The COVID-19 pandemic is having a deeply felt effect across the world. The focus is understandably on the disease itself but, there is no doubt, the mental and social consequences will be far reaching, although not inevitable and not all detrimental.
However, we need to be taking action now to prevent the widening of existing inequalities in mental health and protect the vulnerable in our populations- from those in deprived communities, young people and frontline health care workers to the bereaved, those with existing mental illness and the lonely.
In response the research community, across disciplines, has pulled together with a speed I’ve not been a part of before. There is an almost palpable desire to make a difference, to contribute to the global effort to curb the effects of the pandemic and to raise awareness of the need to mitigate impacts on mental health. The last decade has seen a growing acceptance of the importance of mental health, leaps have been made on the pathway to parity of esteem with physical health but the challenge in these times of pandemic is not to forget the importance and impact of mental health on quality of life, disability and mortality. This is not a time for apathy or old arguments of ‘medicalisation’ with respect to the costs to individuals and society of mental distress. Context, function and diagnosis (or not) will interact as always. The distribution of infection, deaths, the impacts of the measures taken to curb the spread of Covid-19 and the socio-economic impact will not be felt equally in our society and will not have equal effects on mental health. We need to band together to raise awareness and ensure we take actions to mitigate these effects now rather than later.
So where in all this does data science lie? The old challenges remain- volume (size of datasets), variety (multiple sources and types), ‘variability’ and ‘veracity’ i.e the unreliability of some data sources. However, the rapid, ‘real-time’ acquisition of data and our skills in epidemiology and modelling makes our work of fundamental relevance across various study designs at this time. In the past there was never any point in mentioning to friends that I was an epidemiologist- now epidemic curves and modelling is a part of everyday language. The responsibility for researchers, in a climate where there is a thirst for answers, is to highlight the strengths and weaknesses of their design, the parameters of their model, the underlying assumptions, the data source and comprehensiveness (rubbish in, rubbish out) and the uncertainty of any answers. The latter is the most challenging in a world that craves certainty in the face of the unknown.
In the last six weeks we’ve collaborated with others to publish awareness raising position statements and commentaries.
We’ve also analysed a raft of different data. We’ve worked with the Mental Health Foundation on the “Coronavirus: Mental Health in the Pandemic” project where since mid-March, before lockdown, we’ve surveyed more that 4000 adults in the UK at regular intervals to explore peoples’ emotional responses to the pandemic, the social drivers of those responses, coping mechanisms and suicidal thoughts. It’s an important, timely and responsive piece of work – with fast turnarounds to influence actions in real time.
We’ve also collaborated with the University of Bristol on two projects where programming skills have been essential to delivery. One is using Google Trends data to map population mental health concerns related to COVID-19 to inform public health messaging. The other is a living systematic review of the impact of the Covid-19 pandemic on suicidal behaviour with a bespoke platform that searches for relevant literature every 24 hours to support evidence based policy making. Both projects have been disseminated to practitioners and policy makers.
From next week we start work on the SAIL-TAC project, a Wales national e-cohort to study, model and investigate potential trends, outcomes and strategies in the COVID-19 response using almost real time feeds of anonymised privacy protected data. The data comes from patients and is collected by the NHS. It represents data science in its full potential, rising to the challenge, where the research priorities are shaped by the needs of the population and clinical services. Watch this space.
BY PROFESSOR ANN JOHN, SWANSEA UNIVERSITY
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