Search Results (645)

Coinfections with hepatitis C virus (HCV) or human immunodeficiency virus (HIV) among individuals with chronic hepatitis B (CHB) are associated with worse clinical outcomes but remain understudied due to their low prevalence and the sensitivity of associated data. This nationwide, cross-sectional study utilized claims data from the Korean Health Insurance Review and Assessment Service (2014–2021) to investigate the prevalence, comorbidities, treatment patterns, and liver-related complications among patients with HBV monoinfection, HBV/HIV, HBV/HCV, or triple coinfection. Among over 4.5 million patients with chronic hepatitis B, the prevalence of HIV and HCV coinfection ranged from 0.05 to 0.07% and 0.77 to 1.00%, respectively. Patients with HBV/HCV coinfection were older and had significantly higher rates of hypertension, diabetes, dyslipidemia, and major adverse liver outcomes, including hepatocellular carcinoma and liver transplantation, compared to other groups. HBV/HIV coinfection was more common in younger males and was associated with higher dyslipidemia. The use of HBV antivirals increased over time across all groups. These findings highlight the distinct clinical characteristics and unmet needs of coinfected populations, underscoring the importance of tailored screening and management strategies in HBV-endemic settings. Full article

Objectives: Evidence suggests that COVID-19 infection contributes to elevated risks of psychiatric disorders, including depression and anxiety, however, this association remains underexplored. This study aimed to examine the incidence of depression and anxiety in individuals with COVID-19 compared to those without any infection. Method: Using the Utah All Payers Claims Database (2019 to 2021), we examined adult patients with continuous insurance enrollment. Individuals with pre-existing depression or anxiety were excluded. COVID-19 infection in 2020 was identified using diagnostic and procedural codes. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select covariates, followed by entropy balancing to adjust for baseline differences. Weighted logistic regression models were used to estimate the association between COVID-19 infection and incident mental health diagnoses in 2021. Results: Among 356,985 adults included in the final analytic sample for depression analysis, 37.6 percent had a documented COVID-19 infection in 2020. Individuals with prior infection had significantly higher odds of receiving a depression diagnosis in 2021 compared to those without infection (OR = 1.48, p < 0.01). A similar pattern was observed for anxiety: among 371,491 adults, 38.1 percent had a COVID-19 infection, and infected individuals had 46 percent greater odds of receiving an anxiety diagnosis (OR = 1.46, p < 0.01), after adjusting for demographic and clinical characteristics. Conclusions: This study highlights the elevated risk of depression and anxiety among patients who had been infected with COVID-19, emphasizing the importance of addressing the mental health needs of individuals affected by the virus. Full article

This study evaluates the current Spanish insurance framework for catastrophic flood risk, administered by the Consorcio de Compensación de Seguros (CCS), based on nationwide loss data reported by the CCS for the period 1996–2020. The analysis of historical claims data enables a clear differentiation between frequent, low-cost events and infrequent, high-impact catastrophes. While the CCS has fulfilled a critical role in post-disaster compensation, the findings highlight the parallel need for ex ante risk mitigation strategies. The study proposes a more efficient, two-tier risk coverage model. Events whose impacts can be managed through standard insurance mechanisms should be underwritten by private insurers using actuarially fair premiums. In contrast, events with catastrophic implications—due to their scale or financial impact—should be addressed through general solidarity mechanisms, centrally managed by the CCS. Such a risk segmentation would improve the financial sustainability of the system and create fiscal space for prevention-oriented incentives. The current design of the CCS scheme may generate moral hazard, as flood exposure is not explicitly priced into the premium structure. Empirical findings support a shift towards a more transparent, incentive-aligned model that combines collective risk sharing with individual risk responsibility—an essential balance for effective climate adaptation and long-term resilience. Full article

Background/Objectives: Due to limited current evidence, this post-marketing database surveillance study aimed to investigate the occurrence of hospitalization due to community-acquired pneumonia (CAP) among patients with chronic obstructive pulmonary disease in Japan who received single-inhaler triple therapy (fluticasone furoate/umeclidinium/vilanterol; FF/UMEC/VI) or multiple-inhaler triple therapy (MITT). Methods: This retrospective cohort study used health insurance claims data from the Medical Data Vision Co., Ltd. database (November 2017–April 2023) to identify overall and incident users of FF/UMEC/VI or MITT. Index date was the start of FF/UMEC/VI or MITT. Hazard ratios (HRs) for CAP hospitalization were assessed using inverse probability of treatment weighting based on propensity scores (PS). Incidence rates and time to occurrence of CAP hospitalization were also assessed. Adjustments were made to the PS model to address missing body mass index data. Results: In total, 8790 and 10,881 patients were included in the overall FF/UMEC/VI and MITT cohorts, and 3939 and 4017 patients were included in the incident FF/UMEC/VI and MITT cohorts, respectively. HR for CAP hospitalization among incident users ranged from 1.05 to 1.15 across all PS adjustments. Similar incidence rates of CAP hospitalization were reported among both cohorts and across all PS adjustments. The cumulative adjusted incidence rates of first CAP hospitalization at 360 days post-index among incident users was 0.060 and 0.054 in the FF/UMEC/VI and MITT cohorts, respectively. Conclusions: There was no difference in the risk of CAP between patients treated with FF/UMEC/VI and MITT. This safety information may help healthcare providers select appropriate treatments. Full article

Background and Objectives: Air pollutants have been shown to affect hypertensive disorders and placental hypoxia due to vasoconstriction, inflammation, and oxidative stress. The objective of this study was to evaluate whether high levels of maternal exposure to heavy metals during the second trimester of pregnancy are associated with an increased risk of preeclampsia and eclampsia, using national health insurance claim data from South Korea. Methods: Data on mothers and their newborns from 2016 to 2020, provided by the National Health Insurance Service, were used (n = 1,274,671). Exposure data for ambient air pollutants (PM2.5, CO, SO₂, NO₂, and O₃) and heavy metals (Pb, Cd, Cr, Cu, Mn, Fe, Ni, and As) during the second trimester of pregnancy were retrieved from the Korea Environment Corporation. Atmospheric condition data based on the mother’s registration area were matched. A logistic regression model was adjusted for maternal age, infant sex, season of conception, and household income. Results: In total, 16,920 cases of preeclampsia and 592 cases of eclampsia were identified. In the multivariate model, copper exposure remained significantly associated with an increased risk of preeclampsia (odds ratio: 1.011; 95% confidence interval: 1.001–1.023), and higher ozone exposure during pregnancy was associated with an elevated risk of eclampsia. Conclusions: Increased copper exposure during the second trimester of pregnancy was associated with a high incidence of preeclampsia. Full article

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy. Full article

By employing the analytic hierarchy process (AHP), we investigated consumer demand and preferences for InsurTech technologies. A survey was conducted with 350 respondents, yielding 348 completed questionnaires and achieving a response rate of 99.4%. After consistency checks, 78 invalid questionnaires were excluded, resulting in 270 valid responses, with an effective response rate of 77.3%. Using the Analytic Hierarchy Process (AHP), five critical dimensions were identified as top priorities for insurance companies implementing InsurTech solutions: (1) video insurance and mobile applications, (2) blockchain-based claims processing, (3) robo-advisors, (4) the Internet of Things (IoT), and (5) big data analytics. Video and mobile applications, along with blockchain, accounted for over 30% of the total importance of evaluating InsurTech technologies. Among the assessment criteria, “mobile applications” and “remote insurance” had the highest weights, highlighting their roles in the InsurTech service model. Insurance providers need to prioritize these two dimensions when designing their InsurTech service models to enhance service convenience and the customer experience. Full article

In this paper, we introduce a generalised mutually exciting Hawkes process with random and independent jump intensities. This model provides a robust theoretical framework for modelling complex point processes and appropriately characterises the financial system, especially during periods of crisis. Based on this extended Hawkes process, we propose an insurance claim process and demonstrate that claim processes modelled as an aggregated process enable early detection of crises and inform optimal investment strategies in a financial system. Full article

When cells remain permanently trapped in a particular cell cycle stage, they are in senescence. This also occurs in the liver. Such cells are often referred to as “zombie cells”, and an entire organ filled with these “zombie cells” is said to be in a “zombie-like” state, characterized by a lack of function. The senescence-associated secretory phenotype (SASP) encompasses the substances these “zombie cells” release, which can significantly affect nearby cells and tissues. While cellular senescence and SASP are related concepts, they are distinct. This scoping review aims to clarify the role of hepatocyte senescence and hepatocyte SASP in the administration of pharmaceuticals, as well as their relevance to medico-legal practice, disability claims, and insurance coverage. In this context, the effects of pharmaceuticals on senescent hepatocytes are discussed, particularly regarding the medico-legal implications of substances likely to be abused. In conclusion, hepatocyte senescence may be relevant in clinical or medico-legal work because it sheds a new light on interpreting clinical findings and expert witness statements. Full article

The coronavirus (COVID-19) pandemic has profoundly affected public health and socio-economic structures globally. This research conducted a post-COVID-19 analysis of the role of fiscal support interventions on COVID-19 health regulations such as mandatory non-pharmaceutical interventions like face masks, social distancing, periodic lockdowns which include restrictions on movement, and socio-economic dimensions. This quantitative research obtained 302 responses from different households in the Eastern Cape, Gauteng, Kwa-Zulu Natal, and Limpopo Provinces in South Africa. The results reveal that the relief fund (R350 unemployment grant, unemployment insurance fund claim, and food parcel distribution, among others) mediated the relationship between COVID-19 health regulations and poverty levels and the relationship between COVID-19 health regulations and health and well-being. The relief fund also mediated the relationship between COVID-19 health regulations and employment levels. Support packages from the R500 billion government support, which included loan guarantees, job support, tax and payment deferrals and holidays, social grants, wage guarantees, health interventions, and municipalities support, moderate the relationship between COVID-19 health regulations and the family and social support. These results validate the impact of the fiscal support intervention by the government in mitigating its emergency intervention with COVID-19 health regulations. This strengthens the theory of intervention, highlighting that multiple dynamics make interventions complex as shown by mediation and moderation results. Furthermore, this study highlights intervention being central to the management of the crisis. The study highlights the importance of comprehensive intervention for future preparedness, thus advancing the crisis–intervention perspective. Advances in these areas are critical to mitigate the impact of the next pandemic or similar major events in society. This can be achieved through improved pandemic timely response with effective economic stimulus, social relief, strong legal framework, and anti-corruption policies. Full article

The estimation of age-to-age development factors is fundamental to loss reserving, with direct implications for risk management and regulatory compliance in the auto insurance sector. The precise and robust estimation of these factors underpins the credibility of case reserves and the effective management of future claim liabilities. This study investigates the underlying structure and sources of variability in development factor estimates by applying multivariate statistical techniques to the analysis of development triangles. Departing from conventional univariate summaries (e.g., mean or median), we introduce a comprehensive framework that incorporates temporal clustering of development factors and addresses associated modeling complexities, including high dimensionality and temporal dependency. The proposed methodology enhances interpretability and captures latent structures in the data, thereby improving the reliability of reserve estimates. Our findings contribute to the advancement of reserving practices by offering a more nuanced understanding of development factor behavior under uncertainty. Full article

Background/Objectives: Parkinson’s disease (PD) is a major neurodegenerative condition, mainly treated using dopamine-based therapies. However, the side effects and limitations of these therapies hinder their use. This study aimed to analyze the utilization of Korean medicine (KM) by patients with PD in Korea. Methods: Data of the Health Insurance Review and Assessment Service–National Patient Sample were used to investigate the status and trend of KM utilization by patients with PD in Korea from January 2010 to December 2019. Data of 18,562 patients were included, and analyses were performed on the status of KM and Western medicine (WM) utilization, cost of care, prescribed medications, comorbidities, and characteristics of patients with PD. Results: The number of patients who utilized KM services for PD gradually increased over the 10-year period, with 10.6% of all patients with PD using KM services in 2019. In addition, the number of KM users with PD, number of claims, and expenses all showed an increase. The rate of increase in KM service utilization was greater than that of WM. Among KM services, acupuncture had the highest expense (50.6%). Regarding comorbidities in patients with PD, musculoskeletal diseases were the most common (58.6%). Among WM medications prescribed for the KM users, dopa and dopa derivatives (15.5%) and anti-dementia drugs (11.7%) were the most common. Conclusions: This study provides useful information on KM utilization status and trends among patients with PD and the characteristics of these patients. Follow-up research is warranted on the utilization status of more diverse KM services. Full article

Background: Assessing recent changes in mortality among patients undergoing hemodialysis (HD) can help both to identify the causes of death most closely associated with these changes and to develop prevention strategies. This study explored trends in all-cause and cause-specific mortality among patients undergoing HD in South Korea using an analysis of national data. Methods: We used national death certificate and claims data from 2003 to 2021 provided by the National Health Insurance Service. Age-standardized mortality rates (ASRs) were calculated by standardizing to the 2011 population of patients undergoing HD. Joinpoint regression analysis was performed to calculate the annual percentage change (APC) in mortality. All-cause and cause-specific ASRs and APCs were evaluated for the study period. Results: The proportion of male and older adult patients increased over time. In particular, the proportion of patients aged ≥ 80 years in the 2018–2021 period was more than 4 times higher than in the 2003–2007 period. From 2003 to 2021, there were a total of 136,302 deaths among patients undergoing HD in South Korea. Cardiovascular causes accounted for 13.6% of deaths, and the majority (86.4%) were attributed to noncardiovascular causes. In 2003, the all-cause ASR was 174.1 per 1000 person-years, which steadily decreased to 114.5 per 1000 person-years in 2021. The ASR from cardiovascular disease remained unchanged from 2003 to 2013 but increased by 3.9% (95% confidence interval: 1.3 to 14.0) per year from 2013 to 2021. In contrast, the ASR from noncardiovascular disease decreased during the study period. Conclusions: Nationally representative data showed a declining trend in the ASR among patients undergoing HD from 2003 to 2021. Noncardiovascular disease mortality decreased during the study period, while cardiovascular disease mortality increased. Full article

This study addresses key challenges in auto insurance territory risk analysis by examining the complexities of spatial loss data and the evolving landscape of territorial risks before and during the COVID-19 pandemic. Traditional approaches, such as spatial clustering, are commonly used for territory risk assessment but offer limited predictive capabilities, constraining their effectiveness in forecasting future losses, an essential component of insurance pricing. To overcome this limitation, we propose an advanced predictive modeling framework that integrates spatial loss patterns while accounting for the pandemic’s impact. Our Bayesian-based spatial model captures stochastic spatial autocorrelations among territory rating units and their neighboring regions. This approach enables more robust pattern recognition through predictive modeling. By applying this approach to regulatory auto insurance loss datasets, we analyze industry-level trends in claim frequency, loss severity, loss cost, and insurance loading. The results reveal significant shifts in spatial loss patterns before and during the pandemic, highlighting the dynamic interplay between regional risk factors and external disruptions. These insights provide valuable guidance for insurers and regulators, facilitating more informed decision-making in risk classification, pricing adjustments, and policy interventions in response to evolving spatial and economic conditions. Full article