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Research and predictive analysis of the disease burden of bloodstream infectious diseases in China

BMC Infectious Diseases volume 25, Article number: 578 (2025) Cite this article

Abstract

Background

Bloodstream Infection(BSI) are one of the leading causes of infection-related mortality worldwide. However, epidemiological data related to BSI in China remain very limited.

Methods

Based on the Global Burden of Disease(GBD) database, a systematic analysis was conducted on the epidemic trends, pathogen spectrum, and the current status of Antimicrobial Resistance(AMR) related to BSI in China for the year 2021. Additionally, an Autoregressive Integrated Moving Average(ARIMA) time series model was constructed to predict the trend of the disease burden associated with BSI in China from 2022 to 2035.

Results

In terms of pathogens, the top five pathogens causing deaths due to BSI in China are as follows: Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. There are significant differences in the pathogens causing BSI across different age groups. The disease burden is heaviest in the elderly population aged 70 and above. Among children under five years old, Staphylococcus aureus, Streptococcus pneumoniae, and Candida species are predominant. From 1990 to 2021, although there has been a gradual decline in mortality rates due to BSI across different age groups (with an approximately 52.4% reduction in age-standardized rates), the disease burden of BSI increases with age. This is especially evident in the population aged 70 and above, where the burden of disease is significantly higher than in other age groups. For instance, in 2021, the mortality rate for individuals aged 70–74 was 149.29 (per 100 K), while for those aged 95 and older, the mortality rate reached as high as 896.71 (per 100 K). On a global scale, the disease burden caused by BSI in China is at a moderate level. According to time series model projections, the mortality burden of BSI in China shows a complex trend toward 2035: the crude mortality rate across all age groups is expected to increase by approximately 14.26%, whereas the age-standardized mortality rate and Disability-Adjusted Life Years(DALYs) are projected to decrease significantly. Notably, the mortality burden is expected to decline most prominently in the 70 + and under 5 age groups, while the 25–44 age group is projected to see minimal change. Conversely, the mortality rates for the 5–49 age group are anticipated to increase slightly.

Conclusion

Staphylococcus aureus and Escherichia coli are key pathogens contributing to the high mortality burden of BSI. Additionally, the heavy burden associated with AMR poses significant challenges to clinical treatment. From 1990 to 2021, the age-standardized mortality rate mortality of BSI patients is gradually decreasing, and the change in BSI mortality will be mainly affected by the changes in population size and age structure. The forecast analysis for 2022–2035 finds that the death burden of the elderly will be the heaviest, and the mortality of people aged 5–49 years will increase slightly. BSI and its related health problems are still major challenges and need continuous attention.

Clinical trial

Inapplicability.

Peer Review reports

Introduction

Bloodstream infection (BSI) refers to an infection that occurs when pathogens (such as bacteria or fungi) enter the bloodstream and proliferate, leading to systemic infection. It can manifest as bacteremia [1] (the presence of bacteria in the blood) or sepsis [2] (systemic inflammatory response and multiple organ dysfunction). BSI is one of the leading causes of mortality due to infectious diseases [3, 4]. In China, it was estimated that 1.3 million people died from infectious syndrome in 2019, with BSI being the most lethal type of infection, associated with 521,392 deaths [5]. The cornerstone of treating bloodstream infections is identifying the cause and source of infection. However, due to the diversity of infections and the complexity of pathogens, rapidly and accurately identifying the specific causes and sources of bloodstream infections remains a significant challenge in clinical practice. Another major challenge related to BSI is the increasing prevalence of AMR, which contributes to increased global morbidity, mortality, and healthcare costs [6]. The magnitude of its impact is influenced by the type of pathogens and the conditions of the patients [7,8,9]. However, due to the lack of standardized monitoring systems, there is significant heterogeneity in the data related to infectious diseases [10]. In this context, the epidemiological data provided by the Global Burden of Diseases (GBD) database is particularly important, as it enables researchers and clinicians to better understand the epidemiological trends, etiological distribution, and influencing factors of sepsis and BSI. Therefore, more accurate prevention and treatment strategies can be developed to improve the survival rate and quality of life of patients. Meanwhile, time-series models can analyze and predict disease trends over time and are widely used in epidemiological research on infectious diseases. Among these models, the Autoregressive Integrated Moving Average (ARIMA) model is especially popular due to its ability to capture various patterns in time-series data, including trends, seasonality, and cyclicality. By adjusting the parameters (p, d, q) of the model, it can flexibly adapt to different types of data [11,12,13].

Currently, epidemiological data on BSI in China, particularly regarding the spectrum of pathogens and their AMR, remain very limited. This study systematically analyzed the disease burden of BSI in China in 2021 based on the GBD database. Furthermore, to predict the future disease burden of BSI, this study employed the ARIMA algorithm to construct a time series model, aiming to provide scientific evidence and data support for the formulation of public health policies and the effective control of infectious diseases.

Materials and methods

Data source

The data analyzed in this study is sourced from the most recent GBD study [14], which comprehensively examined the all-age and age-specific mortality and disability-adjusted life years (DALYs) associated with 22 pathogens, 84 pathogen-drug combinations, and 11 infectious syndromes across 204 countries and regions from 1990 to 2021. This study extracted data on the burden of infectious diseases in China from 1990 to 2021, with a particular focus on the pathogen spectrum, mortality rates, DALYs, and trends in disease burden related to BSI. All count data were computed for the entire population, and all rates were age-standardized. The GBD dataset is accessible through the Global Health Data Exchange (GHDX) and the interactive GBD Compare platform.

Firstly, we accessed the Global Health Data Exchange (GHDX). We clicked the “GBD Sources Tool” hyperlink located under the “IHME Data” menu. Next, we clicked the “IHME Data Visualizations” hyperlink under the “Resources” menu on the right. Then, we clicked the “INTERACT WITH THE VISUAL” hyperlink under the “MICROBE key finding” menu, which led us to the interactive GBD Compare platform. Following this, we clicked the “https://vizhub.healthdata.org/microbe(link is external)” hyperlink under the “Citation” menu to access the data visualization interface. In the “Key finding” module, we obtained an overview of “Infectious syndrome” prevalence. Next, Clicking on “Infectious syndromes” opened the data search interface. In the left-hand menu, we selected parameters such as: different countries and regions under the “Location” button; different age groups, including “Age-standardized,” under the “Age” button; the years 1990–2021 under the “Year” button; “Deaths” or “DALYs” under the “Measure” button; and “Number” or “Rate (Per 100K)” under the “Metric” button. After selecting the desired parameters, we clicked the “Download” button in the upper right corner to directly download the CSV file.

Methods

This study used R software (version 4.4.1) to process and analyze data obtained from CSV files. The packages “dplyr,” “ggplot2,” and “reshape2” were used for plotting line charts, bar charts, and maps, respectively. An ARIMA (p, d,q) time series model was employed to predict mortality rates for different age groups from 1990 to 2035. The auto.arima() function in R implemented an automatic ARIMA model selection procedure, using a stepwise algorithm to evaluate various combinations of (p, d, q) and selecting the model that minimizes the Corrected Akaike Information Criterion (AICc). The parameter d represents the number of differences required to achieve stationarity in the time series. The selected ARIMA model includes ‘p’ autoregressive (AR) terms, ‘d’ differencing (I) terms, and ‘q’ moving average (MA) terms. After model selection, the forecast() function was applied to generate predictions for 2022–2035, providing both point forecasts and prediction intervals. AICc was chosen for model selection to balance model fit and complexity, aiming to avoid overfitting. The R packages “forecast,” “ggplot2,” “dplyr,” “tidyr,” and “RColorBrewer” were utilized to construct the ARIMA time series model and forecast trends in the burden of BSI disease in China for 2022–2035.

Definition criteria

Associated with AMR

Refers to phenomena or outcomes that are associated with AMR in some way. This association can be either direct or indirect, but AMR is not necessarily the sole cause.

Attributed to AMR

Refers to phenomena or outcomes directly caused by AMR, where AMR is the primary or direct cause.

Results

Burden of BSI by pathogens

In 2021, the top five pathogens responsible for deaths from bloodstream infections in China were Staphylococcus aureus (135,200 [110,183 − 160,217] deaths), Escherichia coli (61,071 [49,842 − 72,300] deaths), Streptococcus pneumoniae (58,597 [47,826 − 69,369] deaths), Pseudomonas aeruginosa (37,201 [30,415 − 43,987] deaths), and Acinetobacter baumannii (32,593 [26,731 − 38,454] deaths). In terms of the burden measured by DALYs, the top five pathogens were Staphylococcus aureus (2,914,529 [2,391,806-3,437,251] DALYs), Streptococcus pneumoniae (1,405,319 [1,159,585-1,651,054] DALYs), Escherichia coli (1,283,708 [1,060,360-1,507,055] DALYs), Pseudomonas aeruginosa (891,860 [737,668-1,046,051] DALYs), and Acinetobacter baumannii (839,440 [692,404–986,476] DALYs). Following these, the pathogens that ranked closely in terms of both mortality and DALYs burden included Candida species, Enterococcus faecalis, Klebsiella pneumoniae, Enterobacter species, and other Gram-negative bacteria (Details can be found in Table 1 ).The results of Table 1 were visualized using R (Fig. 1 Rates are age-standardized rates).

Table 1 Burden of BSI by pathogens in China
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Fig. 1
figure 1

Burden of BSI by Pathogens in China (2021) Note: The X-axis represents pathogens, while the Y-axes of the two bar graphs represent mortality rate and DALYs (per 100 K), respectively

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Burden both associated with and attributable to bacterial antimicrobial resistance by pathogen in China

In 2021, the top five pathogens responsible for the death burden from BSI due to AMR in China were Staphylococcus aureus (101,421 [83,023–119,819] deaths), Escherichia coli (54,669 [44,280 − 65,059] deaths), Streptococcus pneumoniae (52,546 [43,264 − 61,829] deaths), Acinetobacter baumannii (27,419 [22,755 − 32,083] deaths), and Pseudomonas aeruginosa (19,247 [15,677 − 22,817] deaths). Following closely were the pathogens Klebsiella pneumoniae, Enterococcus faecalis, Enterococcus faecium, Enterobacter species, Proteus species, Group A Streptococcus, Salmonella species, Group B Streptococcus, Citrobacter species, Haemophilus influenzae, and Morganella species.

Regarding DALYs associated with AMR, the DALYs for Staphylococcus aureus were 2,186,317 [1,801,743-2,570,892], for Streptococcus pneumoniae were 1,260,212 [1,048,262-1,472,163], for Escherichia coli were 1,149,230 [941,522-1,356,939], for Acinetobacter baumannii were 706,188 [588,987 − 823,390], and for Pseudomonas aeruginosa were 461,441 [379,886 − 542,997] (Details can be found in Table 2; Fig. 2 Rates are age-standardized rates).

Table 2 Burden both associated with and attributable to AMR by pathogen in China (2021)
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Fig. 2
figure 2

Burden by AMR in BSI Note: The X-axis represents pathogens, and the Y-axis of the upper and lower bars represents mortality and DALYs (per 100 K), respectively. Dark blue represents the mortality rate or DALYs (per 100 K) associated with the pathogen, while light blue represents the mortality rate or DALYs (per 100 K) attributable to the pathogen

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Pathogen burden in different age groups

The pathogens causing BSI exhibit significant variation across different age groups. Both in terms of mortality rates and DALYs, the burden of disease is heaviest among individuals aged 70 and older. The main pathogenic microorganisms in this age group include Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Pseudomonas aeruginosa, Enterococcus faecalis, Candida species, Acinetobacter baumannii, and Klebsiella pneumoniae. For the 50 to 69 age group, the predominant pathogens are Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli, and Acinetobacter baumannii. In the 5 to 49 age category, BSI is primarily caused by Staphylococcus aureus. Among children under 5 years old, the leading pathogens are Staphylococcus aureus, Candida species,and Streptococcus pneumoniae. These findings underscore the critical need for age-specific prevention and intervention strategies to effectively address the varied pathogenic profiles and associated health burdens of bloodstream infections across different demographic groups (Details can be found in Annex 1 and Fig. 3).

Fig. 3
figure 3

Burden by Age and Pathogen Note: The X-axis of the heatmap represents different age groups, including: all ages, age-standardized, under 5, 5–49 years, 50–69 years, and 70 years and older. The Y-axis represents different pathogens, and the varying colors and numbers of the squares indicate the mortality rate or DALYs (per 100 K) from BSI caused by various pathogens across different age groups

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Burden of disease in different years and age groups

The crude mortality rate for the entire population slightly decreased from 40 (per 100 K) in 1990 to 37.7 (per 100 K) in 2021, marking an overall decline of approximately 5.75%. However, starting from 32.7 (per 100 K) in 2008, the crude mortality rate gradually increased, reaching 37.7 (per 100 K) in 2021. After age-standardization, the mortality rate showed a continuous downward trend, dropping from 61.4 (per 100 K) in 1990 to 29.2 (per 100 K) in 2021, a decrease of about 52.4%.

Among different age groups, the most significant decline in crude mortality rate was observed in children under 5 years old, which dropped from 129.5 (per 100 K) in 1990 to 12.5 (per 100 K) in 2021, a reduction of approximately 90.4%. For individuals aged 70 and above, the crude mortality rate decreased from 410.0 (per 100 K) in 1990 to 265.1 (per 100 K) in 2021, a decline of about 35.3%. The crude mortality rate for individuals aged 50–69 years dropped by approximately 45.31%. In contrast, the decrease in mortality rate for individuals aged 5–49 years was much smaller, only about 8.77%.

Additionally, the DALYs for all age groups showed a year-on-year decline. The age-standardized DALYs declined from 2,215.3 (per 100 K) in 1990 to 753.3 (per 100 K) in 2021, a reduction of approximately 66.0%. Nevertheless, the DALYs for individuals aged 70 and above remained the highest among all age groups, reaching 3,773.5 (per 100 K) in 2021. Before 2020, the DALYs for children under 5 years old were consistently higher than those for individuals aged 50–69 years. However, starting from 2020, the DALYs for individuals aged 50–69 years surpassed those of children under 5 years old, reaching 1,297.1 (per 100 K) in 2021. (Details can be found in Annex 1 and Fig. 4 Rates are age-standardized rates).

Fig. 4
figure 4

Burden of disease in different years and age groups Note: The X-axis of the heatmap represents different age groups, including: all ages, age-standardized, under 5, 5–49 years, 50–69 years, and 70 years and older. The Y-axis represents the years 1990–2021. The varying colors and numbers of the squares indicate the mortality rate or DALYs (per 100 K) from BSI in different age groups between 1990 and 2021

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Trend and prediction of disease burden of BSI in China

The time series model shows that by 2035, there will be an upward trend in the burden of BSI crude mortality across all age groups, with the mortality rate expected to rise from 37.70 (per 100 K) in 2021 to 43.08 (per 100 K), an increase of approximately 14.26%. However, the crude DALYs are projected to decrease from 884.60 (per 100 K) to 756.12 (per 100 K), a decrease of about 14.53%. The age-standardized mortality rate is expected to drop from 29.21 (per 100 K) in 2021 to 14.76 (per 100 K), a reduction of approximately 49.34%, and DALYs will decrease from 753.27 (per 100 K) to 319.92 (per 100 K), a reduction of about 57.48%. Among those aged 70 and older, the burden of mortality is expected to decline significantly, with the mortality rate projected to decrease from 265.12 (per 100 K) in 2021 to 196.53 (per 100 K) by 2035. The mortality burden for children under 5 years old is also expected to decrease significantly. For the 50–69 age group, the mortality burden is expected to decrease from 43.69 (per 100 K) in 2021 to 32.58 (per 100 K) in 2035, a decline of about 25.4%. In the 5–49 age group, the mortality burden is expected to rise slightly, with the mortality rate projected to increase from 5.21 (per 100 K) in 2021 to 5.49 (per 100 K) in 2035, an increase of approximately 5.37%. The line graph indicates that the BSI disease burden in the 25–44 age group is expected to change little. Overall, although there is a downward trend in the mortality rates among BSI patients in different age groups, the burden of BSI disease is gradually increasing with age, particularly among those aged 70 and older, who have a significantly higher disease burden compared to other groups. In every age group, when subdivided every five years, it is generally observed that the mortality rate tends to increase with age. For example, in 2021, the mortality rates were 149.29 (per 100 K) for those aged 70–74, 220.71 (per 100 K) for those aged 75–79, and as high as 896.71 (per 100 K) for those aged 95 and older (Details can be found in Annex 2 and Fig. 5 Rates are age-standardized rates).

Fig. 5
figure 5figure 5figure 5figure 5

Trend and prediction of disease burden of BSI in China Note: The X-axis of the line charts represents the years 1990–2035, and the Y-axis represents mortality rate and DALYs (per 100 K), respectively. Different colored lines represent different age groups

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Global position of BSI burden in China

In 2021, after age-standardization, the countries with the highest BSI mortality rates included the Central African Republic (110 (per 100 K)), Lesotho (104 (per 100 K)), Guinea-Bissau (102 (per 100 K)), Somalia (96 (per 100 K)), South Sudan (96 (per 100 K)), Chad (94 (per 100 K)), and Zimbabwe (93 (per 100 K)). China’s BSI mortality rate was 29 (per 100 K), placing it in the lower-middle range among the countries listed. In contrast, some countries had considerably lower BSI mortality rates, such as Japan (15 (per 100 K)), San Marino (14 (per 100 K)), and Singapore (10 (per 100 K)). Countries such as the Central African Republic, South Sudan, Chad, Sierra Leone, Somalia, and Mali showed extremely high DALYs, exceeding 4,000 (per 100 K). In comparison, China’s DALYs were 753 (per 100 K), which is at a moderate level globally. Many developed countries, including Sweden, Switzerland, Andorra, Japan, San Marino, and Singapore, generally recorded low DALYs rates, all below 400 (per 100 K) (Details can be found in Annex 3 and Fig. 6).

Fig. 6
figure 6

Global burden of disease from BSI Note: The image above depicts the mortality rate of BSI (per 100 K) by region in 2021, while the image below illustrates the DALYs (per 100 K) of BSI by region in 2021. Different colors represent different numbers

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Discussion

Given the high burden of disease caused by BSI, this study conducts an in-depth analysis of the related pathogens and their antimicrobial resistance patterns. In 2021, Staphylococcus aureus dominated the BSI disease burden across all age groups in China. Research from various regions around the world has also indicated that Staphylococcus aureus is one of the most significant causes of BSI-related mortality [15], with an incidence rate ranging from 15 to 40 (per 100 K) and a case fatality rate of approximately 15–25% [16]. The risk of death following Staphylococcus aureus BSI is high, potentially related to its virulence genes, such as multi-locus sequence type clonal complexes (CC) CC8 [17], CC22, and CC30 [18]. Moreover, methicillin-resistant Staphylococcus aureus (MRSA) is a major pathogen responsible for healthcare-associated bloodstream infections, with its incidence gradually increasing among hospitalized patients and in the community [19,20,21,22]. Data from CHINET in China show a concerning trend in MRSA detection rates, which remained high at 28.3% in 2022 [23]. This study also found that Escherichia coli is another significant pathogen contributing to a high mortality burden, similar to findings in other studies, where its antimicrobial resistance and virulence factors play critical roles in bloodstream infections [24]. Infections caused by these two pathogens are associated with prolonged hospital stays. For Staphylococcus aureus BSI, the 30-day mortality rate is increased by fivefold, and the in-hospital mortality rate is increased by sixfold. In the case of Escherichia coli, the corresponding increases are twofold and threefold, respectively [25]. The mortality burden posed by Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae is also significant. The importance of these pathogens cannot be underestimated, particularly in immunocompromised patients [26,27,28,29] as well as in hospital-associated infections [30,31,32], especially in settings such as intensive care units (ICUs), during invasive procedures, and after exposure to broad-spectrum antibiotics [33,34,35,36,37]. According to data from the China Antimicrobial Surveillance Network(CHINET) reporting in 2021, the isolation rate of carbapenem-resistant Acinetobacter baumannii in tertiary hospitals reached as high as 75.2% [38]. Klebsiella pneumoniae demonstrated resistance rates to most antimicrobial agents that were higher than those of other species within the same genus, with the resistance rates for the majority of antibiotics in 2021 being higher than in 2015 Notably, the resistance rates to enzyme inhibitor combinations and carbapenems showed a particularly significant increase over the seven-year period [39]. Moreover, between 1997 and 2016, the proportion of BSI caused by Gram-negative bacilli (GNB) significantly increased, particularly among multidrug-resistant (MDR) GNB [40, 41]. Previous studies have also shown that de-escalation of antibiotic therapy or the use of narrow-spectrum β-lactam antibiotics can significantly reduce mortality rates [42].

It is noteworthy that the burden of mortality from BSI caused by Streptococcus pneumoniae is significantly high in China. The Asian Network for Surveillance of Resistant Pathogens(ANSORP) study indicates that the resistance rates of pneumococci to macrolides in many Asian countries are significantly higher than those in Western countries [43]. In Asia, 59.3% of isolates were found to be MDR, with resistance to erythromycin exceeding 70% in many Asian countries; in China, this rate is as high as 96.4% [44, 45]. Data from CHINET in 2021 showed that Streptococcus pneumoniae exhibited high levels of resistance to erythromycin and clindamycin, with resistance rates surpassing 90% [46]. This may be associated with the inappropriate use of antibiotics and the accelerated spread of resistant strains [43]. Additionally, the high nasopharyngeal carriage rate of Streptococcus pneumoniae in China may also contribute to the burden of resistant pneumococci [47], as the interaction between community-acquired and hospital-acquired infections could exacerbate antibiotic resistance. Research has also shown that the penicillin resistance rate in Streptococcus pneumoniae is similarly high, with MDR rates steadily increasing [48].

Additionally, Candida species and Enterococcus species are important pathogens in BSI. Risk factors for Candida BSI include prolonged hospitalization, abdominal surgery, antibiotic treatment, neutropenia, and central venous catheter insertion [49]. In recent years, there has been an increase in the incidence of non-susceptible species of azole, such as C. glabrata and fluconazole-resistant C. krusei [50, 51]. Previous studies have indicated that non-albicans species of Candida are generally more resistant to antifungal agents compared to Candida albicans [52]. Enterococcus infections are associated with urinary tract lesions, malignant tumors of the urogenital system, and anatomical abnormalities, while E. faecium infections are linked to gastrointestinal lesions [53]. Treatment options for bloodstream infections caused by vancomycin-resistant enterococci (VRE) are limited, and the mortality rate is high [54]. This resistance not only reduces treatment options but may also prolong hospitalization, increase healthcare costs, and contribute to higher mortality rates.

The pathogens causing BSI exhibit significant differences across various age groups. According to data on mortality and DALYs, the burden of disease is heaviest among individuals aged 70 and older, with the primary pathogenic organisms including Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Pseudomonas aeruginosa, Enterococcus spp., Candida spp., Acinetobacter baumannii, and Klebsiella pneumoniae. Numerous studies from other countries have also confirmed that older adults represent the population group at the highest risk for BSI incidence and mortality [5560]. This increased risk may be related to a high prevalence of comorbidities, immune dysfunction, and more frequent medical contacts among this population [61, 62]. Compared to younger individuals, older adults may present atypical clinical manifestations when experiencing BSI and are more susceptible to infections caused by resistant bacteria, particularly among those with diabetes and infections due to Staphylococcus aureus [63]. In the 50–69 age group, Staphylococcus aureus, Streptococcus pneumoniae, and Escherichia coli remain the primary pathogenic organisms, suggesting a similarity in infection patterns between older and middle-aged populations. However, with increasing age, the contributing factors may become increasingly complex.

It is noteworthy that the incidence of Streptococcus pneumoniae and Candida species is high among children under 5 years of age. Pneumococcal disease (PD) is a leading cause of preventable deaths in children under five globally, and the administration of the pneumococcal conjugate vaccine (PCV) has proven effective in preventing PD [64]. In recent years, the use of the seven-valent pneumococcal conjugate vaccine has significantly reduced the incidence of invasive pneumococcal disease; however, it does not cover all known serotypes of Streptococcus pneumoniae. Currently, there are 93 known serotypes [65], meaning that many serotypes remain uncovered and could potentially lead to infections. Therefore, the development of new pneumococcal vaccines is essential. Candida is an opportunistic pathogen that frequently causes nosocomial infections in immunocompromised patients, with common infection sources being the gastrointestinal tract and skin [66]. In pediatric populations, Candida-related BSI are closely associated with high morbidity and mortality rates [67]. These affected children are at risk of developing other nosocomial infections, with some experiencing recurrent candidemia or bacterial infections [68]. Thus, monitoring and implementing preventive measures specifically for immunocompromised children are particularly important.

From 1990 to 2021, despite the gradual decline in overall mortality rates and DALYs over the past few decades, this does not necessarily indicate a reduction in the burden of diseases. This study reveals that with increasing age, the burden of BSI gradually rises, particularly among individuals aged 70 and above, whose disease burden is significantly higher than that of other age groups. Regardless of the age group, when broken down into finer 5-year intervals, mortality rates generally tend to increase with age. For instance, in 2021, the mortality rate for individuals aged 70–74 was 149.29 (per 100 K), rising to 220.71 (per 100 K) for those aged 75–79, and reaching as high as 896.71 (per 100 K) for those aged 95 and above. Notably, by 2020, the disease burden among middle-aged individuals (50–69 years old) had surpassed that of children under 5, which may be related to factors such as rising incidences of chronic diseases (e.g., diabetes, ischemic heart disease, depression, anxiety), lifestyle changes, and aging populations [69, 70]. These findings highlight the need to optimize health resource allocation in line with population characteristics, prevent and manage chronic diseases, and enhance health support for older adults, in order to reduce the overall health burden.

According to time series predictive analysis, by 2035, although the overall burden of mortality from BSI is expected to rise across all age groups (with an estimated increase in mortality rate of about 14.26%), the age-standardized mortality rate will exhibit a significant decline, with a reduction of approximately 49.34%. This indicates that changes in future BSI mortality rates will be largely influenced by population size and age structure. Additionally, the DALYs are projected to decrease by about 14.53%, reflecting improvements in medical interventions and public health measures that enhance the quality of life for patients, thereby reducing the overall health burden, particularly among those aged 70 and older and children under 5 years. Although the mortality rate of 5–49 years old showed a four-fold decline between 1990 and 2021, the decline was small (8.77%). After ARIMA algorithm, the mortality rate of 5–49 years old was found to increase slightly by 5.37% in 2035, suggesting that there may be new health risks in young adults. Studies have indicated that one of the leading causes of death among individuals aged 5–44 is traffic-related injuries [71], and a significant portion of late mortality in adult trauma patients is attributable to infections [72, 73]. Therefore, accidents could be a contributing factor to the increased burden of BSI mortality in this age group, warranting further attention and research.

This study shows that the disease burden caused by bloodstream infections (BSI) in China is at a moderate level globally. Compared to low-burden countries, the burden of infectious diseases in China remains high. There is a pressing need to strengthen the healthcare system to further reduce infection rates and health burdens, while also promoting improvements in global public health.

This study has the following limitations: Firstly, the GBD data relies on existing disease reporting and surveillance systems, but discrepancies in healthcare conditions across regions may lead to underestimation or overestimation of the disease burden, introducing a certain level of reporting bias. Secondly, the ARIMA model is unable to incorporate variables that could affect future trends, such as policy changes or outbreaks of epidemics. Moreover, this model is more suitable for short-term forecasting, and cumulative errors may occur in long-term predictions. Future research could consider integrating data from multiple sources to reduce the impact of reporting bias, as well as exploring the use of machine learning or other more complex predictive models to improve the accuracy and reliability of forecasts.

Conclusion

This study, based on the GBD database, systematically analyzed the burden of BSI in China in 2021. Staphylococcus aureus and Escherichia coli were identified as significant pathogens contributing to the high fatal burden of BSI, with AMR posing considerable challenges to clinical treatment. The incidence and mortality rates were notably higher among the elderly, highlighting them as a high-risk population requiring urgent attention. Additionally, infections caused by Streptococcus pneumoniae and Candida contributed substantially to the mortality burden among children, underscoring the need for enhanced surveillance and prevention efforts targeting this vulnerable group. Both retrospective and predictive analyses indicated a slight increase in mortality rates among individuals aged 5–49, suggesting that greater emphasis should be placed on health education and disease prevention for young adults in clinical and public health settings. Time series forecasting revealed that although the overall mortality rate of BSI patients is gradually declining from 2022 to 2035, future changes in BSI mortality will primarily be influenced by population size and age structure, particularly in the elderly population. BSI and its associated health challenges remain a significant concern, necessitating sustained attention. China’s BSI burden is at a moderate level globally, yet it remains markedly higher than that of countries with low burden. Furthermore, public health conditions in certain high-risk regions are in urgent need of improvement, requiring the attention and support of the international community. Future research is recommended to focus on the health of specific populations and high-risk regions, optimize existing diagnostic tools for infections, and strengthen antimicrobial stewardship to reduce the burden associated with infectious diseases.

Data availability

All count data were computed for the entire population, and all rates were age-standardized. The GBD dataset is accessible through the Global Health Data Exchange (GHDX) and the interactive GBD Compare platform.

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Funding

This study was supported by the Bethune Public Welfare Foundation’s program “Capacity Building for Community Infectious Disease Research” (J202201E026).

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Authors and Affiliations

  1. First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China

    Xiaoyu Zhang, Xifan Zhang, Baiyi Chen, Jingping Zhang & Xin Zhang

  2. Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China

    Sufei Tian

  3. Department of Emergency, Shengjing Hospital of China Medical University, Shenyang, China

    Feng Guo

  4. Department of Infectious Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China

    Deng Zhang

  5. Intensive Care Unit, Shenyang Fourth People’s Hospital affiliated to China Medical University, Shenyang, China

    Zhihui Ren

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  1. Xiaoyu Zhang
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Contributions

XYZ: Writing-manuscript, conceptualization, data curation, formal analysis, investigation, methodology, visualization and translation. SFT: Writing-first draft, methodology and suggestions. XFZ: Writing - conceptualization, data curation, formal analysis, investigation, translation. FG: Writing - conceptualization, methodology, visualization. BYC: Writing-review, editing, resources. DZ: methodology, visualization. ZHR: Writing-review, editing, modification. JPZ: Writing - Writing-first draft, review, editing, resources. XZ: Writing - Writing-first draft, review, editing, resources. All authors reviewed the manuscript.

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Correspondence to Jingping Zhang or Xin Zhang.

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Zhang, X., Tian, S., Zhang, X. et al. Research and predictive analysis of the disease burden of bloodstream infectious diseases in China. BMC Infect Dis 25, 578 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12879-025-10989-1

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