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PubMed1985

[Population forecasts for the Netherlands after 1984].

The results of the most recent official population projections for the Netherlands are presented. The methods and assumptions used in preparing the projections are described, and three alternative projections are provided up to the year 2035. Projections by sex, age, and marital status are also discussed. "The population forecasting model applied belongs to the class of cohort-component models and its relevant components are fertility, mortality, external migration, administrative corrections, marriage of never-married persons, divorce, marriage of divorced persons, transition to widowhood and marriage of widowed persons...." (summary in ENG)

Age Distribution↗

PubMed2002

Forecasting the impact of a clinical practice guideline for perioperative beta-blockers to reduce cardiovascular morbidity and mortality.

BACKGROUND: Beta-blockers reduce morbidity and mortality when administered to high-risk patients undergoing major noncardiac surgery, yet little is known about how often they are being prescribed. Clinical practice guidelines are tools that can be used to speed the translation of research into practice and may be one method to improve the use of beta-blockers. Before implementing any guideline, it is important to forecast its potential clinical and financial impact. METHODS: We conducted a retrospective cohort study, using administrative and medical record review data, of all adult patients undergoing major noncardiac surgery at Baystate Medical Center, Springfield, Mass, during a 1-month period in 1999. Patients with 2 or more cardiac risk factors or with documented coronary artery disease were classified as high risk and were considered eligible for treatment with a beta-blocker if they had no obvious contraindications to its use. We estimated the potential clinical benefit of treating eligible patients with a beta-blocker by extrapolating the treatment effect observed in a previously reported randomized clinical trial. RESULTS: Of 158 patients undergoing major noncardiac surgery, 67 (42.4%) seemed to be ideal candidates for treatment with perioperative beta-blockers. Of these 67 patients, 25 (37%) received a beta-blocker at some time perioperatively. During the course of a year, we estimate that between 560 and 801 patients who do not receive beta-blockers might benefit from treatment with these medications. Full use of beta-blockers among eligible patients at our institution could result in 62 to 89 fewer deaths each year at an overall cost of $33 661 to $40 210. CONCLUSIONS: There seems to be a large opportunity to improve the quality of care of patients undergoing major noncardiac surgery by increasing the use of beta-blockers in the perioperative period. A clinical practice guideline may be one method to achieve these goals at little cost.

Adrenergic beta-Antagonists↗

PubMed1988

A model for forecasting intermittent skilled home nursing needs.

The problem of forecasting the need and the cost for post-discharge skilled home nursing services is addressed by a simple statistical model. The model, assumptions, and simple calculations are described. Use of the model is illustrated with 7598 cancer patients and 2337 myocardial infarction patients. Simulation of the impact of changes in the health care delivery system toward greater and lesser severity of hospitalized patients is carried out. Two key projections illustrating the model's output are the number of patients with these diseases who will need care and the cost of that care.

Costs and Cost Analysis↗

PubMed1989

The application of time series forecasting methods to an estimation problem using provisional mortality statistics.

Provisional estimates of mortality for selected causes of death are published each month by the National Center for Health Statistics. These estimates are based upon a ten per cent sample of death certificates in the United States. Final mortality results, based upon all the death certificates for a calendar year, are available one to two years after publication of the provisional estimates. This paper explores the potential of time series forecasting techniques for improving mortality estimates by using the correlation structure between the provisional and final series to obtain mortality estimates that are expected to be closer to final values than currently used provisional estimates.

Algorithms↗

PubMed1985

Forecasting staffing needs for productivity management in hospital laboratories.

Daily and weekly prediction models are developed to help forecast hospital laboratory work load for the entire laboratory and individual sections of the laboratory. The models are tested using historical data obtained from hospital census and laboratory log books of a 90-bed southwestern hospital. The results indicate that the predictor variables account for 50%, 81%, 56%, and 82% of the daily work load variation for chemistry, hematology, and microbiology sections, and for the entire laboratory, respectively. Equivalent results for the weekly model are 53%, 72%, 12%, and 78% for the same respective sections. On the basis of the predicted work load, staffing assessment is made and a productivity monitoring system constructed. The purpose of such a system is to assist laboratory management in efforts to utilize laboratory manpower in a more efficient and cost-effective manner.

Efficiency↗

PubMed1994

Nonlinear forecasting of hepatitis and AIDS incidence.

The simplex methods of nonlinear forecasting are used to study the data sets of hepatitis A and AIDS in various regions of the United States. The results are compared with those obtained from the traditional ARIMA methods. In many regions, the simplex methods developed from nonlinear dynamical system theory give smaller errors for the data of hepatitis A. A combination of the simplex methods and the traditional ARIMA methods can produce better results for the AIDS data sets.

Acquired Immunodeficiency Syndrome↗

PubMed1998

Forecasting of hydrophilic contact lens tolerance by means of tear ferning test.

BACKGROUND: The ability of the tear ferning test to predict future hydrophilic contact lens tolerance was studied. METHODS: The tear ferning test (TFT) was performed on one randomly chosen eye of each of the 116 subjects who came to our contact lens clinic for hydrophilic contact lens application. The TFT was performed at the time of enrollment (TO) and then 1 month (T1), 3 months (T2) and 6 months (T3) after contact lens fitting. The specificity and the sensitivity of the TFT in identifying future contact lens tolerance was then studied. The statistical significance of the differences in behavior through the study period among the subjects with different pre-fitting TFT results was evaluated by means of survival curves. RESULTS: When only type I ferning was considered as a marker of good tear film conditions, the ability of the TFT to forecast contact lens tolerance had 78.95% sensitivity, 78.35% specificity and 78.45% diagnostic precision. The TFT showed sensitivity of 100%, specificity of 86.6% and diagnostic precision of 97.4% when performed after 1 month of contact lens wearing (T1). Survival curve analysis showed a statistically significant difference in behavior between the group of subjects with pre-fitting ferning type I and the other three groups (P < 0.001). CONCLUSION: The TFT appears to have good sensitivity and specific for prediction of contact lens tolerance in a clinical setting.

Contact Lenses, Hydrophilic↗

PubMed2000

Forecasting of the flowering time for wild species observed at Guidonia, central Italy.

It is well known that forecasting the flowering time of wild vegetation is useful for various sectors of human activity, particularly for all agricultural practices. Therefore, continuing previous work by Cenci et al., we will present here three new phenoclimatic models of the flowering time for a set of wild species, based on an original data sample of flowering dates for more than 500 species, observed at Guidonia (42 degrees N in central Italy) by Montelucci in the period 1960-1982. However, on applying the bootstrap technique to each species sample to check its basic statistical parameters, we found only about 200 to have data samples with an approximately Gaussian distribution. Eventually only 57 species (subdivided into eight monthly subsets from February to September) were used to formulate the models satisfactorily. The flowering date (represented by the z variable), is expressed in terms of two variables x and y by a nonlinear equation of the form z=axbeta+gammay. The x variable represents either the degree-day sum (in model 1), or the daily-maximum-temperature sum (in model 2), or the daily-global-insolation sum (in model 3), while y for all three models corresponds to the rainy-day sum. Note that all summations involved in the computation of the variables x and y take place over a certain period of time (preceding the flowering phase), which is a parameter to be determined by the fitting procedure. This parameter, together with the threshold temperature (needed to compute the degree-days in model 1), represents the two implicit parameters of the process, thus the total number of parameters (including these last two) becomes respectively, five for model 1, and four for the other two models. The preliminary results of this work were reported at the XVI International Botanical Congress (1-7 August 1999, St. Louis, Missouri USA).

Agriculture↗

PubMed2005

Forecasting length of stay in child residential treatment.

A sample of 126 consecutively admitted residential treatment children (mean age = 9.86, SD=1.84; 70.6% male; 42.1% African American; 50% Caucasian) were studied over a five-year period to identify predictors of length-of-stay. Cox regression was the primary statistical method used to analyze psychiatric and behavioral rating data for children assessed by teachers and treatment staff using the Devereux Scales of Mental Disorders (DSMD). Parental alcohol abuse, and children's age, medication status, race, initial DSMD total and critical pathology scores, were predictive of length-of-stay. Residential length-of-stay was strongly linked to initial levels of psychiatric symptomatology. Models that can help forecast length of stay are vital tools in helping to improve both clinical and utilization management strategies.

Child↗

PubMed2005

Choice of models for the analysis and forecasting of hospital beds.

There is growing concern that current health care services are not sustainable. The compartmental flow model provides the opportunity for improved decision-making about bed occupancy decisions, particularly those of a strategic nature. This modelling can be applied to complement infrastructure and workforce-planning methods. Discussion about appropriateness of the level of model complexity, the degree of fit and the ability to use compartmental flow models for generalization and forecasting has been lacking. The authors investigated model selection and assessment in relation to hospital bed compartment flow models. A compartment model for a range of scenarios was created. The training and test data related to the 1998 and 1999 calendar years, respectively. The majority of scenarios tested were based upon commonly used periods that describe periods of time. The goodness-of-fit achieved by optimisation was measured against the training and test data. Model fit improved with increasing complexity as expected. The analysis of model fit against the test data showed that increasing model complexity did result in over-fitting, and better prediction was achieved with a relatively simple model. In terms of generalisation, the seasonal models performed best. Single day census type models, which have been used by Millard and his colleagues, were also generated. The performance of these models was similar, but inferior to that of the models generated from a full year of training data. The additional data make the models better able to capture the variation across the year in activity.

Bed Occupancy↗

PubMed1984

Forecasting expenditure on capital projects.

S-curves are widely used for planning, forecasting and control of cost, time and resources of a project. In this paper, a comparison of two S-curve models developed at the Department of Health and Social Security (DHSS) and Bradford University is carried out both from the viewpoint of predictive accuracy and ease of use. The models are validated using expenditure data for 21 recent U.K. health building projects. Methods of least squares is used to estimate the parameters of the two models. These parameters are categorized according to the total cost of the projects. Both the models are shown to be of comparable accuracy for fitting actual expenditure data. The DHSS model has a major advantage of simplicity of form and use, although the slightly greater mathematical complexity of the Keller-Singh model is off-set by the readily interpretable nature of its form and basic parameters. It is concluded that both or either of the models could be used by clients/contractors for effective planning and control of project costs.

Capital Expenditures↗

PubMed1991

Forecasting the number of future disabled elderly using Markovian and mathematical models.

The accuracy of forecasting the number of future disabled elderly people depends on the accuracy of projecting mortality rates and the rates of transition to and from functional disability. We describe a new two-step method for constructing mathematical models that project these future rates dynamically. (1) A Markovian model of elders' transitions between functional states is specified. (2) A mathematical model of the probability of each transition is created. We conducted pilot studies of the fundamental mathematical processes of this method using data from the Longitudinal Study of Aging. First we constructed prototypic mathematical models of the probabilities of remaining functionally able and of making transitions to disability and to death within 2 years. Then we used these models to project hypothetical rates of transition for white women of selected ages, morbidity ratings and health statuses.

Activities of Daily Living↗

PubMed1995

An ignorant belief network to forecast glucose concentration from clinical databases.

Ignorant Belief Networks (IBNs) are a class of Bayesian Belief Networks (BBNs) able to reason on the basis of incomplete probabilistic information and to incrementally refine the precision of the inferred probabilities as more information becomes available. In this paper, we will describe how can be used to develop a system able to forecast blood glucose concentration in patients affected by insulin dependent diabetes mellitus (IDDM). The major difference between our approach and the traditional ones is that probability distributions over the IBN are not provided by some human expert or by the current literature but they are directly extracted from a clinical database of IDDM patients. This choice capitalizes on the large amount of information generated by the daily control of blood glucose and allows the system to improve the accuracy of predictions as more information becomes available. We will show how, even with a very small subset of the information needed to specify a BBN, the IBN is able to carry out predictions about the future blood glucose concentration in a patient by explicitly taking into consideration the level of ignorance embedded in the network.

Artificial Intelligence↗

PubMed2005

Stochastic model to forecast ground-level ozone concentration at urban and rural areas.

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,1)24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.

Cities↗

PubMed2005

Forecasting land use change and its environmental impact at a watershed scale.

Urban expansion is a major driving force altering local and regional hydrology and increasing non-point source (NPS) pollution. To explore these environmental consequences of urbanization, land use change was forecast, and long-term runoff and NPS pollution were assessed in the Muskegon River watershed, located on the eastern coast of Lake Michigan. A land use change model, LTM, and a web-based environmental impact model, L-THIA, were used in this study. The outcomes indicated the watershed would likely be subjected to impacts from urbanization on runoff and some types of NPS pollution. Urbanization will slightly or considerably increase runoff volume, depending on the development rate, slightly increase nutrient losses in runoff, but significantly increase losses of oil and grease and certain heavy metals in runoff. The spatial variation of urbanization and its impact were also evaluated at the subwatershed scale and showed subwatersheds along the coast of the lake and close to cities would have runoff and nitrogen impact. The results of this study have significant implications for urban planning and decision making in an effort to protect and remediate water and habitat quality of Muskegon Lake, which is one of Lake Michigan's Areas of Concern (AOC), and the techniques described here can be used in other areas.

Cities↗

PubMed2004

Natural turbidity variability and weather forecasts in risk management of anthropogenic sediment discharge near sensitive environments.

Coastal development activities can cause local increases in turbidity and sedimentation. This study characterises the spatial and temporal variability of turbidity near an inshore fringing coral reef in the central Great Barrier Reef, under a wide range of natural conditions. Based on the observed natural variability, we outline a risk management scheme to minimise the impact of construction-related turbidity increases. Comparison of control and impact sites proved unusable for real-time management of turbidity risks. Instead, we suggest using one standard deviation from ambient conditions as a possible conservative upper limit of an acceptable projected increase in turbidity. In addition, the use of regional weather forecast as a proxy for natural turbidity is assessed. This approach is simple and cheap but also has limitations in very rough conditions, when an anthropogenic turbidity increase could prove fatal to corals that are already stressed under natural conditions.

Animals↗

PubMed2005

Scientific management of Mediterranean coastal zone: a hybrid ocean forecasting system for oil spill and search and rescue operations.

The oil spill from Prestige tanker showed the importance of scientifically based protocols to minimize the impacts on the environment. In this work, we describe a new forecasting system to predict oil spill trajectories and their potential impacts on the coastal zone. The system is formed of three main interconnected modules that address different capabilities: (1) an operational circulation sub-system that includes nested models at different scales, data collection with near-real time assimilation, new tools for initialization or assimilation based on genetic algorithms and feature-oriented strategic sampling; (2) an oil spill coastal sub-system that allows simulation of the trajectories and fate of spilled oil together with evaluation of coastal zone vulnerability using environmental sensitivity indexes; (3) a risk management sub-system for decision support based on GIS technology. The system is applied to the Mediterranean Sea where surface currents are highly variable in space and time, and interactions between local, sub-basin and basin scale increase the non-linear interactions effects which need to be adequately resolved at each one of the intervening scales. Besides the Mediterranean Sea is a complex reduced scale ocean representing a real scientific and technological challenge for operational oceanography and particularly for oil spill response and search and rescue operations.

Disaster Planning↗

PubMed2005

Forecasting versus projection models in epidemiology: the case of the SARS epidemics.

In this work we propose a simple mathematical model for the analysis of the impact of control measures against an emerging infection, namely, the severe acute respiratory syndrome (SARS). The model provides a testable hypothesis by considering a dynamical equation for the contact parameter, which drops exponentially with time, simulating control measures. We discuss the role of modelling in public health and we analyse the distinction between forecasting and projection models as assessing tools for the estimation of the impact of intervention strategies. The model is applied to the communities of Hong Kong and Toronto (Canada) and it mimics those epidemics with fairly good accuracy. The estimated values for the basic reproduction number, R0, were 1.2 for Hong Kong and 1.32 for Toronto (Canada). The model projects that, in the absence of control, the final number of cases would be 320,000 in Hong Kong and 36,900 in Toronto (Canada). In contrast, with control measures, which reduce the contact rate to about 25% of its initial value, the expected final number of cases is reduced to 1778 in Hong Kong and 226 in Toronto (Canada). Although SARS can be a devastating infection, early recognition, prompt isolation, and appropriate precaution measures, can be very effective to limit its spread.

Canada↗