Authors: Jasper Synowski, with Michael Plant and Clare Donaldson.

Thanks to Florian Kuhlmeier for data visualization, and to Derek Foster, Sonia Vallentin and Lucia Coulter for their valuable feedback. Especially, we would like to thank the whole team for their work on this project.

Summary

Relatively little is known about the cost-effectiveness of mental health programmes in low-resource settings, at least compared to physical health. Yet, given the scale of suffering attributed to mental illness, as well as its relative neglectedness and presumed impact on subjective well-being, these programmes appear promising from an (near-term) effective altruism (EA) perspective. As part of the Happier Lives Institute (HLI), a group of volunteers has begun to identify the most cost-effective giving opportunities tackling mental health conditions globally. This post sets out the methodology used for the first of three evaluation rounds. A team of eight screeners made initial evaluations of 76 programmes listed on the Mental Health Innovation Network (MHIN) website. At least three screeners rated each programme and, based on the screening data, we identified 25 programmes for further evaluation. Lack of information on cost-effectiveness for many programmes on the MHIN was a limitation of the process.

Introduction

In the context of global development, the effective altruism community has generally focused on alleviating poverty and improving physical as opposed to mental health (some exceptions are this 2017 post and Giving What We Can, 2016). This was understandable: if measured via the conventional methods used in health economics, programmes targeting mental health mostly seemed less cost-effective than, for example, GiveWell-recommended programmes (Levin and Chisholm, 2016; Founders Pledge, 2019). However, as Michael Plant (2018), (2019, ch7) argues, if the cost-effectiveness of mental health programmes is assessed using subjective well-being (SWB) scores – individuals’ reports of their happiness and/or life satisfaction – mental health programmes appear relatively more cost-effective than they do on the conventional metrics, and may be roughly on a par with poverty alleviation and physical health interventions.

As yet, there has been limited effort among effective altruists to systematically and transparently evaluate organisations working to improve mental health. The most substantial analysis to date is an evaluation conducted by Founders Pledge, which only examined two mental health organizations – StrongMinds and BasicNeeds – in depth. A more comprehensive evaluation of the potential giving opportunities is therefore warranted.

To shed light on this question, a group of volunteers at the Happier Lives Institute (HLI) is researching the cost-effectiveness of mental health programmes. Specifically, our aim is to identify those programmes which (a) are already being implemented and (b) can use funding to scale up their services. In contrast with Charity Entrepreneurship’s approach, we do not aim to identify interventions that new organisations could implement.

Our method has three steps:

1. Longlist: Identify programmes targeting mental illness in low-resource settings and make a shallow assessment of their suitability for EA funding, resulting in a “longlist” to be considered further.
2. Shortlist: Assess the longlisted programmes against relevant criteria to create a shortlist for detailed evaluation.
3. Recommendations: Carry out in-depth evaluations of the shortlisted programmes, potentially resulting in a list of recommended donation opportunities.

This document sets out to describe our approach and findings related to step (1). Later steps will be discussed in future posts.

Methods

Database for initial screening

As a starting point for our investigation, we chose the database provided by the Mental Health Innovation Network (MHIN). In several conversations with experts in the field of global mental health, it was mentioned as the most comprehensive overview of mental health projects and organizations, particularly those working in low- and middle-income countries (LMICs). We appreciated the focus on LMICs because the treatment gap for mental health conditions is especially high in these countries (WHO Mental Health Atlas, 2017), particularly in low-resource (e.g. rural) settings. Further, costs of treatment are usually lower than in high-income countries. We assessed only those innovations targeting depression, generalized anxiety disorder or stress-related disorders. This is because (a) they are responsible for most of the global burden of disease caused by mental disorders (Whiteford et al., 2013), and because of our prior beliefs that they (b) are very bad for well-being per-person (World Happiness Report, 2017) and (c) are mental health conditions that are relatively cheap and easy to treat (compared to, say, schizophrenia (Levin and Chisholm, 2016)).

The screening process

76 innovations were randomly assigned to eight screeners. Each innovation was screened by three screeners independently and blind to the ratings of others. Screeners used the same standardised framework and were advised to spend roughly 20 minutes evaluating each programme. Screenings were conducted over the months of May and June 2019 based only on information from the MHIN database – no additional literature search on the programmes was conducted at this point.

The screeners

Each of the eight screeners has some academic background relevant to mental health (e.g. was a master’s student of Public Health) and was familiar with the framework used for our ratings. In a first pilot testing of ratings, overall proportion of agreement between screeners was tentatively deemed sufficient (see inter-rater reliability analyses here and here).

The screening framework

The screening framework can be reviewed in detail here. It includes different parameters, such as type of mental disorder targeted, costs per beneficiary (rated 1 to 5 on an exponential scale, with each point increase corresponding to a ten-fold increase in costs) and effectiveness score (rated qualitatively 0 to 5, with 0 meaning no effect and 5 an endured cure of moderate or severe mental illness). Additionally, it includes a question on whether the screened programme can potentially be scaled up – either by means of supporting an already existing organization or through setting up an entirely new one. If this was not the case, the screening process was terminated early. Although the project’s focus is now clearly set on recommending already existing organizations, this was not yet decided when we started the screenings. We therefore assessed both types of programmes in our first round of evaluation.

From cost and effectiveness data, a crude ‘mechanical’ score of cost-effectiveness was generated by multiplying cost and effectiveness score. Additionally, raters provided a 0-10 ‘intuitive score’, which represents a subjective evaluation of the programme “as a scalable intervention that EAs could fund and have strong counterfactual impact”. We included these two different assessments as a robustness check. On the one hand, we wanted raters to make a rough estimation of cost-effectiveness using “objective” data, rather than solely relying on their subjective judgement. However, we also wanted to allow raters to make use of their judgement in order to (a) overcome severe limitations of the cost and effectiveness data (e.g. much of it was from clinical trials, which is unlikely to generalise to “real-world” practice), and (b) integrate other factors that may affect the suitability of the programme for receipt of donations, such as assumptions about its scalability, organisational strength, and room for more funding. This is reflected in the intuitive score.

Identifying programmes to investigate in more detail

It was necessary to establish decision rules determining whether a programme would be examined more closely in the second round of our analysis. We chose to base our decision on a combined rule including mechanical score and intuitive score: if a programme crossed the respective cut-off point for either of the two, it would be investigated in more detail regardless of its score on the other. The cut-off points were decided to be defined based on the screening data, taking into account HLI’s limited resources to investigate programmes in more detail.

Results

Description of screening data

All screening data can be found in the master file (the reader is especially referred to the tab “Screening Outcomes Summary”). Central rating outcomes for each innovation were mean mechanical estimate and mean intuitive estimate, shown in Figure 1.

Figure 1. Mean mechanical estimate vs. mean intuitive estimate. The x-axis represents the mean mechanical estimate, the y-axis the mean intuitive estimate. The size of the symbols represents the number of completed ratings (ranging from 1 to 9), whereas the colours indicate the actual number of mechanical estimates which have been made (these may differ due to insufficient information). NB: (a) If no mechanical estimate could be made at all, this was coded as mechanical estimate of -1; (b) programmes screened for assessing inter-rater reliability received more than 3 ratings (max. 9).

For both mechanical and intuitive scores, the majority of programmes fall in the middle of the scales (~5-13 for mechanical score; ~4-7 for intuitive score). A small proportion of programmes score more highly than this on one or both scales. It should be noted that about one third (34%) of all individual ratings did not result in a mechanical estimate, because screeners deemed the available information insufficient to rate either costs or effectiveness. Consequently, the mean mechanical estimate score was sometimes based on just one or two estimates. In general, mean mechanical estimate and mean intuitive estimate were positively correlated (Spearman’s ⍴ = 0.606), as visible in Fig. 1.

There seems to have been considerable disagreement between screeners on both scores. The median range of the intuitive estimate (that is, the difference between the highest and the lowest score) across all programmes was 2.5, and the median range of the mechanical estimate across programmes was 4.0.

As no clear clustering could be identified, we stipulated that to be considered in round 2, a programme needed to have an intuitive estimate ≥7 and/or a mechanical estimate ≥13. Additionally, we included programmes where there was high disagreement (i.e. a relatively high range of either intuitive estimate or mechanical estimate) and where repeating the highest intuitive estimate or mechanical estimate two times (i.e. adding two hypothetical screenings with this score) resulted in a mean score above the threshold. This decision rule resulted in a total of 26 programmes in round 2, of which one was excluded as typing errors were found to be responsible for a severely distorted mean mechanical estimate. The 25 programmes can be seen in a separate document in Table 1, along with their mean mechanical estimate and intuitive score.

Discussion

76 mental health innovations were screened as a first step in finding the most effective programmes targeting mental ailments worldwide. Using our screening procedure and decision rule, we identified 25 promising programmes for further evaluation.

That a relatively high proportion of screenings could not be given even a rough ‘mechanical’ cost-effectiveness estimate on the basis of cost and effectiveness data indicates the challenges of finding cost-effective mental health programmes. Cost data were particularly likely not to be included. Our inability to even vaguely estimate the cost-effectiveness of particular programmes may be either a result of the information existing but not being listed on MHIN, or its not having been collected so far. This lack of information is reflected in the considerable disagreement between raters when assessing both the intuitive estimate and mechanical estimate, and constitutes a major limitation of our analysis.

Our decision rule defining which programmes will be investigated in more detail imposed necessarily arbitrary cut-offs. While we currently believe that the mechanical estimate and the intuitive estimate offer the most promising combination to identify the most cost-effective programmes, this choice is debatable and so are the respective cut-off points. Hence, we do not have high confidence that all of the programmes we screened out are less cost-effective than those we included in the second round.

There are several other noteworthy limitations. First, screening was based on information from the MHIN database, and the extent to which information was provided varied greatly across programmes. This may have introduced bias towards placing higher ratings on the programmes with more available information. Second, we relied on the intuitive estimate as one of two central indicators determining whether an intervention will be investigated in more detail in the second round of ratings. This score, while presumably aggregating a lot more information than the mechanical estimate, may be prone to bias. For example, knowledge of treatments such as StrongMinds or the Friendship Bench may have led screeners to favour programmes which operate in a similar way over others (e.g. programmes using a task-shifting approach). Nonetheless, we believe that incorporating this judgment is important because it reflects the subject matter knowledge of our screeners as well as all other information collected via the framework. In addition, our impression was that the overall quality of data on costs and effectiveness for most of the programmes was relatively poor, which adds further value to the intuitive score compared to the mechanical estimate. Third, as we relied on the MHIN database, which has not been regularly updated since 2015, we will have missed any programme not included on that. To counter this flaw, we are currently conducting additional expert interviews in order to identify any additional promising programmes.

Next steps

We have already collected more information on the programmes which have made it into the second round of our analysis. Based on a scoring framework that we are developing, we are currently narrowing down the number of programmes to examine in more detail. At the end of round two, we plan to publish a post similar to this one.

Conclusions

At least three screeners attempted both a mechanical estimate of cost-effectiveness and an intuitive rating for each of 76 programmes listed on MHIN, though both assessments were often hampered by a lack of information. From these scores, we identified 25 programmes to evaluate in the second round. Ultimately, we aim to make detailed assessments of the most promising giving opportunities, to shed light on the question of whether any mental health programmes are competitive with GiveWell-recommended programmes in terms of cost-effectiveness.