Hasil untuk "Insurance"

Katja Oetinger, Katja Oetinger, Anika Tyana Heudier et al.

IntroductionIndividuals with spinal cord injury (SCI) have a higher prevalence of mental health problems than the general population but face significant barriers to accessing outpatient psychotherapy. Understanding the factors that influence therapists' willingness to treat this population is critical for improving mental healthcare equity.MethodsWe conducted a cross-sectional online survey among licensed outpatient psychotherapists in Southern Germany. All therapists registered with the Association of Statutory Health Insurance Physicians who had an email address or an online contact form were invited to participate. In total, 677 complete datasets were analyzed in this study. Using logistic regression, we examined the associations between therapists' self-reported willingness to accept a hypothetical therapy request from an individual with SCI and nine potential influencing factors, including personal, emotional, and organizational variables.ResultsSix variables were significantly associated with the therapists' willingness. Therapists who agreed to provide home-based therapy had higher odds of being in the willing group [OR = 2.28, 95% CI [1.50, 3.46], p < 0.001], as did those who reported a stronger feeling of preparedness [OR = 1.83, 95% CI [1.51, 2.21], p < 0.001] and greater field experience [OR = 1.34, 95% CI [1.11, 1.61], p = 0.002]. In contrast, older age [OR = 0.83, 95% CI [0.74, 0.92], p < 0.001], higher levels of emotional response [OR = 0.82, 95% CI [0.68, 0.99], p = 0.040], and workload concerns [OR = 0.73, 95% CI [0.55, 0.95], p = 0.020] were associated with lower odds of being in the willing group.ConclusionWillingness to provide psychotherapy for individuals with SCI is associated with both modifiable (e.g., training, preparedness, home visit policies) and non-modifiable (e.g., age) factors. These findings highlight the importance of disability-specific education and structural adjustments to reduce access barriers. Although the present study was limited to Southern Germany, reports from other countries, such as Australia and Switzerland, document a low uptake of psychotherapy among individuals with SCI, suggesting that this may represent a broader challenge across healthcare systems. Our results thus contribute to a better understanding of provider-side barriers in Germany and may stimulate further international research into disability-inclusive mental healthcare provision.

Panyi Dong, Zhiyu Quan

With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to sustain their competitive edge. This paper provides both a conceptual overview and practical case studies of natural language processing (NLP) and its emerging applications within insurance operations, focusing on transforming raw, unstructured text into structured data suitable for actuarial analysis and decision-making. Leveraging real-world alternative data provided by an InsurTech industry partner that enriches traditional insurance data sources, we apply various NLP techniques to demonstrate feature de-biasing, feature compression, and industry classification in the commercial insurance context. These enriched, text-derived insights not only add to and refine traditional rating factors for commercial insurance pricing but also offer novel perspectives for assessing underlying risk by introducing novel industry classification techniques. Through these demonstrations, we show that NLP is not merely a supplementary tool but a foundational element of modern, data-driven insurance analytics.

Samuel J. Eschker, Antik Chakraborty, Melanie Gall et al.

Mortgage default rates, on the one hand, serve as a measure of economic health to support decision-making by insurance companies, and on the other hand, is a key risk factor in the asset-liability management (ALM) practice, as mortgage related assets constitute a significant proportion of insurers' investment portfolios. This paper studies the relationship between economic losses due to natural hazards and mortgage default rates. The topic is greatly relevant to the insurance industry, as excessive insurance losses from natural hazards can lead to a surge in mortgage defaults, creating compounded challenges for insurers. To this end, we apply a state-space modeling approach to decouple the effect of natural hazard losses on mortgage default rates after controlling for other economic determinants through the inclusion of latent variables. Moreover, we consider a sliced variant of the classical SSM to capture the subtle relationship that only emerges when natural hazard losses are sufficiently high. Our model verifies the significance of this relationship and provides insights into how natural hazard losses manifest as increased mortgage default rates.

Laura K. Winstone‐Weide, Kelly Gettig, Cynthia A. Austin

Abstract Introduction The objective of this study was to confirm previous risk factors for concussion recovery in a diverse pediatric sample and to elucidate the pathways by which individual mental health factors influence postconcussive symptom reporting and time to clearance. Methods Subjects between 13 and 17 years of age (N = 642; mean age = 15.40; 45% female) were analyzed from a prospectively completed database associated with a multidisciplinary TBI/concussion clinic in the southwest United States. Fifty‐four percent of participants identified as Hispanic, 41% received medical coverage through Medicaid, and 54% were injured during participation in an organized sports team. Mediation analysis using a structural equational framework was employed to examine the significance of both direct and indirect effects from preinjury factors (e.g., prior concussions, female gender, history of migraines, anxiety, depression, attention‐deficit/hyperactivity disorder [ADHD], and learning disorders) on postinjury symptom reporting (at baseline and visit 1) and time to clearance. Results Higher symptom reporting at baseline was significantly associated with history of anxiety, depression, ADHD, headaches, and female gender. Higher symptom reporting at visit 1 was significantly associated with baseline symptoms, female gender, and history of anxiety. Symptom scores at baseline fully accounted for the relation between history of depression and symptom scores at visit 1 and only partially accounted for the relation between history of anxiety and symptom scores at visit 1. Only history of anxiety indirectly contributed to greater days to clearance through higher symptom scores at visit 1. Discussion This study supports the concept that heterogenous experience following injury is influenced by preinjury factors and extends the generalizability of risk factors to a diverse sample of youth in terms of ethnicity, insurance status/type, and mechanism of injury. Anxiety and depression represent important noninjury factors that warrant considerable attention during concussion treatment and management.

Chul Young Yoon, Junhun Lee, Tae Hoon Kong et al.

<h4>Objective</h4>Vascular disease like small-vessel disease (SVD) is the most likely cause among the potential causes of Sudden sensorineural hearing loss (SSNHL). Understanding the relationship between SVD and SSNHL is crucial for developing effective prevention and treatment strategies. To confirm the relationship between SVD and SSNHL, the effect of SVD is confirmed by focusing on the duration and recurrence of SSNHL.<h4>Methods</h4>This article reports a retrospective observational study that investigated the relationship between SVD and SSNHL using the South Korea Health Insurance Review and Assessment Service (HIRA) database from 2010 to 2020. This retrospective observational study included 319,569 SSNHL patients between 2010 and 2020.<h4>Results</h4>Participant demographics were controlled using Propensity Score Matching. The hazard ratios (HR) for the effect of SVD on the duration of SSNHL were 1.045 for the group with SVD before the onset of SSNHL and 1.234 for the group with SVD after the onset of SSNHL. SVD was statistically significant for the recurrence of SSNHL, with an odds ratio of 1.312 in the group with SVD compared to the group without SVD. The HR for the period until a recurrence in the group with SVD was 1.062.<h4>Conclusions</h4>The study identified SVD as a possible cause of SSNHL and found that the duration of SSNHL increased only in the presence of SVD. SVD also affected the recurrence of SSNHL, with the recurrence rate being 1.312 times higher in the group with SVD.

Nana Yaw Asabere, Isaac Ofori Asare, Gare Lawson et al.

Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand-driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location-aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web-based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F-measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE).

Heidi Silvén, Susanna M. Savukoski, Paula Pesonen et al.

Abstract Introduction To investigate the occurrence of previous cancer diagnoses in women suffering from premature ovarian insufficiency (POI) and compare it with the general population, shedding light on the association between cancer, cancer treatments, and POI. Material and methods We conducted a nationwide case–control study based on registry data from various sources, including the Social Insurance Institution, Finnish Population Information System, and Finnish Cancer Registry spanning from 1953 to 2018. Our participants comprised all women in Finland who, between 1988 and 2017, received hormone replacement therapy reimbursement for ovarian insufficiency before the age of 40 years (n = 5221). Controls, matched in terms of age and municipality of residence, were selected from the Finnish Population Information System (n = 20 822). Our main exposure variable was a history of cancer diagnosis preceding the diagnosis of POI. We analyzed odds ratios (OR) to compare the prevalence of previous cancers in women with POI with that in controls, stratifying results based on cancer type, age at cancer diagnosis, and the time interval between cancer diagnosis and POI. We also assessed changes in OR for previous cancer diagnoses over the follow‐up period. Results Out of the women diagnosed with POI, 21.9% had previously been diagnosed with cancer, resulting in an elevated OR of 36.5 (95% confidence interval [CI] 30.9 to 43.3) compared with 0.8% of the controls. The risk of developing POI was most pronounced during the first 2 years following a cancer diagnosis, with an OR of 103 (95% CI 74.1 to 144). Importantly, this risk remained elevated even when the time interval between cancer and POI exceeded 10 years, with an OR of 5.40 (95% CI 3.54 to 8.23). Conclusions This study reveals that 21.9% of women with POI have a history of cancer, making the prevalence of cancer among these women 27.5 times higher than age‐matched controls in the Finnish population. The risk of developing POI is most substantial in the first 2 years following a cancer diagnosis. These findings underscore the role of cancer treatments as an etiological factor for POI and emphasize the importance of recognizing the risk of POI in cancer survivors for early diagnosis and intervention.

Jayanthi Vajiram, Negha Senthil, Nean Adhith. P

Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims.

Nadine ACeituno-Moya, Fred Torres-Cruz

This article presents a study that uses multiple linear regression analysis to examine the factors influencing the number of people affiliated with different insurance plans within the Comprehensive Health Insurance (SIS) system in Peru.The study highlights the importance of multiple linear regression analysis in understanding the factors that affect SIS Comprehensive Health Insurance affiliates. It also showcases the value of utilizing interactive tools like RShiny to enhance data analysis, providing a dynamic and participatory experience for researchers and users interested in the subject.To facilitate the analysis and visualization of SIS-related data, the researchers developed an interactive application using RShiny. This tool allows for the easy loading, visualization, and analysis of data in a user-friendly and practical manner. By providing an interactive platform, users can effectively explore and understand the factors that impact SIS affiliates.The results of the analysis indicate that the selected variables have a significant positive influence on the total number of affiliates. This suggests that the specific insurance plan examined in this study has a favorable effect on the enrollment of individuals in SIS. Additionally, the data shows a linear trend, supporting the use of a linear regression model to describe this relationship. Active affiliates,Comprehensive health insurance SIS,Data Visualization,Multiple Linear Regression Analysis,RShiny

Sean B. Sequeira, MD, Henry R. Boucher, MD

Background: Unicompartmental knee arthroplasty (UKA) is a common orthopedic procedure with overall good clinical outcomes; however, more recent literature has identified disparities in treatment access and outcomes based on sociodemographic factors. There is a paucity of literature examining whether payor type, including Medicare, Medicaid, and commercial insurance types, impacts early medical complications and rates of reoperation following a UKA. Methods: Patients with Medicare, Medicaid, or commercial payor type who underwent primary medial or lateral UKA between 2010 and 2019 were identified using a large national database. Ninety-day incidence of emergency department visit and 1-year incidence of revision, revision to arthroplasty, reimbursement, and cost of care were evaluated. Propensity score matching was used to control for patient demographic factors and comorbidities as covariates. Results: Medicaid insurance was associated with an increased risk of emergency room visit (odds ratio [OR] 2.77; P < .001), revision surgery (OR 1.85; P < .001), and conversion to total knee arthroplasty (OR 1.50; P = .0292) compared to commercially insured patients. Medicaid insurance was associated with an increased risk of emergency room visit (OR 3.58; P < .001), revision surgery (OR 1.97; P < .001), and conversion to total knee arthroplasty (OR 1.80; P = .003). Medicaid patients were associated with a higher overall cost of care and lower reimbursement than commercial and Medicare patients (P < .001 and P < .001, respectively). Conclusions: These findings demonstrate that payor type is associated with increased rates of reoperation and health-care utilization following UKA despite controlling for covariates. Additional work is required to understand the complex relationship between socioeconomic status and outcomes to ensure appropriate health-care access for all patients and pursue appropriate risk stratification. Level of Evidence: III, retrospective chart review.

Marie Michaelides, Mathieu Pigeon, Hélène Cossette

The occurrence of a claim often impacts not one but multiple insurance coverages provided in the contract. To account for this multivariate feature, we propose a new individual claims reserving model built around the activation of the different coverages to predict the reserve amounts. Using the framework of multinomial logistic regression, we model the activation of the different insurance coverages for each claim and their development in the following years, i.e. the activation of other coverages in the later years and all the possible payments that might result from them. As such, the model allows us to complete the individual development of the open claims in the portfolio. Using a recent automobile dataset from a major Canadian insurance company, we demonstrate that this approach generates accurate predictions of the total reserves as well as of the reserves per insurance coverage. This allows the insurer to get better insights in the dynamics of his claims reserves.

Theis Bathke, Marcus Christiansen

Forward transition rates were originally introduced with the aim to evaluate life insurance liabilities market-consistently. While this idea turned out to have its limitations, recent literature repurposes forward transition rates as a tool for avoiding Markov assumptions in the calculation of life insurance reserves. While life insurance reserves are some form of conditional first-order moments, the calculation of conditional second-order moments needs an extension of the forward transition rate concept from one dimension to two dimensions. Two-dimensional forward transition rates are also needed for the calculation of path-dependent life insurance cash-flows as they occur upon contract modifications. Forward transition rates are designed for doing prospective calculations, and by a time-symmetric definition of so-called backward transition rates one can do retrospective calculations.

Fu-Shun Yen, Ming-Chih Hou, Ming-Chih Hou et al.

Background: Adequate management of diabetes in patients with liver cirrhosis can be challenging. We conducted this study to investigate the liver-related long term outcomes of alpha-glucosidase inhibitors (AGIs) in patients with diabetes and cirrhosis.Methods: From National Health Insurance Research Database (NHIRD) in Taiwan, we recruited propensity-score matched alpha-glucosidase inhibitor users and non-users from a cohort of type 2 diabetes mellitus (T2DM) with compensated liver cirrhosis between 1 January 2000, and 31 December 2017, and followed them until 31 December 2018. Cox proportional hazards models with robust sandwich standard error estimates were used to assess the risk of main outcomes for alpha-glucosidase inhibitor users versus non-users.Results: The incidence rates of mortality during follow-up were 65.56 vs. 96.06 per 1,000 patient-years for alpha-glucosidase inhibitor users and non-users, respectively. The multivariable-adjusted model shows that alpha-glucosidase inhibitor users had significantly lower risks of all-cause mortality (aHR 0.63, 95% CI 0.56–0.71), hepatocellular carcinoma (aHR 0.55, 95% CI 0.46–0.67), decompensated cirrhosis (aHR 0.74 95% CI 0.63–0.87), hepatic encephalopathy (aHR 0.72, 95% CI 0.60–0.87), and hepatic failure (aHR 0.74, 95% CI 0.62–0.88) than alpha-glucosidase inhibitor non-users. Patients who received alpha-glucosidase inhibitors for a cumulative duration of more than 364 days had significantly lower risks of these outcomes than non-users.Conclusion: Alpha-glucosidase inhibitor use was associated with a lower risk of mortality, hepatocellular carcinoma, decompensated cirrhosis, and hepatic failure in patients with diabetes and compensated cirrhosis. alpha-glucosidase inhibitors may be useful for the management of diabetes in patients with compensated liver cirrhosis. Large-scale prospective studies are required to verify our results.

Yung-Chung Tsao, Fu-Jen Cheng, Yi-Hua Li et al.

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients’ health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.

Emmanuel Coffie, Sindre Duedahl, Frank Proske

In modern life insurance, Markov processes in continuous time on a finite or at least countable state space have been over the years an important tool for the modelling of the states of an insured. Motivated by applications in disability insurance, we propose in this paper a model for insurance states based on Markov jump processes with more general state spaces. We use this model to derive a new type of Thiele's differential equation which e.g. allows for a consistent calculation of reserves in disability insurance based on two-parameter continuous time rehabilitation rates.

Wrya Vakili, Alireza Ghaffari-Hadigheh

We investigate the role of reinsurance in maximizing the wealth of an insurance company. We use Liu's uncertainty theory (B. Liu, 2007) for the problem modeling and follow-up computations. The uncertainty measure of ruin for the insurance company is considered as the optimization criterion. Since calculating the ruin index is very difficult, we introduce a simple computational method to identify the uncertain measure of ruin for an insurance company. Finally, a generalized model is presented, granting the model be more practical.

Kate Donahue, Solon Barocas

Consider a cost-sharing game with players of different contribution to the total cost: an example might be an insurance company calculating premiums for a population of mixed-risk individuals. Two natural and competing notions of fairness might be to a) charge each individual the same price or b) charge each individual according to the cost that they bring to the pool. In the insurance literature, these general approaches are referred to as "solidarity" and "actuarial fairness" and are commonly viewed as opposites. However, in insurance (and many other natural settings), the cost-sharing game also exhibits "externalities of size": all else being equal, larger groups have lower average cost. In the insurance case, we analyze a model with externalities of size due to a reduction in the variability of losses. We explore how this complicates traditional understandings of fairness, drawing on literature in cooperative game theory. First, we explore solidarity: we show that it is possible for both groups (high and low risk) to strictly benefit by joining an insurance pool where costs are evenly split, as opposed to being in separate risk pools. We build on this by producing a pricing scheme that maximally subsidizes the high risk group, while maintaining an incentive for lower risk people to stay in the insurance pool. Next, we demonstrate that with this new model, the price charged to each individual has to depend on the risk of other participants, making naive actuarial fairness inefficient. Furthermore, we prove that stable pricing schemes must be ones where players have the anti-social incentive of desiring riskier partners, contradicting motivations for using actuarial fairness. Finally, we describe how these results relate to debates about fairness in machine learning and potential avenues for future research.

Alois Pichler, Dana Uhlig

The Covid-19 pandemic still causes severe impacts on society and the economy. This paper studies excess mortality during the pandemic years 2020 and 2021 in Germany empirically with a special focus on the life insurer's perspective. Our conclusions are based on official counts of German governmental offices on the living and deaths of the entire population. Conclusions, relevant for actuaries and specific insurance business lines, including portfolios of pension, life, and health insurance contracts, are provided.

Yichun Chi, Xun Yu Zhou, Sheng Chao Zhuang

We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle. We derive the optimal policy semi-analytically, which is coinsurance above a deductible when the variance bound is binding. This policy automatically satisfies the incentive-compatible condition, which is crucial to rule out ex post moral hazard. We also find that the deductible is absent if and only if the contract pricing is actuarially fair. Focusing on the actuarially fair case, we carry out comparative statics on the effects of the insured's initial wealth and the variance bound on insurance demand. Our results indicate that the expected coverage is always larger for a wealthier insured, implying that the underlying insurance is a normal good, which supports certain recent empirical findings. Moreover, as the variance constraint tightens, the insured who is prudent cedes less losses, while the insurer is exposed to less tail risk.

Rosy Oh, Youngju Lee, Dan Zhu et al.

Typical risk classification procedure in insurance is consists of a priori risk classification determined by observable risk characteristics, and a posteriori risk classification where the premium is adjusted to reflect the policyholder's claim history. While using the full claim history data is optimal in a posteriori risk classification procedure, i.e. giving premium estimators with the minimal variances, some insurance sectors, however, only use partial information of the claim history for determining the appropriate premium to charge. Classical examples include that auto insurances premium are determined by the claim frequency data and workers' compensation insurances are based on the aggregate severity. The motivation for such practice is to have a simplified and efficient posteriori risk classification procedure which is customized to the involved insurance policy. This paper compares the relative efficiency of the two simplified posteriori risk classifications, i.e. based on frequency versus severity, and provides the mathematical framework to assist practitioners in choosing the most appropriate practice.