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At least 1,621 records · Page 90Linked to original sources

PubMed1995

Are mortality projections always more pessimistic when disaggregated by cause of death?

"It is often observed that mortality projections are more pessimistic when disaggregated by cause of death. This article explores the generality and strength of this relationship under a variety of forecasting models. First, a simple measure of the pessimism inherent in cause-based mortality forecasts is derived. Second, it is shown that the pessimism of cause-based forecasts can be approximated using only data on the distribution of deaths by cause in two pervious time periods. Third, using Japanese mortality data during 1951-1990, the analysis demonstrates that the pessimism of cause-based forecasts can be attributed mainly to observed trends in mortality due to cancer and heart disease, with smaller contribution due to trends in stroke (women only), pneumonia/bronchitis, accidents, and suicide. The last point requires the important qualification, however, that observed trends in cancer and heart disease may be severely biased due to changes in diagnostic practice." (SUMMARY IN FRE)

Asia↗

PubMed2003

Risk adjustment using automated ambulatory pharmacy data: the RxRisk model.

OBJECTIVES: Develop and estimate the RxRisk model, a risk assessment instrument that uses automated ambulatory pharmacy data to identify chronic conditions and predict future health care cost. The RxRisk model's performance in predicting cost is compared with a demographic-only model, the Ambulatory Clinical Groups (ACG), and Hierarchical Coexisting Conditions (HCC) ICD-9-CM diagnosis-based risk assessment instruments. Each model's power to forecast health care resource use is assessed. DATA SOURCES: Health services utilization and cost data for approximately 1.5 million individuals enrolled in five mixed-model Health Maintenance Organizations (HMOs) from different regions in the United States. STUDY DESIGN: Retrospective cohort study using automated managed care data. SUBJECTS All persons enrolled during 1995 and 1996 in Group Health Cooperative of Puget Sound, HealthPartners of Minnesota and the Colorado, Ohio and Northeast Regions of Kaiser-Permanente. MEASURES RxRisk, an algorithm that classifies prescription drug fills into chronic disease classes for adults and children. RESULTS: HCCs produce the most accurate forecasts of total costs than either RxRisk or ACGs but RxRisk performs similarly to ACGs. Using the R(2) criteria HCCs explain 15.4% of the prospective variance in cost, whereas RxRisk explains 8.7% and ACGs explain 10.2%. However, for key segments of the cost distribution the differences in forecasting power among HCCs, RxRisk, and ACGs are less obvious, with all three models generating similar predictions for the middle 60% of the cost distribution. CONCLUSIONS: HCCs produce more accurate forecasts of total cost, but the pharmacy-based RxRisk is an alternative risk assessment instrument to several diagnostic based models and depending on the nature of the application may be a more appropriate option for medical risk analysis.

Adolescent↗

PubMed2005

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice.

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.

Climate↗

PubMed2003

Delusions of success. How optimism undermines executives' decisions.

The evidence is disturbingly clear: Most major business initiatives--mergers and acquisitions, capital investments, market entries--fail to ever pay off. Economists would argue that the low success rate reflects a rational assessment of risk, with the returns from a few successes outweighing the losses of many failures. But two distinguished scholars of decision making, Dan Lovallo of the University of New South Wales and Nobel laureate Daniel Kahneman of Princeton University, provide a very different explanation. They show that a combination of cognitive biases (including anchoring and competitor neglect) and organizational pressures lead managers to make overly optimistic forecasts in analyzing proposals for major investments. By exaggerating the likely benefits of a project and ignoring the potential pitfalls, they lead their organizations into initiatives that are doomed to fall well short of expectations. The biases and pressures cannot be escaped, the authors argue, but they can be tempered by applying a very different method of forecasting--one that takes a much more objective "outside view" of an initiative's likely outcome. This outside view, also known as reference-class forecasting, completely ignores the details of the project at hand; instead, it encourages managers to examine the experiences of a class of similar projects, to lay out a rough distribution of outcomes for this reference class, and then to position the current project in that distribution. The outside view is more likely than the inside view to produce accurate forecasts--and much less likely to deliver highly unrealistic ones, the authors say.

Administrative Personnel↗

PubMed1991

Supply prices to increase slightly.

Expect hospital supply prices to increase by about 3 percent to 4 percent during 1991, says the new economic forecast from the Joint Purchasing Corp. (JPC), New York City. JPC's report may help reduce the uncertainty for those making supply price forecasts during the current recession. The Economic Forecast and Budget Impact Report presents economic information from industry analysts and publishes the data in a single volume. The JPC forecast can be used as a guide to help determine price changes; however, it's also important to consider changes in consumption, utilization, and quality when using the report, says JPC's president.

Equipment and Supplies, Hospital↗

PubMed1997

Death and taxes: longer life, consumption, and social security.

We analyze in three steps the influence of the projected mortality decline on the long run finances of the Social Security System. First, on a theoretical level, mortality decline adds person years of life which are distributed across the life cycle. The interaction of this distribution with the age distribution of labor earnings minus consumption, or of taxes minus benefits, partially determines the corresponding steady state financial consequences of mortality decline. The effect of mortality decline on population growth rates also matters, but is negligible in low mortality populations. Second, examination of past mortality trends in the United States and of international trends in low mortality populations, suggests that mortality will decline faster than foreseen by the Social Security Administration's forecasts. Third, we combine the work of the first two parts in dynamic simulations to examine the implications of mortality decline and of alternative forecasts of mortality for the finances of the social security system. Also, we use stochastic population forecasts to assess the influence of uncertainty about mortality decline on uncertainty about finances; we find that uncertainty about fertility still has more important implications than uncertainty about mortality, contrary to sensitivity tests in the official forecasts.

Actuarial Analysis↗

PubMed1981

Surgical decision making.

The decision-making process in a general surgical outpatient clinic has been studied by following the course of 2000 consecutive newly referred patients of whom 1045 were put on surgical waiting lists. Surgeons were asked to record their reasons for selection for each of four levels of care: main ward, 5-day ward, day bed unit or minor outpatient surgery. Selections were influenced by social and psychological factors in 12 per cent of patients, the remaining 88 per cent were determined by the medical history or physical condition. The decision was assessed by monitoring the progress in hospital and the outcome at a follow-up clinic. In 84 per cent of patients arrangements had gone according to plan. Eleven (1.75 per cent) of 5-day or day care patients were transferred to a higher level of care and 13 (1.4 per cent) of the total series were re-admitted. Surgeons were asked to forecast the duration of postoperative stay. Thirty-six per cent were discharged on the day forecast, 63 per cent within 24 h and 75 per cent within 48 h. Forecasts of length of stay for hernias and varicose veins were accurate but were usually underestimated for major surgery. It is concluded that for the majority of general surgical patients the selection of the level of care and the forecast of postoperative stay were sufficiently reliable to allow confident and definite advice to be given to the patient and to allow resources to be efficiently used.

Decision Making↗

PubMed1994

Growth models and the HIV epidemic in Finland.

Today, we know a lot about HIV and AIDS, yet too little to be able to stop the pandemic by a vaccination or by medication. Until that day is at hand, the only means of controlling the pandemic is by information--information about the diffusion of the virus, about risk behaviour, about safe sex, and about social responsibility. Educating people must be based on concise clinical experience, an up-to-date picture of the epidemiological situation, and reliable forecasts about the future course of the epidemic. Producing reliable forecasts about the HIV epidemic has proved to be a more complicated task than expected. The proven, reliable, and in most cases very useful epidemiological models have been far from successful when applied to the HIV epidemic. The reason for this is mostly due to the lack of reliable data depicting the true HIV seroprevalence. Instead of being handicapped by the problems of inadequate data, geographical modelling of the HIV epidemic is able to rely on its theoretical understanding and good knowledge about the spatial organization and the functioning of societies to make is forecasts. Due to its flexibility of approach, geographical modelling can adjust itself to less accurate data, thus providing an interim forecasting instrument until epidemiological modelling becomes successful. This paper presents the results of an analysis of the HIV epidemic in Finland based on cartographic analysis of municipal data and the use of a simple growth model.

Disease Outbreaks↗

PubMed2006

Predicting the impact of Medicare Part D implementation on the pharmacy workforce.

BACKGROUND: There is currently a shortage of pharmacist manpower, and it is expected to continue into the near future. It is also likely that the implementation of Medicare Part D will further aggravate the shortage by increasing demand, but it is not clear how much impact it will have. OBJECTIVE: The purpose of this study was to estimate the impact that the new Medicare drug benefit program will have on pharmacy workforce demand. METHODS: Analysis was conducted using forecasting techniques, which combines traditional statistical theory with both quantitative and qualitative methods. The Aggregated Demand Index (ADI) was designated as the dependent variable. A number of independent variables were selected for their potential to affect the workforce, demand for prescriptions or clinical services, and patient population. Data for the identified variables were collected from a variety of sources. Supply and demand data were analyzed at a national level. RESULTS: Both historical and univariate forecasts indicated that the demand for pharmacists will continue to exceed the supply of pharmacists. The ADI ratio of pharmacist demand-to-supply has recently leveled off which means that demand and supply are in an equilibrium that falls to the demand side. Consequently, the Medicare Modernization Act (MMA) is not predicted to produce a dramatic increase in prescription volume, which would change the current demand for pharmacists. Multivariate forecasting models were not robust primarily due to the lack of precise predictor variables. CONCLUSIONS: Despite the reliance on preliminary univariate forecasts and imprecise predictor variables, it appears that the increased use of prescriptions due to the MMA Part D will have minimal impact on pharmacist demand.

Humans↗

PubMed2000

Estimating disease risk at the whole plant level with General Circulation Models.

General Circulation Models (GCMs) have been developed to assess the impacts of potential global climate change. However, these models do not provide specific weather information at the whole-plant level and thus provide only very gross estimates of conditions that affect plant and disease development. Also, climatic change may increase the frequency of extreme events that influence plant production more than changes in daily or monthly averages. One solution is a simulation approach that can scale weather information from the global down to the plant scale. Over the last 4 years, we have been developing methods to hierarchically define current and forecast weather conditions down to the whole-plant level based on nested high-resolution atmospheric (mesoscale) numerical models. Two hierarchical mesoscale model approaches were tested to downscale weather data in a vineyard. The first, known as the Localized Mesoscale Forecast System (LMFS) uses surface databases to 'localize' mesoscale output. The second, known as the Canopy-Mesoscale Forecast System (CMFS), uses a boundary layer model to downscale mesoscale output. To illustrate the utility of this approach we focused on surface wetness duration (SWD), a variable with high spatial and temporal variability. SWD is also a critical variable for prediction of plant disease. Simulations of SWD with on-site input data were compared to those derived from the mesoscale models and to on-site sensors. Forecasts of atmospheric variables by the two systems were compared to on-site observations. Success in this effort leads us to extend this method to GCMs where factors such as temperature, rainfall, relative humidity, and surface wetness can be estimated within plant and crop canopies. We explore the implications of this work on evaluating the assessment of climate change on the risk of plant disease development.

Journal Article↗

PubMed1998

A geographic information system on the potential distribution and abundance of Fasciola hepatica and F. gigantica in east Africa based on Food and Agriculture Organization databases.

An adaptation of a previously developed climate forecast computer model and digital agroecologic database resources available from FAO for developing countries were used to develop a geographic information system risk assessment model for fasciolosis in East Africa, a region where both F. hepatica and F. gigantica occur as a cause of major economic losses in livestock. Regional F. hepatica and F. gigantica forecast index maps were created. Results were compared to environmental data parameters, known life cycle micro-environment requirements and to available Fasciola prevalence survey data and distribution patterns reported in the literature for each species (F. hepatica above 1200 m elevation, F. gigantica below 1800 m, both at 1200-1800 m). The greatest risk, for both species, occurred in areas of extended high annual rainfall associated with high soil moisture and surplus water, with risk diminishing in areas of shorter wet season and/or lower temperatures. Arid areas were generally unsuitable (except where irrigation, water bodies or floods occur) due to soil moisture deficit and/or, in the case of F. hepatica, high average annual mean temperature >23 degrees C. Regions in the highlands of Ethiopia and Kenya were identified as unsuitable for F. gigantica due to inadequate thermal regime, below the 600 growing degree days required for completion of the life cycle in a single year. The combined forecast index (F. hepatica+F. gigantica) was significantly correlated to prevalence data available for 260 of the 1220 agroecologic crop production system zones (CPSZ) and to average monthly normalized difference vegetation index (NDVI) values derived from the advanced very high resolution radiometer (AVHRR) sensor on board the NOAA polar-orbiting satellites. For use in Fasciola control programs, results indicate that monthly forecast parameters, developed in a GIS with digital agroecologic zone databases and monthly climate databases, can be used to define the distribution range of the two Fasciola species, regional variations in intensity and seasonal transmission patterns at different sites. Results further indicate that many of the methods used for crop productivity models can also be used to define the potential distribution and abundance of parasites.

Africa, Eastern↗

PubMed2001

Apamin-induced irregular firing in vitro and irregular single-spike firing observed in vivo in dopamine neurons is chaotic.

Dopamine neurons of the substantia nigra often fire action potentials irregularly in vivo, but in vitro fire in a regular, pacemaker-like firing pattern. Bath application of apamin, a blocker of calcium-activated potassium channels, can shift a dopamine neuron from pacemaker-like to irregular firing. To determine whether the irregular firing was caused by intrinsic cellular mechanisms rather than random synaptic input or some other form of noise, spike density functions of interspike interval records were analyzed using non-linear forecasting methods to quantify any non-linear (non-periodic) structure. Intrinsic cellular mechanisms are capable of producing chaotic firing, which is deterministic, non-linear, and loses predictability exponentially with increasing forecast time.To determine whether forecasting spike density functions could reliably measure predictability, forecasting was first applied to spike density functions produced by computer simulations of pacemaker-like, chaotic, and random firing, as well as pacemaker-like and chaotic firing that were randomly synaptically driven. Exponential loss of predictability was successfully detected in both chaotic and randomly driven chaotic firing. Predictability scaled faster than exponentially for random spiking, and linearly (slower than exponentially) for randomly driven pacemaker firing. The method was then applied to experimental records of apamin-induced irregular firing of rat dopaminergic neurons of the substantia nigra in vitro and in vivo. Exponential loss of predictability was detected in both cases, consistent with chaotic firing. Experimental records of pacemaker-like firing in vitro showed linear scaling, consistent with a randomly driven pacemaker. Several schemes for neural encoding of synaptic inputs have been suggested, such as rate codes or temporal codes. However, our results suggest that under some conditions, the irregular firing of dopamine neurons does not reflect the random temporal dynamics of its inputs, but rather the intrinsic, deterministic dynamics of dopamine cells themselves, under the tonic neuromodulatory influence of apamin in vitro and possibly that of an unidentified endogenous modulatory substance in vivo.

Action Potentials↗

PubMed2001

Effect of unexpected outcomes and causal attributions on key workers' expectancies of goal attainment.

Goal planning is an integral part of the role of the key worker in services for people with intellectual disability. The present study investigated the effect of the Weiner stability-expectancy principle on unexpected outcomes in goal planning. Four hundred and twenty-two actual goal attainment scales were constructed, with each scale describing the key workers' expected levels of outcome. Two hundred and twenty-six (54%) unexpected outcomes, both successful and unsuccessful, were then rated by key workers to provide a causal explanation for each outcome. These explanations were then grouped according to whether the attributions represented stable or variable causes. Finally, key workers were asked to provide a new expectancy rating to forecast future goal attainment. Two predictions from Weiner's model were confirmed by statistical analysis: (1) that success attributed to stable factors would lead to higher revised forecasts of goal attainment than success attributed to variable factors; and (2) that failure attributed to stable factors would lead to lower revised forecasts of goal attainment than failure attributed to variable factors. The results of the present study confirm that stability attributions do alter key workers' revised forecasts of goal attainment. The results are discussed in terms of the possible consequences for key worker behaviour, such as goal abandonment, differential treatment of service users and the need for a knowledgeable approach to performance appraisal by managers.

Adult↗

PubMed2005

Climate science and famine early warning.

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Africa South of the Sahara↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2006

Developing global climate anomalies suggest potential disease risks for 2006-2007.

BACKGROUND: El Niño/Southern Oscillation (ENSO) related climate anomalies have been shown to have an impact on infectious disease outbreaks. The Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA/CPC) has recently issued an unscheduled El Niño advisory, indicating that warmer than normal sea surface temperatures across the equatorial eastern Pacific may have pronounced impacts on global tropical precipitation patterns extending into the northern hemisphere particularly over North America. Building evidence of the links between ENSO driven climate anomalies and infectious diseases, particularly those transmitted by insects, can allow us to provide improved long range forecasts of an epidemic or epizootic. We describe developing climate anomalies that suggest potential disease risks using satellite generated data. RESULTS: Sea surface temperatures (SSTs) in the equatorial east Pacific ocean have anomalously increased significantly during July - October 2006 indicating the typical development of El Niño conditions. The persistence of these conditions will lead to extremes in global-scale climate anomalies as has been observed during similar conditions in the past. Positive Outgoing Longwave Radiation (OLR) anomalies, indicative of severe drought conditions, have been observed across all of Indonesia, Malaysia and most of the Philippines, which are usually the first areas to experience ENSO-related impacts. This dryness can be expected to continue, on average, for the remainder of 2006 continuing into the early part of 2007. During the period November 2006 - January 2007 climate forecasts indicate that there is a high probability for above normal rainfall in the central and eastern equatorial Pacific Islands, the Korean Peninsula, the U.S. Gulf Coast and Florida, northern South America and equatorial east Africa. Taking into consideration current observations and climate forecast information, indications are that the following regions are at increased risk for disease outbreaks: Indonesia, Malaysia, Thailand and most of the southeast Asia Islands for increased dengue fever transmission and increased respiratory illness; Coastal Peru, Ecuador, Venezuela, and Colombia for increased risk of malaria; Bangladesh and coastal India for elevated risk of cholera; East Africa for increased risk of a Rift Valley fever outbreak and elevated malaria; southwest USA for increased risk for hantavirus pulmonary syndrome and plague; southern California for increased West Nile virus transmission; and northeast Brazil for increased dengue fever and respiratory illness. CONCLUSION: The current development of El Niño conditions has significant implications for global public health. Extremes in climate events with above normal rainfall and flooding in some regions and extended drought periods in other regions will occur. Forecasting disease is critical for timely and efficient planning of operational control programs. In this paper we describe developing global climate anomalies that suggest potential disease risks that will give decision makers additional tools to make rational judgments concerning implementation of disease prevention and mitigation strategies.

Africa↗

PubMed1999

[Analysis of the method used in the nursing staff performance evaluation at the USP University Hospital].

The aim of this study was to check if the evaluators and evaluated follow the operational criterion that direct the nursing staffs evaluation of HU; to the identify the graduation conferred to obrigatory and optional forecasters by evaluators and evaluated; to identify the optional forecaster chosen by evaluators and evaluated and prepare the researches to a change at the process of nursing staff's evaluation of HU. The results showed that different functional categories had similar points of view; there were high level of agreement between them; both values attribution as optional forecaster selection. It's demonstrated that there's coherence at the process of nursing staff's performance evaluation, the evaluators shaves with evaluated the same pre-supposition about the purpose and importance of the forecasters insert in the instrument of performance evaluation.

Brazil↗

PubMed1996

Impact of selective financing of drugs on pharmaceutical expenditure control in the province of Valladolid, Spain.

In July 1993, public financing for 1692 drug specialities was stopped in Spain (a drug speciality is a medicine with a defined composition, particular pharmaceutical form and dosage, prepared for immediate use, ready and packaged for dispatching to the public, with uniform name, packaging and labelling which has been granted authorisation by the State Administration and registered in the Pharmaceutical Specialities Register). We designed a study to assess the repercussions of this so-called 'selective financing' on expenditure control. The aims of the study were 2-fold: first, to forecast the consumption of drug specialities that are reimbursable on the Spanish National Health Service, had selective financing not been applied; and, secondly, to estimate the impact of selective financing on drug consumption. For short- and medium-term forecast estimates, time-series analysis was used, and was applied to monthly consumption data. The measures of consumption used were the number of prescriptions per 1000 patients, and cost [in Spanish pesetas (Pta; $US1 = Pta123.9, February 1996)] per 1000 patients. Data were analysed from January 1986 up to the introduction of selective financing. We found that drug specialities excluded by selective financing showed, overall, a negative increase (i.e. the consumption of drugs that were excluded under selective financing decreased). The repercussion forecast, both in terms of prescription numbers and cost, of the 13 excluded drug specialities that had the highest pre-selective financing consumption is decreasing compared with total consumption. The assessment of the repercussions of selective financing up to December 1994 was made by calculating the difference between actual expenditures and forecast expenditures in the absence of selective financing. This repercussion, in prescriptions, was lower than the percentage of the 1692 excluded drug specialities relating to total consumption in 1992. The repercussion in cost, nevertheless, was greater than expected during the first year of selective financing application. This could be attributable to average price containment of drug specialities due to causes other than selective financing itself. The economic impact of selective financing after 1 year of application was reduced because of accommodation phenomena.

Cost Control↗