Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults

Abstract

Chronic diseases and their inequalities amongst older adults are a significant public health challenge. Prevention and treatment of chronic diseases will benefit from insight into which population groups show greatest risk. Biomarkers are indicators of the biological mechanisms underlying health and disease. We analysed disparities in a common set of biomarkers at the population level using English national data (n = 16,437). Blood-based biomarkers were HbA1c, total cholesterol and C-reactive protein. Non-blood biomarkers were systolic blood pressure, resting heart rate and body mass index. We employed an intersectionality perspective which is concerned with how socioeconomic, gender and ethnic disparities combine to lead to varied health outcomes. We find granular intersectional disparities, which vary by biomarker, with total cholesterol and HbA1c showing the greatest intersectional variation. These disparities were additive rather than multiplicative. Each intersectional subgroup has its own profile of biomarkers. Whilst the majority of variation in biomarkers is at the individual rather than intersectional level (i.e. intersections exhibit high heterogeneity), the average differences are potentially associated with important clinical outcomes. An intersectional perspective helps to shed light on how socio-demographic factors combine to result in differential risk for disease or potential for healthy ageing.

Understanding unequal ageing: towards a synthesis of intersectionality and life course analyses

“…Emerging research suggests that there are typically large differences in health between intersectional subgroups, e.g. the health of a working-class 55-year-old Black woman is typically worse than that of a 25-year-old White middle-class man, and these are mostly driven by additive rather than multiplicative effects (Holman et al 2020). In other words, they are the result of adding up the average health differences between 25 and 55 year olds, men and women, and so on.…”

Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders

“…With respect to quantitative analysis, we observed the con ation between intersectionality and the use of interaction effects previously highlighted by Bauer and Scheim (2019). While it is necessary to consider statistical interaction in intersectionality research, it is not su cient, because as noted intersectionality is not the testable hypothesis that there are multiplicative interactions between socio-demographic factors but rather it is a framework, perspective, or paradigm (Bowleg, 2012) concerned with the relationship between subgroup heterogeneity and social power, which could exist with only a combination of additive effects (Holman et al, 2020). This confusion reinforces the need for developing training and expertise in the use of quantitative intersectionality.…”

Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders

“…With respect to quantitative analysis, we observed the conflation between intersectionality and the use of interaction effects previously highlighted by Bauer and Scheim [53]. While it is necessary to consider statistical interaction in intersectionality research, it is not sufficient, because as noted, intersectionality is not the testable hypothesis that there are multiplicative interactions between sociodemographic factors, but rather it is a framework, perspective or paradigm [54] concerned with the relationship between subgroup heterogeneity and social power, which additive effects might also evidence [55]. This confusion reinforces the need for developing training and expertise in the use of quantitative intersectionality.…”