Ethnicity and ethnic group measures in social survey research

This article is a review of issues associated with measuring ethnicity and using ethnicity measures in social science research. The review is oriented towards researchers who undertake secondary analyses of large-scale multipurpose social science datasets. The article begins with an outline of two main approaches used in social surveys to measure ethnicity, the ‘mutually exclusive category’ approach and the ‘multiple characteristics’ approach. We also describe approaches to the use of ethnicity measures in cross-national comparative research. We emphasise the value of sensitivity analyses. We also encourage researchers to carefully consider the possible relationships between ethnicity and other important variables in order to avoid spurious interpretations of the effects of ethnicity

Where’s WALY? : A proof of concept study of the ‘wellbeing adjusted life year’ using secondary analysis of cross-sectional survey data

Background The Quality-Adjusted Life Year (QALY) is a measure that combines life extension and health improvement in a single score, reflecting preferences around different types of health gain. It can therefore be used to inform decision-making around allocation of health care resources to mutually exclusive options that would produce qualitatively different health benefits. A number of quality-of-life instruments can be used to calculate QALYs. The EQ-5D is one of the most commonly used, and is the preferred option for submissions to NICE (https://www.nice.org.uk/process/pmg9/). However, it has limitations that might make it unsuitable for use in areas such as public and mental health where interventions may aim to improve well-being. One alternative to the QALY is a Wellbeing-Adjusted Life Year. In this study we explore the need for a Wellbeing-Adjusted Life Year measure by examining the extent to which a measure of wellbeing (the Warwick-Edinburgh Mental Well-being Scale) maps onto the EQ-5D-3L. Methods Secondary analyses were conducted on data from the Coventry Household Survey in which 7469 participants completed the EQ-5D-3L, Warwick-Edinburgh Mental Well-being Scale, and a measure of self-rated health. Data were analysed using descriptive statistics, Pearson’s and Spearman’s correlations, linear regression, and receiver operating characteristic curves. Results Approximately 75 % of participants scored the maximum on the EQ-5D-3L. Those with maximum EQ-5D-3L scores reported a wide range of levels of mental wellbeing. Both the Warwick-Edinburgh Mental Well-being Scale and the EQ-5D-3L were able to detect differences between those with higher and lower levels of self-reported health. Linear regression indicated that scores on the Warwick-Edinburgh Mental Well-being Scale and the EQ-5D-3L were weakly, positively correlated (with R2 being 0.104 for the index and 0.141 for the visual analogue scale). Conclusion The Warwick-Edinburgh Mental Well-being Scale maps onto the EQ-5D-3L to only a limited extent. Levels of mental wellbeing varied greatly amongst participants who had the maximum score on the EQ-5D-3L. To evaluate the relative effectiveness of interventions that impact on mental wellbeing, a new measure – a Wellbeing Adjusted Life Year – is needed

A review of educational attainment measures for social survey research

This article is a review of issues associated with measuring education and using educational measures in social science research. The review is orientated towards researchers who undertake secondary analyses of large-scale micro-level social science datasets. The article begins with an outline of important context, which impinges upon the measurement of education. The United Kingdom is the focus of this review, but similar issues apply to other nation states. We provide a critical introduction to the main approaches to measuring education in social survey research, which include measuring years of education, using categorical qualification based measures and scaling approaches. We advocate the use of established education measures to better facilitate comparability and replication. We conclude by making the recommendation that researchers place careful thought into which educational measure they select, and that researchers should routinely engage in appropriate sensitivity analyses

The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study

Background: Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. Methods: The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. Results: Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. Conclusions: Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups

Developing and testing theory-based and evidence-based interventions to promote switching to arsenic-safe wells in Bangladesh

Millions of people in Bangladesh drink arsenic-contaminated water despite increased awareness of consequences to health. Theory-based and evidence-based interventions are likely to have greater impact on people switching to existing arsenic-safe wells than providing information alone. To test this assumption, we first developed interventions based on an empirical test of the Risk, Attitudes, Norms, Abilities and Self-regulation (RANAS) model of behaviour change. In the second part of this study, a cluster-randomised controlled trial revealed that in accordance with our hypotheses, information alone showed smaller increases in switching to arsenic-safe wells than information with reminders or information with reminders and implementation intentions