Hasil untuk "Insurance"

P. Chiappori, B. Salanié

T. Rolski

Lawrence Crosby, Nancy Stephens

B. Handel

Vicente Cunat Martinez

Sun-Ok Song, C. Jung, Y. Song et al.

Background The National Health Insurance Service (NHIS) recently signed an agreement to provide limited open access to the databases within the Korean Diabetes Association for the benefit of Korean subjects with diabetes. Here, we present the history, structure, contents, and way to use data procurement in the Korean National Health Insurance (NHI) system for the benefit of Korean researchers. Methods The NHIS in Korea is a single-payer program and is mandatory for all residents in Korea. The three main healthcare programs of the NHI, Medical Aid, and long-term care insurance (LTCI) provide 100% coverage for the Korean population. The NHIS in Korea has adopted a fee-for-service system to pay health providers. Researchers can obtain health information from the four databases of the insured that contain data on health insurance claims, health check-ups and LTCI. Results Metabolic disease as chronic disease is increasing with aging society. NHIS data is based on mandatory, serial population data, so, this might show the time course of disease and predict some disease progress, and also be used in primary and secondary prevention of disease after data mining. Conclusion The NHIS database represents the entire Korean population and can be used as a population-based database. The integrated information technology of the NHIS database makes it a world-leading population-based epidemiology and disease research platform.

J. Gallagher

Ankita Kundu, Istvan Fekete, Sakhi Roy

Abstract Background Tobacco use among women in India remains under-researched despite its growing public health burden and strong sociocultural influences. Understanding its determinants is essential for designing gender-sensitive interventions. Methods This study analysed data from 724,115 women aged 15–49 years from the National Family Health Survey (NFHS-5, 2019–2021). A binary logistic regression model estimated the marginal effects of demographic, socioeconomic, and health-related predictors of tobacco consumption (smoked or smokeless). Results Overall, 6.28% of women reported current tobacco use. Prevalence increased sharply with age, peaking among women aged 45–49 years (+8.14 percentage points relative to those aged 15–19 years). Tobacco use was substantially higher among women with no formal education (+10.91 percentage points), those in the poorest wealth quintile (+11.79 percentage points), and residents of select Northeastern states, where prevalence exceeded 50%. In contrast, higher educational attainment and greater household wealth were associated with lower tobacco use. Women covered by health insurance (+2.04 percentage points) and those reporting chronic health conditions (+1.38 percentage points) exhibited higher tobacco use, suggesting possible behavioural risk compensation and stress-related coping mechanisms. After adjustment for socioeconomic and educational factors, rural residence was associated with a modest reduction in tobacco use. All estimated associations were statistically significant at the 1% level (p < 0.01). Conclusions Female tobacco use in India is shaped by intersecting demographic, economic, and health vulnerabilities, with novel evidence of higher use among insured and chronically ill women. Policy implications Targeted cessation efforts for older, less-educated, and low-income women, integration of tobacco screening into primary care and chronic disease management, and insurance-linked cessation incentives are recommended to address these disparities.

Frederik Zuiderveen Borgesius, Marvin van Bekkum, Iris van Ooijen et al.

Two modern trends in insurance are data-intensive underwriting and behavior-based insurance. Data-intensive underwriting means that insurers analyze more data for estimating the claim cost of a consumer and for determining the premium based on that estimation. Insurers also offer behavior-based insurance. For example, some car insurers use artificial intelligence (AI) to follow the driving behavior of an individual consumer in real-time and decide whether to offer that consumer a discount. In this paper, we report on a survey of the Dutch population (N=999) in which we asked people's opinions about examples of data-intensive underwriting and behavior-based insurance. The main results include: (i) If survey respondents find an insurance practice unfair, they also find the practice unacceptable. (ii) Respondents find almost all modern insurance practices that we described unfair. (iii) Respondents find practices for which they can influence the premium fairer. (iv) If respondents find a certain consumer characteristic illogical for basing the premium on, then respondents find using the characteristic unfair. (v) Respondents find it unfair if an insurer offers an insurance product only to a specific group. (vi) Respondents find it unfair if an insurance practice leads to the poor paying more. We also reflect on the policy implications of the findings.

Liang Hong

In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification, (ii) selection effect, and (iii) lack of finite-sample validity. This article addresses these three issues simultaneously by employing conformal prediction -- a general machine learning strategy for valid predictions. The proposed method is both model-free and tuning-parameter-free. It also guarantees finite-sample validity at a pre-assigned coverage probability level. Examples, based on both simulated and real data, are provided to demonstrate the excellent performance of the proposed method and its applications in insurance, especially regarding meeting the solvency capital requirement of European insurance regulation, Solvency II.

Tianhe Zhang, Suhan Liu, Peng Shi

Fairness has emerged as a critical consideration in the landscape of machine learning algorithms, particularly as AI continues to transform decision-making across societal domains. To ensure that these algorithms are free from bias and do not discriminate against individuals based on sensitive attributes such as gender and race, the field of algorithmic bias has introduced various fairness concepts, along with methodologies to achieve these notions in different contexts. Despite the rapid advancement, not all sectors have embraced these fairness principles to the same extent. One specific sector that merits attention in this regard is insurance. Within the realm of insurance pricing, fairness is defined through a distinct and specialized framework. Consequently, achieving fairness according to established notions does not automatically ensure fair pricing in insurance. In particular, regulators are increasingly emphasizing transparency in pricing algorithms and imposing constraints on insurance companies on the collection and utilization of sensitive consumer attributes. These factors present additional challenges in the implementation of fairness in pricing algorithms. To address these complexities and comply with regulatory demands, we propose an efficient method for constructing fair models that are tailored to the insurance domain, using only privatized sensitive attributes. Notably, our approach ensures statistical guarantees, does not require direct access to sensitive attributes, and adapts to varying transparency requirements, addressing regulatory demands while ensuring fairness in insurance pricing.

Mario Ghossoub, Bin Li, Benxuan Shi

We consider a monopoly insurance market with a risk-neutral profit-maximizing insurer and a consumer with Yaari Dual Utility preferences that distort the given continuous loss distribution. The insurer observes the loss distribution but not the risk attitude of the consumer, proxied by a distortion function drawn from a continuum of types. We characterize the profit-maximizing, incentive-compatible, and individually rational menus of insurance contracts, show that equilibria are separating, and provide key properties thereof. Notably, insurance coverage and premia are monotone in the level of risk aversion; the most risk-averse consumer receives full insurance $(\textit{efficiency at the top})$; the monopoly absorbs all surplus from the least-risk averse consumer; and consumers with a higher level of risk aversion induce a higher expected profit for the insurer. Under certain regularity conditions, equilibrium contracts can be characterized in terms of the marginal loss retention per type of consumer, and they consist of menus of layered deductible contracts, where each such layered structure is determined by the risk type of the consumer. In addition, we examine the effect of a fixed insurance provision cost on equilibria. We show that if the fixed cost is prohibitively high, then there will be no $\textit{ex ante}$ gains from trade. However, when trade occurs, separating equilibrium contracts always outperform pooling equilibrium contracts, and they are identical to those obtained in the absence of fixed costs, with the exception that only part of the menu is excluded. The excluded contracts are those designed for consumers with relatively lower risk aversion, who are less valuable to the insurer. Finally, we characterize incentive-efficient menus of contracts in the context of an arbitrary type space.

Abdillah Ahsan, Maulida Gadis Utami, Yuyu Buono Ayuning Pertiwi et al.

Objectives This study investigated the correlation between the type of health insurance membership as a proxy for the economic status of patients and the severity of their type two diabetes mellitus (T2DM) in Indonesia.Design The study conducted a secondary analysis of National Health Insurance (Jaminan Kesehatan Nasional) claim data provided by the Indonesian Social Security Agency, Badan Penyelenggara Jaminan Sosial (BPJS). We used ordered logistic regression with four severity levels for T2DM (0=outpatient, I=mild, II=moderate, III=severe) as dependent variables. The main independent variables (insurance membership categories) included subsidised insurance members (PBI), a combination of formally employed and nonsalaried informal workers (PBPU &amp; PPU) and nonworkers (BP).Setting Secondary healthcare facilities in Indonesia.Participants The dataset included 2 989 618 claims for hospital visits of people with T2DM from 2018 to 2022.Primary outcome measures Severity level of T2DM patients.Result A higher percentage of T2DM patients who visited healthcare facilities with subsidised insurance (PBI), which represents a low-income group, have severe disease (6.9%) than patients in the PBPU &amp; PPU (4.9%) and BP categories (5.5%). Moreover, regression analysis revealed that having PBI membership status was associated with a greater OR of having severe T2DM than nonsubsidised members. Among T2DM patients in the nonsubsidised insurance category, workers (PBPU &amp; PPU) had an OR of 0.74 (95% CI: 0.735 to 0.745; p&lt;0.0001) for having severe disease during hospital visits. Moreover, non-workers (BP) had a lower OR of 0.718 (95% CI: 0.711 to 0.725; p&lt;0.0001) for severe disease than the PBI category.Conclusion These findings illustrate the lack of optimal access to health services for diabetes patients in low-income insurance membership categories and the challenges of better treatment in health facilities for low-income patients.

Sunil J. Wimalawansa

Neglecting preventive healthcare policies has contributed to the global surge in chronic diseases, increased hospitalizations, declining quality of care, and escalating costs. Non-communicable diseases (NCDs)—notably cardiovascular conditions, diabetes, and cancer—consume over 80% of healthcare expenditure and account for more than 60% of global deaths, which are projected to exceed 75% by 2030. Poor diets, sedentary lifestyles, regulatory loopholes, and underfunded public health initiatives are driving this crisis. Compounding the issue are flawed policies, congressional lobbying, and conflicts of interest that prioritize costly, hospital-based, symptom-driven care over identifying and treating to eliminate root causes and disease prevention. Regulatory agencies are failing to deliver their intended functions. For instance, the U.S. Food and Drug Administration’s (FDA) broad oversight across drugs, devices, food, and supplements has resulted in inefficiencies, reduced transparency, and public safety risks. This broad mandate has allowed the release of unsafe drugs, food additives, and supplements, contributing to the rising childhood diseases, the burden of chronic illness, and over-medicalization. The author proposes separating oversight responsibilities: transferring authority over food, supplements, and OTC products to a new Food and Nutraceutical Agency (FNA), allowing the FDA to be restructured as the Drug and Device Agency (DDA), to refocus on pharmaceuticals and medical devices. While complete reform requires Congressional action, interim policy shifts are urgently needed to improve public health. Broader structural changes—including overhauling the Affordable Care Act, eliminating waste and fraud, redesigning regulatory and insurance systems, and eliminating intermediaries are essential to reducing costs, improving care, and transforming national and global health outcomes. The information provided herein can serve as a White Paper to help reform health agencies and healthcare systems for greater efficiency and lower costs in the USA and globally.

Sarah E. MacLean, Nicole E. Edgar, Chloe Ahluwalia et al.

First responders (police, firefighters, and paramedics) are routinely exposed to potentially psychologically traumatic events (PPTE). While the prevalence of mental disorders is difficult to estimate, research has demonstrated that first responders report higher rates of mental health disorders than the general population. They also report significant barriers to accessing mental healthcare, including concerns about the confidentiality of mental health services and stigma by co-workers and organizational leadership. One way to address these barriers to seeking care is through the establishment of a first responder specific mental health clinic. The objective of this qualitative study was to assess how to best implement such a service for first responders in Ottawa, Canada. We conducted 14 in-depth semi-structured qualitative interviews with key interest holders from first responder services, unions/associations, and the Workplace Safety and Insurance Board (WSIB) which explored elements of service delivery and organizational barriers and facilitators to implementing the clinic. Interviews were analyzed and coded using thematic analysis by two independent coders. Four main themes were identified: implementation context (perceived need, workplace culture), design of the clinic (service delivery, confidentiality, cost, and communication about the clinic), the implementation process (barriers and facilitators to implementation), and the broader impact of the implementation of the clinic. Findings show that it is the right time to implement first responder specific clinical services as services begin to prioritize the mental health needs of their members. To increase uptake by first responders, confidentiality and cultural competency of care providers is paramount.

Tiffany P Quock, Eunice Chang, Ashis K Das et al.

Aim: Recent evidence regarding the healthcare resource utilization (HCRU) and associated costs of acromegaly is limited. Materials & methods: This retrospective, cross-sectional administrative claims analysis (IQVIA Pharmetrics Plus) identified patients (≥18 years) with acromegaly between 1 January 2017 and 30 June 2022. HCRU and costs over 1 year were compared in patients with acromegaly and matched patients without acromegaly (age, sex, insurance type, year). Among patients with acromegaly, annual total healthcare costs of comorbidities and procedures consistent with high-risk comorbidities were reported. Costs were adjusted to 2023 USD. Results: Among 2289 patients with acromegaly and 2289 matched patients without acromegaly, mean age was 49.8 years and 51.6% were female. Patients with acromegaly had a significantly (p < 0.001) higher comorbidity burden than patients without acromegaly. A significantly (p < 0.001) greater proportion of patients with acromegaly versus patients without acromegaly had inpatient hospitalizations (20.1 vs 4.9%) and emergency department visits (23.9 vs 15.7%). Total mean healthcare costs were also significantly higher for patients with acromegaly than patients without acromegaly ($51,888 vs $10,601). The majority of acromegaly-related healthcare costs ($30,985) were attributable to acromegaly therapy ($25,895). Hypertension (42.8%) was the most common high-risk comorbidity associated with acromegaly. The costliest high-risk comorbidity was congestive heart failure, with a mean cost difference of $38,123 (p < 0.05) between patients with acromegaly with and without hypertension. Conclusion: Patients with acromegaly had higher HCRU and costs than matched patients without acromegaly, and the presence of acromegaly with high-risk comorbidities was associated with a substantial HCRU and cost burden. This high burden of illness may be alleviated with better disease control.

András Jánosi

The Hungarian Myocardial Infarction Registry (HUMIR) is a comprehensive database, covering 190,230 infarction events in 170,337 patients. In 2024, the National Health Insurance Fund Management Centre financed treatment for 14,555 infarctions, of which 86.3% (13,728 events) were included in the HUMIR. 46.7% of the registered events were STEMI, and 53.3% were NSTEMI diagnoses. In both types of infarction, most patients were male (64% vs. 61%). Patients treated for NSTEMI were older than those treated with a STEMI diagnosis (64.4 vs. 69.1 years). PCI was performed in 86.2% of STEMI patients and 64.7% of NSTEMI patients. The procedures were performed through radial access (95%) and almost exclusively involved the implantation of drug-eluting stents (96%). The median total ischemic time (from the onset of symptoms to the opening of the vessel) in the STEMI patient group was 4 hours and 30 minutes. In 60.2% of NSTEMI patients who underwent PCI, the procedure was performed within 24 hours. The 30-day mortality of STEMI/NSTEMI patients who underwent PCI was 10.3% and 6%, respectively. When examined by the catheterisation center, significant differences were in the frequency of PCI treatment, platelet aggregation inhibitor treatment, and 30-day mortality. The HUMIR participates in the EuroHeart program, and the research entitled Investigation of the effects of atmospheric and air pollution parameters on the development of myocardial infarction. In 2024, we created an interactive query interface on the HUMIR website, allowing external interested parties to learn about basic epidemiological data. HUMIR has continuously monitored the data on the domestic care of patients with infarction since 2014, and the program's effectiveness has been proven in domestic and international forums.

Hao Yu

China successfully achieved universal health insurance coverage in 2011, representing the largest expansion of insurance coverage in human history. While the achievement is widely recognized, it is still largely unexplored why China was able to attain it within a short period. This study aims to fill the gap. Through a systematic political and socio-economic analysis, it identifies seven major drivers for China's success, including (1) the SARS outbreak as a wake-up call, (2) strong public support for government intervention in health care, (3) renewed political commitment from top leaders, (4) heavy government subsidies, (5) fiscal capacity backed by China's economic power, (6) financial and political responsibilities delegated to local governments and (7) programmatic implementation strategy. Three of the factors seem to be unique to China (i.e., the SARS outbreak, the delegation, and the programmatic strategy.) while the other factors are commonly found in other countries’ insurance expansion experiences. This study also discusses challenges and recommendations for China's health financing, such as reducing financial risk as an immediate task, equalizing benefit across insurance programs as a long-term goal, improving quality by tying provider payment to performance, and controlling costs through coordinated reform initiatives. Finally, it draws lessons for other developing countries.

E. Walker, Janet R. Cummings, J. Hockenberry et al.

Zhiyu Quan, Changyue Hu, Panyi Dong et al.

Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies' risk management. Our study further utilizes various machine learning techniques to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house. The quantification process provides a deeper understanding of the value of the InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic-industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.