Age-polarized burden of respiratory syncytial virus and influenza, 1990–2021: global-China evidence for a shift toward older adults

Introduction

Respiratory viruses, notably Respiratory syncytial virus (RSV) and influenza, remain leading causes of acute lower respiratory infection (ALRI) globally, imposing substantial mortality and disability-adjusted life years (DALYs) at the extremes of age (1). While RSV is classically framed as a pediatric pathogen, modern evidence shows a clinically important burden among older adults that has been underestimated in surveillance and policy (2-4). In 2019, the global age-standardized mortality rate (ASMR) for RSV was 4.8 per 100,000, and notably, the mortality rate in adults over 70 years had become higher than in children under 5 (5). Recent evidence suggests that the RSV burden in older adults may now rival or exceed that in children in many regions. This epidemiological shift underscores the growing importance of RSV in aging populations.

Seasonal influenza produces annual epidemics with excess hospitalizations and deaths, particularly among older adults and persons with comorbidities (6). In 2019, the global ASMR for influenza [as a cause of lower respiratory infection (LRI)] was about 6.5 per 100,000 (6). While children can suffer severe complications from flu, especially those under five years old, the burden of severe illness and death is heaviest in older adults. A recent analysis focusing on adults aged 55 and above found that although influenza mortality rates in older adults declined from 1990 to 2019, the absolute number of influenza-related deaths in this group increased by over 85% due to population aging (6). Both RSV and influenza disproportionately affect either end of the age spectrum: young children (under 5 years) and older adults (typically 65 or 70+ years), making them critical targets for intervention.

Global patterns vary with socio-demographic development; countries/regions with lower socio-demographic index (SDI) experience higher mortality and DALYs for RSV/influenza (1). In China, sustained gains in maternal-child health, access to inpatient care, and broader respiratory infection control have reduced pediatric mortality since 1990, whereas disease progress for older adults—especially for RSV—has been less pronounced (1,3). The coronavirus disease 2019 (COVID-19) pandemic introduced widespread non-pharmaceutical interventions (NPIs)—masking, distancing, travel restrictions, school closures—that dramatically suppressed circulation of many respiratory pathogens, including influenza and RSV, with historically low activity in 2020–2021 (7). Understanding how these measures shifted RSV and influenza burden, and how trends may evolve as societies reopen, is crucial for policy making.

Using Global Burden of Disease (GBD) 2021, we (I) characterize 1990–2021 mortality/DALY trends for RSV and influenza in China and globally; (II) analyze age/period/cohort effects; (III) decompose changes around the COVID-19 era; and (IV) assess SDI gradients.

Methods

Data source

We extracted indicators for LRIs attributable to RSV and influenza from the GBD 2021 study via the Global Health Data Exchange (GHDx) results tool (8). GBD 2021 provides the latest estimates of the epidemiological burden for 371 diseases and injuries across 204 countries/regions from 1990 to 2021. The detailed methodology for data collection, processing, and modeling used in the GBD 2021 study has been extensively described in previous publications (9-11). Aligned with the GBD framework, we analyzed deaths, mortality rates, and DALYs with 95% uncertainty intervals (UIs) for China and the global level, stratified by age. We also retrieved the SDI—a composite of education, income, and fertility from the Institute for Health Metrics and Evaluation (IHME)—categorized as low, low-middle, middle, high-middle, and high. Within the GBD framework, the etiology-specific burden of LRIs was estimated using a counterfactual approach. population attributable fractions (PAFs) for RSV and influenza were derived from systematically reviewed literature and vital registration data, mapping to specific International Classification of Diseases (ICD) codes (e.g., ICD-10 codes J09-J11 for influenza and J12.1, J20.5, J21.0 for RSV) before being applied to the overall LRI mortality envelope.

To complement the GBD estimates with real-world clinical evidence and better capture the morbidity burden (e.g., hospitalizations) not fully reflected in mortality metrics, we performed a retrospective analysis of routine respiratory multi-pathogen testing at The First Affiliated Hospital of Xiamen University (2019–2025). The study protocol was reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Xiamen University [ethics approval No. (2025)163], and the requirement for individual informed consent was waived due to the retrospective nature of the analysis and the use of de-identified data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Annual and age-stratified (≤5, 5–14, 15–49, 50–70, and ≥70 years) positivity rates for RSV, influenza A, and influenza B were calculated to provide localized insights into pathogen-specific clinical activity.

Statistical analysis

Age standardization

All mortality and DALY rates were age-standardized to the global population (per 100,000 people) and reported as age-standardized rates (ASRs) (12). The ASR is calculated using the following formula:

ASR=∑iAai∗wi∑i=1Awi[1]

where a_i is the age-specific rate for age group i, w_i is the number of people (or weight) in the same age group i in the chosen standard population, and A is the number of age groups (upper age limit). ASRs reflect patterns in specific population risk factors and help to inform targeted prevention strategies.

Temporal trends [estimated annual percent change (EAPC)]

To quantify the trends of rates such as ASR over time, we calculated the EAPC (13). This was achieved by fitting a linear regression model to the natural logarithm of the rate, where the independent variable is the calendar year (14): y = ln(rate) = α + β(x) + ε [x- year, y- the natural logarithm of rates (such as prevalence and incidence rates), α- the intercept, β- the slope, ε- the random error)] The EAPC was then calculated as 100× [exp(β) −1], with its 95% confidence interval (CI) also derived from the model. Interpretation followed standard rules: a lower CI bound >0>0>0 indicates a significant increase; an upper CI bound <0<0<0 indicates a significant decrease; CIs spanning 0 imply no significant trend (15).

Joinpoint regression

We applied Joinpoint [version 5.1.0, U.S. National Cancer Institute (NCI)] (16) to detect inflection points in log-linear trends and to estimate annual percent change (APC) for each segment and average annual percent change (AAPC) over the full interval. The expected value of the log-linear segmented model was:

E(y|x)=eβ0+β1x+δ1(x−τ1)++…+δk(x−τk)+[2]

where y is disease prevalence or mortality rate, x is year, β₁ is regression coefficient, k is the number of join-points, the τ_k are the unknown join-points and (a)⁺ = max (a,0).

The APC is calculated as:

APC=(yx+1−yxyx)∗100%=(eβ1−1)∗100%[3]

evaluating the trend of independent intervals of piecewise functions.

The AAPC is calculated as:

AAPC=(e∑wiβi∑wi−1)∗100%[4]

assessing the average trend over the entire study interval. A positive trend was considered significant if the AAPC and its 95% CI were greater than zero, while a negative trend was significant if the AAPC and its 95% CI were less than zero.

Age-period-cohort modeling

An age period cohort model was used to disentangle the independent effects of age, time period, and birth cohort on mortality rates. The log-linear regression model is expressed as:

log(Yi)=μ+a∗agei+β*periodi+γ*cohorti+ε[5]

where Y_i is the mortality rate, α, β and γ are the coefficients of age, period and cohort, respectively, µ is the intercept and ε is the residual of model. The intrinsic estimator (IE) method integrated into age-period-cohort model was used to get the net effects for three dimensions (17). It should be noted that the IE approach resolves statistical collinearity but does not establish causality; hence, the derived cohort and period effects represent statistical associations.

Decomposition of changes in deaths

To quantify the drivers of change in the total number of deaths, a decomposition analysis based on the Das Gupta method was employed, which partitions the change over time into effects from population growth, population aging, and changes in age-specific mortality rates (epidemiological change) (18).

Frontier and SDI-gradient analysis of mortality

Frontier analysis was applied to establish benchmarks for the burden of LRIs caused by influenza and RSV, comparing countries/regions against their best-performing counterparts (19). This method identifies leading countries/regions, setting achievable standards and potential targets for others. We computed the ‘effective difference’ for each country and territory, indicating the gap between its current and potential burden from influenza- and RSV-associated LRIs, adjusted for the SDI.

Software

All analyses and visualizations were performed in R 4.3.2. Joinpoint analyses used Joinpoint 5.1.0 (NCI).

Results

National and global burden by age

Across China and globally, LRIs due to RSV and influenza exhibited a clear “high-at-both-ends” age pattern. In 2021, RSV caused 31,525 deaths (95% UI: 23,348–41,871) worldwide, concentrated in children <5 years (27,829; 95% UI: 20,300–37,354); in comparison, influenza caused 98,199 deaths (95% UI: 74,217–126,286), with heavy tolls in both <5 years (27,615; 95% UI: 20,128–36,852) and ≥70 years (45,768; 95% UI: 34,055–60,461) (Table 1). ASMRs reinforced this pattern: in 2019 the global RSV rate was 23.68/100,000 (95% UI: 20.09–27.41) in <5 and 6.68 (95% UI: 5.26–7.62) in ≥70, far exceeding 5–14 years 0.04 (95% UI: 0.03–0.04) and 15–49 years 0.06 (95% UI: 0.05–0.06); China showed a similar gradient [<5 5.7 (95% UI: 4.66–6.79), ≥70 6.33 (95% UI: 4.88–8.02)]. For influenza, the elderly burden was particularly high (2019 global ≥70: 119.57; 95% UI: 95.91–133.22; <5: 21.54; 95% UI: 18.03–25.51; 50–69: 3.59; 95% UI: 3.29–3.89) (Table 1). ASMRs declined over 1990–2019 and fell sharply during 2019–2021 globally [RSV EAPC −1.73 (95% CI: −1.88 to −1.59) then −41.32 (95% CI: −51.65 to −28.78); influenza −0.96 (95% CI: −1.14 to −0.77) then −47.67 (95% CI: −51.67 to −43.34)], with steeper declines in China [e.g., RSV 1990–2019 −6.50 (95% CI: −6.78 to −6.21) then −53.38 (95% CI: −53.96 to −52.79)] (Table 1). Functional loss mirrored these patterns: in 2019 the global DALY ASMRs for RSV was 2,124.65 (95% UI: 1,803.39–2,458.61) in <5 (DALYs 7,025,995; 95% UI: 5,900,100–8,190,976) vs. 62.09 (95% UI: 49.79–70.51) in ≥70 (DALYs 131,442; 95% UI: 115,828–144,727); for influenza, the DALY ASR was 1,929.3 (95% UI: 1,615.42–2,284.37) in <5 (DALYs 6,752,652; 95% UI: 5,583,479–8,037,820) and 1118.96 (95% UI: 913.04–1241.38) in ≥70 (DALYs 2,540,822; 95% UI: 2,285,432–2,745,248), with substantially lower rates at intermediate ages; Chinese DALY ASRs were directionally similar but lower [e.g., influenza 2019 <5: 241.18 (95% UI: 196.42–290.02); ≥70: 737.32 (95% UI: 578.80–925.82)] (Table 2). A key finding was the divergent long-term trend for the elderly: from 1990–2019, the global EAPC for the deaths and DALY ASRs in the ≥70 age group were positive for RSV deaths (+0.27; 95% CI: 0.07–0.47), RSV DALY (+0.22; 95% CI: 0.03–0.41), influenza deaths (+0.51; 95% CI: 0.32–0.71) and influenza DALY (+0.42; 95% CI: 0.23–0.62), indicating increasing burden, whereas rates for children <5 declined markedly; this pattern reversed between 2019 and 2021 (Tables 1,2). Collectively, these results demonstrate a persistent “high-at-both-ends” age profile with overall long-term decreases and an abrupt post-2019 drop.

Geospatial distribution and China’s ranking

Geospatially, when 204 countries/regions were ranked from lowest to highest ASMR and DALY rate, China showed marked improvement by 2019, followed by a relative setback in 2021. For RSV, China’s ASMR rank improved from 125th [1990] to 32^nd [2019] before slipping to 104^th [2021]; its DALY-rate rank likewise moved from 125^th to 59^th and then to 100^th (Figure 1; Figure S1; supplementary tables 1-12 available at https://cdn.amegroups.cn/static/public/jtd-2026-1-0200-1.xlsx). For influenza, the ASMR rank advanced from 92^nd [1990] to 13^th [2019] but fell to 110^th [2021], while the DALY-rate rank rose from 120^th to 5^th and then dropped to 85^th (Figure 1; Figure S1; supplementary tables 1-12 available at https://cdn.amegroups.cn/static/public/jtd-2026-1-0200-1.xlsx). Following widespread NPIs, China’s absolute ASMR for both pathogens declined sharply in 2021; paradoxically, its global rank worsened because many other countries/regions experienced even larger reductions, pushing China down the relative standings (Figure 1; Figure S1; supplementary tables 1-12 available at https://cdn.amegroups.cn/static/public/jtd-2026-1-0200-1.xlsx).

Figure 1 Global geospatial distribution and ranking of ASMRs for RSV and influenza, 1990, 2019, and 2021. Choropleth maps visualize ASMRs per 100,000 population for Global Burden of Disease (GBD) 2021 locations (countries/regions), with corresponding 95% uncertainty intervals (UIs) provided in the underlying data tables. Locations are ranked from lowest (rank =1) to highest (rank =204) ASMR. (A) 1990_RSV. (B) 2019_RSV. (C) 2021_RSV. (D) 1990_influenza. (E) 2019_influenza. (F) 2021_influenza. (Related to Figure S1; supplementary tables 1-12 available at https://cdn.amegroups.cn/static/public/jtd-2026-1-0200-1.xlsx). ASMR, age-standardized mortality rate; RSV, respiratory syncytial virus; UI, uncertainty interval.

Age- and sex-stratified mortality and DALY burden

Across both pathogens, the age distribution shows a persistent “high-at-both-ends” pattern, with <5 years and ≥70 years constituting the two principal peaks for mortality and DALYs (Figure 2). For RSV, the proportion of deaths attributable to the ≥70 years group increased from 1990→2019→2021 in both global and China series; notably, in China in 2021, deaths among ≥70-year-old females exceeded those among females <5 years (Figure 2A,2E). For influenza, the elderly share of deaths rose over the same periods globally and in China, and by 2019 already surpassed that of children <5 years (Figure 2B,2F). Regarding DALYs, RSV showed a rising contribution from ≥70 years over time, yet children <5 years consistently remained the dominant contributor in both global and China data (Figure 2C,2D); in contrast, for influenza, <5 years remained the largest DALY contributor globally, whereas in China the ≥70 years group had already overtaken <5 years by 2019 (Figure 2G,2H).

Figure 2 Age- and sex-stratified death numbers and DALYs for RSV and influenza in 1990, 2019, and 2021. Panels depict deaths and DALYs by age groups and sex across three time points for each pathogen and geography. Bars/lines are accompanied by uncertainty where applicable (95% UI in the source tables). (A) 1990_2019_2021_RSV_deaths_global. (B) 1990_2019_2021_influenza_deaths_global. (C) 1990_2019_2021_RSV_DALYs _global. (D) 1990_2019_2021_influenza_DALYs_global. (E) 1990_2019_2021_RSV_deaths_China. (F) 1990_2019_2021_influenza_deaths_China. (G) 1990_2019_2021_RSV_DALYs_China. (H) 1990_2019_2021_influenza_DALYs_China. DALY, disability-adjusted life-year; RSV, respiratory syncytial virus; UI, uncertainty interval.

Temporal trends, age-period-cohort patterns, and decomposition of burden

Across RSV and influenza, dual-axis time series show that, for both <5 years and ≥75 years—and at the all-ages level—mortality and DALYs declined sharply from 2019 to 2021, consistent with the timing of widespread NPIs (Figure 3).

Figure 3 Dual-axis temporal trends in counts and ASRs for RSV and influenza, 1990–2021. Time series show left y-axis: deaths (or DALYs) and right y-axis: deaths ASR (or DALYs ASR). Where shown, ribbons/whiskers indicate 95% UI. Panels are organized by age group (All ages, <5 years, >70 years). (A) 1990–2021_RSV_deaths_global. (B) 1990–2021_RSV_DALYs_global. (C) 1990–2021_influenza_deaths_global. (D) 1990–2021_influenza_DALYs_global. (E) 1990–2021_RSV_deaths_China. (F) 1990–2021_RSV_DALYs_China. (G) 1990–2021_influenza_deaths_China. (H) 1990–2021_influenza_DALYs_China. ASR, age-standardized rate; DALY, disability-adjusted life-year; RSV, respiratory syncytial virus; UI, uncertainty interval.

Joinpoint analyses of ASMR revealed uniformly steep, statistically significant contractions during 2019–2021 (APC <−45% for both pathogens in China and globally; asterisks denote significance), corroborating a synchronous, large-magnitude downturn across age strata (Figure S2).

Age period cohort modeling clarified the structure of temporal changes. The Long-to-Cross rate ratio (RR)—which compares longitudinal to cross-sectional age gradients at the same age—approached 1 from young to mid-adulthood (~20–60 years) and flattened <1 at older ages, indicating smaller longitudinal than cross-sectional gradients in late life; by definition, values >1 mean the longitudinally estimated risk exceeds the cross-sectional estimate at that age, suggesting greater accumulated risk in historical cohorts. Local Drifts—the age-specific annual percent changes—were predominantly negative at younger ages and trended toward 0 with increasing age, implying faster rate declines in youth and slower declines in older adults; heterogeneity was evident in the elderly (e.g., China-RSV local drift −0.7113, 95% CI: −2.7056 to 1.3239), consistent with attenuated improvement and the comparatively higher mortality burden in older Chinese adults (Figure 4). Period rate ratios (PeriodRR) decreased over time in China for both pathogens (higher in early periods, then falling and stabilizing), while globally they exhibited a transient rise around 2010–2015 followed by sustained declines, with unknown reason (Figure 4). Cohort rate ratios (CohortRR) were highest in early birth cohorts (before 1920) and progressively lower in more recent cohorts (after 1960), consistent with long-run improvements in living conditions and healthcare (Figure 4).

Figure 4 Age period cohort analyses for RSV and influenza. Rows correspond to (A) RSV_global, (B) RSV_China, (C) influenza_global, (D) influenza_China; columns show age period cohort outputs: long-to-cross rate ratio (long vs. cross RR), comparing longitudinal to cross-sectional age gradients; Local Drifts, the age-specific annual percent change over time; PeriodRR adjusted for cohort effects; and CohortRR adjusted for period effects. Where shown, uncertainty bands denote 95% CI. CI, confidence interval; CohortRR, cohort rate ratios; PeriodRR, period rate ratios; RSV, respiratory syncytial virus; RR, rate ratio.

Decomposition analyses attributed the 2019 collapse in deaths and DALYs primarily to a large, negative “epidemiological change” component, whose negative contribution diminished through 2020–2021 and by 2021 became comparable to the demographic components from population size and age structure (Figure 5).

Figure 5 Decomposition of changes in deaths and DALYs into demographic and epidemiological components, 1990–2021. Stacked contributions (y-axis) by year (x-axis) partition total change into population size, age structure, and epidemiological change. Positive values indicate increases in burden; negative values indicate reductions. Black line represents the net overall change each year. (A) Decomposition of annual change in deaths in global. (B) Decomposition of annual change in deaths in China. (C) Decomposition of annual change in DALYs in global. (D) Decomposition of annual change in DALYs in China. DALY, disability-adjusted life-year; RSV, respiratory syncytial virus.

Frontier and SDI-gradient analysis of mortality rate

Using efficiency-frontier plots of mortality rate vs. the SDI, countries/regions were ranked by distance to the frontier (“effective gap”), revealing a systematic gradient whereby the effective gap generally widens at higher SDI, indicating greater unrealized potential for mortality reduction among more developed settings (Figure S3). When evaluated for 1990–2019, ASMR trends were mixed among different countries/regions, but extending to 1990–2021 yielded near-universal declines, consistent with the profound impact of COVID-19-era NPIs on non-COVID respiratory mortality (Figure S3). Age-restricted analyses (>50 years) uncovered deviations from all-age patterns in several countries/regions; notably, in China, RSV mortality rate decreased at the all-age level during 1990–2019 but increased among adults >50 years, whereas influenza mortality rate declined across both all-age and >50-year groups over the same period (Figure S3). Across 21 global regions, SDI was strongly and inversely associated with mortality rate (P<0.001), reinforcing a robust development-mortality gradient (Figure S4).

Real-world evidence from an institutional cohort (2019–2025)

Among 64,947 tests, RSV positivity across all ages ranged from 1.9–11.3% (highest in 2021: 11.3%, and 7.9% in 2025). RSV positivity was greatest in children ≤5 years (peak 34.2% in 2021; 19.4% in 2025). In adults ≥70 years, RSV positivity increased from 1.0% [2023] to 2.2% [2025]. Detailed annual, age- and sex-stratified results are provided in S13, and trends are shown in Figure S5.

Discussion

To enhance the clinical interpretability of GBD-derived burden estimates, we triangulated our macro-level findings with real-world institutional testing data. Among 64,947 respiratory multi-pathogen tests conducted at The First Affiliated Hospital of Xiamen University from 2019 to 2025, RSV positivity across all ages ranged from 1.9% to 11.3%, peaking in 2021 (11.3%) and rebounding in 2025 (7.9%). RSV positivity was consistently highest in children ≤5 years (peak 34.2% in 2021; 19.4% in 2025), and increased among older adults (≥70 years) from 1.0% in 2023 to 2.2% in 2025 (supplementary table 13 available at https://cdn.amegroups.cn/static/public/jtd-2026-1-0200-2.xlsx; Figure S5). Although test positivity does not directly quantify hospitalization incidence or clinical severity, it provides pragmatic evidence of pathogen circulation and diagnostic burden, offering real-world context for interpreting disease burden in age groups where morbidity and healthcare utilization may dominate.

Over three decades, the burden of RSV and influenza displayed a robust U-shaped age pattern, with the highest mortality and DALY concentrations in children <5 years and adults ≥70 years, but with a notable epidemiologic shift toward older adults over time. In particular, we observed positive long-term EAPCs for elderly DALY rates [1990–2019] for both pathogens and a sharp reversal to large negative EAPCs during 2019–2021, consistent with an abrupt, system-wide reduction in transmission. These age-period features are supported by surveillance and synthesis studies showing that COVID-19 NPIs dramatically suppressed RSV and influenza circulation and seasonality across regions (20,21). Furthermore, the estimated reductions during 2019–2021 must be interpreted cautiously. Beyond the true suppression of transmission by NPIs, alternative factors likely contributed to the modelled decline. These include significant disruptions in healthcare-seeking behavior (healthcare avoidance), shifted testing priorities leading to the under-diagnosis of RSV and influenza, and potential misclassification in cause-of-death registries during the peak of the COVID-19 pandemic (22).

The 2019–2021 collapse in RSV/influenza burden aligns with documented, off-season or muted activity during stringent NPIs, followed by heterogeneous rebounds as measures relaxed—initially atypical waves that progressively re-approached pre-pandemic timing. This pattern has been described in multi-country analyses and national reports, including China’s out-of-season influenza resurgence in 2022–2023 (23-25). The “immunity debt” (or “immunity gap”) hypothesis—reduced population exposure during NPIs leading to transient susceptibility build-up—offers a parsimonious interpretation for the intensity and timing of these rebounds, although the concept has been debated and should be applied cautiously (26). This framework also helps explain our “ranking paradox”: China’s stringent, prolonged NPIs produced very low 2021 ASMRs but a worsened relative rank because many other countries/regions saw proportionally even larger declines that year, while later relaxations were followed by pronounced rebounds (25).

Forward-looking implications are clearest for RSV in older adults. Our decomposition and APC indicate that, despite overall declining ASMRs, mortality risk is likely to rise in adults ≥70 years in several settings—underscoring the need to reframe RSV as a lifelong threat rather than a predominantly pediatric infection. The recent availability of countermeasures across the life course—older-adult vaccines (RSVPreF3/Arexvy; RSVpreF/Abrysvo), maternal vaccination to protect infants, and long-acting monoclonal prophylaxis (nirsevimab)—creates an opportunity to blunt this shifting burden if deployed with age- and season-sensitive strategies (27).

The strengths of this work include standardized estimations across locations and years, alongside the triangulation of age-period-cohort analysis, decomposition, and time-series forecasting. However, key limitations inherent to the GBD framework and pandemic-era data remain. Foremost, as GBD outputs rely on complex modeling rather than laboratory-confirmed surveillance, our findings represent estimated macro-level burdens rather than direct clinical observations. Additionally, the data are subject to varying source quality, modeling uncertainty, and ecological inference; moreover, the unique epidemiological perturbations of 2020–2022 inject path-dependence into short-term projections (28). The strengths of this study lie in its standardized estimations across regions and time periods, as well as the triangulation of age-period-cohort analyses in conjunction with decomposition approaches and time-series forecasting. These methodological advantages enhance the robustness of long-term trend interpretation and cross-population comparability. Nevertheless, several limitations warrant consideration. First, inherent constraints of the GBD framework—particularly heterogeneous data quality across sources, model uncertainty, and reliance on ecological inference—introduce unavoidable bias. Second, the unprecedented perturbations of 2020–2022 created path dependence in short-term projections, complicating the extrapolation of counterfactual scenarios. These caveats underscore the importance of cautious interpretation and highlight the value of sustained, high-granularity surveillance systems. Such systems will be critical as health infrastructures progressively scale up vaccine deployment and immunoprophylaxis programs for both RSV and influenza, ensuring timely detection of epidemiological shifts and supporting evidence-based policy adaptation.

Conclusions

In conclusion, RSV- and influenza-attributable LRIs displayed a persistent U-shaped age pattern from 1990 to 2021, with the greatest mortality and DALY burdens concentrated in children <5 years and adults ≥70 years. Although overall age-standardized mortality declined over the long term, an unfavorable signal emerged in older adults, particularly for RSV, consistent with slower improvements in late-life risk amid population aging. During 2019–2021, both pathogens showed abrupt reductions in estimated ASMR and DALY burden, temporally aligned with COVID-19—era NPIs and broader behavioral and health-system changes; however, these estimates should be interpreted cautiously given potential pandemic-related disruptions in healthcare-seeking, testing priorities, and cause-of-death attribution.

To strengthen clinical interpretability beyond model-based GBD estimates, we incorporated real-world evidence from routine respiratory multi-pathogen testing at The First Affiliated Hospital of Xiamen University [2019–2025]. Across 64,947 tests, RSV positivity varied substantially (1.9–11.3%), peaked in 2021, and rebounded in 2025. Children ≤5 years consistently had the highest positivity, while positivity among adults ≥70 years increased from 2023 to 2025. Although test positivity does not directly quantify hospitalization incidence or severity, it provides pragmatic evidence of pathogen circulation and diagnostic burden, supporting the central inference that pediatric risk remains high while older-adult risk warrants renewed emphasis.

These findings argue for differentiated, life-course prevention. Sustained pediatric protection remains foundational, while adults ≥70 years should be prioritized for intensified surveillance and targeted countermeasures as available (e.g., vaccination and immunoprophylaxis strategies tailored to seasonal risk). High-resolution surveillance integrating laboratory confirmation with clinical outcomes (e.g., hospitalization, ICU admission, case fatality, and length of stay) will be essential to detect post-pandemic rebounds, refine burden attribution, and guide evidence-based policy for both RSV and influenza.

Acknowledgments

We are grateful for the support of Natural Science Foundation of Fujian, Xiamen Joint Laboratory for Translational Epigenetics in Oncology and Aging. We thank all participants and investigators who participated in this study. AI-assisted technology (ChatGPT-5) was used to improve the readability and language of the report.

Footnote

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0200/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0200/prf

Funding: The study was supported by the Natural Science Foundation of Fujian (2024J011355), Xiamen Joint Laboratory for Translational Epigenetics in Oncology and Aging.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0200/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. For the institutional retrospective component, the study protocol was reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Xiamen University [ethics approval No. (2025)163], and the requirement for individual informed consent was waived due to the retrospective nature of the analysis and the use of de-identified data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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