Gender and educational differences in the association between smoking and health-related quality of life in Belgium

Previous studies have shown that smoking has a significant and negative association with health-related quality of life (HRQOL). A question remains, however, as to whether this association between smoking and HRQOL differs by gender or educational level. To examine this question, we extracted data from the 2013 Belgian Health Interview Survey (n = 5668). HRQOL was assessed using the descriptive system of the EuroQol 5D-5L that consists of 5 dimensions and the resulting index score. Linear and logistic multivariable regression models were fitted to estimate the association between HRQOL and smoking for each educational level and gender. Also, interaction terms were introduced in the full regression models and the Wald test was used to assess model fit. Our findings show that among men, there is no significant association between smoking and HRQOL, and no effect modification by educational level. Among women, however, daily smokers have shown significantly lower HRQOL scores compared with never smokers, but only among females with a low and intermediate educational level. The lower EQ-5D index scores among female daily smokers with lower education was due to higher odds of reporting problems in anxiety/depression, mobility, pain, and usual activities. To conclude, information on the association between HRQOL and smoking is useful for the development of smoking cessation interventions. Our findings suggest the importance of tailoring these interventions to the needs of the women with lower education

The effect of smoking on the duration of life with and without disability, Belgium 1997-2011

Background: Smoking is the single most important health threat yet there is no consistency as to whether non-smokers experience a compression of years lived with disability compared to (ex-)smokers. The objectives of the manuscript are (1) to assess the effect of smoking on the average years lived without disability (Disability Free Life Expectancy (DFLE)) and with disability (Disability Life Expectancy (DLE)) and (2) to estimate the extent to which these effects are due to better survival or reduced disability in never smokers. Methods. Data on disability and mortality were provided by the Belgian Health Interview Survey 1997 and 2001 and a 10 years mortality follow-up of the survey participants. Disability was defined as difficulties in activities of daily living (ADL), in mobility, in continence or in sensory (vision, hearing) functions. Poisson and multinomial logistic regression models were fitted to estimate the probabilities of death and the prevalence of disability by age, gender and smoking status adjusted for socioeconomic position. The Sullivan method was used to estimate DFLE and DLE at age 30. The contribution of mortality and of disability to smoking related differences in DFLE and DLE was assessed using decomposition methods. Results: Compared to never smokers, ex-smokers have a shorter life expectancy (LE) and DFLE but the number of years lived with disability is somewhat larger. For both sexes, the higher disability prevalence is the main contributing factor to the difference in DFLE and DLE. Smokers have a shorter LE, DFLE and DLE compared to never smokers. Both higher mortality and higher disability prevalence contribute to the difference in DFLE, but mortality is more important among males. Although both male and female smokers experience higher disability prevalence, their higher mortality outweighs their disability disadvantage resulting in a shorter DLE. Conclusion: Smoking kills and shortens both life without and life with disability. Smoking related disability can however not be ignored, given its contribution to the excess years with disability especially in younger age groups

RESISTIRE D3.2 - Summary report on mapping quantitative indicators, Cycle 2

This report provides an overview of the second cycle mapping of quantitative indicators, at both national and European levels, that allow us to measure and monitor the economic, social and environmental impacts of COVID-19. National insights are derived from the mapping of Rapid Assessment Surveys (RAS), which are studies undertaken at fast pace to understand the impact of the pandemic. European-level insights come from reviews of the literature and analysis of relevant large-scale European datasets, such as European Union Statistics on Income and Living Conditions (EU-SILC) and Eurofound “Living working and COVID-19" e-survey. The report also demonstrates how we are addressing research gaps identified in the first cycle of RESISTIRÉ through ongoing quantitative analysis in collaboration with the authors of 'promising’ mapped RAS and through the development of a mobile application (app)

The HLS19-COM-P, a New Instrument for Measuring Communicative Health Literacy in Interaction with Physicians: Development and Validation in Nine European Countries

Background: Sufficient communicative health literacy (COM-HL) is important for patients actively participating in dialogue with physicians, expressing their needs and desires for treatment, and asking clarifying questions. There is a lack of instruments combining communication and HL proficiency. Hence, the aim was to establish an instrument with sufficient psychometric properties for measuring COM-HL. Methods: The HLS19-COM-P instrument was developed based on a conceptual framework integrating HL with central communicative tasks. Data were collected using different data collection modes in nine countries from December 2019 to January 2021 (n = 18,674). Psychometric properties were assessed using Rasch analysis and confirmatory factor analysis. Cronbach’s alpha and Person separation index were considered for reliability. Results: The 11-item version (HLS19-COM-P-Q11) and its short version of six items (HLS19-COM-P-Q6) fit sufficiently the unidimensional partial credit Rasch model, obtained acceptable goodness-of-fit indices and high reliability. Two items tend to under-discriminate. Few items displayed differential item functioning (DIF) across person factors, and there was no consistent pattern in DIF across countries. All items had ordered response categories. Conclusions: The HLS19-COM-P instrument was well accepted in nine countries, in different data collection modes, and could be used to measure COM-HL.publishedVersio

Does health literacy mediate the relationship between socioeconomic status and health related outcomes in the Belgian adult population?

peer reviewed[en] BACKGROUND: Health literacy (HL) has been put forward as a potential mediator through which socioeconomic status (SES) affects health. This study explores whether HL mediates the relation between SES and a selection of health or health-related outcomes. METHODS: Data from the participants of the Belgian health interview survey 2018 aged 18 years or older were individually linked with data from the Belgian compulsory health insurance (n = 8080). HL was assessed with the HLS-EU-Q6. Mediation analyses were performed with health behaviour (physical activity, diet, alcohol and tobacco consumption), health status (perceived health status, mental health status), use of medicine (purchase of antibiotics), and use of preventive care (preventive dental care, influenza vaccination, breast cancer screening) as dependent outcome variables, educational attainment and income as independent variables of interest, age and sex as potential confounders and HL as mediating variable. RESULTS: The study showed that unhealthy behaviours (except alcohol consumption), poorer health status, higher use of medicine and lower use of preventive care (except flu vaccination) were associated with low SES (i.e., low education and low income) and with insufficient HL. HL partially mediated the relationship between education and health behaviour, perceived health status and mental health status, accounting for 3.8-16.0% of the total effect. HL also constituted a pathway by which income influences health behaviour, perceived health status, mental health status and preventive dental care, with the mediation effects accounting for 2.1-10.8% of the total effect. CONCLUSIONS: Although the influence of HL in the pathway is limited, our findings suggest that strategies for improving various health-related outcomes among low SES groups should include initiatives to enhance HL in these population groups. Further research is needed to confirm our results and to better explore the mediating effects of HL

Potential impact of reduced tobacco use on life and health expectancies in Belgium

Objectives: We investigated the potential impact of reduced tobacco use scenarios on total life expectancy and health expectancies, i.e., healthy life years and unhealthy life years. Methods: Data from the Belgian Health Interview Survey 2013 were used to estimate smoking and disability prevalence. Disability was based on the Global Activity Limitation Indicator. We used DYNAMO-HIA to quantify the impacts of risk factor changes and to compare the “business-as-usual” with alternative scenarios. Results: The “business-as-usual” scenario estimated that in 2028 the 15-year-old men/women would live additional 50/52 years without disability and 14/17 years with disability. The “smoking-free population” scenario added 3.4/2.8 healthy life years and reduced unhealthy life years by 0.79/1.9. Scenarios combining the prevention of smoking initiation with smoking cessation programs are the most effective, yielding the largest increase in healthy life years (1.9/1.7) and the largest decrease in unhealthy life years (− 0.80/− 1.47). Conclusions: Health impact assessment tools provide different scenarios for

Comparing health insurance data and health interview survey data for ascertaining chronic disease prevalence in Belgium

Background: Health administrative data were increasingly used for chronic diseases (CDs) surveillance purposes. This cross sectional study explored the agreement between Belgian compulsory health insurance (BCHI) data and Belgian health interview survey (BHIS) data for asserting CDs. Methods: Individual BHIS 2013 data were linked with BCHI data using the unique national register number. The study population included all participants of the BHIS 2013 aged 15 years and older. Linkage was possible for 93% of BHISparticipants, resulting in a study sample of 8474 individuals. For seven CDs disease status was available both through selfreported information from the BHIS and algorithms based on ATC-codes of disease-specific medication, developed on demand of the National Institute for Health and Disability Insurance (NIHDI). CD prevalence rates from both data sources were compared. Agreement was measured using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) assuming BHIS data as gold standard. Kappa statistic was also calculated. Participants’ sociodemographic and health status characteristics associated with agreement were tested using logistic regression for each CD. Results: Prevalence from BCHI data was significantly higher for CVDs but significantly lower for COPD and asthma. No significant difference was found between the two data sources for the remaining CDs. Sensitivity was 83% for CVDs, 78% for diabetes and ranged from 27 to 67% for the other CDs. Specificity was excellent for all CDs (above 98%) except for CVDs. The highest PPV was found for Parkinson’s disease (83%) and ranged from 41 to 75% for the remaining CDs. Irrespective of the CDs, the NPV was excellent. Kappa statistic was good for diabetes, CVDs, Parkinson’s disease and thyroid disorders, moderate for epilepsy and fair for COPD and asthma. Agreement between BHIS and BCHI data is affected by individual sociodemographic characteristics and health status, although these effects varied across CDs. Conclusions: NHIDI’s CDs case definitions are an acceptable alternative to identify cases of diabetes, CVDs, Parkinson’s disease and thyroid disorders but yield in a significant underestimated number of patients suffering from asthma and COPD. Further research is needed to refine the definitions of CDs from administrative data