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PubMed2005

Why is metal bioaccumulation so variable? Biodynamics as a unifying concept.

Ecological risks from metal contaminants are difficult to document because responses differ among species, threats differ among metals, and environmental influences are complex. Unifying concepts are needed to bettertie together such complexities. Here we suggest that a biologically based conceptualization, the biodynamic model, provides the necessary unification for a key aspect in risk: metal bioaccumulation (internal exposure). The model is mechanistically based, but empirically considers geochemical influences, biological differences, and differences among metals. Forecasts from the model agree closely with observations from nature, validating its basic assumptions. The biodynamic metal bioaccumulation model combines targeted, high-quality geochemical analyses from a site of interestwith parametrization of key physiological constants for a species from that site. The physiological parameters include metal influx rates from water, influx rates from food, rate constants of loss, and growth rates (when high). We compiled results from 15 publications that forecast species-specific bioaccumulation, and compare the forecasts to bioaccumulation data from the field. These data consider concentrations that cover 7 orders of magnitude. They include 7 metals and 14 species of animals from 3 phyla and 11 marine, estuarine, and freshwater environments. The coefficient of determination (R2) between forecasts and independently observed bioaccumulation from the field was 0.98. Most forecasts agreed with observations within 2-fold. The agreement suggests that the basic assumptions of the biodynamic model are tenable. A unified explanation of metal bioaccumulation sets the stage for a realistic understanding of toxicity and ecological effects of metals in nature.

Animals↗

PubMed1987

Depression and pessimism for the future: biased use of statistically relevant information in predictions for self versus others.

The cognitive theories of depression emphasize the role of pessimism about the future in the etiology and maintenance of depression. The present research was designed for two reasons: to provide a clear demonstration that depressed individuals' predictions of the likelihood of future outcomes are more pessimistic than those of nondepressed individuals given identical information with which to make forecasts and identical conditions for forecasting, and to test two additional hypotheses regarding possible mechanisms underlying depressives' relative pessimism in forecasting: a social-comparison and a differential attributional-style hypothesis. We used a modification of the cue-use paradigm developed by Ajzen (1977, Experiment 1) and examined depressed and nondepressed people's predictions of the likelihood of future positive and negative outcomes for themselves and for others. The results provided strong support for pessimism on the part of depressed individuals relative to nondepressed individuals in forecasts for both self and others. In addition, whereas nondepressives exhibited a self-enhancing bias in which they overestimated their probability of success and underestimated their probability of failure relative to that of similar others, depressives did not succumb to either positive or negative social comparison biases in prediction. Finally, in line with the attributional-style hypothesis, depressed-nondepressed differences in subjects' cue-use patterns were obtained, especially in forecasts for self. The findings are discussed with respect to the mechanisms underlying predictive optimism and pessimism and the possible functions and implications of these predictive biases.

Adult↗

PubMed1993

Prediction of gentamicin concentrations in neonates and infants using a Bayesian pharmacokinetic model.

This study retrospectively characterized population-based pharmacokinetic parameters for gentamicin in neonates and young infants, and evaluated the predictive performance of these parameters in a Bayesian forecasting program. Population parameter estimates were determined from the serum concentration-time data of 19 neonates and infants using a one-compartment open infusion model and nonlinear least-squares regression analysis. Univariate and multiple stepwise linear regression analyses were used to determine significant relationships between demographic characteristics and gentamicin pharmacokinetic parameters. Creatinine clearance and postnatal age were the most significant predictors of weight-standardized gentamicin clearance (model r2 = 0.86). The relationships between patient characteristics and population-based parameters were incorporated into the one-compartment Bayesian forecasting model. A second group of 17 neonates and infants receiving 35 courses of gentamicin therapy were used to evaluate the predictive performance of the population-based parameters and a Bayesian forecasting model. The population parameters provided accurate prediction of steady state gentamicin concentrations throughout multiple courses of therapy within the same patient. Bayesian forecasting further minimized the mean prediction error (bias) once a set of steady state peak and trough serum gentamicin concentrations became available (peak concentrations: -0.062 vs. -0.273 mg/l; trough concentrations: -0.006 vs. -0.161 mg/l). The mean absolute error (accuracy) was similar for the two sets of parameters. The observed accuracy of both the population parameters and Bayesian forecasting suggests that monitoring of serum gentamicin concentrations can be kept to minimum in neonates and infants.

Aging↗

PubMed2006

Relationship between onset of peptic ulcer and meteorological factors.

AIM: To discuss the relationship between onset of peptic ulcer (PU) and meteorological factors (MFs). METHODS: A total of 24,252 patients were found with active PU in 104,121 samples of gastroscopic examination from 17 hospitals in Nanning from 1992 to 1997. The detectable rate of PU (DRPU) was calculated every month, every ten days and every five days. An analysis of DRPU and MFs was made in the same period of the year. A forecast model based on MFs of the previous month was established. The real and forecast values were tested and verified. RESULTS: During the 6 years, the DRPU from November to April was 24.4 -28.8%. The peak value (28.8%) was in January. The DRPU from May to October was 20.0-22.6%, with its low peak (20.0%) in June. The DRPU decreased from winter and spring to summer and autumn (P<0.005). The correlated coefficient between DRPU and average temperature value was -0.8704, -0.6624, -0.5384 for one month, ten days , five days respectively (P<0.01). The correlated coefficient between DRPU and average highest temperature value was -0.8000,-0.6470,-0.5167 respectively (P<0.01). The correlated coefficient between DRPU and average lowest temperature value was -0.8091, -0.6617, -0.5384 respectively (P<0.01). The correlated coefficient between DRPU and average dew point temperature was -0.7812, -0.6246, -0.4936 respectively (P<0.01). The correlated coefficient between DRPU and average air pressure value was 0.7320, 0.5777, 0.4579 respectively (P<0.01). The average temperature, average highest and lowest temperature, average air pressure and average dew point temperature value of the previous month, ten days and five days could forecast the onset of PU, with its real and forecast values corresponding to 71.8%, 67.9% and 66.6% respectively. CONCLUSION: DRPU is closely related with the average temperature, average highest and lowest temperature,average air pressure and average dew point temperature of each month, every ten days and every five days for the same period. When MFs are changed, the human body produces a series of stress actions.A long-term and median-term based medical meteorological forecast of the onset of PU can be made more accurately according to this.

Atmospheric Pressure↗

PubMed1992

Population, labour force and unemployment in Andalusia: prospects for 1993.

"This paper is concerned with forecasting population, employment, labour force and unemployment in Andalusia [Spain] to 1993. For this purpose, a block-recursive demoeconomic model is presented. The demographic submodel is based on the component-cohort method of forecasting population by age and sex. The economic submodel uses econometric and time series analysis to forecast employment both in the medium and short-term. In the labour market part, the labour force is forecasted taking into account the encouraged-discouraged worker effects of changes in the demand for labour."

Age Factors↗

PubMed1985

Economic-demographic modeling with endogenously determined birth and migration rates: theory and prospects.

"The standard demographic approach to population forecasting consists of extrapolating into the future carefully measured birth, death, and migration rates. An alternative is to forecast changes in those rates on the basis of social science theories. In this paper the prospects for incorporating those theories into forecasting models are assessed. The paper has two parts, the first devoted to fertility and the second to migration. Each contains a description of the demographic methods currently used by the US Bureau of the Census followed by a comprehensive review of the theoretical foundations for forecasting and an assessment of the prospects for doing so."

Americas↗

PubMed1997

The importance of demographics in economic analysis: the unusual suspects.

"The proper use of demographic information can add significantly to the ability to analyze and forecast economic activity. With housing, short-run forecasting is difficult and long-term forecasting impossible without considering changing demographic factors. Combining detailed demographic analysis with sound structural econometric modeling of the cyclical factors underlying the demand for and the supply of housing has resulted in significantly more accurate analyses and forecasts of the [U.S.] housing market."

Americas↗

PubMed1990

[Predictability of international migration].

Population forecasts concerning international migration for the Netherlands are evaluated. "In the Dutch Population Forecasts from 1975 onwards, the uncertainty of the [international] migration projections has been underestimated. The interval between the low and high variants covered only a quarter of the subsequent observations. The...model...[utilized is] appropriate for determining the width of the forecast interval. Average migration in a number of years can be forecast considerably more accurately than migration in separate years." (SUMMARY IN ENG)

Demography↗

PubMed1998

Estimating the benefits of modifying risk factors of cardiovascular disease: a comparison of primary vs secondary prevention.

OBJECTIVES: To compare the potential years of life saved (YOLS) associated with risk factor modification in the primary and secondary prevention of cardiovascular disease (CVD). METHODS: The CVD life expectancy model estimates the risk of death due to coronary disease, stroke, and other causes based on the levels of independent risk factors (such as age, blood pressure, and blood lipid levels) found in the cohort of the Lipid Research Clinics. The model was validated by comparing its predictions with the observed fatal outcomes of 9 randomized clinical trials. We then estimated the YOLS associated with treating hyperlipidemia or hypertension among hypothetical patient groups with and without CVD at baseline. We defined high-risk patients as those with 3 risk factors (hyperlipidemia, cigarette smoking, and hypertension) and low-risk patients as those with isolated hypertension or hyperlipidemia. RESULTS: The fatal events predicted by the model were consistent with the clinical trial results. Among men and women with hyperlipidemia without CVD, the forecasted benefits of lipid therapy were substantially greater among high-risk groups vs low-risk groups (4.74-0.78 YOLS vs 2.50-0.25 YOLS, respectively). Among those with CVD, the forecasted benefits of treatment were similar for both high-risk and low-risk groups (4.65-0.65 YOLS vs 3.84-0.58 YOLS, respectively). The results for hypertension therapy also demonstrated greater benefits for high-risk vs low-risk patients undergoing primary prevention therapy (1.34-0.29 YOLS vs 0.85-0.13 YOLS, respectively), and the forecasted benefits in secondary prevention were similar (1.26-0.23 YOLS vs 1.00-0.23 YOLS, respectively). CONCLUSIONS: The clinical approach to risk factor modification in primary prevention should be different from that in secondary prevention. The forecasted benefits of therapy among patients without CVD are greatest in the presence of other risk factors. Among those with CVD, the benefits of therapy are equivalent, thereby obviating the need to target high-risk patients.

Cardiovascular Diseases↗

PubMed2000

Implications of an aging registered nurse workforce.

CONTEXT: The average age of registered nurses (RNs), the largest group of health care professionals in the United States, increased substantially from 1983 to 1998. No empirically based analysis of the causes and implications of this aging workforce exists. OBJECTIVES: To identify and assess key sources of changes in the age distribution and total supply of RNs and to project the future age distribution and total RN workforce up to the year 2020. DESIGN AND SETTING: Retrospective cohort analysis of employment trends of recent RN cohorts over their lifetimes based on US Bureau of the Census Current Population Surveys between 1973 and 1998. Recent workforce trends were used to forecast long-term age and employment of RNs. PARTICIPANTS: Employed RNs aged 23 to 64 years (N = 60,386). MAIN OUTCOME MEASURES: Annual full-time equivalent employment of RNs in total and by single year of age. RESULTS: The average age of working RNs increased by 4.5 years between 1983 and 1998. The number of full-time equivalent RNs observed in recent cohorts has been approximately 35% lower than that observed at similar ages for cohorts that entered the labor market 20 years earlier. Over the next 2 decades, this trend will lead to a further aging of the RN workforce because the largest cohorts of RNs will be between age 50 and 69 years. Within the next 10 years, the average age of RNs is forecast to be 45.4 years, an increase of 3.5 years over the current age, with more than 40% of the RN workforce expected to be older than 50 years. The total number of full-time equivalent RNs per capita is forecast to peak around the year 2007 and decline steadily thereafter as the largest cohorts of RNs retire. By the year 2020, the RN workforce is forecast to be roughly the same size as it is today, declining nearly 20% below projected RN workforce requirements. CONCLUSIONS: The primary factor that has led to the aging of the RN workforce appears to be the decline in younger women choosing nursing as a career during the last 2 decades. Unless this trend is reversed, the RN workforce will continue to age, and eventually shrink, and will not meet projected long-term workforce requirements. JAMA. 2000.

Adult↗

PubMed1999

On the application of integer-valued time series models for the analysis of disease incidence.

Statistical time series models are practical tools in public health surveillance. Their capacity to forecast future disease incidence values exemplifies their usefulness. Using these forecasts, one can develop strategies to trigger alerts to public health officials when irregular disease incidence values have occurred. Clearly, the better the forecasting performance of the model class used in the time series analysis, the more realistic are the alerts triggered. The time series analysis of disease incidence values has often entailed the Box and Jenkins model class. However, this class was designed to model real-valued variables whereas disease incidences are integer-valued variables. A new class of time series models, called integer-valued autoregressive models, has been developed and studied over the past decade. The objective of this paper is to introduce this new class of models to the application of time series analysis of infectious disease incidence, and to demonstrate its advantages over the class of real-valued Box and Jenkins models. The paper also presents a bootstrap method developed for the calculation of forecast interval limits.

Communicable Diseases↗

PubMed1986

Do the predictions for cancer incidence come true? Experience from Finland.

The observed occurrence of cancer in Finland in 1980 was compared with that predicted by extrapolation of the trends in cancer incidence by site in 1957 to 1968. The predictions had been constructed as 90% confidence intervals. Of the mutually exclusive 33 incidence predictions, 22 (67%) included the observed incidence rate. The incidence rate was successfully predicted for intestinal, pancreatic, and urinary cancers, as well as for lymphomas, whereas incidence forecasts concerning cancers of the lung, breast, and cervix uteri, and melanomas of the skin did not come true. The incidence of male lung cancer did not increase as was expected, breast cancer incidence increased even more than expected, and the decrease in the incidence of cervical cancer was steeper than expected. Because of failure in the population forecast, only 55% of the predictions (90% confidence intervals) for the numbers of new cancer cases included the observed number. For administrative purposes it would be important for the predictions to come true. On the other hand, the successes or failures of the predictions related to specific cancers may be used to study the effect of changes in etiologic factors, diagnostic methods, or the definition of cancer, as well as the effect of mass screening or other intervention measures upon the cancer incidence. Extrapolation as a method of forecasting cancer incidence is adequate as far as the control of random error is concerned when only short-term forecasts, ranging from 5 to 10 years, are needed.

Aged↗

PubMed1982

Scenarios, past, present and future.

A key point in considering strategic planning is that it is not forecasting. The future is unpredictable and is not a product of the past. This low opinion of forecasting has gained in influence recently. But such an approach creates a dilemma for the strategic planner. He needs to take into account potential future forces--but he can only do this with inadequate means. Forecasting methods, however, can suggest trends and it is possible to reduce the number of possible futures to a manageable number. This is, in effect, creating scenarios. The concept of using alternative scenarios in planning was popularized some 20 years back. The proviso was made that they should not be interpreted as forecasts. The methods have proved highly flexible and include the consensus technique, the iteration-through-synopsis technique, and the cross-impact method. Various studies using one or more of these have been implemented, perhaps the best known being by Meadows and by Mesarovic. These have been based on the so-called 'hard' method, using computers. 'Soft' methods are based more on the mind and use psychology and sociology, the most familiar being the Delphi method. A somewhat more sophisticated version is Cross-Impact Analysis.

Forecasting↗

PubMed1989

PPS-87: a new event oriented solar proton prediction model.

A new event-oriented solar proton prediction model has been developed and implemented at the USAF Space Environment forecast facility. This new model generates predicted solar proton time-intensity profiles for a number of user adjustable energy ranges and is also capable of making predictions for the heavy ion flux. The computer program is designed so a forecaster can select inputs based on the data available in near real-time at the forecast center as the solar flare is occurring. The predicted event amplitude is based on the electromagnetic emission parameters of the solar flare (either microwave or soft X-ray emission) and the solar flare position on the sun. The model also has an update capability where the forecaster can normalize the prediction to actual spacecraft observations of spectral slope and particle flux as the event is occurring in order to more accurately predict the future time-intensity profile of the solar particle flux. Besides containing improvements in the accuracy of the predicted energetic particle event onset time and magnitude, the new model converts the predicted solar particle flux into an expected radiation dose that might be experienced by an astronaut during EVA activities or inside the space shuttle.

Computer Simulation↗

PubMed1999

New technologies in medicine: biotechnology and nanotechnology.

In February 1997, researchers created Dolly, a lamb cloned from the DNA of an adult sheep. This was supposed to be impossible (or at least generations away), but suddenly it was here--a clone of a higher mammal. Whatever Dolly's ultimate significance, she conclusively demonstrated the growing power of biotechnology. Many have come to the conclusion that advances in biotechnology will fundamentally transform medicine during the coming decade. Society is in the midst of a technical revolution that will have the same relevance as the development of the printing press, the internal combustion engine, and the microprocessor. Computers have become the key tools in the accelerating progress that is occurring in the field of biotechnology. At the same time, genetic, evolutionary, and other biologic processes are providing new models for the development of computer hardware and software. Today represents the early stages of what has been called the "bionic convergence": the convergence of the biologic revolution with the information revolution, the joining of biology with electronics. Virtually everything that is important to health care practitioners and patients--diagnostic techniques, means of understanding disease causes, methods of treatment, approaches to prevention, health care facility design, medical education, and legal and ethical issues--will be changed by the revolutions currently underway in the fields of biotechnology and genetic medicine. The following monograph includes several forecasts about a range of possible opportunities that may have enormous effects on health care during the next century. These forecasts address the potential impacts of biotechnology on disease detection and diagnosis, treatment, prevention, nanotechnology, and other areas of medical significance. Every area of beneficiary care will be affected as the changes implied by these forecasts begin to develop. Beneficiary care will continue to see the emergence of a "forecast, prevent, and manage" paradigm. The emphasis will be on disease prevention, health promotion, and the creation of healthy communities. New diagnostic and treatment opportunities will be available as a consequence of breakthroughs in genetic medicine. The health care system will view health as a whole, as a person's overall sense of well-being--an entity that encompasses much more than the absence of symptoms.

Biotechnology↗

PubMed2004

[Efficacy of time series analysis in health planning for cancer in Spain].

OBJECTIVE: To evaluate the accuracy of the time series model used to forecast cancer mortality rates in Spain published in Atención Primaria for the year 2000. DESIGN: Descriptive study. SETTING: Spain. PARTICIPANTS: All cancer deaths recorded in Spain during the year 2000. MAIN MEASUREMENTS: The forecast for cancer mortality rates in males and females for the year 2000 was obtained from a paper published in 1999 in the journal Atención Primaria. Official data on cancer mortality rates for the year 2000 were obtained from the Spanish Statistical Office (INE) in 2003. RESULTS: In 2000, 59,212 males and 35,860 females died of cancer. The figures predicted by the time series model were 60,561 for males and 36,183 for females. Thus, the actual cancer mortality figures published by the Statistical Office fell within the confidence intervals of the forecasts for both gross mortality and cancer deaths. CONCLUSIONS: The difference between the true cancer mortality rate and the forecast is very small and not significant for health planning. Time series analysis is highly effective as a basis for decision-taking in cancer health planning. Cancer health policy and management can also benefit from the strength of these models.

Confidence Intervals↗

PubMed2003

Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong.

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of air pollutant parameters becomes an important and popular topic in environmental science. Airborne pollution is a serious, and will be a major problem in Hong Kong within the next few years. In Hong Kong, Respirable Suspended Particulate (RSP) and Nitrogen Oxides NOx and NO2 are major air pollutants due to the dominant diesel fuel usage by public transportation and heavy vehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are of significance to public and the city image. The multi-layer perceptron (MLP) neural network is regarded as a reliable and cost-effective method to achieve such tasks. The works presented here involve developing an improved neural network model, which combines the principal component analysis (PCA) technique and the radial basis function (RBF) network, and forecasting the pollutant levels and tendencies based in the recorded data. In the study, the PCA is firstly used to reduce and orthogonalize the original input variables (data), these treated variables are then used as new input vectors in RBF neural network model established for forecasting the pollutant tendencies. Comparing with the general neural network models, the proposed model possesses simpler network architecture, faster training speed, and more satisfactory predicting performance. This improved model is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP. NOx and NO2 concentrations with the actual data of these pollutants recorded at the monitory station, the effectiveness of the proposed model has been proven. Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and has advantages over the traditional neural network methods.

Air Pollutants↗

PubMed2002

Applying Lee-Carter under conditions of variable mortality decline.

The Lee-Carter method of mortality forecasting assumes an invariant age component and most applications have adopted a linear time component. The use of the method with Australian data is compromised by significant departures from linearity in the time component and changes over time in the age component. We modify the method to adjust the time component to reproduce the age distribution of deaths, rather than total deaths, and to determine the optimal fitting period in order to address non-linearity in the time component. In the Australian case the modification has the added advantage that the assumption of invariance is better met. For Australian data, the modifications result in higher forecast life expectancy than the original Lee-Carter method and official projections, and a 50 per cent reduction in forecast error. The model is also expanded to take account of age-time interactions by incorporating additional terms, but these are not readily incorporated into forecasts.

Australia↗