Accounting for mental health research funding: developing a quantitative baseline of global investments.

High-quality data on funding for mental health research are essential to mapping funding levels, identifying gaps in the funding landscape, and tracking the impact of research funding. To date, quantitative analyses of research funding in mental health have been restricted in scope. In this Health Policy paper, we present a comprehensive analysis of grant funding for mental health research as a starting point for discussion among stakeholders globally. We drew on a major international research database and used existing definitions and automated classification tools for mental health research. Our analysis shows a flat and stable trend over the years 2015-19 and highly unequal geographical distribution of funding, and reveals patterns of funding across different conditions and across the research spectrum. Improvements in data availability and quality, in the definitions delineating mental health research from other areas, and in automated classification tools are needed to ensure funders and policy makers can fully rely on the data and generate bespoke analyses as needed. We argue that collaborative reporting of funding for mental health research globally could help to inform and evaluate efforts to increase investments, to improve strategic dialogue, and to achieve the best possible allocation of finite resources.

Pharmacoepidemiology research: delivering evidence about drug safety and effectiveness in mental health.

Research that provides an evidence base for the pharmacotherapy of people with mental disorders is needed. The abundance of digital data has facilitated pharmacoepidemiology and, in particular, observational research on the effectiveness of real-world medication. Advantages of pharmacoepidemiological research are the availability of large patient samples, and coverage of under-researched subpopulations in their naturalistic conditions. Such research is also cheaper and quicker to do than randomised controlled trials, meaning that issues regarding generic medication, stopping medication (deprescribing), and long-term outcomes are more likely to be addressed. Pharmacoepidemiological methods can also be extended to pharmacovigilance and to aid the development of new purposes for existing drugs. Drawbacks of observational pharmacoepidemiological studies come from the non-randomised nature of treatment selection, leading to confounding by indication. Potential methods for managing this drawback include active comparison groups, within-individual designs, and propensity scoring. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple analytical approaches triangulated to improve confidence in inferred causal relationships. With developments in data resources and analytical techniques, it is encouraging that guidelines are beginning to include evidence from robust observational pharmacoepidemiological studies alongside randomised controlled trials. Collaboration between guideline writers and researchers involved in pharmacoepidemiology could help researchers to answer the questions that are important to policy makers and ensure that results are integrated into the evidence base. Further development of statistical and data science techniques, alongside public engagement and capacity building (data resources and researcher base), will be necessary to take full advantage of future opportunities.

How data science can advance mental health research.

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

How much is spent on mental health research: developing a system for categorising grant funding in the UK.

Knowing how much money is invested in funding mental health research, and in which areas, is essential to inform strategy and track trends to achieve the best allocation of limited resources. However, no comprehensive categorisation system for mental health research is available and, therefore, national and international data on mental health research funding are minimal and not comparable. In this Health Policy paper, we consider the complexities involved in generating such data and propose an approach to classify mental health research grants. We then describe a method using search terms and algorithms for automatic identification and categorisation of mental health research grants listed in a major international database (Dimensions, Digital Science). The automated approach was validated using manually categorised grants data from funders based in the UK, which showed that the accuracy of this approach is satisfactory and comparable to manual classification. Finally, we consider areas of research that are difficult to classify, and how the automated approach can be refined using machine-learning. We argue that agreed definitions and automated approaches could facilitate collaborative reporting of mental health research funders nationally and internationally and improve the strategic dialogue in this area of research.

Pictorial health warnings and wear-out effects: evidence from a web experiment in 10 European countries.

OBJECTIVE: This paper examines whether there are possible wear-out effects associated with repeated exposure to pictorial health warnings on tobacco products. Wear-out effects can be general, that is, people get used to the presence of pictorial warnings in general, or specific to the content of the warnings (ie, the images used). Distinguishing between these two types of wear-out is important for understanding how to maintain the effectiveness of health warnings over time. METHODS: This study used data from two surveys carried out in 10 European countries. Participants (n=12 600) were exposed in a random order to a series of health warnings and assessed the salience of the warnings as well as their effects on smoking intentions. Using these data and country variations in health warning legislation, we tested whether warning pictures are subject to general and/or specific wear-out effects. RESULTS: Responses were stronger to combined text+picture warnings than to text-only warnings. This effect was lower for smokers living in countries where combined warnings were already in place at the time of the data collection, compared with smokers residing in countries where text-only warnings were in use. This result, observed for combined warnings with new pictures, is in line with the presence of general wear-out effects. Combined warnings with an unknown pictorial content were more effective than those including pictorial warnings already in use, suggesting that specific wear-out effects are also at play. CONCLUSIONS: These findings strengthen the evidence that pictorial health warnings are an effective tool for tobacco control policies and suggest that, even in the presence of a general wear-out effect among smokers, periodically introducing new pictures helps to maintain warning effectiveness over time.

The Fox and the Grapes-How Physical Constraints Affect Value Based Decision Making.

One fundamental question in decision making research is how humans compute the values that guide their decisions. Recent studies showed that people assign higher value to goods that are closer to them, even when physical proximity should be irrelevant for the decision from a normative perspective. This phenomenon, however, seems reasonable from an evolutionary perspective. Most foraging decisions of animals involve the trade-off between the value that can be obtained and the associated effort of obtaining. Anticipated effort for physically obtaining a good could therefore affect the subjective value of this good. In this experiment, we test this hypothesis by letting participants state their subjective value for snack food while the effort that would be incurred when reaching for it was manipulated. Even though reaching was not required in the experiment, we find that willingness to pay was significantly lower when subjects wore heavy wristbands on their arms. Thus, when reaching was more difficult, items were perceived as less valuable. Importantly, this was only the case when items were physically in front of the participants but not when items were presented as text on a computer screen. Our results suggest automatic interactions of motor and valuation processes which are unexplored to this date and may account for irrational decisions that occur when reward is particularly easy to reach.

Value signals in the prefrontal cortex predict individual preferences across reward categories.

Humans can choose between fundamentally different options, such as watching a movie or going out for dinner. According to the utility concept, put forward by utilitarian philosophers and widely used in economics, this may be accomplished by mapping the value of different options onto a common scale, independent of specific option characteristics (Fehr and Rangel, 2011; Levy and Glimcher, 2012). If this is the case, value-related activity patterns in the brain should allow predictions of individual preferences across fundamentally different reward categories. We analyze fMRI data of the prefrontal cortex while subjects imagine the pleasure they would derive from items belonging to two distinct reward categories: engaging activities (like going out for drinks, daydreaming, or doing sports) and snack foods. Support vector machines trained on brain patterns related to one category reliably predict individual preferences of the other category and vice versa. Further, we predict preferences across participants. These findings demonstrate that prefrontal cortex value signals follow a common scale representation of value that is even comparable across individuals and could, in principle, be used to predict choice.