Analysis Of Health Data Research Articles

Random Forest and CatBoost with Handling Imbalanced Class for Detection of Risk Factors Anemia in Children (5-12 Years)

The prevalence of anemia in children (5-12 years) remains a public health issue in Indonesia. Early detection and control of risk factors are crucial for prevention. Machine learning models can be employed to address this problem. One practical approach is using ensemble learning models. However, it is expected to encounter imbalanced class problems when analyzing health data. Therefore, this study aims to perform classification modeling using two ensemble learning models: Random Forest (RF) and CatBoost. The proposed methods for handling imbalanced class issues include Random Over Sampling, SMOTE, G-SMOTE, Random Under Sampling, Instance Hardness Threshold (IHT), and SMOTE-ENN. Additionally, SHAP is used to explain the best-performing model based on Shapley values. The research findings indicate that the ensemble learning model using the CatBoost algorithm with G-SMOTE data handling produces the best performance compared to other methods. Based on the average performance metrics from 100 replicate validation, the CatBoost G-SMOTE model produces a sensitivity of 0.7104, specificity of 0.7043, G-Mean of 0.7067, and AUC of 0.7844. Handling the imbalance class problem using the G-SMOTE method effectively increases the sensitivity value in the two proposed ensemble learning models. Meanwhile, the SMOTE-ENN method produces effective G-Mean values for the Random Forest (RF) algorithms. Based on Shapley's value, the features with the highest contribution to predicting anemia in children (5-12 years) are ferritin, vitamin A, consumption of vegetables, diagnosed pneumonia, zinc, calcium total, and consumption of soft or carbonated drinks.

Investigating low birth weight and preterm birth as potential mediators in the relationship between prenatal infections and early child development: a linked administrative health data analysis

BackgroundPrenatal infections are associated with childhood developmental outcomes such as reduced cognitive abilities, emotional problems and other developmental vulnerabilities. However, there is currently a lack of research examining whether this...

A Retrospective Analysis of Southeastern U.S. Police Recruit Health and Fitness From 4 Points in Time Within a 16-Year Period: Implications for Physical Fitness Training.

Gonzales, SM, Orr, RM, Coburn, JW, Hoffmann, MD, Kennedy, K, Dawes, JJ, and Lockie, RG. A retrospective analysis of southeastern U.S. police recruit health and fitness from 4 points in time within a 16-year period: Implications for physical fitness training. J Strength Cond Res 38(6): 1118-1126, 2024-Drawing from the general population for its recruiting needs, police departments often employ academy curricula to improve a recruit's fitness so they can perform occupational tasks. Recently, obesity and physical inactivity have increased in the general population, potentially influencing the health and fitness of incoming recruits. This study involved a retrospective, cross-sectional analysis of health and fitness data of police recruits. Data from 1 police department in southeastern United States were analyzed at 4 specific time points, splitting the recruits into natural immerging groups; 2003 (n = 93), 2006 (n = 137), 2009 (n = 74), and 2019 (n = 242). Health and fitness data for all recruits included age, height, body mass, and body mass index (BMI); systolic and diastolic blood pressure (BP); sit-and-reach; combined grip strength; push-ups and sit-ups completed in 60 seconds; physical ability test time; and 2.4-km run time. Several univariate analyses, with sex and age as covariates (analyses of covariance), and a Bonferroni's post hoc, determined whether there were significant between-group differences. Effect sizes (d) were also calculated. Key results demonstrated that 2019 recruits were heavier than the recruits in 2006 and 2009 (p ≤ 0.032; d = 0.14-0.38); had a greater BMI (p ≤ 0.028; d = 0.24-0.75) and systolic BP (p < 0.001; d = 0.47-0.65), and lesser sit-and-reach distance (p ≤ 0.020; d = 0.26-0.46), than all recruit groups; completed fewer sit-up repetitions than the recruits in 2006 and 2009 (p ≤ 0.025; d = 0.42-0.48); and were slower in the 2.4-km run than the recruits in 2006 (p = 0.009; d = 0.36). Police training staff may need to address lesser health (BMI, BP) and fitness (hamstring flexibility, abdominal endurance, aerobic fitness) in incoming recruits.

Research protocol for an observational health data analysis to assess the applicability of randomized controlled trials focusing on newly diagnosed metastatic prostate cancer using real-world data: PIONEER IMI’s “big data for better outcomes” program

Background: Metastatic prostate cancer (PCa) constitutes ~5% of all new PCa diagnoses in Western countries. For most cases, primary consideration should be given to systemic therapies as the first-line approach based on evidence from randomized controlled trials (RCTs). Despite the importance of RCTs as the pinnacle of evidence in modern medicine, concerns have been raised about their applicability to real-life scenarios. These trials often feature participants who are younger with better performance statuses and prognoses compared to their real-world counterparts. The PIONEER project falls under the Innovative Medicine Initiative’s (IMI) “Big Data for Better Outcomes” initiative, aimed at revolutionizing PCa care in Europe. The central focus lies in improving cancer-related outcomes, enhancing health system efficiency, and elevating the quality of health and social care. This study endeavours to evaluate the generalizability of RCT findings concerning newly diagnosed metastatic PCa. Methods: A systematic review of the literature will be conducted to compile patient characteristics from RCTs addressing this subject within the past decade. To create a real-world benchmark, patients with recently diagnosed metastatic PCa from a network of population-based databases will serve as a comparison group. The objective is to assess the applicability of RCT results in two ways. First, a comparison will be made between the characteristics of patients with newly diagnosed metastatic PCa enroled in RCTs and those with the same condition included in our databases which might represent the real-world setting. Second, an evaluation will be undertaken to determine the proportion of real-world patients with newly diagnosed metastatic PCa who meet the criteria for RCT enrolment. This study will rely on extensive observational data, primarily sourced from population-based registries, electronic health records, and insurance claims data. The study cohort is established upon routinely gathered healthcare data, meticulously mapped to the Observational Medical Outcomes Partnership Common Data Model.

The Effect of "MOLKESPRO" Educational Media on The Level of Knowledge of Early Adolescents about Reproductive Health

Child marriage in Indonesia is a recurring reality in society. Early marriage is a form of gender inequality, especially for women. Globally, 21 percent of women living in 2020 were married before the age of 18, and 12 million girls under 18 are married each year, or about 23 girls married as children every minute. Various reasons for the high rate of early marriage are not only due to educational, economic, and stigmatizing factors but also due to the low understanding of adolescents about reproductive health, which causes adolescents to have no choice, especially for adolescent girls. The purpose of this study was to determine the effect of "MOLKESPRO" monopoly media on adolescents' knowledge about reproductive health. The design of this study was pre-experimental with a pre-post-test design approach. Using the purposive sampling technique, the population was grade 6 elementary school students, as many as 31. In this study, the instrument used was a questionnaire about reproductive health—data analysis using a paired t-test. The results showed that the respondents' knowledge got the less category (35 percent) before being given health education and got a good knowledge category (81 percent) after being given health education. The results obtained p value less than 0.000 (0.000 less than a). Therefore, there is an effect of "MOLKESPRO" on the knowledge of early adolescents about reproductive health. Thus, the "MOLKESPRO" monopoly media can be used by midwives and other health workers in providing health education to early adolescents related to reproductive health, this is because its attractive design can make students enthusiastic to learn materials about reproductive health using the atmosphere created with fun.

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case

BackgroundSecuring adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset’s utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset’s utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.MethodsPredictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two.ResultsAll 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores.ConclusionsAs the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data’s intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

IoT Based Hospital Management

Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient’s care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients’ data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital’s data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system

Analysis of Missingness Scenarios for Observational Health Data.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains.

Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes

INTRODUCTION: University physical education programs still largely use traditional methods without significant innovation in teaching or health evaluation. With the growing capabilities of Internet technology and artificial intelligence, there's a critical need to leverage these advancements to enhance the physical health assessments of college students.OBJECTIVES: The study proposes an integrated APP design for health data collection and analysis both inside and outside physical education classes, utilizing Internet technologies and intelligent learning algorithms. This is aimed at precisely analyzing and improving the health outcomes of university students by fostering more tailored and responsive physical education experiences..METHODS: The method involves constructing an app design analysis index system, integrating a vulture search heuristic optimization algorithm with a convolutional neural network (CNN). This setup uses smart sports APP behavioral data as input to refine and optimize health data integration, aiming to enhance the analysis and feedback mechanisms within university sports programs.RESULTS: The implementation of this method showed that it meets real-time requirements while significantly improving the accuracy and efficiency of integrated APP design analyses for health data. The use of smart algorithms allows for more precise adjustments and feedback in physical education programs, suggesting a substantial improvement over traditional physical health monitoring and evaluation methods.CONCLUSION: The proposed APP design successfully integrates and analyzes health data, enhancing the management and evaluation of physical education programs. It represents a significant step forward in utilizing modern technology to address the stagnation in physical education health monitoring, with potential implications for broader educational and health management practices in universities. Future iterations of the APP could incorporate more diverse data inputs and advanced analytical features to further refine its effectiveness and usability.

Diet quality as assessed by the healthy eating index-2020 among different smoking status: an analysis of national health and nutrition examination survey (NHANES) data from 2005 to 2018

BackgroundCombining smoking with poor eating habits significantly elevates the risk of chronic illnesses and early death. Understanding of how dietary quality shifts post-smoking cessation remains limited. The objective of this study is to examine dietary quality – using Healthy Eating Index (HEI – 2020) and its 13 components, among current, former, and never smokers, and particularly the impact of quitting and the duration of cessation on dietary habits.MethodsA cross-sectional analysis of 31,569 adults from the National Health and Nutrition Examination Survey (NHANES) 2005–2018 was conducted. Dietary quality was assessed using HEI-2020 scores, which were determined by NIH developed - simple HEI scoring algorithm per person. Smoking status was categorized into current, former, and never smokers, with further subdivisions for current (heavy/light smokers) and former smokers (duration post-cessation). Descriptive analysis and multiple regression models weighted to represent the US population were performed.ResultsThe current smoking rate was 19.4%, with a higher prevalence in males (22.5%) than females (17.5%). Current smokers reported statistically significantly lower HEI total score than both former and never smokers. Former smokers exhibited HEI scores similar to those of never smokers. The adjusted HEI total scores for current, former, and never smokers were 49.2, 54.0, and 53.3, respectively, with a statistically significant difference (p < 0.001). Moreover, light smokers had better total HEI score than heavy smokers (46.8 vs. 50.8, p < 0.001, respectively), but former and never smokers scored even higher. Quitting smoking immediately improved dietary quality, with former smokers reaching the dietary levels of never smokers within 5–10 years (53.8 vs. 53.3, p > 0.05, respectively). Compared to current smokers, former smokers tended to consume more beneficial foods (e.g., fruits, vegetables, greens and beans, whole grains, proteins, and fatty acids), while also consuming more sodium and less added sugar.ConclusionsCurrent smokers, particularly heavy smokers, exhibit poorer dietary habits than former and never smokers. The dietary quality of former smokers aligns with never smokers over time, highlighting the positive impact of smoking cessation on diet. This has implications for reducing chronic disease risks associated with poor diet and smoking.

Children with Down syndrome and health management information system

This study examines the use of electronic systems to improve health care for people with Down syndrome in the midst of fast-paced advancements in information technology. It notes that current systems often fail to fully recognize the intellectual abilities of individuals with Down syndrome, especially concerning their health needs. The research highlights the importance of health management information systems (HMIS) in managing patient information effectively. It suggests creating detailed databases that include essential details like diagnoses, analyses, reports, and specific patient information. The goal is to address ongoing health issues and ensure that health services are efficient and uphold high professional standards. This study points out the value of using existing health data for immediate analysis to move health services toward a more professional and electronic future. This work adds to the discussion on technology-driven health management, showing how information systems can be used to improve healthcare outcomes.

What is infodemiology? An overview and its role in public health.

The exponential increase in internet use and the consequent surge in data generation present both opportunities and challenges for public health. Infodemiology, an emerging field at the intersection of information science and public health, seeks to harness the vast amounts of health-related data generated online for public health benefits. This paper provides a comprehensive overview of infodemiology, examining its development, methodologies, and potential to address public health challenges. We discuss the role of infodemiology in identifying and mitigating the spread of misinformation, especially in the context of the COVID-19 pandemic, which underscored the dangers of the "infodemic" - an overabundance of information, both accurate and not, that complicates public health responses. Through both demand and supply-based studies, infodemiology offers unique insights into health trends, misinformation dynamics, and the digital behaviors of health information seekers. Tools such as sentiment analysis are highlighted as essential in navigating the vast digital landscape for real-time health data analysis. Despite the potential of infodemiology, challenges such as data overload or misinformation. The paper concludes by emphasizing the importance of interdisciplinary collaboration, the development of advanced analytical tools, and the need for guidelines to maximize the field's impact on public health policy and practice.

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021

BackgroundThe Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018–2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats.MethodsThe study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas.ResultsThirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times.ConclusionSupport is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

Comparative Analysis of Dental Health Outcomes from Two Outreach Camps in Zanzibar, Tanzania: 2019 and 2023

Background: In low- and middle-income countries, oral health is frequently marginalized due to limited dental care access. This cross-sectional study assesses the outcomes of dental outreach camps in Zanzibar, Tanzania, organized in 2019 and 2023, to understand their impact on addressing local oral health needs over a four-year span. Methods: The study involved a thorough examination and analysis of clinical dental health data from both outreach camps. In 2019, 224 patients were examined, and in 2023, the number increased to 354. The assessment covered various dental health parameters, including dental caries, DMFT/dmft indices, enamel fluorosis, dental trauma, oral mucosa lesions, and non-carious tooth surface loss. Results: The analysis demonstrated a significant reduction in dental caries among adolescents and teens, decreasing from 62.5% in 2019 to 35.59% in 2023 (p &lt; 0.0001). Similarly, the prevalence of teeth missing due to caries showed marked reductions from 40.2% to 9.04% in adolescents and teens (p &lt; 0.0001) and from 25.89% to 11.86% among preschool and school-aged children (p = 0.004). Moreover, a significant change was observed in the Basic Erosive Wear Examination (BEWE) scores, with cases showing no erosive tooth wear (BEWE = 0) decreasing from 49.1% to 33.3% (p = 0.016), and cases with initial loss of enamel surface texture (BEWE = 1) increasing from 47.3% to 61% (p = 0.044). Conclusions: The outreach camps conducted in 2019 and 2023 showed tangible improvements in certain dental health metrics, particularly a reduction in dental caries and missing teeth due to caries among adolescents and teens. However, the uptick in non-carious tooth surface loss points to emerging dental health concerns. These results underline the importance of sustained and targeted dental health interventions to improve oral health outcomes in underserved communities such as Zanzibar.

Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

In the past decade, the revolution in information and computing technology has transformed approaches to breast cancer detection and treatment, with Machine Learning technologies offering significant potential in health data analysis. However, the development of accurate and reliable predictive models is faced with the challenges of data heterogeneity and complexity. This research proposes the development and validation of Machine Learning-based classification models using Support Vector Machine and Principal Component Analysis to address these issues, targeting improved accuracy in the early detection of breast cancer. The methodology applied involved the use of a breast cancer dataset from Kaggle, with data analysis conducted through inductive methods to identify relevant patterns. The combination of Support Vector Machine and Principal component Analysis achieved 89% accuracy in medical image classification, proving its efficacy in breast cancer diagnostics and providing a more reliable model for early detection. The implications of these findings are significant, both theoretically and practically, for the fields of Machine Learning and Breast Cancer, expanding the understanding of the applications of advanced data processing techniques. Although this study faces limitations in the variability of the dataset's patient characteristics, the results offer a basis for further development in diagnostic technology while recommending the integration of Deep Learning and Big Data analysis as a direction for future research.

Transformative Applications of IoT in Diverse Industries: A Mini Review

By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.

Applying Advanced Practice Nurses in Rural Japan: A Mixed-Methods Perspective.

Introduction Rural hospitals, particularly those in geographically isolated regions like Shimane Prefecture, Japan, face significant healthcare delivery challenges. These include limited resources, an aging population, and a scarcity of healthcare professionals. Advanced practice nurses (APNs) have emerged as pivotal in addressing these gaps, offering specialized patient assessment, diagnosis, and management skills. This study aimed to evaluate the demand for APNs in rural community hospitals, focusing on the specific educational needs and clinical competencies required to improve healthcare outcomes in these settings. Method Employing a mixed-methods approach, this research combined qualitative insights from stakeholder interviews with quantitative data analysis of electronic health records (EHRs) at Unnan City Hospital. This sequential exploratory design aimed to capture comprehensive educational needs and outcomes, integrating the depth of qualitative data with the breadth of quantitative evidence to tailor a curriculum for APNs in rural healthcare contexts. Results The study revealed a critical demand for APNs skilled in managing common medical issues in rural settings, such as infections, circulatory failures, and respiratory problems. Stakeholder interviews highlighted the necessity for a curriculum that enhances clinical competencies and emphasizes soft skills like communication and leadership. An analysis of EHRs identified 21 specific diseases across six categories, underlining the importance of targeted education on these prevalent conditions. Conclusion The findings underscore the urgent need for specialized education programs for APNs in rural hospitals to address these communities' unique healthcare challenges. Developing a curriculum that focuses on clinical and soft skills essential for rural healthcare delivery can significantly enhance the quality of care. This study advocates for implementing such tailored educational programs to empower APNs, thereby contributing to healthcare equity and improving patient outcomes in rural settings.

Supporting re-engagement with HIV services after treatment interruption in South Africa: a mixed method program evaluation of MSF’s Welcome Service

Psychosocial challenges impact patients’ ability to remain on antiretroviral therapy lifelong, magnified by disorganized health-systems and healthcare worker (HCW) attitudes. To address this, Médecins Sans Frontières and the Department of Health developed the Welcome Service intervention, to provide person-centered care at re-engagement after HIV treatment interruption. Implemented in Khayelitsha, South Africa, between August 2020 and February 2021, the intervention aimed to reorganize triage, optimize clinical and counselling services and address HCW attitudes. The study used a mixed-methods design, incorporating in-depth interviews, and analyses of programmatic and routine health data. Interviews demonstrated positive patient care experiences. HCWs understood the potential impact of attitudes on patient engagement, however, some continued to demonstrate judgmental attitude. Clinical objectives were variably met at re-engagement: 98% were re-initiated the same day, 50% had a CD4 done, and 45% received tuberculosis prevention. Nevertheless, 4-month retention was 66%, and 88% had a VL < 1000 c/mL. Despite HCWs’ understanding of person-centered care not translating into supportive behaviors, patients had positive care experiences and the intervention ended with a high rate of VL suppression. More efforts are needed to design interventions building on Welcome Service principles to provide person-centered care and sustain retention after re-engagement.

Computer-Aided Design in Sports Nutrition and Health Data Analysis of College Students Using Artificial Intelligence Data Mining

Computer-Aided Design in Sports Nutrition and Health Data Analysis of College Students Using Artificial Intelligence Data Mining