Choice from Non-Choice: Predicting Consumer Preferences from Blood Oxygenation Level-Dependent Signals Obtained during Passive Viewing

Decision-making is often viewed as a two-stage process, where subjective values are first assigned to each option and then the option of the highest value is selected. Converging evidence suggests that these subjective values are represented in the striatum and medial prefrontal cortex (MPFC). A separate line of evidence suggests that activation in the same areas represents the values of rewards even when choice is not required, as in classical conditioning tasks. However, it is unclear whether the same neural mechanism is engaged in both cases. To address this question we measured brain activation with functional magnetic resonance imaging while human subjects passively viewed individual consumer goods. We then sampled activation from predefined regions of interest and used it to predict subsequent choices between the same items made outside of the scanner. Our results show that activation in the striatum and MPFC in the absence of choice predicts subsequent choices, suggesting that these brain areas represent value in a similar manner whether or not choice is required

Comparison of m-mode echocardiographic left ventricular mass measured using digital and strip chart readings: The Atherosclerosis Risk in Communities (ARIC) study

BACKGROUND: Epidemiological and clinical studies frequently use echocardiography to measure LV wall thicknesses and chamber dimension for estimating quantitative measures of LV mass. While echocardiographic M-mode LV images have traditionally been measured using hand-held calipers and strip-chart paper tracings, digitized M-mode LV image measurements made directly on the computer screen using electronic calipers have become standard practice. We sought to determine if systematic differences in LV mass occur between the two methods by comparing LV mass measured from simultaneous M-mode strip chart recordings and digitized recordings. METHODS: The Atherosclerosis Risk in Communities study applied the latter method. To determine if systematic differences in LV mass occur between the two methods, LV mass was measured from simultaneous M-mode strip chart recordings and digitized recordings. RESULTS: We found no difference in LV mass (p > .25) and a strong correlation in LV mass between the two methods (r = 0.97). Neither age, sex, nor hypertension status affected the correlation of LV mass between the two methods. CONCLUSIONS: We conclude that digital estimates of LV mass provide unbiased estimates comparable to the strip-chart method

Evaluation of the public health impacts of traffic congestion: a health risk assessment

Background: Traffic congestion is a significant issue in urban areas in the United States and around the world. Previous analyses have estimated the economic costs of congestion, related to fuel and time wasted, but few have quantified the public health impacts or determined how these impacts compare in magnitude to the economic costs. Moreover, the relative magnitudes of economic and public health impacts of congestion would be expected to vary significantly across urban areas, as a function of road infrastructure, population density, and atmospheric conditions influencing pollutant formation, but this variability has not been explored. Methods: In this study, we evaluate the public health impacts of ambient exposures to fine particulate matter (PM2.5) concentrations associated with a business-as-usual scenario of predicted traffic congestion. We evaluate 83 individual urban areas using traffic demand models to estimate the degree of congestion in each area from 2000 to 2030. We link traffic volume and speed data with the MOBILE6 model to characterize emissions of PM2.5 and particle precursors attributable to congestion, and we use a source-receptor matrix to evaluate the impact of these emissions on ambient PM2.5 concentrations. Marginal concentration changes are related to a concentration-response function for mortality, with a value of statistical life approach used to monetize the impacts. Results: We estimate that the monetized value of PM2.5-related mortality attributable to congestion in these 83 cities in 2000 was approximately $31 billion (2007 dollars), as compared with a value of time and fuel wasted of$60 billion. In future years, the economic impacts grow (to over $100 billion in 2030) while the public health impacts decrease to$13 billion in 2020 before increasing to $17 billion in 2030, given increasing population and congestion but lower emissions per vehicle. Across cities and years, the public health impacts range from more than an order of magnitude less to in excess of the economic impacts. Conclusions: Our analyses indicate that the public health impacts of congestion may be significant enough in magnitude, at least in some urban areas, to be considered in future evaluations of the benefits of policies to mitigate congestion

Neural Random Utility: Relating Cardinal Neural Observables to Stochastic Choice Behavior

We assess whether a cardinal model can be used to relate neural observables to stochastic choice behavior. We develop a general empirical framework for relating any neural observable to choice prediction and propose a means of benchmarking their predictive power. In a previous study, measurements of neural activity were made while subjects considered consumer goods. Here, we find that neural activity predicts choice behavior with the degree of stochasticity in choice related to the cardinality of the measurement. However, we also find that current methods have a significant degree of measurement error which severely limits their inferential and predictive performance

Ultra-processed food consumption and obesity in the Australian adult population

Background: Rapid simultaneous increases in ultra-processed food sales and obesity prevalence have been observed worldwide, including in Australia. Consumption of ultra-processed foods by the Australian population was previously shown to be systematically associated with increased risk of intakes of nutrients outside levels recommended for the prevention of obesity. This study aims to explore the association between ultra-processed food consumption and obesity among the Australian adult population and stratifying by age group, sex and physical activity level. Methods: A cross-sectional analysis of anthropometric and dietary data from 7411 Australians aged ≥20 years from the National Nutrition and Physical Activity Survey 2011–2012 was performed. Food consumption was evaluated through 24-h recall. The NOVA system was used to identify ultra-processed foods, i.e. industrial formulations manufactured from substances derived from foods and typically added of flavours, colours and other cosmetic additives, such as soft drinks, confectionery, sweet or savoury packaged snacks, microwaveable frozen meals and fast food dishes. Measured weight, height and waist circumference (WC) data were used to calculate the body mass index (BMI) and diagnosis of obesity and abdominal obesity. Regression models were used to evaluate the association of dietary share of ultra-processed foods (quintiles) and obesity indicators, adjusting for socio-demographic variables, physical activity and smoking. Results: Significant (P-trend ≤ 0.001) direct dose–response associations between the dietary share of ultra-processed foods and indicators of obesity were found after adjustment. In the multivariable regression analysis, those in the highest quintile of ultra-processed food consumption had significantly higher BMI (0.97 kg/m2; 95% CI 0.42, 1.51) and WC (1.92 cm; 95% CI 0.57, 3.27) and higher odds of having obesity (OR = 1.61; 95% CI 1.27, 2.04) and abdominal obesity (OR = 1.38; 95% CI 1.10, 1.72) compared with those in the lowest quintile of consumption. Subgroup analyses showed that the trend towards positive associations for all obesity indicators remained in all age groups, sex and physical activity level. Conclusion: The findings add to the growing evidence that ultra-processed food consumption is associated with obesity and support the potential role of ultra-processed foods in contributing to obesity in Australia

Teaching clinical informatics to third-year medical students: negative results from two controlled trials

BACKGROUND: Prior educational interventions to increase seeking evidence by medical students have been unsuccessful. METHODS: We report two quasirandomized controlled trials to increase seeking of medical evidence by third-year medical students. In the first trial (1997–1998), we placed computers in clinical locations and taught their use in a 6-hour course. Based on negative results, we created SUMSearch(TM), an Internet site that automates searching for medical evidence by simultaneous meta-searching of MEDLINE and other sites. In the second trial (1999–2000), we taught SUMSearch's use in a 5½-hour course. Both courses were taught during the medicine clerkship. For each trial, we surveyed the entire third-year class at 6 months, after half of the students had taken the course (intervention group). The students who had not received the intervention were the control group. We measured self-report of search frequency and satisfaction with search quality and speed. RESULTS: The proportion of all students who reported searching at least weekly for medical evidence significantly increased from 19% (1997–1998) to 42% (1999–2000). The proportion of all students who were satisfied with their search results increased significantly between study years. However, in neither study year did the interventions increase searching or satisfaction with results. Satisfaction with the speed of searching was 27% in 1999–2000. This did not increase between studies years and was not changed by the interventions. CONCLUSION: None of our interventions affected searching habits. Even with automated searching, students report low satisfaction with search speed. We are concerned that students using current strategies for seeking medical evidence will be less likely to seek and appraise original studies when they enter medical practice and have less time