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The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user‐friendly, farm‐specific, decision‐making tool for dairy producers or their advisers and technology manufacturers.

The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add‐in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail.

Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs.

Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

The purpose of this article is to clarify the social burden of Japan’s three major diseases including Long-term Care (LTC) burden.

A modification of the Cost of Illness (COI)—the Comprehensive-COI (C-COI) was utilized to estimate three major diseases: cancer, heart disease, and cerebrovascular diseases (CVD). The C-COI consists of five parts: medical direct cost, morbidity cost, mortality cost, formal LTC cost and informal LTC cost. The latter was calculated by two approaches: opportunity cost approach (OC) and replacement approach (RA), which assumed that informal caregivers were substituted by paid caregivers.

The C-COI of cancer, heart disease and CVD in 2017 amounted to 10.5 trillion JPY, 5.2 trillion JPY, and 6.7 trillion JPY, respectively (110 JPY= 1 US$). The mortality cost was preponderant for cancer (61 percent) and heart disease (47.9 percent); while the informal LTC cost was preponderant for CVD (27.5 percent). The informal LTC cost of the CVD in OC amounted to 1.8 trillion JPY; while the RA amounted to 3.0 trillion JPY.

The LTC burden accounted for a significant proportion of the social burden of chronic diseases. The informal care was maintained by unsustainable structures such as the elderly providing care for the elderly. This result can affect health policy decisions.

The C-COI is more appropriate for estimating the social burden of chronic diseases including the LTC burden and can be calculated using governmental statistics.

We study changes in age-specific diabetes-related mortality and annual health care utilization. We find that half of the estimated 16% increase of diabetic mortality falls within employable age groups. We estimate that disease combination-specific increase in case fatality has resulted in premature diabetic mortality costing $3.2 billion annually. The estimated annual direct cost of treating high-risk diabetics reaches $36 billion, of which Medicare and Other Federal Programs compensate 54%. Respiratory conditions among diabetics comprise the same proportion of high-risk diabetics as do the disease combinations including coronary heart diseases. Treating of general diabetic conditions has become more efficient as indicated by the estimated declines in per unit health care costs.

The disease management approach to improving health care, at the system as well as the hospital level, has proved to be very powerful, producing unprecedented results in reducing costs while improving quality of care and patient satisfaction. The Boston Consulting Group (BCG), working with a variety of leading managed care providers and pharmaceutical clients, has pioneered the development and dissemination of the disease management concept. Defines the disease management approach and outlines how it differs from the traditional component management approach. Also describes the key elements of disease management, characteristics to look for in a candidate disease, and results achieved so far. Finally, discusses the three strategic roles a health‐care player can take in disease management.

The purpose of this study is to analyze the growth of unit costs per output in the Finnish police force (2002–2015). Is it higher than the growth of prices in the Finnish economy in general; i.e. higher than the inflation rate? Cost disease theory suggests this to be the case. It says that all labor-intensive, slow-progressive industries suffer from relatively rapidly growing real unit costs and that policing is one among these industries.

The rates of growth of the division-specific unit costs of the police are compared to the inflation rate. Costs and prices are treated as indexed schedules, comparable to each other. The functional divisions under scrutiny are surveillance and emergency operations, criminal investigation, and permits and licenses. The period under analysis is 2002–2015.

The surveillance and emergency operations and criminal investigation divisions, but not the permits and licenses division, suffer from cost disease. Owing to the persistent digitalization efforts in permits and licenses, the physical productivity of the division has been growing at a rapid rate, and both the nominal and the real unit costs per output of the division have been decreasing over time.

Owing to data limitations, use of a proxy was needed to estimate one of the key variables of the study.

The policy significance of the research question is potentially very high. Prior to this study, there was a near total lack of empirical evidence in this area.

The purpose of this paper is to investigate the determinants and impacts of outsourcing pest and disease management on rice production in China.

A multinomial endogenous treatment effects model which accounts for selection bias was used.

The results show that outsourcing decisions are driven mainly by the size of the farm, the age of the household head and other household characteristics. Further, the authors find that outsourcing labor for pest and disease control has no significant effect on pest control cost and rice yields, though it reduces the number of pesticide applications. Conversely, outsourcing of professional services can increase rice yields by 4.1 percent, and at the same time it increases pest and disease control costs by 50.6 percent. However, it is found that outsourcing of professional services exerts no significant impact on the farm profitability.

This study suggests that households with large farm size are more likely to outsource professional services and, therefore, service providers and governments should target those farmers to provide incentives and create greater awareness of the benefits from the outsourcing of professional services. Moreover, the increase in yields along with the government subsidy justifies the outsourcing of professional services by farmers. However, service providers and policy makers have a lot of leeway to come up with cheaper methods for pest and disease management in rice production.

This study is the first attempt to simultaneously evaluate the determinants and impacts of outsourcing pest and disease management on rice production in China.

This article aims to provide a description of principal aspects of policy and practice associated with orphan drugs and treatments of rare diseases, and give perspectives for 2011 on new and emerging approaches for addressing patient access.

Information has been synthesized from a wide variety of sources including the authors' experience and familiarity with this topic, having over the past ten years developed market access strategies for many orphan drugs. This is supplemented by pertinent information from conferences, meetings and articles published over the last 20 years and from policy documents released by relevant regulatory bodies.

While a small number of people suffer from any one rare disease, approximately 30 million in the EU and 25 million in the USA are affected by such diseases. Annual orphan drug treatment costs may approach $500,000. High costs coupled with relatively sparse supportive clinical data and difficulty in meeting standard cost‐effectiveness benchmarks raise significant issues for payers in allocating finite budgetary resources. Since the US (1983) and European (European Parliament, 2000) orphan drug regulations were approved, hundreds of agents have been granted orphan status although many fewer have achieved marketing authorization. Agencies have introduced a slew of incentives to help sponsors develop and market orphan medicinal products. The nature and focus of the programs are undergoing a metamorphosis as access to funding is becoming as important as supporting development.

The article represents a contemporary examination of orphan drug policy and practice, bringing together historic elements which influenced practice up to 2010 and the latest, emerging trends and approaches which stakeholders are now embracing and which will shape procedures from 2011.

The estimation of the burden of a disease is one of the tasks with the longest tradition in health economics, which allows us to know the volume of resources that a country allocates to a specific health problem, and to compare countries and diseases. Although the fundamental objective of health systems is not to reduce the cost of the disease, but to improve the health of the population, the studies of burden of disease establish the economic seriousness of the problem, orienting the priorities of action.

Government-funded medical expenditure in Uruguay for the last ten years has tripled in US dollars. The increase in the prevalence of overweight and obesity has contributed to this growth. According to the World Health Organization, Uruguay has the highest growing trend in the prevalence of both overweight and obesity in South America. We have previously estimated that economic burden linked to obesity will be more than US$500 million by 2020, a figure close to 1% of the country’s GDP.

In this study, we tried to generate a measure of value to ascertain the cost of inaction in the fight against obesity and its consequences linked to several non-communicable diseases. The cost of inaction is not defined as the cost of not doing, but as the cost of not implementing the right policies (in this case health prevention policies) at the right time.

The purpose of this paper is to present a new method and model for constructing a new decision-making paradigm of Medicare, which can not only satisfy the needs of the sick people but also reduce the possibility of people slipping back to poverty due to diseases under the policy of Targeted Poverty Alleviation (TPA) of China.

This paper uses the traditional supply chain theory to analyze the Medicare of impoverished people with the policy of TPA of China and transforms it into a multi-layer supply chain optimization decision-making problem. First, a nonlinear integer programming model for poor people’s Medicare decision with opportunity constraints is constructed. To facilitate the solution of the optimal decision scheme, the abovementioned model is transformed into a linear integer programming model with opportunity constraints by using the Newsvendor model for reference. Meanwhile, the scope of the inventory model is discussed, for it can be combined with the construction of the medical insurance system better. Second, the theoretical model is applied to the practical problem. Finally, based on the results of the theoretical model applying the practical problem, we give further improvement and modification of the theoretical model applies it to the actual situation further.

This paper presents a theoretical model about determine the optimal the inventory, under the framework of traditional supply chain decision-making, for it can be combined with the construction of the medical insurance system better. The theoretical model is applied to the practical problem of the fight against poverty in XX County, China. By using the actual data and MATLAB, optimal decision scheme is obtained.

There are two aspects of value. On the one hand, this paper provides a new way to construct a Medicare system of impoverished people with TPA of China. On the other hand, this paper tries making a new way to handle the storage of medicines and related medical devices at basic standard clinics decision-making problems based on above mentioned Medicare system.

We discuss the connections between epidemiology models and the search and matching (SAM) approach and draw conclusions about modeling the trade-offs between lockdowns and disease spread. We review the pre-COVID epidemics literature, which was mainly by epidemiologists, and the post-COVID surge in economics papers that use meeting technologies to model the trade-offs. We argue that modeling the decentralized equilibrium with economic trade-offs gives rise to substantially different results from the earlier epidemics literature, but policy action is still welfare-improving because of several externalities.