Bayes classifiers for imbalanced traffic accidents datasets

[EN] Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. (C) 2015 Elsevier Ltd. All rights reserved.The authors are grateful to the Police Traffic Department in Jordan for providing the data necessary for this research. Griselda Lopez wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit Areas, which has made this work possible. The authors appreciate the reviewers' comments and effort in order to improve the paper.Mujalli, R.; López-Maldonado, G.; Garach, L. (2016). Bayes classifiers for imbalanced traffic accidents datasets. Accident Analysis & Prevention. 88:37-51. https://doi.org/10.1016/j.aap.2015.12.003S37518

Which clinical parameters predict a CSF diagnosis of meningitis in a population with high HIV prevalence?

Background. The HIV epidemic has changed the aetiology of meningitis in sub-Saharan Africa, and frontline clinicians are faced with a variety of meningitic presentations. Doctors working in resource-limited settings have the challenge of appropriately selecting patients for lumbar puncture (LP), a potentially risky procedure that requires laboratory analysis.Methods. In a rural South African hospital, the practice of performing LPs was audited against local guidelines. Data were collected retrospectively between February and June 2013. Symptoms and signs of meningitis, HIV status, investigations performed prior to LP and cerebrospinal fluid (CSF) results were recorded. With the aim of determining statistically significantclinical predictors of meningitis, parameters were explored using univariate and multivariate logistic regression analyses.Results. A total of 107 patients were included, of whom 43% had an  abnormal CSF result. The majority (76%) of patients were HIV-positive (CD4+ cell count <200 cells/ìl in 46%). Cryptococcal meningitis (CCM) was the most prevalent microbiological diagnosis, confirmed in 10 out of 12 patients. Of the non-microbiological diagnoses, lymphocytic predominance was the most common abnormality, present in 17 out of 33 patients. Confusion (p=0.011) was the most statistically significant predictor of anabnormal CSF result. Headache (p=0.355), fever (p=0.660) and  photophobia (p=0.634) were not statistically predictive.Conclusion. The high incidence of CCM correlates with previous data from sub-Saharan Africa. In populations with high HIV prevalence, the classic meningitic symptoms of headache, fever and photophobia, while common presenting symptoms, are significantly less predictive of a meningitis diagnosis than confusion

Evaluation of Injury Severity for Pedestrian VehicleCrashes in Jordan Using Extracted Rules

[EN] Pedestrian safety is a major concern throughout the world because pedestrians are considered to be the most vulnerable roadway users. This paper sought to identify the main factors in pedestrian-vehicle crashes that increase the risk of a fatality or severe injury. Pedestrian-vehicle crashes which occurred in urban and suburban areas in Jordan between 2009 and 2011 were investigated. Extracted rules from Bayesian networks were used to identify factors related to severity of pedestrian-vehicle crashes. To obtain as much information as possible about these factors, three subsets were used. The first and second subsets contain all types of collisions (pedestrian and nonpedestrian), in which the first subset used collision type as a class variable and the second subset used injury severity. The third subset contains pedestrian collisions only and used injury severity as the class variable. The results indicate that when using collision type as the class variable, better performance was obtained and that the following variables increase the risk of fatality or severe injury: roadway type, number of lanes, speed limit, lighting, and adverse weather conditions.Mujalli, R.; Garach, L.; López-Maldonado, G.; Al-Rousan, T. (2019). Evaluation of Injury Severity for Pedestrian VehicleCrashes in Jordan Using Extracted Rules. Journal of Transportation Engineering. 145(7):04019028-1-04019028-13. https://doi.org/10.1061/JTEPBS.0000244S04019028-104019028-13145

Extraction of decision rules via imprecise probabilities

"This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 2017, available online: https://www.tandfonline.com/doi/full/10.1080/03081079.2017.1312359"Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.This work has been supported by the Spanish "Ministerio de Economia y Competitividad" [Project number TEC2015-69496-R] and FEDER funds.Abellán, J.; López-Maldonado, G.; Garach, L.; Castellano, JG. (2017). Extraction of decision rules via imprecise probabilities. International Journal of General Systems. 46(4):313-331. https://doi.org/10.1080/03081079.2017.1312359S313331464Abellan, J., & Bosse, E. (2018). Drawbacks of Uncertainty Measures Based on the Pignistic Transformation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(3), 382-388. doi:10.1109/tsmc.2016.2597267Abellán, J., & Klir, G. J. (2005). Additivity of uncertainty measures on credal sets. International Journal of General Systems, 34(6), 691-713. doi:10.1080/03081070500396915Abellán, J., & Masegosa, A. R. (2010). An ensemble method using credal decision trees. European Journal of Operational Research, 205(1), 218-226. doi:10.1016/j.ejor.2009.12.003(2003). International Journal of Intelligent Systems, 18(12). doi:10.1002/int.v18:12Abellán, J., Klir, G. J., & Moral, S. (2006). Disaggregated total uncertainty measure for credal sets. International Journal of General Systems, 35(1), 29-44. doi:10.1080/03081070500473490Abellán, J., Baker, R. M., & Coolen, F. P. A. (2011). Maximising entropy on the nonparametric predictive inference model for multinomial data. European Journal of Operational Research, 212(1), 112-122. doi:10.1016/j.ejor.2011.01.020Abellán, J., López, G., & de Oña, J. (2013). Analysis of traffic accident severity using Decision Rules via Decision Trees. Expert Systems with Applications, 40(15), 6047-6054. doi:10.1016/j.eswa.2013.05.027Abellán, J., Baker, R. M., Coolen, F. P. A., Crossman, R. J., & Masegosa, A. R. (2014). Classification with decision trees from a nonparametric predictive inference perspective. Computational Statistics & Data Analysis, 71, 789-802. doi:10.1016/j.csda.2013.02.009Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., & Zielosko, B. (2013). Optimization and analysis of decision trees and rules: dynamic programming approach. International Journal of General Systems, 42(6), 614-634. doi:10.1080/03081079.2013.798902Chang, L.-Y., & Chien, J.-T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17-22. doi:10.1016/j.ssci.2012.06.017Chang, L.-Y., & Wang, H.-W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019-1027. doi:10.1016/j.aap.2006.04.009DE CAMPOS, L. M., HUETE, J. F., & MORAL, S. (1994). PROBABILITY INTERVALS: A TOOL FOR UNCERTAIN REASONING. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 02(02), 167-196. doi:10.1142/s0218488594000146DGT. 2011b.Spanish Road Safety Strategy 2011–2020, 222 p. Madrid: Traffic General Directorate.Dolques, X., Le Ber, F., Huchard, M., & Grac, C. (2016). Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis. International Journal of General Systems, 45(2), 187-210. doi:10.1080/03081079.2015.1072927Gray, R. C., Quddus, M. A., & Evans, A. (2008). Injury severity analysis of accidents involving young male drivers in Great Britain. Journal of Safety Research, 39(5), 483-495. doi:10.1016/j.jsr.2008.07.003Guo, J., & Chankong, V. (2002). Rough set-based approach to rule generation and rule induction. International Journal of General Systems, 31(6), 601-617. doi:10.1080/0308107021000034353Huang, H., Chin, H. C., & Haque, M. M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident Analysis & Prevention, 40(1), 45-54. doi:10.1016/j.aap.2007.04.002Kashani, A. T., & Mohaymany, A. S. (2011). Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science, 49(10), 1314-1320. doi:10.1016/j.ssci.2011.04.019Li, X., & Yu, L. (2016). Decision making under various types of uncertainty. International Journal of General Systems, 45(3), 251-252. doi:10.1080/03081079.2015.1086574Mantas, C. J., & Abellán, J. (2014). Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data. Expert Systems with Applications, 41(5), 2514-2525. doi:10.1016/j.eswa.2013.09.050Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis & Prevention, 35(5), 683-691. doi:10.1016/s0001-4575(02)00047-7McCartt, A. T., Mayhew, D. R., Braitman, K. A., Ferguson, S. A., & Simpson, H. M. (2009). Effects of Age and Experience on Young Driver Crashes: Review of Recent Literature. Traffic Injury Prevention, 10(3), 209-219. doi:10.1080/15389580802677807Montella, A., Aria, M., D’Ambrosio, A., & Mauriello, F. (2011). Data-Mining Techniques for Exploratory Analysis of Pedestrian Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 107-116. doi:10.3141/2237-12Montella, A., Aria, M., D’Ambrosio, A., & Mauriello, F. (2012). Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accident Analysis & Prevention, 49, 58-72. doi:10.1016/j.aap.2011.04.025De Oña, J., López, G., & Abellán, J. (2013). Extracting decision rules from police accident reports through decision trees. Accident Analysis & Prevention, 50, 1151-1160. doi:10.1016/j.aap.2012.09.006De Oña, J., López, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis & Prevention, 51, 1-10. doi:10.1016/j.aap.2012.10.016Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety Science, 47(1), 145-154. doi:10.1016/j.ssci.2007.12.001Peek-Asa, C., Britton, C., Young, T., Pawlovich, M., & Falb, S. (2010). Teenage driver crash incidence and factors influencing crash injury by rurality. Journal of Safety Research, 41(6), 487-492. doi:10.1016/j.jsr.2010.10.002Sikora, M., & Wróbel, Ł. (2013). Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. International Journal of General Systems, 42(6), 594-613. doi:10.1080/03081079.2013.798901Walley, P. (1996). Inferences from Multinomial Data: Learning About a Bag of Marbles. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 3-34. doi:10.1111/j.2517-6161.1996.tb02065.xWang, Z., & Klir, G. J. (1992). Fuzzy Measure Theory. doi:10.1007/978-1-4757-5303-5Webb, G. I. (2007). Discovering Significant Patterns. Machine Learning, 68(1), 1-33. doi:10.1007/s10994-007-5006-xWitten, I. H., & Frank, E. (2002). Data mining. ACM SIGMOD Record, 31(1), 76-77. doi:10.1145/507338.50735

Development of safety performance functions for Spanish two-lane rural highways on flat terrain

[EN] Over decades safety performance functions (SPF) have been developed as a tool for traffic safety in order to estimate the number of crashes in a specific road section. Despite the steady progression of methodological innovations in the crash analysis field, many fundamental issues have not been completely addressed. For instance: Is it better to use parsimonious or fully specified models? How should the goodness-of-fit of the models be assessed? Is it better to use a general model for the entire sample or specific models based on sample stratifications? This paper investigates the above issues by means of several SPFs developed using negative binomial regression models for two-lane rural highways in Spain. The models were based on crash data gathered over a 5-year period, using a broad number of explanatory variables related to exposure, geometry, design consistency and roadside features. Results show that the principle of parsimony could be too restrictive and that it provided simplistic models. Most previous studies apply conventional measurements (i.e., R-2, BIC, AIC, etc.) to assess the goodness-of-fit of models. Seldom do studies apply cumulative residual (CURE) analysis as a tool for model evaluation. This paper shows that CURE plots are essential tools for calibrating SPF, while also providing information for possible sample stratification. Previous authors suggest that sample segmentation increases the model accuracy. The results presented here confirm that finding, and show that the number of significant variables in the final models increases with sample stratification. This paper point out that fully models based on sample segmentation and on CURE may provide more useful insights about traffic crashes than general parsimonious models when developing SPF. (C) 2016 Elsevier Ltd. All rights reserved.The authors would like to thank the ERDF of the European Union for financial support via project "Bases para un sistema experto que permita la identificacion probabilistica de Tramos de Concentracion de Crashes (TCA)" under the "Programa Operativo FEDER de Andalucia 2007-2013". We also thank the Public Works Agency and the Regional Ministry of Public Works and Housing of the Regional Government of Andalusia. Griselda Lopez wishes to express her acknowledgement of the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit Areas.Garach, L.; De Oña, J.; López-Maldonado, G.; Baena-Ruiz, L. (2016). Development of safety performance functions for Spanish two-lane rural highways on flat terrain. Accident Analysis & Prevention. 95:250-265. https://doi.org/10.1016/j.aap.2016.07.021S2502659

A proposal for cost-related and market-oriented train running charges

[EN] This paper examines some key aspects of a charging system for promoting railway transport, including charges reflecting a clear relationship with costs (transparency) and charges reflecting the quality of the infrastructure manager¿s service. Train running charges recover track-related costs and can help to develop a charging system that meets these requirements. To orient train running charges to the market, a method for processing track maintenance and renewal costs is proposed whereby the quality of the service provided by an infrastructure is measured according to its utility to the railway undertaking. To achieve transparency, a single indicator is used for cost planning and the subsequent levying of costs on railway undertakings. The paper includes an example of how proposed train running charges would be calculated according to data from 14 European countries. The example shows that short-distance trains generate the lowest maintenance and renewal costs, followed by long-distance trains and freight trains.This work was supported by the Spanish Ministerio de Fomento [grant number PT-2007-056-05CCPP].Calvo, F.; De Oña, J.; De Oña, R.; López-Maldonado, G.; Garach, L. (2014). A proposal for cost-related and market-oriented train running charges. Transportation Planning and Technology. 37(4):354-372. https://doi.org/10.1080/03081060.2014.897127S354372374Baumgartner, J. P. 2001. “Prices and Costs in the Railway Sector.” Laboratoire d'Intermodalité des Transports et de Planification. École Polytechnique Fédérale de Lausanne. Accessed February 4. http://litep.epfl.ch–2014Calvo, F., and J. de Oña. 2012a. “An Approach to Mark-Ups through Capacity Charges.”Proceedings of the ICE – Transport. Accessed February 4. http://www.icevirtuallibrary.com/content/article/10.1680/tran.11.00050.Calvo, F., & De Oña, J. (2012). Are rail charges connected to costs? Journal of Transport Geography, 22, 28-33. doi:10.1016/j.jtrangeo.2011.11.004Calvo, F., de Oña, J., López, G., Garach, L., & de Oña, R. (2013). Rail track costs management for efficient railway charges. Proceedings of the Institution of Civil Engineers - Transport, 166(6), 325-335. doi:10.1680/tran.11.00001Calvo, F., de Oña, J., & Nash, A. (2007). Proposed Infrastructure Pricing Methodology for Mixed-Use Rail Networks. Transportation Research Record: Journal of the Transportation Research Board, 1995(1), 9-16. doi:10.3141/1995-02CENIT, TIS PT, IWW, and HERRY. 2007. “RailCalc. Calculation of Charges for the Use of Rail Infrastructure.” Prepared for the European Commission Directorate General for Energy and Transport. Accessed February 4. http://ec.europa.eu/transport/rail/legislation/doc/railcalc_discussion_paper_final.pdf.ECMT (European Conference of Ministers of Transport). 2005. “Charges for the Use of Infrastructure in ECMT Railways.” Draft final report. ECMT/CS/CF(2005)1/REV1. Accessed February 4. http://lnweb90.worldbank.org/ECA/Transport.nsf/ECADocByUnid/2CF8BE276F63A37D85256FB20043A05D?Opendocument.EPFL (Ecole Polytechnique Féderale de Lausanne). 2003. “IMPROVERAIL: IMPROVEd Tools for RAILway Capacity and Access Management.” Accessed February 4. http://litep.epfl.ch.Network Rail. 2006. “Usage Costs – Assessment Methodology.” Draft for consultation. Accessed February 4. http://www.networkrail.co.uk/browse%20documents/regulatory%20documents/access%20charges%20reviews/consultations%20on%20future%20charging/variable%20track%20access%20charges/g-%20usage%20costs%20methodology%20sept%2006.pdf.Nyström, B., & Söderholm, P. (2010). Selection of maintenance actions using the analytic hierarchy process (AHP): decision-making in railway infrastructure. Structure and Infrastructure Engineering, 6(4), 467-479. doi:10.1080/15732470801990209ORR (Office of Rail Regulation). 2005. “Revision of Variable Usage and Electrification Asset Usage Charges: Final Report.” Accessed February 4. http://www.rail-reg.gov.uk/upload/pdf/bah_variable-usage-initial-report_jan05.pdf.Quinet, E. (2003). Short term adjustments in rail activity: the limited role of infrastructure charges. Transport Policy, 10(1), 73-79. doi:10.1016/s0967-070x(02)00047-1Thomas, J. 2002. “EU Task Force on Rail Infrastructure Charging: Summary Findings on Best Practice in Marginal Cost Pricing.” IMPRINT-EUROPE. Implementing Reform in Transport. Effective Use of Research on Pricing in Europe. A European Commission-funded Thematic Network (2001–2004). Accessed February 4. www.imprint-eu.org/public/Presentations/imprint3_Thomas.ppt.UIC (International Union of Railways). 2008. “Lasting Infrastructure Cost Benchmarking (LICB).” Summary Report. Accessed February 4. http://www.uic.org/spip.php?article582

Predicting pedestrian road-crossing assertiveness for autonomous vehicle control

Autonomous vehicles (AVs) must interact with other road users including pedestrians. Unlike passive environments, pedestrians are active agents having their own utilities and decisions, which must be inferred and predicted by AVs in order to control interactions with them and navigation around them. In particular, when a pedestrian wishes to cross the road in front of the vehicle at an unmarked crossing, the pedestrian and AV must compete for the space, which may be considered as a game-theoretic interaction in which one agent must yield to the other. To inform AV controllers in this setting, this study collects and analyses data from real-world human road crossings to determine what features of crossing behaviours are predictive about the level of assertiveness of pedestrians and of the eventual winner of the interactions. It presents the largest and most detailed data set of its kind known to us, and new methods to analyze and predict pedestrian-vehicle interactions based upon it. Pedestrian-vehicle interactions are decomposed into sequences of independent discrete events. We use probabilistic methods - logistic regression and decision tree regression - and sequence analysis to analyze sets and sub-sequences of actions used by both pedestrians and human drivers while crossing at an intersection, to find common patterns of behaviour and to predict the winner of each interaction. We report on the particular features found to be predictive and which can thus be integrated into game-theoretic AV controllers to inform real-time interactions

Filtration analysis of pedestrian-vehicle interactions for autonomous vehicle control

Interacting with humans remains a challenge for autonomousvehicles (AVs). When a pedestrian wishes to cross the road in front of thevehicle at an unmarked crossing, the pedestrian and AV must competefor the space, which may be considered as a game-theoretic interaction inwhich one agent must yield to the other. To inform development of newreal-time AV controllers in this setting, this study collects and analy-ses detailed, manually-annotated, temporal data from real-world humanroad crossings as they interact with manual drive vehicles. It studies thetemporal orderings (filtrations) in which features are revealed to the ve-hicle and their informativeness over time. It presents a new frameworksuggesting how optimal stopping controllers may then use such data toenable an AV to decide when to act (by speeding up, slowing down, orotherwise signalling intent to the pedestrian) or alternatively, to continueat its current speed in order to gather additional information from newfeatures, including signals from that pedestrian, before acting itself

Long daytime napping is associated with increased adiposity and type 2 diabetes in an elderly population with metabolic syndrome

Research examining associations between objectively-measured napping time and type 2 diabetes (T2D) is lacking. This study aimed to evaluate daytime napping in relation to T2D and adiposity measures in elderly individuals from the Mediterranean region. A cross-sectional analysis of baseline data from 2190 elderly participants with overweight/obesity and metabolic syndrome, in the PREDIMED-Plus trial, was carried out. Accelerometer-derived napping was measured. Prevalence ratios (PR) and 95% confidence intervals (CI) for T2D were obtained using multivariable-adjusted Cox regression with constant time. Linear regression models were fitted to examine associations of napping with body mass index (BMI) and waist circumference (WC). Participants napping ≥90 min had a higher prevalence of T2D (PR 1.37 (1.06, 1.78)) compared with those napping 5 to <30 min per day. Significant positive associations with BMI and WC were found in those participants napping ≥30 min as compared to those napping 5 to <30 min per day. The findings of this study suggest that longer daytime napping is associated with higher T2D prevalence and greater adiposity measures in an elderly Spanish population at high cardiovascular risk

Dietary diversity and nutritional adequacy among an older Spanish population with Metabolic Syndrome in the PREDIMED-Plus study: a cross-sectional analysis

Dietary guidelines emphasize the importance of a varied diet to provide an adequate nutrient intake. However, an older age is often associated with consumption of monotonous diets that can be nutritionally inadequate, increasing the risk for the development or progression of diet-related chronic diseases, such as metabolic syndrome (MetS). To assess the association between dietary diversity (DD) and nutrient intake adequacy and to identify demographic variables associated with DD, we cross-sectionally analyzed baseline data from the PREDIMED-Plus trial: 6587 Spanish adults aged 55–75 years, with overweight/obesity who also had MetS. An energy-adjusted dietary diversity score (DDS) was calculated using a 143-item validated semi-quantitative food frequency questionnaire (FFQ). Nutrient inadequacy was defined as an intake below 2/3 of the dietary reference intake (DRI) forat least four of 17 nutrients proposed by the Institute of Medicine (IOM). Logistic regression models were used to evaluate the association between DDS and the risk of nutritionally inadequate intakes. In the higher DDS quartile there were more women and less current smokers. Compared with subjects in the highest DDS quartile, those in the lowest DDS quartile had a higher risk of inadequate nutrient intake: odds ratio (OR) = 28.56 (95% confidence interval (CI) 20.80–39.21). When we estimated food varietyfor each of the food groups, participants in the lowest quartile had a higher risk of inadequate nutrient intake for the groups of vegetables, OR = 14.03 (95% CI 10.55–18.65), fruits OR = 11.62 (95% CI 6.81–19.81), dairy products OR = 6.54 (95% CI 4.64–9.22) and protein foods OR = 6.60 (95% CI 1.96–22.24). As DDS decreased, the risk of inadequate nutrients intake rose. Given the impact of nutrient intake adequacy on the prevention of non-communicable diseases, health policies should focus on the promotion of a healthy varied diet, specifically promoting the intake of vegetables and fruit among population groups with lower DDS such as men, smokers or widow(er)s. View Full-Tex