Search Results (39)

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

Public buildings are crucial to creating healthy and sustainable cities. These buildings promote social cohesion and enrich urban life by transforming existing facilities into hybrid models that integrate medical content. Historical developments highlight shifts in residential, economic, and healthcare infrastructure. The healthcare system aims to enhance public health while ensuring financial equity. Reforms in healthcare privatization, governed by public health and insurance policies, involve liberalizing service provision and are supported by the Ministry of Health and Finance. This study examines how public buildings can adapt to enhance health and social sustainability. Through case studies, it assesses architectural adaptability in analyzing spatial, economic, and social impacts. Diagrams illustrate spatial dynamics, while surveys compare efficiency, sustainability, and user experience. Statistical analysis highlights the role of spatial adaptability in fostering sustainable urban environments. The results, which express significant differences between means for different locations and citizens’ satisfaction, suggest that the hypothesis offers substantial results in every area. Besides commercial programs in commercial buildings, healthcare also gives satisfactory results. This study advocates for adaptive architecture as a key strategy, aligning with evolving societal and health demands. Hybridizing healthcare facilities and commercial spaces transforms shopping centers into sustainable models, enhancing social cohesion and economic viability. Full article

Background/Objectives: Addressing health research priorities in public institutions is crucial for efficient resource allocation and policy impact. This study aims to describe the development of Peru’s Social Health Insurance (ESSALUD) 2023–2025 research portfolio, which aligns with institutional priorities and focuses on improving decision-making for population health. Methods: The Health Research Directorate (DIS) of ESSALUD led a structured three-phase process, engaging multidisciplinary teams and utilizing a group model-building approach to generate research ideas. Twelve working groups were established, corresponding to ESSALUD’s prioritized health topics, to identify key institutional challenges and propose research ideas. Results: A total of 338 research ideas were generated from 217 identified problems. These ideas were classified using the UK Health Research Classification System (HRCS) and scored based on nine dimensions to prioritize execution. Research ideas primarily focused on health services (57.7%) and disease management (16.9%). High-priority topics included cancer, mental health, malnutrition, and antimicrobial resistance. As a result of this implementation, ESSALUD resources were positively concentrated in the HRCS research activities ‘Health and social care services research’ (51.85%) and ‘Etiology’ (44.44%) for the period 2023–2025. Conclusions: The development of ESSALUD’s research portfolio identified key areas such as health services, health economics, and prevention, essential for evidence-based decisions and sustainability. Multidisciplinary participation ensured solutions aligned with real needs, promoting equity and continuous improvement in Peru’s health system. Full article

Background/Objectives: The Brazilian health system provides healthcare financed by the public and private sector, being the first designed to encompass universal healthcare coverage delivered to the population without charge to patients (Sistema Único de Saúde, SUS), whilst the second refers to healthcare coverage delivered for individuals with the capacity to pay for assistance through health insurance or out-of-pocket disbursements. Health insurance companies with beneficiaries receiving publicly financed healthcare from the SUS are required to provide the reimbursement of healthcare expenditures to the government, considering that the health insurance beneficiaries obtain deductions of income taxes designed to fund the SUS. Therefore, the study investigated patterns of healthcare utilization and public expenditure due to the use of public healthcare by beneficiaries of health insurance between 2003 and 2019. Methods: Datasets including annual information on healthcare utilization by beneficiaries of health insurance from the National Agency of Supplementary Health (Agência Nacional de Saúde Suplementar, ANS) were organized into a single database to allow for the identification of patterns of interest to inform public policies of health. The empirical strategy adopted included the estimation of regression models and agglomerative hierarchical cluster analysis to identify factors associated with public sector expenditure. Results: The regression results indicated lower expenditure with female patients, particularly children and adolescents under 20 years old, receiving treatment in public sector facilities linked to the federal government. The cluster analysis showed five types of health insurance beneficiaries with a higher level of healthcare utilization, being three clusters referring to medium complexity procedures with lower public expenditures, and two clusters with higher public expenditures, one cluster that refers to high complexity procedures, and one cluster referring to health insurance schemes without hospitalization. Conclusions: The findings of the study highlight the existence of patterns of healthcare utilization by health insurance beneficiaries that may compromise the sustainability of public funding within the Brazilian health system. Full article

Poverty reduction is the primary goal of the United Nations 2030 Agenda for Sustainable Development. Enhancing the purchase rate of health insurance is essential for alleviating poverty caused by health shocks, as it serves as a crucial risk management tool for addressing health-related risks. In this paper, we investigate the impact of digital inclusive finance on household participation in terms of health insurance and financial vulnerability, utilizing the Digital Inclusive Finance Index developed by Peking University and survey data from the China Household Finance Survey. Our findings indicate that the advancement of digital inclusive finance can significantly reduce the risk of household financial vulnerability by increasing household health insurance enrollment rate. The findings are robust across various digital inclusive finance indices, different metrics for financial vulnerability, alternative econometric models, and additional control variables. Furthermore, the effects of digital inclusive finance on health insurance enrollments and household financial vulnerability are particularly pronounced among urban households and those led by younger and more risk-averse household heads. Our findings advocate for further development of digital inclusive finance, mainly targeted at rural households and those with elderly heads, to enhance health insurance participation and mitigate the risk of illness-related poverty. Full article

The joint involvement of family and society in elderly care is a crucial factor in improving the health status of older adults and narrowing health disparities, which are essential for achieving sustainable development goals. However, the interactions between these entities and their mechanisms of influence require further investigation. By utilizing data from the China Longitudinal Aging Social Survey (CLASS) spanning 2014 to 2016 and employing the Kakwani index of individual relative deprivation in conjunction with a two-way fixed-effects model for unbalanced panel data, in this study, we investigated the mechanisms through which social elderly care security and familial support influence health inequalities among the elderly. The findings reveal that only senior benefits (=−0.009, p < 0.05) significantly mitigate relative health deprivation in this population. Enrollment in pension insurance amplifies the sense of relative health deprivation among the elderly, but this effect becomes insignificant after controlling for temporal effects. Both economic support (=−0.002, p < 0.05) and emotional support (=−0.004, p < 0.01) from offspring significantly reduce the level of relative health deprivation among the elderly. Mechanism testing results indicate that individual attitudes towards aging serve as a mediator in the relationship between relative health deprivation and preferential treatment, economic support, and emotional support. The results of further heterogeneity tests suggest that the impact of various elderly support models on relative health deprivation differs by age, gender, and residential area.These findings confirm that support from both society and family plays a crucial role in achieving sustainable health outcomes for the elderly. Consequently, it is recommended to enhance the social elderly care security system, bolster familial support functions, cultivate positive individual attitudes towards aging, and address health inequalities among the elderly in accordance with their distinct characteristics, thereby improving their quality of life and sense of fulfillment, and contributing to the broader goals of sustainable development. Full article

This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software’s (FOSS), collectively termed “sustainable geoinformatics.” The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic information system (GIS) to map the terrain, drainage, and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out time-series analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters. Full article

Background: This study seeks to understand the empirical nature of macro-financial factors associated with the worsening of HIV infections and the risks that need to be carefully monitored for a sustainable improvement in HIV outcomes as developing countries seek to achieve the United Nations 95-95-95 targets. Methods: The author used a panel VAR model to study the long-term endogenous relationships between percentage changes in the annual spot price of the most traded commodities, GDP per capita, health spending, and the HIV infection rate of developing countries. Results: The author discovered that shocks of global commodity prices negatively impact GDP per capita, real government health spending, and real private health spending. These shocks have adverse spillover effects characterized by worsening HIV infections. The reactions from price shocks suggest that GDP per capita contract immediately when a commodity price shock hits developing economies. Real government health spending and real private health spending also contract instantly. HIV infections begin worsening three years after the shock in the energy and precious metal blocks of countries. HIV infections also begin to worsen two years after shocks in the agricultural block of counties. These impacts are statistically significant and can potentially reverse the positive HIV infection gains achieved in the previous years. Emergency funds, insurance schemes, and international aid for HIV need to discharge more funds to counter these shocks. Conclusions: There is a significant risk of reversing HIV infection outcomes arising from commodity price shocks. Funding agencies must protect HIV prevention services from global macro-economic shocks as countries move closer to the United Nations 95-95-95 targets. Full article

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network. Full article

Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F₁ score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem. Full article

As a result of the inability of people to meet their demands in the face of increasing demands, people tend to have private health insurance in addition to the general health insurance offered as a public service. Due to the increasing trend of taking out private sustainable health insurance, the number of private sustainable health insurance plans in the health insurance market has increased significantly. Therefore, people may be confronted by a wide range of private health insurance plan options. However, there is limited information about how people analyze private health insurance policies to protect their health in terms of benefit payouts as a result of illness or accident. Thus, the objective of this study is to provide a model to aid people in evaluating various plans and selecting the most appropriate one to provide the best healthcare environment. In this study, a hybrid fuzzy Multiple Criteria Decision Making (MCDM) method is suggested for the selection of health insurance plans. Because of the variety of insurance firms and the uncertainties associated with the various coverages they provide, q-level fuzzy set-based decision-making techniques have been chosen. In this study, the problem of choosing private health insurance was handled by considering a case study of evaluations of five alternative insurance companies made by expert decision makers in line with the determined criteria. After assessments by expert decision makers, policy choices were compared using the Q-Rung Orthopair Fuzzy (Q-ROF) sets Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Q-ROF VIšeKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods. This is one of the first attempts to solve private health policy selection under imprecise information by applying Q-ROF TOPSIS and Q-ROF VIKOR methods. At the end of the case study, the experimental results are evaluated by sensitivity analysis to determine the robustness and reliability of the obtained results. Full article

Despite the growing urgency to curb carbon emissions worldwide, the healthcare industry, particularly the pharmaceutical industry, has received little attention from the sustainability community in terms of its contribution to the global carbon footprint. This paper constructs a differential game model of the secondary pharmaceutical supply chain consisting of pharmaceutical enterprises and medical institutions in the context of centralized drug procurement policy, considering the effects of health insurance reimbursement and consumers’ low-carbon preferences, and compares and analyzes the feedback equilibrium strategies of low-carbon inputs and marketing efforts, supply chain profits, and social welfare levels under four government subsidy models and further discusses them with arithmetic examples. The results illustrated that government subsidies have a significant impact on the low-carbon investment of pharmaceutical enterprises and the low-carbon marketing of medical institutions; subsidies for pharmaceutical enterprises can significantly increase the low-carbon investment and profit level of pharmaceutical enterprises; subsidies for medical institutions can effectively promote the implementation of the “zero-rate” policy and the realization of the emission reduction target under the centralization policy of medical institutions, increase the market demand for low-carbon drugs, and thus gain higher profits; the dual-subsidy model of the government brings higher social welfare than the single-subsidy model, and under a reasonable subsidy ratio, the profit and social welfare of the whole supply chain can be maximized. Full article

Community-based screening for the hepatitis C virus (HCV) decreased during the COVID-19 pandemic. We developed a collaborative referral model between a primary clinic (Liouguei District Public Health Center, LDPHC) and a tertiary referral center to increase HCV screening and treatment uptake in a mountainous region of Taiwan. Once-in-a-lifetime hepatitis B and C screening services established by the Taiwan National Health Insurance were performed at LDPHC. Antibody-to-HCV (anti-HCV)-seropositive patients received scheduled referrals and took a shuttle bus to E-Da hospital for HCV RNA testing on their first visit. Direct-acting antiviral agents (DAAs) were prescribed for HCV-viremic patients on their second visit. From October 2020 to September 2022, of 3835 residents eligible for HCV screening in Liouguei District, 1879 (49%) received anti-HCV testing at LDPHC. The overall HCV screening coverage rate increased from 40% before referral to 69.4% after referral. Of the 79 anti-HCV-seropositive patients, 70 (88.6%) were successfully referred. Of the 38 HCV-viremic patients, 35 (92.1%) received DAA therapy, and 32 (91.4%) achieved sustained virological response. The collaborative referral model demonstrates a good model for HCV screening and access to care and treatment in a Taiwan mountainous region, even during the COVID-19 pandemic. Sustained referral is possible using this routine referral model. Full article

Background: The association between alcohol intake and newly developed heart failure remains unclear. We aimed to measure the change in alcohol intake between two timepoints to evaluate the association of alcohol consumption with incident heart failure using a population-based study in Korea. Methods: Using the Korean National Health Insurance database, participants who underwent two subsequent national health examinations in 2009 and 2011 were included. Participants were classified into four groups according to total alcohol intake (none: 0 g alcohol/day; light: <15 g alcohol/day; moderate: 15–30 g alcohol/day; and heavy: ≥30 g alcohol/day), and changes in alcohol consumption between the two health exams were grouped into the following five categories: abstainers, sustainers (those who maintained their first examination drinking level), increasers, reducers, and quitters. After adjustment for age, sex, smoking status, regular exercise, socioeconomic information, and comorbidities, the Charlson Comorbidity Index, systolic blood pressure, and laboratory results, a Cox proportional hazards model was used to find the risk of newly diagnosed heart failure (according to ICD-10 code I50 from claims for the first hospitalization) as the primary endpoint. A subgroup analysis among those with a third examination was conducted to reflect further changes in alcohol consumption. Results: Among 3,842,850 subjects, 106,611 (3.0%) were diagnosed with heart failure during the mean follow-up period of 6.3 years. Increasers to a light level of drinking had a lower HF risk compared with abstainers (aHR = 0.91, 95% CI: 0.89–0.94). Those who increased their alcohol intake to a heavy level had a higher HF risk (from light to heavy (aHR = 1.19, 95% CI: 1.12–1.26) and from a moderate to heavy level (aHR = 1.13, 95% CI: 1.07–1.19). Reducing alcohol from a heavy to moderate level was associated with lower HF risk (aHR = 0.90, 95% CI: 0.86–0.95). Conclusion: This study found that light and moderate sustainers had lower incident heart failure risk compared with abstainers. Increased alcohol consumption from light to moderate to heavy was associated with a higher incident heart failure risk. Full article

Under the background of tighter resource and environmental constraints, whether and how land transfer can promote the green development of agriculture has become a realistic question that needs to be answered urgently. This paper analyzes the internal mechanism between land transfer and agricultural green production by using property rights theory and sustainable development theory. Taking the data of the “investigation on household energy consumption and green agricultural development” in Sichuan Province in 2022 as a sample, it empirically analyzes the impact of land transfer on agricultural green production by using OLS and 2SLS models. The results show that: (1) land inflow significantly improves the level of agricultural green production, with a unit impact of 22.3%; (2) whereas land outflow will inhibit agricultural green production, with a unit impact of 5.46%; (3) the family’s long-term agricultural labor, social capital, migrant experience, non-agricultural income, and household clean energy use have a promoting effect on agricultural green production; (4) age, education level, health level and agricultural subsidies inhibit agricultural green production; (5) the heterogeneity analysis found that the inflow of land would significantly promote the level of green agricultural production of farmers who have environmental awareness, have been village cadres, have purchased agricultural insurance, and have not suffered from agricultural disasters; (6) agricultural training, farmers’ digital literacy, and agricultural related loans have a positive and strengthened regulatory role in the impact of land transfer on agricultural green production. Based on this, this paper gets policy enlightenment from the government, market, and farmers. Full article