What can mental health researchers learn from the COVID-19 crisis?

Since the COVID‐19 pandemic took hold in the first quarter of 2020, children and their families across the world have experienced extraordinary changes to the way they live their lives – creating enormous practical and psychological challenges for them at many levels. While some of these effects are directly related to COVID‐related morbidity and mortality, many are indirect – linked rather to governmental public health responses designed to slow the spread of infection and minimise the numbers of deaths. These have often involved aggressive programmes of social distancing and quarantine, including extended periods of national social and economic lockdown, unprecedented in the modern age. Debates about the appropriateness of these measures have often referenced their potentially negative impact on people’s mental health and well‐being – impacts which both opponents and advocates appear to accept as being inevitable.

I reflect on this period of history to ask what lessons might be learnt about how to conduct child and adolescent mental health research in the context of society‐wide health crisis. This is important for two reasons. First, it can help us prepare to be more effective in future crises – either further waves of this virus, its mutations or other global threats. Second, studying the specifics of the COVID‐19 situation has the potential to cast light on more general issues in mental health research and stimulate scientific and therapeutic advances in ways similar to those seen during previous national crises (e.g. the Second World War). Given the space available, I have selected three issues which I think are most obvious and important – spanning public engagement with science and health messaging, health economics and developmental psychopathology. My goal is not to compare one response to the pandemic over another or to make specific claims about the impact of government measures on mental health but rather to consider how research might be conducted and evidence communicated most effectively.

1 Are we promoting the public (mis)understanding of science?

Effective public health strategies require simple messaging to engage the public and make it possible for them to follow advice. These messages also need to motivate people to follow that advice, in part, by highlighting the credibility of its source. Central to achieving this second purpose during the COVID‐19 campaigns has been the mantra ‘follow the science’. Indeed, such rhetoric appears to have been effective in motivating parental compliance with official scientific advice about the best way to fight the spread of the virus – even if that risks negative effects on their families’ well‐being. At the same time, it seems to me, it has promoted a dangerously simplistic notion of what science is and what scientists do. Borrowing a military analogy, so popular with politicians during the pandemic, we might say that the language of science has been weaponised as part of the war on the invisible enemy. By simplistic, I mean a model that reduces science to ‘fact collecting’ – where ‘facts’ are by their very nature presented as uncontentious, uncontested and therefore able to be stated with authority and certainty based on unanimity within the scientific community (Davey Smth, Blastland, & Munafo,). However, as scientists themselves know – this is a misleading caricature – science is not simply about collecting ‘facts’ but about testing hypotheses, derived from models and theories, against the best evidence. This means that in all scientific fields there are important unresolved questions and debates about the standing of competing explanations – this is normal and healthy in science – and is obviously most common in situations where less is known about the phenomenon in question – such as, in the COVID‐19 situation, vis‐a‐vis the rates of community acquisition of immunity to a virus or the extent of mental health impacts of lockdown on families.

Now the temptation to simplify your message to speak with authority when you are trying to persuade people to change their behaviour to do something that is difficult or costly to them, even when the evidence is uncertain and contested, is understandable. It is obviously important for official science to gain and keep the public’s confidence in the advice they are being given. However, over and above being potentially misleading in and of itself, there are two other reasons for pause to reconsider the wisdom of the current messaging tactic. First, it risks undermining the public’s general trust in science and scientists if individuals start to doubt the veracity of the specific advice given with confidence and authority by government scientists. This could undermine the public’s willingness to ‘follow the science’ in the future. Second, it may suppress debate amongst scientists themselves and by this hinder scientific progress – as one group of scientists look to lock down the competing views of others – so as to maintain the clarity and power of their public‐facing messaging. If scientists start to go down this slippery slope, they risk creating a pretext for antiscientific actions, including: (a) exaggeration of the fit between existing evidence and preferred models; (b) mischaracterisation of opponent’s models (i.e. setting up, easily refutable, straw men); and/or (c) appeal to eminence or consensus when the evidence is contested or uncertain. At worse, they may even degenerate into ad hominen attacks on the reputations of those making alternative proposals. I fear that when the social history of COVID‐19 science is written, the potentially damaging effects on science of the pursuit of simple and effective public health messaging are likely to form a central narrative theme.

For me the antidote to all this is to encourage open, thoughtful and civilised debate within the science community, avoiding over simplistic assertions about evidence and then to trust the public to grasp a more sophisticated and nuanced notion of what science is, the limits of evidence and the fact that debate and disagreement are signs of a vibrant and healthy science.

2 How can we ensure that public health policymakers take seriously the negative impact of disease control measures (e.g. lockdowns) on mental health?

If ‘follow the science’ is on page 1 of the Pandemic Book of Tropes, and ‘defeat the invisible enemy’ is on page 2, then ‘the cure is doing more harm than the disease’ is on page 3. This of course refers to the claim that governmental measures to reduce transmission are producing more harm through their damage to civil rights, social and economic activity and mental health and well‐being (including access to medical procedures) than benefit in terms of reductions in morbidity and mortality. In fact, because of the extreme nature of the measures introduced, authorities have not tried to deny the potentially calamitous impact of this sort of collateral damage of their policies or play down the sacrifices that individuals and communities have been asked to make to save lives. Strikingly, however, despite the ‘follow the science’ mantra discussed above, governments have typically neither exploited existing or invested in new scientific or statistical methods to answer the fundamental question – do the benefits of measures imposed outweigh their costs, seen in the round? – as would typically be done for all other health interventions (albeit they might lack the scale, scope and potential extreme adverse effects of many COVID‐19 disease control approaches). One defence of this lack of activity is that it is impossible to equate the health benefits of ‘lockdown’ in reducing physical disease and death with its negative economic, psychological and health‐related impact. The answer to this of course is that this is what bodies such as the National Institute of Health and Clinical Excellence (NICE) do all the time when they have to judge if a new intervention should be paid for by the government. Indeed, health economists have developed powerful concepts (such as quality‐adjusted life years or QALYs) and costs–benefit analytical techniques to help them make these tough decisions (Smith, Machalaba, Seifman, Feferholtz, & Karesh,2019). Obviously, a national lockdown presents particular methodological and health economic modelling challenges compared to say a randomised controlled trial of a new drug – however, the principle of translating diverse costs and benefits into common monetary and quality‐of‐life metrics are the same. One challenge relates to how one assesses costs and benefits accurately across a whole population – though the science of demography has cracked the problem of stratified sampling and light touch and portable methods such as app‐based remote measures may have an important role to play (Russell & Gajos, 2020; Watson, Wah, & Thamman,). From a modelling perspective, a major challenge relates to estimating the cost–benefit trade‐off for an intervention, such as lockdown, where there is such enormous variation between different demographics in terms of benefits and, to a lesser extent, costs. With the health benefits likely to be greater in those groups at higher risk of disease (the elderly and those with preexisting health conditions) and the costs likely to be greater for those with preexisting mental health conditions or living in difficult circumstances. To put the problem in an extreme form – poor young people in dysfunctional families with histories of mental health—but not physical health problems, will gain almost no benefit personally from the lockdown but are likely to experience great mental health costs. One might ask does it even make sense to calculate a costs–benefit ratio for the nation as a whole under such circumstances or is a totally different community‐based modelling approach needed that also takes account of the wider societal benefits of reducing pressure on social infrastructure.

3 Can we go beyond mere correlation in studies of the mental health impact of disease control measures?

Understanding the impact of COVID‐19 control measures on the mental health and well‐being of children and young people, both in the short and long terms, should be a public health funding priority. I have already highlighted how vital it is that government decisions to introduce public health measures are informed by a comprehensive estimate of the mental health burden imposed by these on families and children as part of a broader assessment of the costs of those policies. However, research must go beyond estimating the scale of the adverse effects linked to lockdown to understanding their underlying causes. This is important both because (a) it can help with the development of new, and better targeting and tailoring of existing, interventions to alleviate the specific adverse effects of lockdown measures, and (b) provide more general insights into the role adverse experiences play in the development of mental health problems. This can drive therapeutic innovation more broadly as extraordinary events that introduce perturbations within causal systems have the potential to reveal new effects and associations not apparent under other circumstances.

This focus on the importance of understanding the causes of disorder is part of the translational imperative at the heart of what we do anyway – if we can isolate disorders’ causes, we can more effectively target them to alleviate the distress and suffering they create. Of course, experiments – the systematic independent and systematic manipulation of putative causal variables through random assignment to assess their effects on predetermined outcomes while controlling for possible sources of bias and confounding factors – are the gold standard test of causation. It is obviously not ethically acceptable or politically expedient to implement experimental studies of an intervention with such potentially far reaching negative consequences as a “lockdown” – although variations in specifics might be feasible. On the other hand, no amount of cross‐sectional observational studies of the link between lockdown and mental health problems can tell us anything about cause. However, there are various ways to strengthen our ability to make inference about the causal processes linking lockdown to mental health problems. Existing longitudinal cohort studies, with measures already taken before lockdown and assessment of postlockdown mental health, will take us some of the way by providing important information about temporal sequencing of the exposure and its ‘effect’. This is particularly powerful when the timing and duration of the exposure can be precisely measured – as in the case of a lockdown. Comprehensive measurement and statistical control of potentially confounding factors increase the power of this design further (Vander Weele, Jackson, & Li,2016). Even when full experiments are not possible, some degree of ‘natural’ experimental control may be gained by exploiting ‘chance’ regional variations of the type and rigour of lockdown measures imposed. If we were to find systematic variation in mental health as a function of such variation, once other potential confounds have been controlled, this would strengthen causal inference further (Rutter, Kumsta, Schlotz, & Sonuga‐Barke,). If studies could be embedded in genetically informative designs such as those provided by national twin registries, that would add further explanatory precision by controlling for genetic confounds between environmental exposures and mental health outcomes (an approach illustrated by Vermeulen et al., this issue). Within these longitudinal designs, careful observation and modelling of putative mediating pathways linking lockdown exposure and mental health will be essential in the hunt for new intervention targets while strengthening further the plausibility of causal accounts. In this regard, there are likely to be both direct effects of lockdown experiences (for instance linked to lack of education, isolation from friends, lack of exercise, disease‐related anxiety) and indirect effects mediated via the impact of the lockdown on the family (for instance seen in the sociodevelopmental cascade where economic hardship and related stress negatively impacts intrafamily relations in extremis increasing the risk of interparental conflict or child maltreatment). As alluded to above, the effects of lockdown are likely to vary enormously from one person and one setting to another, depending on their circumstances and characteristics. If assessment approaches can capture this variation in outcomes, they may be able to help identify individuals at particular risk in terms of mental health (for instance, those young people with preexisting disorders, living in very challenging circumstances within dysfunctional families) and promote a more selective approach in which interventions are tailored to adjust for individual circumstances and clinical needs. In more general terms, analysis of these variations will provide insights into the nature and sources of resilience in the face of adversity (Sonuga‐Barke, 2019). What factors or characteristics mark those out that respond badly to lockdown‐related adversity and those that respond well or even thrive?

In conclusion

The COVID‐19 pandemic has had devastating effects on people’s lives all over the world – with many hundreds of thousands of people dying with the virus and widespread collateral damage of attempts to control the spread and reduce the health impacts of the virus. It has also presented the scientific community with some difficult challenges – many of them still to be addressed. In this editorial, I have focused on three such challenges relevant to researchers interested in the mental health of children and young people. In doing this, I have highlighted the need to improve public education about science and evidence, for policy decision models to be driven by health economic thinking and for research into the underlying causal pathways from lockdown to mental health problems.

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