
==== Front
Vaccine X
Vaccine X
Vaccine: X
2590-1362
Elsevier

S2590-1362(24)00127-X
10.1016/j.jvacx.2024.100554
100554
Regular paper
Characteristics of inpatient and outpatient respiratory syncytial virus mortality in Gavi-eligible countries
Willemsen Joukje E. ab
Vernooij Femke S. b
Shaaban Farina L. b
Chikoti Chilufya c
Bont Louis J. b
Drylewicz Julia J.Drylewicz@umcutrecht.nl
a⁎
on behalf of
the RSV GOLD study group
a Centre for Translational Immunology, University Medical Centre Utrecht, Utrecht, The Netherlands
b Division of Infectious Diseases, Department of Pediatrics, University Medical Centre Utrecht, Utrecht, The Netherlands
c Right to Care Zambia, Lusaka, Zambia
⁎ Corresponding author at: University Medical Center Utrecht, Center for Translational Immunology, KC 02.085.2, P.O. Box 85090, 3508 AB Utrecht, The Netherlands. J.Drylewicz@umcutrecht.nl
13 9 2024
10 2024
13 9 2024
20 10055414 2 2024
4 9 2024
5 9 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Highlights

• Median age at RSV-related death in Gavi-eligible countries is lower than prior estimates.

• The majority of RSV-related deaths from Gavi-eligible countries occurs before 6 months of life.

• Presence of biases in retrospective data should be considered for cost-effectiveness analyses.

The current understanding of the RSV-related mortality age distribution in low- and lower-middle-income countries (LMICs) relies on a limited number of disease incidence studies reporting wide age bands, and lacking specificity to Gavi-eligible countries. Understanding the age distribution of RSV-related deaths is crucial for the implementation of RSV interventions in LMICs that rely on support from Gavi. This study aims to provide the age profile of RSV mortality specifically in Gavi-eligible countries.

Utilizing data from the RSV GOLD project, an ongoing global online mortality registry focusing on children under the age of 5 with laboratory-confirmed RSV infection, we employed two models (Complete Data Model and Prospective Data Model) to estimate the age profiles. To mitigate biases related to age group representation, we applied post-stratification weighting in our analysis.

We included 423 pediatric deaths, including 145 from the community, under 2 years of age from 15 Gavi-eligible countries. Both models identified a peak age at 1 month and found that the majority of RSV-related mortality cases (59–77 %) from Gavi-eligible countries occur before 6 months of life. However, the models exhibited disparities in other age-related metrics. We present fitted age-at-time-of-death probability distributions to aid impact and cost-effectiveness studies.

We expect that implementing infant RSV immunization strategies, such as maternal vaccination or infant immunoprophylaxis, will have high impact on RSV-related mortality in Gavi-eligible countries. The divergent results from the two models underscore the importance of carefully considering potential biases in retrospective and surveillance data when interpreting the age profile of RSV mortality cases in future research.

Keywords

RSV
Gavi-eligible countries
Age at RSV-related death
Mortality
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pmc1 Introduction

Respiratory syncytial virus (RSV) infection is a leading cause of hospitalization and mortality worldwide in children under 5 years of age due to lower respiratory tract infection (LRTI) [1]. The majority of deaths (97 %) occur in low- and lower-middle-income countries (LMICs) due to the poor accessibility and affordability of healthcare and poor quality of care in health facilities. Gaining insight into the age distribution of children who experience fatal outcomes due to RSV has become more pressing with ongoing research into prevention methods, such as vaccines and monoclonal antibodies, and policy debates on prophylaxis in resource-limited settings [2].

Several RSV intervention products are currently in clinical development, and in 2023, both an RSV maternal vaccine and a long-acting single-dose monoclonal antibody (mAb) have received market approval. However, due to limited resources, maternal vaccination and mAb access in LMICs will require support from international partners [3]. Gavi, the Vaccine Alliance, is an international organization established with the goal of creating equal access to new and underused vaccines for those living in LMICs. To increase the accessibility of RSV prevention methods worldwide, Gavi has included both maternal vaccination and monoclonal antibodies for the prevention of RSV as one of the six priorities in their Vaccine Investment Strategy (VIS) for the 2021–2025 period [4], [5]. Every 5 years, the VIS sets new priorities for Gavi’s vaccine support programmes based on impact, cost, value and programmatic feasibility of underused or new vaccines of highest relevance to Gavi-eligible countries [6].

As both maternal vaccination and RSV monoclonal antibodies are characterized by a temporary protection profile, a good understanding of the age distribution of RSV-related deaths in Gavi-eligible countries will contribute to inform on impact and cost-effectiveness of RSV prophylaxis, thereby aiding Gavi in their decision-making process. The most commonly used estimates for the RSV-related mortality age-distribution in LMICs originate from a systematic review [1], [7]. This study relied on a limited number of studies centered on disease incidence and mortality rates, which were often presented in broad age categories, producing indirect estimates. Furthermore, these estimates were not specific to Gavi-eligible countries. The WHO Strategic Advisory Group of Experts on Immunization (SAGE) underlined the key epidemiological gap of age-stratified data on RSV acute lower respiratory infection (ALRI) during the first months of life to inform the use and anticipated impact of prevention products [5], [4]. Utilizing more detailed age-distributions enables a more precise estimation of the potential impact of RSV prevention strategies [8].

To fill the epidemiological gap of age-stratified disease burden data, the RSV Global Online Mortality Database (RSV GOLD) has been initiated in 2017, to elaborate on the clinical and sociodemographic profile of global RSV-related pediatric mortality. The first RSV GOLD retrospective case study was published in 2017 and included 117 cases from LMICs [9]. In 2021, a new retrospective case study was published comparing RSV-related infant community deaths with in-hospital deaths, including 829 deaths from LMICs [10]. However in that study, the majority of cases were from studies not eligible for Gavi, and the analysis on children below 2 years of age excluded 168 cases from community studies that only enrolled children up to 6 months of age.

In this study, we aim to investigate the characteristics of pediatric RSV mortality specifically in Gavi-eligible countries using all the data available from the RSV GOLD database. The detailed information on the age profile of RSV mortality will improve vaccine impact models and cost-effectiveness analysis, which are important for Gavi decision making.

2 Methods

2.1 Study design and patients

The RSV GOLD project is an ongoing global online mortality registry that collects individual patient data of children below 5 years of age who died with a laboratory-confirmed RSV infection after January 1st, 1995. Individual patient-level data are collected using an online questionnaire. Variables collected in the RSV GOLD database have been published previously [9], [10]. In this article we categorize the data in the RSV GOLD database into two groups based on how the information was collected. The characteristics (country, setting, inclusion criteria, etc.) of all the studies included in the RSV GOLD database are presented in STable 1. We refer to the first as Registry Mortality Data, which comprises data gathered through proactive outreach to researchers and physicians worldwide. These data were predominantly collected as part of larger hospital-based surveillance studies, mostly focused on respiratory infections in children. The sources are diverse, with variations in methodology and inclusion and exclusion criteria (see STable 1 for a detailed overview). The second group, referred to as Prospective Mortality Studies Data, consists of data from Bill & Melinda Gates Foundation (BMGF)-funded community mortality studies. These data were collected through prospective hospital and community-based mortality surveillance. Investigators of Bill & Melinda Gates Foundation (BMGF)-funded community mortality studies were specifically asked to share data collected up until March 2nd, 2021, as described previously in [10]. Two community studies (the Zambia Pertussis RSV Infant Mortality Estimation Study (Z-PRIME), and the Pakistan Community Mortality studies) included children younger than 6 months of age; other studies recruited children up until at least 12 months of age.

All data were thoroughly reviewed by the RSV GOLD research team. Additional queries concerning inconsistencies or missing information have been verified through direct contact with the respective collaborator. In this analysis, RSV-related deaths above 2 years of age, nosocomial deaths, and deaths in high-income countries were excluded (Fig. 1).Fig. 1 Flowchart of mortality cases included in this study. Flowchart shows children excluded via both data quality and per definition of study population. We incorporated the data from the <6 month and <24 months studies in our analysis using post-stratification weighting to adjust for overrepresentation. GOLD I: Pediatric deaths published as a retrospective case series from 1 November 2014 to 31 October 2015 [9]. GOLD II includes pediatric deaths collected after this publication. Abbreviations: m, months; BMGF, Bill & Melinda Gates Foundation; GOLD, Global Online Mortality Database; RSV, respiratory syncytial virus; ZPRIME, Zambia Pertussis RSV Infant Mortality Estimation Study.

2.2 Case definition

For this analysis, we only included mortality cases from Gavi-eligible countries according to the classification from 2023 [11]. As in our previous publications, we included any death with laboratory-confirmed RSV infection and did not require RSV to be the primary cause of death. If there was no information on where the RSV infection had been acquired and in the absence of nosocomial indications, we deemed the case community acquired.

The definition for a community death has been described previously [4], [8], [10]. If a child had not been admitted to hospital, we considered it as a community death. In case of missing data on the place of death, cases were classified as in-hospital deaths if the child had been admitted to hospital or if hospitalization status was not available. In case of missing data on hospitalization, we assumed the child had been hospitalized.

Children with comorbidities had at least one underlying disease, such as congenital heart disease, a genetic or chromosomal disorder, HIV infection, or active tuberculosis. Healthy term children were born without comorbidities at 37 weeks' gestational age or later, and healthy preterm children were born without comorbidities before 37 weeks' gestational age. If data for comorbidities and prematurity were not recorded, we assumed that the child was born healthy term.

2.3 Post-stratification weighting

In the RSV GOLD database, two prospective community mortality studies (Z-PRIME and the Pakistan Community Mortality studies) included only children younger than 6 months of age, while the vast majority of the registry mortality data included data until at least 2 years of age (STable 1). To address a potential issue of over- or under-representation of particular age groups in our analysis, we employed post-stratification weighting. We adjusted the weights of undersampled and oversampled subpopulations with the goal of making the overall sample more representative of the true underlying population. The weights were calculated based on the demographic characteristics of the known population, to which we refer as the population weights. In our case, the oversampled subpopulation was the group of children in Gavi-eligible countries who died with RSV before 6 months of age. We therefore needed to adjust the proportion of children in Gavi-eligible countries dying with RSV before 6 months of age and the proportion of children dying with RSV at 6 months or older. In order to estimate population weights based on our available data, we utilized two distinct approaches referred to as the Complete Data Model and the Prospective Data Model. Without a compelling reason to favor one model over the other, we examined the results of both models side by side.

In short, the Complete Data Model (M1) includes both the Registry Mortality Data and the Prospective Mortality Studies Data, whereas the Prospective Data Model (M2) includes only the Prospective Mortality Studies Data (Fig. 2). A complete illustrative comparison depicting the disparities between the Prospective Data Model and the Complete Data Model is presented in SFig. 1. More details about the models are described in Supplemental Methods.Fig. 2 Visual summary of the difference between the Complete Data Model (M1) and the Prospective Data Model (M2). See SFig. 1 for a more detailed version.

2.4 Statistical analysis

We report the mean age at time of death, the median age at time of death (i.e. the age at which half of the deaths among those under 2 years old had already taken place), and the peak age at time of death (i.e. the age at which RSV-related mortality reaches its peak). To quantify the proportion of cases in age groups, which is relevant for developing prophylactics, we calculated the proportion of cases below 1, 3 and 6 months of age.

For the other clinical features of interest, we calculated descriptive statistics, including measures such as mean, standard deviation, median, mode and interquartile range. Categorical variables were presented as frequencies and percentages.

We used a general linear model with the “glm” function in R to investigate the relationship between the place of death (in-hospital or community) and the data collection methodology (registry or prospective) and the age at time of death.

2.5 Ethical approval

Since solely secondary anonymous data were used in this study, the Medical Ethics Review Committee at the University Medical Centre Utrecht waived parental informed consent. However, adherence to local guidelines was encouraged and collaborators obtained ethical approval whenever necessary.

2.6 Role of the funding source

The RSV GOLD study is funded by the BMGF, which had no role in the study design, data collection, analysis, and interpretation, or the writing of the article.

3 Results

3.1 Study population

We included 423 pediatric deaths under 2 years of age from 15 Gavi-eligible countries classified as LMIC according to the World Bank income group classification of 2023. Of these, 145 deaths occurred in the community (Fig. 1). The Registry Mortality Data consisted of 226 deaths (Fig. 1) from 13 different countries (Fig. 3A), of which 16 were community mortality cases and 210 were in-hospital mortality cases. Place of death was missing for 35 cases, hospitalization was missing for 2 cases.Fig. 3 Countries of origin of included children with RSV-related mortality for the Registry Mortality Data (A) and the Prospective Mortality Studies Data (B), reference data for M1 and M2 respectively. The color gradient indicates the number of deaths shared, with a darker color representing a larger number of deaths shared.

RSV diagnosis was established most often by PCR (76 %). PCR was mostly used for children who died after 2005. The age at time of death distribution for the Registry Mortality Data is plotted in Fig. 4A. The Prospective Mortality Studies Data consisted of 197 deaths (Fig. 1) from 7 different countries (Fig. 3B), of which 129 were community mortality cases and 68 were in-hospital mortality cases. RSV diagnosis was established by PCR for all these cases. Most deaths were from Zambia (76 %, 150/197). The age at time of death distribution for the Prospective Mortality Studies Data is plotted in Fig. 4B.Fig. 4 (A) Histogram of age at RSV-related death for children under 2 years in the Registry Mortality Data (reference data for M1). (B) Histogram of age at RSV-related death for children under 2 years in the Prospective Mortality Studies Data (reference data for M2). The histograms show the number of deaths (count) shared to the registry by age at death in months (rounded to the nearest integer).

3.2 Pooling hospital and community cases

Given the limited number of community mortality cases in the registry data, and the limited number of in-hospital mortality cases in the prospective data, we conducted a permutation test to determine whether it is appropriate to combine the hospital and community mortality cases for the analyses. We computed the difference in medians through 1000 permutations, comparing permuted differences to the observed one to calculate a p-value. The test showed no significant median age difference between community and in-hospital mortality cases (p = 0.69 for the registry data and p = 0.67 for the prospective data below 6 months). We therefore concluded that these cases could be pooled in the main analysis. Similarly, we tested whether the observations below 6 months of age differed significantly between the registry and prospective group. The permutation test did not yield a statistically significant result (p = 0.44), indicating that pooling of the data was acceptable.

3.3 Post-stratification weights

3.3.1 Complete data model (M1)

In this model, we made the assumption that using all available data, except for the Z-PRIME and Pakistan data, which had divergent age inclusion criteria, would provide us with the most accurate representation of the true population. This data encompasses observations across the entire age spectrum up to 2 years. Based on this data, we estimated the true proportion of mortality cases below 6 months of age to be 0.59. This corresponds to a weight of 0.788 for cases before 6 months of age and a weight of 1.633 for 6 months and older.

3.3.2 Prospective data model (M2)

In this model, we assumed that the subset of data obtained from the Prospective Community Mortality Studies is the most informative as this data was collected through active surveillance. We fitted a truncated Burr distribution (type XII) to the data below 6 months of age. The Burr distribution (Burr type XII) has three parameters [12]; scale, shape 1 and shape 2. The cumulative distribution function of the Burr distribution (for x weeks of age) is Fx=1-[1+xscaleshape1]-shape2. Our analysis identified the most suitable fit with parameters Burr(scale = 11.0, shape1 = 1.2, shape 2 = 0.9) (Fig. 5B). Based on the extrapolated distribution, we estimated the proportion of mortality cases below 6 months of age to be 0.77. This corresponds to a weight of 0.839 for cases before 6 months of age and a weight of 2.817 for 6 months and older.Fig. 5 Fits of the Burr distribution to the age at RSV-related death data. The Burr distribution (Burr type XII) has three parameters: scale, shape 1 and shape 2. The fitted parameters are presented in the plot legend as Burr (scale, shape 1, shape 2). The cumulative distribution function of the Burr distribution (for x weeks of age) is Fx=1-[1+xscaleshape1]-shape2. (A) Weighted histogram (in grey) and fitted distribution (in red) for M1 under 2 years of age. (B) Data from the Prospective Community Mortality Studies below 6 months of age (in grey) and the corresponding fitted truncated distribution (in blue). (C) Weighted histogram (in grey) for M2 under 2 years of age and the fitted distribution (in yellow). (D) Comparison of the fitted distributions for M1 (in red the Burr-distribution fitted on the full dataset, in green the fitted Burr-distribution on data below 6 months of age only) and M2 (in yellow the Burr-distribution fitted on the weighted dataset, in blue the fitted Burr-distribution on data below 6 months of age only). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

To test the fit of the truncated distribution, we performed both a visual inspection (Fig. 5B) and a simulation test. We simulated data from the truncated distribution with the same sample size as the observed sample and calculated the median age. This was repeated a 1000 times. The median from the observed sample was compared with the distribution of medians from the simulated samples and a corresponding p-value was calculated. The permutation test did not yield a statistically significant result (p = 0.99), indicating that there is no compelling evidence that the fitted distribution is not representative of the true underlying age distribution.

3.4 Clinical characteristics

The metrics of the age profile (mean, median, and peak age) are presented in Table 1 for both the raw registry data and the raw prospective data (not adjusted for an overrepresentation of <6 months), and according to M1 and M2. Whereas the peak age of RSV-related mortality is consistent among the two models (driven by the prospective data, Table 1), M2 shows a younger age profile for the other metrics, with a median age difference of 2.2 months. Both models agree that the majority of RSV-related mortality cases from Gavi-eligible countries (59–77 %) occur before 6 months of life.Table 1 Clinical characteristics of children under 5 years of age, who died with RSV in-hospital versus in the community in Gavi-eligible countries. Descriptive statistics (%, mean, medium, IQR) were calculated using survey weights, adjusting for the overrepresentation of studies with different age-related inclusion criteria. n is the reported frequency for a specific outcome in the dataset, N is the number of non-missing observations for the variable. n/N is the observed proportion in the database, not adjusted for survey weights.

Clinical characteristics	Registry data
N = 226	Unadjusted prospective data*
N = 197	Complete data model (M1)
N = 423	Prospective data model (M2)
N = 197	
Sex, male, % (n)	52 (117/226)	48 (87/183)	51 (210/412)	47 (87/185)	
Age at death, months, peak age (%)	2 (14)	1 (30)	1 (16)	1 (26)	
Age at death, months, mean (SD)	6.0 (4.8)	2.9 (3.5)	5.9 (5.2)	4.4 (5.1)	
Age at death, months, median (IQR)	4.9 (2.2–8.0)	1.7 (0.8–3.9)	4.3 (1.8–8.9)	2.1 (0.9–5.5)	
Age <1 m at death, % (n)	7.5 (17)	30 (60)	14 (61)	26 (50)	
Age <3 m at death, % (n)	30 (68)	66 (131)	37 (157)	56 (110)	
Age <6 m at death, % (n)	60 (136)	92 (181)	59 (250)	77 (152)	
Comorbidity, % (n)	37 (83)	18 (35)	31 (133)	30 (59)	
Prematurity, % (n)	8.4 (19)	12 (23)	9.0 (38)	9.8 (19)	
Hospitalized, % (n)	96 (217)	37 (73)	73 (311)	40 (79)	
Gestational age (weeks) mean (SD)	37.1 (4.5)	35.9 (3.5)	36.4 (4.1)	37.0 (2.9)	
n	13	16	32	25	
Birth weight (kg)
median (IQR)	2.6 (1.8–3.0)	2.8 (2.3–3.3)	2.8 (2.1–3.2)	2.9 (2.5–3.3)	
n	22	31	59	53	
Year of death, minimum–maximum	1995–2022	2017–2021	1995–2022	2017–2021	
Not immunized, % (n/N)	12 (8/69)	53 (41/78)	26 (39/152)	39 (36/93)	
Other children in household, % (n/N)	82 (28/34)	88 (29/33)	86 (64/75)	93 (44/47)	
Mother uneducated, % (n/N)	21 (8/39)	11 (13/114)	15 (20/130)	11 (11/96)	
Father uneducated, % (n/N)	0 (0/21)	4.0 (4/100)	3.2 (3/98)	4.0 (3/84)	
* Not adjusted for the overrepresentation of cases <6 months of age.

Besides describing the age profile, our goal is to provide direct input for cost-effectiveness models. To accomplish this, we fitted a truncated Burr distribution to the M1 data for up to two years of age (Fig. 5A). Given that, for M2, the Burr distribution is fitted using information from below six months of age (Fig. 5B), we similarly utilized a truncated Burr distribution exclusively for the M1 data below six months. This approach ensures a fair comparison between the fitted distributions for both models and provides insight into the extent to which different age cut-offs impacted the fitted distribution. M2 yields a younger age-distribution compared to M1 (Fig. 5D). When only data below 6 months of age is used to fit M1, the age-distribution is still considerably younger compared to that of M2.

The q-q plots show a reasonably close alignment between the quantiles of the observed data and those predicted by the fitted distributions (SFigs. 3–6), except for the data fitted based on M2 up to two years of age. This is understandable, as the number of observations past 6 months are very limited in M2 and not evenly spread out (Fig. 5C). A visual comparison suggests a favorable fit of the distributions to the empirical data (Fig. 5A–C).

3.4.1 Comorbidity and prematurity

Our estimations indicate that a minimum of 31 % and 30 % of mortality cases, for M1 and M2 respectively, had severe comorbidities (Table 1). Furthermore, based on M1 and M2, we calculated that a minimum of 9 % and 10 % of mortality cases, respectively, were born prematurely. Below 6 months of age, HIV/AIDS was the second most common reported comorbidity after congenital heart disease (STable 2). However, data on comorbidities and prematurity were often missing, especially for the Prospective Community Mortality Data cases (STable 3), limiting the power to analyze this characteristic. To address potential bias and test our assumption that children with missing data were born healthy term, we performed a sensitivity analysis excluding cases with missing data for prematurity or comorbidities. When excluding children with missing data for comorbidities and gestational age (STable 3), the percentage of children with comorbidities or prematurity more than doubled (to 59 % and 26 % for M1, and to 82 % and 27 % for M2, respectively).

3.4.2 Community deaths vs in-hospital deaths

To address potential bias stemming from differences in data collection methodology (registry or prospective), a separate analysis was conducted for community and in-hospital deaths in both data collection approaches below 6 months of age (STable 4). Under 6 months of age, the observed age at time of death for in-hospital cases is older compared to community deaths. For both in-hospital cases and community deaths, the estimates for age at time of death are consistently older based on the Registry Mortality Data compared to the Prospective Community Mortality Data. We used a general linear model to compare the age at time of death (in months) for community deaths and in-hospital deaths below 6 months of age, while taking the differences in methodology into account. We found a significant effect of methodology on age at time of death, with the Prospective Community Mortality Studies resulting in lower estimates compared to the Registry Mortality Data (β = −0.7, p = 0.002), while the place of death (in community or in-hospital) showed no significant effect (β = 0.2, p = 0.35).

4 Discussion

To the best of our knowledge, this is the first global mortality study characterizing children dying with RSV specifically in Gavi-eligible countries. We found a peak age of RSV-related mortality at 1 month of age and the majority of RSV-related mortality cases from Gavi-eligible countries occur before 3–6 months of life. Thus, we expect that implementing infant RSV immunization strategies, such as maternal vaccination or infant immunoprophylaxis, will have high impact on RSV-related mortality in Gavi-eligible countries.

Impact and cost-effectiveness studies are required to further evaluate health and economic impact of RSV interventions for Gavi eligible countries. To inform these studies, we reported the fitted age at time of death distributions. We utilized a Burr distribution to model our data, as this continuous probability distribution is commonly employed to describe the duration of survival. Furthermore, the Burr-distribution is used as input for the UNIVAC model (step 2 of inputs page) in [13]); a decision-support model with a universal framework for evaluating the potential impact and cost-effectiveness of different vaccines. An adaptation of the UNIVAC model allows the evaluation of RSV maternal vaccines and infant mAbs [14], [15] and is therefore particularly useful in cost-effectiveness studies for RSV interventions.

We used two different models to estimate the age profile. The first model (M1) was based on all available data in the RSV GOLD database. The second model (M2) was based on only the data shared with the RSV GOLD project that were collected as part of the Prospective Community Mortality studies. Both M1 and M2 show a peak age of RSV-related mortality at 1 month of age. The majority of RSV-related mortality cases from Gavi-eligible countries occur before 6 months of life. According to M2, the majority of RSV-related mortality cases occur even before 3 months of life, suggesting a younger age-distribution compared to M1. The younger age profile of RSV mortality according to M2 is most in line with new available surveillance data. These contain thirty additional mortality cases from Gavi-eligible countries, gathered as part of the RSV GOLD ICU Network study, a recent active RSV surveillance study focusing on children under 2 years old, which have not been incorporated into the present analysis. The age profile of these cases is similar to that described in M2 [16].

On the other hand, the age distribution derived from M1 aligns more closely with previously described age distributions in the context of cost-effectiveness analyses in LMICs (SFig. 2). In a cost-effectiveness study conducted in Vietnam [15], a Burr-distribution was fitted to severe RSV-ALRI hospital admission data below 2 years of age. This distribution corresponds to a median age of 35 weeks. In another study by Mahmud et al. [14], data from RSV hospital admission in six LMICs were analyzed as part of their cost-effectiveness analysis. They identified the best fit for a Burr-distribution corresponding to a median age 23 weeks. Notably, their fitted age-distributions for severe RSV-ALRI exhibited significant variability across countries (see Fig. S1 in [14].). In both cost-effectiveness studies, the age-distribution for mortality cases was assumed to follow the age-distribution for hospital admission. However, this could result in an overestimation of the age-distribution, as mortality cases are generally younger [16]. This might explain why their estimates are notably older compared to our estimates.

For the first model (M1), it was assumed that using all available data except for the two community mortality studies, which had divergent age inclusion criteria, would provide us the most accurate representation of the true population. This assumption was based on two main factors: firstly, this data encompasses observations across the entire age spectrum up to 2 years, allowing for a comprehensive understanding of the age distribution below 2 years of age. Secondly, this is a relatively large and geographically diverse dataset. We acknowledge potential limitations in study methodology for the regular RSV GOLD registry cases; most of this data is gathered by our collaborators from hospital surveillance data or by systematically searching through hospital files. Heterogeneities in factors such as study setting, health-care access and seeking behaviour and eligibility for RSV testing could affect our estimates. For example, it has been speculated that younger children with RSV may present with nonspecific symptoms to the hospital and will therefore be overlooked [10]. Furthermore, children with reported severe comorbidities (who are generally older [9]) have better access to health care and health monitoring and therefore a higher probability of being tested and reported as RSV related death in the RSV GOLD database. Additionally, data pollution may arise due to the inclusion of children who died with severe comorbidities, where RSV was not a causal factor. These factors possibly explain the older age-distribution compared to M2.

For the second model, it was assumed that the subset of data obtained from the community mortality studies offers the most reliable depiction of the true population below 6 months of age. Unlike the regular GOLD registry cases, all these cases were collected through active surveillance. This active surveillance approach enhances the likelihood of capturing a comprehensive representation of the target population. An obvious limitation of this model is that the majority of the data originated from only one study site (Z-PRIME), and that it thereby makes the implicit assumption that the results of a single country are representative for all Gavi-eligible countries. Additionally, the reference data for this model only included children below 6 months of age. Therefore, M2 relies on the strong assumption that we can extrapolate the fitted distribution, obtained from data below 6 months, to up to 2 years of age.

Consistent with our previous analysis [10], we observed a lower median age at death in the community compared with in-hospital, although differences were not statistically significant when correcting for data collection methodology. Data collection methodology, on the other hand, was a significant predictor for age at time of death; with the Prospective Community Mortality Studies resulting in lower estimates compared to the Registry Mortality Data. This stresses the need for more high-quality prospectively collected mortality data from Gavi-eligible countries, to allow for more robust conclusions regarding the age profile of RSV mortality cases.

5 Conclusion

We expect that implementing infant RSV immunization strategies, such as maternal vaccination or infant immunoprophylaxis, will have high impact on RSV-related mortality in Gavi-eligible countries. We further conclude that the potential biases in retrospective and surveillance data influencing the age profile of RSV mortality cases should be considered when performing cost-effectiveness analyses and making policy decisions. We underscore the importance of collecting more high-quality prospectively collected mortality data from Gavi-eligible countries.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: LJB has regular interaction with pharmaceutical and other industrial partners. He has not received personal fees or other personal benefits. The University Medical Centre Utrecht (UMCU) has received major funding (>€100 000 per industrial partner) for investigator-initiated studies from AbbVie, MedImmune, Janssen, the Bill & Melinda Gates Foundation, Nutricia (Danone), and MeMed Diagnostics. UMCU has received major cash or in-kind funding as part of the public–private partnership IMI-funded RESCEU project from GSK, Novavax, Janssen, AstraZeneca, Pfizer, and Sanofi. UMCU has received major funding by GlaxoSmithKline and Julius Clinical for participating in the INFORM study sponsored by MedImmune. UMCU has received minor funding for participation in trials by Regeneron and Janssen from 2015 to 2017 (total annual estimate <€20 000). UMCU received minor funding for consultation and invited lectures by AbbVie, MedImmune, Ablynx, Bavaria Nordic, MabXience, Novavax, Pfizer, and Janssen (total annual estimate <€20 000). LJB. is the founding chairman of the ReSViNET Foundation. NIM has regular interaction with pharmaceutical and other industrial partners. She has not received personal fees or other personal benefits. Cheryl Cohen (C. C. has received minor funding from Sanofi and support to attend meetings from Parexel. Sandra Chaves (S. S. C.) has left the Centers for Disease Control and Prevention and is currently working for Sanofi Pasteur, France. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Appendix A Supplementary data

The following are the Supplementary data to this article:Supplementary Data 1

Data availability

Data will be made available on request.

Acknowledgments

We thank José Borghans, Angela Guo and Padmini Srikantiah for their scientific advice and support. We thank all members of the RSV GOLD team: in particular Annemijn van den Doel, Nynke van Haastregt, Lisa de Krosse, Femke Vernooij, and Zoe Wesselink.

This publication is based on research funded in part by the 10.13039/100000865 Bill & Melinda Gates Foundation (grant number OPP1148988.8 ).

Authors’ contributions

LJB is PI on the RSV GOLD project. JW and JD conceptualized the study. JW did the analysis with assistance from FSV. FSV performed the datacleaning. JW, JD and LJB interpreted the data. JW and JD wrote the first draft of the article. LJB, FV, FS, CC reviewed and commented on the manuscript.

RSV GOLD collaborators

Josep Estrada1, MSc, PhD, Angela Gentile2, Prof, MD, Maria Florencia Lucion2, MD, Marcela Echavarria3, PhD, Noelia Reyes3, MSc, Fernando P. Polack4, MD, Mauricio T. Caballero4, MD, Annette Alafaci5,6, BSc, Nigel Crawford5,6, MD, PhD, Jenny Thompson6, Warwick Butt5,6,7, MBBS, FRACP, Nusrat Homaira8, MBBS, MPH, PhD, Adam Jaffe8, BSc, MBBS, MD, FRCPCH, FRCP, FRACP, FThorSoc, Gemma Saravanos9, PhD, Philip Britton9, PhD, MD, FRACP, Christoph Binder10, MD, Angelika Berger10, Prof, MD, PhD, MBA, Bernhard Resch11, Prof, MD, Gülsen Sever Yildiz 11, MD, Fahmida Chowdhury12, MD, MPH, Ariful Islam12, MPH, Senjuti Saha13, PhD, Samir Saha13, PhD, José Gareca Perales14, MD, Felipe Cotrim de Carvalho15, Sergio de Andrade Nishioka, MD, PhD, on behalf of the Influenza Surveillance Team at the Brazil Ministry of Health15,16, Maria Tereza da Costa Oliveira17, PhD, Carla Cecília de Freitas Lázaro Emediato17, MD, Heloisa Giamberardino18, MD, MSc, Jane Melissa Webler18, MD, Regina Grigolli-Cesar19,20, MD, Daniel Jarovsky21, MD, Daniela Gregória Bomfim Prado da Silva21, MD, Patricia Gomes de Matos Bezerra22, MD, PhD, Maria do Carmo Menezes Bezerra Duarte22, MD, PhD, Fernanda de-Paris23, PhD, Patrícia da Silva Fernandes 24, MD, MSc, Sonia M. Raboni25, MD, PhD, Tani Sagna26, PhD, Serge Diagbouga26, Prof, PhD, DVM, Daniel Garros27, MD, Michael Hawkes28, MD, PhD, Joanne M. Langley29, MD, MSc, FRCPC, Jill Mutch29, BScN, RN, CCRP, Kirk Leifso30, MD, MSc, Shaun K. Morris31, MD, MPH, FRCPC, FAAP, DTM&H, Waison Wong31, MD, Bosco A. Paes32, MD, Jesse Papenburg33, MD, MSc, Franco Diaz34,35, MD, Rodrigo A. Fasce36, BSBC, Olga Lopez37, MD, Ting F. Leung38, MD, Wei Su39, MD, Chiang Chun Yuan39, MD, Zhengde Xie40, PhD, MD, Junhong Ai40, MD, Evelyn Obando Belalcazar41, MD, Jaime Fernández-Sarmiento42, MD, Ledys Izquierdo43,44,45, MD, Rubén Lasso43,44,46, MD, Rosalba Pardo-Carrero43,47, MD, Pablo Vasquez43,44,48, MD, MSc, Eliana Zemanate43,44,49, MD, Srđan Roglić50, MD, Thea K. Fischer51, Prof, Sune Rubak52, MD, PhD, Mette Holm52, MD, PhD, Domenica de Mora53, MSc, Alfredo Bruno53,54, MD, MSc, Jenny Ojeda55, MSc, Lida Zamora55, MSc, Erica Dueger56,173, Terho Heikkinen57, Prof, MD, PhD, Gilles Cambonie58, MD, PhD, Jean-Christophe Dubus59, Prof, MD, PhD, Melina Messaoudi60, MSc, Juliet Bryant60, MSc, PhD, Haoua Tall61, MPH, Bradford D. Gessner61, MD, MPH, Dominique Ploin62, MD, PhD, Come Horvat62, MD, Nour Hanna63, MD, Christian Vogelberg63, MD, PhD, Christoph Härtel64, MD, Andrea Streng64, PhD, Johannes Liese64, MD, MSc, Barbara A. Rath65,66, MD, PhD, Jürgen Seidenberg67, MD, PhD, Geeske Stelljes67, FWJ, Evangeline Obodai68, PhD, John Kofi Odoom68, PhD, Irini Eleftheriou69, MD, Maria Tsolia69, MD, PhD, Vassiliki Papaevangelou70, Prof, MD, Elpiniki Kartsiouni70, MD, MSc, Tapan Dhole71, MD, Sheetal Verma71, MD, Rashmi Ranjan Das72, MD, Ashish Satav73, MD, Agustinus Sutanto74, MD, Dario Prais75,76, MD, Liat Ashkenazi-Hoffnung75, MD, Orli Megged77, MD, Francesca Tortora78, MD, Fabrizio Chiusolo78, MD, Giovanni Gabutti79, MD, Kazuki Iio80, MD, Satoshi Kusuda81, MD, Hitoshi Oshitani82, MD, Hisato Ito83, MD, Najwa Khuri-Bulos84, MD, Loai Saadah85, PhD, Iman Basheti85, Prof, PhD, Sandra S. Chaves86, MD, MSc, Gideon Emukule86, MSc, PhD, Patrick K. Munywoki87,88, PhD, D. James Nokes87,89, PhD, Grieven Paul Otieno87,89, BSc, Ieva Silina90, MD Abdulla Alfraij91, MD, Mohammad Alghounaim92, MD, Ghassan Dbaibo93, MD, Rima Hanna-Wakim93, MD, Yoke-Fun Chan94, PhD, Jamal I-Ching Sam94, MRCPath, Teck-Hock Toh95, MD, Jeffrey Soon-Yit Lee95, MD, David Pace96, MD, Pg Dip PID (Oxf), FRCPCH, PhD, Socorro P Lupisan97, MD, Marilla G. Lucero97, PhD, Daniel E. Noyola98, MD, PhD, Andreu Comas-García98, MD, PhD, Túfaria Mussá99, DVM, PhD, Brigitte Buiteman100, RN, Diederick E. Grobbee101,102, MD, PhD, Rosalie Linssen103, MD, Namrata Prasad104, MPH, José F. Sánchez105, MD, Uzma Bashir Aamir106, PhD, Qalab Abbas107, MD, Abdul Momin Kazi107, MD, MPH, Sidra Asif Khan107, MBBS, Rodrigo DeAntonio108,109, MD, MSc, DrPH, Xavier Saez-Llorens108,110, MD, Juana del Valle-Mendoza111, PhD, Wojciech Feleszko112, Prof, Karolina Dumycz112, MD, Sara Cristina de Tavares Ferreira113, MD, Ana Beatriz de Sousa Pereira Luzio Vaz113, MD, Mohammed Ahmed Abdullah Qashnon114, MD, Khalid Alansari114,115,116,117, Prof, MD, Eun Hwa Choi118, PhD, Yae-Jean Kim119, MD, PhD, Eun Lee120, MD, PhD, Eun-Ae Yang121, MD, Hyun Mi Kang121, MD, Kirill Stolyarov122, MD, Thoon Koh Cheng123, MD, Kee Thai Yeo124, MD, Chee Fu Yung124,125,126, MD, Stefan Grosek127, Prof, MD, PhD, Marko Pokorn127, MD, PhD, Cheryl Cohen128,129, MSc, PhD, Jocelyn Moyes129, MD, Shabir A. Madhi130, PhD, Michelle J. Groome130, PhD, Marietjie Venter131, Prof, PhD, Adele Visser132, Cristina O'Callaghan-Gordo133, PhD, Quique Bassat133,134,135,136,137, Prof, MSc, MD, PhD, Cristina Calvo138, MD, PhD, Xavier Carbonell-Estrany139, MD, PhD, Francisco J. Elola140, MD, Manuel Sánchez Luna140, MD, PhD, Rosa Rodriguez Fernandez141, MD, PhD, Cristian Launes137, 142, MD, PhD, Carmen Muñoz-Almagro137, 142,143, MD, PhD, Saif Awlad Thani 144, MD, Sampath Jayaweera145, MD, Martin W. Weber146, MD, PhD, Joachim Luthander147, MD, Ulrich Heininger148, Prof, MD, PhD, Daniel Trachsel148, Claudia E. Kuehni149, Prof, Cristina Ardura-Garcia149, PhD, Chia-Yu Chi150, MD, Hsin Chi151, MD, PhD, Shuenn-Nan Chiu152, Prof, MD, Jou-Kou Wang152, MD, Yhu-Chering Huang153, Prof, MD, PhD, Piyarat Suntarattiwong154, MD, MPH, Somsak Thamthitiwat155, MD, Nasamon Wanlapakorn156, MD, PhD, Aida Borgi157, MD, Ahmed Ayari157, MD, Imen Bel Hadj158, MD, Khadija Boussetta158, Prof, Benan Bayrakci159, MD, Esra Koçkuzu159, MD, Muhterem Duyu160, MD, Sule Gökçe161, MD, Aykut Eşki162, MD, Tanil Kendirli163, MD, PhD, Emrah Gün163, MD, Edward A. Goka164, PhD, Simon Nadel165, MD, PhD, Marwa Ghazaly165, MD, PhD, Kentigern Thorburn166,167, MD, Paul S. McNamara166,167, MD, PhD, Soledad Menta168,169, MD, Nicolás Monteverde168,170, MD, Sebastián González-Dambrauskas168,171,172, MD, Dianna M. Blau173, DVM, PhD, Katherine Horton173, BSc, MPH, Robert F. Breiman174, Prof, MD, Andrea Buchwald175, MD, Helen Chu176, MD, MPH, Leah Forman177, MPH, Christopher J. Gill177, MD, Lawrence Mwananyanda177, Aubree Gordon178, PhD, Natasha Halasa179, MD, Danielle Hessong180, MPH, Diego Raul Hijano181, MD, MSc, Kim J. Allison181, RN, Matthew S. Kelly182, MD, Veena Kumar183, MD, Asuncion Mejias184,185, PhD, MD, Octavio Ramilo184,185, MD, Katherine O'Brien, MD, on behalf of the PERCH Study Group186, Saad B. Omer187, MD, MPH, PhD, Thyyar Ravindranath188, MD, Steven L. Shein189, MD, Ananya Parlapalli189, Eric A F Simões190, MD, Michael C Spaeder191, MD, Pham Thi Minh Hong192, Prof, Tran Anh Tuan193, MD, PhD, Mohammed Al Amad194, MD, Abdul Wahed Al Serouri194, MD, Patrick Gavin 195, MD, Eilish Moore 195, RN, Olivier Fléchelles 196, MD, PhD, Lisa Kemp 197, MD, Michael Bolton 197, MD, PhD, Burmaajav Badrakh 198, Prof, PhD, MD, Oyuchimeg Myagmardorj 198, PhD, Amadu Juliana 199, MD, PhD, Bérenger Kabore 200, MD, PhD, Halidou Tinto 200, Prof, PhD, Nickson Murunga 87, BSc, Ejaz Ahmed Khan 201, Prof, MD, MBBS, Hafiz Muhammad Faras 201, MBBS, Lorenzo Lodi 202, 203, MD, Federica Barbati 203, MD, David Ng Chun-Ern 204, MD, Vindhu Venugopal 204, MRCPCH, Simon Drysdale 205, PhD, FRCPCH, Suba Guruprasad 205, MRCPCH, Sanath Thushara Kudagammana 206, Prof, MD, Hewapedi genara sunjani premathilake 207, Alireza Tahamtan 208, PhD, Vahid Salimi 209, PhD, Catalina Pírez210, Prof, MD, Elizabeth Assandri 210,MD.1. SAAS, Escaldes Engordany, Andorra

2. Department of Epidemiology, Ricardo Gutiérrez Children's Hospital, Buenos Aires, Argentina

3. CEMIC University Hospital, Buenos Aires, Argentina

4. Fundación Infant, Buenos Aires, Argentina

5. Murdoch Children's Research Institute, Melbourne, Australia

6. The Royal Children's Hospital, Melbourne, Australia

7. Department of Paediatrics, University of Melbourne, Melbourne, Australia

8. University of New South Wales, Sydney, Australia

9. The University of Sydney Children’s Hospital Westmead Clinical School, Sydney, Australia

10. Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University Vienna, Vienna, Austria

11. Medical University of Graz, Graz, Austria

12. Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh

13. Dhaka Shishu Hospital, Dhaka, Bangladesh

14. Centro de Pediatria Especializada “CRECER”, Santa Cruz de la Sierra, Bolivia

15. Department of Transmissable Diseases, Ministry of Health, Brasília, Brazil

16. Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil

17. Health Secretariat of the City of Belo Horizonte, Belo Horizonte, Brazil

18. Hospital Pequeno Principe, Curitiba, Brazil

19. Hospital Infantil Sabará, São Paulo, Brazil

20. Red Colaborativa Pediátrica de Latinoamérica (LARed Network), São Paulo, Brazil

21. Santa Casa de São Paulo, São Paulo, Brazil

22. Instituto de Medicina Integral Prof Fernando Figueira (IMIP), Recife, Brazil

23. Transplant Immunology and Personalized Medicine Unit, Laboratory Diagnostics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil

24. Infection Control Commission, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil

25. Universidade Federal do Paraná, Paraná, Brazil

26. Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso

27. Stollery Children’s Hospital, Edmonton, Canada

28. University of Alberta, Alberta, Canada

29. Izaak Walton Killam Health Centre, Halifax, Canada

30. Kingston General Hospital, Kingston, Canada

31. Division of Infectious Diseases, The Hospital for Sick Children, Toronto, Canada

32. Neonatal Division, Department of Pediatrics, McMaster University, Hamilton, Canada

33. Montreal Children’s Hospital, McGill University Health Centre, Montreal, Canada

34. Hospital La Florida Dra Eloísa Díaz, Santiago, Chile

35. Red Colaborativa Pediátrica de Latinoamérica (LARed Network), Santiago, Chile

36. Public Health Institute, Santiago, Chile

37. Hospital Dr Ernesto Torres Galdames, Iquique, Chile

38. Department of Paediatrics, Faculty of Medicine and Chinese University of Hong Kong- University Medical Center Utrecht Joint Research Laboratory of Respiratory Virus and Immunobiology, Chinese University of Hong Kong, Hong Kong, China

39. CanAm International Medical Center, Guang Zhou, China

40. Beijing Children Hospital, Beijing, China

41. Instituto Roosevelt, Bogotá, Colombia

42. Universidad de La Sabana, Fundación Cardioinfantil-Instituto de Cardiología, Bogotá, Colombia

43. Red Colaborativa Pediátrica de Latinoamérica (LARed Network), Bogotá, Colombia

44. BACON, Bogotá, Colombia

45. Hospital Militar Central, Bogotá, Colombia

46. Fundación Valle de Lili, Cali, Colombia

47. Clínica Infantil Colsubsidio, Bogotá, Colombia

48. Hospital de San José, Bogotá, Colombia

49. Hospital Susana López de Valencia ESE, Popayán, Colombia

50. Department of Paediatric Infectious Diseases, University Hospital for Infectious Diseases, Zagreb, Croatia

51. University of Copenhagen, Institute of Public Health, Copenhagen, Denmark

52. Danish Center of Pediatric Pulmonology and Allergology, Department of Pediatrics and Adolescents Medicine, Aarhus University Hospital, Aarhus, Denmark

53. Instituto Nacional de Investigacion en Salud Publica, Guayaquil, Ecuador

54. Universidad Agraria del Ecuador, Guayaquil, Ecuador

55. Ministerio de Salud Pública del Ecuador, MSP, Quito, Ecuador

56. Global Disease Detection and Response Program, US Naval Medical Research Unit No. 3, Cairo, Egypt

57. Turku University Hospital, Turku, Finland

58. Montpellier University Hospital Center, Montpellier, France

59. CHU Timone-Enfants, Marseille, France

60. Emerging Pathogens Laboratory - Fondation Merieux, Lyon-Gerland, France

61. Agence de Medecine Preventive, Paris, France; currently with Pfizer, Inc., New York, NY, USA

62. Services de Reanimation et d'Urgences Pediatriques, Hopital Femme Mere Enfant des Hospices Civils de Lyon, Lyon, France

63. Pediatric Department, University Hospital Carl Gustav Carus, Dresden, Germany

64. Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany

65. Vienna Vaccine Safety Initiative, Berlin, Germany

66. University of Nottingham School of Medicine, Nottingham, Germany

67. University Hospital for Children, Klinikum Oldenburg AöR, Oldenburg, Germany

68. Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana

69. Children's Hospital ‘P& AKyriakou’, Athens, Greece

70. Third Department of Pediatrics, National and Kapodistrian University of Athen, ATTIKON Hospital, Athens, Greece

71. King George's Medical University, Lucknow, India

72. All India Institute of Medical Sciences Bhubaneswar (AIIMS), Bhubaneswar, India

73. MAHAN trust (Mahatma Ghandi Tribal Hospital), Kadhava, India

74. West Nusa Tenggara Provincial Government, Lombok, Indonesia

75. Schneider Children‘s Medical Center of Israel, Petah Tikva, Israel

76. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

77. Shaare Zedek Medical Center, Jerusalem, Israel

78. Bambino Gesù Children's Hospital, Rome, Italy

79. University of Ferrara, Department of Medical Sciences, Section of Public Health Medicine, Ferrara, Italy

80. Tokyo Metropolitan Children’s Medical Center, Tokyo, Japan

81. Kyorin University, Tokyo, Japan

82. Department of Virology, Tohoku University Graduate School of Medicine, Sendai, Japan

83. Department of Pediatrics, Nantan General Hospital, Ueno, Yagichoyagi, Nantan-shi, Kyoto, Japan

84. Department of Pediatrics, University of Jordan, Amman, Jordan

85. Applied Science Private University, Amman, Jordan

86. Centers for Disease Control and Prevention (CDC), Influenza Program, Nairobi, Kenya

87. KEMRI Welcome Trust Research Programme, Kilifi, Kenya

88. Department of Nursing Sciences, Pwani University, Kilifi, Kenya

89. University of Warwick, Coventry, UK

90. Children's Clinical University Hospital, Riga, Latvia

91. Pediatric Intensive Care Unit, Department of Pediatrics, Farwaniya Hospital, Kuwait City, Kuwait

92. Department of Pediatrics, Amiri Hospital, Kuwait City, Kuwait

93. American University of Beirut, Beirut, Lebanon

94. University of Malaya, Kuala Lumpur, Malaysia

95. Clinical Research Centre, Sibu Hospital, Ministry of Health Malaysia, Sibu, Sarawak, Malaysia

96. Mater Dei Hospital, Msida, Malta

97. Research Institute for Tropical Medicine, Alabang Muntinlupa City, Metro Manila Philippines

98. Universidad Autonoma de San Luis Potosi, San Luis Potosi, Mexico

99. Universidade Eduardo Mondlane, Maputo, Mozambique

100. University Medical Center Utrecht, Utrecht, Netherlands

101. Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands

102. Julius Clinical Science, Zeist, Netherlands

103. Pediatric Intensive Care Unit, Emma Children’s Hospital Amsterdam UMC, Amsterdam, The Netherlands

104. Institute of Environmental Science and Research, Auckland, New Zealand

105. Department of Medicine, Hospital Infantil Manuel de Jesus Rivera, Managua, Nicaragua

106. Department of Virology, National Institute of Health, Islamabad, Pakistan

107. Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan

108. Sistema Nacional de Investigación-Secretaría Nacional de Ciencia, Tecnología e Innovación (SNI-SENACYT), Panama City, Panama

109. Centro de Vacunación e Investigación CEVAXIN, Panama City, Panama

110. Hospital Del Niño Dr José Renán Esquivel, Departamento de Infectología, Panama City, Panama

111. School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima, Peru

112. Medical University of Warsaw, Warsaw, Poland

113. Department of Pediatrics Hospital Dona Estefânia, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal

114. Department of Pediatrics, Division of Pediatric Emergency Medicine, Hamad Medical Corporation, Doha, Qatar

115. Department of Emergency Medicine, Sidra Medicine, Doha, Qatar

116. Weill Cornell Medicine, Doha, Qatar

117. Clinical Department, College of Medicine, QU Health, Qatar University, Doha, Qatar

118. Seoul National University College of Medicine, Seoul, South Korea

119. Sungkyunkwan University School of Medicine, Seoul, South Korea

120. Chonnam National University Hospital, Gwangju, South Korea

121. Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, South Korea

122. WHO Russian Federation, Leningrad, Russian Federation

123. KK Women's and Children's Hospital, Kampong Java, Singapore

124. Department of Neonatology, KK Women's & Children's Hospital, Singapore, Singapore

125. Duke-NUS Medical School, Singapore, Singapore

126. Lee Kong Chian School of Medicine, Imperial College, NTU Singapore

127. UMC Ljubljana, Ljubljana, Slovenia

128. School of Public Health, Faculty of Health Science, University of the Witwatersrand, Johannesburg, South Africa

129. Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, Johannesburg, South Africa

130. Medical Research Council: Respiratory and Meningeal Pathogens Research Unit and Department of Science and Technology/National Research Foundation: Vaccine Preventable Diseases, University of the Witwatersrand, Johannesburg, South Africa

131. University of Pretoria, Pretoria, South Africa

132. Department of Philosophy, Practical and Systematic Theology, School of Humanities, University of South Africa, Pretoria, South Africa

133. ISGlobal, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain

134. ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain

135. Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Esplugues, Barcelona, Spain

136. Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique

137. Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

138. Hospital La Paz IdiPAZ Foundation, CIBER Infectious Diseases (ISCIII), Translational Research Network in Pediatric Infectious Diseases (RITIP), Madrid, Spain

139. IRIS group Coordinator, Hospital Clinic, Institut d’Investigacions Biomediques August Pi Suñer (IDIBAPS), Barcelona, Spain

140. Fundación Instituto para la Mejora de la Asistencia Sanitaria / Research Institute Gregorio Marañón, Madrid, Spain

141. Hospital Gregorio Marañón, Madrid, Spain

142. Institut de Recerca Pediátrica Sant Joan de Déu, Hospital Sant Joan de Déu, Barcelona, Spain

143. Department of Medicine, Universitat Internacional de Cataluñya, Barcelona, Spain

144. The Royal Hospital, Muscat, Oman

145. Department of Microbiology, Faculty of Medical and Allied Sciences, Rajarata University of Sri Lanka, Saliyapura, Sri Lanka

146. Department of Child and Adolescent Health and Development, World Health Organization, Geneva, Switzerland

147. Astrid Lindgren Children's Hospital, Karolinska University Hospital, Solna, Sweden

148. Institute of Social and Preventive Medicine, University of Basel Children’s Hospital, Basel, Switzerland

149. University of Bern, Bern, Switzerland

150. National Health Research Institutes, Zhunan, Taiwan

151. Department of Pediatric Infectious Disease, MacKay Children's Hospital, Taipei, Taiwan

152. Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan

153. Chang Gung Memorial Hospital, Taoyuan City, Taiwan

154. Queen Sirikit National Institute of Child Health, Bangkok, Thailand

155. Division of Global Health Protection, Thailand Ministry of Public Health-US Centers for Disease Control and Prevention Collaboration, Nonthaburi, Thailand

156. Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

157. Hôpital d'enfants Béchir Hamza , Tunis, Tunisia

158. Bechir Hamza Children's Hospital, Tunis, Tunisia

159. Department of Pediatric Intensive Care Unit, Hacettepe University Faculty of Medicine, Ankara, Turkey

160. Istanbul Medeniyet University, Goztepe Training and Research Hospital, Istanbul, Turkey

161. Ege University Medical Faculty, Izmir, Turkey

162. Ege University Children's Hospital, Izmir, Turkey

163. Department of Pediatric Intensive Care, Ankara University School of Medicine, Ankara, Turkey

164. School of Health and Related Research, University of Sheffield, Sheffield, UK

165. Saint Mary's Hospital, London, UK

166. Department of Paediatric Intensive Care, Alder Hey Children's Hospital, Liverpool, UK

167. Department of Child Health (University of Liverpool), Institute in the Park, Alder Hey Children’s Hospital, Liverpool, UK

168. Hospital Tacuarembó, Tacuarembó, Uruguay

169. Red Colaborativa Pediátrica de Latinoamérica (LARed Network), Montevideo, Uruguay

170. Médica Uruguaya, Montevideo, Uruguay

171. Cuidados Intensivos Pediátricos Especializados (CIPe), Casa de Galicia, Montevideo, Uruguay

172. Facultad de Medicina, Unidad de Cuidados Intensivos de Niños del Centro Hospitalario Pereira Rossell (UCIN-CHPR), Universidad de la República, Montevideo, Uruguay

173. Centers for Disease Control (CDC) and Prevention, Atlanta, USA

174. Emory University, Global Health Institute, Atlanta, USA

175. University of Maryland Baltimore, Baltimore, USA

176. University of Washington, Seattle, USA

177. Boston University School of Public Health, Boston, USA

178. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, USA

179. Vanderbilt University Medical Center, Nashville, USA

180. Children's Hospital Colorado, Colorado, USA

181. St Jude Children's Research Hospital, Memphis, USA

182. Division of Pediatric Infectious Diseases, Duke University, Durham, USA

183. AstraZeneca, Hershey, USA

184. Department of Pediatrics, Division of Infectious Diseases, Ohio State University, Columbus, USA

185. Center for Vaccines and Immunity at Nationwide Children's Hospital, Ohio State University, Columbus, USA

186. International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA

187. Yale Institute for Global Health, New Haven, USA

188. Morgan Stanley Children's Hospital of New York-Presbyterian, New York, USA

189. Rainbow Babies and Children's Hospital, Cleveland, USA

190. Department of Pediatrics and Center for Global Health, University of Colorado, Aurora, USA

191. Division of Pediatric Critical Care, University of Virginia School of Medicine, Charlottesville, USA

192. Department of Pediatrics, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh city, and Children Hospital No.2, Ho Chi Minh city, Vietnam

193. Respiratory department, Children's Hospital No.1, Ho Chi Minh City, Vietnam

194. Yemen-FETP, Sana'a, Yemen

195. Children’s Health Irelan, Dublin, Ireland

196. PICU & NICU, University Hospital of Martinique, Porte de France, Martinique, France

197. Our Lady of the Lake Children’s Hospital, Baton Rouge, USA

198. Ach Medical University, Ulaanbaatar, Mongolia

199. Academisch Ziekenhuis Paramaribo, Paramaribo, Suriname

200. Clinical Research Unit of Nanoro – Institute of Research in Health Sciences (CRUN/IRSS), Nanoro, Burkina Faso

201. Shifa International Hospital, Islamabad, Pakistan

202. Pediatric Immunology Unit, Meyer Children’s Hospital, Florence, Italy

203. University of Florence, Florence, Italy

204. Pediatric Department, Hospital Tuanku Ja’afar, Seremban, Malaysia

205. Department of Pediatrics, St George’s University, Hospitals NHS Foundation Trust, London, UK

206. Department of Pediatics, University of Peradeniya, Peradeniya, Sri Lanka

207. Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka

208. Department of Microbiology, Golestan University of Medical Sciences, Gorgan, Iran

209. Department of Virology, Tehran University of Medical Sciences, Tehran, Iran

210. School of Medicine, University of the Republic (UDELAR), Montevideo, Uruguay

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jvacx.2024.100554.
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