Use of Biomeasures in Social Surveys 3 |
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| Session Organisers |
Ms Anne Conolly (NatCen Social Research) Dr Shaun Scholes (Research Department of Epidemiology and Public Health, University College London) Mr Matt Brown (Centre for Longitudinal Studies, UCL Institute of Education) Dr Emily Gilbert (Centre for Longitudinal Studies, UCL Institute of Education) |
| Time | Thursday 18th July, 16:00 - 17:30 |
| Room | D26 |
Many social surveys, both cross-sectional and longitudinal, include the collection of biomeasures. While traditional questionnaires can obtain self-reported assessments of general health, diagnosed disease and health behaviours, it is known that they can be prone to misreporting. Objective health measures can add to survey data considerably, enabling researchers to discover things that cannot be captured through self-reported measures. The inclusion of objective measurements within social surveys allows us to assess health with significantly greater accuracy and therefore to deepen our understanding of the interplay between social and biological factors in explaining human behaviour.
Such measurements encompass a range of anthropometric (e.g. height, weight, waist), functional (e.g. grip strength, balance), and sensory measurements (e.g. hearing), as well as biological samples (e.g. blood, saliva, urine), other physiological health measurements (e.g. blood pressure, lung function), and device-based measurement of physical activity.
Typically these data are collected either in clinics or at participants’ homes and may be carried out by trained field interviewers or by those with medical training and expertise (such as nurses). Technological advances and the development of minimally invasive techniques of data collection have increased the feasibility of collecting biomeasures at home and by fieldworkers with no medical training. Respondent-led collection of their own biomedical data is also now emerging as a data collection method – for example, some studies now ask respondents to self-collect dried blood spots. Additionally, the increase in the use of smartphone apps (e.g. activity tracking, food logs) and wearable technology (e.g. fitness trackers, smart watches, smart eyewear) has led to a growing interest in using such technology for data collection in survey research.
This session invites survey practitioners to share their experiences of incorporating the collection of biomeasures into social surveys. We welcome submissions relating to:
• Innovative approaches to the collection of biomeasures
• Comparisons of objective measures with self-reported data
• Analyses to assess the diagnostic ability of biomarkers
• Training of fieldworkers to collect biomeasures
• Respondent-led collection of biomeasures
• Methods to maximise response to and/or representativeness of biomeasures
• Collecting biomeasures in special populations (e.g. older people)
• Ethical challenges in the collection of biomeasures (e.g. relating to feedback of results, consent for ongoing use of biological samples)
Papers need not be restricted to these specific examples.
Keywords: biomeasures, health data, biosocial, biomarkers
Dr Jessica Faul (University of Michigan) - Presenting Author
Dr Eileen Crimmins (University of Southern California)
Dr Bharat Thyagarajan (University of Minnesota)
Dr David Weir (University of Michigan)
Over the last several years, research documenting the interconnectedness of biology, social measures, and health has increased significantly. Many studies are now moving to a biosocial approach to research including the collection of biomarkers alongside more typical survey approaches. However, for many of these measures we lack data from a population representative sample. There is concern that much of the health and biosocial research discoveries to date may not be generalizable and may be providing a false sense of progress. The primary aim of the Health and Retirement Study (HRS) is to collect and distribute multi-disciplinary data to support research on aging. Because of their value in assessing health status and elucidating pathways connecting social experience to health, biomarkers have become a key element of the HRS approach to measurement. In 2016 the HRS began collecting venous blood from respondents and assays were selected to 1) enhance harmonization with other studies of aging, 2) advance research on mechanisms of social disparities in health and aging and 3) measure inflammation, immune system function, and related molecular and cellular age-related changes. Innovative assays include cytokines, flow cytometry, DNA methylation and transcriptomics. Many of these measures have interest longitudinally and are hypothesized to be influenced by social and environmental exposures related to key health disparities in chronic disease prevalence and mortality. We will show differences in means and standard deviation for these assays by demographic subgroups and by socioeconomic status. We compare the distribution of these measures in HRS to other studies with available data to emphasize the importance of a representative sample for biological data that is socially and demographically pattered. However, differential consent to the collection of biological data should also be considered. Sampling weights using propensity models should be created to address sample composition differences by key demographics.
Mr Patrick Lazarevic (Vienna Institute of Demography) - Presenting Author
Introduction: Self-ratings of health (SRH) are the most widely used single-indicator of health in survey research. How these ratings come about, however, is still relatively unexplored. For instance, little is known about the role undiagnosed diseases or the severity of diagnosed diseases plays in rating one's health. Biomarkers, e.g., HbA1c for diabetes, give objective and pertinent information beyond what respondents are willing - or able - to tell. Thus, biomarkers offer the opportunity of investigating the relevance of diseases unknown to respondents and the severity of known diseases.
Method: Using lab-measured HbA1c in the respondents' blood, this paper analyzes the relative role of diagnoses and biomarkers in rating health. This paper draws on health data from 2,890 respondents aged 50-79 collected in the 2007 and 2009 waves of the Canadian Health Measures Survey to quantify the contribution of self-reported diabetes diagnoses, biomarker-indicated diabetes, undiagnosed diabetes, prediabetes, and diabetes-severity to explain SRH. These contributions are compared by gender, age, and education. Separate analyses of all 359 diagnosed diabetics in the sample were conducted to investigate the influence of diabetes-severity in diabetics.
Results: Across all subgroups, the influence of undiagnosed (pre)diabetes as well as HbA1c on SRH appears to be subordinate to diagnoses. All subgroups, except older men (65-79), show a greater influence of diagnoses than lab-indicated diabetes while lab-indicated prediabetes generally does not affect SRH. General and subgroup analyses of the the disease's severity in diabetics reveal that HbA1c influences their SRH, although only in older respondents (65+) and respondents with higher education.
Conclusions: While further research is needed, these results highlight the importance of knowledge in subjective health. This paper also suggests that SRH is primarily based on information known to and relevant for the daily lives of respondents, giving insight in its basis.
Ms Judith Lehmann (Goettingen University) - Presenting Author
In their study “Beyond BMI”, Burkhauser and Cawley (2008) recommend to stop using the Body Mass Index (BMI) for research on social inequality, which had been the modus operandi in most of the previous studies (i.e. Cawley 2004, Gortmaker et al. 1993). They show that using percent body fat instead of BMI yields diverging results regarding the classification of respondents as obese but also the correlation of obesity with employment (Burkhauser & Cawley 2008). Other studies report similar findings: increased body fat correlates with lower wages irrespective of race or gender, which is not true for BMI (Wada & Tekin 2010, Bozoyan & Wolbring 2011). It remains unclear whether measuring Waist Circumference in addition to BMI will yield similar findings as body fat measures, and such increase data quality of surveys.
Using two data sets conducted by the Robert Koch Institute in Germany – the German National Health Interview and Examination Survey 1998 (BGS98) and the German Health Interview and Examination Survey for Adults 2008 (DEGS1) – I compare the correlation of both measures of obesity (BMI and Waist Circumference) and an array of socio-economic outcomes such as income, employment and job prestige. Men and women are analyzed separately because men might be misclassified more often than women due to more muscle mass (Burkhauser & Cawley 2008). First results indicate that using Waist Circumference instead of BMI leads to more robust results in some aspects of social inequality but not all of them.
Ms Anne Conolly (NatCen Social Research) - Presenting Author
It is well documented that estimates of overweight and obesity prevalence are higher when using objective measures of height and weight than those using self-reported data (Bolton-Smith et al. (2000); Visscher et al (2006); Shields et al. (2011)). In general, self-reported data tends to overestimate height and underestimate weight.
This study uses objective physical measurements and subjective self-assessments of weight from the Health Survey for England (HSE). Since 1993, the HSE has collected measurements of height, weight and waist circumference from adults and children in sampled households. In some years of the survey, participants were also asked to give an assessment of their weight; whether they felt they were: a) too light, b) about the right weight, or c) too heavy. Similarly, parents were asked to provide an assessment of their children’s weight using the same response options.
Using individual and household level data the relationship between parental and child BMI were examined. The relationship between objective weight and subjective self-assessments were also explored.
Children’s overweight and obesity was associated with that of their parents. Most obese adults assessed themselves as being too heavy, whereas only half of adults who were overweight but not obese thought they were too heavy. The majority of overweight and obese children did not describe themselves as too heavy and the parents of overweight and obese children often thought that their child was the right weight.
Objective physical measurements of height, weight and waist circumference are recommended for obtaining accurate estimates of overweight and obesity, including abdominal obesity. A household level approach sheds light on the relationships between parental and child weight.