Accurately Assessing the Expected Impact of Universal First Respiratory Syncytial Virus (RSV) Season Immunization With Nirsevimab Against RSV-Related Outcomes and Costs Among All US Infants
Bibliographic record
Abstract
To the Editor—We read with interest the recent publication by Kieffer et al [1] regarding the impact of nirsevimab on respiratory syncytial virus (RSV)–related outcomes and costs in the United States This is undoubtedly an important topic to address if nirsevimab is to be made widely available for use in clinical practice. However, we believe that several key issues ideally should have been addressed to maximize the value and utility of the study. First, there are some points of potential confusion that could lessen confidence in this important work. In particular, no costs were provided for palivizumab or nirsevimab in the Methods or the Discussion, and it is stated that “costs associated with purchasing and administering nirsevimab are not included in this study.” The outcomes of the study, however, clearly incorporate the costs of both nirsevimab and palivizumab, as evidenced by their inclusion in the sensitivity analyses provided in a figure [1, figure 4]. Clarity on the inclusion of these costs and their value is required for payers and clinicians to fully appreciate the scope of this work and the substantial value of this analysis for policy decision making. As another point of confusion, the RSV season is variously cited, in the Methods, as both October to February and October to March. Second, it would be more useful if the authors had explained how many of the key assumptions in the study were derived and/or applied. The widespread use of unpublished or proprietary data is also a salient limitation, particularly when, in several instances, publicly available data could have been used instead. As one example, the proportion of palivizumab-eligible infants was calculated using combined published and unpublished data, and information was not provided on how the data sources were used and how the calculations were performed. In another example, calculation of the proportion of RSV cases that would present as RSV-associated medically attended lower respiratory tract illness was based on the proportion of RSV cases for a given month, the incidence rate per month of age, age at the start of the season, and a multiplier for clinical severity derived from Shi et al [2]. It would have been helpful to report the actual multiplier used in this calculation. Furthermore, although the text refers to RSV-associated medically attended lower respiratory tract illness, the supporting figure cited in the text pertains to hospitalizations alone [1, figure 1]. Details of how these data related to the calculated inpatient hospitalization rates for the palivizumab-eligible group would be useful. In a third example, the impact of nirsevimab and palivizumab were calculated from efficacy rates, derived from a Cochrane review [3] of the available clinical trials, combined with a calculated coverage (uptake) rate. The latter seems to be derived from unpublished data. It is helpful for readers to understand the considerations underpinning the choice of values, and while the absence of such detail is understandable for nirsevimab, which is not yet in clinical use, it is unfortunate that the published data on palivizumab uptake and adherence in practice were not used [4–8]. Because the rate of coverage is a key driver of the impact of immunization, and therefore cost, greater detail on how the rate of coverage was derived would be useful. As it stands, we do not believe it is possible for investigators to replicate the analysis and independently validate the results or to adapt the model to their own healthcare system and clinical circumstances. More rigorous and systematic signposting to important information contained in the supplementary information may have addressed some of these issues. Finally, the analysis focused solely on direct costs, omitting the important component of indirect costs. Indirect costs associated with RSV and its prevention, treatment, and management in young children can be substantial; these include costs related to working parents’ absenteeism and presenteeism (attending work but not being productive because of worries/stress about sick child) [9]. Inclusion of indirect costs would allow for a more accurate depiction of the value of nirsevimab immunization from the societal perspective [10]. This exclusion, together with some of our other critiques, may reflect the absence among the authors of clinical experts and researchers in the RSV field. Involving such coinvestigators in this industry-supported study would likely have ensured that the best and most relevant data were included and that the model was more applicable to the true clinical scenario. We look forward to further publications on this important topic to support improvements in the prevention of RSV. Financial support. This work was not funded/financially supported.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".