. These developments hold strong potential for strengthening global efforts against antimicrobial resistance.

Utility of Plasma Level of Macromolecules to Predict Dysmetabolism and Energy Imbalance among Individuals Living with HIV at Nnewi, Nigeria

2026-05-28T13:17:00+00:00

Introduction: HIV infection is well-documented to disrupt normal metabolic processes and elevate energy demands, with these effects becoming more pronounced as the disease advances. The present investigation measured circulating concentrations of Nicotinamide Adenine Dinucleotide (NADH), Flavin Adenine Dinucleotide (FAD), Acetyl-CoA (ACA), and Adenosine Diphosphate (ADP) in a cohort of HIV-positive individuals.

Methods: A longitudinal, prospective case-controlled design was adopted, enrolling 77 HAART-naive, HIV-seropositive adults (aged 18–60 years) attending the antiretroviral treatment clinic at Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi, Nigeria, alongside 36 HIV-seronegative healthy volunteers serving as controls. NADH, FAD, ACA, and ADP concentrations were quantified using enzyme-linked immunosorbent assay (ELISA), and overall energy balance was estimated through a validated mathematical formula. All data were analyzed with SPSS version 23.0.

Results: ACA and NADH concentrations were significantly reduced (P<0.05) in HIV-infected individuals (both HAART and Naïve groups) regardless of treatment status relative to controls. ADP and FAD levels were additionally depressed (P<0.05) in patients who had completed 12 months of HAART compared with both the pre-treatment HIV cohort and healthy controls. Calculated energy balance scores were markedly lower (P<0.05) across all HIV groups versus controls, with post-treatment patients recording the greatest significantly energy deficit compared to their treatment-naive counterparts.

Conclusion: Statistically significant alterations in key metabolic macromolecules among HIV subjects—both prior to and during antiretroviral therapy suggests metabolic disruption. These findings indicate that circulating levels of NADH, ADP, ACA, and FAD hold promise as accessible biomarkers for detecting early energy deficits and guiding nutritional or therapeutic interventions in HIV-infected populations.

Predictive Modeling of Stature from Arm Length, Shoulder Breadth, and Foot Length among the Yoruba Ethnic Group in Nigeria

2026-06-02T12:28:28+00:00

Background: In the fields of bioarchaeology, forensic science, and physical anthropology, stature estimation is essential. It entails using quantifiable body factors to estimate or anticipate a person's height.

Aim: This study aims to investigate whether arm length, shoulder breadth, and foot length can be used to predict stature among the indigenous Yoruba people in Ibadan, Oyo State, Nigeria.

Method: The study employed a descriptive cross-sectional design; including 300 subjects (150 males and 150 females) aged 18 to 35 years. The anthropometric characteristics were measured using a mega-size calliper. The data were analysed using an IBM version of the Statistical Package for the Social Sciences (SPSS, version 23). Linear regression was employed to estimate gender and stature. A probability less than 0.05 (p<0.05) was considered statistically significant, with a 95% confidence level.

Result: A strong positive correlation existed in all subjects between stature, arm length, shoulder breadth, and foot length (R = 0.78, SEE = 4.968). The stature predictive power of males (R = 0.65) and females (R =0.73). Significant sexual dimorphism was noted in all parameters.

Conclusion: This shows a strong positive correlation between the subjects. These findings will be useful in forensic sciences, anthropology, medical sciences, and bioarchaeology.

An Integrated Multi-modal Wearable Sensor System for Real-time Monitoring of Athlete Stress, Hydration, and Tendon Stiffness Analysis

2026-06-10T07:16:02+00:00

Continuous monitoring of physiological and musculoskeletal conditions is essential for improving athlete performance and preventing sports-related injuries. Advancements in wearable sensor technology enable non-invasive, real-time tracking of stress, hydration, and tendon health through integrated multi-modal systems. This study presents the development of an integrated multi-modal wearable sensor system for real-time monitoring of athlete stress, hydration, and tendon stiffness. The system leverages an Arduino Uno microcontroller interfaced with multiple physiological and biomechanical sensors, including heart rate and heart rate variability sensors (MAX30102/05), galvanic skin response (GSR) modules, bioimpedance circuits, skin temperature sensors, high-resolution inertial measurement units (MPU6050/ICM-20948), and strain sensors (Flexi Force). By collecting and pre-processing analog and digital signals, the system provides continuous assessment of athlete physiological states and musculoskeletal load without the need for EMG. Data are transmitted wirelessly to a mobile or PC interface for further analysis, while real-time alerts are delivered through vibration motors, LEDs, or buzzers to prevent stress-related issues, dehydration, and tendon overuse injuries. Validation demonstrates the feasibility of integrating multiple sensing modalities in a compact wearable platform, enabling proactive athlete health management and performance optimization.

Knowledge, Prevalence and Factors Associated with Self-medication among University Students in Kano, Nigeria: A Cross-sectional Study

2026-06-10T12:19:57+00:00

Introduction: Self-medication is common in university settings and carries well-documented risks including antimicrobial resistance, adverse drug reactions, and delayed care. Students are particularly exposed, given their autonomy and easy access to medicines without prescriptions. This study aimed to assess the knowledge, prevalence, and factors associated with self-medication among students at Bayero University, Kano, Nigeria.

Methods: A descriptive cross-sectional study was conducted among 354 students selected by multi-stage sampling, combining proportionate stratified allocation across academic levels with consecutive recruitment within strata. Data were collected using a semi-structured, self-administered questionnaire. Knowledge of self-medication was assessed with a validated ten-item scale and categorised as good (score ≥7), fair (5–6), or poor (≤4). Data were analysed in IBM SPSS version 27 using descriptive statistics, chi-square tests, and binary logistic regression (P ≤ .05).

Results: All 354 questionnaires were completed (response rate 100%). Self-medication was reported by 235 respondents (66.4%, 95% CI: 61.5%–71.3%). Most respondents (232, 65.5%) demonstrated good knowledge of self-medication, assessed using a ten-item scale. Across 1,408 total responses, CNS stimulants (13.4%), antimalarials (12.7%), and antibiotics (12.6%) were the most frequently reported drug categories. On chi-square analysis, ethnicity (χ² = 7.94, P = .047) and income source (χ² = 9.42, P = .024) were significantly associated with self-medication behaviour; knowledge level was not (χ² = 1.41, P = .494). On binary logistic regression, Yoruba students were significantly less likely to self-medicate than Hausa students (aOR = 0.475, 95% CI: 0.238–0.948, P = .035), and students earning through personal work were significantly less likely to self-medicate than those supported by parents or guardians (aOR = 0.500, 95% CI: 0.278–0.900, P = .021).

Conclusion: Self-medication was highly prevalent among students at Bayero University, Kano, despite most demonstrating good knowledge of associated risks. The absence of a significant knowledge-practice association, alongside the significance of ethnicity and income source, suggests that structural and socio-cultural factors are stronger drivers of the practice than awareness alone. Interventions should prioritise improving affordable healthcare access, regulating drug sales at the point of purchase, and addressing the specific socio-cultural contexts that shape health-seeking behaviour among university students in Northern Nigeria.

Prevalence and Determinants of Spirometric Abnormalities among Individuals Undergoing Pulmonary Function Testing in South Gujarat, India

2026-06-25T09:30:02+00:00

Introduction: Pulmonary function tests (PFTs) are widely used to detect ventilatory abnormalities and assess respiratory health. Spirometry plays an important role in the diagnosis, classification and monitoring of respiratory diseases. However, data on spirometric patterns and their associated determinants in the Indian population remain limited. This study aimed to determine the prevalence and distribution of spirometric abnormalities and to evaluate their association with demographic and lifestyle factors among individuals undergoing pulmonary function testing.

Methods: A cross-sectional study was conducted from January to June 2024 among 875 participants undergoing spirometry at tertiary care hospitals and community-based health check-up camps in South Gujarat, India. Spirometry was performed in accordance with American Thoracic Society (ATS) and European Respiratory Society (ERS) guidelines using a Spirolab portable spirometer. Pulmonary function patterns were classified as normal, obstructive, restrictive or mixed ventilatory defects. The severity of impairment was graded as mild, moderate or severe according to standard interpretative criteria. Associations between spirometric patterns and demographic or lifestyle variables were tested using chi-square tests.

Results: Of the 875 participants, 50.4% had normal spirometry and 49.6% had abnormal pulmonary function findings. The most common abnormality was a restrictive spirometric pattern (44.7%), followed by obstructive (3.2%) and mixed ventilatory defects (1.7%). Mild impairment was observed in 25.26% of participants, moderate impairment in 18.63% and severe impairment in 4.0%. Significant associations were observed between spirometric abnormalities and age group (p < 0.0001), sex (p = 0.0081) and smoking status (p < 0.0001). No significant association was found between body mass index (BMI) and spirometric patterns (p = 0.6155).

Conclusion: Abnormal spirometric findings were common among participants in the study population. The association of age, sex and smoking status with pulmonary function abnormalities highlights the importance of spirometry in respiratory screening and smoking-related health interventions.