A toolkit for capturing a representative and equitable sample in health research

Research participants often do not represent the general population. Systematic exclusion of particular groups from research limits the generalizability of research findings and perpetuates health inequalities. Groups considered underserved by research include those whose inclusion is lower than expected based on population estimates, those with a high healthcare burden but limited research participation opportunities and those whose healthcare engagement is less than others. The REP-EQUITY toolkit guides representative and equitable inclusion in research. The toolkit was developed through a methodological systematic review and synthesis and finalized in a consensus workshop with 24 participants. The REP-EQUITY toolkit describes seven steps for investigators to consider in facilitating representative and equitable sample selection. This includes clearly defining (1) the relevant underserved groups, (2) the aims relating to equity and representativeness, (3) the sample proportion of individuals with characteristics associated with being underserved by research, (4) the recruitment goals, (5) the strategies by which external factors will be managed, (6) the methods by which representation in the final sample will be evaluated and (7) the legacy of having used the toolkit. Using the REP-EQUITY toolkit could promote trust between communities and research institutions, increase diverse participation in research and improve the generalizability of health research. National Institute for Health and Care Research PROSPERO identifier: CRD42022355391.


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Considerable interest among statisticians in diversity and including underserved groups, but they wanted advice on 'how to work it out' and 'where to go to progress it'.

What are the barriers for use of the Toolkit?
 Consider how comfortable people are with sharing their data.People may be used to being asked about things like ethnicity but in cases where data relating to gender identity or marital status, for example, are requested, people may be wondering how these data will be used.There is a trend whereby people are asked to share information on the nine protected characteristics in the UK Equality Act 2010, but onus on researcher to rationalise what is "nice to know versus need to know"  Researchers must earn the public's trust in relation to sharing their data.

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Trial teams under pressure to recruit in a set time-frame or close, and a time-consuming sampling and recruitment process may not be compatible.

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For basic fundamental science -how do you get access to the required information?
 Expectation of any given study: In the beginning, you expect pragmatic evaluation of it but then in the context, that may not be possible due to the number of participants.So, you set your aim, but you may not achieve.or health relevance in the foreseeable future to improve human health, or would the effect be aligned with public trust and confidence and therefore impinge on the sustainability of your discipline from the public support perspective?Sometimes at a minimal level, you might have a duty to share your knowledge to inform, be publicly accountable, and to empower the public.In support of this, case studies could be made widely available to exemplify how good EDI can be done.They could include how underserved communities could be involved and engaged.

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There could be more support provided to fill gaps in understanding, in the form of more knowledge sharing through training, discussion forums between disciplines and involving people with insights and experience in EDI in research.It therefore seems imperative that a case will need to be made by each researcher in the first instance.If it is strong enough, and patient and public benefit can be accounted for, then it stands a much better chance of getting funded.

Search results from OVID MEDLINE, Embase, searches from the references of the relevant Articles and Grey literature.
Teams have started to match samples we recruited to the population using the local hospital's health records; but it is still difficult to ensure you are being fully inclusive and that you have got a diverse data set when dealing with small sample sizeConducting research using a national survey to look at clinical population versus the general population.But when looking at research papers that have the same biases as we have, you may be comparing good practices to good More sites, more diversity?Depends on the area where we link patient representation to e.g., areas with small ethnic minority populations -Hence, yes, more sites benefit but also disadvantages.Even hyperdiverse cities are difficult to rely on for recruitment of large trials.
Look to example of patient and public involvement and engagement (PPIE) where originally, it was 'nice to do' and not 'necessary to do' as there was push-back from certain funders and additional PPIE costs made grant applications uncompetitive.o For research centre in question: resource implication and being able to access the resource quite quickly would be a challenge that the centre would need to support/facilitate o Thus, funders need to be aware and supportive of the implementation of this toolkit as, if they are not, researchers cannot implement this.o It can become a disadvantage for your application when applying for grants explaining extra costs for the implementation of the toolkit.
A lot of this will be really difficult for people that do not have access to patients and do not have access to data about patient groups or demographics.It is much easier, probably, if you are in applied health research or data research.But if you're doing basic or fundamental science, how would you get access to this information?Other than looking at other studies, which could just have the same biases from research that has been done without this guidance.o Research centres could utilise existing health data and flag this material for researchers, because for some this is just inaccessible. Ethnicity and the challenges, especially on the genomics work, so we just expect pragmatic evaluation of that.Now we discussed the deprivation, and how you might assess that, and you can go down that long list.But then, in the context of a ten, to fifteen to twenty-five patients' study that may not be possible to go all the way down the list.I think we just require something pragmatic, that gives you a sense of "if I am doing that study in this region; This is broadly what I expect to look like, and then how you achieve that".o We might not be able to achieve that, but at least you would be clear as to what it should look like, and how you might do that, and if it does not work the next time around, you can say, well, what do we need to do to improve that going forward.That's still a lot further forward than the way we currently undertake studies.While it is obvious that EDI is well established in clinical research, other researchers such as those engaged in health data and methodologies may not find immediate relevance.It is for them to ask the question: would you be intersecting with translational research?Will your specialised field have knowledge 