Underserved Areas Research Articles

Title: Initial Challenges and Learning Experience on Fields of Family Adoption Program.

India’s healthcare system faces challenges due to its large population and resource constraints. The Family Adoption Program aims to bridge healthcare gaps by involving medical graduates in community-based care, particularly in underserved areas. Through direct engagement, students gain practical experience and a deeper understanding of community health needs. Key challenges include cultural barriers, language differences, and resource limitations. However, building trust, collaborating with local health workers, and engaging community leaders help improve healthcare outcomes. This program enhances medical graduates' skills while strengthening community health services, contributing to health equity in India.

Telemedicine in Tanzania: asystematic literature review.

This review explores the challenges and opportunities of implementing telemedicine in rural Tanzania. The study examines how telemedicine can address healthcare gaps in underserved areas and identifies key barriers that hinder its full adoption. It also provides insights into strategies for improving healthcare delivery through telemedicine in rural settings. A systematic literature review methodology was employed following the PRISMA guidelines. The study collected and analysed relevant academic and grey literature from PubMed, Google Scholar and Scopus databases. The search focused on telemedicine in rural Tanzania, with a thematic analysis used to categorise the challenges and opportunities identified in the literature. The review found that the major challenges to telemedicine implementation in rural Tanzania include inadequate telecommunications infrastructure, limited digital literacy among healthcare providers, insufficient financial investment and weak regulatory frameworks. Despite these barriers, telemedicine presents significant opportunities to improve healthcare access, particularly by connecting rural patients with specialists, reducing travel times and integrating telemedicine with national health insurance systems. Innovations such as drone-based medical deliveries and SMS-based health interventions also show promise. This paper comprehensively overviews telemedicine's potential to transform healthcare delivery in rural Tanzania. It synthesises existing literature, highlights critical gaps in telemedicine adoption and proposes actionable strategies for overcoming these barriers. The study contributes valuable insights for policymakers, healthcare providers and stakeholders interested in leveraging telemedicine to enhance healthcare outcomes in underserved regions.

Comparative analysis of SD biosensor standard™ M10 HPV and seegene anyplex™ II HPV HR for detecting high-risk human papillomavirus: a concordance study

BackgroundCervical cancer, primarily caused by persistent high-risk human papillomavirus (hrHPV) infections, is a significant health burden, particularly in low-resource settings such as Sarawak, Malaysia. Effective prevention depends on effective vaccination and early hrHPV detection. This study compares the performance of the point-of-care test (POCT) SD Biosensor Standard™ M10 HPV and laboratory-based Seegene Anyplex™ II HPV HR assay, focusing on their ability to detect and genotype hrHPV in self-collected high vaginal swab samples.MethodsA total of 151 archived self-sampled high vaginal swabs from the Sarawak Urban and Rural Action for Cervical Cancer Elimination Programme (Program SUARA) were analyzed. hrHPV detection and genotyping were performed using Anyplex, which identifies 14 hrHPV genotypes, and M10, which detects HPV16, HPV18, and other hrHPV categorized into six genogroups. Agreement between the assays was evaluated using Cohen’s Kappa (κ), McNemar’s test, and overall agreement percentages. Statistical significance was determined with p-values, and discordant results were further analyzed for potential diagnostic implications.ResultsThe overall agreement between M10 and Anyplex for hrHPV detection was 92.05% (κ = 0.84, 95% CI 0.75–0.93), indicating almost perfect agreement. M10 demonstrated comparable sensitivity for detecting HPV16, HPV18, and other hrHPV genotypes, achieving 96.91% agreement (κ = 0.89, 95%CI 0.73-1.00) in hrHPV classification when discordant results were excluded. Genogrouping also showed almost perfect agreement (κ = 0.91, 95% CI 0.82–0.98). McNemar’s test indicated no significant difference in hrHPV detection rates (p > 0.05), affirming their comparable performance in detecting clinically significant hrHPV infections.ConclusionThe SD Biosensor Standard™ M10 HPV POCT and the Seegene Anyplex™ II HPV HR assay demonstrated almost perfect agreement in hrHPV detection and classification, supporting their complementary roles in cervical cancer prevention. M10’s rapid, field-deployable design makes it suitable for resource-limited settings, while Anyplex provides enhanced genotyping capability in laboratory environments, allowing informed vaccine strategy. Incorporating both assays into cervical cancer prevention programs can improve screening coverage and accessibility, particularly in underserved areas. These findings align with the World Health Organization’s cervical cancer elimination goals, reinforcing the importance of adaptable diagnostic tools in diverse healthcare contexts.

Design and implementation of a photovoltaic system for health facilities in rural areas of Uganda

The persistent issue of unreliable electricity in rural Ugandan healthcare facilities significantly hinders the provision of essential medical services, affecting everything from diagnostic testing to emergency care. This study addresses the urgent need for dependable, renewable energy solutions by designing and implementing a photovoltaic (PV) system specifically tailored to meet the energy requirements of rural health centres. The primary aim of this research was to develop a cost-effective, portable solar power system that can provide continuous power for critical medical equipment, including portable ultrasound scanners, medical centrifuges, vaccine refrigerators, and other essential diagnostic and monitoring devices. The PV system comprises two 50 W monocrystalline solar panels, a buck-boost converter for voltage regulation, a 12 V, 25 Ah maintenance-free battery for energy storage, and a single-phase inverter capable of producing 1 kW of power with an integrated inverter driver system to handle three-phase load requirements. Experimental testing was conducted over a three-month period, during which the system operated from 8:55 AM to 5:00 PM, five days a week. Data on voltage, current, power, and efficiency were recorded, showing an average system efficiency of 92.46%, even under varying sunlight conditions. The high efficiency and portability of the PV system underscore its suitability for off-grid healthcare facilities, enabling reliable operation of life-saving equipment and improving the quality and continuity of care. Compared with existing systems in similar regions, this study’s approach offers a portable, robust, and low-maintenance solution that directly addresses the challenges faced by rural healthcare facilities. The key contribution of this research is its practical, adaptable PV system model, which can be scaled and modified for use in other resource-limited settings. By providing a renewable and consistent energy source, this study advances the field of sustainable healthcare infrastructure and highlights the potential of PV technology to improve healthcare accessibility in underserved areas.

Enterprise-led internet healthcare provision in China: insights from a leading platform.

China's healthcare resources are limited and unevenly distributed, with a notable urban-rural gap. Enterprise-led internet healthcare platforms have become an important solution for optimizing resource allocation, improving accessibility, and enhancing efficiency in mainland China. However, detailed analysis of their online consultation services from both healthcare provider and patient perspectives is still lacking. The online consultation data of an enterprise-led internet healthcare platform was depicted and analyzed to understand the temporal trend and current situation of enterprise-led internet healthcare development in mainland China, which provided insights for the further development of internet healthcare. We gathered information from an enterprise-led internet healthcare platform (i.e., Good Doctor Online) covering the period from January 2008 to December 2022, including the characteristics of doctors, healthcare institutions, and patients. Based on the above data, we sketched and analyzed the situation of online consultation services provided by the enterprise-led internet healthcare platform in mainland China. A total of 149,890 doctors from 7,584 healthcare institutions provided 40,462,801 online consultations from January 2008 to December 2022. Doctors and healthcare institutions providing online consultation services were primarily distributed in the economically developed eastern and southern provinces of China. Doctors with intermediate (30.15%) and senior titles (58.12%) were the main providers of online consultations and most doctors were from tertiary hospitals (88.18%). The consultation price {median [interquartile range (IQR)]} was 49.00 (15.00, 100.00) RMB. The health issues with the highest consultation frequency included upper respiratory tract infections or fever (16.19%), gynecological disorders (11.98%), and skin diseases (8.65%), with variations in gender and age. The age distribution of patients showed two peaks in age groups <5 years and 20-39 years, with the median age (IQR) 29.00 (19.00-43.00) years. Enterprise-led internet healthcare platforms enhance access to care and reduce offline resource strain, especially during COVID-19. They mainly address non-urgent conditions but cannot fully replace in-person care. Policies should focus on increasing elderly participation, engaging senior doctors, optimizing male-oriented services, expanding access to underserved areas, standardizing pricing, and broadening insurance reimbursement coverage to improve equity and sustainability.

Bilingual Perspectives of Functional Dyspepsia Management in People From Underserved Areas.

Disorders of gut-brain interaction, such as functional dyspepsia (FD), are prevalent and challenging conditions. In other gastrointestinal (GI) disorders, individuals from underserved areas (UAs) have difficulty accessing care. Little is known about UA FD patient perspectives of their care, especially in those with limited English proficiency. We aimed to characterize patients' experiences with FD management with the goal of informing future studies targeting disorders of gut-brain interaction management in potentially vulnerable communities residing in UAs. Participants meeting FD criteria were identified in 2 community health centers affiliated with a large academic medical center in the Northeastern United States. Semistructured interviews were conducted in English and Spanish. Transcripts were reviewed by a bilingual panel of investigators using the constant comparative method of iterative data acquisition. Psychosocial stressors and GI symptom severity were assessed. A total of 26 participants were interviewed (12 English-speaking and 14 Spanish-speaking). Broadly, GI symptoms were mild and there was mild-to-moderate psychological distress present. Adverse social determinants of health were highly prevalent. Despite mild symptom severity on objective scales, FD severely affected quality of life and interfered with physical, psychological, and social well-being, including avoidance of certain foods and professional/social situations. Study participants (particularly those with limited English proficiency status) reported difficulty in receiving care. Thematic saturation was achieved. Even when symptoms were mild, interviewees from UAs reported significant FD-related impairment, along with psychological distress. Education interventions targeting FD-related care in UAs should be designed to improve shared decision making in FD, sensitive to the burden of social determinants of health.

An examination of factors contributing to different anesthesia models in underserved areas.

An examination of factors contributing to different anesthesia models in underserved areas.

Bridging global neurosurgical gaps: the potential role of medical students in Europe.

Unmet global neurosurgical needs pose a significant challenge, as do unequal opportunities for neurosurgical education and training across the globe. Mission:BRAIN is a nonprofit organization working to provide neurosurgical expertise and resources to both patients and healthcare providers in underserved areas. It operates globally, with local chapters involving primarily medical students. Its presence in Europe has expanded in the last few years with initiatives that contribute to shaping an international approach to global neurosurgery challenges. The authors aim to investigate the impact of student-led organizations, such as Mission:BRAIN, on global neurosurgery, with a focus on low-income countries and low- to middle-income countries, while also assessing the changes observed within the European group during its initial 2 years. Quantitative and qualitative analyses were employed to investigate changes in the number of chapters, members, and events. The perceived impact was evaluated using a Likert scale survey administered to European members. Between 2022 and 2023, the number of European chapters increased by 150% (n = 10) and memberships doubled. Of the 102 survey respondents, 56.9% were female and 42.2% were male. Similarly, the number of events grew by 240%, with a significant increase in educational events (325%, p = 0.007). Participation in events increased by 10.13%, reflecting growing interest and engagement. For most members (72.6%), the activities in the European region have increased global awareness of the importance of neurosurgical care in underserved areas; for 45.1% of them, outreach events have effectively communicated the importance of neurosurgical care to nonmedical audiences. For 67.6% of the members, European chapters have contributed to fostering collaboration and knowledge sharing among medical students and healthcare providers. Furthermore, 59% agreed that Mission:BRAIN initiatives have contributed to addressing disparities in neurosurgical care and education. Mission:BRAIN's growth in Europe showcases the pivotal role of medical students. The impact of the organization's activities demonstrates the students' potential to contribute to the overarching effort of bridging worldwide gaps in healthcare and education and calls for their active participation and recognition.

Interpretable AI for Skin Lesion Detection: Enhancing Diagnostic Accuracy with CNN and Score-CAM in IoMT Systems

An AI-powered system for detecting skin lesions integrates IoMT platforms with CNN for feature extraction and Score-CAM for enhanced interpretability. The primary aim is to develop a system that accurately classifies skin lesions while providing visual justifications to boost clinician confidence, particularly in regions with limited dermatological resources. The system employs CNN-based feature extraction on dermoscopic images pre-processed using BM3D for noise reduction and CLAHE for contrast enhancement. Score-CAM integration improves model interpretability, while IoMT platforms ensure robustness and enable real-time diagnostics across diverse skin tones and lesion types. The proposed approach outperformed previous methods, achieving 99.20% accuracy, with real-time diagnostic capabilities offering significant benefits for remote and underserved areas. The model enhances accessibility, accuracy, and interpretability, making it highly beneficial for diverse populations.

AI and data-driven innovations in healthcare: Enhancing cancer detection, workforce optimization, and comprehensive care for people living with HIV

The integration of artificial intelligence (AI) and data-driven technologies is revolutionizing healthcare by enhancing diagnostic accuracy, optimizing workforce efficiency, and improving chronic disease management. This manuscript explores how AI-assisted imaging can improve early cancer detection, particularly in underserved areas, through advanced image recognition and predictive modeling. Additionally, the role of predictive analytics in optimizing healthcare workforce distribution is examined, highlighting its potential to enhance resource allocation, reduce clinician burnout, and improve patient outcomes. The manuscript also delves into the importance of lifestyle interventions in managing comorbidities among people living with HIV (PLWH), emphasizing the role of digital health technologies in promoting adherence to healthy behaviors. Finally, the paper discusses how data-driven decision-making can strengthen health systems, reduce disparities, and improve public health outcomes. By synthesizing these themes, this manuscript underscores the transformative potential of AI and data analytics in creating resilient, equitable, and efficient healthcare systems globally. Keywords: Artificial Intelligence (AI), Data-Driven Healthcare, Early Cancer Detection, AI-Assisted Imaging

Advances in Medical Toxicology: Innovative Approaches for Poisoning and Overdose Management

Medical toxicology has rapidly evolved with innovative approaches to managing poisoning and overdose, integrating advances in pharmacology, biotechnology, and precision medicine. Poisoning from chemicals, drugs, and environmental agents remains a major public health challenge globally. Novel diagnostic tools, such as highthroughput mass spectrometry and point-of-care toxicological assays, have significantly improved the speed and accuracy of identifying toxic agents. Precision medicine has introduced individualized detoxification protocols tailored to genetic profiles and specific metabolic pathways, enhancing the efficacy and safety of treatments. Recent developments in extracorporeal treatments, including hemodialysis and hemoperfusion, allow for more effective removal of toxins, particularly for substances that are poorly eliminated by the body. Artificial intelligence and machine learning algorithms are being integrated into clinical toxicology to predict outcomes, optimize antidote dosing, and improve clinical decision-making. Mobile health platforms and telemedicine have expanded access to toxicology expertise, particularly in remote or underserved areas, facilitating real-time consultations in emergency scenarios. The adoption of these cutting-edge technologies is reshaping the landscape of poisoning and overdose management, moving toward more targeted, efficient, and patient-specific interventions. This review highlights key innovations in medical toxicology and emphasizes the need for continued research and interdisciplinary collaboration to improve outcomes for patients affected by toxic exposures.

A scoping review of empirical evidence on the nurse practitioner workforce in Thailand.

This scoping review aimed to synthesize research findings on the state of the development of the nurse practitioner (NP) workforce and its contribution to care outcomes in Thailand. Policies assuring universal access to healthcare in Thailand have increased the demand for primary care. The Thai NP workforce is helping to meet this demand, and this workforce is growing. However, a comprehensive assessment of the existing evidence on NPs has yet to be conducted. A comprehensive search of five electronic databases, including PubMed, Embrace, Clarivate, ProQuest, and ThaiJo (a Thai database), was conducted. The inclusion criteria were peer-reviewed research papers focused on NPs practicing in primary care in Thailand and published in English or Thai between 2002 and 2022. Studies with data not attributable to NPs in Thailand were excluded. The search yielded 118 records, with 42 meeting the inclusion criteria. Most research studies were cross-sectional and grouped into five categories focusing on NP education, patient outcomes associated with NP care, NP performance, NP competency, and NP well-being. Findings suggest that the NP workforce in Thailand contributes positively to patient outcomes. Future research should further explore NP contributions to patient and organizational outcomes and factors impacting NP well-being. Findings suggest policymakers should establish a master's degree as the minimum requirement for NPs, aligning with international standards. Healthcare organizations should improve the work environments for NPs, while health policies should expand NPs' role in primary care, particularly in underserved areas. Expanding the scope of practice for NPs in managing chronic conditions independently is recommended.

Dismantling Occupational Therapy Provider Licensure Barriers for Individuals Who Are Underdocumented.

The occupational therapy profession faces critical workforce shortages, and the demand for services to meet the needs of diverse communities continues to rise. Underdocumented individuals represent a diverse pool of potential practitioners who face barriers to licensure if they pursue a career in occupational therapy. Despite being eligible for occupational therapy education and the National Board for Certification in Occupational Therapy exam, inconsistent state licensure requirements, which often mandate a Social Security number (SSN), pose a significant barrier to licensure and employment. We examine the challenges underdocumented individuals encounter in pursuing occupational therapy education and careers and argue for removing such barriers to enable them to become licensed practitioners. Addressing these barriers could diversify the workforce, enhance access to culturally responsive care, and alleviate shortages in underserved areas. We propose six actionable steps for the profession: (1) investigating and clarifying state licensure requirements; (2) ensuring accurate information dissemination to the public and students; (3) advocating for inclusive licensure policies, such as accepting Individual Taxpayer Identification Numbers instead of SSNs; (4) addressing inconsistencies in reciprocity agreements across states; (5) funding research to better understand workforce demographics and needs; and (6) advocating for increased tuition equity and scholarship opportunities. By fostering an inclusive pathway to licensure and embracing diversity in cultural backgrounds, languages, and life experiences, the occupational therapy profession can become a meaningful career option for underdocumented individuals who can contribute to the development of a more resilient and representative workforce to effectively serve diverse populations and address health disparities.

Exploring tele-speech therapy: A scoping review of interventions, applications, benefits, and challenges.

Exploring tele-speech therapy: A scoping review of interventions, applications, benefits, and challenges.

Does Broadband Infrastructure Promote Foreign Direct Investment? Evidence From a Quasi-Natural Experiment in China

ABSTRACT: Traditionally, research on FDI has focused on macro-level factors like market size, institutional quality, financial development and policy measures. This paper, however, uniquely examines whether and to what extent broadband infrastructure influences FDI, Using the "Broadband China" strategy implemented during 2014 and 2016 as a quasi-natural experiment, this paper is one of the first to document that network infrastructure development affects FDI. Estimation results from a staggered difference-in-differences model based on a panel data of 279 prefecture-level cities in China during 2011 to 2021 reveal that pilot cities are able to attract significantly more FDI following the "Broadband China" policy. In addition, the development of digital finance and the upgrading of industrial structure can strengthen the positive relationship between broadband infrastructure and FDI. Furthermore, significant regional disparities exist in the effectiveness of broadband infrastructure on FDI, with regions that have agglomerated modern industrial sectors playing a more crucial role in attracting FDI and driving economic development. The results are robust to parallel tests, propensity score matching, placebo tests, and exclusion of samples of mega cities and provincial capitals. The study underscores the critical role of broadband infrastructure in attracting FDI, highlighting that reliable, high-speed internet connectivity is not just a technical requirement but a fundamental economic driver in today's globalized market. For policymakers, the results suggest that investments in broadband and information technology infrastructure should be prioritized as part of national and regional economic development strategies in attracting international capital. This means not only expanding broadband coverage to underserved areas but also continuously upgrading and optimizing existing infrastructure to keep pace with technological advancements. Moreover, the results highlight the importance of simultaneously developing network infrastructure, information technology, digital finance, and upgrading industrial structures to foster an environment that is highly appealing to foreign investors. By prioritizing these areas of development, policymakers can enhance their countries' or regions' competitiveness in attracting FDI and promoting economic growth.

MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan populations.

Machine learning algorithms are emerging as valuable aides for radiologists in medical image segmentation due to their accuracy and speed. However, existing approaches, including both conventional machine learning and Segment Anything (SA)-based models, face challenges with the complex, multimodal, and varied quality of magnetic resonance imaging (MRI) scan images used for brain tumor segmentation. To address these challenges, we propose MD-SA2, adapting Segment Anything 2 (SA2) to medical image segmentation and introducing a lightweight U-Net "aggregator" model. Various modifications are incorporated to enhance segmentation accuracy and throughput. SA2 is first customized and fine-tuned for greater efficiency than the original Segment Anything. Slices from multiple image modalities are concatenated for input into the image encoder to improve the delineation of tumor subtypes. In addition, a lightweight U-Net aggregator model is integrated with SA2 to introduce depth awareness. The 2023 BraTS-Africa dataset, containing low-resolution MRI images from 60 sub-Saharan patients, is used to evaluate the algorithm's performance. MD-SA2 attains notable improvements over existing approaches under challenging data circumstances. It achieves a tenfold cross-validated, statistically significant improvement over current methods with a 0.7893 Dice coefficient. It also reaches a higher Intersection over Union and lower 95% Hausdorff distance metrics. An ablation study verifies the impact of key components. MD-SA2 displays strong potential for supporting the diagnosis and treatment planning of brain tumors. It may contribute to narrowing health inequities, especially in medically underserved areas where data quantity and quality limitations reduce the efficacy of traditional automated approaches.

Neighborhood Built Environment and Home Dialysis Utilization: Varying Patterns by Urbanicity-Dependent Patterns and Implications for Policy.

Neighborhood Built Environment and Home Dialysis Utilization: Varying Patterns by Urbanicity-Dependent Patterns and Implications for Policy.

Carcinogenic air pollutants and breast cancer risk in the Arkansas rural community health study: A nested case-control study.

Carcinogenic air pollutants and breast cancer risk in the Arkansas rural community health study: A nested case-control study.

Retrospective Analysis of the Effectiveness of Outreach Dental Camps in Underserved Areas

ABSTRACT Objective: This retrospective analysis aims to evaluate the effectiveness of outreach dental camps in addressing oral health disparities and improving access to dental care in underserved areas. Methods: Data from outreach dental camps conducted in underserved areas over the past five years were analyzed. A comprehensive search identified studies reporting quantitative data on oral health outcomes and dental care utilization among camp participants. Descriptive statistics and meta-analysis techniques were employed to summarize findings and estimate overall effect sizes. Results: The analysis revealed a significant improvement in oral health outcomes among camp participants. Dental caries prevalence decreased from 60% to 35%, and mean probing depth improved from 4.5 mm to 3.0 mm (P &lt; 0.001 for both). Dental treatment utilization increased, with dental cleaning prevalence rising from 10% to 40% and filling prevalence increasing from 5% to 25% (P &lt; 0.001 for both). Conclusion: Outreach dental camps demonstrate effectiveness in reducing dental caries prevalence, improving periodontal health, and increasing dental treatment utilization in underserved areas. These findings highlight the critical role of outreach strategies in promoting oral health and addressing disparities among vulnerable populations. Further research should explore the scalability and long-term impact of outreach dental interventions for sustainable oral health improvement.

Artificial Intelligence in Logistics and Distribution: The function of AI in dynamic route planning for transportation, including self-driving trucks and drone delivery systems

Artificial Intelligence (AI) is revolutionizing logistics and distribution by enhancing efficiency, reducing costs, and improving the overall delivery experience. One of the key applications of AI in this sector is dynamic route optimization, where machine learning algorithms analyze real-time data such as traffic patterns, weather conditions, and road closures to continuously adjust delivery routes. This reduces fuel consumption, optimizes delivery times, and mitigates operational disruptions. In parallel, autonomous delivery systems, including drones and self-driving trucks, are transforming last-mile and long-haul transportation by minimizing human intervention and increasing speed and reliability. AI-powered autonomous delivery systems leverage advanced technologies such as computer vision, sensors, and machine learning to navigate and make real-time decisions. Drones, for example, are already being utilized for time-sensitive deliveries, especially in remote or underserved areas, while self-driving trucks promise to revolutionize long-haul freight transportation with the potential for 24/7 operation, cost reduction, and increased safety. Despite the clear benefits, challenges such as regulatory frameworks, safety concerns, and public acceptance remain as significant barriers to large-scale adoption. This review explores the role of AI in dynamic route optimization, drones, and self-driving trucks within logistics, providing insights into how these technologies are currently being implemented and the challenges that lie ahead. It further discusses the potential for AI to reshape the future of logistics and transportation by driving innovation in automation, reducing operational inefficiencies, and contributing to the development of smarter, more sustainable supply chains. The future of AI in logistics promises a more integrated, cost-effective, and agile system that can adapt to global challenges and evolving consumer demands.