Derek

• Derek 19 Mar 2024 17:37 UTC

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  on: Trans­for­ma­tive trust­build­ing via ad­vance­ments in de­cen­tral­ized lie detection

  As you note, this was written in 2012. Have you looked for more recent research into lie detection? As well as fMRI and polygraph, there’s voice stress analysis, non-verbal cues, microexpressions, and cognitive interviewing. I did a brief search a while ago but couldn’t find anything particularly accurate. I would be extremely keen to hear about any methods with high sensitivity and/​or specificity, or with the potential to achieve that in the near future. I might be willing to pay someone a modest amount to review the evidence and predict when accurate techniques will become available, and/​or to recommend the best existing method (or combination or methods).

• Derek 22 Jan 2024 19:39 UTC

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  in reply to: Stan Pinsent’s comment on: Does a DALY ap­proach un­der­rate the bad­ness of men­tal ill­ness?

  DALYs appear to weight pain very lightly. For example, terminal illness with constant, untreated pain has a disability (DALY) weight of 0.569, which is only 0.029 more than the weight for the same condition with pain medication. QALYs are better at capturing pain: physical pain is the dimension given the highest weight in the EQ-5D, and instrument used to measure quality of life.

  You might want to check disability weights for other painful conditions; I don’t remember if they were generally low.

  I suspect QALYs still underweight extreme pain, for various reasons, e.g. the arbitrary cap on negative values, and the lack of experience of such states among most respondents (typically the general public in high- and middle-income countries). The distribution of responses typically suggest ‘floor effects’, with some respondents likely to give lower values if it were permitted. The Devlin et al paper I linked to previously gives good evidence of that, but here is a graph from a different paper (UK sample) for illustration (note the cluster at −1).

  

  My point was more that pain gets a high weight relative to other dimensions of the EQ-5D...though not always the highest. As shown in the graph below, the original EQ-5D-3L UK tariff (Dolan, 1997) had pain as second (after mobility) for extreme, and roughly equal first (with self-care) for moderate, based on TTO responses from the general public. (I can give you the Excel version of the graph if you want to modify it.)

  

  The preliminary UK tariff for the newer EQ-5D-5L gave pain the highest weight, followed by depression/​anxiety, for the extreme level. Full results below… but note that NICE rejected the value set for methodological reasons so, last I checked, it still recommends mapping the old 1997 3L figures onto the 5L with an algorithm.

  

  There are many other tariffs from many other countries, for both the 3L and 5L, if you want to compare: https://​​euroqol.org/​​information-and-support/​​resources/​​value-sets/​​

  even sufferers may underestimate the badness of depression

  I think this may be true (given some plausible-to-me philosophical and psychological assumptions), but it’s also more generally that studies done in sufferers likely underestimate the badness. For example, because studies exclude the most severe cases, the badness of severe depression would be underestimated even if the study participants gave fully ‘valid’ responses (and even if an instrument were used that was able to capture the full range of experience).

  • I see from the summary you linked that IHME have used sequelae to identify ailments that are present in multiple health conditions. That seems sensible. I guess the kind of problem I often face is “What will be reduction in someone’s disability weight if they are—protected from getting diabetes /​ cured of depression /​ etc. ?”

    • In the diabetes example, it seems fair to count DALYs averted by not having diabetes and DALYs averted by depression-caused-by-diabetes. Maybe not fair to count, say, obesity, since the increased risk of obesity associated with diabetes is likely to be correlational, not causal. Am I thinking along the right lines?

    • If we go with the depression example, it seems fair to count both prevented suicide and prevented depression (but not prevented depression-while-dead-by-suicide)

  I don’t remember the details of the DALY/​GBD methods, and I don’t know a great deal about diabetes, but I’m pretty sure it can be a cause as well as consequence of obesity. At least insulin therapy can cause weight gain. And obviously you’d want to count only the proportion of diabetics who would have got depressed/​gained weight as a result of diabetes.

  Not sure I follow the depression example, but yes, you would sum the YLL from suicide (i.e. ‘standard’ or counterfactual life expectancy minus the actual number lived) and YLD (i.e. years lived with depression * disability weight). The formula/​steps and examples are here and here.

• Derek 20 Jan 2024 13:00 UTC

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  on: Does a DALY ap­proach un­der­rate the bad­ness of men­tal ill­ness?

  Thanks for writing this—there’s some good stuff here. A few comments:

  1 QALY is equal to a year of life in full health, while 0 QALYs is a health state equivalent to death … The QALY scale admits scores below zero, which represent states worse than death

  Minor point, but I think ‘being dead’ is more accurate than ‘death’. The latter suggest permanency, whereas values <0 can represent temporary states that are deemed worse than being dead. That said, there is some uncertainty over the meaning of negative valuations, and the best interpretation may depend on the methods used to elicit the values.

  They appear to weight pain very lightly. For example, terminal illness with constant, untreated pain has a disability (DALY) weight of 0.569, which is only 0.029 more than the weight for the same condition with pain medication.

  IIRC, physical pain is the dimension given the highest weight in the EQ-5D, so I’m not sure this is accurate for QALYs at least. I haven’t looked into it fully, but one might expect DALYs to underweight pain, as in the example above, because (intuitively) one is no less ‘unhealthy’ if, say, terminal cancer is treated with painkillers. In contrast, your ‘quality of life’ is higher with lower pain, and most people have a strong preference for less pain, which is what QALYs aim to capture. In general, QALYs and DALYs give similar weights, so I’m not sure how much it matters in practice, but I haven’t looked at differences across types of health state. EDIT: A useful project would be to compare DALY and QALY values for painful and mental disorders, but it wouldn’t be that straightforward as QALYs are normally based on generic descriptions of health states while DALYs refer to specific conditions.

  They only aim to measure the impact of the health state, not its comorbidities

  If done properly, I think comorbidities are captured by both QALYs and DALYs. An individual’s QALY value is normally based on their self-reported score on a generic health state questionnaire, e.g. the EQ-5D has mobility, self-care, usual activities, pain/​discomfort, and anxiety/​depression. This is done without reference to specific health conditions (e.g. arthritis, cancer), so the impact of any comorbidities should be reflected in the valuation. When individual data are not available, I think the impact of comorbidities is typically estimated by multiplying the weights, e.g. 0.3*0.7=0.21, though alternatives have been suggested.

  DALYs do focus on health conditions but, at least when assessing the burden of disease, they try to account for sequelae (consequences of a condition). See the links in my summary here: https://​​forum.effectivealtruism.org/​​posts/​​Lncdn3tXi2aRt56k5/​​health-and-happiness-research-topics-part-1-background-on#Population_health_summaries1

  That said, I’m not sure whether comorbidities/​sequelae are always adequately captured in cost-effectiveness analyses, especially model-based analyses that use a hypothetical treated population. I can see it would be tempting for a modeller to ignore other conditions when evaluating the impact of an intervention on a particular disease.

  On depression:

  There are reasons to suspect that even the weights given by sufferers underestimate its badness. For example [EDIT: I’ve expanded this list]:

  • The most severe cases (e.g. in suicidal individuals, those with a terminal illness, or those with comorbidities such as dementia or psychosis) are typically excluded from studies for ethical reasons: it’s potentially problematic to ask such people whether they would be better off dead, and the ability to provide (meaningful) answers may be limited by acute suffering.

  • There are potential cognitive ‘biases’ at work, e.g. an evolutionarily-ingrained aversion to death.

  • There is some evidence from qualitative studies that respondents take into account effects on others, e.g. wanting to be alive to look after their children, even if they are asked to answer only based on self-interest.

  • As you know, DALYs don’t permit SWD and QALYs cap negative values at −1 or higher for individual responses in valuation studies, so the average value ends up much higher than that. In studies where they’re allowed to go lower, the mean values are also lower. See e.g. this paper.

  But I’m not sure it’s right to correct the weights for suicide. The QALY gain/​DALY loss from an intervention is a function of both duration and quality/​healthfulness of life, so adjusting the quality dimension risks double-counting the effect of suicide. EDIT: But there are reasons to think that the DALY-based Global Burden of Disease studies have underestimated the burden of mental illness. I haven’t kept up to date on the methodology since writing my posts, but see this paper from 2016.

  Some things, like extreme pain, depression and psychosis, may never be fully captured by bounded scales

  My PhD supervisor made a similar point this week. Some kinds of wellbeing (e.g. objective lists) may not have an upper or lower limit, and it may be unnecessary to impose them: to trade off duration and quality of life, we need a zero point (e.g. ‘as bad as being dead’) and some unit (e.g. a quantity of wellbeing), but a fixed upper and lower bound might not be essential. For hedonists, there may be some physical limit to pain/​pleasure, which could potentially serve as the bounds. For desire theorists, I’m not sure: I suppose intensity of preference is also a mental state that might also have a physical limit. But whether it’s feasible to develop a practical measure that captures the extremes without neglecting important gradations of more common states is as-yet unclear to me. Maybe one option is to anchor scales at something non-extreme, e.g. WELBY 1 = the 95th percentile of some population, and simply allow some individuals to score arbitrarily higher than 1 (e.g. WELBY 5 during some extreme pleasures). Mutatis mutandis for negative states. But I need to think about it more.

• Derek 11 Nov 2022 20:15 UTC

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  on: Reliev­ing ex­treme phys­i­cal pain in hu­mans – an op­por­tu­nity for effec­tive funding

  The urgency of relieving severe physical pain reveals the serious limitations of the “QALYs gained” approach to measuring scale of impact. A person with terminal cancer treated with morphine for two months might remain highly disabled and in a very poor state of health, and gain only a fraction of a QALY, yet be spared two months of agony.

  I’m not sure I follow this. QALYs allow negative values, so if morphine treatment increased health-related quality of life from, say, −0.5 to +0.1, it would gain 0.6 QALYs per year. Most/​all currently-used value sets would give less weight than that to pain relief, but I don’t think that’s primarily because of their other health states. Extending that life would gain few QALYs, but that doesn’t seem to be your concern.

  That said, it might depend on the method used to combine utility decrements for various health states. The most common approach to dealing with comorbidities is to multiply utility decrements, e.g. if the decrement for cancer is 0.4 and for pain is 0.6, you’d end up with (0.4*0.6) = 0.24 (assuming a baseline/​counterfactual of full health). Maybe that’s what you were getting at?

• Derek 4 Nov 2022 21:10 UTC

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  in reply to: Derek’s comment on: Prob­a­bil­ity dis­tri­bu­tions of Cost-Effec­tive­ness can be misleading

  Note that there are also methods for calculating confidence intervals around ICERs that avoid issues with ratios. The best I’m aware of is by Hatswell et al. I have an Excel sheet with all the macros etc set up if you want.

• Derek 20 Jul 2022 19:28 UTC

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  in reply to: Lorenzo Buonanno🔸’s comment on: Prob­a­bil­ity dis­tri­bu­tions of Cost-Effec­tive­ness can be misleading

  MAICER = maximum acceptable incremental cost-effectiveness ratio. This is often called the willingness to pay for a unit of outcome, though the concepts are a little different. It is typically represented by lambda.

  The CE plane is also useful as it indicates which quadrant the samples are in, i.e. NE = more effective but more costly (the most common), SE = more effective and cheaper (dominant), NW = less effective and more costly (dominated), and SW = less effective and cheaper. When there are samples in more than one quadrant, which is very common, confidence/​credible intervals around the ICER are basically meaningless, as are negative ICERs more broadly. Distributions in Guesstimate, Causal, etc can therefore be misleading.

  The standard textbook for heath economic evaluation is Drummond et al, 2015, and it’s probably the best introduction to these methods.

  For more details on the practicalities of modelling, especially in Excel, see Briggs, Claxton, & Sculpher, 2006.

  For Bayesian (and grudgingly frequentist) approaches in R, see stuff by Gianluca Baio at UCL, e.g. this book, and his R package BCEA.

  Cost-effectiveness planes are introduced in Black (1990). CEACs, CEAFs, and value of information are explained in more detail in Barton, Briggs, & Fenwick (2008); the latter is a very useful paper.

  For more on VOI, see Wilson et al., 2014 and Strong, Oakley, Brennan, & Breeze, 2015.

  For a very clear step-by-step explanation of calculating and interpreting ICERs and net benefit, see Paulden 2020. In the same issue of PharmacoEconomics there was a nice debate between those who favour dropping ICERs entirely and those who think they should be presented alongside net benefit. (I think I’m in the latter camp, though if I had to pick one I’d go for NB as you can’t really quantify uncertainty properly around ICERs.)

  For an application of some of those methods in EA, you can look at the evaluation we did of Donational. I’m not sure it was the right tool for the job (a BOTEC + heuristics might have been as good or better, given how speculative much of it was), and I had to adapt the methods a fair bit (e.g. to “donation-cost ratio” rather than “cost-effectiveness ratio”), but you can get the general idea. The images aren’t showing for me, though; not sure if it’s an issue on my end or the links are broken.

  Here is a more standard model in Excel I did for an assignment.

  Hope that helps. LMK if you want more.

• Derek 19 Jul 2022 22:03 UTC

  19 points

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  on: Prob­a­bil­ity dis­tri­bu­tions of Cost-Effec­tive­ness can be misleading

  This is a recognised issue in health technology assessment. The most common solution is to first plot the incremental costs and effects on a cost-effectiveness plane to get a sense of the distributions:

  

  Then to represent uncertainty in terms of the probability that an intervention is cost-effective at different cost-effectiveness thresholds (e.g. 20k and 30k per QALY). On the CEP above this is the proportion of samples below the respective lines, but it’s generally better represented by cost-effectiveness acceptability curves (CEACs), as below:

  

  Often, especially with multiple interventions, a cost-effectiveness acceptability frontier (CEAF) is added, representing the probability that the optimal decision (i.e. the one with highest expected net benefit) is the most cost-effective.

  

  I can dig out proper references and examples if it would be useful, including Excel spreadsheets with macros you can adapt to generate them from your own data (such as samples exported from Guesstimate). There are also R packages that can do this, e.g. hesim and bcea.

  What links here?

  • Prob­a­bil­ity dis­tri­bu­tions of Cost-Effec­tive­ness can be misleading by Lorenzo Buonanno🔸 (18 Jul 2022 17:42 UTC; 70 points)

• Derek 19 Jul 2022 19:58 UTC

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  in reply to: Hank_B’s comment on: Welfare Es­ti­ma­tion 101: Money is not Fungible

  For traditional QALY calculations, researchers simply ask people how they feel when experiencing certain things (like a particular surgery or a disease) and normalize/​aggregate those responses to get a scale where 0 quality is as good as death, 1 is perfect health, and negative numbers can be used for experiences worse than death.

  This isn’t correct. QALY weights are typically based on hypothetical preferences, not experiences.

  What Richard described is more like a WELBY, which has a similar structure but covers wellbeing in some sense rather than just health. See Part 1 of my (unfinished) sequence on this if you’re interested.

• Derek 19 Jul 2022 18:08 UTC

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  in reply to: Aaron Bergman’s comment on: Some re­search ques­tions that you may want to tackle

  Glad you found it useful. I am not qualified to comment on the role of neuron count in sentience; you may want to look at work by Jason Schukraft and others at Rethink Priorities on animal sentience and/​or get in touch with them.

  If you haven’t already, you may also want to review the 2018 Humane Slaughter Association report, which was the best I could find in early 2019. While looking for it, I also just came across one from Compassion in World Farming, which I don’t think I’ve read.

• Derek 11 Jul 2022 15:09 UTC

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  on: Some re­search ques­tions that you may want to tackle

  On fish, there were several comments here, including this one from me.

  The 2018 Humane Slaughter Association report was probably the best info available at the time; not sure what’s happened since.

• Derek 11 Jul 2022 13:57 UTC

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  in reply to: Derek’s comment on: Should I in­vest my run­way?

  There are also easy-access savings accounts giving a bit more than 1.3%: https://​​www.moneysavingexpert.com/​​savings/​​savings-accounts-best-interest/​​

• Derek 11 Jul 2022 13:53 UTC

  2 points

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  on: Should I in­vest my run­way?

  If you are under 40 and might want to spend the money on a first property costing <450k, you could consider a Lifetime ISA (either cash or stocks & shares):

  https://​​www.gov.uk/​​lifetime-isa https://​​www.moneysavingexpert.com/​​savings/​​lifetime-isas/​​

• Derek 16 Jun 2022 18:01 UTC

  11 points

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  on: Me­gapro­jects for animals

  There is a lot of potential in fish welfare/​stunning. In addition to what others have mentioned, IIRC from some reading a few years ago:

  • The greatest bottleneck in humane slaughter is research, e.g. determining parameters/​designing machines for stunning each major species, as they differ so much. There just aren’t many experts in this field, and the leading researchers are mostly very busy (and pretty old), but perhaps financial incentives would persuade some people with the right sort of background to go into this area.

  • As well as electrical and percussive stunning, anaesthetising with clove oil/​eugenol seems a promising and under-researched method of reducing the pain of slaughter. Because it may just involve adding a liquid/​powder to a tank containing the fish, it may also require less tailoring to each species than than other methods (though it can affect the flavour if “too much” is used). I have some notes on this if anyone is interested.

  • Crustastun could be mass-produced and supplied cheaply/​freely to places that would otherwise boil crustaceans alive. I seem to recall a French lawyer had invented another machine that was even better (or cheaper) but was too busy to promote it; maybe EAs could buy the patent or something?

  What links here?

  • Derek's comment on Some re­search ques­tions that you may want to tackle by Aaron Bergman (11 Jul 2022 15:09 UTC; 5 points)

• Derek 12 Jun 2022 9:52 UTC

  9 points

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  on: Where can I learn about how DALYs are calcu­lated?

  Some/​all answers are in here, or in papers linked in that post. https://​​forum.effectivealtruism.org/​​posts/​​Lncdn3tXi2aRt56k5/​​health-and-happiness-research-topics-part-1-background-on

• Derek 5 Jun 2022 15:05 UTC

  2 points

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  in reply to: Elizabeth Crewe’s comment on: Stuff I buy and use: a lis­ti­cle to boost your con­sumer sur­plus and productivity

  Yeah that’s what I use, and it’s cheaper than the fancy Wiley-branded fish-based product he linked to. You can get much cheaper fish oil, but if you’re going to get the expensive stuff anyway (I guess due to concerns about the quality of the cheaper brands), why not get vegan?

• Derek 29 Mar 2022 15:33 UTC

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  in reply to: Derek’s comment on: Health and hap­piness re­search top­ics—Part 1: Back­ground on QALYs and DALYs

  [Recording of the talk and related papers] You can now view the recording of the talk from Professor John Brazier—Extending the QALY beyond health—the EQ HWB (Health and Wellbeing) Kaltura https://​​digitalmedia.sheffield.ac.uk/​​media/​​t/​​1_8k5slrc4 YouTube https://​​www.youtube.com/​​watch?v=KTlsIvqyhNI Papers associated with this talk Special issue of Value in Health Development papers: Brazier, J et al. ‘The EQ-HWB: overview of the development of a measure of health and well-being and key results’. Value in Health. https://​​www.sciencedirect.com/​​science/​​article/​​pii/​​S1098301522000833 Mukuria, C et al. “Qualitative Review on Domains of Quality of Life Important for Patients, Social Care Users, and Informal Carers to Inform the Development of the EQ Health and Wellbeing.” Value in Health (2022). https://​​www.sciencedirect.com/​​science/​​article/​​pii/​​S1098301521032277 Carlton, J et al. “Generation, Selection, and Face Validation of Items for a New Generic Measure of Quality of Life: The EQ Health and Wellbeing.” Value in Health (2022). https://​​www.sciencedirect.com/​​science/​​article/​​pii/​​S1098301522000109 Peasgood, T et al. “Developing a New Generic Health and Wellbeing Measure: Psychometric Survey Results for the EQ Health and Wellbeing.” Value in Health (2022). https://​​www.sciencedirect.com/​​science/​​article/​​pii/​​S1098301521031922 International papers: Monteiro AL, et al. A Comparison of a Preliminary Version of the EQ Health and Wellbeing Short and the 5-Level Version EQ-5D. Value Health. 2022 Mar 8:S1098-3015(22)00051-1. doi: 10.1016/​j.jval.2022.01.003. Epub ahead of print. PMID: 35279371. Augustovski F, Argento F, Rocío R, Luz G, Mukuria C, Belizán M. The Development of a New International Generic Measure (EQ Health and Wellbeing): Face Validity And Psychometric Stages In Argentina. https://​​www.sciencedirect.com/​​science/​​article/​​abs/​​pii/​​S1098301522000134

• Derek 24 Mar 2022 14:37 UTC

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  in reply to: Derek’s comment on: Health and hap­piness re­search top­ics—Part 1: Back­ground on QALYs and DALYs

  FYI the E-QALY work has been progressing quite well since you asked that question; I’ve just come out of a webinar on it. Let me know if you want me to send you notes/​slides.

  A few key points:

  1. The measure has been named the EuroQol Health and Wellbeing (EQ-HWB); E-QALY seems to be what they are calling the broader project of extending the scope of the QALY.

  2. Psychometric work and stakeholder consultation resulting in a 25-item ‘long’ measure, then further consultation resulted in a 9-item EQ-HWB-S (Short Form) covering 9 domains: Mobility, Daily activitie, Pain, Fatigue, Loneliness, Concentration & thinking clearly, Depression, Anxiety, Control.

  3. A feasibility valuation study in 521 members of the UK public uses the time tradeoff (TTO, EQ-VT protocol) and discrete choice experiments (DCE). Due to covid this was done using video conferencing.

  4. There was also a deliberative exercise with a 12-member panel of experts at NICE which reviewed the valuation results.

  5. Based on the size of the utility decrement associated with the most severe level of each dimension, the order of importance is: Pain (by a long way); Mobility; Daily activities; Depression; Loneliness; Anxiety; Fatigue; Control; Concentration. (To me, the weight given to Mobility in particular might indicate that this measure does not overcome some of the biggest problems with earlier measures like the EQ-5D, though it seems to be much better overall.)

  6. Other valuation studies, using different methodologies, are underway or planned. As far as I know, these don’t include ones that obtain weights based on SWB, but I think they will be looking at own-state utilities (i.e. weights derived from preferences of people with the relevant conditions).

  7. Several papers are being published on it this year in a special edition of the journal Value in Health.

  8. It started with a grant of 850,000 GBP; more has been spent since, but I’m not sure how much.

  9. NICE still seems wedded to the EQ-5D for the foreseeable future, at least in standard health technology assessments, but they may use/​accept the EQ-HWB in cases where broader effects are particularly important, e.g. impacts on carers.

• Derek 11 Jan 2022 17:17 UTC

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  in reply to: RayTaylor’s comment on: Sleep: effec­tive ways to im­prove it

  Thanks. I tried 5-HTP a few years ago and didn’t notice any benefit, but maybe I’ll give it another go.

• Derek 4 Dec 2021 15:25 UTC

  12 points

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  in reply to: JoelMcGuire’s comment on: Donat­ing money, buy­ing hap­piness: new meta-analy­ses com­par­ing the cost-effec­tive­ness of cash trans­fers and psy­chother­apy in terms of sub­jec­tive well-being

  Thanks for the reply. I don’t have much more time to think about this at the moment, but some quick thoughts:

  1. On time discounting: It might have been reasonable to omit discounting in this case for the reasons you suggest, but (a) it limits comparability across analyses if you or others do it elsewhere; (b) for various reasons, it would be good to have some estimate of the absolute, not just relative, costs and effects of these interventions; and (c) it’s pretty easy to implement in most software, e.g. Excel and R (maybe less so in Guesstimate), so there isn’t usually much reason not to do it.

  2. On costs: (a) You only seem to measure depression, so if costs affect some other aspect of SWB then your analysis will not account for it. (b) It is also a good idea, where feasible, to account for non-monetary costs, such as lost time spent with family, and informal caregiver time. In this case, these are probably best covered by SWB outcomes, rather than being monetised, but since they involve spillovers on people other than the patient, they were not captured in this case. (c) Your detailed CEA of StrongMinds does not make it entirely clear what you mean by “all costs”; it just says “Our estimates of the average cost for treating a person in each programme are taken directly from StrongMinds’ accounting of its costs from 2019,” with no details about those accounts. For example, if they bought an expensive building in which to deliver training in 2018, that cost should normally be amortised over future years (roughly speaking, shared among future beneficiaries for the life of the building). So simply looking at 2019 expenditure does not necessarily capture “all costs”. I suggest reading Chapter 7 of Drummond et al to begin with, for a discussion of practical and conceptual issues in costing of health interventions.

  3. On the focus on depression data: My “loading the dice” comment wasn’t about SDB/​demand effects. Suppose, for example, that you want to compare intervention A, which treats both depression and severe physical pain; and intervention B, which only treats depression. You find that B reduces depression by more per dollar than A, so you conclude it is more cost-effective than A, and recommend it to donors. But it’s not really a fair comparison: you don’t know whether the overall benefit per dollar is greater in B than A, because you are ignoring the pain-relieving effects, which are likely greater in A. I haven’t looked at the GD data recently, but I can imagine something like that going on here, e.g. the cash has all sorts of benefits that aren’t captured by the depression measure, whereas the psychotherapy could have few such benefits.

  4. On spillovers: I’m glad you are updating the analysis. To be frank, I think you probably shouldn’t have published this analysis in its current state, primarily due to the omission of spillovers. It’s just too misleading.

  5. On sensitivity analysis: Also pleased you are going to add some of these. You’re right that some take longer than others, and it’s hard/​impossible to do some of them in Guesstimate. But I think you can export the samples from Guesstimate to Excel, which should allow you to do some of the key ones without too much work, e.g. EVPI and CEAC/​CEAF just need a simple macro and graph; see my Donational model for examples. (For extra usability and flexibility, you can do it in R and make a Shiny web app, but that takes a lot more work.)

  This paper, the Drummond book above, and this book are good starting points if you want to learn how to do cost-effectiveness analysis (including sensitivity analysis).

  A couple nitpicks:

  • Your title is misleading: this isn’t/​these aren’t “meta-analyses comparing the cost-effectiveness of cash transfers and psychotherapy”. AFAICT, you are doing a cost-effectiveness analysis informed by meta-analyses of the effects of the two interventions. You aren’t doing a meta-analysis of cost-effectiveness studies.

  • The y axes of your graphs, and some of your tables, say things like “Effects of Depression Improvement”. As far as I can tell, these are showing the effects of the interventions on depression/​SWB/​MHa in terms of SD. They aren’t, for example, showing the effects of depression (i.e. the consequences of depression for something else), as implied by this wording.

• Derek 26 Nov 2021 14:44 UTC

  14 points

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  on: Donat­ing money, buy­ing hap­piness: new meta-analy­ses com­par­ing the cost-effec­tive­ness of cash trans­fers and psy­chother­apy in terms of sub­jec­tive well-being

  There is much to be admired in this report, and I don’t find it intuitively implausible that mental health interventions are several times more cost-effective than cash transfers in terms of wellbeing (which I also agree is probably what matters most). That said, I have several concerns/​questions about certain aspects of the methodology, most of which have already been raised by others. Here are just a few of them, in roughly ascending order of importance:

  1. Outcomes should be time-discounted, for at least two reasons. First, to account for uncertainty as to whether they will obtain, e.g. there could be no counterfactual benefit in 10 years because of social upheaval, catastrophic events (e.g. an AI apocalypse, natural disaster), or the availability of more effective treatments for depression/​ill-being/​poverty. Second, to account for generally improving circumstances and opportunities for reinvestment: these countries are generally getting richer, people can invest cash transfers, etc. This will be even more important when assessing deworming and other interventions with benefits far in the future. (There is probably no need to discount costs as it seems they are incurred around the time the intervention is delivered in both cases.)

  2. I’ve only skimmed the reports, but it isn’t clear to me what exactly is included in the costs for StrongMinds, e.g. sometimes capital costs (buildings etc), or overheads like management salaries and rent, are incorrectly left out of cost-effectiveness analyses. If you haven’t already, you might also want to consider any costs to the beneficiaries, e.g. if therapy recipients had to travel, pay for materials, miss work, etc. As you note, most of the difference in the cost-effectiveness is determined by the programmes’ costs rather than their consequences, so it’s important to get this right (which you may well have done).

  3. You note that both interventions are assessed only in terms of their effect on depression. A couple years ago I summarised the findings of the four available evaluations of GiveDirectly in an unpublished draft post ( see Appendix 2.1, copied below, and the “GiveWell” subsection of section 2.2, the relevant part of which is copied below). The studies recorded data on many other indicators of wellbeing, which were sometimes combined into indices of “psychological wellbeing” with up to 10 components (as well as many non-wellbeing outcomes like consumption and education). Apologies if you explain this somewhere, but why did you only use the data on depression? Was it to facilitate an ‘apples to apples’ comparison, or something like that? If so, I wonder if it that was loading the dice a bit: at first blush, it seems unfair to compare two interventions in terms of outcome A when one is aimed solely at improving outcome A and the other is aimed at improving outcomes A, B, C, D, E, F, G and H (at least when B–H are relevant, i.e. indicators of subjective wellbeing).

  4. I share others’ concerns about the omission of spillovers. In the draft post I linked above (partly copied below), I recorded my impression that the evidence so far, while somewhat lacking, suggests only null or positive spillovers to other households (at least for the current version of the programme, which ‘treats’ all eligible households in the village). As part of a separate project I did last year (which I’m not allowed to share), I also concluded that non-recipients within the household benefited considerably: “Only about 1.6 members of each household (average size ~4.3) were surveyed to get the wellbeing results, of which only 1 actually received the money. There was no statistically significant wellbeing difference between the recipients and surveyed non-recipient household members, and there is evidence of many benefits to non-recipients other than psychological wellbeing (e.g. education, domestic violence, child labour). Nevertheless, we expect the effects to be a little lower among non-recipients…” Omitting the inter-household spillovers is perhaps reasonable for the primary analysis, but it seems harder to justify ignoring benefits to others within the household.

  5. Whatever may be justified for the base case, I don’t understand why you haven’t done a proper sensitivity analysis. Stochastic uncertainty is captured well by the Monte Carlo simulations, but it is standard practice in many fields (including health economics) to carry out scenario analyses that investigate the effects of contestable structural and methodological assumptions. It should be quite straightforward to adapt the model so as to include/​exclude (or vary the values of) spillovers, non-depression data, certain kinds of costs, discount rates, etc. You can present the results of these analyses yourself, but users can also put their own set of assumptions in a well-constructed model to see how that changes things. (Many other analyses are also potentially helpful, especially when the difference in cost-effectiveness between the alternatives is relatively small, e.g. deterministic one-way and two-way analyses that show how the cost-effectiveness ratio changes with high/​low values for each parameter; threshold analyses that show what value a parameter must attain for the ‘worse’ programme to become the more cost-effective; value of information, showing how much it would be worth spending on further studies to reduce uncertainty; and perhaps most usefully in this case, a cost-effectiveness acceptability curve indicating the probability that StrongMinds is cost-effective at a given threshold, such as the 3-8x GiveDirectly that GiveWell is currently using as its bar for new charities. Some examples are here.)

  ---

  My out-of-date notes:

  Topic 2.2: (Re-)prioritising causes and interventions

  […]

  GiveWell

  […]

  Spillover effects

  Secondly, there are also potential issues with ‘spillover effects’ of increased consumption, i.e. the impact on people other than the beneficiaries. This is particularly relevant to GiveDirectly, which provides unconditional cash transfers; but consumption is also, according to GiveWell’s model, the key outcome of deworming (Deworm the World, Sightsavers, the END Fund) and vitamin A supplementation (Hellen Keller International). Evidence from multiple contexts suggests that, to some extent, the psychological benefits of wealth are relative: increasing one person’s income improves their SWB, but this is at least partly offset by decreases in the SWB of others in the community, particularly on measures of life satisfaction (e.g. Clark, 2017). If increasing overall wellbeing is the ultimate aim, it seems important to factor these ‘side-effects’ into the cost-effectiveness analysis.

  As usual, GiveWell provides a sensible discussion of the relevant evidence. However, it is somewhat out of date and does not fully report the findings most relevant to SWB, so I’ve provided a summary of wellbeing outcomes from the four most relevant papers in Appendix 2.1. In brief:

  • All four studies found positive treatment effects, i.e. improvement to the psychological wellbeing of cash recipients, though in two cases this finding was sensitive to particular methodological choices.

  • Two studies of GiveDirectly found negative psychological spillovers.

  • Two found only null or positive spillovers.

  As GiveWell notes, it is hard to aggregate the evidence on spillovers (psychological and otherwise) because of:

  • Major differences in study methodology (e.g. components of the psychological wellbeing index, type of control, inclusion/​exclusion criteria, follow-up period).

  • Major differences in the programs being studied (e.g. size of transfers, proportion of households in a village receiving transfers).

  • Absence of key information (e.g. how many non-recipient households are affected by spillover effects for each treated household, how the magnitude of spillovers changes with distance and over time, how they differ among eligible and ineligible households).

  Like GiveWell, I suspect the adverse happiness spillovers from GiveDirectly’s current program are fairly small. In order of importance, these are the three main reasons:

  • The negative findings were based on within-village analyses, i.e. comparing treated and untreated households in the same village. These may not be relevant to the current GiveDirectly program, which gives money to all eligible households in treated villages (and sometimes all households in the village). The two studies that investigated potential spillovers in untreated villages in the same area as the treated ones found no statistically significant effect.

  • Eggers et al. (2019) (the “general equilibrium” study), which found only null or positive spillovers, was by far the largest, seems to have had the fewest methodological limitations, and investigated a version of the program most similar to current practice.

  • At least one of the ‘negative’ studies, Haushofer & Shapiro (2018), had significant methodological issues, e.g. differential attrition rates and lack of baseline data on across-village controls (though results were fairly robust to authors’ efforts to address these).

  In addition, any psychological harm seems to be primarily to life satisfaction rather than hedonic states. As noted in Haushofer, Reisinger, & Shapiro (2019): “This result is intuitive: the wealth of one’s neighbors may plausibly affect one’s overall assessment of life, but have little effect on how many positive emotional experiences one encounters in everyday life. This result complements existing distinctions between these different facets of well-being, e.g. the finding that hedonic well-being has a “satiation point” in income, whereas evaluative well-being may not (Kahneman and Deaton, 2010).” This is reassuring for those of us who tend to think feelings ultimately matter more than cognitive evaluations.

  Nevertheless, I’m not extremely confident in the net wellbeing impact of GiveDirectly.

  • Non-trivial comparison effects are found in many other contexts, so it is perhaps reasonable to expect them here too. (I haven’t properly looked at that evidence so I’m not sure how strong my prior should be.)

  • As with any metric, there are various potential biases in wellbeing measures that could lead to under- or over-estimation of effects. When assessing the actual effect on wellbeing/​welfare/​utility (rather than on the specific measures of wellbeing used in the study), we should consider the evidence in the context of other findings that I haven’t discussed here.

  • Even a negative spillover with a very small effect size, which seems plausible in this case, could offset much or all of the positive impact. For instance, if recipient households gain 1 happiness point from the transfer, but every transfer causes 10 other households to lose 0.1 points for the same duration, the net effect is neutral.

  • I have only summarised the relevant papers; I haven’t tried to critique them in detail. GiveWell has also not analysed the latest versions of some of the key studies, which differ considerably from the working papers, so they might uncover some issues that I haven’t spotted.

  A few more notes on interpreting the wellbeing effects of GiveDirectly:

  • As with other health and poverty interventions, I suspect the overall, long-run impact will be more sensitive to unmeasured and unmodeled indirect effects (e.g. consumption of factory-farmed meat, population size, CO2 emissions) than to methods for estimating welfare (e.g. SWB instruments vs consumption). But I’m leaving these broader issues with short-termist methodology aside for now.

  • The mechanisms of any adverse wellbeing effects have not been established in this case, and may not be pure psychological ‘comparison effects’ (jealousy, reduced status, etc). For instance, they could be mediated through consumption (e.g. poorer households selling goods to richer ones) or through some other, perhaps culture-specific, process.

  • Like any metric, SWB measures are imperfect. So even when SWB data are available, an assessment of the SWB effects of an intervention may be improved by taking into account information on other outcomes, plus ‘common sense’ reasoning.

  In addition, I would note that the other income-boosting charities reviewed by GiveWell could potentially cause negative psychological spillovers. According to GiveWell’s model, the primary benefit of deworming and vitamin A supplementation is increased earnings later in life, yet no adjustment is made for any adverse effects this could have on other members of the community. As far as I can tell, the issue has not been discussed at all. Perhaps this is because these more ‘natural’ boosts to consumption are considered less likely to impinge on neighbours’ wellbeing than windfalls such as large cash transfers. But I’d like to see this justified using the available evidence.

  I make some brief suggestions for improving assessment of psychological spillover effects in the “potential solutions” subsection below.

  Appendix 2.1

  Four studies investigated psychological impacts of GiveDirectly transfers. Two of these found wellbeing gains for cash recipients (“treatment effects”) and only null or positive psychological spillovers:

  • Haushofer & Shapiro (2016) (9-month follow-up)

    • 0.26 standard deviation (SD; p<0.01), positive, within-village treatment effect (i.e. comparing treated and untreated households in the same village) on an index of psychological wellbeing with 10 components (Table IV, p. 2011).

      • Statistically significant benefits for (in decreasing order of magnitude) Depression, Stress, Life Satisfaction, and Happiness at the 1% level, and Worries at the 10% level. Null effects (at the 10% level) on Cortisol, Trust, Locus of Control, Optimism, and Self-esteem (though point estimates were mostly positive).

    • Null, precise, within-village spillover effect on the index of psychological wellbeing; point estimate positive (0.1 SD; Table III, p. 2004).

  • Egger et al. (2019) (the “general equilibrium” study)

    • 0.09 SD (p<0.01) within-village treatment effect (i.e. assuming all spillovers are contained within a village) on a 4-item index of psychological wellbeing.

      • Driven entirely by Life Satisfaction; no effect on Depression, Happiness, or Stress. (See this table, which the authors kindly sent to me on request.)

    • 0.12 SD (p<0.1) “total” treatment effect (both within-village and across-village) on psychological wellbeing.

      • Driven by Happiness (0.15 SD; p<0.05); no others significant at the 10% level. (See this table.)

    • Null, fairly precise “total” spillover effect (combining within- and across-village effects) on the index of psychological wellbeing (and on every individual component); point estimate small and positive (0.08 SD). (See this table.)

    • Note: GiveWell reports a positive, statistically significant within-village spillover effect on psychological wellbeing of about 0.1 SD, based on an earlier draft of the paper. I can’t find this in the published paper; perhaps it was cut because of the authors’ stated preference for the “total” specification.

  However, two studies are more concerning:

  • Haushofer & Shapiro (2018) (3-year follow-up; working paper)

    • Within-village 0.16 SD (p<0.01) treatment effect on an 8-component index of psychological wellbeing (Table 3, p. 16).

      • Driven primarily by improvements to Depression and Locus of Control (p<0.05), followed by Happiness and Life Satisfaction (p<0.1). No statistically significant (at the 10% level) change in Stress, Trust, Optimism, and Self-esteem. (Table B.7, p. 55)

    • Null across-village treatment effect on psychological wellbeing (Table 5, p. 22).

    • Approx. −0.2 SD (p<0.01) adverse psychological wellbeing spillover on untreated households in treated villages (Table 7, p. 26).

      • Driven by Stress (p<0.01), Depression (p<0.05), Happiness (p<0.1), and Optimism (p<0.1). No statistically significant (at the 10% level) change in Life Satisfaction, Trust, Locus of control, or Self-esteem. (Table B.15, p. 63)

  • Haushofer, Reisinger, & Shapiro (2019)

    • A 1 SD increase in own wealth causes a 0.13 SD (p<0.01) increase in the psychological well-being index (p.13; Table 3, p. 27).

      • At the average change in own wealth of eligible (thatched-roof) households of USD 354, this translates into a treatment effect of 0.09 SD.

    • At the average transfer of $709 among treated households, this translates into a treatment effect of 0.18 SD.

    • Driven by Happiness and Stress (p<0.01) then Life Satisfaction and Depression (p<0.05). No statistically significant (at the 10% level) effect on Salivary Cortisol. (Table 5, p. 29)

    • A 1 SD increase in village mean wealth (i.e. neighbours in one’s own village having a larger average transfer size) causes a decrease of 0.06 SD in psychological well-being over a 15 month period, only significant at the 10% level (p. 14; Table 3, p. 27).

      • At the average cross-village change in neighbours’ wealth of $327, this translates into an effect of −0.2 SD.

    • Driven entirely by Life Satisfaction (0.14 SD; p<0.01; p. 15; Table 5, p. 29)

      • At a change in neighbours’ wealth of $327, this translates into a Life Satisfaction effect of −0.4 SD (which is much larger than the own-wealth benefit, but less precisely estimated).

    • Subgroup analysis 1: No statistically significant within-village difference between treated and untreated households in psychological wellbeing effects of a change in neighbours’ wealth. (This suggests that what matters is how much more your neighbours received, not whether you received any transfer.)

    • Subgroup analysis 2: No statistically significant within-village difference in the psychological wellbeing effect of a change in neighbours’ wealth between households below versus above the median wealth of their village at baseline. (This suggests poorer households did not suffer more adverse psychological spillovers than wealthier ones.)

    • Methodological variations: Broadly similar results using alternative measures of the change in village mean wealth. (See p. 17 and Tables A.9–A.14 for details.)

  • No effect of village-level inequality on psychological wellbeing (holding constant one’s own wealth) over any time period and using three alternative measures of inequality.

  Note: GiveWell’s review of an earlier version of the paper reports a “statistically significant negative effect on an index of psychological well-being that is larger than the short-term positive effect that the study finds for receiving a transfer, but the negative effect becomes smaller and non-statistically significant when including data from the full 15 months of follow-up… The authors interpret these results as implying that cash transfers have a negative effect on well-being that fades over time.” I’m not sure why the authors removed those analyses from the final version.