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

Descriptive Epidemiology of New York City Older Adult Patients With Multiple Chronic Conditions

We characterized comorbidity profiles and cardiometabolic risk factors among older adults with multiple chronic conditions (MCCs) in New York City using an intersectionality approach. Electronic health record data were obtained from the INSIGHT Clinical Research Network on 367,901 New York City residents aged 50 years or older with MCCs. Comorbidity profiles were heterogeneous. The most common profile across sex and racial and ethnic groups was co-occurring hypertension and hyperlipidemia; prevalence of these 2 conditions differed across groups (4.7%-7.3% co-occurrence alone, 65.1%-88.0% with other conditions). Significant sex and racial and ethnic differences were observed, which may reflect accumulated disparities in risk factors and health care access across the life course.

Psychosocial and biological pathways to aging : The role(s) of the behavioral and social sciences in geroscience

While the biological hallmarks of aging are widely recognized as fundamental mechanisms of biological aging, more recently, there have been calls within geroscience to understand the aging process more comprehensively by adding a perspective of the social hallmarks of aging. Social and behavioral factors, such as socioeconomic status, life events or behavior and beliefs can alter the aging process per se and act in complex interactions with biological pathways. In addition, underlying biological pathways have been proposed for various psychosocial concepts, such as views on age and relationship quality. The aim of the present article is to provide narrative insights into theoretical and empirical developments between social and behavioral gerontology and geroscience or biogerontology. This article focuses on the potential of an interdisciplinary aging research but it also sets out the critical perspective that social gerontology provides.

Intersectional Inequalities in the Transition to Grandparenthood and Cognitive Functioning: A Longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy

OBJECTIVES

In aging societies, more people become vulnerable to experiencing cognitive decline. Simultaneously, the role of grandparenthood is central for older adults and their families. Our study investigates inequalities in the level and trajectories of cognitive functioning among older adults, focusing on possible intersectional effects of social determinants and grandparenthood as a life course transition that may contribute to delaying cognitive decline.

METHODS

Using longitudinal data from the Survey of Health, Aging, and Retirement in Europe, we analyzed a sample of 19,953 individuals aged 50-85 without grandchildren at baseline. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy to investigate variation in cognitive functioning across 48 intersectional strata, defined by sex/gender, migration, education, and occupation. We allowed the impact of becoming a grandparent on cognitive functioning trajectories to vary across strata by including random slopes.

RESULTS

Intersectional strata accounted for 17.43% of the overall variance in cognitive functioning, with most of the stratum-level variation explained by additive effects of the stratum-defining characteristics. Transition to grandparenthood was associated with higher cognitive functioning, showing a stronger effect for women. Stratum-level variation in the grandparenthood effect was modest, especially after accounting for interactions between grandparenthood and the stratum-defining variables.

DISCUSSION

This study highlights the importance of social determinants for understanding heterogeneities in older adults' level of cognitive functioning and its association with the transition to grandparenthood. Cumulative disadvantages negatively affect cognitive functioning, hence adopting an intersectional lens is useful to decompose inequalities and derive tailored interventions to promote equal healthy aging.

Clarifications on the intersectional MAIHDA approach: A conceptual guide and response to Wilkes and Karimi (2024)

Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) has been welcomed as a new gold standard for quantitative evaluation of intersectional inequalities, and it is being rapidly adopted across the health and social sciences. In their commentary "What does the MAIHDA method explain?", Wilkes and Karimi (2024) raise methodological concerns with this approach, leading them to advocate for the continued use of conventional single-level linear regression models with fixed-effects interaction parameters for quantitative intersectional analysis. In this response, we systematically address these concerns, and ultimately find them to be unfounded, arising from a series of subtle but important misunderstandings of the MAIHDA approach and literature. Since readers new to MAIHDA may share confusion on these points, we take this opportunity to provide clarifications. Our response is organized around four important clarifications: (1) At what level are the additive main effect variables defined in intersectional MAIHDA models? (2) Do MAIHDA models have problems with collinearity? (3) Why does the Variance Partitioning Coefficient (VPC) tend to be small, and the Proportional Change in Variance (PCV) tend to be large in MAIHDA? and (4) What are the goals of MAIHDA analysis?

The Extent, Range, and Nature of Quantitative Nutrition Research Engaging with Intersectional Inequalities: A Systematic Scoping Review

Addressing malnutrition for all requires understanding inequalities in nutrition outcomes and how they intersect. Intersectionality is increasingly used as a theoretical tool for understanding how social characteristics intersect to shape inequalities in health outcomes. However, little is known about the extent, range, and nature of quantitative nutrition research engaging with intersectional inequalities. This systematic scoping review aimed to address this gap. Between 15 May 2021 and 15 May 2022, we searched 8 databases. Studies eligible for inclusion used any quantitative research methodology and aimed to investigate how social characteristics intersect to influence nutrition outcomes. In total, 55 studies were included, with 85% published since 2015. Studies spanned populations in 14 countries but were concentrated in the United States (n = 35) and India (n = 7), with just 1 in a low-income country (Mozambique). Race or ethnicity and gender were most commonly intersected (n = 20), and body mass index and overweight and/or obesity were the most common outcomes. No studies investigated indicators of infant and young child feeding or micronutrient status. Study designs were mostly cross-sectional (80%); no mixed-method or interventional research was identified. Regression with interaction terms was the most prevalent method (n = 26); 2 of 15 studies using nonlinear models took extra steps to assess interaction on the additive scale, as recommended for understanding intersectionality and assessing public health impacts. Nine studies investigated mechanisms that may explain why intersectional inequalities in nutrition outcomes exist, but intervention-relevant interpretations were mostly limited. We conclude that quantitative nutrition research engaging with intersectionality is gaining traction but is mostly limited to the United States and India. Future research must consider the intersectionality of a wider spectrum of public health nutrition challenges across diverse settings and use more robust and mixed-method research to identify specific interventions for addressing intersectional inequalities in nutrition outcomes. Data systems in nutrition must improve to facilitate this. This review was registered in PROSPERO as CRD42021253339.

A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.

Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualization of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009-2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.

Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey

BACKGROUND

Differences in type 2 diabetes risk have been reported for several sociodemographic determinants including sex/gender or socioeconomic status. From an intersectional perspective, it is important to not only consider the role of social dimensions individually, but also their intersections. This allows for a deeper understanding of diabetes risk and preventive needs among diverse population groups.

METHODS

As an intersectionality-informed approach, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was used in a population-based sample of adults without known diabetes in Germany from the cross-sectional survey "Disease knowledge and information needs- Diabetes mellitus (2017)". Diabetes risk was assessed by the German Diabetes Risk Score (GDRS, range 0-122 points), estimating the individual risk of developing type 2 diabetes within the next 5 years based on established self-reported risk factors. Nesting individuals in 12 intersectional strata defined by combining sex/gender, educational level, and history of migration, we calculated measures to quantify the extent to which individual differences in diabetes risk were explained at strata level, and how much this was due to additive or multiplicative intersectional effects of social determinants.

RESULTS

Drawing on data of 2,253 participants, we found good discriminatory accuracy of intersectional strata (variance partition coefficient = 14.00% in the simple intersectional model). Model-predicted GDRS means varied between 29.97 (corresponding to a "low risk" of < 2%) in women with high educational level and a history of migration, and 52.73 ("still low risk" of 2-5%) in men with low educational level without a history of migration. Variance in GDRS between strata was mainly explained by additive effects of social determinants (proportional change in variance to intersectional interaction model = 77.95%) with being male and having low educational level being associated with higher GDRS. There was no evidence of multiplicative effects in individual strata.

CONCLUSIONS

Type 2 diabetes risk differed between intersectional strata and can to some extent be explained at strata level. The role of intersectional effects was minor and needs to be further investigated. Findings suggest a need for specific preventive measures targeted at large groups with increased diabetes risk, such as men and persons with low educational level.

Overcoming combination fatigue: Addressing high-dimensional effect measure modification and interaction in clinical, biomedical, and epidemiologic research using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Growing interest in precision medicine, gene-environment interactions, health equity, expanding diversity in research, and the generalizability results, requires researchers to evaluate how the effects of treatments or exposures differ across numerous subgroups. Evaluating combination complexity, in the form of effect measure modification and interaction, is therefore a common study aim in the biomedical, clinical, and epidemiologic sciences. There is also substantial interest in expanding the combinations of factors analyzed to include complex treatment protocols (e.g., multiple study arms or factorial randomization), comorbid medical conditions or risk factors, and sociodemographic and other subgroup identifiers. However, expanding the number of subgroup category combinations creates combination fatigue problems, including concerns over small sample size, reduced power, multiple testing, spurious results, and design and analytic complexity. Creative new approaches for managing combination fatigue and evaluating high-dimensional effect measure modification and interaction are needed. Intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) has already attracted substantial interest in social epidemiology, and has been hailed as the new gold standard for investigating health inequities across complex intersections of social identity. Leveraging the inherent advantages of multilevel models, a more general multicategorical MAIHDA can be used to study statistical interactions and predict effects across high-dimensional combinations of conditions, with important advantages over alternative approaches. Though it has primarily been used thus far as an analytic approach, MAIHDA should also be used as a framework for study design. In this article, I introduce MAIHDA to the broader health sciences research community, discuss its advantages over conventional approaches, and provide an overview of potential applications in clinical, biomedical, and epidemiologic research.

An Intersectional Approach to Quantifying the Impact of Geographic Remoteness and Health Disparities on Quality-Adjusted Life Expectancy: Application to Australia

OBJECTIVES

An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a novel method for exploring the interaction between sociodemographic characteristics that affect health outcomes. This study explores the interaction between geographic remoteness and socioeconomic status on health outcomes in Australia from an intersectional perspective.

METHODS

Data from a cross-sectional survey were matched with data from the Australian Bureau of Statistics and the Australian Institute of Health and Welfare. To explore the effect of health-related quality of life on life expectancy, quality-adjusted life expectancy (QALE) was estimated through applying utility values derived from the EQ-5D-5L to life table data from the Australian Bureau of Statistics. The effect of geographic remoteness on QALE was quantified using multivariable linear regression. An intersectional MAIHDA was performed to explore differences in mean QALE across strata formed by intersections of age, sex, and Socioeconomic Indexes for Areas score.

RESULTS

Based on multivariable linear modeling, QALE declined significantly with increasing remoteness (inner regional, -1.0 years [undiscounted]; remote/very remote, -3.3 years [undiscounted]) (P < .001). In contrast, life expectancy was only significantly different between participants in remote/very remote areas and major cities (β-coefficient, -2.4; 95% CI -4.4 to -0.4; P = .016). No intersectional interaction effects between strata on QALE were found in the MAIHDA.

CONCLUSIONS

QALE has considerable value as a metric for exploring disparities in health outcomes. Given that no intersectional interactions were identified, our findings support broad interventions that target the underlying social determinants of health appropriately reduce disparities versus interventions targeting intersectional interactions.

Intersectional inequalities in the transition to grandparenthood and cognitive functioning: A longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

Objectives

With aging societies, more people become vulnerable to experiencing cognitive decline. While normal aging is associated with a deterioration in certain cognitive abilities, little is known about how social determinants intersect to create late-life cognitive functioning inequalities. Simultaneously, the role of grandparenthood is central for older adults and their families. There are indications that social determinants intersect to modulate the effect of the transition to grandparenthood, but further evidence is needed. Our study investigates the relation of transition to grandparenthood with cognitive functioning and explores differences across intersectional strata.

Methods

Using longitudinal data from the Survey of Health, Ageing and Retirement in Europe, we analyzed a sample of 19,953 individuals aged 50-85 without grandchildren at the baseline. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to investigate cognitive functioning differences across 48 intersectional strata, defined by sex/gender, migration, education, and occupation. We allowed the impact of becoming a grandparent to vary across strata by including random slopes.

Results

Intersectional strata accounted for 17.43% of the overall variance in cognitive functioning, with most of the stratum-level variation explained by additive effects of the stratum-defining characteristics. Transition to grandparenthood was associated with higher cognitive functioning, with a stronger effect for women. Stratum-level variation in the grandparenthood effect was modest.

Discussion

This study highlights the importance of social determinants for understanding heterogeneities in the association of transition to grandparenthood with cognitive functioning. Adopting an intersectional lens is useful to decompose inequalities and derive tailored interventions to promote equal healthy aging.

Educational Inequalities at the Intersection of Multiple Social Categories: An Introduction and Systematic Review of the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) Approach

Intersectional approaches have become increasingly important for explaining educational inequalities because they help to improve our understanding of how individual experiences are shaped by simultaneous membership in multiple social categories that are associated with interconnected systems of power, privilege, and oppression. For years, there has been a call in psychological and educational research for quantitative approaches that can account for the intersection of multiple social categories. The present paper introduces the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach, a novel intersectional approach from epidemiology, to study educational inequalities. The MAIHDA approach uses a multilevel model as the statistical framework to define intersectional strata that represent individuals’ membership in multiple social categories. By partitioning the variance within and between intersectional strata, the MAIHDA approach allows identifying intersectional effects at the strata level as well as obtaining information on the discriminatory accuracy of these strata for predicting individual educational outcomes. Compared to conventional quantitative intersectional approaches, MAIHDA analyses have several advantages, including better scalability for higher dimensions, model parsimony, and precision-weighted estimates of strata with small sample sizes. We provide a systematic review of its past application and illustrate its use by analyzing inequalities in reading achievement across 40 unique intersectional strata (combining the social categories of gender, immigrant background, parental education, and parental occupational status) using data from 15-year-old students in Germany (N = 5451). We conclude that the MAIHDA approach is a valuable intersectional tool to study inequalities in educational contexts.

Racial differences in sympathetic nervous system indicators: Implications and challenges for research

Growing evidence indicates the presence of racial differences in sympathetic nervous system (SNS) functioning, yet the nature of these differences is unclear and appears to vary across different indices of SNS activity. Moreover, racial differences among commonly used indices of SNS activity are under-investigated. This systematic review examines racial differences among widely used resting SNS indices, such as electrodermal activity (EDA), pre-ejection period (PEP), and salivary alpha-amylase (sAA). Our review reveals that Black participants have consistently been found to display lower resting EDA compared to White participants. The few studies that have investigated or reported racial differences in PEP and sAA yield mixed findings about whether racial differences exist. We discuss potential reasons for racial differences in SNS activity, such as index-specific factors, lab confounds, psychosocial environmental factors, and their interactions. We outline a framework characterizing possible contributors to racial differences in SNS functioning. Lastly, we highlight the implications of several definitional, analytic, and interpretive issues concerning the treatment of group differences in psychophysiological activity and provide future recommendations.

Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25% of the challenges were related to privacy, cybersecurity, and data ownership (10%), technology (7.5%), and implementation (7.5%). The most frequent benefits were related to cost-saving (17.5%), nurse engagement improvement (10%), and caregiver stress reduction (7.5%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use PMH technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits.

The effects of social determinants on children's health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Empirical evidence suggests that the health outcomes of children living in slums are poorer than those living in non-slums and other urban areas. Improving health especially among children under five years old (U5y) living in slums, requires a better understanding of the social determinants of health (SDoH) that drive their health outcomes. Therefore, we aim to investigate how SDoH collectively affects health outcomes of U5y living in Bangladesh slums through an intersectionality lens. We used data from the most recent national Urban Health Survey (UHS) 2013 covering urban populations in Dhaka, Chittagong, Khulna, Rajshahi, Barisal, Sylhet, and Rangpur divisions. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the Discriminatory Accuracy (DA) of the intersectional effects estimates using Variance Partition Coefficient (VPC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). We also assessed the Proportional Change in Variance (PCV) to calculate intersectional effects. We considered three health outcomes: cough, fever, and acute respiratory infections (ARI) in U5y.We found a low DA for cough (VPC = 0.77%, AUC-ROC = 61.90%), fever (VPC = 0.87%, AUC-ROC = 61.89%) and ARI (VPC = 1.32%, AUC-ROC = 66.36%) of intersectional strata suggesting that SDoH considered do not collectively differentiate U5y with a health outcome from those with and without a health outcome. The PCV for cough (85.90%), fever (78.42%) and ARI (69.77%) indicates the existence of moderate intersectional effects. We also found that SDoH factors such as slum location, mother's employment, age of household head, and household's garbage disposal system are associated with U5y health outcomes. The variables used in this analysis have low ability to distinguish between those with and without health outcomes. However, the existence of moderate intersectional effect estimates indicates that U5y in some social groups have worse health outcomes compared to others. Therefore, policymakers need to consider different social groups when designing intervention policies aimed to improve U5y health outcomes in Bangladesh slums.

Optimising the selection of outcomes for healthy ageing trials: a mixed methods study

Advancing age is associated with chronic diseases which are the largest cause of death and disability in developed countries. With increasing life expectancy and an ageing population, there is a need to conduct trials to extend healthy ageing, including targeting biological ageing processes, and prevent ageing-related diseases. The main objectives of the study are as follows: (i) to review outcome measures utilised in healthy ageing trials focusing on pharmacological therapies, nutritional supplements and medical devices; (ii) to summarise the views of key stakeholders on outcome selection for healthy ageing trials. An analysis of records from the Clinicaltrials.gov database pertaining to healthy ageing trials from inception to May 2022 was conducted. In addition, the findings of a workshop attended by key stakeholders at the 2022 annual UKSPINE conference were qualitatively analysed. Substantial heterogeneity was found in the interventions evaluated and the outcomes utilised by the included studies. Recruitment of participants with diverse backgrounds and the confounding effects of multi-morbidity in older adults were identified as the main challenges of measuring outcomes in healthy ageing trials by the workshop participants. The development of a core outcome set for healthy ageing trials can aid comparability across interventions and within different settings. The workshop provided an important platform to garner a range of perspectives on the challenges with measuring outcomes in this setting. It is critical to initiate such discussions to progress this field and provide practical answers to how healthy ageing trials are designed and structured in the future.

Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England

While social and spatial determinants of biomarkers have been reported, no previous study has examined both together within an intersectional perspective. We present a novel extension of quantitative intersectional analyses using cross-classified multilevel models to explore how intersectional positions and neighbourhood deprivation are associated with biomarkers, using baseline UK Biobank data (collected from 2006 to 2010). Our results suggest intersectional inequalities in biomarkers of healthy ageing are mostly established by age 40-49, but different intersections show different relationships with deprivation. Our study suggests that certain biosocial pathways are more strongly implicated in how neighbourhoods and intersectional positions affect healthy ageing than others.

The Intersections of Structural Racism and Ageism in the Time of COVID-19: A Call to Action for Gerontological Nursing Science

The health consequences of systemic racism and ageism have received growing attention as the coronavirus disease 2019 pandemic has illuminated long-standing inadequacies and injustices that are structurally engrained in our health systems. The current State of the Science Commentary addresses the intersecting influences of systemic racism and ageism, and other "-isms" that conspire to create disparate health outcomes for older adults from historically excluded and marginalized backgrounds. We focus specifically on the long-term care sector as a representative microcosm of structural inequities, while recognizing that these unjust barriers to health are widespread, endemic, and pervasive. We present a call to action for gerontological nursing science to engage deeply and robustly in these realities, and the ethical and scientific imperative they present to ensure that all older adults encounter just conditions for maximizing their health and well-being. [Research in Gerontological Nursing, 15(1), 6-13.].

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

Background

The concept of “intersectionality” is increasingly employed within public health arenas, particularly in North America, and is often heralded as offering great potential to advance health inequalities research and action. Given persistently poor progress towards tackling health inequalities, and recent calls to reframe this agenda in the United Kingdom and Europe, the possible contribution of intersectionality deserves attention. Yet, no existing research has examined professional stakeholder understandings and perspectives on applying intersectionality to this field.

Methods

In this paper we seek to address that gap, drawing upon a consultation survey and face-to-face workshop (n = 23) undertaken in the United Kingdom. The survey included both researchers (n = 53) and policy and practice professionals (n = 20) with varied roles and levels of engagement in research and evaluation. Topics included familiarity with the term and concept “intersectionality”, relevance to health inequalities work, and issues shaping its uptake. Respondents were also asked to comment on two specific policy suggestions: intersectionally targeting and tailoring interventions, and evaluating the intersectional effects of policies. The workshop aims were to share examples of applying intersectionality within health inequalities research and practice; understand the views of research and practice colleagues on potential contributions and challenges; and identify potential ways to promote intersectional approaches.

Results

Findings indicated a generally positive response to the concept and a cautiously optimistic assessment that intersectional approaches could be valuable. However, opinions were mixed and various challenges were raised, especially around whether intersectionality research is necessarily critical and transformative and, accordingly, how it should be operationalized methodologically. Nonetheless, there was general agreement that intersectionality is concerned with diverse inequalities and the systems of power that shape them.

Conclusions

We position intersectionality within the wider context of health inequalities policy and practice, suggesting potential ways forward for the approach in the context of the United Kingdom. The views of policy and practice professionals suggest that intersectionality has far to travel to help counter individualistic narratives and to encourage an approach that is sensitive to subgroup inequalities and the processes that generate them. Examples of promising practice, albeit mostly in North America, suggest that it is possible for intersectionality to gain traction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12961-021-00742-w.

Intersectionality and heart failure: what clinicians and researchers should know and do

PURPOSE OF REVIEW

To review the application of intersectionality to heart failure. Intersectionality refers to the complex ways in which disenfranchisement and privilege intersect to reproduce and influence health and social outcomes.

RECENT FINDINGS

Intersectionality challenges approaches that focus on a single or small number of socio-demographic characteristics, such as sex or age. Instead, approaches should take account of the nature and effects of a full range of socio-demographic factors linked to privilege, including: race and ethnicity, social class, income, age, gender identity, disability, geography, and immigration status. Although credible and well established across many fields - there is limited recognition of the effects of intersectionality in research into heart disease, including heart failure. This deficiency is important because heart failure remains a common and burdensome syndrome that requires complex pharmacological and nonpharmacological care and collaboration between health professionals, patients and caregivers during and at the end-of-life.

SUMMARY

Approaches to heart failure clinical care should recognize more fully the nature and impact of patients' intersectionality- and how multiple factors interact and compound to influence patients and their caregivers' behaviours and health outcomes. Future research should explicate the ways in which multiple factors interact to influence health outcomes.

Rethinking long-term condition management: An actor-level framework

To understand the complexities of managing long-term conditions and develop appropriate responses, micro-, meso- and macrolevels must be considered. However, these levels have not been combined in a single analytical framework of long-term condition management (LTCM). This article aims to describe a framework of LTCM practice and research that combines societal levels and key agents. The actor-level framework, based on the works of Abram De Swaan and Randall Collins, provides a broader understanding of LTCM as an interdisciplinary research field compared to previous contributions. The framework has three main advantages. First, it encourages knowledge production across levels and actors that address the complexity of long-term illness management. Second, it broadens the scope of LTCM as an interdisciplinary research field and practice field. Finally, it facilitates the integration of knowledge production from different disciplines and research traditions. The framework could stimulate interdisciplinary research collaboration to enhance knowledge of processes and interactions influencing the lives of individuals with long-term conditions.

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

Intersectionality has received an increasing amount of attention in health inequalities research in recent years. It suggests that treating social characteristics separately—mainly age, gender, ethnicity, and socio-economic position—does not match the reality that people simultaneously embody multiple characteristics and are therefore potentially subject to multiple forms of discrimination. Yet the intersectionality literature has paid very little attention to the nature of ageing or the life course, and gerontology has rarely incorporated insights from intersectionality. In this paper, we aim to illustrate how intersectionality might be synthesised with a life course perspective to deliver novel insights into unequal ageing, especially with respect to health. First we provide an overview of how intersectionality can be used in research on inequality, focusing on intersectional subgroups, discrimination, categorisation, and individual heterogeneity. We cover two key approaches—the use of interaction terms in conventional models and multilevel models which are particularly focussed on granular subgroup differences. In advancing a conceptual dialogue with the life course perspective, we discuss the concepts of roles, life stages, transitions, age/cohort, cumulative disadvantage/advantage, and trajectories. We conclude that the synergies between intersectionality and the life course hold exciting opportunities to bring new insights to unequal ageing and its attendant health inequalities.