Abstract
Background
Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality, with influenza viruses being a significant cause. Despite advances in vaccination and antiviral therapies, the burden of influenza-associated LRIs remains high, particularly in low-income regions and high-risk populations. Understanding long-term trends and regional disparities is crucial for developing effective prevention strategies.
Methods
Using data from the Global Burden of Disease (GBD) 2021 study, we analyzed age-standardized mortality rates (ASMR), and disability-adjusted life years (DALYs) for influenza-associated LRIs across 21 global regions and 204 countries and territories from 1990 to 2021. Joinpoint regression was utilized to analyze temporal trends in the disease burden of influenza-associated LRIs. The relationship between influenza-associated LRIs burden and the socio-demographic index (SDI) was examined using a smoothing spline model. Frontier analysis was employed to estimate achievable outcomes based on development levels.
Results
Globally, ASMR declined from 5.87 (95% UI: 5.33–6.40) per 100,000 population in 1990 to 1.30 (0.98–1.66) per 100,000 population in 2021, with an average annual percent change (AAPC) of -0.69% (1990–2019) and − 49.74% (2019–2021). Despite declining rates, absolute deaths increased by 0.85% annually from 1990 to 2019, reflecting population growth and aging. In 2021, Central Sub-Saharan Africa had the highest ASMR (10.84/100,000 population) and ASDR (271.71/100,000 population), while high SDI regions (e.g., High-income Asia Pacific) approached near-zero mortality. Age-specific analysis revealed bimodal burdens: children under 5 and adults ≥ 70 years faced the highest risks.
Conclusions
Influenza-associated LRIs remain a significant global health challenge, particularly in low-income and high-risk populations. While global trends indicate progress, regional disparities and the impact of demographic factors highlight the need for tailored interventions. Targeted strategies—including equitable vaccine access, healthcare system strengthening, and integrated surveillance—are critical to mitigating burden in high-risk regions and populations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-025-12282-7.
Keywords: Influenza, Lower respiratory infections, Global burden of disease, Prevention strategies
Background
Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality [1], posing a persistent challenge to public health systems. Among the pathogens that cause LRIs, influenza viruses are of particular concern due to their seasonal epidemics, antigenic drift, and the potential to cause severe complications such as pneumonia and acute respiratory distress syndrome [2]. According to data from the World Health Organization, LRIs were responsible for approximately 2.5 million deaths globally in 2021, remaining one of the leading causes of death worldwide [3]. The mortality rate associated with LRIs continues to represent a public health crisis, with influenza-related cases being particularly prominent in low-income regions and high-risk populations [4]. Despite advances in vaccination and antiviral therapies, the incidence and mortality of influenza-associated LRIs remain high, highlighting the urgent need for optimized prevention strategies based on large-scale disease burden analyses.
Influenza viruses evade host immunity through antigenic drift and shift of haemagglutinin and neuraminidase, driving periodic epidemics [5]. The virus can invade the lower respiratory tract, leading to severe outcomes, particularly in immunocompromised populations such as infants and the elderly [6]. Despite widespread recognition of the public health threat posed by influenza-associated LRIs, existing research is marked by significant limitations. Most regional analyses, constrained by fragmented data sources, limited temporal scope, or lack of standardized methods, fail to comprehensively assess long-term trends and heterogeneity in disease burden [7–9]. Furthermore, differential risk profiles across regions with varying socio-demographic indices (SDI), age groups, and sexes have not been systematically elucidated, hindering the development of targeted prevention strategies. For instance, low-income regions may face elevated risks due to limited healthcare resources and low vaccination coverage, yet evidence remains predominantly case-based and lacks global quantitative comparisons [10–12]. Additionally, the impact of sudden public health interventions during the COVID-19 pandemic—such as social distancing and mask mandates—on influenza transmission patterns and subsequent disease burden has not been integrated into frameworks analyzing long-term trends.
The Global Burden of Disease (GBD) database offers a breakthrough solution to the aforementioned challenges by integrating multisource data and employing standardized statistical models to fill data gaps, thereby providing disease metric estimates across countries and populations [13]. Its time-series data from 1990 to 2021 are particularly valuable for analyzing the dynamic evolution of disease burden, including the intervention effects of public health emergencies such as COVID-19 [14]. By analyzing these data, we can delve into the global disease burden of influenza-associated LRIs, including trends in mortality, disability-adjusted life years (DALYs), and age-standardized mortality rates (ASMR). This study, based on GBD 2021 data, aims to: (1) quantify trends in mortality and DALYs of influenza-associated LRIs globally from 1990 to 2021; (2) uncover disparities in disease burden across regions with varying socio-demographic indexes (SDIs), age groups, and sexes. Investigating these disparities can reveal geographical patterns of disease burden and inform resource allocation and prevention strategies globally. The findings can provide empirical evidence for optimizing vaccine distribution, high-risk population interventions, and global health resource planning, with significant policy implications for addressing overlapping risks of emerging and seasonal infectious diseases.
Methods
Data sources
Data for this study were sourced from the GBD 2021 database (http://ghdx.healthdata.org/gbd-results-tool), which integrates epidemiological surveillance data, hospital records, census data, and literature sources from 204 countries and territories worldwide. The GBD study employs standardized methods to model and fill gaps in data from regions with insufficient or low-quality data, using a Bayesian statistical framework to estimate disease parameters and quantify uncertainty through Markov chain Monte Carlo algorithms. Data quality is rigorously evaluated through the GBD collaborative network’s validation process, which includes consistency checks, outlier removal, and the calculation of 95% uncertainty intervals (UIs) [13, 15, 16].
This study focused on LRIs caused by influenza viruses. Specifically, using the GBD Results Tool, we directly extracted the pre-estimated metrics of mortality and DALYs for the cause “Lower respiratory infections attributed to influenza”, which is listed under the ‘Etiologies’ branch of the GBD cause hierarchy. The analysis covered 20 age groups (“under 5 years,” “5–9 years,” “10–14 years,” “15–19 years,” “20–24 years,” “25–29 years,” “30–34 years,” “35–39 years,” “40–44 years,” “45–49 years,” “50–54 years,” “55–59 years,” “60–64 years,” “65–69 years,” “70–74 years,” “75–79 years,” “80–84 years,” “85–89 years,” “90–94 years,” and “95 + years”) and was stratified by sex and region (including regions categorized by the SDI). The annual changes in the ASMR and age-standardized DALY rate (ASDR) were analyzed.
Developed by the GBD research team, the SDI serves as a composite indicator closely associated with health outcomes and developmental status. This index is constructed as the geometric mean of three standardized components normalized to a 0–1 scale: total fertility rate under age 25, average years of education among populations aged 15 and older, and lag-distributed income per capita [17]. The SDI employs a 0–1 measurement framework: a value of 0 indicates a theoretical minimum level of development relevant to health outcomes, while a value of 1 represents the theoretical maximum developmental benchmark [18]. Regions and countries are classified into five tiers of development according to SDI thresholds (0, 0.47, 0.62, 0.71, 0.81, 1): low SDI, low-middle SDI, middle SDI, high-middle SDI, and high SDI [19].
Statistical analysis
Disease burden metrics
This study quantified the disease burden of influenza-associated LRIs using the ASMR and ASDR, alongside absolute numbers of deaths and DALYs. The age-standardized rates (ASRs) and their 95% UIs were extracted from the GBD 2021 database, calculated as follows:
 |
where ai is the incidence or mortality rate of the i age group, wi is the GBD standard population weight, and n is the total number of age groups [13, 15, 20].
Time trends
To analyze temporal trends, this study employed Joinpoint regression models to calculate annual percentage changes (APCs) and average annual percentage changes (AAPCs), along with their 95% UIs, for influenza-associated LRIs from 1990 to 2021. The formulas used are as follows:
 |
 |
where βi represents the regression coefficients for each linear segment, and wi denotes the weights for each segment. The optimal number and placement of join points were determined using the grid search method. An increasing trend in the ASR was indicated if the AAPC was greater than 0 and its 95% UI did not include 0. A decreasing trend was identified if the AAPC was less than 0 and its 95% UI did not include 0. If neither condition was met, the ASR was considered stable over time [21, 22].
Correlation between disease burden and SDI
To evaluate the relationship between the ASMR and ASDR for influenza-associated LRIs and the SDI, this study utilized a smoothing spline model to analyze data from global and 21 super-region levels. Locally weighted scatterplot smoothing (LOESS) was applied to fit spline curves, with the number and location of knots determined automatically based on data distribution. The Spearman rank correlation coefficient was used to assess the correlation between influenza-associated LRIs burden and SDI, where p < 0.05 indicated statistical significance [23, 24].
Frontier analysis
To further explore the relationship between the burden of influenza-associated LRIs and socioeconomic development, this study used frontier analysis to model the ASDR as a function of the SDI. LOESS approach was applied to fit the frontier curve. This method accounted for the nonlinear relationship between ASDR and SDI and estimated the theoretical minimum ASDR achievable at different SDI levels [25].
Results
Global burden of influenza-associated LRIs
Globally, the ASMR for influenza-associated LRIs has shown an overall declining trend. The ASMR was higher in males than in females, with the most significant decline observed after 2019 (Fig. S1). In 1990, the ASMR was 5.87 (95% UI: 5.33, 6.40) per 100,000 population, decreasing to 4.75% (95% UI: 4.32, 5.13) per 100,000 population in 2019, with an AAPC of -0.69% (95% UI: -0.82, -0.56). By 2021, the ASMR had decreased more markedly to 1.30 (95% UI: 0.98, 1.66) per 100,000 population, with an AAPC of -49.74% (95% UI: -52.59, -46.72) (Table 1).Unlike the declining trend in ASMR, the number of deaths attributable to influenza-associated LRIs increased from 274,041 (95% UI: 245,890.82, 305,362.72) in 1990 to 349,384 (95% UI: 318,605.15, 376,703.69) in 2019, with an AAPC of 0.85% (95% UI: 0.72, 0.98). However, in 2021, the number of deaths dropped sharply to 98,199 (95% UI: 74,217.38, 126,285.73), with an AAPC of -48.62% (95% UI: -49.81, -47.40) (Table S1).
Table 1.
Age-standardized mortality rate of influenza-associated LRIs in 1990, 2019, and 2021, with average annual percent change during 1990–2019 and 2019–2021 for global burden of disease regions
| Location | 1990 year (95% UI) (per 100000) | 2019 year (95% UI) (per 100000) | AAPC% (95% UI), 1990–2019 | 2021 year (95% UI) (per 100000) | AAPC% (95% UI), 2019–2021 |
|---|---|---|---|---|---|
| Global | 5.87 (5.33, 6.40) | 4.75 (4.32, 5.13) | -0.69 (-0.82, -0.56) | 1.30 (0.98, 1.66) | -49.74 (-52.59, -46.72) |
| Female | 5.31 (4.73, 5.94) | 4.08 (3.58, 4.48) | -0.86 (-0.99, -0.73) | 1.15 (0.84, 1.52) | -49.41 (-52.04, -46.63) |
| Male | 6.75 (6.23, 7.29) | 5.68 (5.25, 6.12) | -0.56 (-0.71, -0.42) | 1.49 (1.16, 1.88) | -50.00 (-52.51, -47.36) |
| High SDI | 2.90 (2.60, 3.09) | 2.80 (2.42, 3.02) | -0.17 (-0.73, 0.40) | 0.17 (0.13, 0.25) | -61.22 (-73.87, -42.44) |
| High-middle SDI | 2.86 (2.62, 3.12) | 2.47 (2.22, 2.65) | -0.49 (-0.72, -0.27) | 0.32 (0.23, 0.42) | -59.59 (-65.69, -52.41) |
| Middle SDI | 5.63 (5.14, 6.12) | 4.11 (3.71, 4.43) | -1.17 (-1.32, -1.03) | 0.94 (0.68, 1.26) | -54.83 (-58.54, -50.78) |
| Low-middle SDI | 7.52 (6.73, 8.37) | 6.59 (5.95, 7.23) | -0.39 (-0.56, -0.22) | 2.55 (1.63, 3.74) | -46.20 (-54.29, -36.69) |
| Low SDI | 12.77 (11.17, 14.34) | 10.68 (9.39, 12.03) | -0.68 (-0.84, -0.52) | 5.25 (3.89, 6.75) | -35.23 (-40.92, -29.00) |
| Andean Latin America | 10.89 (9.78, 11.96) | 11.50 (9.74, 13.08) | 0.03 (-0.27, 0.33) | 4.99 (1.75, 8.12) | -48.07 (-68.57, -14.21) |
| Australasia | 1.56 (1.37, 1.69) | 1.92 (1.59, 2.12) | 1.13 (0.19, 2.08) | 0.01 (0.00, 0.03) | -93.08 (-98.89, -56.83) |
| Caribbean | 5.60 (5.12, 6.12) | 5.38 (4.84, 5.96) | -0.08 (-0.46, 0.29) | 0.02 (0.01, 0.11) | -69.83 (-93.28, 35.52) |
| Central Asia | 5.46 (5.05, 5.85) | 4.04 (3.68, 4.45) | -0.93 (-1.16, -0.69) | 0.46 (0.26, 0.74) | -44.17 (-55.00, -30.73) |
| Central Europe | 2.51 (2.38, 2.62) | 2.84 (2.65, 2.98) | 0.60 (-0.13, 1.34) | 0.04 (0.01, 0.11) | -55.05 (-82.97, 18.62) |
| Central Latin America | 4.77 (4.46, 5.03) | 4.61 (4.29, 4.90) | -0.09 (-0.47, 0.28) | 0.49 (0.32, 0.73) | -59.92 (-69.09, -48.02) |
| Central Sub-Saharan Africa | 15.42 (12.57, 18.95) | 15.09 (11.49, 19.53) | -0.07 (-0.16, 0.01) | 10.84 (5.53, 17.02) | -15.39 (-16.83, -13.93) |
| East Asia | 5.80 (5.04, 6.45) | 2.02 (1.73, 2.39) | -3.64 (-4.01, -3.26) | 0.40 (0.23, 0.66) | -55.50 (-59.62, -50.95) |
| Eastern Europe | 1.03 (0.99, 1.07) | 1.42 (1.36, 1.49) | 1.50 (0.02, 3.01) | 0.17 (0.07, 0.33) | -2.12 (-2.59, -1.64) |
| Eastern Sub-Saharan Africa | 15.08 (13.08, 16.99) | 13.55 (11.95, 15.56) | -0.51 (-0.65, -0.37) | 7.09 (4.67, 10.17) | -35.09 (-42.12, -27.21) |
| High-income Asia Pacific | 4.67 (4.15, 5.00) | 3.05 (2.55, 3.35) | -1.56 (-1.93, -1.19) | 0.00 (0.00, 0.02) | -65.52 (-94.48, 115.49) |
| High-income North America | 2.45 (2.15, 2.62) | 2.22 (1.93, 2.39) | -0.02 (-0.70, 0.67) | 0.26 (0.12, 0.47) | -45.98 (-62.08, -23.06) |
| North Africa and Middle East | 4.98 (4.41, 5.71) | 4.10 (3.60, 4.61) | -0.57 (-0.71, -0.44) | 1.10 (0.65, 1.72) | -45.61 (-50.91, -39.73) |
| Oceania | 8.78 (7.59, 10.23) | 8.20 (6.91, 9.90) | -0.22 (-0.33, -0.12) | 0.33 (0.17, 0.56) | -71.01 (-76.12, -64.82) |
| South Asia | 6.83 (5.92, 7.75) | 5.39 (4.81, 5.99) | -0.79 (-1.05, -0.52) | 2.68 (1.28, 4.46) | -41.82 (-58.09, -19.23) |
| Southeast Asia | 5.81 (5.11, 6.88) | 5.78 (4.88, 6.30) | -0.03 (-0.09, 0.03) | 0.47 (0.29, 0.71) | -64.77 (-66.55, -62.90) |
| Southern Latin America | 3.97 (3.65, 4.20) | 9.78 (8.85, 10.39) | 3.19 (2.19, 4.21) | 0.56 (0.25, 1.09) | -78.90 (-89.58, -57.29) |
| Southern Sub-Saharan Africa | 9.21 (8.13, 10.38) | 14.57 (13.11, 15.89) | 1.65 (1.48, 1.83) | 2.14 (0.84, 4.14) | -70.36 (-76.34, -62.86) |
| Tropical Latin America | 5.27 (4.85, 5.62) | 7.57 (6.63, 8.11) | 1.30 (0.53, 2.07) | 1.49 (0.45, 3.25) | -59.71 (-76.05, -32.21) |
| Western Europe | 2.37 (2.11, 2.52) | 2.55 (2.18, 2.75) | 0.57 (-0.09, 1.22) | 0.11 (0.07, 0.16) | -62.16 (-74.91, -42.91) |
| Western Sub-Saharan Africa | 13.88 (12.08, 15.73) | 12.85 (10.88, 15.05) | -0.24 (-0.31, -0.18) | 5.92 (3.74, 8.67) | -33.87 (-36.49, -31.14) |
In line with the declining trend in ASMR, the ASDR for influenza-associated LRIs also exhibited a downward trajectory (Fig. 1). The ASDR was 304.31 (95% UI: 267.40, 346.58) per 100,000 population in 1990, which fell to 174.72 (95% UI: 154.54, 197.69) per 100,000 population in 2019, corresponding to an AAPC of -2.00% (95% UI: -2.12, -1.88) per 100,000. By 2021, the rate had plummeted to 59.69 (95% UI: 44.18, 77.17) per 100,000 population, with the AAPC dropping sharply to -45.20% (95% UI: -48.79, -41.36) (Table 2). Unlike the rise in mortality observed in 2019, the DALY count demonstrated a consistent downward trend. The AAPC for the period from 1990 to 2019 was − 1.23% (95% UI: -1.31, -1.15), and from 2019 to 2021, the AAPC was − 45.77% (95% UI: -48.34, -43.07) (Table S2).
Fig. 1.
Global trends in influenza-associated LRIs mortality (1990–2021): (A) Age-standardized mortality rate and total death counts by sex; (B) Age-standardized DALY rate and total DALY counts by sex; (C) Age-standardized mortality rate stratified by SDI; (D) Age-standardized DALY rate stratified by SDI
Table 2.
Age-standardized DALY rate of Influenza-Associated LRIs in 1990, 2019, and 2021, with average annual percent change during 1990–2019 and 2019–2021 for global burden of disease regions
| Location | 1990 year (95% UI) (per 100000) |
2019 year (95% UI) (per 100000) |
AAPC% (95% UI), 1990–2019 | 2021 year (95% UI) (per 100000) | AAPC% (95% UI), 2019–2021 |
|---|---|---|---|---|---|
| Global | 304.31 (267.40, 346.58) | 174.72 (154.54, 197.69) | -2.00 (-2.12, -1.88) | 59.69 (44.18, 77.17) | -45.20 (-48.79, -41.36) |
| Female | 292.34 (251.96, 335.14) | 160.19 (138.80, 179.62) | -2.15 (-2.26, -2.04) | 55.33 (40.44, 72.45) | -45.34 (-48.64, -41.82) |
| Male | 319.76 (278.69, 365.26) | 192.00 (170.14, 216.81) | -1.86 (-1.98, -1.73) | 64.46 (48.81, 83.26) | -45.14 (-48.78, -41.25) |
| High SDI | 53.12 (49.38, 55.69) | 47.69 (43.52, 50.21) | -0.48 (-0.87, -0.09) | 3.62 (2.57, 5.15) | -57.37 (-74.02, -30.04) |
| High-middle SDI | 130.21 (117.19, 145.62) | 53.60 (50.17, 56.73) | -3.02 (-3.23, -2.81) | 7.54 (5.69, 9.82) | -56.21 (-60.64, -51.29) |
| Middle SDI | 261.08 (236.39, 287.79) | 113.41 (103.89, 124.03) | -2.91 (-2.99, -2.84) | 26.62 (19.27, 34.86) | -53.40 (-54.67, -52.09) |
| Low-middle SDI | 402.86 (351.57, 460.56) | 234.51 (207.06, 264.66) | -1.85 (-1.92, -1.78) | 88.16 (58.50, 124.95) | -46.09 (-48.83, -43.21) |
| Low SDI | 643.53 (538.92, 754.39) | 375.22 (317.57, 434.22) | -1.94 (-2.09, -1.78) | 173.24 (129.24, 222.95) | -36.48 (-41.26, -31.31) |
| Andean Latin America | 414.39 (370.90, 462.83) | 253.25 (219.75, 287.73) | -1.66 (-1.95, -1.37) | 99.09 (36.55, 157.79) | -52.14 (-61.14, -41.04) |
| Australasia | 26.09 (24.06, 27.67) | 27.59 (24.22, 29.94) | 0.66 (-0.22, 1.54) | 0.18 (0.05, 0.41) | -93.08 (-99.36, -25.19) |
| Caribbean | 226.92 (196.79, 261.16) | 172.42 (143.90, 204.92) | -0.68 (-0.94, -0.43) | 0.75 (0.23, 3.69) | -70.53 (-86.40, -36.13) |
| Central Asia | 417.30 (382.43, 451.86) | 206.04 (180.39, 236.74) | -2.37 (-2.52, -2.23) | 19.77 (11.21, 31.82) | -45.04 (-50.78, -38.65) |
| Central Europe | 100.88 (95.79, 106.00) | 68.78 (65.98, 71.27) | -1.17 (-1.82, -0.52) | 0.96 (0.30, 2.27) | -54.41 (-88.47, 80.30) |
| Central Latin America | 180.15 (168.80, 192.36) | 133.61 (122.42, 145.58) | -1.07 (-1.36, -0.77) | 14.89 (9.95, 21.76) | -60.02 (-68.93, -48.55) |
| Central Sub-Saharan Africa | 631.74 (508.25, 749.25) | 392.29 (309.93, 491.20) | -1.62 (-1.72, -1.53) | 271.71 (141.60, 418.39) | -17.15 (-19.79, -14.41) |
| East Asia | 266.57 (231.92, 306.20) | 40.92 (36.17, 46.78) | -6.29 (-6.55, -6.03) | 8.45 (4.83, 13.97) | -54.35 (-58.53, -49.74) |
| Eastern Europe | 60.25 (57.27, 63.25) | 52.32 (50.19, 54.66) | -0.26 (-1.61, 1.10) | 6.26 (2.72, 11.88) | -3.66 (-4.09, -3.24) |
| Eastern Sub-Saharan Africa | 649.94 (550.03, 761.13) | 390.07 (337.49, 442.74) | -1.91 (-2.05, -1.78) | 196.43 (131.73, 284.31) | -36.50 (-43.30, -28.89) |
| High-income Asia Pacific | 74.57 (68.28, 78.78) | 44.43 (38.86, 47.88) | -1.87 (-2.22, -1.52) | 0.07 (0.01, 0.35) | -65.19 (-96.36, 232.95) |
| High-income North America | 43.63 (40.24, 45.65) | 43.87 (40.56, 46.01) | 0.21 (-1.33, 1.78) | 5.11 (2.37, 9.12) | 0.97 (0.52, 1.42) |
| North Africa and Middle East | 264.29 (227.13, 322.32) | 119.13 (105.96, 134.18) | -2.64 (-2.73, -2.56) | 28.45 (16.43, 47.36) | -47.35 (-50.31, -44.21) |
| Oceania | 439.43 (366.61, 530.31) | 389.45 (314.54, 480.02) | -0.23 (-0.34, -0.13) | 15.54 (7.51, 27.17) | -71.68 (-76.19, -66.31) |
| South Asia | 378.53 (322.25, 437.07) | 212.33 (186.13, 243.86) | -2.03 (-2.14, -1.93) | 95.02 (46.15, 153.24) | -44.70 (-50.83, -37.80) |
| Southeast Asia | 290.20 (253.69, 333.28) | 155.35 (137.47, 174.39) | -2.06 (-2.12, -2.00) | 11.53 (7.39, 17.03) | -65.82 (-67.54, -64.02) |
| Southern Latin America | 93.30 (88.71, 97.64) | 170.32 (158.57, 178.79) | 2.26 (1.58, 2.95) | 9.86 (4.36, 19.19) | -79.27 (-94.67, -19.37) |
| Southern Sub-Saharan Africa | 376.12 (336.61, 414.39) | 473.58 (420.45, 526.88) | 0.69 (0.48, 0.89) | 70.64 (28.80, 138.71) | -69.62 (-77.67, -58.66) |
| Tropical Latin America | 197.30 (180.61, 213.11) | 158.19 (145.50, 169.27) | -0.75 (-1.24, -0.26) | 30.77 (9.09, 68.03) | -60.45 (-73.55, -40.87) |
| Western Europe | 37.41 (34.77, 39.03) | 37.34 (33.37, 39.54) | 0.08 (-0.59, 0.76) | 1.63 (1.03, 2.39) | -60.78 (-78.67, -27.86) |
| Western Sub-Saharan Africa | 705.51 (583.00, 839.38) | 468.36 (372.31, 569.61) | -1.44 (-1.62, -1.26) | 205.81 (123.54, 303.07) | -34.81 (-36.97, -32.57) |
Regional burden of influenza-associated LRIs
In different SDI regions, the ASMR for influenza-associated LRIs exhibited distinct trends. In High SDI regions, the ASMR remained relatively stable, with the highest mortality rate observed in 1995 at 3.12 (95% UI: 2.78, 3.33) per 100,000 population. In High-middle SDI regions, the highest mortality rate was recorded in 1990 at 2.86 (95% UI: 2.62, 3.12) per 100,000 population. In Middle SDI regions, the mortality rate gradually decreased from 5.63 (95% UI: 5.14, 6.12) per 100,000 population in 1990 to 3.92 (95% UI: 3.59, 4.22) per 100,000 population in 2008, and then stabilized at 4.11 (95% UI: 3.71, 4.43) per 100,000 population by 2019.In Low-middle SDI and Low SDI regions, the ASMR for influenza-associated LRIs followed a similar pattern, decreasing steadily from 1990 to around 2010, followed by a gradual increase until 2019 (Fig. 1).
In 2021, among the super regions, the highest ASMR for influenza-associated LRIs was observed in Central Sub-Saharan Africa at 10.84 (95% UI: 5.53, 17.02) per 100,000 population, followed by Eastern Sub-Saharan Africa at 7.09 (95% UI: 4.67, 10.17) per 100,000 population, and Western Sub-Saharan Africa at 5.92 (95% UI: 3.74, 8.67) per 100,000 population. Similarly, the highest ASDR was also recorded in Central Sub-Saharan Africa at 271.71 (95% UI: 141.60, 418.39) per 100,000 population, followed by Eastern Sub-Saharan Africa at 196.43 (95% UI: 131.73, 284.31) per 100,000 population, and Western Sub-Saharan Africa at 205.81 (95% UI: 123.54, 303.07) per 100,000 population. Conversely, the lowest ASMR was observed in High-income Asia Pacific at 0.00 (95% UI: 0.00, 0.02) per 100,000 population, Australasia at 0.01 (95% UI: 0.00, 0.03) per 100,000 population, and Caribbean at 0.02 (95% UI: 0.01, 0.11) per 100,000 population. The lowest ASDR was also recorded in these regions: High-income Asia Pacific at 0.07 (95% UI: 0.01, 0.35) per 100,000 population, Australasia at 0.18 (95% UI: 0.05, 0.41) per 100,000 population, and Caribbean at 0.75 (95% UI: 0.23, 3.69) per 100,000 population (Tables 1 and 2). The regions with the highest number of deaths in 2021 were South Asia with 34,339 (95% UI: 16,336.94, 56,925.92), Western Sub-Saharan Africa with 17,966 (95% UI: 10,770.41, 26,490.26), and Eastern Sub-Saharan Africa with 13,784 (95% UI: 9,252.69, 19,968.65). These regions also recorded the highest DALYs: South Asia at 1,411,322.87 (95% UI: 689,110.46, 2,272,145.21), Western Sub-Saharan Africa at 1,143,646.12 (95% UI: 684,777.30, 1,715,744.97), and Eastern Sub-Saharan Africa at 707,239.81 (95% UI: 477,316.21, 1,040,971.50).In contrast, the fewest deaths were observed in Australasia with 8 (95% UI: 2.27, 19.24), Caribbean with 12 (95% UI: 3.71, 59.27), and High-income Asia Pacific with 32 (95% UI: 2.84, 173.87). The corresponding DALYs for these regions were Australasia at 101.56 (95% UI: 27.10, 231.29), Caribbean at 341.38 (95% UI: 102.24, 1,645.90), and High-income Asia Pacific at 389.29 (95% UI: 35.53, 2,039.98) (Table S1, S2).
At the national level, the highest ASMR in 2021 was recorded in Central African Republic(13.03 (95%UI: 6.53, 20.29) per 100,000 population), Democratic Republic of the Congo(11.74 (95%UI: 5.81, 19.33) per 100,000 population), and Togo(11.64 (95%UI: 6.72, 17.30) per 100,000 population), while the lowest ASMR was observed in South Korea(0.00 (95%UI: 0.00, 0.00) per 100,000 population), Japan(0.01 (95%UI: 0.00, 0.030) per 100,000 population), and Brunei(0.01 (95%UI: 0.00, 0.03) per 100,000 population) (Fig. 2).
Fig. 2.
Global map of age-standardized mortality rates for influenza-associated LRIs, 2021
The association between ASRs and SDI
From 1990 to 2021, both the ASMR (ρ = -0.712, p < 0.001) and ASDR (ρ = -0.864, p < 0.001) for influenza-associated LRIs decreased with increasing SDI. The ASMR and ASDR stabilized when SDI values were between 0.5 and 0.6 and above 0.8. Notably, in Sub-Saharan Africa, the ASMR for influenza-associated LRIs initially decreased and then increased with rising SDI, unlike other regions where the ASMR generally remained stable or decreased. The ASDR exhibited a predominantly decreasing or stable trend across all regions (Fig. 3).
Fig. 3.
Age-standardized rates of mortality and DALYs of influenza-associated LRIs globally and for 21 super regions, by SDI, from 1990 to 2021
Age and sex-specific burden of influenza-associated LRIs
Across different age groups, the ASMR for influenza-associated LRIs was generally lowest among young and middle-aged adults (15–49 years) and highest among the elderly (≥ 70 years), with ASMR increasing with age. The ASMR for children under 5 years was slightly higher than that of young and middle-aged adults but remained substantially lower than that of the elderly. Unlike the ASMR, the ASDR for children under 5 years was comparable to that of the elderly and significantly higher than that of young and middle-aged adults (Fig. 4). In terms of absolute numbers, the distribution of deaths and DALYs was broadly similar between males and females, with males slightly exceeding females. Children under 5 years accounted for the highest number of deaths and DALYs, with these metrics showing a declining trend over the years. Apart from children under 5 years, the elderly consistently recorded the highest number of deaths and DALYs, while young and middle-aged adults maintained the lowest (Fig. S2).
Fig. 4.
Age-specific mortality rates and DALY rates for influenza-associated LRIs: (A, B, C) Age-specific mortality rates in 1990, 2019, and 2021; (D, E, F) Age-specific DALY rates in 1990, 2019, and 2021
Frontier analysis of influenza-associated LRIs burden
Based on data from 1990 to 2021, we conducted a frontier analysis to evaluate the potential for improvement in the ASDR of influenza-associated LRIs across countries and regions at varying levels of development. The results indicated that in 2021, the top 10 countries and regions with the greatest potential for improvement were Central African Republic, Togo, Eritrea, Zambia, Angola, Cameroon, Rwanda, Nigeria, Kenya, and Ghana. Among low-SDI countries and regions, Mali, Afghanistan, Papua New Guinea, Haiti, and Timor-Leste exhibited relatively lower disease burdens and were located near the efficiency frontier, despite their developmental constraints. In contrast, among high-SDI countries and regions, Ireland, Belgium, Sweden, Lithuania, and Iceland showed considerable room for improvement compared to other nations with similar developmental levels. (Fig. 5)
Fig. 5.
Frontier analysis examining the relationship between SDI and ASDR for influenza-associated LRIs across 204 countries and territories. (A) Temporal progression of ASDR represented by a chromatic gradient ranging from light blue (1990) to dark blue (2021). (B) Efficiency frontier analysis for 2021: The black frontier line represents the optimal health outcomes achievable based on SDI levels. Black-labeled markers indicate the 10 countries or territories with the largest gaps from the frontier. Blue-labeled markers denote low-SDI countries near the frontier, while red-labeled markers highlight high-SDI countries with the largest gaps. Red dots signify a decline in ASDR from 1990 to 2021
Discussion
Despite a significant decline in the global ASMR and ASDR for influenza-associated LRIs from 1990 to 2019 (with AAPCs of -0.69% and − 2.00%, respectively), the absolute number of deaths increased during the same period (AAPC = 0.85%). This discrepancy likely reflects the amplifying effects of population aging and growth on disease burden [1]. The decline in ASMR indicates the effectiveness of medical advancements, such as antiviral medications and vaccine distribution, as well as public health interventions [26]. Notably, in 2021, both the ASMR and the number of deaths exhibited a sharp decline (AAPCs of -49.74% and − 48.62%, respectively). This decline is closely associated with the widespread implementation of social distancing, mask-wearing, and increased influenza vaccination rates during the COVID-19 pandemic [4, 14]. While encouraging, these reductions may reflect transient behavioral changes rather than sustained improvements in influenza management. Future research should monitor post-pandemic rebound effects to assess the durability of these trends.
Furthermore, we observed a notable increasing trend in the ASMR for influenza-associated LRIs between 2010 and 2019, particularly in low and low-middle SDI regions. This reversal of the previous decline is likely multifactorial in origin. First, demographic shifts provide an underlying context: although age-standardized rates adjust for age structure, the absolute population growth and aging in these regions may have expanded the size of high-risk groups, thereby increasing the absolute number of deaths and influencing the overall trend [27]. Second, health systems faced persistent pressures: many low- and low-middle SDI regions continue to carry a high burden of other infectious diseases such as HIV, tuberculosis, and malaria, which may divert limited health resources and undermine capacity for seasonal influenza surveillance, vaccination coverage, and clinical case management [28]. Additionally, the growing challenge of antimicrobial resistance may have reduced the success rate of treating secondary bacterial pneumonia following influenza infection, representing another potential driver of increasing mortality [29]. This concerning pattern highlights the fragility and instability of public health gains in resource-limited settings and underscores the urgent need for sustained investment and targeted interventions in these regions.
The negative correlation between the SDI and the ASMR underscores the role of socioeconomic factors in the burden of influenza. In high SDI regions, such as the High-income Asia Pacific, the ASMR approaches zero. In contrast, regions like Central Sub-Saharan Africa exhibit substantially higher ASMR and ASDR, at 10.84 per 100,000 population and 271.71 per 100,000 population, respectively. This disparity aligns closely with the social and economic development levels reflected in the SDI. High SDI regions benefit from robust healthcare systems, high vaccine coverage, and strong early diagnostic capabilities, which collectively reduce disease burden [30]. Conversely, low and middle SDI regions, particularly in Sub-Saharan Africa, face challenges such as limited healthcare resources, low vaccine accessibility, and a high prevalence of comorbidities [31]. To address these gaps, targeted investments are essential in resource-limited settings. These should include ensuring equitable access to vaccines, training healthcare workers, and establishing integrated surveillance systems to enhance preparedness and response to influenza and other respiratory infections [32].
Age distribution analysis revealed a bimodal risk pattern, with children under 5 years and elderly populations (particularly those aged ≥ 70 years) bearing the primary disease burden. Although the elderly had the highest ASMR, linked to immunosenescence and comorbidities [33], the ASDR was comparable between these age groups. This highlights the long-term health losses from childhood infections, such as growth faltering and recurrent hospitalizations [34]. The ASMR was consistently higher in males than in females, potentially due to occupational exposure, differences in smoking rates, or sexual dimorphism in immune responses [35]. The decline in under-5 mortality since 1990 likely reflects global efforts in immunization (e.g., pneumococcal conjugate vaccines) and improvements in maternal and child health [36, 37], yet progress remains uneven across regions. To address these high-burden groups, tailored interventions are needed: expanding childhood immunization programs (e.g., incorporating influenza vaccines into national schedules) and optimizing early antiviral treatment and complication management for high-risk elderly populations.
To explore the potential reasons underlying the suboptimal performance of the identified high-SDI countries (e.g., Ireland, Belgium, Sweden, Lithuania, and Iceland) in our frontier analysis, we performed a cross-validation with external data on influenza vaccination coverage. Data from the Our World in Data on influenza vaccination rates in the elderly population reveal a compelling pattern [38]. For instance, Lithuania consistently reported vaccination rates notably below the average, aligning with its position as having considerable room for improvement. While comprehensive data for all listed countries varies in availability, this observation suggests that suboptimal vaccination coverage is a key, modifiable factor likely contributing to the relative inefficiency in mitigating influenza mortality in these high-resource settings. This finding highlights that even within nations possessing strong healthcare infrastructure, public health priorities and the effective implementation of preventive strategies, such as vaccination campaigns, are critical for achieving optimal health outcomes.
Looking ahead, sustaining the decline in the global burden of influenza-associated LRIs presents both opportunities and challenges. Promising developments, such as the pursuit of universal influenza vaccines [39], wider adoption of rapid diagnostic technologies [40], and the strengthening of global influenza control frameworks [41], hold the potential to accelerate progress. However, the epidemiology of influenza-associated LRIs is embedded within a complex and dynamic system of co-circulating respiratory pathogens. As highlighted during the COVID-19 pandemic, while non-pharmaceutical interventions can profoundly suppress influenza transmission, viral competition and interference mean that suppressing one pathogen can create an ecological niche for others to rebound, sometimes with unusual intensity or timing [42]. Furthermore, influenza is just one of many pathogens that can precipitate severe outcomes in vulnerable individuals, particularly the elderly at the end of life. In this context, a decline in influenza-specific mortality may not always translate into a net reduction in all-cause respiratory mortality, as individuals protected from influenza may subsequently contract other circulating viruses or bacterial infections [43]. Therefore, future strategies must adopt an integrated “One Health” approach to respiratory virus surveillance and control. Research priorities should include longitudinal studies on viral interactions and the impact of syndemics on population health, especially in high-risk groups.
Projections suggest that the ASMR for influenza-associated LRIs will continue to decline and plateau after 2030. This optimistic trend is likely driven by global vaccine development (e.g., universal influenza vaccines), the widespread adoption of diagnostic technologies (e.g., rapid antigen testing), and implementation of the WHO influenza prevention and control framework. However, several factors could affect the accuracy of these projections. First, climate change may alter the seasonal transmission patterns of influenza viruses. Second, the emergence of antiviral resistance and fluctuations in the match between vaccine and circulating strains require ongoing surveillance. Third, the fragility of health systems in low and middle SDI regions may slow the reduction in disease burden. Future research should integrate multidisciplinary data, such as climate modeling and resistance monitoring, and promote international collaboration to address resource allocation gaps.
While this study leveraged comprehensive GBD data, several limitations should be considered. First, data gaps or inaccuracies in low-income countries and remote areas may lead to an inaccurate estimation of the true disease burden [19]. Second, GBD estimates are generated through statistical models designed to produce complete and comparable time series across regions and over time. This process involves temporal smoothing that may attenuate short-term, random year-to-year fluctuations, meaning it might not fully capture the intensity of specific high-intensity influenza epidemics. Therefore, our analysis is more focused on revealing long-term trends rather than capturing specific annual variations [18]. Additionally, The definition of “influenza-associated LRIs” used in the GBD study, and consequently in our analysis, is distinct from the broader concept of “influenza-attributable excess mortality.” The latter, estimated through statistical models in specific studies, encompasses deaths where influenza is a triggering factor but the underlying cause is recorded as other conditions, such as cardiovascular diseases [44]. Consequently, when interpreting our mortality estimates, it is crucial to recognize that they yield lower values compared to studies reporting influenza-attributable or excess mortality. Substantial evidence indicates that a significant proportion of influenza-associated deaths, particularly among the elderly, have their underlying cause recorded as cardiovascular or other systemic diseases [44–46]. Thus, our findings should be interpreted as a conservative estimate of deaths directly manifested as influenza-associated LRIs. A more comprehensive assessment of the total influenza burden requires integration with estimates from excess mortality studies. Finally, While non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic are known to have suppressed the transmission of other respiratory viruses like influenza, the GBD 2021 model estimates for individual countries may not fully align with their perceived NPI stringency, as the model is designed for long-term trends rather than capturing the immediate, country-specific effectiveness of single-year policies. Differences in intervention strategies (e.g., a strategy focused solely on SARS-CoV-2 elimination versus a broader mix of NPIs) and data reporting lags may further contribute to this discrepancy [4, 47]. Therefore, the map for 2021 should be viewed as the model’s best estimate given its global framework, rather than a direct real-time reflection of each country’s policy effectiveness against influenza in that particular year. Future research should adopt a more multidimensional approach to analyze the specific effects of policies in different countries and regions, enhancing result accuracy and robustness.
Conclusions
In conclusion, the influenza-associated LRIs remain a significant global health challenge, exacerbated by socioeconomic disparities, population vulnerabilities, and pandemic outbreaks. To achieve a sustainable reduction in influenza- associated morbidity and mortality, it is crucial to prioritize high-risk regions and populations through vaccination campaigns, strengthened healthcare systems, and modernized surveillance. This integrated approach not only addresses existing vulnerabilities but also builds resilience against future outbreaks. By targeting areas and groups with the highest burden, we can optimize resource allocation and enhance the effectiveness of public health interventions. Such strategies are essential for mitigating the long-term impact of influenza on global health.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Abbreviations
- LRIs
Lower respiratory infections
- GBD
Global Burden of Disease
- DALYs
Disability-Adjusted Life Years
- ASMR
Age-Standardized Mortality Rates
- SDI
Socio-Demographic Index
- UIs
Uncertainty Intervals
- ASDR
Age-Standardized DALY Rate
- ASR
Age-Standardized Rate
- APC
Annual Percentage Change
- AAPC
Average Annual Percentage Change
- NPI
Non-Pharmaceutical Intervention
Author contributions
CS, XH and ZL contributed to the conception and design and manuscript drafting. CS, XW, LQ and HS contributed to data collection, assembly, analysis of the data. CS, SZ and HZ contributed to the interpretation of data; SZ and ZL contributed to manuscript revising. All authors approved the final version of the manuscript.
Funding
This work was funded by the National Natural Science Foundation of China (82174286, 82205005), Science and Technology Commission of Shanghai Municipality (21Y21920400, 21S21900200, 22XD1423500), Multidisciplinary Innovation Team of Traditional Chinese Medicine of China (ZYYCXTD-D-202208), Shanghai Municipal Health Commission (2022CX010, 2022XD027) and Three-year action plan for strengthening the construction of the public health system in Shanghai (GWVI-11.1-08).
Data availability
The datasets analyzed during the current study are available at https://vizhub.healthdata.org/gbd-results/.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chenglong Shao and Xing Huang contributed equally to this work.
Contributor Information
Shaoyan Zhang, Email: zhangshaoyan000@163.com.
Zhenhui Lu, Email: Dr_luzh@shutcm.edu.cn.
References
- 1.Diseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Petrova VN, Russell CA. The evolution of seasonal influenza viruses. Nat Rev Microbiol. 2018;16(1):47–60. [DOI] [PubMed] [Google Scholar]
- 3.The top 10. causes of death https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.
- 4.Infections GBDLR, Antimicrobial Resistance C. Global, regional, and National incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990–2021: a systematic analysis from the global burden of disease study 2021. Lancet Infect Dis. 2024;24(9):974–1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mosmann TR, McMichael AJ, LeVert A, McCauley JW, Almond JW. Opportunities and challenges for T cell-based influenza vaccines. Nat Rev Immunol. 2024;24(10):736–52. [DOI] [PubMed] [Google Scholar]
- 6.Zhao Y, Gao Y, Guyatt G, Uyeki TM, Liu P, Liu M, Shen Y, Chen X, Luo S, Li X, et al. Antivirals for post-exposure prophylaxis of influenza: a systematic review and network meta-analysis. Lancet. 2024;404(10454):764–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Li J, Chen Y, Wang X, Yu H. Influenza-associated disease burden in Mainland china: a systematic review and meta-analysis. Sci Rep. 2021;11(1):2886. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Giacchetta I, Primieri C, Cavalieri R, Domnich A, de Waure C. The burden of seasonal influenza in italy: A systematic review of influenza-related complications, hospitalizations, and mortality. Influenza Other Respir Viruses. 2022;16(2):351–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Barbieri E, Porcu G, Dona D, Cavagnis S, Cantarutti L, Scamarcia A, McGovern I, Haag M, Giaquinto C, Cantarutti A. Epidemiology and burden of influenza in children 0–14 years over ten consecutive seasons in Italy. Pediatr Infect Dis J. 2023;42(12):e440–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.de Francisco Shapovalova N, Donadel M, Jit M, Hutubessy R. A systematic review of the social and economic burden of influenza in low- and middle-income countries. Vaccine. 2015;33(48):6537–44. [DOI] [PubMed] [Google Scholar]
- 11.Tafalla M, Buijssen M, Geets R, Vonk Noordegraaf-Schouten M. A comprehensive review of the epidemiology and disease burden of influenza B in 9 European countries. Hum Vaccin Immunother. 2016;12(4):993–1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Thommes EW, Kruse M, Kohli M, Sharma R, Noorduyn SG. Review of seasonal influenza in canada: burden of disease and the cost-effectiveness of quadrivalent inactivated influenza vaccines. Hum Vaccin Immunother. 2017;13(4):867–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Diseases GBD, Injuries C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Collaborators GBDD. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):1989–2056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Collaborators GBDCD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Collaborators GH. Global, regional, and National burden of household air pollution, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2025;405(10485):1167–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Global Nutrition Target C. Global, regional, and National progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the global burden of disease study 2021. Lancet. 2025;404(10471):2543–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhang SX, Miao FY, Yang J, Zhou WT, Lv S, Wei FN, Wang Y, Hu XJ, Yin P, Zheng PY, et al. Global, regional, and National burden of HIV-negative tuberculosis, 1990–2021: findings from the global burden of disease study 2021. Infect Dis Poverty. 2024;13(1):60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Mu S, Wang Y, Cui J, Chen L, Qiu L, Li C, Jiang Y, Lu Z, Ma Z. Global, Regional, and National Temporal trend and patterns of change in the burden of asthma from 1990 to 2021: an analysis of the global burden of disease study 2021. Clin Exp Pharmacol Physiol. 2025;52(5):e70039. [DOI] [PubMed] [Google Scholar]
- 20.Sun Z, Liu S, Zhang L, Li X, Chen X, Chen D, Liu S, Wu J. Age-period-cohort analysis of global, regional, and National trends in laryngeal cancer among older adults, 1990–2021: insights from the 2021 global burden of disease study. Front Public Health. 2025;13:1547801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhang SX, Liu YJ, Tan EL, Yang GB, Wang Y, Hu XJ, Li MZ, Duan L, Lv S, Tian LG, et al. Global, regional, and National burden of upper respiratory infections, 1990–2021: findings from the global burden of disease study 2021. Sci One Health. 2024;3:100084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Liao L, Wang H, Cui W, Zhang Q, He X, Wang L, Xiong Y, Jiang L, Xie Y. Global, regional and National burden and trends of larynx cancer among adults aged 55 and older from 1990 to 2021: results from the global burden of disease study 2021. BMC Public Health. 2025;25(1):906. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lv C, Chen Y, Cheng Z, Zhu Y, Chen W, Zhou N, Chen Y, Li Y, Deng W, Guo X, et al. Global burden of zoonotic infectious diseases of poverty, 1990–2021. Infect Dis Poverty. 2024;13(1):82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Meng Y, Zhao T, Zhang R, Zhu X, Ma C, Shi Q. Global burden of polycystic ovary syndrome among women of childbearing age, 1990–2021: a systematic analysis using the global burden of disease study 2021. Front Public Health. 2025;13:1514250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bai Z, Han J, An J, Wang H, Du X, Yang Z, Mo X. The global, regional, and National patterns of change in the burden of congenital birth defects, 1990–2021: an analysis of the global burden of disease study 2021 and forecast to 2040. EClinicalMedicine. 2024;77:102873. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kumari R, Sharma SD, Kumar A, Ende Z, Mishina M, Wang Y, Falls Z, Samudrala R, Pohl J, Knight PR, et al. Antiviral approaches against influenza virus. Clin Microbiol Rev. 2023;36(1):e0004022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Talbird SE, La EM, Carrico J, Poston S, Poirrier JE, DeMartino JK, Hogea CS. Impact of population aging on the burden of vaccine-preventable diseases among older adults in the united States. Hum Vaccin Immunother. 2021;17(2):332–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sauer M, Vasudevan P, Meghani A, Luthra K, Garcia C, Knoll MD, Privor-Dumm L. Situational assessment of adult vaccine preventable disease and the potential for immunization advocacy and policy in low- and middle-income countries. Vaccine. 2021;39(11):1556–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sanches Santos Rizzo Zuttion M, Parimon T, Bora SA, Yao C, Lagree K, Gao CA, Wunderink RG, Kitsios GD, Morris A, Zhang Y et al. Antibiotic use during influenza infection augments lung eosinophils that impair immunity against secondary bacterial pneumonia. J Clin Invest. 2024;134(21). [DOI] [PMC free article] [PubMed]
- 30.Ruscio B, Bolster A, Bresee J, London Shaping Meeting P. Shaping meeting to explore the value of a coordinated work plan for epidemic and pandemic influenza vaccine preparedness. Vaccine. 2020;38(16):3179–83. [DOI] [PubMed] [Google Scholar]
- 31.Kumar R, Amarchand R, Narayan VV, Saha S, Lafond KE, Kapoor SK, Dar L, Jain S, Krishnan A. Challenges in conducting a community-based influenza vaccine trial in a rural community in Northern India. Hum Vaccin Immunother. 2018;14(8):1909–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Calabro GE, D’Ambrosio F, Fallani E, Ricciardi W. Influenza vaccination assessment according to a value-based health care approach. Vaccines. 2022;10(10). [DOI] [PMC free article] [PubMed]
- 33.Ciabattini A, Nardini C, Santoro F, Garagnani P, Franceschi C, Medaglini D. Vaccination in the elderly: the challenge of immune changes with aging. Semin Immunol. 2018;40:83–94. [DOI] [PubMed] [Google Scholar]
- 34.Souquette A, Thomas PG. Variation in the basal immune state and implications for disease. eLife. 2024;13. [DOI] [PMC free article] [PubMed]
- 35.Cervantes O, Cruz Talavera I, Every E, Coler B, Li M, Li A, Li H, Adams Waldorf K. Role of hormones in the pregnancy and sex-specific outcomes to infections with respiratory viruses. Immunol Rev. 2022;308(1):123–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Olson SM, Newhams MM, Halasa NB, Feldstein LR, Novak T, Weiss SL, Coates BM, Schuster JE, Schwarz AJ, Maddux AB, et al. Vaccine effectiveness against Life-Threatening influenza illness in US children. Clin Infect Diseases: Official Publication Infect Dis Soc Am. 2022;75(2):230–8. [DOI] [PubMed] [Google Scholar]
- 37.Sahni LC, Olson SM, Halasa NB, Stewart LS, Michaels MG, Williams JV, Englund JA, Klein EJ, Staat MA, Schlaudecker EP, et al. Maternal vaccine effectiveness against Influenza-Associated hospitalizations and emergency department visits in infants. JAMA Pediatr. 2024;178(2):176–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Flu vaccination coverage. in the over-65’s https://ourworldindata.org/grapher/influenza-vaccination-rate-elderly-oecd?time=latest.
- 39.Estrada LD, Schultz-Cherry S. Development of a universal influenza vaccine. J Immunol (Baltimore Md: 1950). 2019;202(2):392–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Lin X, Liu XY, Zhang B, Qin AQ, Hui KM, Shi K, Liu Y, Gabriel D, Li XJ. A rapid influenza diagnostic test based on detection of viral neuraminidase activity. Sci Rep. 2022;12(1):505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Hay AJ, McCauley JW. The WHO global influenza surveillance and response system (GISRS)-A future perspective. Influenza Other Respir Viruses. 2018;12(5):551–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Lei H, Yang L, Yang M, Tang J, Yang J, Tan M, Yang S, Wang D, Shu Y. Quantifying the rebound of influenza epidemics after the adjustment of zero-COVID policy in China. PNAS Nexus. 2023;2(5):pgad152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Zheng X, Song Z, Li Y, Zhang J, Wang XL. Possible interference between seasonal epidemics of influenza and other respiratory viruses in Hong Kong, 2014–2017. BMC Infect Dis. 2017;17(1):772. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Goldstein E, Viboud C, Charu V, Lipsitch M. Improving the Estimation of influenza-related mortality over a seasonal baseline. Epidemiology. 2012;23(6):829–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Schanzer DL, Tam TW, Langley JM, Winchester BT. Influenza-attributable deaths, Canada 1990–1999. Epidemiol Infect. 2007;135(7):1109–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Walkowiak MP, Walkowiak D. From respiratory diseases to nervous system disorders: unraveling the certified causes of influenza-associated deaths in Poland from 2000 to 2019. Influenza Other Respir Viruses. 2023;17(11):e13214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J. The WHO estimates of excess mortality associated with the COVID-19 pandemic. Nature. 2023;613(7942):130–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets analyzed during the current study are available at https://vizhub.healthdata.org/gbd-results/.