Enhancing Private Healthcare Effectiveness in Lagos State, Nigeria: An Overview of the Effect of Quality Improvement Initiatives and Implications for Sustainable Healthcare Delivery

Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge of ensuring quality healthcare access. The overview of the effect of quality improvement initiatives in this paper focuses on private healthcare providers in Lagos State, Nigeria. The study assesses the impact of donor-funded quality improvement projects on these private healthcare facilities. It explores the level of participation, perceived support, and tangible effects of the initiatives on healthcare delivery within private healthcare facilities. It also examines how these initiatives influence patient inflow and facility ratings, and bring about additional benefits and improvements, provides insights into the challenges faced by private healthcare providers in implementing quality improvement projects and elicits recommendations for improving the effectiveness of such initiatives. Methods: Qualitative research design was employed for in-depth exploration, utilizing semi-structured interviews. Private healthcare providers in Lagos involved in the SP4FP Quality Improvement Project were purposively sampled for diversity. Face-to-face interviews elicited insights into participation, perceived support, and project effects. Questions covered participation levels, support perception, changes observed, challenges faced, and recommendations. Thematic analysis identified recurring themes from interview transcripts. Adherence to ethical guidelines ensured participant confidentiality and informed consent. Results: Respondents affirmed active involvement in the SP4FP Quality Improvement Project, echoing literature emphasizing private-sector collaboration with the public sector. While acknowledging positive influences on facility ratings, respondents highlighted challenges within the broader Nigerian healthcare landscape affecting patient numbers. Respondents cited tangible improvements, particularly in staff management and patient care processes, validating the positive influence of quality improvement projects. Financial constraints emerged as a significant challenge, aligning with existing literature emphasizing the pragmatic difficulties faced by private healthcare providers. Conclusions: This study illuminates the complex landscape of private healthcare provision in Lagos State, emphasizing the positive impact of donor-funded quality improvement projects. The findings provide nuanced insights, guiding policymakers, healthcare managers, and practitioners toward collaborative, sustainable improvements. As Nigeria progresses, these lessons will be crucial in shaping healthcare policies prioritizing population well-being.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Levels of Polycyclic Aromatic Hydrocarbons (PAHs) in Healthcare Waste Incinerators’ Bottom Ash from Five County Hospitals in Kenya

Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care waste requires safe handling, treatment and disposal procedures. While incineration reduces the volume and quantity of waste for final disposal, it leads to the production of fly and bottom ashes laden with toxic incomplete combustion products such as Polycyclic Aromatic Hydrocarbons (PAHs), dioxins, furans and heavy metals. This exposes workers who handle and dispose the bottom ashes, hospital patients, the general public and environment. The goal of this study was to determine the total and individual levels of 16 most prevalent and toxic PAHs. Bottom ash samples were collected from incinerators in five county hospitals in Kenya, namely;Moi-Voi, Narok, Kitale, Makindu and Isiolo. Bottom ash samples were collected over a period of six months from the five hospitals. The samples were then sieved, homogenised and stored at 4°C in amber coloured glass containers. The PAHs were extracted using 30 ml of a hexane-acetone solvent (1:1) mixture by ultrasonication at room temperature (23°C) for 45 minutes. The PAHs were then analyzed with a GC-MS spectrophotometer model (Shimadzu GCMS-QP2010 SE) connected to a computer work station was used for the PAHs analysis. The GC-MS was equipped with an SGE BPX5 GC capillary column (30 m × 0.25 mm × 0.25 μm) for the separation of compounds. Helium was used as the carrier gas at a flow rate of 15.5 ml/minute and 14.5 psi. 1 μl of the sample was injected at 280°C, split mode (10:1). The oven programming was set for a total runtime of 40 minutes, which included: 100°C (2-minute hold);10°C /min rise to 200°C;7°C /min rise to 249°C;3°C /min rise to 300°C (2-minute hold). The interface temperature was set at 290°C. Analysis was done in Selected Ion Monitoring (SIM) mode and the peak areas of each of the PAHs were collected from the chromatograph and used for quantification of the 16 PAHs listed by the U.S. Environmental Protection Agency (EPA) which included, BaA (benz[a]anthracene: 4 rings), BaP (benzo[a]pyrene: 5 rings), BbF (benzo [b]fluoranthene: 5 rings), BkF (benzo[k]fluoranthene: 5 rings), Chr (chrysene: 4 rings), DbA (dibenz[a,h]anthracene: 5 rings), InP (indeno[1,2,3 - cd] pyrene: 6 rings) and Acp (acenaphthene: 3 rings), Acpy (acenaphthylene: 3 rings), Ant (anthracene: 3 rings), BghiP (benzo[g,h,i]perylene: 6 rings), Flu (fluorene: 3 rings), FluA (fluoranthene: 4 rings), Nap (naphthalene: 2 rings), PhA (phenanthrene: 3 rings) and Pyr (pyrene: 4 rings). Ion source-interface temperature was set at 200°C - 250°C. Internal standards from Sigma Aldrich were used in the analysis and the acquired mass spectra data were then matched against the NIST 2014 library [1] [2]. The mean PAHs concentration in the bottom ashes of each hospital varied broadly from 0.001 mg/kg to 0.4845 mg/kg, and the mean total concentration levels of individual PAHs ranged from 0.0072 mg/kg to 1.171 mg/kg. Low molecular weight PAHs (Phenanthrene, Naphthalene and Fluorene) were predominant in all the hospital wastes whereas Kitale and Narok presented the lowest PAHs concentrations and the lowest number of individual PAHs. Moi/Voi recorded the highest total PAHs concentration at 1.3129 ± 0.0023 mg/kg from a total of 11 PAHs being detected from the bottom ash samples. Narok had only three PAHs being detected at very low concentrations of 0.0041 ± 0.00 mg/kg, 0.0076 ± 0.00 mg/kg and 0.012 ± 0.00 mg/kg for phenanthrene, anthracene and chrysene respectively. This study presents hospital incinerator bottom ash as containing detectable levels of both carcinogenic and non-carcinogenic PAHs. Continued unprotected exposure of hospital workers (waste handlers) to the bottom ash PAHs could be hazardous to their health because of their cumulative effect. Preventive measures e.g. the use of Personal protective equipment (PPE) should be prioritised to minimise direct contact with the bottom ash. The study recommends an upgrade on incinerator technology for efficient combustion processes thus for better pollution control.

Differences between the healthcare systems of Quebec and France for the treatment of pain due to spinal disorders

In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Exploring the intersection of the medical metaverse and healthcare ethics:future considerations and caveats

The medical metaverse and digital twin are set to revolutionise healthcare.Like all emerging technologies their benefits must be weighed against their ethical and social,impacts.If we consider the advances of medical technology as an expression of our values,such as the pursuit of knowledge,cures and healing,an ethical study allows us to align our values and steer the technology towards an agreed goal.However,to appreciate the long-term consequents of a technology,those consequences must be considered in the context of a society already shaped by that technology.This paper identifies the technologies currently shaping society and considers the ethical,and social consequences of the medical metaverse and digital twin in that future society.

ERAD: Enhanced Ransomware Attack Defense System for Healthcare Organizations

Digital integration within healthcare systems exacerbates their vulnerability to sophisticated ransomware threats, leading to severe operational disruptions and data breaches. Current defenses are typically categorized into active and passive measures that struggle to achieve comprehensive threat mitigation and often lack real-time response effectiveness. This paper presents an innovative ransomware defense system, ERAD, designed for healthcare environments that apply the MITRE ATT&CK Matrix to coordinate dynamic, stage-specific countermeasures throughout the ransomware attack lifecycle. By systematically identifying and addressing threats based on indicators of compromise (IOCs), the proposed system proactively disrupts the attack chain before serious damage occurs. Validation is provided through a detailed analysis of a system deployment against LockBit 3.0 ransomware, illustrating significant enhancements in mitigating the impact of the attack, reducing the cost of recovery, and strengthening the cybersecurity framework of healthcare organizations, but also applicable to other non-health sectors of the business world.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing capacities.The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption.The system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context.This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial applications.Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn O.Moreover,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption.This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

The Use of Smart Textiles in the Healthcare Space: Towards an Improvement of the User-Patient Experience

This article explores the role of smart textiles in transforming healthcare environments into spaces that prioritize patient well-being. We will examine the advantages of smart textiles in healthcare settings, such as the real-time monitoring of vital signs through connected clothing. Additionally, we will introduce metadesign as a design approach that considers the interactions between users, healthcare environments, and technologies to create fulfilling experiences. By combining the advanced features of smart textiles with a patient-centered metadesign approach, it becomes possible to create care spaces that cater to patient needs. The objective of this article is to present the integration of metadesign in the design of smart textiles as a process aimed at enhancing the quality of the patient user experience. In this process, we will emphasize the collaborative approach and embrace technological innovation to harness the potential for ongoing improvement and provide users with high-quality experiences. Lastly, we will underscore the significance of adopting a multidimensional approach to evaluate the impact of smart textiles on the patient user experience.

Digital Disparities:How Artificial Intelligence Can Facilitate Anti-Black Racism in the U.S.Healthcare Sector

This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.

Revolutionizing Healthcare—The Integration of Virtual Worlds, AR, and Metaverse Technology

This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

BIoMT:A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.

United States Healthcare Data Breaches: Insights for NIST SP 800-66 Revision 2 from a Review of the NIST SP 800-66 Revision 1

Healthcare security and privacy breaches are occurring in the United States (US), and increased substantially during the pandemic. This paper reviews the National Institute of Standards and Technology (NIST) publication base as an effective solution. The NIST Special Publication 800-66 Revision 1 was an essential standard in US healthcare, which was withdrawn in February 2024 and superseded by SP 800-66 Revision 2. This review investigates the academic papers concerning the application of the NIST SP 800-66 Revision 1 standard in the US healthcare literature. A systematic review method was used in this study to determine current knowledge gaps of the SP 800-66 Revision 1. Some limitations were employed in the search to enforce validity. A total of eleven articles were found eligible for the study. Consequently, this study suggests the necessity for additional academic papers pertaining to SP 800-66 Revision 2 in the US healthcare literature. In turn, it will enhance awareness of safeguarding electronic protected health information (ePHI), help to mitigate potential future risks, and eventually reduce breaches.

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Falling is among the most harmful events older adults may encounter.With the continuous growth of the aging population in many societies,developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential.This paper presents a new healthcare Internet of Health Things(IoHT)architecture built around an ensemble machine learning-based fall detection system(FDS)for older people.Compared to deep neural networks,the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters.The number of cascaded random forest stages is automatically optimized.This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system.The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch.Each scenario in this dataset is classified and labeled as a fall or a non-fall.In comparison to the three machine learning models—K-nearest neighbors(KNN),decision tree(DT),and standard random forest(SRF),the proposed ensemble classifier outperformed the other models and achieved 98.4%accuracy.The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

Knowledge and associated factors of healthcare workers on measles vaccine and cold chain management at health institutions in Gondar,Ethiopia

Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar City Administration public health institutions among 165 healthcare workers.Data were collected using a structured questionnaire.In addition,an on-spot observation checklist was used to assess the availability,status and management of the cold chain.A logistic regression model was used to assess the relationship between the outcome and predictor variables.Crude and adjusted odds ratios were calculated with 95%confidence intervals.Results:Overall,87(52.7%;95%CI 44.8%-60.5%)of the healthcare workers had unsatisfactory knowledge regarding the measles vaccine and its cold chain management.One hundred thirty-six(82.4%)healthcare workers correctly mentioned the recommended range of temperature(2-8℃)for measles vaccine storage.Healthcare workers aged 18-29 years(P=0.001)and 30-44 years(P=0.014)were observed as determinants of unsatisfactory knowledge on the measles vaccine and its cold chain management.One hundred and five(63.6%)of the healthcare workers did not correctly mention the type of measles vaccine used in routine immunization.More than one-third(36.4%)of the healthcare workers perceived that the measles vaccine is not safe and could cause measles.Conclusions:More than half of the healthcare workers in the study area had unsatisfactory knowledge on the measles vaccine and its cold chain management.It is necessary to provide technical support and in-service training for healthcare workers to ensure optimal immunization effectiveness.

Accessibility and utilization of healthcare services among diabetic patients:Is diabetes a poor man’s ailment?

Diabetes is a non-communicable ailment that has adverse effects on the individual’s overall well-being and productivity in society.The main objective of this study was to examine the empirical literature concerning the association between diabetes and poverty and the accessibility and utilization of medical care services among diabetic patients.The diabetes literature was explored using a literature review approach.This review revealed that diabetes is an ailment that affects all individuals irrespective of socioeconomic status;however,its prevalence is high in low-income countries.Hence,despite the higher prevalence of diabetes in developing countries compared with developed countries,diabetes is not a poor man’s ailment because it affects individuals of all incomes.While the number of diabetic patients that access and utilize diabetes medical care services has increased over the years,some personal and institutional factors still limit patients’access to the use of diabetes care.Also,there is a lacuna in the diabetes literature concerning the extent of utilization of available healthcare services by diabetic patients.