Cost-effectiveness analysis of interventions for migraine in four low- and middle-income countries

We adopted a core set of drug interventions, focusing on those included in WHO’s Essential Medicines list [6-8] but also those backed by substantial evidence of effectiveness. We included first-line (simple analgesics, eg, acetylsalicylic acid [ASA] 1,000 mg, but not paracetamol, for lack of evidence) and second-line medications (sumatriptan 50 mg because of its low price compared to other triptans, and almotriptan 12.5 mg because it had earlier been signified as the most cost-effective triptan [9]) for acute treatment of attacks, and assumed the latter would be used only by non-responders to the former in a stepped-care treatment paradigm (Table 1). We also included prophylactic drugs (propranolol 160 mg, topiramate 100 mg, amitriptyline 100 mg daily). We followed guidelines for dosages [6,7]. We added to the model the expected consequences of consumer education (posters and leaflets in pharmacies explaining how to acquire and best use these medications) and of training of health-care providers.

We used the sectoral, population-based approach to cost-effectiveness analysis (CEA) and the methods and tools developed by WHO-CHOICE [14]. Specifically, we ran a population model for two scenarios over a lifetime analytical horizon (100 years) to give the total number of healthy years lived by the population. Scenario 1 represented the natural history of migraine (no interventions in place); scenario 2 reflected the population-level impact of each specified intervention implemented for 10 years (thereafter, epidemiological variables and health-state valuations returned to natural history values). The difference between these two simulations represented the population-level health gain (healthy life years [HLYs] gained) from the intervention, relative to doing nothing.

We applied separate disability weights (DWs) (health state valuations on a 0–1 scale, where 1 equals full health) to the times spent in the ictal (within-attack) state of migraine and the interictal state (between attacks, but susceptible). Ictal DW (0.43) was available from GBD 2010 [2]. For interictal DW we applied the lowest weighting of 0.01, and only to those with high-frequency attacks. These DWs were multiplied by the estimated amounts of time spent in an ictal or interictal state by persons with migraine, with and without intervention.

Analyses were limited to the population aged 18–65 years because neither efficacy nor epidemiological data were reliably available for other age groups.

Sex-specific prevalences (Table 2) were drawn from epidemiological surveys performed in the four countries as projects within the Global Campaign against Headache by Lifting The Burden (LTB) [11,15-18]. The same sources provided mean attack frequencies and durations (from which we calculated times spent in ictal and interictal states).

We assessed the impact of acute management of migraine – both with first-line (ASA) and with second-line drugs (sumatriptan or almotriptan) for those not responding to the former – and its combination with a range of prophylactic drugs (for high-frequency cases). For each of these strategies, we also assessed the potential impact of enhanced consumer education and provider training on treatment adherence rates.

We modelled impact as reduction in total time spent in the ictal state. For acute drugs, we used the clinical endpoint of “sustained headache relief” (SHR), defined as reduction in headache intensity from moderate or severe to mild or none (which we assumed was not associated with disability [10] within 2 hours, without recurrence or further medication during 24 hours (Table 1). We assumed baseline pain of migraine was always at least moderate (Table 1 [11]). SHR therefore implied full recovery of the remaining hours of the attack that would have been spent with disability. We assumed that treatment was taken at attack onset (Table 1), so that hours recovered were attack duration minus 2 hours. We obtained SHR values for each acute drug from systematic literature reviews [19-21]. As these were based on clinical trials reporting single doses, we assumed each acute medication was used once per attack (Table 1).

To enable an estimation of real-world effectiveness of prophylactic drugs, data were collected only from trials with a placebo-free baseline period and when fully reported for a minimum of 100 representative and evaluable patients in the active treatment group [22-24]. The median percentage of migraine attacks averted was calculated (Table 3). We assumed prophylaxis would be offered only to the proportion of people with ≥3 migraine attacks/month (Table 1), and derived this proportion for each country from the LTB surveys (Table 2). The potential effect of acute treatment was projected onto attacks not averted by prophylaxis.

Estimates of efficacy obtained from clinical trials were adjusted better to reflect effectiveness in the real world by reference to treatment coverage (the proportion of people in need of the treatment who receive it) and adherence (Table 3). We assumed ASA had high current coverage (80%) everywhere except in Zambia (50%) (Table 1). We based estimates of triptan coverage on sales data obtained from the Intercontinental Medical Statistics (IMS) database: 2% for sumatriptan (Zambia 1%) and 0% for almotriptan in all countries modelled. We set target coverage at 90% (Zambia 80%) for acute drugs and at 30% (reflecting need limited to high-frequency cases) for prophylactic drugs (Table 3).

To the extent that we could, we based estimates of current patient adherence rates on a systematic literature review. No evidence was found for patient adherence to OTC-drugs in monotherapy, so 80% was used. This was based on assumptions (Table 1) regarding the following components of non-adherence: [I] Not taking the OTC-drugs at all. Conceivably, this is true for 10% of patients covered and thus contributing with 10% to a total percentage of OTC-drug non-adherence. [II] Taking OTC-drugs too late (1 hour or more after onset). We assumed that 10% use it after 1 hour, another 10% use it after 2 hours, another 10% after 3 hours, and another 10% after 4 hours. They are losing an average of 2.5 hours from the possible gain of 16.7 hours (global mean attack duration minus 2 hours). This contributes with 6% (40% of patients * 15% of possible gain) non-adherence. [III] Using OTC-drugs in a too low dose. We assumed that 10% use half of the dose. Of them, probably one half has an effect, so this would contribute with another 5% to the total OTC-drug non-adherence. [IV] We assumed the model could not cope with overusage, so that was ignored. For prescribed drugs (triptans and prophylactics), we used the median current patient adherence rate reported in the literature. This was 56% for triptans [25-30], 71% for propranolol (betablockers) [22,31-34], 60% for topiramate (antiepileptics) [22,24,31,32,34-37], and 42% for amitriptyline [24,31,32,34]. Where combinations of drugs were involved, we modelled the effects of each separately, implying no interaction between adherences to each component. There was no published evidence for provider adherence data. However, there was reason to say it was not 100%. For triptans, we knew some physicians were unduly cost-conscious (and limit supply inappropriately) and that for prophylactics there was under-dosing (both correctable in theory through education). Therefore, we assumed provider adherence to be 75% (Table 1). Target consumer and provider adherence rates were calculated based on the assumption that public education would not improve adherence to 100% but by 50% of the current deficit (Tables 1 and 3) [38-41].

We adopted an “ingredients” approach with a societal perspective, including, as applicable, drug dosage and frequency, primary-care visits, consumer education and provider training. The base year was 2008, and the time horizon used was 10 years of full implementation.

We obtained supplier prices for generically produced drugs (Table 4) from the International Drug Price Indicator Guide for 2008, adjusting to include domestic margin [42]. The cheapest retailer prices for drugs not in the guide (sumatriptan, almotriptan and topiramate) were obtained from the IMS database (third quarter 2007). For sumatriptan (and topiramate in Russia), we used 2014 prices because of price collapses. We imputed prices for countries where drugs were not currently available. For acute medications, we multiplied mean costs per attack by the number of attacks per year in the country population to give the total cost of the intervention per year of implementation.

We assumed that three-monthly doctor visits were needed for monitoring and prescription of triptans and prophylactics (Table 1). Unit-costs of primary-care services were derived from an econometric analysis of a multinational dataset of hospital costs, using gross national income per capita (plus other explanatory variables) to predict unit costs. The mean duration of a doctor’s visit was defined as 10 minutes (Table 1).

For consumer education, we assumed the number of leaflets needed was 50% of the disease prevalence, and one poster was required per 2,000 of the population (Table 1). We applied WHO-CHOICE default prices to leaflets and posters (and their associated distribution). For provider education, we assumed that one physician per primary health-care centre per year would be trained for one day (Table 1).