Search Results (188)

Background/Objectives: Laboratory tests are a cornerstone of modern medicine, and their interpretation depends on reference intervals (RIs) that define expected values in healthy populations. Standard RIs are obtained in cohort studies that are costly and time-consuming and typically do not account for demographic factors such as age, sex, and ethnicity that strongly influence biomarker distributions. This study establishes a data-driven approach for deriving RIs directly from routinely collected laboratory results. Methods: Multidimensional joint distributions of lipid biomarkers were estimated from large-scale real-world laboratory data from the Puerto Rican population using a Gaussian Mixture Model (GMM). GMM and additional statistical analyses were used to enable separation of healthy and pathological subpopulations and exclude the influence of comorbidities all without the use of diagnostic codes. Selective mortality patterns were examined to explain counterintuitive age trends in lipid values while comorbidity implication networks were constructed to characterize interdependencies between conditions. Results: The approach yielded sex- and age-stratified RIs for lipid panel biomarkers estimated from the inferred distributions (total cholesterol, LDL, HDL, triglycerides). Apparent improvements in biomarker profiles after midlife were explained by selective survival. Comorbidities exerted pronounced effects on the 95% ranges, with their broader influence captured through network analysis. Beyond fixed limits, the method yields full distributions, allowing each individual result to be mapped to a percentile and interpreted as a continuous measure of risk. Conclusions: Population-specific and sex- and age-segmented RIs can be derived from real-world laboratory data without recruiting healthy cohorts. Incorporating selective mortality effects and comorbidity networks provides additional insight into population health dynamics. Full article

Background: Hearing impairment is a significant public health issue globally, yet national data for Saudi Arabia remain sparse. Methods: Using data from the 2017 Disability Survey, we analysed 12 hearing-related indicators across 13 administrative regions. Descriptive statistics, logistic regression, cluster analysis, and residual mapping were applied to identify socio-demographic disparities and service gaps. Findings: Among 20,408,362 Saudi nationals, about 1,445,723 (7.1%) reported at least one functional difficulty. Of these, 289,355 individuals (1.4%) had hearing impairment, either alone or with other difficulties—229,541 (1.1%) had hearing impairment combined with other disabilities, while 59,814 (0.3%) had only hearing impairment. Females and males were equally affected. Notably, educational attainment and marital status significantly influenced device uptake; less-educated and divorced individuals were particularly underserved. Regionally, southern provinces (Al-Baha, Jazan, and Najran) demonstrated the highest unmet need due to geographic barriers, limited audiological resources, and socioeconomic constraints, reflecting compounded risks from consanguinity and rural isolation. Cluster analyses identified provinces requiring urgent attention, recommending mobile audiology units, tele-audiology services, and means-tested vouchers to enhance coverage. Conclusions: Despite Saudi Arabia’s existing public audiology services and a National Newborn Hearing Screening programme achieving 96% coverage, substantial gaps remain in follow-up care and specialist distribution, underscoring the necessity for systematic workforce tracking and enhanced rural incentives. International evidence from India and Brazil underscores the feasibility and cost-effectiveness (approximately USD 5200/QALY) of these recommended interventions. Implementing targeted provincial strategies, integrating audiological screening into routine healthcare visits, and aligning resource allocation with the WHO and Vision 2030 benchmarks will significantly mitigate hearing impairment’s health, social, and economic impacts, enhancing the quality of life and societal inclusion for affected individuals. Full article

Background/Objectives: As COVID-19 policies shift from government mandates to individual responsibility, understanding how voluntary protective behaviors shape social interactions remains a public health priority. This study examines the association between voluntary mask wearing and interpersonal distance (IPD) preferences in a post-mandate context, focusing on Taiwan, where mask wearing continues to be culturally prevalent. Methods: One hundred university students (50 males, 50 females) in Taiwan completed an online IPD simulation task. Participants adjusted the distance of a virtual avatar in response to targets that varied by gender and mask status. Mask-wearing status upon arrival was recorded naturally, without manipulation. A four-way ANOVA tested the effects of participant gender, participant mask wearing, target gender, and target mask wearing on the preferred IPD. Results: Voluntary mask wearing was more common among female participants (72%) than males (44%). Mask-wearing individuals maintained significantly greater IPDs, suggesting heightened risk perception, whereas masked targets elicited smaller IPDs, possibly due to social signaling of safety. Gender differences emerged in both protective behavior and spatial preferences, with females showing stronger associations between mask use and distancing behavior. Conclusions: These findings offer actionable insights into how voluntary behavioral adaptations continue to shape spatial interaction norms after mandates are lifted. The integration of real-time simulation and statistical modeling highlights the potential of digital behavioral tools to support culturally adaptive, person-centered public health strategies. Full article

Objective: This study aimed to investigate the impact of depression and anxiety disorders on mortality in women diagnosed with gynecologic cancers, utilizing nationwide retrospective cohort data. Methods: Data from the Korean National Health Insurance Service (NHIS) database, covering women diagnosed with cervical, endometrial, or ovarian cancers between 2007 and 2014, were analyzed. Women diagnosed with depression or anxiety disorders within one year after cancer diagnosis were identified and compared with a control group comprising patients with gynecologic cancers who did not develop either disorder during the same post-diagnosis period. Mortality was evaluated as the primary outcome. Results: Of 85,327 women analyzed, 784 (0.9%) were diagnosed with depression or anxiety disorders. Patients with depression or anxiety exhibited significantly higher mortality (38.4% vs. 29.9%; p < 0.001). Multivariate analysis indicated that depression significantly increased mortality risk (OR 1.46, 95% CI 1.27–1.66), whereas anxiety alone showed no significant effect (OR 0.97, 95% CI 0.74–1.27). Combined depression and anxiety showed the highest mortality risk (OR 1.47, 95% CI 1.31–1.65). Conclusions: Depression and anxiety disorders are significant predictors of increased mortality in women with gynecologic cancers, emphasizing the necessity for integrated mental health assessment and interventions in oncologic care to improve both survival and quality of life. Full article

Background: Mobility limitation is a pivotal but under-documented dimension of disability in Saudi Arabia. Leveraging the 2017 National Disability Survey, this cross-sectional study provides a population-wide profile of mobility-related physical difficulty. Objectives: Five research aims were pursued: (1) estimate national prevalence and severity by sex; (2) map regional differentials; (3) examine educational and marital correlates; (4) characterize cause, duration, and familial context among those with multiple limitations; and (5) describe patterns of assistive-aid and social-service use. Methods: Publicly available aggregate data covering 20,408,362 Saudi citizens were cleaned and analyzed across 14 mobility indicators and three baseline files. Prevalence ratios and χ² tests assessed associations. Results: Overall, 1,445,723 Saudis (7.1%) reported at least one functional difficulty; 833,136 (4.1%) had mobility difficulty, of whom 305,867 (36.7%) had mobility-only impairment. Severity was chiefly mild (35% of cases), with moderate (16%) and severe (7%) forms forming a descending pyramid. Prevalence varied more than threefold across the thirteen regions, peaking in Aseer (9.4%) and bottoming in Najran (2.9%). Mobility difficulty clustered among adults with no schooling (36.1%) and widowed status (18.5%), with sharper female disadvantage in both domains (p < 0.001). Among those with additional limitations, chronic disease dominated etiology (56.3%), and 90.1% had lived with disability for ≥25 years; women were overrepresented in the longest-duration band. Aid utilization was led by crutches (47.7%), personal assistance (25.3%), and wheelchairs (22.6%), while 83.8% accessed Ministry rehabilitation services, yet fewer than 4% used home or daycare support. Conclusions: These findings highlight sizeable, regionally concentrated, and gender-patterned mobility burdens, underscoring the need for education-sensitive prevention, chronic-care management, investment in advanced assistive technology, and distributed community services to achieve Vision 2030 inclusion goals. Full article

Background: With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. Objective: This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification, thus improving operational efficiency in healthcare settings. Methods: We present MedScrubCrew, a multi-agent framework integrating established technologies: Gale-Shapley stable matching algorithm for optimal patient-provider allocation, knowledge graphs for semantic compatibility profiling, and specialized large language model-based agents. The framework is designed to emulate the collaborative decision making processes typical of medical teams. Results: Our evaluation demonstrates that combining these components within a cohesive multi-agent architecture substantially enhances operational efficiency, task completeness, and contextual relevance in healthcare scheduling workflows. Conclusions:MedScrubCrew provides a practical, implementable blueprint for healthcare automation, addressing significant inefficiencies in real-world appointment scheduling and patient triage scenarios. Full article

Background/Objectives: Accurately estimating survival times is critical for clinical decision-making, treatment evaluation, resource allocation, and other purposes. Yet data from relatively recent diagnosis cohorts is strongly affected by right censoring that biases average survival times downward. For example, 5-, 10-, or 20-year survival time averages are not available until 5, 10, or 20 years later, which may be in the future, thus presenting a challenge to obtain in the present. An approach to addressing this problem is described in this report. Here it is demonstrated for kidney cancer survival but could also be applied to survival questions for other types of cancer, other diseases, stage progression times, and similar problems in medicine and other fields in which there is a need for up-to-date analyses of survival improvement trends. Methods: This study introduces STETI, an approach to survival estimation that integrates information about survival times of diagnosis year cohorts with information about survival times of death year cohorts. By leveraging data from death year cohorts in addition to the more familiar diagnosis year cohorts, STETI incorporates recent survival data often excluded by traditional approaches due to right censoring, caused when the post-diagnosis time period of interest has not yet elapsed. Using data from SEER, we explain how the proposed approach integrates diagnosis year cohorts with the death year cohorts of recent years. We demonstrate that incorporating death year cohorts addresses an important source of right censorship that is inherent in diagnosis year cohorts from relatively recent years. This permits survival time trend analysis that accounts for recent improvements in survival time that would be difficult to account for using diagnosis year cohorts alone. We tested linear and exponential models to demonstrate the method’s ability to derive survival time trends using valuable data that would otherwise risk being left unused. Conclusions: Improved survival estimation can better support personalized treatment planning, healthcare benchmarking, and research into cancer subtypes as well as other domains. To this end, we introduce a hybrid analytical approach that addresses an important source of right censorship. Demonstrating it within the domain of kidney cancer is expected to help pave the way to other applications in oncology and beyond, and offers a case study of STETI, an approach to quantifying and projecting trends in survival time associated with therapeutic advancements. Full article

Background and Objective: Despite growing emphasis on quality and safety in healthcare, there remains a limited understanding of how Quality Improvement and Patient Safety (QI/PS) training for health workers has evolved in response to global events like the COVID-19 pandemic and the WHO Global Patient Safety Action Plan. This rapid scoping review aimed to not only identify existing curricula but also uncover trends, innovation gaps, and global inequities in QI/PS education—providing timely insights for reshaping future training strategies. Methods: We searched MEDLINE and Scopus for English-language studies published between January 2020 and April 2024, describing QI and/or PS curricula across graduate, postgraduate, and continuing education levels. All healthcare worker groups were eligible, with no geographic limitations. Two reviewers conducted independent screening and data extraction; a third verified the results. Results: Among 3290 records, 74 curricula met inclusion criteria, with a majority originating from the US (58, 78.4%) and targeting physicians—especially residents and fellows (43/46, 93.5%). Only 27% of curricula were multidisciplinary. While traditional didactic (66.2%) and interactive (73%) approaches remained prevalent, curricula launched after 2020 introduced novel formats such as Massive Open Online Courses and gamification, with long-term programs uniformly leveraging web-based platforms. Common thematic content included Root Cause Analysis, Plan-Do-Study-Act cycles, QI tools, communication skills, and incident reporting. English-language peer-reviewed published literature indicated a marked lack of structured QI/PS training in Europe, Asia, and Africa. Conclusions: This review reveals both an uneven development and fragmentation in global QI/PS training efforts, alongside emerging opportunities catalyzed by digital transformation and pandemic-era innovation. The findings highlight a critical gap: while interest in QI/PS is growing, scalable, inclusive, and evidence-based curricula remain largely concentrated in a few high-income countries. By mapping these disparities and innovations, this review provides actionable direction for advancing more equitable and modern QI/PS education worldwide, whilst showcasing the need to systematically delve into QI/PS training in underrepresented regions. Full article

Background/Objectives: Nonattendance at healthcare appointments remains a major challenge, particularly in chronic diseases like diabetes. Dietary therapy is essential in diabetes care, yet nonattendance at dietitian appointments persists. This study aimed to identify key drivers of nonattendance at dietitian appointments, explore prior experiences with dietary counseling, and determine factors motivating attendance. Methods: A mixed-methods approach was used in this quality improvement project, drawing on multiple data sources to explore nonattendance at dietitian appointments. This included combining a retrospective analysis of clinical and attendance data from patient records at a Danish outpatient diabetes clinic with semi-structured interviews with 25 individuals who had recently missed a dietitian appointment. Quantitative and qualitative data were analyzed separately and then integrated to characterize overall nonattendance patterns. Interview data were analyzed using systematic text condensation. Results: Individuals who missed dietitian appointments were also more likely to miss other healthcare appointments. Vulnerable individuals (i.e., those with complex health conditions or mental health issues) were more likely to miss appointments. Four principal barriers to attendance were identified: administrative, digital, and logistical challenges; competing health concerns; personal priorities; and unclear referral communication and patient involvement. Conclusions: Improving attendance at dietitian appointments requires a multifaceted approach. Key recommendations include optimizing scheduling practices, implementing digital reminders, offering continuity of care and virtual consultation options. Referring clinicians should improve patient communication by clearly explaining the purpose of the dietitian consultation and involving patients in shared decision-making prior to referral. Dietitians should collaborate with patients to establish realistic, personalized goals to foster engagement in their diabetes management. Full article

Background: With the global population aging, the proportion of the elderly is increasing, leading to health challenges. The decline in the elderly’s physical function raises their fall risk, which affects their health and burdens the healthcare system. Traditional fall risk assessment methods like Short Physical Performance Battery (SPPB) have limitations, while computer vision technology shows potential but also has drawbacks. Objective: This study aims to use computer vision technology to quantify the elderly’s gait movement features, analyze their correlations with SPPB test scores and duration consumption, and explore a solution for long-term monitoring and more efficient fall risk assessment. Methods: Data from 19 elderly Japanese subjects, including SPPB test data and camera-captured body movement data, were analyzed. Python (Version 3.12.6) was used to obtain JSON data, calculate movement distances, and construct a comprehensive index. Correlation analysis and principal component analysis (PCA) were performed. Results: The variance mean indicator of the comprehensive index associated with movement distance had a significant negative correlation with the completion duration of Test 2 in SPPB, indicating that greater gait variability might be related to better physical vitality. PC1 (Muscle-Control Reserve) and PC2 (Learning-Fatigue Response) obtained from PCA had a positive relationship with the test duration. The comprehensive index had a positive but not highly significant correlation with test scores. Conclusions: This study analyzed the correlation between the elderly’s gait movement features and SPPB test performance. It innovated in data collection and analysis methods. Future research can be improved by expanding the sample size, adding more parameters, and applying deep-learning techniques. Full article

Background: Metabolic syndrome (MetS), characterized by the co-occurrence of obesity, hypertension, hyperglycemia, and dyslipidemia, substantially increases the risk of cardiovascular disease and type 2 diabetes. In South Korea, the prevalence of MetS is steadily increasing. While physical activity is known to mitigate this risk, recent evidence suggests that the timing of activity, not just its volume, may also be important. Methods: We analyzed accelerometer data from Korean adults who participated in the 2014–2016 Korea National Health and Nutrition Examination Survey (KNHANES). Functional principal component analysis (FPCA) was applied to minute-level physical activity trajectories to extract key temporal patterns. Logistic regression models assessed associations between the resulting principal component (PC) scores and MetS, adjusting for demographic, behavioral, and occupational factors, as well as total moderate-to-vigorous physical activity (MVPA). Results: Among the four extracted components, the third principal component (PC3)—reflecting higher morning and evening activity with reduced afternoon variability—was significantly associated with increased risk of MetS in the fully adjusted model (adjusted OR = 1.117; 95% CI: 1.003–1.244). Conclusions: These findings suggest that temporal patterns of physical activity, particularly reduced variability in the afternoon, may be linked to adverse metabolic outcomes. Beyond overall activity volume, the timing and distribution of daily physical activity should be considered in metabolic health research and interventions. Full article

Background/Objectives: Dementia involves progressive cognitive and functional deterioration that leads to dependence and overload on family caregivers. This overload has a negative impact on the physical, mental, emotional, and occupational health of caregivers, leading to occupational imbalance and problems arising from an inadequate distribution of time devoted to caregiving. This project aims to evaluate the effects of the technology-based CAREFIT programme, structured around physical activity interventions, education, and psychoemotional and social support, on the health-related quality of life and emotional well-being of informal caregivers. Methods: The experimental group will develop the intervention programme, which will last 8 weeks and combine educational activities, physical activities, and psychoemotional and social support. Before beginning the intervention, the entire experimental group will receive a training session and educational materials on how to access and use the platform. The CAREFIT platform will consist of two educational sessions and two weekly physical sessions, combined with psychoemotional and social support activities that participants must complete. Initial, final, and follow-up evaluations will be conducted. The HRQoL and psychoemotional health (stress, anxiety, depression, and perceived social support and burden) of caregivers of people with dementia will be the main outcome measures. The effects of the intervention on the study variables will be assessed using a repeated-measures analysis of variance (ANOVA). Conclusions: The proposed protocol for the CAREFIT programme represents an innovative and multidisciplinary initiative that leverages a digital platform to promote the well-being of informal caregivers of people with dementia. This approach combines health literacy and strengthened psychoemotional and social support. Through this integration, the goal is to reduce the levels of burden, stress, anxiety, and depression among primary caregivers, while strengthening their self-care capabilities and social support networks. Full article

Multimorbidity is the conjoint presence of multiple conditions in patients, which is a public health problem. Multimorbidity is like the elephant in the clinic room because it remains the unaddressed challenge we face in healthcare. Clinical health psychology has a role to play in this undertaking because it recognizes the intersection and interface of concurrent mental and/or behavioral problems and physical diseases. The current article will define multimorbidity, describe current statistics, how it differs from comorbidity, how to use the biopsychosocial model, and ways in which clinical health psychologists can manage and prevent it in their clinics. A model of how to address multimorbidity will be shared using the role of a clinical health psychologist working in a multidisciplinary pain clinic in a hospital setting serving patients who are socioeconomically disadvantaged. Full article

Background: The MULTIPLAT_AGE is a network project which developed a digital platform based on the Comprehensive Geriatric Assessment (CGA) for collecting data and identifying personalized healthcare programs for older people at home. In this article, the final recommendations of the MULTIPLAT_AGE Working Group are reported. Methods: The MULTIPLAT_AGE project included five independent studies developed and carried out by five research centers according to two common principles previously shared by the researchers: (i) the multidimensional approach to older people through the CGA-based Multidimensional Prognostic Index (MPI); (ii) the use of a common web-based platform for collecting data to facilitate healthcare interventions of older people at their home according to the aging in place approach. At the end of the studies, a series of recommendations have been proposed by an expert panel including the principal investigators and discussed by all researchers involved in the MULTIPLAT_AGE project in formal meetings. After discussion, the recommendations have been approved with formal vote by all the researchers during the final meeting of the MULTIPLAT_AGE project. Results: The recommendations are addressed to healthcare providers, policy decision-makers, caregivers, and patients. In summary, the CGA-based interventions and technologies adopted in the MULTIPLAT_AGE project reduced length of hospital stay, improved multidimensional frailty, walking safety, physical and cognitive performances, and reduced fear of falling in older people across different clinical settings and suffering from different diseases. Conclusions: The final recommendations of the MULTIPLAT_AGE Working Group could be a useful instrument to facilitate the use of technologies along with CGA-based interventions to improve the management of older people at home. Full article