Modelling a preference-based index for EQ-5D-3L and EQ-5D-3L + Sleep using a Bayesian framework.

BACKGROUND: Conventionally, frequentist approach has been used to model health state valuation data. Recently, researchers started to explore the use of Bayesian methods in this area. OBJECTIVES: This paper presents an alternative approach to modelling health state valuation data of the EQ-5D-3L and EQ-5D-3L + Sleep descriptive systems, using a Bayesian framework, and demonstrates its superiority to conventional frequentist methods. METHODS: The valuation study is composed of 18 EQ-5D-3L health states and 18 EQ-5D-3L + Sleep health states valued by 160 members of the general public in South Yorkshire, UK, using the time tradeo-ff technique. Three different models were developed for EQ-5D-3L and EQ-5D-3L + Sleep accordingly using Bayesian Markov chain Monte Carlo simulation methods. Bayesian methods were applied to models fitted included a linear regression, random effect and random effect with covariates. The models are compared based on their predictive performance using mean predictions, root mean squared error (RMSE) and deviance information criterion (DIC). All analyses were performed using Bayesian Markov chain Monte Carlo simulation methods. RESULTS: The random effects with covariates model performs best under all criterions for the two preference-based measures, with RMSE (0.037) and DIC (637.5) for EQ-5D-3L and RMSE (0.019), DIC (416.4) for EQ-5D + Sleep. Compared with models previously estimated using frequentist approach, the Bayesian models reported in this paper provided better predictions of observed values. CONCLUSION: Bayesian methods provide a better way to model EQ-5D-3L valuation data with and without a sleep 'bolt-on' and provide a more flexible in characterizing the full range of uncertainty inherent in these estimates.

Food insecurity is associated with compromised dietary intake and quality among Lebanese mothers: findings from a national cross-sectional study.

OBJECTIVE: Examine the associations between household food insecurity (HFI) with sociodemographic, anthropometric and dietary intakes of mothers. DESIGN: Cross-sectional survey (2014-2015). In addition to a sociodemographic questionnaire, data collection included the validated Arabic version of the Household Food Insecurity Access Scale, which was used to evaluate HFI. Dietary intake was assessed using 24-h dietary recall of a single habitual day, and maternal BMI was calculated based on weight and height measurements. Associations between HFI and maternal dietary intake (food groups, energy and macronutrients' intake) were examined. Simple and multiple logistic regression analyses were conducted to explore the associations between HFI status with odds of maternal overweight and measures of diet quality and diversity (Healthy Eating Index (HEI) and Minimum Dietary Diversity for Women of Reproductive Age (MDD-W)). SETTING: Lebanon. PARTICIPANTS: Mothers, nationally representative sample of Lebanese households with children (n 1204). RESULTS: HFI was experienced among almost half of the study sample. Correlates of HFI were low educational attainment, unemployment and crowding. Significant inverse associations were observed between HFI and dietary HEI (OR 0·64, 95 % CI 0·46, 0·90, P = 0·011) and MDD-W (OR 0·6, 95 % CI 0·42, 0·85, P = 0·004), even after adjusting for socioeconomic correlates. No significant association was observed between HFI and odds of maternal overweight status. CONCLUSIONS: HFI was associated with compromised maternal dietary quality and diversity. Findings highlight the need for social welfare programmes and public health interventions to alleviate HFI and promote overall health and wellbeing of mothers.

Use of Bayesian methods to model the SF-6D health state preference based data.

BACKGROUND: Conventionally, models used for health state valuation data have been frequentists. Recently a number of researchers have investigated the use of Bayesian methods in this area. The aim of this paper is to put on the map of modelling a new approach to estimating SF-6D health state utility values using Bayesian methods. This will help health care professionals in deriving better health state utilities of the original UK SF-6D for their specialized applications. METHODS: The valuation study is composed of 249 SF-6D health states valued by a representative sample of the UK population using the standard gamble technique. Throughout this paper, we present four different models, including one simple linear regression model and three random effect models. The predictive ability of these models is assessed by comparing predicted and observed mean SF-6D scores, R2/adjusted R2 and RMSE. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods freely available in the specialist software WinBUGS. RESULTS: The random effects model with interaction model performs best under all criterions, with mean predicted error of 0.166, R2/adjusted R2 of 0.683 and RMSE of 0.218. CONCLUSIONS: The Bayesian models provide flexible approaches to estimate mean SF-6D utility estimates, including characterizing the full range of uncertainty inherent in these estimates. We hope that this work will provide applied researchers with a practical set of tools to appropriately model outcomes in cost-effectiveness analysis.

Valuation of preference-based measures: can existing preference data be used to generate better estimates?

BACKGROUND: Experimental studies to develop valuations of health state descriptive systems like EQ-5D or SF-6D need to be conducted in different countries, because social and cultural differences are likely to lead to systematically different valuations. There is a scope utilize the evidence in one country to help with the design and the analysis of a study in another, for this to enable the generation of utility estimates of the second country much more precisely than would have been possible when collecting and analyzing the country's data alone. METHODS: We analyze SF-6D valuation data elicited from representative samples corresponding to the Hong Kong (HK) and United Kingdom (UK) general adult populations through the use of the standard gamble technique to value 197 and 249 health states respectively. We apply a nonparametric Bayesian model to estimate a HK value set using the UK dataset as informative prior to improve its estimation. Estimates are compared to a HK value set estimated using HK values alone using mean predictions and root mean square error. RESULTS: The novel method of modelling utility functions permitted the UK valuations to contribute significant prior information to the Hong Kong analysis. The results suggest that using HK data alongside the existing UK data produces HK utility estimates better than using the HK study data by itself. CONCLUSION: The promising results suggest that existing preference data could be combined with valuation study in a new country to generate preference weights, making own country value sets more achievable for low and middle income countries. Further research is encouraged.

Modelling a preference-based index for EQ-5D using a non-parametric Bayesian method.

BACKGROUND: Conventionally, models used for health state valuation data have been parametric. Recently, a number of researchers have investigated the use of non-parametric Bayesian methods in this area. OBJECTIVES: In this paper, we present a non-parametric Bayesian model to estimate a preference-based index for a five-dimensional health state classification, namely EQ-5D. METHODS: A sample of 2997 members of the UK general population valued 43 health states selected from a total of 243 health states defined by the EQ-5D using time trade-off technique. Findings from non-parametric modelling are reported in this paper and compared to previously used parametric estimations. The impact of respondent characteristics on health state valuations is also reported. RESULTS: The non-parametric models were found to be better at predicting scores in populations with different distributions of characteristics than observed in the survey sample. Additionally, non-parametric models were found to be better at allowing for the impact of respondent characteristics to vary by health state. The results show an important age effect with sex having some effect. CONCLUSION: The non-parametric Bayesian models provide more realistic and better utility estimates from the EQ-5D than previously used parametric models have done. Furthermore, the model is more flexible in estimating the impact of covariates.

Bayesian statistical models to estimate EQ-5D utility scores from EORTC QLQ data in myeloma.

It is well documented that the modelling of health-related quality of life data is difficult as the distribution of such data is often strongly right/left skewed and it includes a significant percentage of observations at one. The objective of this study is to develop a series of two-part models (TPMs) that deal with these issues. Data from the UK Medical Research Council Myeloma IX trial were used to examine the relationship between the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30/QLQ-MY20 scores and the European QoL-5 Dimensions (EQ-5D) utility score. Four different TPMs were developed. The models fitted included TPM with normal regression, TPM with normal regression with variance a function of participant characteristics, TPM with log-transformed data, and TPM with gamma regression and a log link. The cohort of 1839 patients was divided into 75% derivation sample, to fit the different models, and 25% validation sample to assess the predictive ability of these models by comparing predicted and observed mean EQ-5D scores in the validation set, unadjusted R2 , and root mean square error. Predictive performance in the derivation dataset depended on the criterion used, with R2 /adjusted-R2 favouring the TPM with normal regression and mean predicted error favouring the TPM with gamma regression. The TPM with gamma regression performs best within the validation dataset under all criteria. TPM regression models provide flexible approaches to estimate mean EQ-5D utility weights from the EORTC QLQ-C30/QLQ-MY20 for use in economic evaluation.

Bayesian nonparametric estimation of EQ-5D utilities for United States using the existing United Kingdom data.

BACKGROUND: Valuations of health state descriptors such as EQ-5D or SF6D have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analyzing its data separately. METHODS: Data from 2 EQ-5D valuation studies were analyzed using the time trade-off technique, where values for 42 health states were devised from representative samples of the UK and US populations. A Bayesian non-parametric approach has been applied to predict the health utilities of the US population, where the UK results were used as informative priors in the model to improve their estimation. RESULTS: The findings showed that employing additional information from the UK data helped in the production of US utility estimates much more precisely than would have been possible using the US study data alone. CONCLUSION: It is very plausible that this method would serve useful in countries where the conduction of large evaluation studies is not very feasible.

A comparison of Hong Kong and United Kingdom SF-6D health states valuations using a nonparametric Bayesian method.

OBJECTIVES: There is interest in the extent to which valuations of health may differ between different countries and cultures, but few studies have compared preference values of health states obtained in different countries. The present study applies a nonparametric model to estimate and compare two HK and UK standard gamble values for six-dimensional health state short form (derived from short-form 36 health survey) (SF-6D) health states using Bayesian methods. METHODS: The data set is the HK and UK SF-6D valuation studies in which two samples of 197 and 249 states defined by the SF-6D were valued by representative samples of the HK and UK general populations, respectively, both using the standard gamble technique. We estimated a function applicable across both countries that explicitly accounts for the differences between them, and is estimated using the data from both countries. RESULTS: The results suggest that differences in SF-6D health state valuations between the UK and HK general populations are potentially important. In particular, the valuations of Hong Kong were meaningfully higher than those of the United Kingdom for most of the selected SF-6D health states. The magnitude of these country-specific differences in health state valuation depended, however, in a complex way on the levels of individual dimensions. CONCLUSIONS: The new Bayesian nonparametric method is a powerful approach for analyzing data from multiple nationalities or ethnic groups to understand the differences between them and potentially to estimate the underlying utility functions more efficiently.

Modeling a preference-based index for two condition-specific measures (asthma and overactive bladder) using a nonparametric Bayesian method.

BACKGROUND: Conventionally, parametric models were used for health state valuation data. Recently, researchers started to explore the use of nonparametric Bayesian methods in this area. OBJECTIVES: We present a nonparametric Bayesian model to estimate a preference-based index for two condition-specific five-dimensional health state classifications, one for asthma (five-dimensional Asthma Quality of Life Utility Index) and the other for overactive bladder (five-dimensional Overactive Bladder Quality of Life-Utility Index). METHODS: Samples of 307 and 311 members of the UK general population valued 99 health states selected from a total of 3125 health states defined by each of the measures using the time trade-off technique. The article presents the results of the nonparametric model and compares it with the original model estimated using a conventional parametric random-effects model. The different methods are compared theoretically and in terms of empirical performance across the two data sets. It also reports the effect of respondent characteristics on health state valuations. RESULTS: The nonparametric models were found to be better at predicting health state values within the estimation sample than without in terms of root mean square error and the patterns of standardized residuals. Some respondent characteristics were found to explain variation in health state values, but these did not have a significant effect on the health states values when estimates were adjusted for sample differences with the general population. CONCLUSIONS: The nonparametric Bayesian models are theoretically more appropriate than previously used parametric models and provide better utility estimates from the two condition-specific measures. Furthermore, the model is more flexible in estimating the effect of covariates.

Estimating the SF-6Dv1 Value Set for a Population-Based Sample in Lebanon.

OBJECTIVES: The SF-6Dv1 is a preference-based measure derived from the SF-36 for use in quality-adjusted life-year estimation for cost-utility analysis. Country-specific value sets for SF-6Dv1 are needed to reflect societal preferences but none are available for Lebanon and other Arabic countries. This study aimed to generate a value set for SF-6Dv1 for Lebanon and to compare results with the UK set. METHODS: A sample of 249 health states defined by the SF-6Dv1 were valued by a representative sample of 577 members of the Lebanon general population, using standard gamble. Several multivariate regression models at mean and individual level were fitted to estimate utilities for all SF-6Dv1 states with selection of best fitting models based on predictive ability, consistency, and model fit. The best fitting models were compared with those fitted in the UK study. RESULTS: Data from 553 eligible respondents providing 3308 valuations were used for the analysis. Lebanese values were consistently higher than UK values, indicating differences in preferences, and there were no negative values. The random effects model using only main effects was the best performing model. There were inconsistencies in 2 dimensions, thereby consistent models were estimated with values ranging from 0.367 to 1. The results are consistent with the UK results. CONCLUSIONS: This study provides the first population-based value set for SF-6Dv1 health states for Lebanon, making it possible to generate quality-adjusted life-years for cost-utility analysis studies. The potential for applications of a standardized utility measure is enormous both in Lebanon and all Arab countries.

Assessing the Relationship between Physical Health, Mental Health and Students' Success among Universities in Lebanon: A Cross-Sectional Study.

BACKGROUND: Achieving high academic success is known to be influenced by many factors including, but not limiting to, physical and mental health. The present study aimed to assess the relationship between physical health, mental health, and university students' success, and to explore the associations between these factors and their academic achievement. METHODS: A cross-sectional, self-administered online survey was used to collect data from college students in three different universities in Lebanon during the Fall 2023 semester. Mental health was evaluated using validated screening tools for depression, anxiety, and stress, specifically the Patient Health Questionnaire (PHQ-9), the General Anxiety Disorder (GAD-7), and Cohen's Perceived Stress Scale (PSS), respectively. Additionally, general questions regarding physical health and lifestyle factors were incorporated into the questionnaire. Academic achievement was measured using students' grade point average (GPA). RESULTS: A total of 261 students completed the self-administered online survey. The results revealed that approximately 42% and 36% of students were experiencing moderate to severe symptoms of depression and anxiety, respectively, and 75.1% of students exhibited symptoms of moderate stress. The majority of participants (99.2%) did not report any physical disability. Chi-square analysis revealed a significant association between mental health status (depression, anxiety, and stress) and GPA level (p = 0.03, p = 0.044, p = 0.015, respectively). Multiple logistic regression models identified eight correlates of GPA and highlighted the relationship between physical health and student success. For instance, students who considered themselves moderately active had lower odds of achieving a higher GPA than those who considered themselves active (OR = 0.41, p = 0.045). CONCLUSIONS: This is the first investigation into Lebanese university students' academic success in relation to lifestyle and mental health profiles. The findings indicate that implementing public health programs and interventions targeting mental health and lifestyle behaviors is essential for enhancing student success.

Modeling SF-6D Hong Kong standard gamble health state preference data using a nonparametric Bayesian method.

OBJECTIVES: This article reports on the findings from applying a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm. METHODS: A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated by using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. RESULTS: Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect. CONCLUSIONS: The nonparametric Bayesian model is argued to be more theoretically appropriate than previously used parametric models. Furthermore, it is more flexible to take into account the impact of covariates.

Knowledge, Attitudes, and Practices of Consumers on Food Allergy and Food Allergen Labeling: A Case of Lebanon.

The epidemiology of food allergies is increasing worldwide. International labeling standards were developed to enhance consumers' awareness of allergen-free foods. The main objective of the present study is to assess the characteristics of allergen labeling and consumers' knowledge, attitudes, and purchasing habits of food products with allergens in Lebanon. We evaluated the allergen labeling of 1000 food products form Lebanese supermarkets. A random sample of 541 consumers was recruited through an online survey (November 2020-February 2021). Descriptives and regression analysis were conducted. Results showed that wheat represents the largest group of food allergens on food labels, followed by milk and soybean. Furthermore, 42.9% of supermarket food products had a precautionary allergen labeling with "may contain traces of allergens". The majority of food products complied with local regulations for locally manufactured and imported products. One-quarter of survey respondents had a food allergy or were caregivers of food-allergic individuals. Regression analyses showed that "previous experience of a severe reaction" was negatively associated with food allergy-related knowledge and attitude scores respectively (ß = -1.394, 95% CI: (-1.827, -1.034) and ß = -1.432, 95% CI: (-2.798, -0.067)). The findings of this study provide practical insights on food allergy labeling issues for stakeholders and policymakers in the food supply chain.

Raw Meat Consumption and Food Safety Challenges: A Survey of Knowledge, Attitudes, and Practices of Consumers in Lebanon.

A key contributor to foodborne illnesses is consuming contaminated ready-to-eat foods, including raw meats. The latter is a common practice in Lebanon, a country that suffers from widespread pollution and food safety challenges. However, studies on the safety of raw meat consumption in Lebanon are limited. In this study, an attempt was made to investigate the knowledge, attitudes, and practices (KAPs) of the Lebanese population toward the potential risk associated with the consumption of raw meats, and to identify factors that affect KAP levels. An online survey (n = 577) was administered to Lebanese adults aged 18 years and above to assess their KAPs. The results showed that 74.5% of the participants consumed raw meat, 44% had good food safety knowledge, and 30.7% exhibited good practices. However, more than half of the participants (61.9%) showed a positive attitude toward food safety. There was a significant association between knowledge and attitude (p < 0.001), attitude and practices (p < 0.001), and knowledge and practices (p < 0.001), thereby indicating that an increase in food safety education could translate into better practices in this population. Accordingly, efforts to enhance education on food safety are warranted to reduce the potential risk of food poisoning associated with raw meat consumption in Lebanon.

Assessment of the Microbiological Acceptability of White Cheese (Akkawi) in Lebanon and the Antimicrobial Resistance Profiles of Associated Escherichia coli.

Dairy foods are a staple in Lebanon, a low- and middle-income country that has been experiencing serious challenges to food safety and antimicrobial stewardship among other issues. The microbiological acceptability of dairy products has been of increasing concern. This is partially due to the failing economy and prolonged power outages that affect the quality of raw material and disrupt the dairy cold chain, respectively. Therefore, we assessed the microbiological acceptability of Akkawi, a popular white-brined cheese in Lebanon. For this purpose, we quantified the densities of Escherichia coli (a fecal indicator) and Staphylococcus aureus in cheeses collected from Lebanese retail stores. Additionally, we evaluated the antibiotic resistance profiles of the E. coli isolated from the cheese. E. coli and S. aureus were detected in 40 (80%) and 16 (32%) of the 50 cheese samples, respectively. Notably, 40 (80%) and 16 (32%) of the samples exceeded the maximum permissible limit of E. coli and S. aureus, respectively. A high percentage of the 118 E. coli isolated from the cheeses showed resistance to clinically and agriculturally important antibiotics, while 89 (75%) isolates were classified as multidrug-resistant (MDR). Given that Akkawi can be consumed without cooking, our findings highlight serious food safety and antimicrobial resistance problems that require immediate interventions.

A Bayesian Nonparametric Approach for Modeling SF-6D Health State Utility Scores.

OBJECTIVES: Typically, models that were used for health state valuation data have been parametric. Recently, many researchers have explored the use of nonparametric Bayesian methods in this field. In this article, we report on the results from using a nonparametric model to predict a Bayesian short-form 6-dimension (SF-6D) health state valuation algorithm along with estimating the effect of the individual characteristics on health state valuations. METHODS: A sample of 126 Lebanese members from the American University of Beirut valued 49 SF-6D health states using the standard gamble technique. Results from applying the nonparametric model were reported and compared with those obtained using a standard parametric model. The covariates' effect on health state valuations was also reported. RESULTS: The nonparametric Bayesian model was found to perform better than the parametric model at (1) predicting health state values within the full estimation data and in an out-of-sample validation in terms of mean predictions, root mean squared error, and the patterns of standardized residuals and (2) allowing for the covariates' effect to vary by health state. The findings also suggest a potential age effect with some gender effect. CONCLUSIONS: The nonparametric model is theoretically more flexible and produces better utility predictions from the SF-6D than previously used classical parametric model. In addition, the Bayesian model is more appropriate to account the covariates' effect. Further research is encouraged.

Use of a Non-parametric Bayesian Method to Model Health State Preferences: An Application to Polish and Irish EQ-5D-5L Valuations.

Valuations of preference-based measures for health are conducted in different countries. There is scope to use results from existing countries' valuations to generate better valuation estimates than analyzing the data from each country separately. We analyse data from two smaller design EQ-5D-5L valuation studies where a sample of 119 Polish migrants and 123 native Irish valued 30 common health states using similar composite time trade-off protocols. We apply a non-parametric Bayesian method to provide better predictions of the Polish (Irish) population utility function when the existing Irish (Polish) results were used as informative priors. The resultant new estimates were then compared to those obtained by analyzing the data from each country by itself via different prediction criterions. The results suggest that existing countries' valuations could be used as potential informative priors to produce better valuation estimates under all prediction criterions used. The implications of these results will be hugely important in countries where valuation studies are expensive and hard to conduct. Future application to other countries and to other preference-based health measures are encouraged.

Sex-differences in COVID-19 diagnosis, risk factors and disease comorbidities: A large US-based cohort study.

Introduction: Morbidity and mortality from COVID-19 are higher among men, however, underlying pathways remain controversial. We aim to investigate sex-gender differences in COVID-19 in a large US-based cohort, namely COVID-19 Research Database. More specifically, the objectives are to explore the socio-economic characteristics of COVID-19 male and female patients and to examine potential sex differences in lifestyle factors and disease comorbidities among diagnosed patients. Methods: This is a retrospective cohort study contrasting male vs. female patients with test-confirmed COVID-19. The study used Healthjump electronic medical records (e.g., demographics, encounters, medical history, and vitals) extracted from January 2020 to December 2021 (N = 62,310). Results: Significant sociodemographic and comorbidity differences were observed between males and females (p < 0.05). For example, a significantly higher proportion of males (vs. females) were aged ≥70-year-old (17.04 vs. 15.01%) and smokers (11.04 vs. 9.24%, p < 0.0001). In addition, multiple logistic regression showed that hypertension and diabetes were significantly more frequent in males [adjusted odds ratio (ORa) = 66.19 and ORa = 22.90]. Conclusions: Understanding the differences in outcomes between male and female patients will inform gender equity responsive approach to COVID-19 and enhance the effectiveness of clinical practice, health policy and interventions.

Knowledge, Attitudes and Practices of the Lebanese Community toward Food Adulteration.

Food adulteration is the purposeful act of decreasing the quality of food goods offered for sale, whether by adding or replacing inferior substances or by the removal of some valuable ingredient. A limited number of studies have explored the knowledge, attitudes and practices (KAPs) concerning food adulteration in Lebanon. The objectives of the present study were to determine the knowledge, attitudes and practices of identifying adulteration in the process of food purchase by Lebanese adult consumers, and to identify factors associated with food adulteration. An online survey (n = 499) was administered among Lebanese adults aged 18 years and above. Results showed that the majority had a low food adulteration knowledge score (73.1%). During shopping, fewer than half of the participants checked the ingredients (42%) and nutrition facts label (33.9%). Regression analyses showed that six predictors were significantly associated with participants' knowledge scores including gender, age, marital status, education (undergraduate and master degree) and employment status (student). The results of this study show that knowledge and practices of identifying adulteration in the process of food purchase by consumers are lacking among most respondents. Increasing knowledge, awareness and motivation to identify food adulteration products during food shopping will empower consumers to improve buying practices, especially for the public with a lower level of education.

Total Usual Nutrient Intakes and Nutritional Status of United Arab Emirates Children (<4 Years): Findings from the Feeding Infants and Toddlers Study (FITS) 2021.

Background: The transition from a predominantly milk-based diet to a diverse family diet is a window of opportunity for optimal child growth and development. Objectives: The study aims to examine the nutritional status and food-consumption patterns of children under 4 y of age in the United Arab Emirates (UAE) and their adherence to nutrient and dietary recommendations. Methods: A population-based cross-sectional survey of 525 children aged 0-47.9 mo was conducted in 3 major emirates: Abu Dhabi, Dubai, and Sharjah. Anthropometric measurements were obtained, and dietary assessment was conducted using the 24-h dietary recall approach. Usual intakes of energy, macronutrient, and micronutrients, including from supplements, were assessed using PC-SIDE software. Adherence to food-group recommendations was evaluated based on the American Heart Association/American Academy of Pediatrics dietary guidelines. Results: Among 0-4-y-old children, 10% were stunted, 6% were wasted, 17% were at risk of overweight, 5% were overweight, and 3% were obese. The contribution of sweets and sugar sweetened beverages to energy intake increased from 5% in 6-11.9-mo-old children to 17% in 36-47.9-mo-old children. Compared with dietary guidelines, the lowest adherence was for fruit (13-18%) and vegetables (7-12%), while protein was within the recommendations, and 92% and 89% of children (aged 24 to 35.9 mo and 36 to 47.9 mo, respectively) had high intakes of saturated fat. Almost all toddlers failed to meet the Adequate Intake for fiber. The proportions of children exceeding the free-sugar upper limit increased from 10.6% in infants (0-5.9 mo) to 56.7% in toddlers (12- 23.9 mo). Micronutrient inadequacies were observed, particularly for calcium, zinc, folate, and vitamins A and D. Conclusions: This study suggests a triple burden of malnutrition among infants and young children in the UAE. Results call for national nutrition intervention strategies aimed at improving dietary quality in the pediatric population.