As America burns and the #BlackLivesMatter campaign goes global and spreads to the UK people of colour have been asking white people to say something. The quote Megan Markle “….. the only wrong thing to say is to say nothing”. At the same time, my eldest daughter is adamant that keyboard activism is wrong; “it easy to type and post something to social media”, she says “but it much is harder to something proactive and sincere”. As a secondary school teacher in a state comprehensive school in South London where a lot of her students have social problems and come from a BAME (black, Asian and minority ethnic) background, she has the moral high ground.
This discussion reminds me of a stinging criticism we at Barts-MS had from a person who was then working in a very senior position for one of the MS charities in the UK. She said that Barts-MS pandered to the rich, white, educated, middle-class person with MS, who came to our centre to get what they wanted and that we were neglecting our local population of patients who were much more needy. She claimed we had an unconscious bias against BAME (black, Asian and minority ethnic) patients with MS. This was a stinging attack on our MS service.
I am acutely aware of unconscious bias in healthcare and her criticism hurt. For example, a very prestigious neurorehabilitation centre refused to publish an audit in the early naughties, which showed that people from upper-income groups (socioeconomic classes 1 & 2) were massively overrepresented in their unit compared to patients from lower socioeconomic classes. How and why unconscious biases creep into healthcare are well studied and understood, but to be accused of it yourself was sobering.
To counter the criticism against Barts-MS, which serves the most diverse population in London and arguably in the UK, we decided to do an audit of the patients on disease-modifying therapies in our centre. We argued that if we did have unconscious biases that favoured the well-educated and rich white middle classes they would more likely to be on higher efficacy DMTs than the less well educated, poorer local patients under our care. We felt somewhat vindicated when we showed that within our service socioeconomic class did not predict a person’s likelihood of being on any particular tier of DMT. In other words, if you get into our service regardless of who you are we will treat you the same.
The exercise of doing this audit also triggered a deep desire in me to find out more about the social determinants of health (SDoH) and how they impact on MS outcomes. I have spent the better part of 5 years studying the SDoH, which has led to our #ThinkSocial campaign, our social capital research projects and for a SDoH workstream to be a part of our Raising-The-Bar initiative. Our motto is ‘no patient should be left behind’ and we mean it when we say it.
In fact, I may have developed a conscious bias in favour of BAME patients with MS. As BAME patients with MS have a worse prognosis they are often given a worse prognostic profile, which results in us steering them towards higher efficacy therapies. The patient I described yesterday, who I am now fast-tracking through diagnostic tests despite the COVID-19 restrictions on our service, is being driven by the fact that he comes from a BAME background. I am now questioning myself if this patient happened to be white would he be getting the same treatment approach from me? I sincerely hope so.
Saúl Reyes et al. Socioeconomic Status and Disease-Modifying Therapy Prescribing Patterns in People With Multiple Sclerosis. Mult Scler Relat Disord. 2020 Feb 24;41:102024.
Aims: To examine the association between socioeconomic status (SES) and disease-modifying therapy (DMT) prescribing patterns in people with relapsing-remitting multiple sclerosis (pwRRMS).
Methods: A cross-sectional analysis was conducted among pwRRMS treated with a DMT in the neuroinflammation service at The Royal London Hospital (Barts Health NHS Trust). Study data were collected between July and September 2017. SES was determined by patient income and education extracted from the English Index of Multiple Deprivation. Based on their efficacy, DMTs were categorized as moderate efficacy (Glatiramer Acetate and Beta-Interferons), high efficacy (Cladribine, Fingolimod and Dimethyl Fumarate) and very-high efficacy therapies (Natalizumab and Alemtuzumab). Multinomial logistic regressions were performed for univariate and multivariate models to assess differences between SES and DMT prescribing patterns.
Results: Treatment consisted of moderate efficacy (n = 76, 12%), high efficacy (n = 325, 51.3%) and very-high efficacy therapies (n = 232, 36.7%). Medians for income and education deciles were 4 (IQR 3-7) and 6 (IQR 4-8), respectively. After multinomial logistic regression analysis, patient income was not associated with increased odds of being treated with high efficacy (OR, 0.92; 95% CI, 0.82-1.04; p = 0.177) or very-high efficacy DMTs (OR, 0.95; 95% CI, 0.85-1.06; p = 0.371). Similarly, patient education was not associated with being treated with high efficacy (OR, 0.91; 95% CI, 0.80-1.03; p = 0.139) or very-high efficacy therapies (OR, 0.92; 95% CI, 0.81-1.04; p = 0.188).
Conclusions: SES was not predictive of DMT prescribing patterns in pwRRMS. Whilst this appears reassuring within this universal health care setting, the same methodology needs to be applied to other MS services for comparison. Data could then be further interrogated to explore potential socioeconomic inequities in DMT prescribing patterns across the UK.
CoI: multiple