Socio-economic differences in participation of households in a Belgian national health survey

Demarest, Stefaan; Heyden, Johan Van der; Charafeddine, Rana; Tafforeau, Jean; Oyen, Herman Van; Hal, Guido Van

doi:10.1093/eurpub/cks158

Cited by 69 publications

(60 citation statements)

References 15 publications

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“…Previous studies have shown that such surveys tend to slightly over-represent those categorised in the highest socioeconomic class. 44 If this is the case, and the excluded cases from lower socioeconomic groups were to have even more adverse risk factor profiles, then we may have underestimated inequalities in our study. However, our findings accord well with broader international comparisons which have adopted similar methods for studying trends in absolute and relative inequality in CVD incidence 45 but it should be noted that a substantial proportion of the educational differentials in incidence is not explained by trends in risk factors (or indeed case fatality).…”

Section: Strengths and Limitationsmentioning

confidence: 88%

Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

et al. 2017

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ObjectivesThis study compares trends in socioeconomic inequalities related to key cardiovascular risk factors in neighbouring countries Northern Ireland (NI) and the Republic of Ireland (RoI).DesignRepeated cross-sectional studies.SettingPopulation based.Participants3500–4000 in national surveys in NI and 5000–9000 in RoI, aged 20–69 years.MeasuresEducational attainment was used as a socioeconomic indicator by which the magnitude and direction of trends in inequalities for smoking, diabetes, obesity and physical inactivity in NI and RoI were examined between 1997/1998 and 2007/2011. Gender-specific relative and absolute inequalities were calculated using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII) for both countries.ResultsIn both countries, the prevalence of diabetes and obesity increased whereas levels of smoking and physical inactivity decreased over time. In NI relative inequalities increased for obesity (RII 1.1 in males and 2.1 in females in 2010/2011) and smoking (RII 4.5 in males and 4.2 in females in 2010/2011) for both genders and absolute inequalities increased for all risk factors in men and increased for diabetes and obesity in women. In RoI greater inequality was observed in women, particularly for smoking (RII 2.8 in 2007) and obesity (RII 8.2 in 2002) and in men for diabetes (RII 3.2 in 2002).ConclusionsInterventions to reduce inequalities in risk factors, particularly smoking, obesity and diabetes are encouraged across both countries.

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Section: Strengths and Limitationsmentioning

confidence: 88%

Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

et al. 2017

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“…Nevertheless, a possible over-representation of individuals with a higher socioeconomic status in the childhood leukemia survivor population may also explain the positive results regarding employment and should be discussed both in light of social disparities in childhood AL survival rates previously shown [36,37] and also of ethnical disparities in childhood AL incidence [38], even if a link with a socioeconomic bias is not reported. Furthermore, the socioeconomic status of survey respondents is often higher than for non-respondents [39][40][41]. Unfortunately, the available data did not allow us to compare the repartition of socioeconomic status between our cohort and the general French population.…”

Section: Discussionmentioning

confidence: 99%

Employment in French young adult survivors of childhood leukemia: an LEA study (for Leucemies de l’Enfant et de l’Adolescent—childhood and adolescent leukemia)

et al. 2016

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“…For a target sample of about 100 residents in each neighbourhood, with an estimate of 10% response rate, approximately 1,000 residents were sampled in each neighbourhood. Given varying expected response rate according to neighbourhood SES , 1,200 adults were sampled in low SES neighbourhoods and 800 in high SES neighbourhoods. Finally, a total of 12 neighbourhoods per neighbourhood type were selected.…”

Section: Methodsmentioning

confidence: 99%

Neighbourhood typology based on virtual audit of environmental obesogenic characteristics

et al. 2016

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SummaryVirtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features.

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Socio-economic differences in participation of households in a Belgian national health survey

Abstract: Socio-economic inequalities in participation can introduce a bias in the health survey findings. Strategies targeting improvement of the participation of lower socio-economic groups need to be considered.

Cited by 69 publications

References 15 publications

Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

Employment in French young adult survivors of childhood leukemia: an LEA study (for Leucemies de l’Enfant et de l’Adolescent—childhood and adolescent leukemia)

Neighbourhood typology based on virtual audit of environmental obesogenic characteristics

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