909 resultados para Coverage Insurance.


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Reports for 1895-1914 have each pt. issued as separate vol.: pt. 1. Fire and marine insurance; pt. 2. Life and casualty insurance; 1897-1915, pt. 3. Local mutual fire insurance.

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Cover title.

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Cover title.

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In the last few decades, private health insurance rates have declined in many countries. In countries and states with community rating, a major cause is adverse selection. In order to address age-based adverse selection, Australia has recently begun a novel approach which imposes stiff penalties for buying private insurance later in life, when expected costs are higher. In this paper, we analyze Australiarsquos Lifetime Cover in the context of a modified version of the Rothschild-Stiglitz insurance model (Rothschild and Stiglitz, 1976). We allow empirically-based probabilities to increase by age for low-risk types. The model highlights the shortcomings of the Australian plan. Based on empirically-based probabilities of illness, we predict that Lifetime Cover will not arrest adverse selection. The model has many policy implications for government regulation encouraging long-term health coverage.

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In recent years, most low and middle-income countries, have adopted different approaches to universal health coverage (UHC), to ensure equity and financial risk protection in accessing essential healthcare services. UHC-related policies and delivery strategies are largely based on existing healthcare systems, a result of gradual development (based on local factors and priorities). Most countries have emphasized on health financing, and human resources for health (HRH) reform policies, based on good practices of several healthcare plans to deliver UHC for their population.

Health financing and labor market frameworks were used, to understand health financing, HRH dynamics, and to analyze key health policies implemented over the past decade in Kenya’s effort to achieve UHC. Through the understanding, policy options are proposed to Kenya; analyzing, and generating lessons from health financing, and HRH reforms experiences in China. Data was collected using mixed methods approach, utilizing both quantitative (documents and literature review), and qualitative (in-depth interviews) data collection techniques.

The problems in Kenya are substantial: high levels of out-of-pocket health expenditure, slow progress in expanding health insurance among informal sector workers, inefficiencies in pulling of health are revenues, inadequate deployed HRH, maldistribution of HRH, and inadequate quality measures in training health worker. The government has identified the critical role of strengthening primary health care and the National Hospital Insurance Fund (NHIF) in Kenya’s move towards UHC. Strengthening primary health care requires; re-defining the role of hospitals, and health insurance schemes, and training, deploying and retaining primary care professionals according to the health needs of the population; concepts not emphasized in Kenya’s healthcare reforms or programs design. Kenya’s top leadership commitment is urgently needed for tougher reforms implementation, and important lessons from China’s extensive health reforms in the past decade are beneficial. Key lessons from China include health insurance expansion through rigorous research, monitoring, and evaluation, substantially increasing government health expenditure, innovative primary healthcare strengthening, designing, and implementing health policy reforms that are responsive to the population, and regional approaches to strengthening HRH.

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"Prepared by G. Joachim [i.e. Joachim G.] Elterich and Linda Graham"--Prelim. p.

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The 2014 Farm Bill created Supplemental Coverage Option (SCO), a new add-on crop insurance option which provides supplemental coverage on a producer’s underlying crop insurance policy. SCO operates by mimicking a producer’s individual crop insurance coverage and covering a portion of the deductible based on county-level yield or revenue. SCO is available in select Maryland counties for apples, barley, corn, grain sorghum, green peas, oats, peaches, processing beans, soybeans, sweet corn, and winter wheat, as of the 2017 crop year. USDA’s Risk Management Agency (RMA) continues to expand covered counties and crops covered, and begin distinguishing by practices (such as irrigated compared to non-irrigated).

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O objetivo foi analisar a evolução do perfil de utilização de serviços de saúde, entre 2003 e 2008, no Brasil e nas suas macrorregiões. Foram utilizados dados da PNAD. A utilização de serviços de saúde foi medida pela proporção de pessoas que procuraram e foram atendidas nas 2 semanas anteriores e pelos que relataram internação nos últimos 12 meses, segundo SUS e não SUS. Foram analisadas as características socioeconômicas dos usuários, o tipo de atendimento e de serviço e os motivos da procura. A proporção de indivíduos que procuraram serviços de saúde não se alterou, assim como a parcela dos que conseguiram atendimento (96%), entre 2003 e 2008. O SUS respondeu por 56,7% dos atendimentos, realizando a maior parte das internações, vacinação e consultas e somente 1/3 das consultas odontológicas. Em 2008, manteve-se o gradiente de redução de utilização de serviços de saúde SUS conforme o aumento de renda e escolaridade. Houve decréscimo da proporção dos que procuraram serviços de saúde para ações de prevenção e aumento de procura para problemas odontológicos, acidentes e lesões e reabilitação. O padrão de utilização do SUS por região esteve inversamente relacionado à proporção de indivíduos com posse de planos privados de saúde.

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Modern Integrated Circuit (IC) design is characterized by a strong trend of Intellectual Property (IP) core integration into complex system-on-chip (SOC) architectures. These cores require thorough verification of their functionality to avoid erroneous behavior in the final device. Formal verification methods are capable of detecting any design bug. However, due to state explosion, their use remains limited to small circuits. Alternatively, simulation-based verification can explore hardware descriptions of any size, although the corresponding stimulus generation, as well as functional coverage definition, must be carefully planned to guarantee its efficacy. In general, static input space optimization methodologies have shown better efficiency and results than, for instance, Coverage Directed Verification (CDV) techniques, although they act on different facets of the monitored system and are not exclusive. This work presents a constrained-random simulation-based functional verification methodology where, on the basis of the Parameter Domains (PD) formalism, irrelevant and invalid test case scenarios are removed from the input space. To this purpose, a tool to automatically generate PD-based stimuli sources was developed. Additionally, we have developed a second tool to generate functional coverage models that fit exactly to the PD-based input space. Both the input stimuli and coverage model enhancements, resulted in a notable testbench efficiency increase, if compared to testbenches with traditional stimulation and coverage scenarios: 22% simulation time reduction when generating stimuli with our PD-based stimuli sources (still with a conventional coverage model), and 56% simulation time reduction when combining our stimuli sources with their corresponding, automatically generated, coverage models.

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This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

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Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

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This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

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This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.