Epidemiological and etiological characteristics of respiratory infections among hospitalized patients: insights from a 12-pathogen surveillance study in Eastern China

Abstract

Background

Severe acute respiratory infections pose a significant global public health threat, yet their pathogen profiles and epidemiological characteristics vary regionally. Lianyungang, a temperate monsoon climate region in eastern China, lacks comprehensive data on the etiological composition and coinfection patterns of respiratory infections. To improve clinical management and preventive measures, this study aimed to investigate the distribution of pathogens and trends in coinfections among hospitalized patients in this region.

Methods

A retrospective analysis was conducted on 2,132 hospitalized patients with respiratory infections at a comprehensive hospital in Lianyungang from January to December 2024. Respiratory samples (throat swabs, sputum and nasopharyngeal aspirate) were collected and tested via multiplex PCR for 12 pathogens, including influenza A virus, influenza B virus, respiratory syncytial virus, adenovirus, human rhinovirus, M. pneumoniae, K. pneumoniae, S. pneumoniae, H. influenzae, P. aeruginosa, L. pneumophila, and S. aureus. Demographic characteristics, seasonal trends, departmental differences, and coinfection patterns were analyzed.

Results

Pathogens were detected in 61.73% (1,317/2,132) of patients, with bacterial dominance (71.34%). S. pneumoniae (25.60%), H. influenzae (18.55%), and K. pneumoniae (12.36%) were the most prevalent. Viral pathogens exhibited the highest prevalence in pediatric populations (75.60%), with rhinovirus, respiratory syncytial virus, and adenovirus dominating in 0–4-year-olds. Mycoplasma pneumoniae detection significantly increased in 5–14-year-olds (38.89%), whereas bacterial pathogen detection predominated in patients over 15 years of age. Male patients significantly outnumbered female patients (61.35% vs. 38.65%), with higher S. pneumoniae detection in males (27.98% vs. 21.41%). The peak hospital visits (42.14%) were recorded in winter, with S. pneumoniae circulating year-round. Influenza B virus and M. pneumoniae levels peak early in the year, whereas influenza A virus levels increase markedly late in the year. Mixed infections occurred in 33.79% of the cases, predominantly bacterial‒bacterial combinations (68.31%), such as S. pneumoniae-H. influenzae coinfections. Rhinovirus frequently appeared in bacterial–viral coinfections (38.71%), whereas influenza A virus dominated viral–viral combinations (44.44%).

Conclusion

This study highlights bacterial predominance, age-specific pathogen distributions, and high coinfection rates in Lianyungang. The findings underscore the need for age- and season-tailored clinical management, enhanced viral testing in pediatrics, and prioritized pneumococcal vaccination for elderly individuals. These insights provide critical evidence for optimizing local antimicrobial stewardship and public health strategies in regions with similar climates.

Introduction

Severe acute respiratory infections (SARIs) are one of the leading causes of hospitalization and mortality worldwide, particularly affecting children, elderly individuals, and immunocompromised individuals. According to the World Health Organization (WHO), lower respiratory infections rank as the fourth leading cause of death worldwide, with approximately 2.5 million deaths annually [1]. It is well known that bacterial and viral pathogens, including respiratory syncytial virus (RSV), Haemophilus influenzae, and Streptococcus pneumoniae, are the main causes of acute respiratory infections and community-acquired pneumonia (CAP) [2]. However, pathogen prevalence is often influenced by geographic, climatic, demographic, and healthcare factors. For example, low temperatures and indoor crowding are linked to winter influenza virus peaks [3], whereas immunosuppression or chronic comorbidities may be linked to bacterial infections such as Klebsiella pneumoniae [4]. Therefore, clarifying the pathogen spectrum and epidemiological characteristics of respiratory infections is critical for optimizing clinical management and formulating public health strategies [5].

As a populous country, China faces a particularly significant burden of respiratory infections characterized by the diversity of pathogens, including bacteria, viruses, atypical pathogens, and an increasing focus on polymicrobial infections [6]. Polymicrobial infections not only increase the difficulty of clinical diagnosis and treatment but also may lead to more severe complications and a poor prognosis [7]. Therefore, the epidemiological patterns and clinical significance of mixed infections (such as bacterial-viral or viral-viral coinfections) still require further exploration. Lianyungang, a coastal city in eastern China with a temperate monsoon climate and an aging population, presents unique environmental conditions that may influence pathogen transmission and prevalence [8]. However, current research on the pathogenic characteristics and epidemiological patterns of polymicrobial infections in hospitalized patients in Lianyungang city remains limited. Additionally, with the widespread use of molecular diagnostic techniques, the detection rate of pathogens has significantly increased [9]. Therefore, conducting pathogenic and epidemiological studies on hospitalized patients in this region and integrating these data to guide clinical practice holds important public health significance.

This retrospective study analyzed the clinical data of 2132 hospitalized patients with respiratory infections at a comprehensive hospital in Lianyungang city, China, to systematically characterize their etiological profiles, epidemiological features, and coinfection patterns. The investigation focused on (1) determining the distribution of predominant pathogens and their associations with demographic characteristics (gender, age); (2) exploring variations in pathogen spectra across clinical departments, seasons, and age groups; and (3) identifying prevalent coinfection combinations and their clinical implications. By elucidating pathogen-specific epidemiological trends and infection complexities, this study hopes to provide critical evidence to inform region-specific prevention strategies for respiratory infections in Lianyungang city and climatically similar areas while offering practical references for optimizing clinical diagnosis and treatment protocols.

Methods

Participants

Lianyungang, located on the eastern coast of China, is a city with a population exceeding four million. Guan Yun People’s Hospital, a large comprehensive medical institution in Lianyungang city, admits a significant number of patients with respiratory tract infections annually, making it highly representative. Patients admitted to respiratory, pediatric, hematology, and infectious disease departments between January 1 and December 31, 2024, with severe respiratory symptoms, were retrospectively enrolled.

Data collection

At admission, demographic and clinical data, including age, sex, admission date, department, symptom onset, clinical manifestations, and other relevant factors, were recorded.

Sample collection and transportation

Within 24 h of admission (prior to treatment initiation), experienced nurses collected respiratory samples from all patients according to standard protocols for viral and bacterial detection. For each patient, both throat swabs and sputum samples were collected. For patients who cannot collect sputum, nasopharyngeal aspirate is used as a substitute specimen representing the lower respiratory tract. For throat swabs, sterile swabs were used to collect samples from both the tonsils and the posterior pharyngeal wall. The swabs were placed in tubes containing sampling solution (Yi Kang Biotechnology, MT0301-1), the tubes were tightly closed, and the samples were sent for testing. The samples were stored at 4 °C and sent to the laboratory within 48 h. The sputum samples were collected in sterile containers with physiological saline, stored at 4 °C, and sent to the laboratory within 48 h.

Laboratory testing

For throat swab samples, nucleic acid detection was performed using Sansure Biotechnology Pathogen Detection Kit (20213400256, Sansure Inc., Nanjing, China). A 200 µL aliquot of the sample was used for nucleic acid extraction, and multiplex real-time fluorescent quantitative PCR detection technology was employed, following the instructions of the Pathogen Detection Kit (20213400256) to detect six respiratory pathogens: influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), human adenovirus (HAdV), human rhinovirus (HRV), and Mycoplasma pneumoniae (M. pneumoniae). For the sputum samples, a 200 µL aliquot was similarly used for nucleic acid extraction via a nucleic acid extraction kit (Xiangchang Medical Device Filing No. 20150021) from Shengxiang Biotechnology Co., Ltd. The extracted samples were tested via multiplex real-time fluorescent quantitative PCR detection technology, following the instructions of the Sansure Biotechnology Pathogen Detection Kit (20223400597, Sansure Inc., Nanjing, China), to detect six respiratory bacteria: Klebsiella pneumoniae (K. pneumoniae), Streptococcus pneumoniae (S. pneumoniae), Haemophilus influenzae (H. influenzae), Pseudomonas aeruginosa (P. aeruginosa), Legionella pneumophila (L. pneumophila), and Staphylococcus aureus (S. aureus).

Quality control

For the swab samples, lentiviral particles was used as positive control and internal standard, and normal saline was used as negative control, the following detection channels were assigned: FAM for IAV/HAdV, HEX/VIC for IBV/HRV, and CY5 for RSV/M. pneumoniae. The ROX channel was used to detect the internal standard nucleic acid. The following criteria must be simultaneously met: (1) Negative control: The ROX channel must exhibit a distinct S-shaped amplification curve with Ct value ≤ 40, while the FAM, HEX/VIC, and CY5 channels should show no amplification (No Ct) or the CT value > 40. (2) Positive control: The FAM, HEX/VIC, and CY5 channels must all display distinct S-shaped amplification curves with Ct values between 27 and 33, whereas the ROX channel should show no amplification (No Ct) or the Ct value > 40.

For sputum samples, plasmid and normal saline were used as positive and negative controls, respectively. The following detection channels were assigned: FAM for K. pneumoniae/L. pneumophila, HEX for S. pneumoniae/internal standard, ROX for H. influenzae, and CY5 for P. aeruginosa/S. aureus. The following criteria must be simultaneously met: (1) Negative control: No amplification curve (No Ct) or Ct value > 39 in the FAM, HEX, ROX, and CY5 channels, and no melting curve observed. (2) Positive control: Distinct S-shaped amplification curves with Ct values ≤ 33 in the FAM, HEX, ROX, and CY5 channels, along with characteristic melting peaks in the FAM (68.8–71.3 °C), HEX (65.8–68.5 °C), and CY5 (66.8–70.0 °C) channels.

Statistical methods

Statistical analysis was conducted via SPSS 24.0 software, and all graphical representations were generated and analyzed via GraphPad Prism (version 8.0, GraphPad Software, San Diego, CA). Normally distributed data are expressed as the means. Categorical data are presented as case numbers and percentages, and compared using the χ² test or Fisher’s exact test. Benjamini-Hochberg FDR was used to adjust the P value for the comparison of multiple groups. A P-value of < 0.05 was considered statistically significant.

Results

Demographic characteristics

A total of 2,132 patients were included in the study, with 1,317 confirmed to have pathogen identified and 815 showing no detectable pathogens. Among the patients with identified pathogens, 61.35% were male and 38.65% were female, yielding a male-to-female ratio of 1.59:1. In terms of age composition, 748 patients were elderly individuals aged ≥ 60 years, significantly outnumbering other age groups. Seasonal analysis revealed a greater proportion of infections during winter (42.14%), followed by spring (29.38%), whereas summer and autumn had fewer cases. Among the four departments analyzed, the respiratory medicine department accounted for the majority of cases (68.87%), followed by pediatrics (24.68%), with hematology and infectious disease departments contributing minimally. The detailed demographic distributions are summarized in Table 1.

Table 1.

Demographic characteristics of 2,132 patients

Characteristics	SARI patients
	All (%) [n = 2132]	With confirmed pathogens (%) [n = 1317]	Without confirmed pathogens (%) [n = 815]
Male	1264	808(61.35)	456(55.95)
Female	868	509(38.65)	359(44.05)
Age group (years)
<5	308	190(14.43)	118(14.48)
5–14	222	139(10.55)	83(10.18)
15–60	373	240(18.2)	133(16.32)
>60	1229	748(56.80)	481(59.02)
Season at the time of visit [n (%)]
Spring (March-May)	575	387(29.38)	188(23.07)
Summer (June-August)	312	179(13.59)	133(16.32)
Autumn (September-November )	350	196(14.88)	154(18.90)
Winter ( December-February)	895	555(42.14)	340(41.72)
Departments [n (%)]
Respiratory Medicine	1470	907(68.87)	563(69.08)
Pediatrics	524	325(24.68)	199(24.42)
Hematology	96	62(4.71)	34(4.17)
Infectious disease	42	23(1.75)	19(2.33)

Analysis of the respiratory pathogen spectrum of 1317 patients

In this investigation, twelve pathogens were tested. The cumulative detection frequency of 12 pathogens was 1,844. S. pneumoniae was the most frequently detected pathogen, followed by H. influenzae and K. pneumoniae. No L. pneumophila was detected. Among 1,317 individuals, 872 (66.21%) had single-pathogen infections, 366 (27.79%) had dual-pathogen infections, 76 (5.77%) had triple-pathogen infections, and 3 (0.23%) had quadruple-pathogen infections. Among single-pathogen infections, the top three pathogens were S. pneumoniae, H. influenzae, and IBV. Among patients infected with multiple pathogens, the three most prevalent pathogens detected were S. pneumoniae, H. influenzae, and K. pneumoniae. As shown in Fig. 1.

Fig. 1.

The respiratory pathogen spectrum of 1317 patients with confirmed pathogens detected was categorized into separate infections and mixed infections, as presented in a stacked diagram. The detection frequency indicated on the Y-axis represents the total number of positive respiratory pathogens detected

Differences in the pathogen spectrum among patients of different sexes

Among the 808 male population, a total of 1176 pathogens were detected (including pathogens detected in coinfections), with the highest detection frequency being S. pneumoniae, followed by H. influenzae and K. pneumoniae. However, viral pathogens were detected less frequently, and their proportions were similar among the various types. This phenomenon was also observed in the female population, where a total of 668 pathogens (including those from coinfections) were detected among 509 females. The top three pathogens detected in females were S. pneumoniae, H. influenzae, and K. pneumoniae. For male patients, the positive detection rates of S. pneumoniae and K. pneumoniae were significantly higher than those in female patients, whereas the positive detection rate of M. pneumoniae was significantly higher in females than in males. In contrast, no statistically significant differences were observed between genders for the majority of other pathogens. As shown in Fig. 2; Table 2.

Fig. 2.

Respiratory pathogen spectrum of 1317 patients of different sexes. A. The detection frequency indicated on the Y-axis represents the total number of positive respiratory pathogens detected. B. Y-axis indicates the detection rate of positive samples

Table 2.

Difference of pathogen positive rate among patients of different sexes

Pathogens	Positive rate(%)		χ2	P value	95% confidence intervals
	Male	Female
IAV	9.65	8.06	0.97	0.32	[0.82, 1.81]
IBV	9.16	11.20	1.45	0.23	[0.56, 1.15]
HAdV	5.82	5.89	0.003	0.95	[0.62, 1.58]
HRV	9.78	10.02	0.02	0.86	[0.67, 1.41]
RSV	5.94	4.52	1.24	0.27	[0.80, 2.22]
P. aeruginosa	9.53	13.36	4.67	0.03	[0.48, 0.97]
H. influenzae	27.10	24.17	1.4	0.24	[0.9, 1.51]
S. aureus	0.99	0.2	2.9	0.09	[0.63, 40.74]
M. pneumoniae	7.30	11.98	8.27	0.004	[0.40, 0.84]
S. pneumoniae	40.72	28.09	21.64	<0.001	[1.38, 2.23]
K. pneumoniae	19.55	13.75	7.34	0.006	[1.12, 2.07]

Differences in the pathogen spectrum among patients in different departments

In different departments, respiratory medicine has the highest number of patient visits and the greatest total frequency of pathogen detection. The majority of these detections were bacterial pathogens, with S. pneumoniae, H. influenzae, and K. pneumoniae being the most frequently identified. Conversely, viral pathogens were detected less frequently, and this trend was also observed in hematology departments. However, in infectious disease departments and pediatrics, the detection rate of viral pathogens was relatively high. In the infectious diseases department, the most frequently detected pathogens were IBV and IAV. In pediatric, the three most common pathogens detected were M. pneumoniae, HRV, and HAdV. S. pneumoniae, K. pneumoniae, P. aeruginosa, and S. aureus were not detected, and only one case of H. influenzae was detected.

There are statistical differences in the detection rate of most pathogens among these four departments. Compared to other departments, patients in the Infectious Diseases Department exhibited significantly higher positive detection rates for both IAV and IBV. Conversely, HAdV and HRV detections were significantly more frequent in pediatric patients. A significantly elevated detection rate for H. influenzae was observed in the Hematology Department. Moreover, the detection rates of S. pneumoniae and K. pneumoniae were closely matched and significantly higher in the Infectious Diseases and Respiratory Departments, respectively, than in the other two departments.For more details, please refer to Fig. 3.

Fig. 3.

Respiratory pathogen spectrum of 1317 patients from different departments. The detection frequency indicated on the Y-axis represents the total number of positive respiratory pathogens detected. A. The detection frequency on the Y-axis represents the total number of positive samples. B. Y-axis indicates the detection rate of positive samples

Differences in the pathogen spectrum among patients in different age groups

The distribution of pathogens exhibited distinct patterns across different age groups. In infants and young children (0–4 years), viral pathogens predominated, with HRV, RSV, and HAdV being the most prevalent, collectively accounting for 78.42%. The spectrum shifted markedly in the 5–14 year age group, where M. pneumoniae became the most frequently detected agent, representing 50.36% of cases, while the prevalence of RSV decreased substantially to 5.04%. Overall, among individuals aged 0–14 years, viral pathogens (including HRV, RSV, HAdV, and influenza virus) constituted the vast majority of detections. Bacterial pathogens were rarely identified in this demographic, with only one case of H. influenzae; other common bacteria such as S. pneumoniae, K. pneumoniae, S. aureus, and P. aeruginosa were not detected. In contrast, bacterial pathogens were predominant in adults aged 15–59 years and those ≥ 60 years. The leading pathogens in the 15–59 year age group were S. pneumoniae and H. influenzae. A similar profile was observed in the ≥ 60 years cohort, with S. pneumoniae and H. influenzae being the most prevalent. As depicted in Fig. 4.

Fig. 4.

Respiratory pathogen spectrum of 1317 patients in different age groups. The detection frequency indicated on the Y-axis represents the total number of positive respiratory pathogens detected. A. The detection frequency indicated on the Y-axis represents the total number of positive samples. B. Y-axis indicates the detection rate of positive samples

Differences in the pathogen spectrum among patients in different months of visits

The months with higher detection frequencies of pathogens were concentrated from January to April and December, whereas they were relatively lower from May to November, accounting for only 34.38% of the total. The months with the highest detection frequencies were February, January, and March. Among the 11 detected pathogens, IAV showed a low-level epidemic trend from January to November, with a sharp increase in December, whereas IBV exhibited the opposite trend, peaking in January and gradually declining thereafter, maintaining a low-level epidemic trend throughout the rest of the year. HAdV, RSV, and M. pneumoniae followed a similar trend to IBV, primarily concentrated at the beginning of 2024, with lower detection rates after March. Rhinoviruses had the highest detection rate in January, followed by a fluctuating decline until September, with zero detections in September, and then an increasing trend starting in October, reaching another peak in December, although this peak did not exceed the January peak. The monthly distribution of bacterial pathogens did not show the same clear seasonal concentration trends as those of the viral pathogens. H. influenzae gradually increased in the first three months of 2024, reaching a peak in March before declining and maintaining a relatively stable epidemic trend from May onward. K. pneumoniae showed an increasing trend from January to April, peaking in April, followed by a continuous decline and a fluctuating epidemic trend after May. S. pneumoniae showed a fluctuating epidemic trend throughout the year with no obvious seasonality, with two small peaks in March and December. P. aeruginosa followed a fluctuating epidemic trend from January to October with no clear seasonality, reaching only a small peak in November. S. aureus had relatively low detection rates, maintaining a low-level epidemic trend throughout the year. As presented in Fig. 5.

Fig. 5.

Detection of respiratory pathogens in 1317 patients during different months of visits. Each image illustrates the temporal distribution of pathogens. The detection frequency indicated on the Y-axis represents the total number of positive respiratory pathogens detected in the corresponding month

Multiple pathogens coexist: distribution and frequency patterns

Among all patients with multiple pathogens detected, 366 individuals were infected with two pathogens, and we identified a total of 32 combinations. Specifically, bacterial-bacterial, bacterial-viral, and viral-viral mixed infections occurred 250, 62, and 54 times, respectively. The combination of S. pneumoniae and H. influenzae was the most common bacterial–bacterial mixed infection, occurring 108 times. Apart from 14 cases involving P. aeruginosa and K. pneumoniae, all combinations included either S. pneumoniae or H. influenzae. The most common combination in bacterial-viral mixed infections was M. pneumoniae and HRV, with 17 occurrences. Additionally, combinations related to M. pneumoniae were the most common, occurring 37 times. Furthermore, bacterial-viral mixed infections associated with influenza viruses were also prevalent, specifically with IAV and IBV, occurring 23 times. In mixed viral-viral infections, the combination of IAV and IBV was the most common, with 17 occurrences, followed by HAdV and HRV coinfections, which occurred 12 times.

A total of 76 individuals tested positive for three pathogens in their bodies, resulting in a total of 21 combinations, including 53 instances of mixed bacterial positive, 20 instances of mixed bacterial-viral positive, and 3 instances of mixed viral positive. The most frequently observed combination was a positive for S. pneumoniae, H. influenzae, and K. pneumoniae, occurring 31 times, followed by S. pneumoniae, P. aeruginosa, and K. pneumoniae mixed positive, a total of 10 times. Other combinations were relatively rare. Bacterial pathogens were identified 188 times, with S. pneumoniae being the most common, accounting for 61 occurrences, whereas viral pathogens were identified 40 times, with HRV showing a significantly greater occurrence than other viral pathogens and influenza virus having a relatively lower occurrence. Among the three individuals infected with the four pathogens, all exhibited mixed bacterial-viral infections, specifically those involving IAV. For detailed information, refer to Table 3: Distribution of pathogens detected in patients.

Table 3.

Distribution of pathogens detected in patients

Single and dual pathogen positive (872 and 366 cases)
	S. pneumoniae	H. influenzae	K. pneumoniae	P. aeruginosa	S. aureus	IAV	IBV	HRV	RSV	HAdV	M. pneumoniae
S. pneumoniae	206	108	55	29	2	4	4	2	0	0	0
H. influenzae	-	134	25	15	2	3	1	5	0	0	0
K. pneumoniae	-	-	83	14	0	3	0	0	0	0	0
P. aeruginosa	-	-	-	60	0	0	2	0	0	1	0
S. aureus	-	-	-	-	4	0	0	0	0	0	0
IAV	-	-	-	-	-	109	17	5	1	1	0
IBV	-	-	-	-	-	-	56	2	3	3	6
HRV	-	-	-	-	-	-	-	69	3	12	17
RSV	-	-	-	-	-	-	-	-	46	7	2
HAdV	-	-	-	-	-	-	-	-	-	33	12
M. neumoniae	-	-	-	-	-	-	-	-	-	-	72
Triple pathogen positive (76 cases)
Combination name				frequency	Combination name						frequency
S. pneumoniae & H. influenzae & K. pneumoniae				31	S. pneumoniae & HRV & IBV						1
S. pneumoniae & H. influenzae & P. aeruginosa				7	S. pneumoniae & HRV & M. pneumoniae						1
S. pneumoniae & H. influenzae & HRV				3	K. pneumoniae & RSV & HAdV						1
S. pneumoniae & H. influenzae & IAV				1	K. pneumoniae & P. aeruginosa & H. influenzae						3
S. pneumoniae & H. influenzae & HAdV				1	HRV & RSV & M. pneumoniae						3
S. pneumoniae & H. influenzae & M. pneumoniae				1	HRV & RSV & HAdV						2
S. pneumoniae & P. aeruginosa & K. pneumoniae				10	HRV & RSV & IBV						1
S. pneumoniae & P. aeruginosa & IAV				1	HRV & M. pneumoniae & HAdV						2
S. pneumoniae & P. aeruginosa & M. pneumoniae				1	HRV & M. pneumoniae & IAV						1
S. pneumoniae & IAV & K. pneumoniae				2	HAdV & RSV & M. pneumoniae						2
S. pneumoniae & IAV & IBV				1
Quadruple pathogen positive (3 cases)
Combination name					frequency
S. pneumoniae & H. influenzae & HRV & IAV					1
P. aeruginosa & H. influenzae & IAV & IBV					1
P. aeruginosa & K. pneumoniae & S. aureus & IAV					1

Discussion

To systematically clarify the etiological composition, epidemiological features, and coinfection patterns in this area, this study retrospectively analyzed clinical data from 2,132 hospitalized patients with respiratory infections in a hospital in Lianyungang city, Jiangsu Province. The results not only offer vital information for the clinical diagnosis, management, and prevention of respiratory infections in Lianyungang but also offer a solid scientific basis for developing public health initiatives in regions with comparable climates.

According to this study, the most common bacterial pathogens among hospitalized patients were S. pneumoniae, H. influenzae, and K. pneumoniae. These findings aligned with the global pathogen spectrum of community-acquired pneumonia (CAP). Numerous studies have confirmed that S. pneumoniae is the most prevalent bacterial pathogen of CAP [10, 11]. However, the detection rate of H. influenzae in Lianyungang was significantly higher than that reported in some northern Chinese regions. This could be due to differences in antibiotic prescribing practices or local climatic factors, such as higher humidity in the temperate monsoon climate [12]. Additionally, viral pathogens accounted for a relatively high proportion of positive among pediatric and infectious disease departments (e.g., 75.60% of pediatric patients were infected with viruses), which is in line with the traits of children’s developing immune systems and vulnerability to respiratory viruses [13]. Notably, L. pneumophila was not detected in this investigation, possibly due to rigorous local water management or limitations in diagnostic sensitivity, warranting further validation with environmental surveillance data.

There were significantly more male patients than female patients, and male patients had a higher S. pneumoniae detection rate. Additional confirmation is needed in lifestyle-based studies, as this discrepancy may be linked to factors such as men’s higher smoking rates and occupational exposure, such as dust exposure [5, 14]. Elderly patients (≥ 60 years) constituted 56.8% of the cases and were predominantly infected with bacterial pathogens, likely due to age-related immune decline and comorbidities such as chronic obstructive pulmonary disease [15]. On the other hand, rhinovirus, RSV, and HAdV were the most common infections in infants and toddlers aged 0–4 years, which is consistent with worldwide patterns in pediatric viral infections [16]. A notable spike in M. pneumoniae detection was observed among children aged 5–14 years, suggesting that this group represents a high-risk population requiring targeted prevention in school settings [17]. Seasonal analysis revealed a winter peak in hospital visits, which was consistent with increased pathogen transmission in crowded, poorly ventilated indoor environments during colder months [18, 19]. In contrast to reports of winter bacterial infection surges, there were no discernible seasonal variations in S. pneumoniae prevalence, suggesting that complex local drivers, such as air quality or vaccination coverage, warrant further exploration [20]. The IBV rate peaked in January, whereas the IAV rate increased in late winter, potentially because of viral mutation cycles and the timing of vaccination [21].

Co-detection of two or more pathogens was identified in 33.79% (445/1,317) of the patients, and bacterial–bacterial mixed positive were the most prevalent, with S. pneumoniae and H. influenzae (108 cases) accounting for the majority. This pattern is similar to the results of a national surveillance study in China [12]. However, the proportion of bacterial–viral mixed positive in this study was significantly lower than that in regions with high viral prevalence, such as tropical regions, which may be related to the relatively mild climate in Lianyungang [22]. Notably, there were 17 cases of mixed positive with IAV and IBV, which may present with more severe systemic symptoms, and the public health risk of viral recombination should be considered [23]. Additionally, the frequent occurrence of M. pneumoniae in mixed positive suggests that coinfection with viruses may exacerbate respiratory symptoms, which calls for clinical attention [24].

Significant clinical and public health ramifications result from these findings, which offer reference value for the clinical management of mixed infections, seasonal prevention and control strategies, and empirical treatment strategy optimization. The distribution of pathogens among hospitalized patients in the Lianyungang region varied significantly across departments and age groups. For example, viral and M. pneumoniae infections are predominant in pediatric patients, whereas bacterial pathogens are more common in respiratory medicine patients and elderly patients. This suggests that clinicians should adjust empirical treatment plans on the basis of patient age, department, and season: for elderly patients, coverage of S. pneumoniae and H. influenzae should be prioritized, whereas for pediatric patients, enhanced viral testing is warranted to curb antibiotic overuse [25–27]. Common patterns of coinfection between bacteria or between bacteria and viruses may increase the risk of antibiotic resistance and inflammatory reactions [28]. Multiple-pathogen combined testing is advised for high-risk patients, such as those with chronic lung diseases, and the common mixed combinations found in this study can serve as early warnings for clinical practice. Despite being uncommon, influenza coinfections necessitate different antiviral therapies, and more investigations into their molecular mechanisms are needed [29, 30]. The importance of seasonal interventions, such as influenza vaccination campaigns, improved outpatient screening, and public health messaging on indoor ventilation, is underscored by winter peaks [8, 31]. Furthermore, year-round S. pneumoniae prevalence underscores the need to expand pneumococcal vaccination among elderly individuals and monitor serotype dynamics for vaccine optimization [32, 33].

In this study, PCR technology was used to detect the pathogen spectrum of hospitalized patients with pneumonia, and traditional bacterial culture was not used as a parallel reference, which is one of the limitations of this study. Traditional microbial culture is regarded as the “gold standard” for etiological diagnosis, which has irreplaceable value in evaluating the sensitivity/specificity of a new detection method [34, 35]. However, for patients with acute and critical pneumonia, especially those who have received empirical antibiotic treatment before or at the initial stage of admission, traditional culture methods have inherent limitations. The pre-use of antibiotics will significantly inhibit the growth of pathogens in vitro, resulting in false negative culture results, thus seriously underestimating the true detection rate of pathogens [relevant literature can be cited here]. In contrast, nucleic acid amplification technology does not depend on the activity and culturability of pathogens, and can directly detect the genetic material of pathogens in samples, so it is often superior to culture method in detection sensitivity, especially suitable for hospitalized pneumonia people who have been partially treated in this study. Although all samples were not synchronized in this study, the commercial multiplex real-time PCR kit we used has been proved to be highly accurate and reliable in many prospective studies [36–38]. It should be noted that the sequences of primers and probes used in the commercial PCR kits used in this study are proprietary information of the manufacturer and have not been disclosed. However, their detection performance has been verified in the public literature [36–38]. More importantly, from the perspective of clinical practice, the results of this study highlight the irreplaceable value of molecular detection technology for early diagnosis and treatment of hospitalized pneumonia patients. Bacterial culture usually takes 48 to 72 h to get the final result, and the delay of this time window poses a great challenge to the initial empirical antibiotic selection of patients with severe pneumonia. The PCR detection used in this study can quickly identify more than ten common respiratory pathogens in a few hours. This kind of rapid etiological information can greatly assist clinicians to realize the early transition from broad-spectrum empirical treatment to targeted precise treatment. This is not only expected to improve the prognosis of patients and reduce the abuse of antibiotics, but also in line with the core principles of current antibacterial drug management.

Our findings can be contextualized with a recent study conducted in inland central China [39]. While that survey provided valuable insights into the epidemiological and etiological characteristics of severe acute respiratory infections, our study from eastern coastal cities reveals both complementary and distinct patterns. The geographical disparity—inland versus coastal—likely underlies differences in climate and living habits, which are known factors influencing the circulation of respiratory pathogens. Consequently, the variations in our respective pathogen profiles are not contradictory but rather enrich the geographical nuance necessary for comprehensive public health planning. Furthermore, our study’s focus on hospitalized patients presenting with a broader range of respiratory symptoms or related diagnoses may offer a more immediate and practical reference for frontline clinicians in hospital settings, facilitating timely diagnosis and treatment.

A regrettable limitation of this study lies in the absence of common drug resistance gene markers in the molecular detection methods used. Consequently, while our findings can guide initial empirical antibiotic treatment decisions, they cannot provide precision-based treatment recommendations based on drug resistance genes. The lack of real-time drug resistance data means clinicians still rely on traditional bacterial culture and phenotypic susceptibility testing (which typically require an additional 24–48 h) to finalize antibiotic gradient reduction or adjustment plans. This may delay optimal treatment timing. Future research should focus on developing and validating detection protocols that integrate critical drug resistance genes from local epidemiology, aiming to deliver more comprehensive microbiological information to clinicians at the initial diagnostic stage.

This study is limited by the unavailability of detailed clinical data corresponding to pathogen findings, including information on symptoms, imaging specifics, and severity scores. Consequently, we could not definitively discriminate between true pathogens and colonizing bacteria, nor explore correlations between specific pathogens and clinical presentations. This constraint somewhat hampers the translation of our results into directly actionable insights for personalized therapeutic decision-making. This limitation highlights a clear need for future prospective studies that systematically integrate clinical data with molecular testing. By correlating pathogen findings with clinical phenotypes (e.g., community-acquired vs. hospital-acquired pneumonia, disease severity), such work would ultimately facilitate a transition from etiological diagnosis to precision clinical management.

Conclusion

This study delineates the etiological spectrum and coinfection patterns of respiratory infections among hospitalized patients in eastern China, emphasizing bacterial predominance, elderly vulnerability, and seasonal transmission dynamics. The research results provide an important basis for optimizing local diagnosis and treatment plans and formulating seasonal prevention and control strategies. In the future, multidisciplinary collaboration is essential to deepen mechanism research and foster precision medicine in respiratory infection management.

Author contributions

Feihu Shen is involved in research design, data collection and collation, statistics analysis and paper writing. Haipeng Li is involved in research design, data collection, paper revision and fund support. Shoujuan Chen, Min Wang and Hanhan Li participated in data collection, and paper revision. Xin Zhou and Xingyu Mu participated in statistics analysis, Jialing Zhang is involved in fund support and paper revision. Lei Xu participated in paper revision, submission and fund support.

Funding

This work was supported by Key R&D Program of the City (Social Development), Lianyungang Health Commission [Grant No. SF2419] and the Lianyungang Health Science and Technology Project [Grant No. 202428]. The funding body had no role in the design, collection, analysis and interpretation of data and in the writing of the manuscript.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The present study was performed according to the Declaration of Helsinki or relevant guidelines and regulations, and ethics approval was obtained from the Ethics Committees of Guan Yun people’s hospital. As a retrospective study, informed consent were obtained from all patients or the patient’s legal guardian(s) and/or their parents prior to study commencement. Information concerning the participants was kept confidential and specimens collected from them were used only for the intended purposes.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.