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Introduction
Universal varicella vaccination of infants in Germany was shown to be effective and cost-saving by the EVITA-model (Economic Varicella VaccInation Tool for Analysis).[1] However, affordability proves to be a “fourth hurdle”. The objective of this study is to examine budget impacts of universal varicella vaccination from a payer’s (sickness funds’) perspective.
Methods
The validated, dynamic infections disease model EVITA was used to analyse budget impacts over 30 years. Universal vaccination of children in their second life year was compared to the currently recommended risk-group strategy. In the USA, universal varicella vaccination was implemented in 1996. Based on US-experience, three scenarios for the development of the coverage rates have been estimated. The price level of 2002 was used. Future costs were not discounted to show the full budget impact in future years. Aspects concerning the incidence of herpes zoster have not been included in the analysis.
Results
Under the current risk-group strategy, an annual average of 721,400 cases, 38,700 complications and 5,500 hospitalisations occur in Germany. Annual varicella costs for sickness funds amount to 73 million €, 50 % thereof due to sick-leave costs of parents staying at home to care for their sick child covered by sickness funds. Other costs relate to hospitalisations (24 %), physician services (12 %), medication (11 %) and vaccination (3 %). With a universal vaccination strategy, morbidity is on average reduced by 75 % to 80 % (this and the following results depend on the analysed scenario). Vaccination costs rise to a maximum of 42–43 million € p. a. within about 10–12 years and therefore account — at maximum — for about 0.03 % of the total payers’ health care budget. Other varicella costs however decrease by more than 80 % in the same time. Overall varicella costs rise by 8 % to 13 % in the first few years because of the additional investment in vaccination. In the 6th to 8th year, however, savings in treatment and work loss costs compensate vaccination costs, leading to net savings. Over 30 years, average annual varicella costs decrease by 31 % to 33 % to a level between 49 and 51 million €. Sensitivity analyses showed only marginal variations in these results with maximum coverage rate, price of vaccine, and work loss costs being the most influential variables.
Conclusions
Under the current risk-group vaccination strategy, varicella causes a high burden of disease resulting in considerable utilisation of health care resources and related costs. Universal vaccination can effectively reduce morbidity and utilisation. This strategy has a small impact on overall health care costs. For a short time, investment in varicella vaccination causes additional costs, which are low compared to current annual varicella costs, and does not influence sickness funds’ premiums. Reduced morbidity rapidly leads to savings and significant net-savings occur in 6–8 years.
This paper shows how a mobile robot equipped with sonar sensors and an odometer is used to test ideas about cognitive mapping. The robot first explores an office environment and computes a "cognitive map" which is a network of ASRs [1]. The robot generates two networks, one for the outward journey and the other for the journey home.
It is shown that both networks are different. The two networks, however, are not merged to form a single network. Instead, the robot attempts to use distance information implicit in the shape of each ASR to find its way home. At random positions in the homeward journey, the robot calculates its orientation towards home. The robot's performances for both problems are evaluated and found to be surprisingly accurate.
When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home.
Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information.
Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used for localization?
In this paper, we present an approach using a mobile robot to generate a “cognitive map”, the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a “cognitive map” and uses distance and orientation information for localization.
We present an approach for indoor mapping and localisation using sparse range data, acquired by a mobile robot equipped with sonar sensors.
The chapter consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localisation on the return home journey.
The robot computes local confidence maps for two simple localisation strategies based on distance and relative orientation of regions. These local maps are then fused to produce overall confidence maps.
When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to developa varietyof methodsthat tend to utilize two key pieces of information,namely distance and orientation information.
Their methods differ depending on how they sense their environment.
Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used to return home?
In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a “cognitive map” and uses distance and orientation information to find its way home.
The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping.
This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home.
Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately.
Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing.
In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home.
Background Free movement of the limbs is a prerequisite of mobility and autonomy in old age. Joint contractures, i.e. restrictions in full range of motion of any joint due to deformity, disuse or pain, are common problems of frail older people, particularly in nursing home residents.
Contractures are among the most unexplored and underreported syndromes in clinical and homecare settings. Epidemiological studies indicate a wide range of prevalence of joint contractures in older individuals between 20% and 80%. This variation is due to different definitions of contracture and varying diagnostic criteria or data collection methods, different research settings, sample size and study participants? characteristics.
The aetiology of joint contractures is multifaceted. In older people contractures may be caused by a variety of health conditions and situations, but immobility due to an acute injury or disease seems to be the major risk factor. Upper limb joint contractures may result in loss of ability to dress or eat independently while lower limb contractures may lead to instability and inability to walk independently and higher risk of bed confinement.
Joint contractures further increase the risk of other adverse patient outcomes like pain, pressure ulcers and risk of falls. Thus, joint contractures are a major cause for excess disability in older people with a significant impact on overall quality of life and functioning. Preventive and rehabilitation interventions targeting joint contractures may decrease morbidity, increase functioning and quality of life, and, ultimately, prevent long-term disability. In the United States of America, presence of joint contractures is an established indicator of quality of care in nursing facilities.
In Germany, joint contracture risk assessment and prevention have recently been defined as a quality indicator of nursing home care that should be regularly monitored by experts from the statutory health insurance system. Nursing homes are obliged to report whether they regularly assess the risk of joint contracture and administer relevant preventive measures. In clinical settings, joint contractures are assessed by measuring the range of motion.
However, from a patient- and nursing-oriented perspective the relevance of a systematic registration of contractures in care-dependent older people is unclear unless their impact on functioning is understood. Contracture assessment is only an intermediate step in the evaluation of patient-relevant outcomes such as quality of life, functioning, and the ability to participate in everyday life and social participation. Arguably, a clinical definition of joint contracture is difficult because the contracture?s severity is determined by the consequences on activities of daily living, quality of life and social participation. In addition, there is no consensus on aspects most relevant to the affected individuals.
A variety of functional measures is currently used for the assessment and evaluation of geriatric patients. To date, there is no consensus on common concepts for the choice of outcome measures specifically for evaluating the impact of interventions targeted on joint
We propose a reinforcement learning approach to heating control in home automation, that can acquire a set of rules enabling an agent to heat a room to the desired temperature at a defined time while conserving as much energy as possible. Experimental results are presented that show the feasibility of our method.