Quantifying the Global Burden of Extreme Pain from Cluster Headaches

A systematic analysis examining the prevalence, intensity, and treatment of cluster headaches worldwide, with emphasis on measuring extreme suffering and evaluating psychedelic interventions

Alfredo Parra (Qualia Research Institute)https://www.qri.org/
Nov 1, 2024

Warning: This post discusses statistics about extreme pain that may be distressing. While cluster headaches are a neglected, high-impact issue, understanding their true burden requires appreciating the intensity of suffering involved. The pain often reaches levels far beyond typical human experience, making subjective accounts a valuable datapoint until we have robust methods for quantifying pain intensity. For further context, links to firsthand accounts are provided in the footnote.1

You no longer have a headache, or pain located at a particular site: you are literally plunged into the pain, like in a swimming pool. There is only one thing that remains of you: your agitated lucidity and the pain that invades everything, takes everything. There is nothing but pain. At that point, you would give everything, including your head, your own life, to make it stop.

Key Takeaways

1. Introduction

1.1. Clinical Features and Pain Comparisons

Cluster headaches (sometimes also referred to as “suicide headaches”) are a disorder characterized by attacks of severe pain behind the eye, typically lasting between 15 minutes and 3 hours (Black, Bordini, and Russell 2016). The onset of the attack is quite sudden, starting as mild discomfort and reaching maximal intensity on average within 9 minutes in 86% of sufferers (Torelli and Manzoni 2003). The pain is often described as similar to being repeatedly stabbed in the eye with a knife (Rossi et al. 2018; Schindler and Burish 2022). The term “cluster” refers to the fact that attacks come in bouts typically lasting 1–12 weeks, followed by periods of remission lasting a few months to a few years (episodic subtype) (Ekbom 1970). During a cluster bout, patients experience on average 3–4 attacks per day at predictable times of the day (Burish et al. 2021; Gaul et al. 2012), with a long tail extending to 10 attacks or more per day (Gómez-Emilsson 2019a; Sewell, Halpern, and Pope 2006). Up to 20% of cluster headache sufferers are of the chronic subtype, with remission periods shorter than 3 months (Schindler and Burish 2022).3 The ability of many patients to anticipate the timing of their attacks, both seasonally and daily, is perhaps one of the most sinister aspects of cluster headaches, adding a layer of psychological torment to the already excruciating pain.4

A recent international survey of 1,604 cluster headache patients revealed new data about how sufferers rate their pain compared to other severely painful conditions (Burish et al. 2021). Notably, respondents rated cluster headache pain as 9.7 ± 0.6 on a 0–10 scale, with 72% (n=1,157) rating it as 10.0. Respondents also rated other severely painful conditions they had experienced:

It’s worth pointing out that these are all-things-considered, retrospective ratings, which means that individual attacks aren’t necessarily 9.7 painful on average. Indeed, studies show discrepancies in patients’ estimates of the duration and intensity of attacks between prospective reports (i.e., asking patients to report attack data in a diary right after the attack happens) and retrospective reports (i.e., relying on patients’ memory during interviews) (Snoer et al. 2018; Torelli and Manzoni 2003).

1.2. Treatment and Prevention

Treatment options for cluster headaches are limited and only partly effective. In the survey above, the most commonly used treatments were:

Triptans were considered “completely effective” or “very effective” by 14% and 39% of those who use them, respectively; for oxygen, the numbers were 13% and 41%; and for opioids, only 1% and 4% (Pearson et al. 2019). Standard painkillers that can be effective against migraine and other types of headaches are ineffective against cluster headaches (Nesbitt and Goadsby 2012).

Indoleamines, and in particular tryptamines, have been found to be highly effective in both aborting attacks and helping patients go into remission, often indefinitely, after only a few low doses (Schindler and Burish 2022). A study of 53 patients who had used psilocybin or LSD found that:

Another online survey of 270 cluster headache sufferers revealed that 68% of respondents who used tryptamines had a 4 or 5 out of 5 relief, with 5 being “completely eliminated the cluster headaches” (Frerichs 2019). The tryptamine N,N-DMT has been suggested as a potentially effective treatment due to its very fast action and requiring very low, sub-hallucinogenic doses. However, patients face major challenges accessing tryptamines due to their legal status, which has led to a proliferation of online communities discussing, e.g., ways to grow one’s own psilocybin mushrooms. Only very recently (June 2024), Canada approved the first psilocybin treatment for cluster headaches.

We recommend the 2020 policy paper “Legalising Access to Psilocybin to End the Agony of Cluster Headaches” by the Organisation for the Prevention of Intense Suffering (OPIS) for a more detailed overview of treatment options and policy recommendations, among others.

1.3. The Heavy-Tailed Valence Hypothesis and Existing Metrics

Descriptions of cluster headache pain are one of multiple lines of evidence in support of the “heavy-tailed valence” (HTV) hypothesis. This hypothesis posits that “the accessible human capacity for emotional experiences of pleasure and pain spans a minimum of two orders of magnitude” (Gómez-Emilsson and Percy 2023). The HTV hypothesis suggests that the difference between the mildest and most intense pain is not merely tenfold (as commonly interpreted when using the 0–10 scale) but rather spans a much broader range, with the most extreme experiences potentially being hundreds of times more intense than milder ones. In their survey (n=77), Gómez-Emilsson & Percy (Gómez-Emilsson and Percy 2023) found that 50% of respondents rated their most intense experience as at least twice as intense as the second most intense, lending support to the HTV hypothesis. Further lines of evidence in support of the HTV hypothesis are outlined in Gómez-Emilsson (Gómez-Emilsson 2019b).

Widely used health metrics such as the DALY (Disability-Adjusted Life-Year) are insensitive to these vast differences in our capacity to experience pain and pleasure. Recall that the DALY is calculated as the sum of years of life lost due to premature death (YLLs) and years of healthy life lost due to disability (YLDs), i.e., DALY = YLL + YLD. Since headache disorders are not considered a cause of death according to the Global Burden of Disease (GBD), YLL is zero and DALY = YLD (Stovner et al. 2018). Headache YLDs are then calculated as the product of the number of people with headaches worldwide, the average time spent with headaches, and, crucially, a 0–1 weight measuring the degree of disability caused by headaches, where 0 = full health and 1 = death. For instance, the disability weight for migraines according to the GBD is 0.44, whereas cluster headaches are not included at all due to their relatively low prevalence (“Global Burden of Disease Study 2021 (GBD 2021)” 2021)6. As (Stovner et al. 2018) points out:

“Although other headache disorders such as cluster headache undoubtedly impose a great burden on individual patients, the total societal burden of this and other severe but relatively rare headaches is probably quite small compared with that of the common headache types.”

As (Leighton 2023) has pointed out, there’s a need for alternative metrics that adequately capture the nature of suffering, especially at the extremes. Leighton argues that the Wellbeing-Adjusted Life-Year (WELLBY) (Frijters et al. 2024) and the Suffering Intensity-Adjusted Life-Year (SALY) (Knaul et al. 2018) are steps in the right direction but do not suffice, suggesting instead two potential metrics:

There are several possible explanations why cluster headaches currently receive relatively little attention within the effective altruism community and more broadly. Some include:

1.4. Goal

The goal of this study is to estimate the annual global burden of cluster headache pain according to different metrics. We aggregate statistical data from the medical literature into a numerical model available at cluster-headaches.streamlit.app (full code). We then compare our results with the burden of migraine, the 4th largest source of Years Lived with Disability worldwide (Disease Collaborative Network 2024). We show under which assumptions and metrics cluster headaches result in a larger burden than that of migraines, which lends itself to useful comparisons and is significantly more prevalent. In particular, we find that a straightforward aggregationist approach that weighs the most severe pain (10/10) four orders of magnitude more heavily than the mildest pain (1/10) results in a higher total annual burden for cluster headaches. However, we also estimate that cluster headaches cause about 5 million Days Lived with Extreme Suffering (at ≥9/10 pain) per year, so individuals who prioritize reducing extreme suffering may be convinced to tackle this problem regardless of whether they believe it “outweighs” less severe but more prevalent pain.

We conclude with some suggestions for philanthropists, donors, policymakers, and other relevant decision-makers who take the HTV hypothesis seriously or otherwise put more weight on alleviating the burden of extreme suffering.

2. Methods

We’d like to answer the question: How much time is spent globally per year with cluster headache pain, at different pain intensities? So we need to estimate the following quantities:

  1. Prevalence: How many people worldwide suffer from cluster headaches in a given year?
  2. Frequency: How many attacks per year do cluster headache sufferers experience?
  3. Duration: How long do attacks last?
  4. Intensity: How painful are the attacks?

Each of these quantities will follow a certain probability distribution. We can then calculate the resulting burden distribution as:

Burden = Prevalence × Frequency × Duration × Intensity

We can then derive relevant metrics, such as YLSS and DLES. Finally, we can compare these metrics for cluster headaches with other conditions. For this report, we chose migraine, which lends itself to useful comparisons and is significantly more prevalent.

We find it helpful to distinguish between four different types of cluster headache patients, depending on whether they are chronic vs episodic, and whether they have access to treatment (either preventative or abortive) or not. This distinction can help us see how the burden is distributed among the groups and potentially prioritize them accordingly.

2.1. Prevalence

Level of confidence in the data: Medium

The literature presents a somewhat complex picture of cluster headache prevalence, with reported prevalence rates varying somewhat. Individual studies have reported 1-year prevalence rates as low as 0 (in Malaysia) and 32 (in Ethiopia) per 100,000, and as high as 150 per 100,000 (in Germany). A meta-analysis by (Fischera et al. 2008) of all available epidemiological studies (16 papers) estimated the 1-year worldwide prevalence at 53 per 100,000 (CI 26, 95), with the lifetime prevalence being 124 per 100,000 (CI 101, 151) for adults8 of all ages and sexes. They note that cluster headaches might be less frequent in developing countries, which more recent studies from previously understudied regions seem to confirm (Kim et al. 2023). This could bring the figure of 53 per 100,000 down slightly. Underdiagnosis and misdiagnosis complicate the picture further (Bahra and Goadsby 2004).

For our simulations, we use the default figure of 53 per 100,000, but the user can change this parameter.

The default value used for the fraction of chronic patients is 20% (vs 80% episodic), taken from the meta-analysis by (Schindler and Burish 2022).

2.2. Frequency

Level of confidence in the data: High

Multiple studies have documented the frequency of cluster headache attacks in detail. For episodic patients who typically experience annual bouts lasting a few weeks, ample data exist on the frequency and duration of the bouts. For our simulations, we considered the following:

2.3. Duration

Level of confidence in the data: High

The duration of each attack has also been well documented (Bahra, May, and Goadsby 2002; Russell 1981; Snoer et al. 2018). Most attacks last between 15 minutes and 3 hours, with 4–13% of patients reporting attacks longer than 3 hours (Kim et al. 2023). Attacks tend to be somewhat shorter at the beginning and end of a cluster period (Black, Bordini, and Russell 2016). Additionally, the onset and offset of each attack are quite sudden, reaching peak intensity within just 8.9 minutes on average and subsiding similarly quickly (Torelli and Manzoni 2003).

Less well documented is how the duration of an attack depends on factors such as the patient being episodic or chronic, whether patients have access to treatment (either preventative or abortive), or the intensity of the attack. In our model, we start with typical durations and incorporate the following adjustments:

2.4. Intensity

Level of confidence in the data: Medium-low

We’re interested in modeling the distribution of time spent at different pain intensities on the 0–10 scale. The main challenge in answering this question is that most studies rely on retrospective reports (i.e., by interviewing or surveying patients about their attacks generally) as opposed to prospective reports (i.e., by asking patients to record the intensity in a diary immediately after the attack). We found three studies14 asking patients to record the intensity of their attacks in a diary. The results are summarized in Table 1 below.

Study Cohort details Results
Russell (1981) 24 patients recorded n=77 attacks
23 episodic, 1 chronic
22 men, 2 women
No attacks were treated
Attacks rated on a 1–5 scale		 - Extremely severe (5/5): 23 (30%)
- Severe (4/5): 17 (22%)
- Moderate (3/5): 20 (26%)
- Slight (2/5): 5 (7%)
- Very slight (1/5): 12 (15%)
Mean: 3.44, SD: 1.39
Torelli & Manzoni (2003) 42 patients recorded one “typical” attack
42 episodic, 0 chronic
No attacks were treated
Attacks rated on a 0–10 scale		 - 9.0–10: 29 (69%)
- 8.0–8.9: 7 (17%)
- 7.0–7.9: 3 (7%)
- 6.0–6.9: 3 (7%)
Mean: 9.17, SD: 1.0
Snoer et al. (2019) 57 patients recorded a total of 500 attacks
24 episodic, 33 chronic
32 men, 25 women
54 (95%) patients used abortive treatments; 423 (85%) attacks were treated with abortives
Attacks rated on a 0–10 scale		 - Median (IQR), treated attacks (n=423): 7.3 (5.9–8.7)
- Median (IQR), untreated attacks (n=77): 7.0 (5.0–8.4)

Table 1: Data from prospective studies of cluster headache pain intensity.

To model the pain distribution for untreated attacks, we fitted a truncated (at 10/10) normal distribution with the combined data from (Russell 1981) and (Torelli and Manzoni 2003). The truncation introduces some artifacts into our calculations, discussed in greater detail in Section 3.3, but we’ll limit the scale to 10 to align with established medical practice. For treated attacks, we fitted a truncated normal distribution to the (Snoer et al. 2018) data. Since the three papers likely underestimate the frequency of mild attacks, we adapted the distributions to increase the density of mild attacks.

Finally, to model the time profile of each single attack, we assume that the onset and offset each last 15% of the total attack time, and the remaining 70% is spent at the maximum pain intensity (sampled from the distributions above). While this simplification ignores fluctuations in pain, we believe it is a good approximation given observations by (Nesbitt and Goadsby 2012)15 and (Ekbom 1975).16

2.5. Burden Metrics

Our simulations calculate the average time spent at different pain intensities on the 0–10 scale, in steps of 0.1, for a representative sample of each of the four patient groups. We then extrapolate these results by multiplying the average time with the total global prevalence to arrive at the global burden (as a function of pain intensity). We can then derive a few metrics from this distribution. We focus on three, all global and annual:

  1. Person-Years in Pain17: the sum of person-years spent at all pain intensities, optionally weighing more intense pain more heavily, either linearly or nonlinearly (more below);
  2. (Person-)Years Lived with Severe Suffering (YLSS): as introduced by (Leighton 2023), the sum of person-years spent at ≥7/10 intensity, again with the option of weighing more intense pain more heavily;
  3. (Person-)Days Lived with Extreme Suffering: captures pain at ≥9/10 intensity.

We want to point out an important distinction between pain and suffering, which are correlated but different. Indeed, it is possible to experience intense pain with high equanimity, which reduces the suffering caused by the experience. The expression Suffering = Pain × Resistance, where resistance is the inverse of equanimity, can serve as a good first approximation (young2016pain?). However, we believe that pain and suffering correlate strongly at the very top of the scale. That is, no amount of equanimity can fully get rid of the suffering caused by pain near or at 10/10. So at least for ≥9/10 pain, we will use “pain” and “suffering” interchangeably, while noting that there is room for more research on this question.

Our simulations let the user define how to weigh more intense pain relative to mild pain. Figure 1 shows three different options:

Mapping the pain scale linearly to a 0–1 weight has been proposed by (Stovner et al. 2018) as a way to approximate the disability weight of different types of headaches for DALY calculations. However, this ignores data suggesting that very intense pain is experienced as significantly more intense than what a linear scale might suggest (Gómez-Emilsson 2019b; Gómez-Emilsson and Percy 2023). Power and exponential weights might reflect this fact better while noting that there could be other possible transformations.

Figure 1: Linear (~x), power (~x^p, here with p = 2) and exponential (~b^a x, here with b = e, a = 1) weight transformations for the 0–10 pain scale.

3. Results

The full simulation results can be accessed at cluster-headaches.streamlit.app.

3.1. Global Burden of Cluster Headache Pain

We ran Monte Carlo simulations for four different groups of cluster headache patients, depending on whether they are of the chronic vs episodic subtype, and whether they have access to treatment (preventative or abortive) or not. We assume that 20% of patients are chronic and 80% are episodic, and that 43% of patients worldwide have access to treatment.18 We simulated a total of 3,036 individuals and extrapolated the results of this sample population to estimate the total global burden.

Figure 2 summarizes our main results. On average—and as expected—patients of the chronic subtype without access to treatment will spend the most time in cluster headache pain annually—596 hours per year, compared to 99 hours for the average episodic patient with access to treatment (Figure 2a). However, since the episodic subtype is ~4x more common than the chronic subtype, the total global burden is dominated by the episodic untreated group (Figures 2b,c). We estimate that all cluster headache patients worldwide spend ~74,200 person-years per year in pain at any pain intensity, of which ~46,200 are spent at ≥7/10 intensity and ~13,600 at ≥9/10 intensity (Figure 2d). Using the terminology introduced by (Leighton 2023), the figure of 46,200 person-years would correspond to the Years Lived with Severe Suffering (YLSS) for cluster headaches. The Days Lived with Extreme Suffering (DLES) would then be 13,600 years × 365 days/year = 4,964,000.

Image (a)
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Image (b)
(b)
Image (c)
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Image (d)
(d)
Figure 2: (a) Average hours per year that a patient in each of the four groups spends experiencing cluster headaches, at different intensities on the 0–10 pain scale (in steps of 0.1). (b) Total number of person-years of cluster headache pain experienced annually worldwide by all patients in the four groups, at different intensities on the 0–10 pain scale. (c) Total amount of cluster headache time (person-years) spent annually worldwide at any pain intensity, per patient group. (d) Aggregation of all time (person-years) spent in cluster headache pain worldwide by all patients at any intensity, at ≥7/10 intensity, and at ≥9/10 intensity.

3.2. Reweighting of Extreme Pain

We now explore ways to give larger weight to more extreme pain, a consequence of accepting the heavy-tailed valence hypothesis. For illustrative purposes, we compare the global burden of cluster headaches to that of migraines.

To compare our cluster headache burden results (Figure 2) with the burden of migraines, it would be necessary to know the distribution of time spent in migraine pain at different intensities on the 0–10 scale. One crucial challenge in doing so is that migraine sufferers who have not experienced more extreme forms of pain might overestimate the pain intensity of migraines. While severe migraines can indeed be very painful, it is generally acknowledged that severe cluster headaches are much more painful. As (Torelli and Manzoni 2003) point out:

“Literature reports and clinical practice suggest that the adjective ‘severe’ is inappropriate to describe the intensity of pain in CH, which cannot be compared to that of migraine either in quality or quantity.”

Additional evidence in support of this argument stems from the fact that cluster headache patients “appear agitated, restless, and feel an impulse to move around or go outside in an attempt to cope with the torment of excruciating pain” (Black, Bordini, and Russell 2016). During very severe attacks, “subjects may strike their heads on the wall or with their hands” (Black, Bordini, and Russell 2016). Contrast this behavior with that of migraine patients, who typically avoid movement, lie down or rest, or might want to ‘sleep it off’—forms of passive coping that the severity of cluster headache pain renders impossible.

In exploring this question, we consulted Dr. Mark J. Burish, Neurologist and Pain Medicine Specialist at UTHealth Houston and 1st author of the largest international survey of cluster headache patients (Burish et al. 2021). In his questionnaire:

Given our uncertainties as to how to best model the underlying intensity distribution of migraines, in our simulations, the user can specify the shape parameters of a skewed normal distribution. As an illustrative example, assuming a distribution with a median of 4.2, mean of 3.0, and SD of 1.2, we arrive at the distribution shown in Figure 3.

Figure 3: Annual global burden of migraine vs cluster headache as a function of pain intensity. Note the different scales in the two y-axes, reflecting the large differences in prevalence.

We can now ask: How much more heavily would we have to weigh intense pain compared to mild pain such that the intensity-adjusted burden of cluster headaches is larger than that of migraines? Or, referring to Figure 1, how steep does the weighting curve have to be, where 10/10 pain gets a weight of 1.0? Figure 4a shows the annual intensity-adjusted person-years assuming an exponential transformation proportional to eˣ. Note how the curves get compressed down for lower pain intensity values, causing the high-intensity pain values to become more prominent. However, even this exponential transformation is not steep enough to cause the overall burden (area under the curve) of cluster headaches to be larger than that of migraine. For this choice of migraine parameters, the intensity transformation would have to scale as ~6ˣ for the burden of cluster headaches to dominate (Figure 4b). This would mean, for example, that:

Our simulations also allow the user to ignore pain below 7/10 and focus on the intensity reweighting for the higher end of the pain scale. In this regime, much less steep transformations lead to cluster headaches dominating the total global burden. That is, one need not make strong assumptions about the relative importance of 7/10 pain compared to 10/10 pain (but at the cost of giving no weight to pain below 7/10). Using the same parameters as above, a transformation that gives:

would result in a larger burden for cluster headaches (i.e., the weight scales roughly as 2x).

Intensity-adjusted global burden for migraine (a)
(a)
Intensity-adjusted global burden for cluster headache (b)
(b)
Figure 4: Intensity-adjusted annual global burden (person-years) for migraine vs. cluster headache, assuming transformations proportional to eˣ (a) and 6ˣ (b). Our simulations include 3D plots illustrating how the two distributions evolve as we move from linear to exponential weighting. Note the unnatural shape of the cluster headache distribution results from a ceiling effect, truncated at 10/10.

One might debate whether aggregating different pain intensities into a single metric—however heavily one decides to weigh extreme pain—is necessary or philosophically sound. Indeed, under minimalist axiologies or lexical views (Ajantaival 2024), one might decide to focus one’s efforts on reducing the most extreme forms of suffering (say, involving pain at a level of 9/10 or higher) regardless of whether there also exist other large amounts of milder suffering. Under this lens, relieving cluster headache suffering clearly takes priority over relieving migraine suffering. Whether cluster headaches are the top source of extreme suffering in humans worldwide is still unclear, but ongoing research will hopefully shed light on this question.22

3.3. Ceiling Effects

There are at least three major challenges when trying to quantify the pain intensity of various conditions:

  1. As mentioned in the previous section, patients with different pain conditions might interpret the scale differently (such as migraine patients who have never experienced the pain of cluster headaches). For example, imagine that a patient with both cluster headaches and migraines learns that migraine patients rate migraine pain at an average of 8/10. When asked to rate cluster headache pain, the cluster headache patient might instinctively think “12/10,” but they would answer “10/10” since that’s the highest score possible.23
  2. The distribution of answers to the question “How intense is pain from cluster headaches?” (or other conditions) will differ from the distribution of time actually spent at different pain intensities (i.e., how painful and how long each individual attack was, using headache diaries). Data on the former is more common and easier to gather (such as the survey by (Burish et al. 2021)).
  3. Individual patients themselves might have difficulties assessing the severity of each attack, for example due to recall bias or because what they thought was a 10/10 gets surpassed in future, even more severe attacks.

In each of these cases, there is a ceiling effect that distorts the true underlying distributions. A useful analogy would be asking university students to take an elementary school test, and getting an average score of 97/100. That average should not be interpreted as “slightly below 100” but rather as “much higher than what the test can measure, plus some room for minor mistakes.24”

A prediction of ceiling effects is that we should expect many values to cluster at the scale maximum, such as in the Burish et al. (2021) survey (and to some extent in the prospective reports from Table 1).

While a full treatment of this problem could be the subject of a separate research project, we can hint at potential approaches. For instance, one could assume an underlying normal distribution that extends beyond 10/10. Then, using statistical techniques (such as the tobit model), one could try to reconstruct the underlying distribution using (part of) the measured data. Figure 5 illustrates this approach.

Figure 5: Illustration of the ceiling effect when measuring pain intensity. The blue histogram represents responses by patients (either all-things-considered or per attack), with a clustering of responses at the ceiling of 10/10. The orange curve represents the theoretical underlying distribution of pain intensity. The green curve is an attempt to reconstruct the theoretical curve using the measured data.

Incorporating ceiling effects would allow for more fair comparisons across conditions and a more accurate calculation of pain burden for extremely painful conditions. Indeed, we expect that our intensity calculations from Section 2.4 (based on a truncated normal distribution) lead to underestimating the total burden calculations—and it is precisely the behavior at the far end of the scale that we argue has the most weight. The resulting Figure 4 would look smooth as opposed to having an unnatural peak at the end, allowing us to capture the behavior of the underlying long tail fully. The overall case for prioritizing cluster headaches would be strengthened.

How exactly to capture such effects in patient data is a question for further research. One could consider giving patients the opportunity to enter scores higher than 10 in their diaries if their assessment of the pain evolves over time. Also, since patients who have had the condition for longer are better calibrated about the nature of the pain, their assessments could be weighted more heavily. Collecting data on the differences in relative, subjective intensity of attacks would also be valuable.

4. Recommendations and Conclusions

Our simulations suggest that cluster headaches cause a substantial amount of extreme human suffering globally—nearly 5 million Days Lived with Extreme Suffering (≥9/10 pain) per year. The fact that the prevalence of cluster headaches is much lower than that of other major diseases is actually advantageous and presents an attractive opportunity: we can get rid of a significant proportion of the most extreme human suffering worldwide at a small fraction of the cost of addressing more prevalent conditions. We invite readers to think ambitiously and recognize that reducing global cluster headache suffering by more than, say, 80% is achievable within a few years using currently available interventions.

Another reason to be optimistic about our ability to significantly reduce the burden of extreme suffering from cluster headaches is the low cost of some of the emerging treatments. As an example, an attack (roughly an hour long) could be aborted with anywhere between 3–20mg of N,N-DMT, costing as little as $1–6 per dose (assuming 0.5g cost $150). This excludes costs of research, policy reform, and implementation, but it gives us a sense of what’s possible (see also (Frerichs 2019) for further discussions).

Based on our findings, we offer several recommendations for different stakeholders:

For Philanthropists and Donors:

For Researchers:

For EA Organizations:

For Policy Advocates:

For everyone else:

Acknowledgements

I’m particularly grateful to Andrés Gómez Emilsson for suggesting I work on this project, for feedback and discussions, and for bringing my attention to this problem through his previous research. I’m also grateful to Jonathan Leighton, Teo Ajantaival, Stefan Torges, Mark J. Burish, Hunter Meyer, Jim Buhler, Lucius Caviola, Clare Diane Harris, and Imma Six for their constructive feedback and encouragement. I thank Polaris Ventures for financially supporting this project.

Pain Research, Global Health, Cluster Headaches, Psychedelics, Heavy-Tailed Valence, N,N-DMT, Burden of Disease, Effective Altruism

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  1. Rossi et al. (2018) published a series of extracts from stories of European cluster headache patients who recount what it’s like to live with the condition (highly recommended). The website clusterheadaches.com also features testimonials by patients. This YouTube playlist is a compilation of videos of patients during attacks (disturbing). This artistic rendition (drawing) may be illustrative. Finally, the active, public Facebook group Cluster Headache Patients as well as r/clusterheads are a good place to get a sense of the daily struggles of cluster headache sufferers.↩︎

  2. While we focus on simple aggregation across individuals and time, this work can be generalized to explore more elaborate assumptions, such as how suffering might cluster temporally or aggregate differently in single individuals, as well as the role of mixed valence (Gómez-Emilsson, 2023).↩︎

  3. The remission period cut-off has been revised to 1 month (ICHD-3, 2013) but most papers we analyzed use the 3-month cut-off.↩︎

  4. At the same time, the predictability can also help patients prepare better, especially if they have access to treatment options.↩︎

  5. However, the survey did not ask whether intravenous or spinal/epidural anesthesia was used.↩︎

  6. An attempt to estimate the DALY disability weight for cluster headaches can be found in Sharma et al. (2020), who suggest 0.624. Stovner et al. (2007) have suggested using reported pain as a proxy for the disability weight of headache disorders, but this alone wouldn’t be enough to address the concern raised by the HTV hypothesis.↩︎

  7. Sharma et al. (2020) have made a similar observation: “There is also a seemingly strange result: the disability weights for terminal cancer patients with vs without pain relief are implausibly similar – 0.569 vs 0.540 – implying that cancer without pain relief is barely worse. This is especially odd considering that multiple randomized controlled trials have captured the significant effect opioids have on pain (see Wiffen et al., 2017 and Appendix 1).“↩︎

  8. The authors don’t specify what age group qualifies as “adult”. For our simulations, we’re assuming ages 18+, for which the world population is 5,728,759,000 according to the UN. While the mean age of onset is somewhere between 23 and 34 years, the range spans 6 to 75 years (Kim et al., 2023). This would mean that 3.03 million adults worldwide will suffer from cluster headaches in a given year (CI: 1.49 million, 5.44 million). We also note that the authors didn’t specify whether they controlled for the age pyramid in each country, which could also affect the prevalence calculations.↩︎

  9. The very large values reported for the standard deviation of remission periods relative to the mean (e.g. in Rozen et al., 2001) is indicative of a right-skewed distribution, possibly indicating many patients having shorter remissions and a few having very long ones.↩︎

  10. See anecdotal evidence of patients going for a year or longer without any remission here.↩︎

  11. See also further evidence of the long-tail distribution of annual attack frequency in Gómez-Emilsson (2019a), which could be partly or largely explained by the fraction of chronic patients with very short remission periods.↩︎

  12. Although Gaul et al. (2012) report no significant difference in the retrospective reports of their cohort.↩︎

  13. Although Snoer et al. (2018) found that untreated attacks were shorter in the prospective reports of their cohort. This is likely due to the fact that patients might decide not to use abortive treatment if they notice that the attack is mild (an observation also made by Hagedorn et al., 2019), and milder attacks tend to be shorter. 95% of the participants in Snoer et al.’s study had access to and used abortive treatment.↩︎

  14. Plus Hagedorn et al. (2019), but they followed just one patient (for six years).↩︎

  15. “The pain usually stays at maximal intensity for the duration of the attack, although it may wax and wane slightly, or be punctuated by super-intense stabs of pain.” (Nesbitt & Goadsby, 2012)↩︎

  16. “Within a few minutes these symptoms became accentuated, assuming the character of real pain. This was usually very intense within 10-15 min. When the patient experienced maximum pain it was felt to be excruciating and generally occurred without fluctuations. Sometimes there were short repeated exacerbations of pain added to the continuous basic headache.” (Ekbom, 1975)↩︎

  17. This metric might be somewhat analogous to the Suffering-Adjusted Life Years (SALY) metric suggested by Knaul et al. (2018) to quantify the burden of serious health-related suffering (SHS), but there are notable differences (e.g., the authors suggest including estimates of the value to the patient and their family of averting suffering), so we prefer not to use this term.↩︎

  18. There is little data on the availability of treatments for cluster headache patients worldwide, so we made the following assumptions. Rossi et al. (2020) estimated that 47% of patients in the EU had full access to treatments, while 35% had limited access and 18% lacked access. Assuming that (a) these numbers are representative of developed countries worldwide, (b) ~15% of the world population lives in developed regions, ~25% of the world population lives in intermediate regions, and 60% of the world population lives in developing regions, (c) patients in intermediate regions have ~25% complete access, ~40% restricted access, and ~35% lacking access, while (d) patients in developing regions have ~10% complete access, ~30% restricted access, and ~60% lacking access, and (e) ~50% of patients with “restricted” access have access to treatment while ~15% of patients with “lacking” access have some access to treatment, we arrive at a global estimate of 43%. This parameter can be adjusted in our simulations.↩︎

  19. As a reference, ischemic heart disease contributed 188 million DALYs and malaria contributed 55 million.↩︎

  20. For the reasons discussed earlier, these numbers don’t necessarily reflect the average pain of attacks—they are an all-things-considered evaluation of the pain, which overestimates the actual pain distribution for both migraine and cluster headache.↩︎

  21. The resulting scaling as 6x would mean that the 0–10 scale would have to span 4 orders of magnitude. While Gómez-Emilsson & Percy (2023) suggest the scale spans “at least two orders of magnitude”, private communication with the authors indicates their central estimates might be closer to 4 orders of magnitude, with uncertainty ranging from 2 to 8 OOMs.↩︎

  22. An ongoing study by the Organisation for the Prevention of Intense Suffering (OPIS) aims to quantify the global burden of extreme suffering from all sources. Lack of access to pain relief for terminal or life-threatening illnesses (especially in developing regions) is a strong candidate, potentially causing more extreme human suffering than any other single source: More than 25 million people who died in 2015, including 2.5 million children, experienced “serious health-related suffering”, much of which is easily treatable (Knaul et al., 2018).↩︎

  23. In fact, testimonials such as the following are easy to come by: “Imagine how it feels to be subjected to a knife attack on your head, like someone boring a screwdriver into your eye over and over again, sometimes for hours. […] On a scale of one (no pain) to ten (worst imaginable pain), you rate your pain at eleven and more.” (Rossi et al., 2018)↩︎

  24. I thank Andrés Gómez Emilsson for pointing out the ceiling effect and suggesting this analogy.↩︎

  25. While there are no detailed analyses on the effectiveness of their work, the existing data on the effectiveness of indoleamines, together with the fact that Clusterbusters are the main advocates of this intervention (and of cluster headache relief more generally), makes them stand out as one of the most promising organizations to support. More in the 2020 OPIS policy paper.↩︎

  26. These two interviews with the founder of Eleusinia could be informative: Interview 1, interview 2.↩︎

References

Citation

For attribution, please cite this work as

Parra (2024, Nov. 1). Quantifying the Global Burden of Extreme Pain from Cluster Headaches. Retrieved from https://www.qri.org/blog/cluster-headaches

BibTeX citation

@misc{parra2024quantifying,
  author = {Parra, Alfredo},
  title = {Quantifying the Global Burden of Extreme Pain from Cluster Headaches},
  url = {https://www.qri.org/blog/cluster-headaches},
  year = {2024}
}