Towards theoretically robust evidence on health equity: a systematic approach to contextualising equity-relevant randomised controlled trials

Wester, Gry; Bærøe, Kristine; Norheim, Ole Frithjof

doi:10.1136/medethics-2017-104610

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…In some cases, the biomedical "gold standard" designs (e.g., the randomized controlled trial) can be used, including cluster-randomized trials [41] and stepped-wedge designs [42]. There is growing literature on how to conduct randomized trials that are equity-relevant [43][44][45]. In other cases, particularly when the independent variable (e.g., a policy) cannot be randomized, non-randomized designs and methods are appropriate (e.g., time-series designs, quasi-experiments, natural experiments, difference in difference studies) [41,46].…”

Section: Underdeveloped Measures and Methodsmentioning

confidence: 99%

Implementation science should give higher priority to health equity

et al. 2021

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Background There is growing urgency to tackle issues of equity and justice in the USA and worldwide. Health equity, a framing that moves away from a deficit mindset of what society is doing poorly (disparities) to one that is positive about what society can achieve, is becoming more prominent in health research that uses implementation science approaches. Equity begins with justice—health differences often reflect societal injustices. Applying the perspectives and tools of implementation science has potential for immediate impact to improve health equity. Main text We propose a vision and set of action steps for making health equity a more prominent and central aim of implementation science, thus committing to conduct implementation science through equity-focused principles to achieve this vision in U.S. research and practice. We identify and discuss challenges in current health disparities approaches that do not fully consider social determinants. Implementation research challenges are outlined in three areas: limitations of the evidence base, underdeveloped measures and methods, and inadequate attention to context. To address these challenges, we offer recommendations that seek to (1) link social determinants with health outcomes, (2) build equity into all policies, (3) use equity-relevant metrics, (4) study what is already happening, (5) integrate equity into implementation models, (6) design and tailor implementation strategies, (7) connect to systems and sectors outside of health, (8) engage organizations in internal and external equity efforts, (9) build capacity for equity in implementation science, and (10) focus on equity in dissemination efforts. Conclusions Every project in implementation science should include an equity focus. For some studies, equity is the main goal of the project and a central feature of all aspects of the project. In other studies, equity is part of a project but not the singular focus. In these studies, we should, at a minimum, ensure that we “leave no one behind” and that existing disparities are not widened. With a stronger commitment to health equity from funders, researchers, practitioners, advocates, evaluators, and policy makers, we can harvest the rewards of the resources being invested in health-related research to eliminate disparities, resulting in health equity.

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Section: Underdeveloped Measures and Methodsmentioning

confidence: 99%

Implementation science should give higher priority to health equity

et al. 2021

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“…As an analytical framework, translational ethics provides a broad and useful frame within which to structure the required reflexivity to support an ethics for researchers who are pursuing impact. Importantly, this analytical framework is context-sensitive and puts the burden of providing justification on the researchers, but it does not provide any resources with which to identify substantial limitations to appropriate impact [ 18 ]. According to our working hypothesis, such assessments require support from substantive ethical and political discourses that address the issue of distribution of power at the intersection of producing and applying knowledge.…”

Section: Main Textmentioning

confidence: 99%

Pursuing impact in research: towards an ethical approach

et al. 2022

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Background Research proactively and deliberately aims to bring about specific changes to how societies function and individual lives fare. However, in the ever-expanding field of ethical regulations and guidance for researchers, one ethical consideration seems to have passed under the radar: How should researchers act when pursuing actual, societal changes based on their academic work? Main text When researchers engage in the process of bringing about societal impact to tackle local or global challenges important concerns arise: cultural, social and political values and institutions can be put at risk, transformed or even hampered if researchers lack awareness of how their ‘acting to impact’ influences the social world. With today’s strong focus on research impacts, addressing such ethical challenges has become urgent within in all fields of research involved in finding solutions to the challenges societies are facing. Due to the overall goal of doing something good that is often inherent in ethical approaches, boundaries to researchers’ impact of something good is neither obvious, nor easy to detect. We suggest that it is time for the field of bioethics to explore normative boundaries for researchers’ pursuit of impact and to consider, in detail, the ethical obligations that ought to shape this process, and we provide a four-step framework of fair conditions for such an approach. Our suggested approach within this field can be useful for other fields of research as well. Conclusion With this paper, we draw attention to how the transition from pursuing impact within the Academy to trying to initiate and achieve impact beyond the Academy ought to be configured, and the ethical challenges inherent in this transition. We suggest a stepwise strategy to identify, discuss and constitute consensus-based boundaries to this academic activity. This strategy calls for efforts from a multi-disciplinary team of researchers, advisors from the humanities and social sciences, as well as discussants from funding institutions, ethical committees, politics and the society in general. Such efforts should be able to offer new and useful assistance to researchers, as well as research funding agencies, in choosing ethically acceptable, impact-pursuing projects.

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“…Unjust health inequality is influenced by inequality in the socioeconomical, cultural and environmental factors (eg, access to clean water) that shape people’s living conditions 13. Although theories diverge as to what makes the resulting health disparities unfair, there is broad consensus that health inequality associated with socioeconomic determinants of health creates inequity and calls for amendment 14. For this reason, ML fairness should not only be about avoiding prejudices and favouritism, but also about reducing unfair health inequalities,15 particularly those associated with socioeconomic health determinants .…”

Section: Introductionmentioning

confidence: 99%

“…13 Although theories diverge as to what makes the resulting health disparities unfair, there is broad consensus that health inequality associated with socioeconomic determinants of health creates inequity and calls for amendment. 14 For this reason, ML fairness should not only be about avoiding prejudices and favouritism, but also about reducing unfair health inequalities, 15 particularly those associated with socioeconomic health determinants. In line with Rajkomar and colleagues’ reasoning, 15 to avoid ML in healthcare contributing to maintaining or reinforcing health inequities, fairness should be operationalised into ML processes by ensuring equal outcome across socioeconomic status, equal performance of models across socioeconomic groups, as well as equal allocation of resources.…”

Section: Introductionmentioning

confidence: 99%

Can medical algorithms be fair? Three ethical quandaries and one dilemma

Bærøe

Gundersen

Henden

et al. 2022

BMJ Health Care Inform

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ObjectiveTo demonstrate what it takes to reconcile the idea of fairness in medical algorithms and machine learning (ML) with the broader discourse of fairness and health equality in health research.MethodThe methodological approach used in this paper is theoretical and ethical analysis.ResultWe show that the question of ensuring comprehensive ML fairness is interrelated to three quandaries and one dilemma.DiscussionAs fairness in ML depends on a nexus of inherent justice and fairness concerns embedded in health research, a comprehensive conceptualisation is called for to make the notion useful.ConclusionThis paper demonstrates that more analytical work is needed to conceptualise fairness in ML so it adequately reflects the complexity of justice and fairness concerns within the field of health research.

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Towards theoretically robust evidence on health equity: a systematic approach to contextualising equity-relevant randomised controlled trials

Cited by 3 publications

References 20 publications

Implementation science should give higher priority to health equity

Implementation science should give higher priority to health equity

Pursuing impact in research: towards an ethical approach

Can medical algorithms be fair? Three ethical quandaries and one dilemma

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