Samer A. Kharroubi

Role of inflammation in the association between the western dietary pattern and metabolic syndrome among Lebanese adults

Abstract The aim of this study was to investigate whether inflammation mediates the previously observed direct association between the western dietary pattern (WDP) and metabolic syndrome (MetS) among Lebanese adults. Sociodemographic, lifestyle, dietary pattern scores, anthropometric and biochemical data of 331 adults were used in this study. Inflammation indicators considered were: serum C-reactive protein (CRP) and the dietary inflammatory index (DII). The scores of the WDP were significantly associated with DII (r = .64) but not with serum CRP. Higher CRP levels increased the odds of MetS and four out of five of its components, while no association was found between the DII and MetS. Conclusion: The findings of this study confirmed the association of serum CRP with MetS but did not support mediation effect of inflammation on the association between the WDP and MetS. These findings are important to direct future investigations on diet, inflammation and association with diseases risk. Graphical Abstract

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.

Valuations of EQ-5D health states: could United Kingdom results be used as informative priors for the United States

ABSTRACT Valuations of health state descriptors such as the generic EuroQol five-dimensional (EQ-5D) or the six-dimensional short form (SF-6D) 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 analysing its data separately. This article analyses data from 2 EQ-5D valuation studies where, using similar time trade-off protocols, values for 42 common health states were elicited from representative samples of the US and UK general adult populations. We apply a nonparametric Bayesian method to improve the accuracy of predictions of the US population utility function where the UK results were used as informative priors. The results suggest that drawing extra information from the UK data produces a better estimation of the US population utility than analysing its data separately. The implications of these results will be extremely crucial in countries where valuation studies are limited.

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

BackgroundValuations 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.MethodsData 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.ResultsThe 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.ConclusionIt is very plausible that this method would serve useful in countries where the conduction of large evaluation studies is not very feasible.

Investigating the job satisfaction of healthcare providers at primary healthcare centres in Lebanon: A national cross-sectional study

Low job satisfaction is linked to higher staff turnover and intensified shortages in healthcare providers (HCP). This study investigates the level of, and factors associated with, HCP job satisfaction in the national primary healthcare (PHC) network in Lebanon. The study adopts a cross-sectional design to survey HCP at 99 PHC centres distributed across the country between October 2013 and May 2014. The study questionnaire consisted of four sections: socio-demographics/professional background, employment characteristics, level of job satisfaction (Measure of Job Satisfaction scale) and level of professional burnout (Maslach Burnout Inventory-HSS scale). A total of 1,000 providers completed the questionnaire (75.8% response rate). Bivariate and multivariate regression analyses were used to identify factors significantly associated with job satisfaction. Findings of the study highlight an overall mean job satisfaction score of 3.59 (SD 0.54) indicating that HCP are partially satisfied. Upon further examination, HCP were least satisfied with pay, training and job prospects. Gender, age, career plans, salary, exposure to violence, and level of burnout were significantly associated with the overall level of job satisfaction which was also associated with increased likelihood to quit. Overall, the study highlights how compensation, development and protection of PHC HCP can influence their job satisfaction. Recommendations include the necessity of developing a nationally representative committee, led by the Ministry of Public Health, to examine the policies and remuneration scales within the PHC sector and suggest mechanisms to bridge the pay differential with other sectors. The effective engagement of key stakeholders with the development, organisation and evaluation of professional development programmes offered to HCP in the PHC sector remains crucial. Concerned stakeholders should assess and formulate initiatives and programmes that enrich the physical, psychological and professional well-being of their HCP. The aforementioned suggestions are necessary to strengthen and sustain PHC HCP and support the provision of universal health coverage to the Lebanese population.

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

BackgroundConventionally, 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.ObjectivesIn 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.MethodsA 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.ResultsThe 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.ConclusionThe 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.

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

BackgroundExperimental 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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.

Understanding CAM Use in Lebanon: Findings from a National Survey

The main objective of this study was to identify predictors of Complementary and Alternative Medicine (CAM) use in Lebanon. Data for this study were drawn from a national survey conducted among Lebanese adults (n=1500). A modified version of the Social Behavioral Model (SBM) was used to understand CAM use in the study population. In this version, predisposing factors included sociodemographic characteristics (age, gender, education, and employment) and Push and Pull factors. Additionally, enabling resources included income, and medical need encompassed presence of chronic disease and perceived health status. Simple and multiple logistic regressions were used to examine the predictors of CAM use in the study population. Results of the multiple logistic regression showed that younger and older adults were less likely to use CAM as compared to middle-aged respondents. The Push factor “dissatisfaction with conventional medicine” was associated with higher odds of CAM use. For three of the six Pull factors, compared to participants who strongly disagreed, those who had a tendency of taking care of ones health were more likely to use CAM. Income and presence of chronic disease were also associated with higher odds of CAM use. The findings of this study affirmed the utility of the SBM in explaining the use of CAM and proposed a new version of this model, whereby the Push and Pull factors are integrated within the predisposing factors of this model.

Valuation of preference-based measures: can existing preference data be used to select a smaller sample of health states?

BackgroundDifferent countries have different preferences regarding health, and there are different value sets for popular preference-based measures across different countries. However, the cost of collecting data to generate country-specific value sets can be prohibitive for countries with smaller population size or low- and middle-income countries (LMIC). This paper explores whether existing preference weights could be modelled alongside a small own country valuation study to generate representative estimates. This is explored using a case study modelling UK data alongside smaller US samples to generate US estimates.MethodsWe analyse EQ-5D valuation data derived from representative samples of the US and UK populations using time trade-off to value 42 health states. A nonparametric Bayesian model was applied to estimate a US value set using the full UK dataset and subsets of the US dataset for 10, 15, 20 and 25 health states. Estimates are compared to a US value set estimated using US values alone using mean predictions and root mean square error.ResultsThe results suggest that using US data elicited for 20 health states alongside the existing UK data produces similar predicted mean valuations and RMSE as the US value set, while 25 health states produce the exact features.ConclusionsThe promising results suggest that existing preference data could be combined with a small valuation study in a new country to generate preference weights, making own country value sets more achievable for LMIC. Further research is encouraged.