Artigos de revistas sobre o tema "Purchase Probability Prediction"

This paper discusses various methods for improving recommendation systems. A comparative analysis of two models for solving classification problems is performed: random forest and CatBoostClassifier. The research was performed on the data of the purchase history of Ozon customers. Standard methods that are often used in recommendation systems were used. We implemented collaborative filtering methods, cosine similarity of products from customer views per site visit, and similarity of text data. To evaluate the results, we used special metrics that evaluate the quality of predictions of the first k objects from the recommendations: Mean average precision (map@K) and Recall at K (recall@k). When generating additional features based on various methods that reveal the similarity of objects, an increase in the quality of model forecasts is noted. The CatBoostClassifier model showed the best results.

For a more accurate prediction of the probability of consumers to purchase a commodity, this paper build a users’ behavior model based on correlation analysis with apriori algorithm. The model is built by learning from users’ history data and behaviors’ at present, an experimental result demonstrates that this model can effectively predict consumer buying behavior, and it is better than some traditional methods.

The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.

Purpose The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling. Design/methodology/approach For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted. Findings Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage. Research limitations/implications The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance. Practical implications This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices. Originality/value This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.

This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.

The estimation of operating costs of agricultural and forestry machineries is a key factor in both planning agricultural policies and farm management. Few works have tried to estimate operating costs and the produced models are normally based on deterministic approaches. Conversely, in the statistical model randomness is present and variable states are not described by unique values, but rather by probability distributions. In this study, for the first time, a multivariate statistical model based on Partial Least Squares (PLS) was adopted to predict the fuel consumption and costs of six agricultural operations such as: ploughing, harrowing, fertilization, sowing, weed control and shredding. The prediction was conducted on two steps: first of all few initial selected parameters (time per surface-area unit, maximum engine power, purchase price of the tractor and purchase price of the operating machinery) were used to estimate the fuel consumption; then the predicted fuel consumption together with the initial parameters were used to estimate the operational costs. Since the obtained models were based on an input dataset very heterogeneous, these resulted to be extremely efficient and so generalizable and robust. In details the results show prediction values in the test with r always ≥ 0.91. Thus, the approach may results extremely useful for both farmers (in terms of economic advantages) and at institutional level (representing an innovative and efficient tool for planning future Rural Development Programmes and the Common Agricultural Policy). In light of these advantages the proposed approach may as well be implemented on a web platform and made available to all the stakeholders.

The author develops a probabilistic model that converts stated purchase intents into purchase probabilities. The model allows heterogeneity between nonintenders and intenders with respect to their probability to switch to a new “true” purchase intent after the survey, thereby capturing the typical discrepancy between overall mean purchase intent and subsequent proportion of buyers (bias). When the probability to switch of intenders is larger (smaller) than that of nonintenders, the overall mean purchase intent overestimates (underestimates) the proportion of buyers. As special cases, the author derives upper and lower bounds on proportions of buyers from purchase intents data and shows the consistency of those bounds with observed behavior, except in predictable cases such as new products and business markets. However, a straightforward modification of the model deals with new product purchase forecasts.

The study is aimed at investigating the possibility of using machine learning methods to build models for predicting the probability of purchase and the amount of purchase by online store customers. As a sample, we used data of users transactions of the site ponpare.jp in the period from 01.07.2011 to 23.06.2012. The description and comparative analysis of the most common methods for solving similar problems are given. The metrics used to measure the results in the case of forecasting the fact and amount of the purchase are being described. The results obtained make it clear that within the framework of the problem of predicting the probability of a purchase, gradient boosting, namely its implementation of LGBMClassifier, shows the most accurate estimate. For the problem of predicting the amount of a customer’s purchase, using gradient boosting also gave the best results.

ABSTRACTThis study aims to analyze the ease of use variable is a positive predictor of purchase intention, to analyze the ease of use variable is a positive predictor of customer satisfaction, to analyze whether trust is a positive predictor of purchase intention, to analyze whether trust is a positive predictor of customer satisfaction and to analyze whether customer satisfaction is a positive predictor of purchase intention. The population used in this study were all users of web sites or Tokopedia applications in Indonesia. Considering the number of web site users or Tokopedia applications, the researchers took samples from the population to be respondents in the study. The research sample was collected from customers who use the Tokopedia website or application and are domiciled in the Greater Jakarta area. Hypothesis testing is done with the main data of 300 respondents. The technique in sampling in this study uses probability sampling techniques. The type of probability sampling used is the simple random sampling method. Data analysis tools were analyzed using SPSS ver 23 to measure the ease of use and trust variables of purchase intention through customer satisfaction in the Tokedia application and found that ease of use is a positive and significant predictor of purchase intention, ease of use is a positive predictor and significant to customer satisfaction, trust is a positive and significant predictor of purchase intention, trust is a positive and significant predictor of customer satisfaction, customer satisfaction is a positive and significant predictor of purchase intention.Keywords : ease of use, trust, purchase intention, customer satisfaction

AbstractThe purpose of this paper is to investigate the influence of regional images of Italy on the purchase probability of wine. Instead of regarding country image as a homogeneous national construct, this article focuses on revealing regional differences in Italian country image and investigating whether they are related with purchase probability. Moreover, several scholars state, that the development of larger regional brands can improve consumer choice especially for the product wine. In the light of growing global competition from ‘New World’ wine countries and simultaneously decreasing competitivity of the main European wine-producing countries (including Italy), the possibility to actively shape the development of regional brands and thereby potentially strengthening the competitive advantage within the European Union and on a global level, seems to offer an adequate approach to maintain the leading role of Italian wine producers in European and international trade. Therefore, this study aims to test, whether using larger regions (and not specific wine regions) as origin information for wine is applicable and improving consumer choice. The empirical framework was tested on a random sample of consumers living in Germany and comprises (a) the description of image differences between Northern and Southern Italy, (b) measuring the influence of regional indication of origin on purchase probability, (c) determining the predictive value of image components for purchase probability, and (d) the investigation of moderation effects of country knowledge on the results. Data from 388 respondents were analysed using multiple linear regression and paired t test. Results show significant image differences, which affects purchase probability. It is shown that the image of Southern Italy does not provide respondents with a suitable decision-making heuristic for buying wine. Furthermore, country knowledge negatively influences the predictive value of the measured regional image for purchase probability. As the predictive value of Southern Italian image does not contribute to the explanation of purchase probability, the present work corroborates the hypothesis of region-specific tendencies in the relationship between regional Italian image and consumer behaviour. Thus, this work provides a fundamental understanding of the regional composition of the Italian country image. It therefore serves practitioners as a decisional basis for the utilization of origin-related product information.

The authors examine the effects of a manufacturer coupon on brand choice behavior. The level of coupon redemption and changes in brand choice behavior after redemption are examined as a function of the household's prior probability of purchasing the promoted brand, likelihood of buying a favorite competitive brand, and coupon face value. A model of the coupon redemption decision is developed to predict response to the coupon promotion by different consumer segments. Predictions from the model are tested by using scanner panel data from a field experiment on coupon face values. Coupon redemption rates are found to be much higher among households that have purchased the brand on a regular basis in the past. The results also suggest that most consumers revert to their precoupon choice behavior immediately after their redemption purchase. These and other findings have important implications for the profitability of coupon promotions.

AbstractThe link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

Purpose The purpose of this paper is to propose a method to help firms assess lifetime profitability of customers whose buying behaviors are characterized by purchasing cycles, which are determined by both intrinsic purchasing cycles and cumulative effects of firms’ marketing solicitations. Design/methodology/approach This paper first proposes a probability model to predict customers’ responses to firms’ marketing solicitations in which a customer’s inter-purchase times are assumed to follow a Poisson distribution, whose parameters vary across customers and follow a gamma distribution. The paper then proposes a customer profitability scoring model that uses customers’ responses as an input to assess their lifetime profitability at a given point of time. Findings The paper illustrates the proposed method using individual-level purchasing data of 529 customers from a catalog firm. The paper shows that the proposed model outperforms the benchmark model in terms of both explaining and predicting customers’ purchases. The paper also demonstrates significant profit consequences to the firm if incorrect methods are used instead of the proposed method. Practical implications The proposed method can help firms select or eliminate customers based on their lifetime profitability so that firms can focus their marketing efforts in a more targeted manner to increase total profits. Originality/value The proposed Gamma-Poisson probability model and the profitability scoring method are easy to implement due to the attractive conjugacy property. It is valuable for firms’ customer relationship management applications from the standpoint of making customer selection and inventory management decisions.

We utilize the data of a very large UK automobile loan firm to study the interaction of the characteristics of borrowers and loans in predicting the subsequent loan performance. Our broader findings confirm the earlier research on the issue of subprime auto loans. More importantly, unmarried borrowers living with furnished tenancy agreements who have relatively new jobs have a probability of defaulting of more than 60% compared to an average 7% default rate in overall subprime borrowers in the dataset. Also, in the above category are those who live in a less prosperous part of the UK such as the north-west, are full-time self-employed, have other large loan arrears, fall into the bottom 25% percentile of monthly income, secure loans with high loan to total value (LTV), purchase expensive automobiles with shorter loan duration payment plans, and have a high dependency on government support. This in fact is also true of those who go into arrears, except that the highest probability in this context is around 40% compared to 6% for an overall sample. These findings shall help in the understanding of subprime auto loans performance in relation to borrowers and loan features alongside helping auto finance firms improve predictive models and decision-making.

As passengers are proved to be preference heterogeneous in air travel, this paper tries to model the air ticket purchase behavior incorporating market segmentation. In the research, a latent-NL model, established on the latent class structure and the nested logit model, integrates the personal features as well as the purchase preferences into the forecast of segment-specific purchase probability. In order to calibrate the model, a stated preference survey is designed with the choice profiles using real service information, and the survey is conducted in four cities in China for data collection. The results show that the proposed model provides an effective approach for predicting the air travel demand in particular for air ticket pricing, and the estimation results outperforms the traditional-nested logit model with higher goodness-of-fit. Besides, the model is then adopted to test the efficiency of different pricing strategies, showing its advantages in improving the flight revenues.

This paper mainly studies the impact of evaluation information on e-commerce platform on the future of products. Through natural language processing and rating, an evaluation model based on user rating and evaluation is defined to measure product quality. Among them, evaluations are differentiated: review sentiment coefficient (R) and review length (L).The evaluation model is:D=0.3*S+0.7*( 0.3*L+0.7*R). In order to predict the future reputation of products, based on the above evaluation model, time series is used to rank the products studied. Each customer purchases the product through Markov chain model, so as to predict the probability of future word-of-mouth spread of the product. Use TOPSIS method to select monthly sales, stars and comment sentiment coefficient as indicators. The comprehensive measurement method based on text and score is determined to predict whether the product is successfully promoted.

The objective in the study was to establish whether there was any relationship between certain information usage categories and four selected predictor variables namely (1) new or used car purchase, (2) other-than-white or white buyer, (3) male or female, and (4) first-time buyer or experienced buyer. Certain external sources of information (non-market dominated and market dominated) which are available to the buyer of a car and the development of a model of the probability of buyers using the source are investigated. The technique of ordinal logistic regression is assumed to be the appropriate modelling tool in this study where the response variables of interest are ordinal.

The method of discounted cash flows (DCF) is one of the main and popular methods of economic assessment of business, which is used all over the world. However, the actual behavior of business projects evaluated by this method often differs from that predicted, and the difference can be tens of times.It should be noted that at present, the discounted cash flow method is a subject of extensive literature, but there are no analytical arguments for large discrepancies between the theory and practice of the method. The aim of the study is to provide a theoretical explanation of the forecasting errors inherent in the discounted cash flow method. The research method is related to the analysis of the traditional method of discounted cash flows, which shows that the key indicator that affects the final result is the net income for a certain period of time. Analyzing the economic content of the flows that appear in the formation of net income, we can conclude that for a trade-type enterprise, the cash flow of receipts associated with current operations is significantly random and, therefore, requires the use of stochastic description methods.The paper offers a mathematical model of the mentioned cash flow. It is assumed that the event associated with a purchase (cash receipt) is modeled on the time axis by a point with a random time of occurrence. Then, obviously, the number of points n that appear on a fixed time interval will be a random number. A justification is given for the fact that the point process is a Poisson random point process or simply a Poisson point process, in which the times of occurrence of points W1 ,W2 , ..., Wi and their number N(t) at time t are random variables. We introduce the function λ(t), which characterizes the average number of cash receipts (purchases) per unit of time. From an economic point of view, it is driven by consumer preferences of buyers, and from a mathematic point of view it is a function of the intensity of appearance of points of the Poisson process. The monetary values of purchases made by customers are described by random positive ui values which arise at the Wi moments of the occurrence of shopping events, simulate a random process of cash receipts at the enterprise.Introduction to the consideration of the random Poisson flow of business receipts and their values, which are also random positive values with an arbitrary probability distribution, is the key assumption of the work. The proposed approach allowed us to develop a stochastic model of the company’s revenues, generalize the method of discounted cash flows, obtain a number of simple ratios, and on this basis explain the growth of the method forecast error with an increase in the duration of the forecast horizon.New results of the study are the use of stochastic methods to describe business revenues and expressions obtained on this basis for the variance and standard deviation of the company’s net cash flow, depending on the number of forecasting periods. It is shown that the growth of the standard deviation of the net cash flow, i.e. the forecasting errors, is a fundamental feature of the method in this interpretation. For the initial estimates, a simple expression is obtained and corresponding graphs are given.In conclusion, it is noted that the presented graphs of the behavior of the standard deviation of the method estimates show that the estimate from below of the mentioned deviation slowly grows with an increase in the number of prediction periods and depends only on the number of periods. It is noted that this growth is calculated in relation to the first forecast period, which itself may contain errors, and it is determined only by consumer preferences. Of course, you can choose the forecast period not a month, but, for example, a year, but then the error of the first period will be significantly increased. Thus, this review makes it possible to explain some aspects of the growth of the error of the discounted cash flow method with the forecast time.

In conditions of market volatility, an important issue for industrial enterprises is the issue of creating an efficient resource allocation mechanism. The article gives an example about using of individually adapted economic and mathematical model for forecasting the cost of materials and purchased products, that takes into account both internal and external factors affecting the planning figures. In order to create an effective predictive model, an analysis was conducted of statistical data for the period from 2009 to 2016, data was represented by high-tech enterprises of the radioelectronic industry. As a result of analysis it was revealed the presence of statistical regularities in the nature of the distribution of the analyzed data.On the basis of the calculated distribution parameters, a prediction procedure was performed using the exponential smoothing method and the total projected cost of materials and purchased products was obtained. The use of elements of probability theory and mathematical statistics, as well as methods for forecasting time series as basic methods of the model allows to take into account probabilistic economic factors, such as, for example, a change in the exchange rate of a foreign currency, as well as the presence of defects in the production process. Application of a special mathematical apparatus provides an ability to create a flexible, individually-adapted forecasting model. As a result of application of the model intended for forecasting the cost of materials and purchased products at one of industry enterprises it was revealed that the developed model has lover calculation error than the method that is used at the enterprise at present. Thus economic and mathematical model allows increasing the efficiency of the enterprise’s planned system and ensuring a rational resource allocation by increasing the accuracy of the forecasting process.

This letter considers Bayesian binary classification where data are assumed to consist of multiple time series (panel data) with binary class labels (binary choice). The observed data can be represented as {yit, xit}T,t=1 i = 1, … , n. Here yit ∈ {0, 1} represents binary choices, and xit represents the exogenous variables. We consider prediction of yit by its own lags, as well as by the exogenous components. The prediction will be based on a Bayesian treatment using a Gibbs posterior that is constructed directly from the empirical error of classification. Therefore, this approach is less sensitive to the misspecification of the probability model compared to the usual likelihood-based posterior, which is confirmed by Monte Carlo simulations. We also study the effects of various choices of n and T both numerically (by simulations) and theoretically (by considering two alternative asymptotic situations: large n and large T). We find that increasing T helps to reduce the prediction error more effectively compared to increasing n. We also illustrate the method in a real data application on the brand choice of yogurt purchases.

Abstract Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

Mutual fund, as a financial investment option, has gained reasonable acceptance in India since its inception. While traditional forms of investment have its own merits, the sheen of mutual fund has also been realized by Indian investors. A huge investor class have scored mutual fund higher than its counterparts on many counts. While the popularity of this new age investment option is on a rise, however, a mixed view is also experienced. The present study explores the behaviour of investors’ towards mutual fund. The study is based on the premise of regression analysis and binary logistic regression has been used to develop a model that best represents the consumer behaviour. The best model selection is based on the information criteria of Akike. Also, from the model, the researchers have evaluated the probability of mutual fund purchase by consumers. Finally, the research work shows computation of odds ratio that signifies the extent to which the probability of purchasing mutual fund would change with unit change in the levels of the covariates. This study is descriptive in nature and is based on primary survey with a sample size of 376. The results reveal that high returns are the most preferred determinant of investment behavior followed by the liquidity which is also evident from the odds ratio computation.

The authors develop a model to describe and predict consumer stock-keeping-unit choice in frequently bought product categories. The model posits that a product category consists of several salient attributes with many attribute levels and represents a stockkeeping unit as an attribute-level combination. The number of parameters of the model does not increase with the number of stockkeeping units and the number of attribute levels. The authors demonstrate the descriptive and predictive power of their model using 133,492 purchase incidences in 16 product categories. Their model fits 7% better in sample and predicts 8% better out of sample in hit probability than two leading models and requires only half the number of parametes.

Many online stores are designed such that shoppers can easily access any available discounted products. The authors propose that deliberately increasing search frictions by placing obstacles to locating discounted items can improve online retailers’ margins and even increase conversion. The authors demonstrate this using a simple theoretical framework that suggests inducing consumers to inspect higher-priced items first may simultaneously increase the average price of items sold and the overall expected purchase probability by inducing consumers to search more products. The authors test and confirm these predictions in a series of field experiments conducted with a dominant online fashion and apparel retailer. Furthermore, using information in historical transaction data about each consumer, the authors demonstrate that price-sensitive shoppers are more likely to willingly incur search costs when locating discounted items. Our results show that increasing search frictions can be used as a self-selecting price discrimination tool to match high discounts with price-sensitive consumers and full-priced offerings with price-insensitive consumers.

A model of sheep lice management incorporating different assumed levels of lice prevalence in different regions of Australia was used to determine the conditions under which it would be cost-effective to treat sheep for lice at shearing when no lice had been detected. The probability that a flock might be infested was calculated from the probability that the previous treatment had failed to eradicate lice, purchased sheep may have introduced lice, or lice may have entered on straying sheep. The model showed that a flock should be treated if the probability of infestation is greater than 10%. It was projected that acceptance of a risk level greater than 10% would reduce treatment of flocks where no lice were present, thus reducing treatment costs. This higher risk level, however, would increase the proportion of louse-infested flocks that remained untreated, resulting in increased direct lice costs and an increase in the regional prevalence of lice, but little overall change in costs to the sheep industry. The model indicated that treatment of sheep at lower levels of risk (less than 10%) would incur unnecessary costs due to treatment of flocks where no lice were present and would not reduce the regional prevalence of lice more effectively than a 10% intervention level. The model suggested that more accurate methods of detecting lice at shearing would allow higher levels of risk, reduce the use of treatment and hence reduce overall costs associated with managing lice, without increasing the prevalence of lice.

Pollution detection and ordinary tracking is extraordinarily good-sized errand in this point in time of in our present international. To make a higher and greater steady circumstance for individual, creatures, and plants. We ought to display screen and manage the infection via the Internet of factors innovation. This research proposes air infection and checking version which distinguishes infection in air primarily based totally on statistics mining calculation. The sensor System is used to apprehend the sensor regards from one of a kind fueloline sensors. The microcontroller is applied to trade the features from ADC to server. Information mining is applied to compute the pollution from numerous regions. ID3 set of rules is used to figuring the bottom of the traits on probability. Bluetooth module is applied to interface the controller with the purchaser and the purchaser related to the server via internet administrations. Remote sensors are applied to envision the extent of risky gases gives major all-round that, at last, offers a lower in infection. This framework now no longer simply computes the poisons gift major all-round yet, in addition, we are able to parent to preserve a strategic distance from destiny infection in and might ship the notification message to the particular dirtied location. Here we recall essentially the concoction Industry near Delhi and the metro city groups.These studies paper in particular centered at the evidence of pollutants tracking and locate on precise region or geographical area for an IOT bodily devices that collects statistics with admire to bodily parameters, making use of a complicated microcontroller stage, from one of a kind varieties of sensors, via various techniques of correspondence and after that transfers the statistics to the an internet. The displayed device has been supposed for far flung checking of weather numerous environments. These article facilities across the approach of moving received statistics at the internet with the intention that the device may be applied to remotely display screen weather parameters and in the end take a look at environmental extrade designs like temperature, humidity, Atmospheric Pressure. The paper additionally discusses the simple idea of Internet of Things and its capacity programs, specially for climate tracking.

Abstract This paper reports the results from a laboratory experiment designed to study political distortions in the accumulation of public debt. A legislature bargains over the levels of a public good and of district specific transfers in two periods. The legislature can issue or purchase risk-free bonds in the first period and the level of public debt creates a dynamic linkage across policymaking periods. In line with the theoretical predictions, we find that public policies are inefficient and efficiency is increasing in the size of the majority requirement, with higher investment in public goods and lower debt associated with larger majority requirements. Debt is lower when the probability of a negative shock to the economy in the second period is higher indicating that even in a political equilibrium debt is used to smooth consumption and to insure against economic uncertainty. Also in line with the theoretical predictions, we find that dynamic distortions are eliminated when the first period proposer can commit to a policy for both periods. The experiment, however, highlights two phenomena that are surprising in terms of standard theory and have not been previously documented. First, balancing the budget in each period is a focal point, leading to lower distortions than predicted. Second, higher majority requirements induce significant delays in reaching an agreement.

Consumer Perceived Value (CPV) perceived to have been derived from the consumption of product or service is the Value that a consumer believes that he receives for a certain amount of money. This study was conceptualized so as to understand the most important CPV dimensions and its role in predicting Attitudinal Brand Loyalty in pre purchase situations in the Indian Retail Banking context on the basis of SPERVAL (Services Perceived Value) Scale. The empirical study was based on descriptive research design (cross-sectional) and used questionnaire as the key research instrument. The questionnaire was administered to 100 respondents using a mix of judgmental and convenience non-probability sampling methods. The most important CPV dimensions, which were also the major predictors towards attitudinal brand loyalty in the context of Indian retail banking, were Functional Value, Economic Value, After Sale Value and Epistemic Value. The study has implications for retail banking marketers and can help them understand the consumer psychology behind evaluating the product offering. The service providers can also use the findings from the research to build on their competitive advantage by developing core competencies in strategic arenas. The limitations of the study included limited coverage with respect to sample size.

The role of packaging has changed with the move to self-service retail formats. Marketers have transformed packaging to become one of the major promoting tools to products. There was a necessity to explore packaging and its elements in order to understand which of these elements influence international university students purchase decisions. Thus, this paper seeks to determine the relationship between international university students’ buying behaviour and package elements through linear regression analysis. A quantitative, non-probability research approach was employed. A convenience sample was used to select study 400international students from two South African universities. Research findings reveal that international students’ perception of packaging elements influence their buying behaviour. The results also show that there is a significant relationship between consumer perception and students buying behaviour. Findings also reveal that there is a significant relationship between consumer buying behaviour and seven predictor variables; colour, graphics, packaging technology, label information, brand name, time pressure and level of involvement. It is thus imperative for the marketers to understand that packaging elements have various influences on the consumer depending on context and on product. If the package elements are properly combined, the package will be more attractive and attention grabbing.

This paper studies the effect of introducing a new vertical differentiation strategy, paying for an upgrade to a premium product after purchasing the base product, on the price dispersion of the base product arising from existing price discrimination strategies. In particular, we examine how a major U.S. airline’s price dispersion in the coach cabin changes after introducing the option to upgrade to a new type of premium economy seating within the coach cabin. We first provide a theoretical analysis that highlights two competing pressures that the new premium economy seating upgrades created on coach class prices. On the one hand, the airline benefits from lowering its prices because by allowing more customers to purchase in the first place, it increases the probability of selling upgrades (admission effect). On the other hand, for some customers, the value of flying with the airline increases because of the upgrade availability, therefore the airline may find it optimal to increase its prices (valuation effect). In the second part of the paper, we conduct an empirical investigation of the impact of upgrade introduction on coach class prices, based on a proprietary transaction-level data set from a major U.S. airline company. The empirical analysis tests the main predictions of our theoretical model and examines further nuances. The results show that the introduction of the premium economy seating upgrades is associated with an increase in the price dispersion and revenues in the coach class, the admission effect is stronger than the valuation effect on the low end of the price distribution, and the opposite is true on the high end of the price distribution. Finally, we discuss implications of our results for firm revenues and consumer welfare. This paper was accepted by Serguei Netessine, operations management.

Health messages may be an important predictor in the selection of healthier food choices among young adults. The primary objective of our study is to test the impact of labeling whole grain pasta with a health message descriptor displayed at the point-of-purchase (POP) on consumer choice in a campus dining setting. The study was conducted in a large US college dining venue during lunch service; data were collected during a nine-week period, for a total of 18 days of observation. Each day, an information treatment (i.e., no-message condition; vitamin message; fiber message) was alternated assigned to whole grain penne. Over the study period, the selection of four pasta options (white penne, whole grain penne, spinach fettuccine, and tortellini) were recorded and compiled for analysis. Logistic regression and pairwise comparison analyses were performed to estimate the impact of health messages on diners’ decisions to choose whole grain penne among the four pasta types. Our results indicate that only the message about vitamin benefits had a significant effect on this choice, with a 7.4% higher probability of selecting this pasta than the no-message condition and 6.0% higher than the fiber message condition. These findings suggest that psychological health claims (e.g., reduction of fatigue) of whole grains seem more attractive than physiological health claims (e.g., maintaining a healthy weight) for university students. In line with the 2015–2020 Dietary Guidelines for Americans, our results suggest that small changes made at the POP have the potential to contribute to significant improvements in diet (e.g., achieving recommended levels of dietary fiber). These findings have important implications for food service practitioners in delivering information with the greatest impact on healthy food choices.

Based on the findings of the current study, policymakers must take a hard look at the media and themselves, because the world can no longer blame the subprime mortgage industry for causing the Global Financial Crisis of 2007 and 2008. The public must demand answers from the media and policymakers explaining how an economic crisis that could have been avoided resulted in the collapse of the global economy. The lack of evidence supporting the theory of a financial bubble and a real estate bubble called for further investigation of factors leading to the Global Financial Crisis of 2007 and 2008. Evidence presented from data analysis in Walters (2018) suggested no financial bubble existed in developed or developing countries around the world, preceding the Global Financial Crisis of 2007 and 2008. Based on data analysis in Walters (2018) the evidence also suggested, the lasting effect of economic policies in response to the Global Financial Crisis of 2007 and 2008 for both developed and developing countries around the world, had no significant impact on the financial sector but pointed to a lack of economic growth. The findings raised significant questions about the existence of a real estate bubble in both developed and developing countries. Evidence from data analysis presented in Walters and Djokic (2019) suggested the existence of a real estate bubble in the United States real estate market preceding the Global Financial Crisis of 2007 and 2008 was a false conclusion. Data analysis in Walters (2019) resulted in, 0.989 Adjusted R-square, 194.041 Mean Dependent Variable, 5.908 Square Error of Regression, 488.726 Sum-of- Square Residual, and 0.00000 Probability (F-statistic), for correlation between the independent variable representing advancement in technology, and the dependent variable representing home purchase price in the United States preceding the Global Financial Crisis of 2007 and 2008. The findings in Walters (2019) concluded the rapid increase in home purchase price in the United States real estate market, was due to increased demand for homes from the adaptation of advancement in technology in the real estate and mortgage industries. The current study expanded the investigation of the growth in home purchase price to fifteen developed countries around the world, building on the findings of previous research by the current researcher. The researcher in the current study concluded, the existence of significant and near-perfect correlation in many cases, between the dependent variable representing growth in home purchase price, and the independent variable representing advancement in technology. The analysis was based on data analyzed from fifteen developed countries around the world, which was collected between 1990 and 2006. The data analysis included home purchase price data from, Canada, United Kingdom, Denmark, Finland, France, Italy, New Zealand, Sweden, Netherlands, Australia, Ireland, Belgium, Norway, Spain, and Portugal. Data preceding the Global Financial Crisis of 2007 and 2008 were analyzed in the current study. The researcher in the current study concluded the existence of overwhelming evidence suggesting advancement in technology was responsible for the rapid increase in home prices in developed countries around the world preceding the Global Financial Crisis of 2007 and 2008. The result of data analysis in the current study provided further confirmation of the accuracy of former Federal Reserve Board Chairmen, Alan Greenspan and Ben Bernanke 2005 assessment which concluded, the occurrence of a real estate bubble developing was impossible due to the Efficient Market Hypothesis, before reversing course subsequent their assertion in 2005 (Belke & Wiedmann, 2005; Starr,2012). The result of the current study provided additional evidence supporting Eddison Walters Risk Expectation Theory of The Global Financial Crisis of 2007 and 2008. The result from data analysis also confirmed the need for the adaptation of Eddison Walters Modern Economic Analysis Theory. As a result of the findings in the current study, the researcher concluded the development of a real estate bubble is impossible where there exists real estate price transparency, as is the case in most developed and developing countries. The researcher presented Walters Real Estate Bubble Impossibility Price Transparency Theory based on the findings. False information of a real estate bubble and predictions of a real estate crash disseminated through the mainstream media and social media can be a destructive force with a disastrous effect on the economy around the world. The failure by the media to hold themselves and policymakers to a higher standard resulted in the Global Financial Crisis of 2007 and 2008. The result of the failure by the media was a worldwide economic crisis and the Great Recession that followed the Global Financial Crisis of 2007 and 2008. Lessons learned from the Global Financial Crisis of 2007 and 2008 can assist in preventing another economic crisis in the future.

Abstract The diagnosis of heparin-Induced thrombocytopenia (HIT) is based on the presence of a compatible clinical picture combined with laboratory evidence of heparin-dependent, platelet-activating IgG antibodies. The 4T's Score is a clinical prediction rule that determines the likelihood that a patient has HIT before laboratory testing is performed. A rapid assay (H/PF4-PaGIA, Diamed, Switzerland) uses gel centrifugation to measure binding of antibodies to antigen-coated polystyrene beads (15 min turnover time). The purpose of this study is to evaluate the clinical utility of a diagnostic strategy which combines the 4T's Score with a H/PF4-PaGIA result to guide management of patients with suspected HIT while awaiting results of the serotonin-release assay (SRA). Methods Prospective cohort study of 538 consecutive adult patients with suspected HIT at 4 Canadian hospitals. Physicians completed a standardized 4T's Score sheet and the H/PF4-PaGIA was performed using fresh plasma in a central lab by technologists blinded to the 4T's Score (frozen plasma was used for 85 patients due to disruptions in worldwide availability of the assay.) The SRA and an in-house IgG anti-PF4/H enzyme-immunoassay (EIA) were performed on all patients by blinded technologists. Serologically-confirmed HIT (“HIT positive”) was defined as >50% serotonin release (mean) at three reaction conditions (0.1 U/mL heparin; 0.3 U/mL heparin; enoxaparin, 0.1 U/ml), as well as inhibition (<20% release or >50% inhibition) at 100 U/mL heparin and in the presence of Fc receptor-blocking monoclonal antibody, and a positive EIA. Thrombotic events, major bleeding events, and mortality were captured at day 30. Recommendations for management of patients while awaiting the SRA: patients with a Low 4T's Score (irrespective of H/PF4-PaGIA result) and patients with an Intermediate 4T's Score and negative H/PF4-PaGIA were to receive low-dose danaparoid or fondaparinux. Therapeutic-dose non-heparin anticoagulation was recommended for all patients with an Intermediate 4T's Score and positive H/PF4-PaGIA and for all patients with a High 4T's Score irrespective of H/PF4-PaGIA result. The primary outcome measure was the frequency of management failures defined as a patient with serologically-confirmed HIT who had one of the following combinations of diagnostic testing (a) Low 4T's Score and negative H/PF4-PaGIA; (b) Low 4T's Score and positive H/PF4-PaGIA or (c) Intermediate 4T's Score and negative H/PF4-PaGIA. Results 527 patients with mean age 66.5 yr (sd 15.4) were analyzed; 11 patients with missing diagnostic testing results were excluded. Clinical outcomes of the management of patients according to the diagnostic strategy will be reported separately. Results of diagnostic accuracy of the 4T's Score and H/PF4-PaGIA compared to the SRA are provided below. The prevalence of serologically-confirmed HIT in the study population was 6.5%. Two patients with indeterminate SRAs but IgG>1.0 were reported as HIT Positive. A negative H/PF4-PaGIA result reduced the probability of HIT based on the 4T's Score from 2.5% to 0.7% (95% CI: 0.1-2.6%) in the Low group, from 6.1% to 0% (95% CI: 0-2.7%) in the Intermediate group and from 35.7% to 0% (95% CI: 0-14.3%) in the High group. A positive H/PF4-PaGIA result increased the probability of HIT based on the 4T's Score to 15.4% (Low 4T's), 38.5% (intermediate 4T's) and 83.3% (High 4T's). The proportion of management failures was 1.5% (95% CI : 0.7%-3.0%). Of the 8 patients who were identified as management failures, 2 (Low 4T's) had a negative H/PF4-PaGIA. Out of 33 HIT Positive patients, 8 (24.2%) would have been missed based on a Low 4T's Score alone and 2 (6.1%) based on negative H/PF4-PaGIA alone. The combination of a Low or Intermediate 4T's Score and a negative H/PF4-PaGIA result had a negative predictive value for HIT of 99.5% (95% CI: 98.3-99.9). Conclusions The proportion of management failures was low (1.5%) and within acceptable limits (95% CI : 0.7%-3.0%). Combining the 4T's Score with the result of H/PF4-PaGIA excludes the diagnosis of HIT in the majority of patients with a Low or Intermediate probability for HIT and raises the likelihood of HIT in patients with a High probability. Disclosures: Linkins: BioRad DiaMed: PaGIA assays purchased at cost for study Other. Bates:BioRad Diamed: provided assays for study at cost Other. Lee:BioRad Diamed: provided assays for study at cost Other. Warkentin:GSK: Research Funding; WL Gore: Consultancy; Immucor GTI Diagnostics: Research Funding; Paringenix: Consultancy; Pfizer Canada: Honoraria; BioRad Diamed: provided assays for study at cost, provided assays for study at cost Other.

Introduction: Fibrin degradation products, D-Dimer and X-oligomers, are biomarkers for activation of coagulation. D-Dimer testing is recommended as an additional diagnostic tool for proximal deep vein thrombosis (DVT) and pulmonary embolism (PE), in conjunction with clinical presentation and pre-test probability (Lim, et al. Blood Adv. 2018). D-Dimer levels naturally increase in patients over the age of 50 years, which reduces the specificity of D-Dimer levels as a diagnostic aid for proximal DVT and PE in older patients. We conducted a study to assess the diagnostic accuracy of the Tina-quant® D-Dimer Gen.2 (D-DI2; Roche Diagnostics) assay in patients with a low or intermediate pretest probability for proximal DVT or PE (Bertsch, et al. ISTH 2020; abstract PB0600). An exploratory objective of the study was to evaluate the diagnostic accuracy of the D-DI2 assay in the total study population after applying an age-adjusted cut-off value for patients aged &gt;50 years. Methods: This prospective, observational, multi-center study was conducted between July 2017 and August 2019. Samples were collected at six European hospitals or specialist referral centers, or purchased from a commercial vendor (BioPartners Inc.). The 3.2% citrated plasma samples were analyzed at a central site using the D-DI2 assay on the cobas t 711 analyzer (Roche Diagnostics). Eligible patients were aged ≥18 years, presenting with symptoms suggestive of proximal DVT and/or PE, and with a low or intermediate pretest probability of DVT or PE by Wells score. Main exclusion criteria included DVT and/or PE symptoms for &gt;7 days or a previous diagnosis of DVT and/or PE. DVT and/or PE were diagnosed according to local imaging protocols and standard-of-care procedures, in line with current clinical guidelines. Patients were followed-up for ≥90 days after hospital discharge to verify the clinical diagnosis and record any adverse events. In the primary analysis, a D-DI2 assay cut-off value of 0.5 μg fibrinogen equivalent units (FEU)/mL was applied across all patients, regardless of age. For the purpose of this exploratory analysis, age-adjusted cut-off values were calculated by multiplying age by 0.01 μg FEU/mL for patients aged &gt;50 years. Results: In total, 2516 patients were included in the analysis (1741 DVT cohort; 775 PE cohort); of these, 1538 patients were aged &gt;50 years (996 DVT cohort; 542 PE cohort) and the D-DI2 assay cut-off values for these patients were age-adjusted accordingly. The parameters for diagnostic accuracy for both the DVT and PE cohorts, unadjusted and age-adjusted values, are provided in the Table. Both negative predictive value (NPV) and sensitivity remained high after adjustment for age across both cohorts; however, there were slight decreases compared with the unadjusted values. Positive predictive value (PPV) was increased in both cohorts after age adjustment when compared with the unadjusted values. A reduction in false positives were reported for both the DVT (27%) and PE (26%) cohorts when adjusted for age compared with the unadjusted values. After age-adjustment, the number of false negatives in the total study population increased slightly in the DVT cohort (from zero to one case) and in the PE cohort (from one to five cases). Overall, specificity was increased when the results were adjusted for age. Conclusions: In our study population, when used in conjunction with a low or intermediate pretest probability for DVT and/or PE according to Wells criteria, the D-DI2 assay identifies patients at very low risk for proximal DVT and PE. Adjustment of the cut-off value for age resulted in a slight decrease in NPV, though still below the diagnostic threshold. The benefit of a reduction in false positives observed after age adjustment has the potential to prevent further unnecessary diagnostic procedures for patients. Disclosures Bertsch: Nuremberg General Hospital/Paracelsus Medical University: Current Employment. Blaschke:BMBF: Research Funding; Innovationsfonds: Research Funding; DGINA: Membership on an entity's Board of Directors or advisory committees. Body:University of Manchester: Current Employment; Beckman Coulter: Consultancy; LumiraDx: Consultancy; Roche Diagnostics: Consultancy; Siemens Healthineers: Consultancy; Abbott Point of Care: Consultancy; Abbott Point of Care: Research Funding; Roche Diagnostics: Research Funding; American Association of Clinical Chemistry (sponsored session from Roche, Abbott, ET Healthcare, Ortho, Siemens, Beckman): Speakers Bureau; LumiraDx advisory committee: Membership on an entity's Board of Directors or advisory committees; Creavo (chair of Trial Steering Committee): Membership on an entity's Board of Directors or advisory committees. Davidson:Roche Diagnostics International: Current Employment. Rieger:Roche Diagnostics GmbH: Current Employment. Horner:Salford Royal NHS Foundation Trust: Current Employment. Sonner:Roche Diagnostics GmbH: Consultancy; TRIGA-S Scientific Solutions: Current Employment. Sun:Roche Diagnostics GmbH: Current Employment. Turnes:ICON Clinical Research UK Ltd: Current Employment. Hoffman:Roche Diagnostics GmbH, Germany: Current Employment.

AbstractPurchase prediction has an important role for decision-makers in e-commerce to improve consumer experience, provide personalised recommendations and increase revenue. Many works investigated purchase prediction for session logs by analysing users’ behaviour to predict purchase intention after a session has ended. In most cases, e-shoppers prefer to be anonymous while browsing the websites and after a session has ended, identifying users and offering discounts can be challenging. Therefore, after a session ends, predicting purchase intention may not be useful for the e-commerce strategists. In this work, we propose and develop an early purchase prediction framework using advanced machine learning models to investigate how early purchase intention in an ongoing session can be predicted. Since users could be anonymous, this could help to give real-time offers and discounts before the session ends. We use dynamically created session features after each interaction in a session, and propose a utility scoring method to evaluate how early machine learning models can predict the probability of purchase intention. The proposed framework is validated with a real-world dataset. Computational experiments show machine learning models can identify purchase intention early with good performance in terms of Area Under Curve (AUC) score which shows success rate of machine learning models on early purchase prediction.

Approximate Nearest Neighbor (ANN) has developed an immense demand for many tasks. This ANN methodology was being used for product quantization. These product quantization methods were being used for e-commerce sites. However, this quantization maybe sometimes misleading due to a lack of accuracy in technique. So, we managed to increase the accuracy of quantization by adding Logistic Regression in the process. This helps to increase the accuracy of the method by having a probability value. This helps to make correlated items much more accurate when compared to pure quantization. This method is helpful for e-commerce sites for efficiency in the prediction of purchase by the customer.

As revealed by Malaysia’s bankruptcy statistics, around a quarter of bankruptcy in Malaysia is due to default of vehicle loan. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by the banks. This warrants a study to estimate the determinants of vehicle loan default in Malaysia. This paper estimates the determinants of vehicle loan default probability which could be used to build a loan default prediction or forecasting model for credit risk scoring purposes. Using a simple random sample of 138 car loan borrowers that was provided by an established bank in Malaysia, the descriptive statistical procedures and econometrics modelling were performed to unveil these vehicle loan default determinants. Results of descriptive statistics revealed that more than half of the borrowers were default. Results of logit models further revealed that loan related characteristics are the most important determinants of probability of default. Specifically, the significant determinants of loan default were: areas of residence, vehicle purchase price, length of service, existing relationship with bank, interest rate, and available guarantor. Borrowers who are in high risk of default are characteristically those who reside in rural areas, secure higher vehicle purchase price, have longer length of employment service, are borrowers new to the bank, acquire loans charged with high interest rates, and are without a guarantor. Keywords: Loan default; Vehicle loan; Logit model; Forecasting model.

Abstract Background Bar code- or radio frequency identification (RFID)-based medical instrument management systems have gradually been introduced in the field of surgical medicine for the individual management and identification of instruments. We hypothesized that individual management of instruments using RFID tags can provide previously unavailable information, particularly the precise service life of an instrument. Such information can be used to prevent medical accidents caused by surgical instrument failure. This study aimed to predict the precise service life of instruments by analyzing the data available in instrument management systems. Methods We evaluated the repair history of instruments and the usage count until failure and then analyzed the data by the following three methods: the distribution of the instrument usage count was determined, an instrument failure probability model was generated through logistic regression analysis, and survival analysis was performed to predict instrument failure. Results The usage count followed a normal distribution. Analysis showed that instruments were not used uniformly during surgery. In addition, the Kaplan–Meier curves plotted for five types of instruments showed significant differences in the cumulative survival rate of different instruments. Conclusions The usage history of instruments obtained with RFID tags or bar codes can be used to predict the probability of instrument failure. This prediction is significant for determining the service life of an instrument. Implementation of the developed model in instrument management systems can help prevent accidents due to instrument failure. Knowledge of the instrument service life will also help in developing a purchase plan for instruments to minimize wastage.

Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

in an event when there is lots of risk factor then the logistic regression is used for predicting the probability. For binary and ordinal data the medical researcher increase the use of logistic analysis. Several classification problems like spam detection used logistic regression. If a customer purchases a specific product in Diabetes prediction or they will inspire with any other competitor, whether customer click on given advertisement link or not are some example. For two class classification the Logistic Regression is one of the most simple and common machine Learning algorithms. For any binary classification problem it is very easy to use as a basic approach. Deep learning is also its fundamental concept. The relationship measurement and description between dependent binary variable and independent variables can be done by logistic regression.

Abstract Background This study aims to evaluate the informational content of people’s subjective probability expectations for using various health services. Methods Using a sample of 1,528 Australian adults (25-64 years), I compared stated probabilities of visiting various health service providers (hospitals, dentists, optometrists, physiotherapists and related care providers, naturopaths and massage therapists) with past utilization and with predicted utilization estimated out-of-sample. I also estimated whether past utilization and subjective expectations were predicted by the same covariates. Finally, I estimated whether subjective expectations had predictive power for the choice to purchase private health insurance conditional on past utilization and other controls. Results Subjective expectations closely reflect patterns of observed utilization, are predicted by the same covariates as observed utilization, and correlate with objective measures of risk. Subjective expectations also add predictive power to models estimating insurance take-up, even after conditioning on prior health care use and other risk factors. Conclusion The findings are indicative that on average people form quite accurate expectations, and support collecting subjective expectations about health services in household surveys for use in applied research.

Abstract Background Between 700 thousand and 1.2 million citizens of Tajikistan currently live in the Russian Federation, one of the only countries where the HIV epidemic continues to worsen. Given the previously reported barriers to healthcare access for migrants to the Russian Federation, and the rapidly expanding HIV epidemic in Eastern Europe and Central Asia, this present study set out to determine whether these barriers impact late presentation with HIV among Tajikistani migrants upon their return to Tajikistan. Method This study uses data from the Tajikistan Ministry of Health surveillance system (2006 – 2019). At time of diagnosis, patients are interviewed by staff of AIDS centers, and doctors complete routine intake forms and complete medical exams. Descriptive characteristics of migrants with HIV who had lived in the Russian Federation (n=503) were calculated and compared with those of non-migrants with HIV (n=9519). Missing data were imputed using multiple imputation (predictive means matching, logistic regression imputation, and polytomous regression imputation). Two logistic models were created to model the probability of late presentation for HIV. The first model shows unadjusted associations between predictor variables and late presentation for HIV. The second model shows multivariable associations between significant study variables identified in the univariate model, and late presentation. Results Compared to non-migrants, migrants with HIV are more likely to be from Gorno-Badakhshan region, are less likely to use illicit drugs, and are more likely to have purchased the services of sex workers. The unadjusted logistic model found that for every year spent in the Russian Federation, the risk of late presentation for a Tajikistani migrant with HIV increases by 4.0% (95% CI: 0.3-7.7). The multivariate model showed that when age, sex, and region of origin are held constant, the risk of late presentation for a Tajikistani migrant with HIV increases by 4.0% (95% CI: 0.1-7.8) for each year spent in the Russian Federation. Conclusion The results of this paper suggest that if the Russian Federation were to loosen its restrictions on HIV care for foreign nationals, it might improve the treatment outcomes of migrant laborers. As this analysis is only correlational in nature, further research is needed to explicate the causal pathways of the associations found in the present analysis.

Background: Canadian residency programs have adopted competency-based medical education, where time-based learning systems are replaced with core competency “milestones” that must be achieved before a student progresses. Assessment tools must be developed to predict performance prior to high-stakes milestones, so interventions can be targeted to improve performance.Objectives: 1. To characterize how well each of three practice written exams predicts passing the Canadian Internal Medicine Royal College (RC) exam. 2. To determine if writing practice exams is perceived to improve performance on the RC exam.Methods: Three 105-question multiple choice question exams were created from a range of internal medicine topics, and offered one month apart to 35 residents. Percentile ranks on each practice exam were compared to the result (pass/fail) on the RC exam. Surveys were completed within 1 month after the RC exam.Results: There were 35 residents invited to participate. Practice exams (PE) 1, 2, and 3 were taken by 33, 26, and 22 residents, for an exam participation rate of 94.3, 74.3, and 62.9%, respectively. Failure on the RC exam could be predicted by percentile ranking <15% on PE1 (OR 19.5, p=0.017) or PE2 (OR 63.0, p=0.006), and by percentile ranking <30% on PE1 (OR 28.8, p=0.003), PE2 (OR 24.0, p=0.010) or PE3 (OR 15.0, p=0.046). The survey was sent out to the 33 participants. Of those, the total number of respondents was 25, with a response rate of 75.5%. Survey takers agreed that practice written exams improved performance on the RC exam (18/25, 88%).Conclusions: Performance in the Canadian Internal Medicine RC Exam can be predicted by performance on any of three practice written exams. This tool can therefore identify trainees for whom additional resources should be invested, to prevent failure of a high-stakes milestone within the competency based medical education framework.RÉSUMÉContexte : Les programmes canadiens de résidence ont choisi de diffuser un enseignement médical axé sur les compétences dans lequel les systèmes d’apprentissage structurés en fonction du temps sont remplacés par des « jalons » liés aux compétences fondamentales que l’étudiant doit atteindre pour aller de l’avant. Il faut élaborer des outils d’évaluation pour prédire la probabilité de résultats escomptés par un étudiant avant que celui-ci ne se présente à certains événements dont les enjeux sont élevés. Ainsi, il devient possible d’intervenir de manière à améliorer les résultats escomptés.Objectifs : 1. Déterminer dans quelle mesure chacun des trois examens de pratique écrits prédit la réussite à l’examen du Collège royal des médecins et chirurgiens du Canada (CRMCC) en médecine interne; 2. Évaluer si le fait de se soumettre à des examens de pratique écrits est perçu comme un élément qui améliore les résultats à l’examen du CRMCC.Méthodologie : Trois examens écrits comportant chacun 105 questions à choix de réponses portant sur un éventail de sujets relatifs à la médecine interne ont été préparés et proposés à 35 résidents à intervalle d’un mois. Les rangs-centiles de chaque examen de pratique ont été comparés avec le résultat obtenu à l’examen du CRMCC (succès/échec). Les sondages ont été effectués dans le mois suivant l’examen du CRMCC.Résultats : Trente-cinq résidents ont été invités aux examens de pratique écrits (EP) 1, 2 et 3. La participation a été respectivement de 33, 26 et 22 résidents, soit de 94,3 %, 74,3 % et 62,9 %. L’échec à l’examen du CRMCC pouvait être prédit par un rang-centile < 15 % à l’EP1 (OR 19,5 et p = 0,017) ou à l’EP2 (OR 63,0 et p = 0,006) et un rang-centile < 30 % à l’EP1 (OR 28,8 et p = 0,003), à l’EP2 (OR 24,0 et p = 0,010) ou à l’EP3 (OR 15,0, et p = 0,046). Le sondage a été envoyé aux 33 participants. Le nombre total de répondants a été de 25, pour un taux de réponse de 75,5 %. La majorité des répondants (18/25, 88 %) sont d’avis que les examens de pratique écrits leur ont permis d’obtenir de meilleurs résultats à l’examen du CRMCC.Conclusions : Les résultats à l’examen du Collège royal des médecins et chirurgiens du Canada (CRMCC) en médecine interne peuvent être prédits par les résultats obtenus à l’un des examens de pratique écrits. Par conséquent, cet outil peut être utilisé dans le cadre de l’enseignement de la médecine axé sur les compétences pour identifier sur qui l’on devrait investir des ressources additionnelles en vue d’éviter un échec à cet événement aux enjeux élevés.Competency-based medical education (CBME) has generated increased attention over the last decade,1–3 and become entrenched within several national medical education frameworks including Canada.4 Proponents of CBME suggest that older medical education models focus on medical knowledge rather than skills, or higher order aspects of practice. 5 Focus on time spent in training can take away from the abilities acquired during that time frame.6 Furthermore, flexible time periods may be more efficient and focused, compared to time-based curriculum.3,6,7 In light of these advantages, the Royal College of Physicians and Surgeons of Canada (RCPSC) has committed to transform medical education to a CBME model for all residency programs by 2017.4While residency programs reorganize toward the CBME model, residents will still be required to perform oral and written exams. It is thus essential that CBME-based programs incorporate assessment tools to predict performance on high-stakes milestones, such as RC exams.We created three written PEs, and evaluated how well each predicted performance on a high-stakes milestone, the RCPSC Internal Medicine exam (RC exam). We also evaluated how well PE were perceived to improve performance on the same high-stakes milestone RC exam.METHODSSetting and ParticipantsThe RC exam contains both written and oral components. All residents sitting both components of the RC exam, within 12 months, who were post-graduate medical residents at Western University (London, Ontario, Canada), were invited to participate. The study was conducted in 2013-2014.Western University Health Sciences Research Ethics Board provided an ethics waiver for this study, since the study was performed as part of the standard operations of an educational program.Intervention: ExamsTwo authors (HS, BT) separately created multiple choice questions (MCQ) reflecting all areas of internal medicine, based on the Objectives of Training of the RC Internal Medicine exam. RC exam questions are not available for purchase, and examinees are forbidden to share RC exam questions. Therefore, PE content and question style was informed by questions purchased for American Board of Internal Medicine (ABIM) course reviews.8,9 MCQ creators had each completed the RC exam within 3 years, and were thus familiar with MCQ and exam format.All authors independently reviewed each PE question to assure quality of content, grammar, spelling, and syntax. Each PE covered all subspecialty areas within internal medicine, including allergy and immunology ( n=4), cardiology (n=13), dermatology (n=2), endocrinology (n=8), gastroenterology (n=10), hematology (n=10), infectious diseases (n=15), nephrology ( n=9), neurology (n=6), oncology (n=4), respiratory and critical care medicine (n=7), rheumatology ( n=14) and statistics (n=3). This topic allocation included 7 questions for JAMA Rational Clinical Exam, and 5 for interpretation of medical images (e. g., chest X-ray, electrocardiogram). PE size (105 questions) and length (3 hours) were chosen to reflect the RC exam.Each PE was offered at two separate times, to assure flexibility within ongoing clinical responsibilities. PE1, PE2, and PE3 were offered approximately 7, 6, and 5 months prior to RC exam, respectively. This timing was chosen so that trainees had sufficient time to improve their performance before the RC exam if a poor PE result was found.Examinees were provided a personalized report for each exam, within 7 days of completing the PE. The personalized report included the examinee’s overall mark, average within each subspecialty, and percentile rank within the entire cohort of examinees. Two separate 1-hour periods were available to review each PE results, with the questions and key, supervised by BT.Intervention: SurveyAll study participants were invited to participate in a survey. The survey assessed how well PE simulated the RC exam, whether the PE were recommended to the next year’s cohort of examinees, and whether the PE improved performance on the RC exam.Outcomes: ExamsEach study participant agreed to provide the RC exam result (“pass” or “fail”) once he or she had received it. Each candidates verbally communicated RC exam result was confirmed online 3 months after the RC exam results were reported (cpso.on.ca).Odds ratios were calculated. The adverse outcome was failure on the RC exam. Exposures evaluated included percentile rank < 15% and <30%. Odds ratios of infinity were prevented by adding 1 adverse outcome to any exposure group without any adverse outcomes; this was performed for 3 exposure groups, but did not impact whether statistical significance was attained. Results are detailed in Table 1.Outcome: SurveySurvey results were on a Likert Scale. The proportion of those respondents who agreed or disagreed were calculated.All data was analyzed using Statistical Package for the Social Sciences (SPSS) version 21.0.RESULTSSetting and ParticipantsThere were 35 residents invited to participate, the total number of possible participants. PE1, PE2, and PE3 were taken by 33, 26, and 22 residents, for an exam participation rate of 94.3, 74.3, and 62.9%, respectively. The majority of invitees took 3 (n=17) or 2 ( n=14), while a minority took 1 (n=2) or 0 (n=2) practice exams.ExamsOf all examinees of the RC exam (n=35), 7 failed. RC exam pass rates were lower when PE1 percentile rank was lower than 15% (40.0 vs. 92.9%, p<0.001) or 30% (44.4 vs. 100%, p<0.004), when PE2 percentile rank was lower than 15% (0.0 vs. 100.0%, p <0.001) or 30% (42.9 vs. 100.0%, p=0.038), and when PE3 percentile rank was lower than 30% (50 vs. 93.75%, p=0.046) (Figure 1). Figure 1. License exam practice pass rate versus percentile rate (PR) on practice exams. Examinees were more likely to fail the RC exam if percentile rank was less than 15% (OR 19.5, p=0.017) or 30% (OR 28.8, p=0.003) in PE1, less than 15% (OR 63.0, p=0.006) or 30% (OR 24.0, p =0.010) in PE2, or less than 30% (OR 15.0, p=0.046) in PE3.SurveyOnly residents who had taken at least 1 practice exam were invited to participate. The survey was sent out to the 33 participants, the total number of possible participants. Of those, the total number of respondents was 25, with a response rate of 75.5%. Most survey respondents agreed that the PEs were an accurate simulation of the written component of the RC examination (20/25, 80%) (Figure 2A). Most survey respondents agreed that the PEs improved performance on the RC written examinations (18/25, 72%) (Figure 2B). Most survey respondents recommended future residents to take the PEs (22/25, 88%) (Figure 2C).DISCUSSIONWe describe the creation of a tool to assess performance on a high-stakes milestone examination, the RC exam. This tool is easy to create, affordable, and is administered on a voluntary basis with high uptake amongst candidates writing the RC exam. The assessment tool has been shown to predict performance well so that resources can be invested in those at risk for failing.There is a possibility that mere participation in the assessment tool itself improves performance on this high-stakes exam. There were insufficient numbers of study participants to determine a correlation between number of exams taken and pass rates. Even still, unwillingness to participate in the study may reflect a general unwillingness to prepare, which means the results would be confounded and correlative rather than causative. One way to look into this is to perform a randomized trial in which half of residents take the assessment tool and the other half doesn’t. Unfortunately, almost all invited residents were anxious to participate, rendering such a possible study impossible. On the other hand, exam takers were able to communicate usefulness of the exam and to provide feedback on how it might be improved for future years.As CBME develops and becomes entrenched, there will continue to be a need to prepare for knowledge based written exams. This exam will continue to be considered a core competency between the stages to transition to practice. Thus, tools are needed to assess exam competence. This study confirms that such tools can and should be developed to assure that trainees are prepared.Ideally, residents with low performance would be identified early enough to intervene to change the outcome. It is uncertain what the ideal time frame is or what the intervention should be. It is reasonable to assume that taking the examination earlier in their training may allow candidates to become aware of their performance and implications thereof and implement earlier changes in learning strategies. For example, in past years, candidates contacted their program directors to ease the clinical workload to allow more study time. Others sought counselling and mentorship from staff, while others were self-directed in their learning and became more motivated to study. On the other hand, poor performance on this formative examination could potentially discourage some residents from studying if they felt their studying was futile. Future research efforts should focus on identifying which intervention is optimal to modify exam performance.The failure rate of 20% on the RC exam the year the study was conducted was unusually high for the program; however, this allowed for a correlation to be established between the PEs and the RC exam. The PEs were able to identify all those who failed the RC exam. However, there were those who scored below the 30th percentile on the PEs and still passed the RC exam. It must be kept in mind that the purpose of these formative exams is to identify those at high risk of failure so they can receive remedial support and improve their chances of passing. Thus, it is possible that through increased remedial support, those candidates who did poorly on the in-training exam managed to pass the RC exam.Only 2 trainees chose to write none of the PEs. While both of these trainees ultimately failed the RC exam, statistical significance could not be established due to the small sample size. It thus remains uncertain whether the act of writing PEs predicts passing the RC exam. However, the study objective was to identify candidates at high risk of failing the RC exam; the next step will be to determine which interventions can improve RC exam result. However, it must be acknowledged that PEs could not only identify candidates at risk of failing RC exam, but also improve their performance. This requires future study before any firm conclusions can be found.This study confirms that formative exams’ results can predict failure on the RC exam. The questions were written by authors who had recently written the RC exam, familiar with its format, and knowledgeable of the current Canadian guidelines, which are a focus of the actual RC exam. Because of confidentiality agreements with the RCPSC, actual RC exam questions can’t be shared, and thus can’t be used as part of the practice exams. However, we attempted to overcome this limitation by having all PE questions reviewed by at least 3 physicians who’d recently successfully completed the RC exam, to assure syntax and format was as similar as possible between PE and RC exam. Furthermore, this limitation does not impact the PEs statistically significant prediction of candidates at risk of failing the RC exam. The study objective was to identify candidates at risk of failing the RC exam, and the PEs are indeed a valid predictor of RC exam performance. There are limitations to this study. Firstly, this was a single centre study. However, Western University has a wide range of subspecialty programs available, and the trainees’ demographics resemble that at other Canadian centers. Secondly, new questions need to be created annually to reflect updated literature and guidelines; this requires ongoing commitment and dedication from staff. These “updated” exams could become more difficult to validate if candidates no longer fail the RC exam. However, if the act of taking the PE predicts passing RC, future research could focus on comparing RC pass rates at programs with and without the PEs. Thirdly, it's entirely possible that the use of questions from old RC exam would be more predictive, but these questions cannot be shared or used for PE due to the confidentiality agreement with the RCPSC. Therefore, creation of independent questions is still required. This is the first study of an assessment tool to predict performance on the Canadian internal medicine examination within the CBME framework. This strategy can easily be replicated and feedback is rapidly provided in a time sensitive manner. This could help trainees direct their preparation and identify knowledge gaps more easily.CONCLUSIONWe report an assessment tool to predict performance on the RC exam that can be a valid and useful form of feedback. This strategy can easily be replicated for other subspecialties or internal medicine programs. 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