Comparison of Lost Productivity Due to Eculizumab and Ravulizumab Treatments for Paroxysmal Nocturnal Hemoglobinuria in France, Germany, Italy, Russia, Spain, the United Kingdom, and the United States
Bibliographic record
Abstract
INTRODUCTION Little is known about the productivity loss experienced by patients and their caregivers due to treatment of paroxysmal nocturnal hemoglobinuria (PNH) in countries of different economic status. Some insurers in the United States and other countries require pharmaceutical manufacturers to submit economic evaluations on direct medical costs alone, which excludes productivity costs such as absenteeism, presenteeism, time lost from normal activities, and burden of illness to society. Productivity costs are particularly consequential for PNH because substantial time commitments are required from patients and their caregivers for the intravenous administration of treatment (eculizumab or ravulizumab) at infusion clinics. When patients rely on unpaid caregivers, a complete accounting of costs needs to include caregivers' time loss. However, lost productivity for PNH patients and their caregivers has not been reported. The objective of this study was to assess productivity loss for PNH patients and caregivers due to PNH treatments administered at infusion clinics in France, Germany, Italy, Russia, Spain, the United Kingdom, and the United States. METHODS With a cost-consequence approach and published inputs, productivity costs were estimated for PNH patients who were treated with eculizumab once every 2 weeks (q2w) or ravulizumab once every 8 weeks (q8w) only in infusion clinics in 7 countries for 2 years. Inputs included the estimated PNH patient population size (prevalence: 1 in 500,000 of country population size; Schuller Y, et al. Orphanet J Rare Dis. 2015), the published mean annual income (World Bank, 2019), and assumption that all were employed. Total duration of therapy required for treatment was estimated by considering travel time to the infusion center, wait time in the clinic for medication preparation, infusion time for loading and maintenance doses, and recovery time (240 minutes for eculizumab and 330 minutes for ravulizumab) and was assumed to be the same across all countries. Lost wages were estimated using the human capital approach based on the hourly wage rate, which was derived from each country's 2018 gross national income per capita and assumed a 40-hour work week. Costs for each country were converted to 2018 USD. In the baseline analysis for each country, we did not consider caregivers. We then undertook 11 simulations (each composed of 1000 iterations) in which patient population size and lost wages were treated as normally distributed random variables and the proportion of patients with caregivers increased by 10% intervals. RESULTS The mean annual income varied 6-fold between the included countries. The table shows the total productivity loss due to eculizumab and ravulizumab treatment for a population of PNH patients and a 10% increase in caregivers in each country. For eculizumab, lost productivity ranged from $344,000 in Russia to $4.3 million in the United States, without caregivers. Within each country, the lost productivity due to ravulizumab treatment was reduced by approximately three-quarters relative to eculizumab treatment due to the less frequent dosing and ranged from $123,000 in Russia to $1.5 million in the United States, without caregivers. When the individual effects of population size and lost wages were isolated, the latter had larger proportionate effects in lower-income countries (data not shown). CONCLUSIONS Omitting productivity costs related to unpaid caregiving in PNH underestimates the total burden of illness on society. Switching from a q2w to a q8w treatment results in substantial savings to the patient's productivity. This study showed that this effect was numerically greater in higher-income countries (United States and Germany), in which the opportunity cost of a patient's time is greater. In chronic diseases such as PNH that require lifelong therapies, when the value of treatment is assessed, the burden of illness on society and the relative treatment effect should be considered. Disclosures Levy: Alexion: Consultancy. Dysart:Alexion: Consultancy. Patel:Alexion: Employment. Briggs:ALK: Consultancy; Merck: Consultancy; CVRx: Consultancy; Bayer Steering Committee: Consultancy; Sword Health: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Eisai: Consultancy. Myren:Alexion Pharmaceuticals: Employment. Tomazos:Alexion: Employment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".