Search Results (5,062)

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda. Full article

Background/Objectives: Benign paroxysmal positional vertigo (BPPV) is one of the most common vestibular disorders and is characterized by transient but very severe vertigo, increasing fall risk, especially in older people. While many risk factors have been reported, there are still contradicting papers and evidence from large-scale studies remains limited. Methods: This nationwide, nested case–control study utilized Korean National Health Insurance Service-Health Screening Cohort data to investigate possible risk factors for BPPV. In particular, it examined the association between prior head trauma and BPPV, proposing prior head trauma as a plausible and clinically relevant risk factor. From an initial cohort of 514,866 participants, 29,467 BPPV cases were matched 1:4 with 117,868 controls based on age, sex, income, region, and index date. Conditional logistic regression, with overlap weighting, assessed the risk of BPPV associated with head trauma and other potential factors. Results: Head trauma was modestly more prevalent in the BPPV group (2.29% vs. 1.83%) and was significantly associated with BPPV (adjusted OR 1.28, 95% CI 1.17–1.40, p < 0.001). The corresponding Absolute Risk Increase (ARI) was 0.82 percentage points over the entire follow-up and 0.66 percentage points within 1 year. The association persisted across most subgroups including both demographic and clinical factors except underweight individuals and those with high comorbidity scores. Conclusions: This large-scale analysis reinforces head trauma as a significant risk factor for BPPV, providing population-level evidence that may guide clinical assessment and prevention strategies. Full article

Background and Objectives: Sjögren’s syndrome (SS) is a chronic autoimmune disease that affects the salivary glands and increases the risk of developing dental diseases (DDs). Chinese herbal medicines (CHMs) represent a promising alternative strategy for SS treatment; however, the association between CHMs and DD risk has not been confirmed. In this retrospective, cohort-based, nested case-control study, we explored whether or not combining CHMs with routine treatments for SS can reduce the chance of DDs. Materials and Methods: In the beginning, we recruited subjects aged 20–80 years with newly diagnosed SS who were free of DDs between 2001 and 2009 from a nationwide insurance database. We identified DD events that occurred after SS diagnosis until 31 December, 2013. Corresponding controls were randomly selected from the remaining enrollees using a pair-matched approach. We then exploited conditional logistic regression to explore the association between CHM use and subsequent risk of DD development. Results: Based on the recruited 586 DD cases and 586 non-DD controls, we noted that adding CHMs to routine SS treatment substantially correlated with a lower risk of developing DDs (adjusted odds ratio = 0.68; 95% confidence interval = 0.52–0.90). Notably, for those receiving CHM treatment for more than 365 days, CHM use greatly reduced DD susceptibility, by 44%. Conclusions: Embedding CHMs within routine SS care can prevent subsequent DDs incidence, implying the urgent need for interdisciplinary collaboration and careful treatment planning. Full article

Background: Migraine and chronic periodontitis are prevalent conditions that may share common inflammatory and neurovascular pathways. Growing evidence suggests an association between periodontal inflammation and migraine, yet large-scale population-based studies are limited. Objective: To investigate the association between chronic periodontitis and the occurrence of migraine using a nested case-control design in a nationally representative Korean adult cohort. Methods: This study utilized data from the Korean National Health Insurance Service-Health Screening Cohort (2002–2019). A total of 43,359 individuals diagnosed with migraine (ICD-10: G43) were matched 1:4 by age, sex, income, and residence with 173,436 controls. Chronic periodontitis was identified using ICD-10 code K053. Conditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for demographic, behavioral, and clinical covariates. Results: A significant association was observed between chronic periodontitis and migraine. Individuals with at least one diagnosis of periodontitis within one year prior to migraine onset had increased odds of migraine (adjusted OR = 1.10, 95% CI: 1.08–1.13). Similar associations were observed for two diagnoses within one year (OR = 1.05; 95% CI: 1.01–1.09) and one diagnosis within two years (OR = 1.10; 95% CI: 1.08–1.13). No association was found with three or more diagnoses in one year. Subgroup analyses confirmed consistent associations across migraine subtypes and demographic strata. Conclusions: This study demonstrated a statistically significant association between chronic periodontitis and migraine, suggesting a potential shared inflammatory or neurovascular mechanism. Recognizing periodontal disease as a modifiable factor may offer new insights into migraine prevention and management. Further longitudinal and interventional studies are warranted to establish causality. Full article

The coronavirus disease 2019 (COVID-19) pandemic has put a renewed focus on the value of vaccines in combatting potentially life-threatening diseases. The primary aim was to conduct a longitudinal study on the dispensing patterns of vaccines (from 2017 to 2021) in a section of the private healthcare sector in South Africa. A descriptive cross-sectional pharmacoepidemiological study on health insurance data covering 5 years was conducted. The study included all vaccines available on the South African market (childhood, adult, travel, and other vaccines). The study population consisted of 3.8 million individuals. The descriptive statistics were calculated. The vaccine-dispensing patterns were distinctly different in 2021 compared to the preceding four years. The COVID-19 vaccine was introduced in 2021 in South Africa. Although the total number of medical insurance scheme members stayed relatively constant, the number of vaccine claims increased approximately seven-fold in 2021 compared to the average for the preceding four years (2017 to 2020). The tetanus and pneumococcal vaccines were the most dispensed bacterial vaccines, whilst the influenza and COVID-19 vaccines were the most dispensed viral vaccines. COVID-19 vaccines accounted for 55.74% of all vaccines dispensed over the 5 years, and for 85.70% of the vaccines dispensed in 2021. An increase in the number of bacterial vaccines dispensed was observed towards the middle of 2020, which was attributed to the pneumococcal vaccine. Pneumococcal vaccines were administered during the COVID-19 pandemic to prevent morbidity and mortality from co-/secondary infections and superinfections. Similar ongoing studies on vaccine-dispensing patterns in the post-COVID-19 era are necessary, especially since the outbreak of various vaccine-preventable diseases has recently been observed. Full article

Background: While attention deficit/hyperactivity disorder (ADHD) is characterized by neurodevelopmental heterogeneity, the influence of familial structural factors—particularly parental age and skipped-generation caregiving—on comorbidity patterns remains insufficiently studied. This study examined the associations between parent–child age gaps, skipped-generation family structures, and psychiatric comorbidities in children with ADHD. Methods: Data came from Taiwan’s NHIRD (2009–2013), including 79,163 ADHD cases and 395,815 matched controls. Key variables included maternal and paternal age at childbirth and grandparent-paid insurance premiums as a proxy for skipped-generation caregiving. Conditional logistic regression was used to estimate odds ratios (ORs) for 20 psychiatric and developmental comorbidities. Results: Children with ADHD exhibited significantly higher odds of various comorbidities, including oppositional defiant disorder (OR = 147.05, 95% CI = 101.0–214.1), somatoform disorder (OR = 25.78, 95% CI = 7.96–83.46), anxiety disorder (OR = 24.49, 95% CI = 17.9–33.5), emotional disturbances during childhood and adolescence (OR = 13.99, 95% CI = 9.15–21.4), and autism spectrum disorder (OR = 8.07, 95% CI = 6.63–9.82). Advanced maternal age (>35 years) was associated with increased odds of autism spectrum disorder (OR = 1.47, 95% CI: 1.29–1.67) and speech/language delay (OR = 1.33, 95% CI: 1.17–1.52), whereas younger maternal age (≤25 years) was linked to higher odds of anxiety disorder (OR = 1.23, 95% CI: 1.13–1.33) and adjustment reaction (OR = 1.41, 95% CI: 0.95–2.11). Maternal age under 20 years showed the highest odds for bipolar disorder (OR = 2.01, 95% CI: 1.04–3.88). For paternal age, older age (>35 years) was associated with increased odds of autism (OR = 1.14, 95% CI: 1.04–1.26) and speech/language delay (OR = 1.15, 95% CI: 1.04–1.27), whereas paternal age ≤20 years was strongly linked to bipolar disorder (OR = 3.58, 95% CI: 1.54–8.32) and anxiety (OR = 1.39, 95% CI: 1.01–1.93). Children from skipped-generation families—defined as grandparent-paid insurance premiums without parental cohabitation—had significantly higher odds of bipolar disorder (OR = 2.88, 95% CI: 1.36–6.11), personality disorder (OR = 9.23, 95% CI: 2.23–38.20), adjustment reaction (OR = 2.23, 95% CI: 1.39–3.59), and emotional disturbances during childhood/adolescence (OR = 1.69, 95% CI: 1.13–2.54). Conclusions: Both extremes of parental age and skipped-generation caregiving are linked to specific associations with certain psychiatric comorbidity patterns in children with ADHD. These findings highlight the importance of integrating family structure into diagnostic assessments and treatment planning and support the development of targeted early interventions. Full article

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate–carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22–35% on average and narrows the 95% confidence interval by 25–40% compared to the Kalman filter or LSTM alone. The three-dimensional spatio-temporal analysis demonstrates that the KF–LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series. Full article

Relevance: Obstructive sleep apnea (OSA) is a major public health problem with significant social and economic consequences. With increasing prevalence associated with urbanization and aging, untreated OSA is a considerable burden to the healthcare system, work productivity, and accident costs. Objectives: To analyze the global socioeconomic burden of OSA and evaluate epidemiological, economic, and healthcare policy perspectives across different regions and income levels. Materials and Methods: We conducted a narrative comprehensive review of published studies and WHO reports, covering direct medical costs, indirect social costs, and the cost-effectiveness of both existing and emerging diagnostic and therapeutic techniques. Results: OSA is estimated to afflict some 936 million adults around the world, and if it remains untreated, OSA results in 2.5 times higher healthcare costs compared to non-OSA individuals. The annual societal cost of untreated OSA in the U.S. now exceeds USD 150 billion, when considering direct medical expenses, productivity losses, and accident-related costs. Recent studies also highlight significant global costs, with annual per-patient estimates up to EUR 28,000 in the U.S. and EUR 1700–5000 in Europe. The inequality of treatment access continues between the affluent and the poor regions. Novel approaches as AI diagnostics and telemedicine, hold promise for reducing costs and improving treatment adherence among underserved populations with limited access to conventional care. Discussion: This review underscores the importance of uniform care throughout the world, timely diagnosis initiatives using portable technology, and scalable technological solutions to help reduce the social toll of OSA. Policymaker support, public education campaigns, and insurance changes are necessary to optimize both the cost and effectiveness of OSA management worldwide. Full article

Background/Objectives: Later-stage melanoma at diagnosis is associated with increased mortality. Health care access, socioeconomic status, and neighborhood-level factors likely influence stage at presentation. This study aimed to examine whether neighborhood disadvantage, as measured by the Area Deprivation Index (ADI), is associated with later-stage melanoma diagnosis. Methods: We conducted a cross-sectional analysis of a retrospective cohort of 941 patients diagnosed with melanoma at a large academic medical center between 2010 and 2019. Residential addresses were geocoded and linked to ADI and rurality data. Covariates included race, ethnicity, age, gender, and insurance status. Multivariable logistic regression models with robust standard errors clustered at the census tract level were used to assess associations with melanoma stage at diagnosis. Results: Of 941 patients (63% male, 92.8% non-Hispanic White, mean age 64 years), 432 (46%) were diagnosed with late-stage melanoma. Mean ADI was higher among late-stage cases (5.4) compared to early-stage cases (3.3) (p < 0.001), even after adjustment for covariates. Non-Hispanic White race, private insurance, older age, and urban residences were associated with earlier stage at diagnosis. Racial disparities were attenuated after adjusting for ADI, with no significant interaction between race and ADI. Conclusions: Neighborhood disadvantage is significantly associated with later-stage melanoma diagnosis and contributes to observed racial and socioeconomic disparities. These findings highlight the need for targeted educational interventions and health policy initiatives to reduce late-stage melanoma diagnoses in vulnerable populations. Full article

Designing fair and sustainable pricing mechanisms for health insurance requires accurate risk assessment and the formulation of incentive-compatible strategies among stakeholders. This study proposes a hybrid framework that integrates machine learning with game theory to determine optimal, risk-based premium rates. Using a comprehensive dataset of insured individuals, the XGBoost algorithm is employed to predict medical claim costs and calculate corresponding premiums. To enhance transparency and explainability, SHAP analysis is conducted across four risk-based groups, revealing key drivers, including healthcare utilization and demographic features. The strategic interactions among the insurer, insured, and employer are modeled as a repeated game. Using the Folk Theorem, the conditions under which long-term cooperation becomes a sustainable Nash equilibrium are explored. The results demonstrate that XGBoost achieves high predictive accuracy (R² ≈ 0.787) along with strong performance in error measures (RMSE ≈ 1.64 × 10⁷ IRR, MAE ≈ 1.08 × 10⁶ IRR), while SHAP analysis offers interpretable insights into the most influential predictors. Game-theoretic analysis further reveals that under appropriate discount rates, stable cooperation between stakeholders is achievable. These findings support the development of equitable, transparent, and data-driven health insurance systems that effectively align the incentives of all stakeholders. Full article

Farmers’ land transfer practices optimize the allocation of agricultural resources by transferring them to more efficient operators. This enhances agricultural productivity and advances rural revitalization. However, due to the lack of financial institution outlets in rural areas, the availability of financial services in rural areas is limited, which in turn hinders the transfer of rural land. This study examines the impact of digital financial inclusion, characterized by the deep integration of internet technology and financial services, on farmers’ land transfer behavior in China. The study uses data from the China Family Panel Studies (2012–2022) and provincial digital financial inclusion data. The results show that digital financial inclusion significantly promotes rural land transfer-out. The mechanisms reveal two pathways: (1) digital financial inclusion expands non-agricultural entrepreneurship by easing credit constraints and reducing reliance on land livelihoods; (2) it increases participation in commercial insurance, mitigating risks of land abandonment. Heterogeneity analysis reveals stronger effects in eastern China and among educated households. Theoretically, the study identifies the dual role of financial technology in reshaping rural land markets through credit access and risk management. Practically, it reveals how DFI influences land transfer behavior, providing a basis for the government to formulate policies that combine the two, ultimately enhancing the production capacity, operational efficiency, and market competitiveness of smallholder farmers. The findings offer global insights for developing countries that are leveraging digital finance to activate rural land markets and achieve digital financial inclusion. Full article

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further address the limited research on the evaluation of machine learning (ML) and hybrid approaches, this study considers a broad spectrum of ML techniques, including supervised ML, unsupervised ML, deep learning, and hybrid ML approaches such as SMOTE-ENN, explainable AI, federated learning, and ensemble learning. The study also explored their potential use in enhancing fraud detection in imbalanced and multidimensional datasets. A significant finding of the study was the identification of commonly employed datasets, such as Medicare, the List of Excluded Individuals and Entities (LEIE), and Kaggle datasets, which serve as a baseline for evaluating machine learning (ML) models. The study’s findings comprehensively identify the challenges of employing machine learning (ML) in healthcare systems, including data quality, system scalability, regulatory compliance, and resource constraints. The study provides actionable insights, such as model interpretability to enable regulatory compliance and federated learning for confidential data sharing, which is particularly relevant for policymakers, healthcare providers, and insurance companies that intend to deploy a robust, scalable, and secure fraud detection infrastructure. The study presents a comprehensive framework for enhancing real-time healthcare fraud detection through self-learning, interpretable, and safe machine learning (ML) infrastructures, integrating theoretical advancements with practical application needs. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

Background: The present study aims to evaluate the effectiveness of ESYSTA^® (Emperra GmbH E-Health Technologies, Germany), a CE-certified digital health application made to support insulin-treated diabetes patients to improve their disease management through better self-empowerment. Methods: To evaluate the effectiveness of ESYSTA^®, data from patients who used ESYSTA^® for at least 12 months and participated in an originally prospective one-arm study were evaluated. This study was conducted in cooperation with the German health insurance company AOK Nordost (2012–2015). From a real-world data pool of insured AOK Nordost patients, a control group was matched to mimic a controlled trial that allows the use of ESYSTA^® to be compared with standard care in the context of a disease management program (DMP). Results: The study results show significant and clinically relevant reductions in HbA1c values of at least 0.4% in ESYSTA^® users after 6 months. After 12 months, users achieved, on average, an HbA1c reduction of approximately 0.7%. These reductions are more pronounced compared to the matched control group. Conclusions: The present study shows the effectiveness of the digital health application ESYSTA^®. Using a matched control group further increased the internal and external validity of the study results. Full article

Background/Objectives: Metabolic syndrome (MetS) comprises a cluster of cardiometabolic risk factors that vary dynamically under environmental and behavioral influences. Although there are data suggesting seasonal variability in individual metabolic components, few studies have comprehensively assessed MetS as a composite condition across seasons using a large, nationally representative population. In this study, we aimed to evaluate the seasonal and monthly patterns of MetS prevalence and component burden, with a focus on sex-specific differences. Methods: We analyzed 5,507,251 health screening records from 2,057,897 Korean adults aged ≥40 years between 2013 and 2022, obtained from the National Health Insurance Service database. Seasons were categorized as: spring (March–May), summer (June–August), fall (September–November), and winter (December–February). Trends in MetS prevalence and its components were evaluated monthly and seasonally, stratified by sex. Results: MetS prevalence significantly varied by season in both sexes (p < 0.001), ranging from 30.2% to 34.5% in men and from 21.5% to 25.5% in women. Among men, a U-shaped pattern was observed, with the lowest prevalence during summer and a progressive increase through winter. Women showed a steady decline in prevalence from January to September, followed by a slight rebound. Winter was associated with increased odds of MetS in both sexes. A significant interaction between sex and season (p for interaction < 0.001) indicated the presence of sex-specific temporal patterns. Conclusions: This nationwide study revealed clear seasonal variation in MetS prevalence and component burden, with sex-specific patterns. These findings highlight the importance of incorporating seasonality and sex in cardiometabolic risk assessments and public health interventions. Full article