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.

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.

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.

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.

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.

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.

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.

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.

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.

Analysis of Food Safety Management Systems in the Beef Meat Processing and Distribution Chain in Uganda.

Meat production is an essential component in food security and the economy in Uganda. However, food safety concerns pose a challenge to public health in Uganda and impede access to regional and global markets. Here, food safety management (FSM) practices in the Ugandan beef supply chain were evaluated. A cross-sectional survey was conducted in major slaughterhouses (n = 3), butcher shops (n = 184), and supermarkets (n = 25) in Uganda's capital, Kampala. The three slaughterhouses had low scores in core control and assurance activities of FSM. Packaging interventions were weak in all the slaughterhouses, while only one slaughterhouse had a functional cooling facility. Supermarkets implemented better hygienic and preventative practices in comparison to butcher shops. However, both sourced from slaughterhouses that had low-to-poor hygiene practices, which weakened the efforts implemented in the supermarkets. Furthermore, most butcher shops did not offer training to meat handlers on HACCP (Hazard Analysis and Critical Control Point)-based practices. The low food safety performance in the supply chain was primarily attributed to poor sanitation, hygiene, and handling practices. Therefore, HACCP-based training and robust preventive, intervention, and monitoring systems are needed in the Ugandan beef supply chain to benefit public health and increase competitiveness.

Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?

BACKGROUND: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries' valuations to produce better valuation estimates. METHODS: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. RESULTS: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. CONCLUSIONS: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs.

Bayesian modeling of health state preferences: could borrowing strength from existing countries' valuations produce better estimates.

BACKGROUND: Valuations of preference-based measure such as EQ-5D and/or SF6D have been conducted in different countries. There is potential to borrow strength from existing countries' valuations to generate better representative utility estimates. This is explored using two case studies modelling UK data alongside Japan samples to generate Japan estimates. METHODS: Data from two SF-6D valuation studies were analyzed, where using similar standard gamble protocols, values for 241 and 249 states were devised from representative samples of Japan and UK general adult populations, respectively. Two nonparametric Bayesian models were applied to estimate a Japan value set, where the UK results were used as informative priors in the first model and subsets of the Japan data set for 25 and 50 health states were modelled alongside the full UK data set in the second. Generated estimates were compared to a Japan value set estimated using Japan values alone using different prediction criterion. RESULTS: The results allowed the UK data to provide significant prior information to the Japan analysis by generating better estimates than using Japan data alone. Also, using Japan data elicited for 50 health states alongside the existing UK data produces roughly similar predicted valuations as the Japan data by itself. CONCLUSION: The promising results suggest that the existing preference data could be combined with data from a valuation study in a new country to generate preference weights, thus making own country value sets more achievable for low-middle income countries. Further research and application to other countries and preference-based measures are encouraged.

Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (-1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.

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.

The Importance of Accounting for Parameter Uncertainty in SF-6D Value Sets and Its Impact on Studies that Use the SF-6D to Measure Health Utility.

BACKGROUND: The parameter uncertainty in the six-dimensional health state short form (SF-6D) value sets is commonly ignored. There are two sources of parameter uncertainty: uncertainty around the estimated regression coefficients and uncertainty around the model's specification. This study explores these two sources of parameter uncertainty in the value sets using probabilistic sensitivity analysis (PSA) and a Bayesian approach. METHODS: We used data from the original UK/SF-6D valuation study to evaluate the extent of parameter uncertainty in the value set. First, we re-estimated the Brazier model to replicate the published estimated coefficients. Second, we estimated standard errors around the predicted utility of each SF-6D state to assess the impact of parameter uncertainty on these estimated utilities. Third, we used Monte Carlo simulation technique to account for the uncertainty on these estimates. Finally, we used a Bayesian approach to quantifying parameter uncertainty in the value sets. The extent of parameter uncertainty in SF-6D value sets was assessed using data from the Hong Kong valuation study. RESULTS: Including parameter uncertainty results in wider confidence/credible intervals and improved coverage probability using both approaches. Using PSA, the mean 95% confidence intervals widths for the mean utilities were 0.1394 (range: 0.0565-0.2239) and 0.0989 (0.0048-0.1252) with and without parameter uncertainty whilst, using the Bayesian approach, this was 0.1478 (0.053-0.1665). Upon evaluating the impact of parameter uncertainty on estimates of a population's mean utility, the true standard error was underestimated by 79.1% (PSA) and 86.15% (Bayesian) when parameter uncertainty was ignored. CONCLUSIONS: Parameter uncertainty around the SF-6D value set has a large impact on the predicted utilities and estimated confidence intervals. This uncertainty should be accounted for when using SF-6D utilities in economic evaluations. Ignoring this additional information could impact misleadingly on policy decisions.

Translation, cultural adaptation and psychometric validation of the SF-6D measure of health-related quality of life for use in Arabic-Speaking countries.

BACKGROUND: The SF-6D is a generic, six-dimensional health-related quality of life (HRQoL) measure derived from a selection of items from the SF-36. OBJECTIVES: To translate, culturally adapt and validate the SF-6D for use in Arabic-speaking countries. METHODS: The International Quality of Life Assessment (IQOLA) methodology was followed. Two forward translations, one consensus and one backward translation were undertaken. Difficulties encountered were categorized as grammatical, idiomatic, semantic/conceptual, and cultural. The content validity of the final version was tested and Cronbach's alpha test of internal consistency was used for assessing reliability. Confirmatory factor analysis (CFA), was also used to assess construct validity and to test a pre-specified relationship of observed measures. RESULTS: Minor changes were made to the forward translation to improve cultural appropriateness. The Backward translation did not reveal major problems and equivalence to the original was confirmed following committee review. A total of 470 participants from Jordan, Egypt, UAE, Qatar and Palestine completed the translated SF-6D. All the incremental indices values are ≥0.90 and close to 1. Item loading values ranged from 0.52 to 0.87. The measurement model weight for those with chronic health conditions ranged from 0.68 to 0.91, and from 0.42 to 0.73 for those without. The percentage of variation in self-reported health state was about 55%. The measurement weight of SF-6D on self-reported health state among chronic responders was 0.87 while among responders reporting no chronic disease was 0.61. The t-value for the difference in measurement weight was -8.93 (p ≤ 0.01). CONCLUSION: Arabic translation and cultural adaptation of SF-6D has resulted in an acceptable and culturally-adapted version that can be used in Arabic-speaking countries. Reliability and validity have been confirmed as well as ability to assess the difference in quality of life between patients with chronic health conditions and healthy individuals.

Examining the Feasibility and Acceptability of Valuing the Arabic Version of SF-6D in a Lebanese Population.

Objectives: The SF-6D is a preference-based measure of health developed to generate utility values from the SF-36. The aim of this pilot study was to examine the feasibility and acceptability of using the standard gamble (SG) technique to generate preference-based values for the Arabic version of SF-6D in a Lebanese population. Methods: The SF-6D was translated into Arabic using forward and backward translations. Forty-nine states defined by the SF-6D were selected using an orthogonal design and grouped into seven sets. A gender-occupation stratified sample of 126 Lebanese adults from the American University of Beirut were recruited to value seven states and the pits using SG. The sample size is appropriate for a pilot study, but smaller than the sample required for a full valuation study. Both interviewers and interviewees reported their understanding and effort levels in the SG tasks. Mean and individual level multivariate regression models were fitted to estimate preference weights for all SF-6D states. The models were compared with those estimated in the UK. Results: Interviewers reported few problems in completing SG tasks (0.8% with a lot of problems) and good respondent understanding (5.6% with little effort and concentration), and 25% of respondents reported the SG task was difficult. A total of 992 SG valuations were useable for econometric modeling. There was no significant change in the test-retest values from 21 subjects. The mean absolute errors in the mean and individual level models were 0.036 and 0.050, respectively, both of which were lower than the UK results. The random effects model adequately predicts the SG values, with the worst state having a value of 0.322 compared to 0.271 in the UK. Conclusion: This pilot confirmed that it was feasible and acceptable to generate preference values with the SG method for the Arabic SF-6D in a Lebanese population. However, further work is needed to extend this to a more representative population, and to explore why no utility values below zero were observed.

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.