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As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts. [ABSTRACT FROM PUBLISHER]

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction. [ABSTRACT FROM AUTHOR]

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators. [ABSTRACT FROM AUTHOR]

This study provides a statistical forecast for the development of total elbow arthroplasties (TEAs) in Germany until 2045. The authors used an autoregressive integrated moving average (ARIMA), Error-Trend-Seasonality (ETS), and Poisson model to forecast trends in total elbow arthroplasty based on demographic information and official procedure statistics. They predict a significant increase in total elbow joint replacements, with a higher prevalence among women than men. Comprehensive national data provided by the Federal Statistical Office of Germany (Statistisches Bundesamt) were used to quantify TEA's total number and incidence rates. Poisson regression, exponential smoothing with Error-Trend-Seasonality, and autoregressive integrated moving average models (ARIMA) were used to predict developments in the total number of surgeries until 2045. Overall, the number of TEAs is projected to increase continuously from 2021 to 2045. This will result in a total number of 982 (TEAs) in 2045 of mostly elderly patients above 80 years. Notably, female patients will receive TEAs 7.5 times more often than men. This is likely influenced by demographic and societal factors such as an ageing population, changes in healthcare access and utilization, and advancements in medical technology. Our projection emphasises the necessity for continuous improvements in surgical training, implant development, and rehabilitation protocols. [ABSTRACT FROM AUTHOR]

To forecast future trends in diabetes prevalence, morbidity, and costs in the United States, the Institute for Alternative Futures has updated its diabetes forecasting model and extended its projections to 2030 for the nation, all states, and several metropolitan areas. This paper describes the methodology and data sources for these diabetes forecasts and discusses key implications. In short, diabetes will remain a major health crisis in America, in spite of medical advances and prevention efforts. The prevalence of diabetes (type 2 diabetes and type 1 diabetes) will increase by 54% to more than 54.9 million Americans between 2015 and 2030; annual deaths attributed to diabetes will climb by 38% to 385,800; and total annual medical and societal costs related to diabetes will increase 53% to more than $622 billion by 2030. Improvements in management reducing the annual incidence of morbidities and premature deaths related to diabetes over this time period will result in diabetes patients living longer, but requiring many years of comprehensive management of multiple chronic diseases, resulting in dramatically increased costs. Aggressive population health measures, including increased availability of diabetes prevention programs, could help millions of adults prevent or delay the progression to type 2 diabetes, thereby helping turn around these dire projections. [ABSTRACT FROM AUTHOR]

Great progress has been made in mathematical models of cholera transmission dynamics in recent years. However, little impact, if any, has been made by models upon public health decision-making and day-to-day routine of epidemiologists. This paper provides a brief introduction to the basics of ordinary differential equation models of cholera transmission dynamics. We discuss a basic model adapted from Codeço (2001), and how it can be modified to incorporate different hypotheses, including the importance of asymptomatic or inapparent infections, and hyperinfectious V. cholerae and human-to-human transmission. We highlight three important challenges of cholera models: (1) model misspecification and parameter uncertainty, (2) modeling the impact of water, sanitation and hygiene interventions and (3) model structure. We use published models, especially those related to the 2010 Haitian outbreak as examples. We emphasize that the choice of models should be dictated by the research questions in mind. More collaboration is needed between policy-makers, epidemiologists and modelers in public health. [ABSTRACT FROM AUTHOR]

Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data. [ABSTRACT FROM AUTHOR]

Incubation period of the new variant Creutzfeldt-Jakob disease (vCJD) from infection to clinical onset and the eventual impact of the disease remain major concerns. Based on i) epidemiological conceptualization of human exposure to BSE contaminated material, ii) exponentially decreasing susceptibility after 15 years of age, and iii) typical incubation period (IP) distributions for time from infection to onset, we have previously estimated mean incubation period and projected number of vCJD cases. In this paper, we investigate the robustness of these estimates with respect to i-iii using the UK's 113 vCJD cases with clinical onset before December 2000. Mean incubation period was estimated at 16.4 years (95% CI 11.4-24.8), 15.9 years (95% CI 11.4-22.0), 14.1 years (95% CI 10.4-24.2) with the log-normal, Gamma and Weibull distributions respectively. Corresponding predictions for the total size of the epidemic ranged from 183 to 304. Maximal susceptibility to infection between 1.3 and 15.9 years and decreasing by 15% per year of age thereafter yielded the best fit. The shape of the IP distribution did not affect the predictions. In summary, within a set of reasonable assumptions, mean incubation period for vCJD ranged from 15 to 20 years, and the eventual impact of vCJD was a few hundred patients. [ABSTRACT FROM AUTHOR]

As the number of elderly people in society increases, the use of technology to support the care system is being examined. However, there is no agreed model of a technology-based health-care system that fully integrates the technical and social aspects to meet the needs of both clients and care providers. This paper examines a possible model from the perspective of both the client and the service provider, and estimates the costs, and hence the potential savings, associated with the introduction of a health-care system based on this model. Cost data are presented in the context of an installation as might be anticipated within a city such as Birmingham, UK. Installation costs of an advanced telecare system would be higher than the set-up costs of conventional systems. Expected savings in control centre costs and emergency services costs would be small. However, a reduction in the average annual length of hospital stay by one day and a reduction in the proportion of elderly people being treated in hospital from 32.4% to 32.0%, directly as a result of the use of advanced telecare, would produce significant savings. A financing scheme with a 5-year or 10-year repayment period would then be financially viable for a 10,000-home advanced telecare system. [ABSTRACT FROM AUTHOR]

Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee–Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950–2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li–Lee models in both single- and multi-population scenarios. [ABSTRACT FROM AUTHOR]

The U.S. Bureau of the Census recently released a set of population projections that include middle and high projections that we argue are too conservative. The projections discount the possibility of future baby booms and assume slow rates of mortality decline and low levels of immigration. In this article we explore the impact on the size and age composition of the U.S. population of alternative scenarios of plausible fertility, mortality, and immigration assumptions. We conclude that (1) the Census Bureau's highest projection might be interpreted as a reasonable middle projection, (2) a reasonable high projection would yield a U.S. population in 2080 some 300 million persons larger than the Bureau's highest projection, with the population 85 and older more than twice the Bureau's greatest estimate, and (3) uncertainty about the pace of population growth is substantially greater than the Bureau's projections suggest. [ABSTRACT FROM AUTHOR]

Background: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare.Methods: We randomly divided 1,694 patients with biopsy-proven LN, who had achieved remission after treatment, into a derivation cohort (n = 1,186) and an internal validation cohort (n = 508), at a ratio of 7:3. The risk of renal flare 5 years after remission was predicted using an eXtreme Gradient Boosting (XGBoost) method model, developed from 59 variables, including demographic, clinical, immunological, pathological, and therapeutic characteristics. A simplified risk score prediction model (SRSPM) was developed from important variables selected by XGBoost model using stepwise Cox regression for practical convenience.Results: The 5-year relapse rates were 39.5% and 38.2% in the derivation and internal validation cohorts, respectively. Both the XGBoost model and the SRSPM had good predictive performance, with a C-index of 0.819 (95% confidence interval [CI]: 0.774-0.857) and 0.746 (95% CI: 0.697-0.795), respectively, in the validation cohort. The SRSPM comprised 6 variables, including partial remission and endocapillary hypercellularity at baseline, age, serum Alb, anti-dsDNA, and serum complement C3 at the point of remission. Using Kaplan-Meier analysis, the SRSPM identified significant risk stratification for renal flares (p < 0.001).Conclusions: Renal flare of LN can be readily predicted using the XGBoost model and the SRSPM, and the SRSPM can also stratify flare risk. Both models are useful for clinical decision-making and individualized management in LN. [ABSTRACT FROM AUTHOR]

When developing risk prediction models on datasets with limited sample size, shrinkage methods are recommended. Earlier studies showed that shrinkage results in better predictive performance on average. This simulation study aimed to investigate the variability of regression shrinkage on predictive performance for a binary outcome. We compared standard maximum likelihood with the following shrinkage methods: uniform shrinkage (likelihood-based and bootstrap-based), penalized maximum likelihood (ridge) methods, LASSO logistic regression, adaptive LASSO, and Firth's correction. In the simulation study, we varied the number of predictors and their strength, the correlation between predictors, the event rate of the outcome, and the events per variable. In terms of results, we focused on the calibration slope. The slope indicates whether risk predictions are too extreme (slope < 1) or not extreme enough (slope > 1). The results can be summarized into three main findings. First, shrinkage improved calibration slopes on average. Second, the between-sample variability of calibration slopes was often increased relative to maximum likelihood. In contrast to other shrinkage approaches, Firth's correction had a small shrinkage effect but showed low variability. Third, the correlation between the estimated shrinkage and the optimal shrinkage to remove overfitting was typically negative, with Firth's correction as the exception. We conclude that, despite improved performance on average, shrinkage often worked poorly in individual datasets, in particular when it was most needed. The results imply that shrinkage methods do not solve problems associated with small sample size or low number of events per variable. [ABSTRACT FROM AUTHOR]

Objectives: To develop a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer (BCa).Methods: This retrospective study involved 106 eligible patients from two independent clinical centers. All patients underwent a preoperative 3.0 T MRI scan with T2-weighted image (T2WI) and multi-b-value diffusion-weighted image (DWI) sequences. In total, 1404 radiomics features were extracted from the largest region of the reported tumor locations on the T2WI, DWI, and corresponding apparent diffusion coefficient map (ADC) of each patient. A radiomics signature, namely the Radscore, was then generated using the recursive feature elimination approach and a logistic regression algorithm in a training cohort (n = 64). Its performance was then validated in an independent validation cohort (n = 42). The primary imaging and clinical factors in conjunction with the Radscore were used to determine whether the performance could be further improved.Results: The Radscore, generated by 36 selected radiomics features, demonstrated a favorable ability to predict muscle-invasive BCa status in both the training (AUC 0.880) and validation (AUC 0.813) cohorts. Subsequently, integrating the two independent predictors (including the Radscore and MRI-determined tumor stalk) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.924 and 0.877 in both cohorts, respectively.Conclusions: The proposed multisequence MRI-based radiomics signature alone could be an effective tool for quantitative prediction of muscle-invasive status of BCa. Integrating the Radscore with MRI-determined tumor stalk could further improve the discriminatory power, realizing more accurate prediction of nonmuscle-invasive and muscle-invasive BCa.Key Points: • DWI is superior to T2WI sequence in reflecting the heterogeneous differences between NMIBC and MIBC, and multisequence MRI helps in the preoperative prediction of muscle-invasive status of BCa. • Co-occurrence (CM), run-length matrix (RLM), and gray-level size zone matrix (GLSZM) features were the favorable feature categories for the prediction of muscle-invasive status of BCa. • The Radscore (proposed multisequence MRI-based radiomics signature) helps predict preoperatively muscle invasion. Combination with the MRI-determined tumor stalk further improves prediction. [ABSTRACT FROM AUTHOR]

Background: Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream infections may cause inflammation of the body, even organ failure or death. However, existing work mainly adopts statistical models with laboratory indicators, and fails to make full use of textual description information from EHRs.Methods: We study the problem of positive blood culture prediction by using neural network model. Specifically, we first construct dataset from raw EHRs. Then we propose a hybrid neural network which incorporates attention based Bi-directional Long Short-Term Memory and Autoencoder networks to fully capture the information in EHRs.Results: In order to evaluate the proposed method, we constructe a dataset which consists of totally 5963 patients who had one or more blood cultures tests during hospitalization. Experimental results show that the proposed neural model gets 91.23% F-measure for this task.Conclusions: The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models. [ABSTRACT FROM AUTHOR]

The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data. [ABSTRACT FROM AUTHOR]

Background: Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diagnosis prediction approaches mainly employ recurrent neural networks (RNN) with attention mechanisms to make predictions. However, these approaches ignore the importance of code descriptions, i.e., the medical definitions of diagnosis codes. We believe that taking diagnosis code descriptions into account can help the state-of-the-art models not only to learn meaning code representations, but also to improve the predictive performance, especially when the EHR data are insufficient.Methods: We propose a simple, but general diagnosis prediction framework, which includes two basic components: diagnosis code embedding and predictive model. To learn the interpretable code embeddings, we apply convolutional neural networks (CNN) to model medical descriptions of diagnosis codes extracted from online medical websites. The learned medical embedding matrix is used to embed the input visits into vector representations, which are fed into the predictive models. Any existing diagnosis prediction approach (referred to as the base model) can be cast into the proposed framework as the predictive model (called the enhanced model).Results: We conduct experiments on two real medical datasets: the MIMIC-III dataset and the Heart Failure claim dataset. Experimental results show that the enhanced diagnosis prediction approaches significantly improve the prediction performance. Moreover, we validate the effectiveness of the proposed framework with insufficient EHR data. Finally, we visualize the learned medical code embeddings to show the interpretability of the proposed framework.Conclusions: Given the historical visit records of a patient, the proposed framework is able to predict the next visit information by incorporating medical code descriptions. [ABSTRACT FROM AUTHOR]

Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and R0) and short- and long-term predictions (e.g. one and three-week forecasts, timing and number of cases at the epidemic peak, and final epidemic size). Applying the extended MSS approach to a humidity-based stochastic compartmental influenza model, we were able to accurately predict influenza-like illness activity reported by U.S. Centers for Disease Control and Prevention from 10 regions as well as city-level influenza activity using real-time, city-specific Google search query data from 119 U.S. cities between 2003 and 2014. [ABSTRACT FROM AUTHOR]

Background: Air pollution damages health by promoting the onset of some non-communicable diseases (NCDs), putting additional strain on the National Health Service (NHS) and social care. This study quantifies the total health and related NHS and social care cost burden due to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in England.Method and Findings: Air pollutant concentration surfaces from land use regression models and cost data from hospital admissions data and a literature review were fed into a microsimulation model, that was run from 2015 to 2035. Different scenarios were modelled: (1) baseline 'no change' scenario; (2) individuals' pollutant exposure is reduced to natural (non-anthropogenic) levels to compute the disease cases attributable to PM2.5 and NO2; (3) PM2.5 and NO2 concentrations reduced by 1 μg/m3; and (4) NO2 annual European Union limit values reached (40 μg/m3). For the 18 years after baseline, the total cumulative cost to the NHS and social care is estimated at £5.37 billion for PM2.5 and NO2 combined, rising to £18.57 billion when costs for diseases for which there is less robust evidence are included. These costs are due to the cumulative incidence of air-pollution-related NCDs, such as 348,878 coronary heart disease cases estimated to be attributable to PM2.5 and 573,363 diabetes cases estimated to be attributable to NO2 by 2035. Findings from modelling studies are limited by the conceptual model, assumptions, and the availability and quality of input data.Conclusions: Approximately 2.5 million cases of NCDs attributable to air pollution are predicted by 2035 if PM2.5 and NO2 stay at current levels, making air pollution an important public health priority. In future work, the modelling framework should be updated to include multi-pollutant exposure-response functions, as well as to disaggregate results by socioeconomic status. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability. For this purpose, M5′ model trees method (M5′) is introduced and applied to classify the crash data and then calibrate a model for each homogenous class. The data for 1134 and 345 randomly selected links on urban arterials in the city of Charlotte, North Carolina was used to develop and validate models, respectively. The outputs from the hybrid approach are compared with the outputs from cluster-based negative binomial regression (NBR) and general NBR models. Findings indicate that M5' has high predictability and is very reliable to interpret the role of different attributes on crash frequency compared to other developed models. [ABSTRACT FROM PUBLISHER]

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods. [ABSTRACT FROM AUTHOR]

The study examines whether the number of alcohol-specific deaths can be predicted by population total and/or beverage-specific alcohol consumption and if, how precisely. The data are annual series of spirits, wine, beer and total consumption and alcohol-specific deaths in Finland in the years 1969-2015.~Aims~Unassigned~We specify a Auto Regressive Distributed Lags model with cointegrated variables, to be used in prediction. In our model, the number of alcohol-specific deaths is the response variable, and log of spirits consumption and log of non-spirits consumption, are the explanatory variables. The response variable has one added annual lag and the explanatory variables have both four annual added lags in the model.~Methods~Unassigned~In our data alcohol-specific deaths, log of spirits and log of non-spirits consumption are significantly cointegrated. The precision of the estimated model is good. The prediction results include prediction of the 2008 downturn in alcohol deaths, using the data from the years 1969-2004, forecasting the as yet unknown 2016 alcohol deaths on the basis of known values of alcohol consumption up to 2016, and forecasts of future (2017-2020) alcohol deaths from 2016 on. Forecasted effects of a proposed Finnish alcohol policy change, leading to six percent total consumption increase, are estimated.~Results~Unassigned~The number of alcohol-specific deaths can be predicted with an appropriate time-series regression model on the basis of population consumption. It is important to consider also beverage type because of the improved predictive power. The model is useful in an evaluation of proposed alcohol policy changes.~Conclusions~Unassigned [ABSTRACT FROM AUTHOR]

BACKGROUND: A number of studies have assessed possible climate change impacts on the Lyme disease vector, Ixodes scapularis. However, most have used surface air temperature from only one climate model simulation and/or one emission scenario, representing only one possible climate future. OBJECTIVES: We quantified effects of different Representative Concentration Pathway (RCP) and climate model outputs on the projected future changes in the basic reproduction number (R0) of I. scapularis to explore uncertainties in future R0 estimates. METHODS: We used surface air temperature generated by a complete set of General Circulation Models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to hindcast historical (1971-2000), and to forecast future effects of climate change on the R0 of I. scapularis for the periods 2011-2040 and 2041-2070. RESULTS: Increases in the multimodel mean R0 values estimated for both future periods, relative to 1971-2000, were statistically significant under all RCP scenarios for all of Nova Scotia, areas of New Brunswick and Quebec, Ontario south of 47°N, and Manitoba south of 52°N. When comparing RCP scenarios, only the estimated R0 mean values between RCP6.0 and RCP8.5 showed statistically significant differences for any future time period. CONCLUSION: Our results highlight the potential for climate change to have an effect on future Lyme disease risk in Canada even if the Paris Agreement's goal to keep global warming below 2°C is achieved, although mitigation reducing emissions from RCP8.5 levels to those of RCP6.0 or less would be expected to slow tick invasion after the 2030s. [ABSTRACT FROM AUTHOR]

Background: In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals.Methods: This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment Instrument - Home Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve).Results: The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care).Conclusion: The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Globally, pollen allergy is a major public health problem, but a fundamental unknown is the likely impact of climate change. To our knowledge, this is the first study to quantify the consequences of climate change upon pollen allergy in humans. OBJECTIVES: We produced quantitative estimates of the potential impact of climate change upon pollen allergy in humans, focusing upon common ragweed {Ambrosia artemisiifolia) in Europe. METHODS: A process-based model estimated the change in ragweed's range under climate change. A second model simulated current and future ragweed pollen levels. These findings were translated into health burdens using a dose-response curve generated from a systematic review and from current and future population data. Models considered two different suites of regional climate/ pollen models, two greenhouse gas emissions scenarios [Representative Concentration Pathways (RCPs) 4.5 and 8.5], and three different plant invasion scenarios. RESULTS: Our primary estimates indicated that sensitization to ragweed will more than double in Europe, from 33 to 77 million people, by 2041-2060. According to our projections, sensitization will increase in countries with an existing ragweed problem (e.g., Hungary, the Balkans), but the greatest proportional increases will occur where sensitization is uncommon (e.g., Germany, Poland, France). Higher pollen concentrations and a longer pollen season may also increase the severity of symptoms. Our model projections were driven predominantly by changes in climate (66%) but were also influenced by current trends in the spread of this invasive plant species. Assumptions about the rate at which ragweed spreads throughout Europe had a large influence upon the results. CONCLUSIONS: Our quantitative estimates indicate that ragweed pollen allergy will become a common health problem across Europe, expanding into areas where it is currently uncommon. Control of ragweed spread may be an important adaptation strategy in response to climate change. CITATION: Lake IR, Jones NR, Agnew M, Goodess CM, Giorgi F, Hamaoui-Laguel L, Semenov MA, Solomon F, Storkey J, Vautard R, Epstein MM. 2017. Climate change and future pollen allergy in Europe. Environ Health Perspect 125:385-391; http://dx.doi.org/10.1289/EHP173 [ABSTRACT FROM AUTHOR]

INTRODUCTION: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. OBJECTIVES: We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. METHODS: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children's Health Study (CHS), and the Inhalable Particulate Network (IPN). RESULTS: In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84-0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00-0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. CONCLUSIONS: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. CITATION: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38-6; http://dx.doi.org/10.1289/EHP131 [ABSTRACT FROM AUTHOR]

Background: The real state about the spread of the HIV epidemic in Nepal is not clear since the details available are on the basis of repeated integrated biological and behavioral surveillance. Objective To study the trends of People lost follow up on ART in future. Material and methods: A retrospective study was carried out on the data collected from the Health ministry records of Nepal, between 2006 and 2012. Descriptive statistics and statistical modelling were used for the analysis and forecasting of data. Results: Including the constant term from the equation, the quadratic model was the best fit, for the forecasting of People lost follow up on ART. Using quadratic equation, it is estimated that 4331 reported number of People lost follow up on ART will be there in Nepal by the year 2020. Conclusion: The People lost follow up on ART in Nepal are having an increasing trend. Estimates of the total number of People lost follow up on ART attributable to the major routes of infection make an important contribution to public health policy. They can be used for the planning of healthcare services and for contributing to estimates of the future numbers with People lost follow up on ART used for planning health promotion programmes. [ABSTRACT FROM AUTHOR]

We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior. [ABSTRACT FROM AUTHOR]

Objective: To quantify uncertainty in forecasts of health expenditures.Study Design: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death.Data Sources/study Setting: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example.Principal Findings: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita.Conclusions: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP. [ABSTRACT FROM AUTHOR]

Using the Survey of Income and Program Participation, we document the importance of education in accounting for declines in functional limitations among older Americans from 1984 to 1993. Of the eight demographic and socioeconomic variables considered, education is most important in accounting for recent trends. The relationship between educational attainment and functioning has not changed measurably, but educational attainment has increased greatly during this period. Our analysis suggests, all else being equal, that future changes in education will continue to contribute to improvements in functioning, although at a reduced rate. [ABSTRACT FROM AUTHOR]

This article links parameterized model mortality schedules with time series methods to develop forecasts of U.S. mortality to the year 2000. The use of model mortality schedules permits a relatively concise representation of the history of mortality by age and sex from 1900 to 1985, and the use of modern time series methods to extend this history forward to the end of this century allows for a flexible modeling of trend and the accommodation of changes in long-run mortality patterns. This pilot study demonstrates that the proposed procedure produces medium-range forecasts of mortality that meet the standard tests of accuracy in forecast evaluation and that are sensible when evaluated against the comparable forecasts produced by the Social Security Administration. [ABSTRACT FROM AUTHOR]

Background: Metastatic colorectal cancer (mCRC) remains a lethal disease. Survival, however, is increasing due to a growing number of treatment options. Yet due to the number of prognostic factors and their interactions, prediction of mortality is difficult. The aim of this study is to provide a clinical model supporting prognostication of mCRC mortality in daily practice.Methods: Data from 1104 patients with mCRC in three prospective cancer datasets were used to construct and validate Cox models. Input factors for stepwise backward method variable selection were sex, RAS/BRAF-status, microsatellite status, treatment type (no treatment, systemic treatment with or without resection of metastasis), tumor load, location of primary tumor, metastatic patterns and synchronous or metachronous disease. The final prognostic model for prediction of survival at two and 3 years was validated via bootstrapping to obtain calibration and discrimination C-indices and dynamic time dependent AUC.Results: Age, sidedness, number of organs with metastases, lung as only site of metastasis, BRAF mutation status and treatment type were selected for the model. Treatment type had the most prominent influence on survival (resection of metastasis HR 0.26, CI 0.21-0.32; any treatment vs no treatment HR 0.31, CI 0.21-0.32), followed by BRAF mutational status (HR 2.58, CI 1.19-1.59). Validation showed high accuracy with C-indices of 72.2 and 71.4%, and dynamic time dependent AUC's of 76.7 ± 1.53% (both at 2 or 3 years), respectively.Conclusion: The mCRC mortality prediction model is well calibrated and internally valid. It has the potential to support both, clinical prognostication for treatment decisions and patient communication. [ABSTRACT FROM AUTHOR]