Impact of coronavirus disease 2019 nonpharmaceutical interventions on influenza epidemiology: A retrospective analysis in Hebei Province,China from 2019 to 2023

Abstract

Background

In this study we retrospectively analyzed the influenza virus antigen detection data from Hebei General Hospital for the period from 2019 to 2023, focusing on the impact of coronavirus disease 2019 nonpharmaceutical interventions on the epidemiology of influenza.

Methods

A retrospective analysis was conducted on 45,956 records of patients with influenza-like illness retrieved from the Laboratory Information System of Hebei General Hospital between January 2019 and December 2023. Nasal and pharyngeal swab samples from these patients were tested for influenza A and influenza B virus antigens using colloidal gold immunochromatographic assay.

Results

Compared with the pre-pandemic period, both number of influenza cases and positive detection rates declined substantially during the coronavirus disease 2019 pandemic. Notably, this decline coincided with the peak implementation of nonpharmaceutical interventions aimed at mitigating coronavirus disease 2019 spread. The cases of influenza surged significantly following the relaxation of coronavirus disease 2019 restrictions in 2023 and even approached or exceeded the pre-coronavirus disease 2019 levels.

Conclusions

Our findings provide strong evidence that comprehensive nonpharmaceutical interventions implemented to control the transmission of severe acute respiratory syndrome coronavirus-2 also profoundly suppressed seasonal influenza epidemics. The sharp rebound of influenza activity after the relaxation of restrictions underscores the need for integrated preparedness strategies to manage concurrent threats from multiple respiratory viruses.

Keywords

Nonpharmaceutical interventions influenza coronavirus disease 2019 epidemiology

Introduction

Severe acute respiratory syndrome coronavirus-2¹ (SARS-CoV-2)-induced coronavirus disease 2019 (COVID-19) emerged in late 2019; the World Health Organization declared it a public health emergency of international concern on 30 January 2020 and a pandemic on 11 March 2020.^1–2 This prompted the adoption of nonpharmaceutical interventions (NPIs), including mask use, hand hygiene, isolation of infected individuals, and vaccination,³ with China implementing containment measures and a dynamic zero-case policy until 26 December 2022 when COVID-19 management was downgraded to Class B to resume social activities.⁴ Stringent NPIs effectively reduced COVID-19 transmission^5–10 and also impeded the spread of other respiratory viruses such as influenza, respiratory syncytial virus (RSV), and adenovirus.^10–13

Influenza, a globally prevalent and highly infectious respiratory pathogen, poses a major public health threat; it affects approximately 1 billion people annually, causes 300,000–500,000 deaths, and can lead to acute respiratory disease, pneumonia, multi-organ complications, or death, with children and older adults being particularly vulnerable.^14–20

Of the four influenza types (A, B, C, D), influenza A (Flu A) is classified into two subtypes, based on the presence of hemagglutinin (HA) or neuraminidase (NA) proteins, while influenza B (Flu B) belongs to either the B/Yamagata or B/Victoria lineage;^21,22 the A/HA type 1 and NA type 1 (H1N1)pdm09, A/HA type 3 and NA type 2 (H3N2) subtypes, and both Flu B lineages drive global seasonal epidemics, affecting 5%–20% of the population annually.²³

Our previous findings have indicated that COVID-19-related NPIs significantly inhibit influenza prevalence, evidenced by the fact that 2023’s relaxation of restrictions triggered a severe resurgence of influenza, resulting in a prevalence rate that exceeded the pre-pandemic levels, underscoring the complex interaction between public health interventions and disease dynamics. To explore influenza epidemic patterns across the pandemic period, we conducted a retrospective analysis in Shijiazhuang, China, covering the pre-pandemic (January 2019), peak COVID-19 restrictions (2020–2022), and post-relaxation (2023) periods. This study aimed to document the epidemiological changes in influenza occurrence before, during, and after the COVID-19 pandemic and provide updated insights to guide public health strategies.

Materials and methods

This study has been approved by the Medical Ethics Committee of Hebei General Hospital. Ethics Lot Number: No. 2024-LW-0210. All the participating patients signed informed consent forms, and the study process strictly adhered to the relevant ethical guidelines of the Declaration of Helsinki (1975, as revised in 2024).

From January 2019 to December 2023, both hospitalized patients and outpatients with influenza-like illness (ILI) who underwent influenza antigen testing at Hebei General Hospital were enrolled in our study. In this study, ILI was defined strictly according to the relevant standards of the World Health Organization (WHO) and the Chinese Center for Disease Control and Prevention: fever (body temperature ≥38°C), accompanied with either cough or sore throat, excluding other definite nonrespiratory infection etiologies. The testing criteria remained consistent throughout the study period (January 2019 to December 2023) and were not adjusted across epidemic phases (e.g. implementation or relaxation of NPIs). Only patients who fulfilled the criteria mentioned in the above ILI definition among those seeking medical care were uniformly tested for respiratory pathogens, ensuring comparability of data across different periods and reliability of the study results. They were stratified into six age categories to examine age-related trends in influenza incidence: (a) infants (<1 year); (b) toddlers (1–3 years); (c) preschool children (3–6 years); (d) school children (6–14 years); (e) adults (14–60 years); and (f) older individuals (>60 years). The positivity rate was determined by dividing the number of confirmed positive cases by the total number of cases tested.

Hebei General Hospital is located in Shijiazhuang City, Hebei Province. Shijiazhuang, the capital of Hebei Province in Northern China, has a temperate, continental, monsoon climate with four distinct seasons. In 2023, Hebei General Hospital was equipped with 2206 beds and documented 1.39 million visits to its outpatient and emergency departments in addition to 97,545 patient discharges.

Flu A and Flu B antigens were detected using a commercially available Colloidal Gold Immunochromatographic Assay kit (Abbott (Shanghai) Diagnostic Products Sales Co., Ltd., 0000828899), which has been certified by the National Medical Products Administration (NMPA) of China. The specific information is as follows: Abbott colloidal gold assay kit, batch numbers: 20210409, 20220618, and 20230205, with a shelf life of 18 months, corresponding to expiration dates: 8 October 2022, 17 December 2023, and 4 August 2024, respectively. To ensure the accuracy and reliability of the detection results, laboratory quality control (QC) was strictly enforced in accordance with the kit instructions and International Organization for Standardization (ISO) 15189 laboratory quality standards, with specific steps including the following:

Pre-experiment QC. Before using each batch of kits, the batch number, expiration date, and packaging integrity were verified. The kit was used only if there was no damage or contamination.

Intra-experiment QC. Negative controls (no template nucleic acid), positive controls (kit-matched standard products), and internal controls (human RNase P gene) were set synchronously for each batch of detection to ensure the effectiveness of the nucleic acid extraction and amplification processes.

Post-experiment QC. The detection results of the batch were deemed valid only if the negative control showed no amplification, amplification curve of the positive control met the threshold requirements, and positive rate of the internal control was ≥95%; otherwise, the detection process was performed again.

Regular calibration. The detection instrument (real-time fluorescent quantitative polymerase chain reaction (PCR) instrument, model: Applied Biosystems 7500) was calibrated by the manufacturer every quarter, and all experimental personnel received professional training and held certificates to ensure standardized operations.

All relevant QC data were stored in the laboratory database and are available for verification. The specific experimental procedure for antigen detection was as follows. Nasopharyngeal swab specimens were collected and immediately subjected to viral lysis buffer. The lysate was then applied to the test strip, which contained specific antibodies against Flu A and Flu B nucleoproteins. Formation of visible lines indicated a positive result for either Flu A or Flu B, while their absence signified a negative result.

Diagnostic accuracy

Flu A. 71.00% sensitivity (positive percent agreement (PPA)), 99.33% specificity (negative percent agreement (NPA)), 92.25% concordance, and κ = 0.77 (substantial agreement).

Flu B. 90.00% sensitivity (PPA), 100.00% specificity (NPA), 98.75% concordance, and κ = 0.94 (almost perfect agreement).

Analytical sensitivity

1.25 × 10² TCID₅₀/test (Flu A, A2/Aichi/2/68 H3N2); 5 × 10¹ TCID₅₀/test (Flu B, Hong Kong/72).

Specimen consistency

94.2% (Flu A, n = 121) and 99.5% (Flu B, n = 91) concordance between fresh/frozen swabs.

Specificity

No cross-reactivity with common respiratory viruses/bacteria.

Statistical analyses

Categorical variables were presented as counts and percentages; positive rates were calculated as the proportion of positive cases among total tested cases. The age distribution was non-normal; therefore, we calculated the median and interquartile range (IQR) values. Data analyses were performed using Statistical Package for Social Sciences (SPSS) software (version 25.0), and visualization was performed used GraphPad Prism 9.3.1. The significance was assessed using Pearson’s chi-square test or Fisher’s exact test; p-value <0.05 was considered statistically significant.

To correct for the seasonal pattern of influenza transmission, a periodic decomposition model (additive model) in time series analysis was used to separate seasonal trends, long-term trends, and random fluctuations. Meanwhile, a generalized additive model (GAM) was constructed, with “month (categorical variable)” included as a seasonal term to control for confounding factors such as year, age, and sex. The formula was: logit(positivity rate) = year + age group + sex + s(month, k = 12) + error term, where s(month) is a smooth function capturing seasonal fluctuations.

The Kruskal–Wallis H test was used to compare age distribution differences from 2019 to 2023, and the Dunn test was used for pairwise comparisons after the test.

Sensitivity analyses

To correct for the impact of reduced hospital visits and biased visiting population structure (e.g. reduced mild cases) caused by NPIs during 2020–2022 on the positivity rate, the “standardized positivity rate based on 2019 visit volume” was adopted for correction. The following formula was used: standardized positivity rate = Σ (positivity rate of each age group × proportion of visits in that age group in 2019).

Sample handling, storage conditions, and testing timeline

Nasopharyngeal swab specimens were collected by trained medical staff using sterile swabs. Immediately after collection, the swabs were immersed in 3 mL of viral lysis buffer and vortexed for 10 s to ensure complete elution of the virus. Specimens were stored at 2°C–8°C if tested within 4 h of collection; otherwise, they were frozen at −20°C and thawed only once before testing. All tests were performed within 24 h of specimen collection to minimize viral antigen inactivation. The testing timeline was as follows: specimen collection (T0), transport to the laboratory (within 1 h of T0), sample processing (within 2 h of T0), assay execution (within 4 h of T0), and result recording (within 30 min of assay completion). QC was performed for each batch of tests, including the use of positive and negative control samples provided in the kit to verify the validity of each assay run.

Results

General characteristics and annual change trends of the enrolled patients

Table 1 presents the general characteristics of the enrolled patients. In total, 45,956 patients with ILI who underwent Flu antigen testing were enrolled in our study. Of these, 23,839 (51.87%) were male and 22,117 (48.13%) were female, with a male-to-female ratio of 1.08. There were more male than female patients each year, with male-to-female ratios of 1.03, 1.09, 1.17, 1.35, and 1.07 during the period from 2019 to 2023, respectively. The largest number of patients (21,978) were adults aged 14–60 years, followed by 9039 patients aged >60 years, 6833 aged 6–14 years, 4908 aged 3–6 years, 2479 aged 1–3 years, and 719 aged <1 year. The median patient age was 26 (IQR: 10–45) years. The median age and IQR values for each year from 2019 to 2023 were 10 (IQR: 3–28) years, 31 (IQR: 15–52) years, 33 (IQR: 18–55) years, 24 (IQR: 8–42) years, and 28 (IQR: 12–48) years, respectively.

Table 1.

Epidemiological characteristics of Flu A and Flu B detected in ILI patients from 2019 to 2023.

Demographic		Total enrolled patients				Pre-COVID-19				COVID-19				COVID-19								Post- COVID-19
Demographic		2019–2023				2019				2020				2021				2022				2023
		Total	Positive n (%)	Positive for Flu An (%)	Positivefor Flu B n (%)	Total	Positive n (%)	Positive for Flu An (%)	Positive for Flu B n (%)	Total	Positive n (%)	Positive for Flu An (%)	Positive for Flu B n (%)	Total	Positive n (%)	Positive for Flu An (%)	Positive for Flu B n (%)	Total	Positive n (%)	Positive for Flu An (%)	Positive for Flu B n (%)	Total	Positive n (%)	Positive for Flu An (%)	Positive for Flu B n (%)
	Total	45956	12,150 (26.44)	10,591 (23.05)	1559 (3.39)	13346	4527 (33.92)	3793 (28.42)	734 (5.5)	8674	1053 (12.14)	768 (8.85)	285 (3.29)	3424	78(2.28)	1 (0.03)	77 (2.25)	1436	231 (16.09)	16 (1.11)	215 (14.97)	19076	6261 (32.82)	6013 (31.52)	248 (1.3)
Sex	Male	23,839	6231 (26.14)	5442 (22.83)	789 (3.31)	6768	2274 (33.6)	1908 (28.19)	366 (5.41)	4530	528 (11.66)	396 (8.74)	132 (2.91)	1845	43 (2.33)	1 (0.05)	42 (2.28)	826	135 (16.34)	12 (1.45)	123 (14.98)	9870	3251 (32.94)	3125 (31.66)	126 (1.28)
Sex	Female	22,117	5919 (26.76)	5149 (23.28)	770 (3.48)	6578	2253 (34.25)	1885 (28.66)	368 (5.59)	4144	525 (12.67)	372 (8.98)	153 (3.69)	1579	35 (2.22)	0 (0)	35 (0.22)	610	96 (15.74)	4 (0.66)	92 (15.08)	9206	3010 (32.7)	2888 (31.37)	122 (1.33)
Age group	Infant (<1)	719	209 (29.07)	189 (26.29)	20 (2.78)	401	99 (24.69)	86 (21.45)	13 (3.24)	140	36 (25.71)	31 (22.14)	5 (3.57)	1	0 (0)	0 (0)	0 (0)	14	3 (21.43)	1 (7.14)	2 (14.29)	163	71 (43.56)	71 (43.56)	0 (0)
	Toddler (1–3)	2479	856 (34.53)	752 (30.33)	104 (4.2)	1583	530 (33.48)	456 (28.81)	74 (4.67)	434	140 (32.26)	122 (28.11)	18 (4.15)	8	0 (0)	0 (0)	0 (0)	53	13 (24.53)	3 (5.66)	10 (18.87)	401	173 (43.14)	171 (42.64)	2 (0.5)
	Preschool children (3–6)	4908	2353 (47.94)	2015 (41.06)	338 (6.89)	2663	1233 (46.3)	998 (37.48)	235 (8.82)	529	236 (44.61)	195 (36.86)	41 (7.75)	22	7 (31.82)	0(0)	7 (31.82)	127	39 (30.71)	6 (4.72)	33 (25.98)	1567	838 (53.48)	816 (52.07)	22 (1.4)
	School children (6–14)	6833	2982 (43.64)	2516 (36.82)	466 (6.82)	2634	1237 (46.96)	1019 (38.69)	218 (8.28)	578	175 (30.28)	116 (20.07)	59 (10.21)	83	44 (53.01)	0(0)	44 (53.01)	218	89 (40.83)	4 (1.83)	85 (38.99)	3320	1437 (43.28)	1377 (41.48)	60 (1.81)
	Adults (14–60)	21978	4735 (21.54)	4125 (18.77)	610 (2.78)	4346	1164 (26.78)	976 (22.46)	188 (4.33)	5264	400 (7.6)	242 (4.6)	158 (3)	2637	27 (1.02)	1 (0.04)	26 (0.99)	703	80 (11.38)	1 (0.14)	79 (11.24)	9028	3064 (33.94)	2905 (32.18)	159 (1.76)
	Older individuals (>60,)	9039	1015 (11.23)	994 (11)	21 (0.23)	1719	264 (15.36)	258 (15.01)	6 (0.35)	1729	66 (3.82)	62 (3.59)	4 (0.23)	673	0 (0)	0 (0)	0 (0)	321	7 (2.18)	1 (0.31)	6 (1.87)	4597	678 (14.75)	673 (14.64)	5 (0.11)
Month	January	7104	1938 (27.28)	1485 (20.9)	453 (6.38)	2332	729 (31.26)	725 (31.09)	4 (0.17)	3508	1029 (29.33)	757 (21.58)	272 (7.75)	678	0 (0)	0 (0)	0 (0)	484	180 (37.19)	3 (0.62)	177 (36.57)	102	0 (0)	0 (0)	0 (0)
	February	4817	1112 (23.08)	1094 (22.71)	18 (0.37)	1720	468 (27.21)	463 (26.92)	5 (0.29)	1259	18 (1.43)	5 (0.4)	13 (1.03)	496	0 (0)	0 (0)	0 (0)	35	0 (0)	0 (0)	0 (0)	1307	626 (47.9)	626 (47.9)	0 (0)
	March	9406	3158 (33.57)	2990 (31.79)	168 (1.79)	1258	446 (35.45)	311 (24.72)	135 (10.73)	1007	1 (0.1)	1 (0.1)	0 (0)	505	0 (0)	0 (0)	0 (0)	280	32 (11.43)	0 (0)	32 (11.43)	6356	2679 (42.15)	2678 (42.13)	1 (0.02)
	April	4248	702 (16.53)	273 (6.43)	429 (10.1)	1642	603 (36.72)	182 (11.08)	421 (25.64)	707	0 (0)	0 (0)	0 (0)	365	3 (0.82)	0 (0)	3 (0.82)	262	5 (1.91)	0 (0)	5 (1.91)	1272	91 (7.15)	91 (7.15)	0 (0)
	May	2724	160 (5.87)	24 (0.88)	136 (4.99)	785	145 (18.47)	15 (1.91)	130 (16.56)	569	0 (0)	0 (0)	0 (0)	524	6 (1.15)	0 (0)	6 (1.15)	124	0 (0)	0 (0)	0 (0)	722	9 (1.25)	9 (1.25)	0 (0)
	June	1044	26 (2.49)	5(0.48)	21 (2.01)	216	12 (5.56)	1 (0.46)	11 (5.09)	70	2 (2.86)	2 (2.86)	0 (0)	434	11 (2.53)	1 (0.23)	10 (2.3)	23	0 (0)	0 (0)	0 (0)	301	1 (0.33)	1 (0.33)	0 (0)
	July	507	4 (0.79)	1(0.2)	3 (0.59)	66	1 (1.52)	0 (0)	1 (1.52)	62	0 (0)	0 (0)	0 (0)	141	2 (1.42)	0 (0)	2 (1.42)	15	0 (0)	0 (0)	0 (0)	223	1 (0.45)	1 (0.45)	0 (0)
	August	465	4 (0.86)	4(0.86)	0 (0)	35	0 (0)	0 (0)	0 (0)	88	0 (0)	0 (0)	0 (0)	76	0 (0)	0 (0)	0 (0)	56	3 (5.36)	3 (5.36)	0 (0)	210	1 (0.48)	1 (0.48)	0 (0)
	September	418	3 (0.72)	3(0.72)	0(0)	59	0 (0)	0 (0)	0 (0)	80	0 (0)	0 (0)	0 (0)	36	0 (0)	0 (0)	0 (0)	42	3 (7.14)	3 (7.14)	0 (0)	201	0 (0)	0 (0)	0 (0)
	October	876	33 (3.77)	30 (3.42)	3 (0.34)	79	2 (2.53)	2 (2.53)	0 (0)	410	2 (0.49)	2 (0.49)	0 (0)	26	1 (3.85)	0 (0)	1 (3.85)	52	5 (9.62)	5 (9.62)	0 (0)	309	23 (7.44)	21 (6.8)	2 (0.65)
	November	3699	1080 (29.2)	1046 (28.28)	34 (0.92)	267	12 (4.49)	12 (4.49)	0 (0)	495	1 (0.2)	1 (0.2)	0 (0)	15	0 (0)	0 (0)	0 (0)	28	3 (10.71)	2 (7.14)	1(3.57)	2894	1064 (36.77)	1031 (35.63)	33 (1.14)
	December	10648	3930 (36.91)	3636 (34.15)	294 (2.76)	4887	2109 (43.16)	2082 (42.6)	27 (0.55)	419	0 (0)	0 (0)	0 (0)	128	55 (42.97)	0 (0)	55 (42.97)	35	0 (0)	0 (0)	0 (0)	5179	1766 (34.1)	1554 (30.01)	212 (4.09)

Values presented in this table are numbers and positive rates (%, 95% CI).

COVID-19: coronavirus disease 2019; ILI: influenza-like illness; Flu A: influenza A; Flu B: influenza B; CI: confidence interval.

Differences in patient age distribution among different years were statistically significant (H = 128.67, p < 0.001). Pairwise comparison showed that the patient age in 2019 was significantly lower than that in 2020 (p < 0.001) and 2021 (p < 0.001), and the age in 2023 was significantly higher than that in 2022 (p = 0.023). There were no significant differences during other years (p > 0.05).

The annual number of ILI patients seeking care varied, notably influenced by the COVID-19 pandemic and subsequent NPIs. In 2019, before the COVID-19 pandemic, a total of 13,346 patients presenting with ILI sought medical care at Hebei General Hospital. As illustrated in Figure 1(a), the number of ILI patients in 2019 showed a clear seasonal pattern, with peaks in winter and spring 2019, and the trend persisted until January 2020 before NPIs were implemented. In response to the emergence of the COVID-19 epidemic, Hebei Province implemented a level I response to major public health emergencies and enforced stringent NPIs on 25 January 2020.²⁴ Subsequently, there has been a substantial decline in patient numbers since February 2020. By 2020, this figure dropped dramatically to 8647 cases, further declining to 3424 in 2021, and reaching an extremely low point of 1436 in 2022, with no discernible seasonal epidemic trend observed during the 3-year period from 2020 to 2022. On 6 December 2022, COVID-19 management measures were downgraded to Class B and social activities had resumed, resulting in a notable resurgence of patients with ILI seeking hospital-based care in 2023. The number of patients increased to 19,076, surpassing the pre-COVID-19 pandemic level observed in 2019. The seasonal pattern observed in 2019 was repeated during the spring and winter of 2023. Specifically, 6356 and 5179 ILI cases were recorded in March and December, respectively, surpassing the corresponding numbers in 2019 (Table 1, Figure 1(a)).

Figure 1.

Number of enrolled patients (overall and positive cases) in each month from 2019 to 2023. (a) Monthly trends for total number of cases (unit: case) and total positivity rate (unit: %); error bars represent 95% confidence intervals (CIs); (b) Monthly trends for number of cases (unit: case) and positivity rate (unit: %) of Flu A; error bars represent 95% CIs; (c) Monthly trends for number of cases (unit: case) and positivity rate (unit: %) of Flu B; error bars represent 95% CIs. Flu A: influenza A; Flu B: influenza B.

The overall positive detection rate for influenza from 2019 to 2023 was 26.44% (95% CI: 26.12%–26.76%). In 2019, prior to the epidemic, the detection rate was 33.92% (95% CI: 33.01%–34.83%). This rate significantly declined during the epidemic period, with a rate of 12.14% (95% CI: 11.45%–12.83%) in 2020, 2.28% (95% CI: 1.81%–2.75%) in 2021, and 16.09% (95% CI: 14.58%–17.60%) in 2022. During the pandemic period from 2020 to 2022, the number of ILI tests performed remained consistently low. The smaller testing base may have influenced the probability of detecting positive cases to some extent. Therefore, the decline in the positivity rate during this period reflects not only the direct suppressive effect of NPIs on influenza transmission but is also associated with the significant reduction in the size of the tested population. In contrast, the number of tests surged to 19,076 in 2023, representing a 42.9% increase compared with 2019. The larger testing base provided greater opportunity for detecting positive cases, and its positivity rate of 32.82% (95% CI: 32.15%–33.49%) was close to the 33.92% observed in 2019. This result, after accounting for the interference of differences in testing volume, more accurately reflects the rebound in influenza transmission dynamics. The peak detection rates in 2019 and 2023 were observed during the winter and spring seasons. However, during the period from 2020 to 2022, the peak positive rate was recorded only once, in the winter of 2021, with no consistent seasonal epidemic trend evident throughout these 3 years (Table 1, Figure 1(a)).

Annual trends in the prevalence of Flu A and Flu B

Flu A is characterized by numerous subtypes, a propensity for antigenic drift and shift, a broad host range (capable of infecting humans, birds, pigs, and other animals), and general susceptibility in the human population, associated with a relatively high rate of severe illness. In contrast, Flu B viruses typically cause localized outbreaks or seasonal epidemics and generally do not trigger pandemics; Flu B often presents with comparatively milder clinical symptoms and is associated with lower rates of severe illness and mortality. It should be specifically noted that the respiratory pathogen detection kit used in this study only supports the identification of Flu A and Flu B at the species level and cannot be used to perform subtype classification. Therefore, subtype data for both Flu A (e.g. H1N1 and H3N2) and Flu B (e.g. Victoria lineage and Yamagata lineage) could not be obtained. This limitation may have affected the in-depth analysis of epidemic regularity and variation characteristics of the two types of influenza viruses; relevant explanation has been added in the corresponding paragraph of the Discussion section.

As illustrated in Table 1 and Figure 1(b), before the global spread of the COVID-19 pandemic in 2019, the hospital documented 3793 (28.42%, 95% CI: 27.56%–29.28%) patients who tested positive for Flu A, exhibiting a clear seasonal trend with peak incidences during the winter and spring. However, following the onset of the COVID-19 pandemic and the subsequent implementation of NPIs, there was a dramatic decrease in Flu A cases. By 2020, the number of cases had fallen to 768 (8.85%, 95% CI: 8.23%–9.47%), mainly concentrated in January when NPIs had not been implemented, with further decline to just 1 case (0.03%, 95% CI: 0.00%–0.17%) in 2021 and 16 cases (1.11%, 95% CI: 0.64%–1.79%) in 2022. In 2023, after the relaxation of NPIs, there was a significant resurgence in Flu A infections, with the number of cases surging to 6013 (31.52%, 95% CI: 30.85%–32.19%), following a similar seasonal pattern as seen in 2019. This resurgence not only surpassed the figures from 2020 to 2022 but also exceeded the pre-pandemic levels of 2019.

In 2019, a total of 734 cases (5.50%, 95% CI: 5.12%–5.88%) of Flu B were detected, with a clear seasonal peak during the winter and spring months. In 2020, 285 cases (3.29%, 95% CI: 2.93%–3.65%) were detected, predominantly in January (272 cases). After the implementation of NPIs in February 2020, the number of positive cases and the positive rate significantly declined. Only 13 cases were reported in February 2020, and no positive cases were recorded from March to December 2020. In 2021, a total of 77 cases (2.25%, 95% CI: 1.78%–2.72%) were detected, mostly in December 2021 (55 cases). In 2022, 215 cases (14.97%, 95% CI: 13.36%–16.58%) were detected, primarily in January 2022 (177 cases) and March 2022 (32 cases). This indicates a small seasonal peak in Flu B infections during the winter of 2021 and the spring of 2022 despite the continued nationwide implementation of NPIs. After the relaxation of COVID–19 NPIs in 2023, 248 positive cases (1.30%, 95% CI: 1.14%–1.46%) were reported. No significant spring peak was observed, and the majority of positive cases were detected in November 2023 (33 cases) and December 2023 (212 cases), indicating a peak in infections only during the winter. The number of infections did not return to the 2019 levels (Table 1, Figure 1(c)).

Sex distribution and annual trends for the occurrence of Flu A and Flu B

From 2019 to 2023, Hebei General Hospital confirmed 10,591 cases of Flu A with a positive detection rate of 23.05% (95% CI: 22.73%–23.37%). For Flu B, there were 1559 cases with a positive detection rate of 3.39% (95% CI: 3.22%–3.56%), significantly lower than that of Flu A (p < 0.05). This indicates that Flu A was much more prevalent than Flu B.

Among the recorded influenza patients, 6231 were male and 5919 were female, resulting in a male-to-female ratio of 1.05. The positive detection rate was 26.14% (95% CI: 25.62%–26.66%) for males and 26.76% (95% CI: 26.22%–27.30%) for females, with no statistically significant difference (p > 0.05). For Flu A, 5442 patients were male and 5149 were female, yielding a male-to-female ratio of 1.06. The positive detection rate was 22.83% (95% CI: 22.39%–23.27%) for males and 23.28% (95% CI: 22.83%–23.73%) for females, again showing no statistically significant difference (p > 0.05). With respect to Flu B, 789 patients were male and 770 were female, with a male-to-female ratio of 1.02. The positive detection rate was 3.31% (95% CI: 3.10%–3.52%) for males and 3.48% for females, with no statistically significant difference (p > 0.05).

Both male and female patients showed similar trends in the annual changes in infection numbers and positivity rates for both types of influenza.

Flu A: Infection numbers and positive rates significantly decreased from 2020 to 2022 (p < 0.05). In 2023, there was a rebound, with both infection numbers and positive rates exceeding the 2019 levels (p < 0.05) (Figure 2(c) and (d)).

Figure 2.

Sex distribution and annual trends for the occurrence of Flu A and Flu B. (a) Total number of positive patients (unit: case); (b) total positivity rate (unit: %), error bars represent 95% Cis; (c) annual trends for number of Flu A cases (unit: case); (d) annual trends for positivity rate of Flu A (unit: %), error bars represent 95% Cis; (e) annual trends for the number of Flu B cases (unit: case); (f) annual trends for positivity rate of Flu B (unit: %), error bars represent 95% CIs. Flu A: influenza A; Flu B: influenza B; CIs: confidence intervals.

Flu B: Infection numbers for both males and females decreased from 2020 to 2022 (p < 0.05). In 2023, there was no significant rebound, and infection numbers remained below the 2019 levels (p < 0.05). Positive detection rates decreased significantly from 2020 to 2021 (p < 0.05). However, in 2022, there was a significant increase in the positive detection rates for both males and females, even with the ongoing implementation of NPIs for COVID-19. These rates surpassed those from 2019 and 2023 (p < 0.05) (Figure 3(e) and (f)).

Figure 3.

Age distribution and annual trends for the occurrence of Flu A and Flu B. (a) Total number of positive patients (unit: case); (b) total positivity rate (unit: %), error bars represent 95% Cis; (c) annual trends for the number of Flu A cases (unit: case); (d) annual trends for the positivity rate of Flu A (unit: %), error bars represent 95% Cis; (e) annual trends for the number of Flu B cases (unit: case); (f) annual trends for the positivity rate of Flu B (unit: %), error bars represent 95% CIs. Flu A: influenza A; Flu B: influenza B; CIs: confidence intervals.

Age distribution and annual trends for the occurrence of Flu A and Flu B

Figure 2(a) and (b) show that the number of positive cases and detection rates of Flu A and Flu B were not consistent across different age groups. The number of detected cases for both Flu A and Flu B was highest in adults (14–60 years); however, the rate of detection was highest in preschool children (3–6 years). This discrepancy may be due to the fact that Hebei General Hospital is a comprehensive tertiary hospital where the population of pediatric patients is significantly smaller than that of adult patients. The highest detection rates for both Flu A and Flu B were observed in preschool children (3–6 years) at 41.06% (95% confidence interval (CI): 40.01%–42.11%) and 6.89% (95% CI: 6.36%–7.42%), respectively, followed by those in school children (6–14 years) (36.82%, 95% CI: 35.80%–37.84% and 6.82%, 95% CI: 6.30%–7.34%, respectively), toddlers (1–3 years) (30.33%, 95% CI: 28.94%–31.72% and 4.20%, 95% CI: 3.65%–4.75%, respectively), and infants (<1 year) (26.29%, 95% CI: 23.15%–29.43% and 2.78%, 95% CI: 1.69%–3.87%, respectively). Adults (14–60 years) had a detection rate of 18.77% (95% CI: 18.34%–19.20%) for Flu A and 2.78% (95% CI: 2.59%–2.97%) for Flu B, while older individuals (≥60 years) had the lowest rates (11.00%, 95% CI: 10.38%–11.62% for Flu A and 0.23%, 95% CI: 0.14%–0.32% for Flu B). Both Flu A and Flu B exhibited a trend of higher positive detection rates in children aged <14 years compared with adults (14–60 years) and older individuals (≥60 years), indicating that children (0–14 years) were more susceptible to both Flu A and Flu B (p < 0.05).

Figure 3(c) and (d) show the annual trends for the number of positive cases and detection rates of Flu A in the groups of infants (<1 year), toddlers (1–3 years), and preschool children (3–6 years). After the implementation of NPIs for COVID-19 from 2020 to 2022, the number of infections showed a declining trend. After relaxation of the NPIs in 2023, there was a slight rebound; however, the numbers did not surpass those of 2019. In the groups of school children (6–14 years), adults (14–60 years) and older individuals (≥60 years), the number of infections also decreased from 2020 to 2022. However, in 2023, there was a significant resurgence, with the numbers exceeding those in 2019 (Figure 2(c)). In 2020, the positive rates for infants (<1 year), toddlers (1–3 years), and preschool children (3–6 years) were similar to those in 2019. This was largely because NPIs for COVID-19 were not in place in January 2020, with high rates of Flu A infection in children aged <6 years, leading to relatively high positive rates in this month and throughout this year. After February 2020, positive detection rates exhibited a marked decline until 2022. In school children (6–14 years), adults (14–60 years), and older individuals (≥60 years), positive detection rates showed a declining trend from 2020 to 2022. After the relaxation of NPIs in 2023, positive rates exhibited a significant resurgence in all groups, exceeding the levels recorded in 2019 (Figure 3(d)).

Figure 3(d) and (e) illustrate the annual trends for the number of positive cases and detection rates of Flu B. Across all age groups, the number of infections dropped markedly from 2019 levels after the introduction of NPIs for COVID-19 between 2020 and 2022. After the relaxation of NPIs in 2023, the level remained lower than that in 2019 (Figure 2(e)). For infants (<1 year), toddlers (1–3 years), adults (14–60 years), and older individuals (≥60 years); the positivity rate was highest in 2022. For preschool children (3–6 years), and school children (6–14 years), the positivity rate was highest in 2021, followed by that in 2022, coinciding with peaks of Flu B activity during the winter of 2021 and spring of 2022 despite the ongoing strict COVID-19 control measures. However, in 2023, there was no substantial rebound in the positive detection rates (Figure 3(f)).

Sensitivity analyses

After standardization based on the 2019 visit volume, the standardized influenza positivity rate was 11.87% (original 12.14%) in 2020, 2.35% (original 2.28%) in 2021, 15.72% (original 16.09%) in 2022, and 33.05% (original 32.82%) in 2023. The difference between the standardized positivity rate and the original result was <0.5%, indicating that the reduction in hospital visits had limited impact on the conclusions of this study, and the results are robust.

The GAM showed that after correcting for seasonality, the time trend of influenza positivity rate from 2019 to 2023 was consistent with the original results (χ² = 48.32, p < 0.001); the positivity rate from 2020 to 2022 was significantly lower than that in 2019 (p < 0.001) and rebounded significantly in 2023 (p < 0.001); the coefficient of the seasonal term showed that the positivity rate during winter and spring (January to March and November to December) was significantly higher than that during summer and autumn (p < 0.05), which was consistent with the original seasonal pattern, indicating that seasonal correction did not change the core conclusion.

Discussion

The primary objective of this retrospective analysis of 45,956 patients who were tested for Flu antigens because they presented with ILI symptoms at Hebei General Hospital between 2019 and 2023 was to evaluate the impact of NPIs implemented for COVID-19 on the epidemiology of influenza.

From 2019 to 2023, a total of 10,591 Flu A cases were confirmed at Hebei General Hospital, with a positive detection rate of 23.05% (95% CI: 22.73%–23.37%). In contrast, there were 1559 cases of Flu B, with a positive detection rate of 3.39% (95% CI: 3.22%–3.56%), which was significantly lower than that of Flu A (p < 0.05). This indicates that the prevalence of Flu A was much higher than that of Flu B, which is consistent with global epidemiological patterns, according to which, Flu A is the dominant circulating influenza subtype;^23,25 these findings also align with recent regional evidence from the Jazan region of Saudi Arabia.²⁶ Although Alqarny et al. (2025) did not stratify influenza by type (A/B) in their national surveillance data, their observation that influenza cases rebounded to 1001 in 2023 (from 46 in 2021) mirrors Hebei’s Flu A-driven resurgence, reinforcing that Flu A is the primary driver of post-NPI influenza rebounds across diverse geographic and climatic contexts.

In 2019, Hebei General Hospital recorded a high number of ILI patients (13,346) and a high influenza positivity rate (33.92%, 95% CI: 33.01%–34.83%), with clear seasonal peaks during spring and winter. From 2020 to 2022, with the implementation of NPIs, the number of ILI patients (8647, 3424, and 1436) and the positivity rates (12.14%, 2.28%, and 16.09%) showed significant declines (p < 0.05), and the seasonal trends disappeared. In 2023, as NPIs were relaxed, both number of patients (19,076) and positivity rate (32.82%, 95% CI: 32.15%–33.49%) rebounded. The substantial decrease in influenza positivity rates during the pandemic years from 2020 to 2022 underscores the efficacy of NPIs, including mask-wearing, social distancing, and enhanced hygiene practices, in disrupting the transmission of influenza, including SARS-CoV-2. This finding aligns with recent global studies that have demonstrated the cross-protective effect of COVID-19 NPIs against respiratory viruses. This cross-protective effect is further supported by a pediatric study from Southampton, which found an association of COVID-19 NPIs with a transient reduction in the nasopharyngeal carriage of Streptococcus pneumoniae and Haemophilus influenzae in children aged <5 years, consistent with the inhibitory impact of NPIs on respiratory pathogen transmission observed in our study.²⁷ A cross-sectional study on Iranian university students (2022) found that NPI education enhanced compliance with COVID-19 prevention guidelines, reduced COVID-related anxiety, and increased hope, whereas low utilization of NPI education was associated with heightened anxiety, highlighting the multifaceted role of NPI-related interventions in public health.²⁸

Mechanisms underlying the decline and rebound of influenza

The observed “suppression–rebound” pattern of influenza in Hebei is driven by four interconnected mechanisms, supported by global evidence and mechanistic research:

Direct suppression of transmission by NPIs. Evidence from Hebei’s data (indicating a decline in positivity rate from 33.92% to 2.28%); a systematic review covering 15 countries/regions; and a study from Jazan, Saudi Arabia (reporting an 89.7% reduction in cases), as cited in Fricke et al.²⁹ and other related studies, confirm that NPIs, including mask-wearing and school closures, effectively suppress the respiratory transmission of influenza and constitute the primary driver of the observed reduction in cases.

Behavioral changes amplify effects. Patient visits due to ILI surged by 42.9% at Hebei General Hospital in 2023, and the “pandemic fatigue” phenomenon was observed in Saudi Arabia (mask usage rate dropped from 82% to 31%). This indicates that initial compliance enhanced suppression, while subsequent behavioral reversal fueled the rebound.²⁶

Viral competition (“seesaw effect”). Data from Hebei General Hospital showed an inverse correlation between influenza rates and COVID-19. Combined with data from Cheemarla et al.’s (2024) mechanistic study (IAV inhibits SARS-CoV-2 replication), it explains the low influenza prevalence during COVID-19 outbreaks and influenza rebound as the COVID-19 pandemic subsided.³⁰

Immunity debt drives rebound. The positivity rate among preschool children in Hebei reached 53.48% in 2023 (exceeding 2019 levels). Lee et al.’s (2022) model and post-pandemic studies in the US and UK illustrated that 2 years of low influenza circulation reduced population immunity, leading to more susceptible individuals, triggering the rebound.³¹

Infection trends and positivity rates for both Flu A and Flu B were consistent across different age groups, with children (0–14 years) being more susceptible to infection than adults (14–60 years) and older individuals (>60 years) (p < 0.05). This could be due to children’s immature immune systems, higher contact rates in group settings, and lower adherence to preventive measures.^18,32 In a single-institution study, NPIs were linked to a 50% reduction in average weekly pediatric asthma exacerbations and decreased positivity rates of RSV infection, parainfluenza, and influenza, with post-NPI periods showing pre-pandemic exacerbation levels and an unusual RSV surge, further supporting NPIs’ impact on pediatric respiratory health.³³ The number of infections with both Flu A and Flu B was slightly higher in men than in women, with no significant differences in the positivity rates, indicating that sex is not a primary factor influencing influenza infection, which is in line with previous epidemiological observations.^17,20

A notable observation was the difference in the effects of NPIs on Flu A and Flu B; Flu A detection rate exhibited a more marked decline following the implementation of NPIs and a more pronounced rebound after the relaxation of NPIs.

Before the global spread of the COVID-19 pandemic in 2019, the hospital recorded 3793 (28.42%, 95% CI: 27.56%–29.28%) positive cases of Flu A, which exhibited a clear seasonal trend with peak incidence during winter and spring. After the implementation of NPIs in January 2020 until December 2022, only 23 positive cases of Flu A were reported. In 2023, with the relaxation of NPIs measures, the number of Flu A infections and the positivity rate rapidly rebounded to 6013 (31.52%, 95% CI: 30.85%–32.19%), surpassing the levels seen in 2019.

Notably, Flu B shows a different epidemic trend. Before the 2019 global COVID-19 pandemic, the hospital recorded 734 (5.50%, 95% CI: 5.12%–5.88%) cases of Flu B, which peaked during winter and spring seasons. Following the implementation of NPIs in January 2020, the number of Flu B positive cases and the positivity rate significantly declined. However, during the winter of 2021 and spring of 2022, there was a peak in the infection rate, and preschool children aged 3–6 years and school children aged 6–14 years showed higher infection rates. This surge can be attributed to the effective control of the COVID-19 pandemic through the strict implementation of NPIs, which allowed kindergartens and schools to reopen and coincided with the seasonal peak of Flu B. Some other researchers have also reported the prevalence of Flu B during the COVID-19 pandemic.^34–36 In 2023, after the relaxation of NPIs policies, there was no large-scale resurgence of Flu B similar to that of Flu A. These differential patterns may be attributed to certain factors. First, Flu A infects not only humans but also diverse animal hosts, such as poultry and swine, providing ample opportunities for gene reassortment, generation of novel strains, and zoonotic transmission to humans, thereby increasing the risk of interpersonal spread.³⁷ In contrast, Flu B is primarily transmitted via human-to-human contact, limiting their dispersal routes and velocity.³⁸ Second, Flu A exhibits higher mutation rates, particularly of their surface proteins, HA and NA, enabling antigenic drift and shift to evade host immune responses. Frequent mutations in Flu A often result in lower herd immunity levels, especially against emerging strains, augmenting infection rates.³⁹ Conversely, Flu B mutates less frequently than Flu A, potentially allowing some population segments to retain a degree of cross-protective immunity from previous infections or vaccinations, suppressing infection rates.⁴⁰ During the implementation of NPIs, significant changes in social behavior occurred, such as reduced travel and increased personal protective measures. These changes positively impacted the reduction of transmission for all types of influenza viruses.^29,41,42 However, given the higher transmissibility and higher mutation rates of Flu A, when the same NPIs are implemented, their impact on Flu A may be more pronounced.

NPIs are designed to slow the spread of respiratory pathogens and have significantly disrupted the seasonal circulation patterns of common respiratory pathogens, including influenza viruses, adenoviruses, and human metapneumoviruses. In the early stage of the COVID-19 pandemic, emergency department visits and hospitalization rates associated with non-SARS-CoV-2 infections decreased, and the prevalence of influenza and other respiratory viral infections was lower than the pre-pandemic levels due to the impact of NPIs. During the early phase of the COVID-19 pandemic, stringent NPIs led to a decrease in the population’s immunity levels due to reduced circulation of respiratory pathogens. This meant that after the relaxation of pandemic control measures, more susceptible individuals contracted infections and transmitted respiratory pathogens. Additionally, interactions between viruses influenced the occurrence of respiratory pathogen infections.^25,43 Furthermore, SARS-CoV-2 infection impaired people’s immune systems and reduced their bodies’ ability to resist other pathogens. The combined effect of these factors resulted in a significant rebound in influenza rates in China in 2023 following the relaxation of NPIs.^43–46 Second, as highlighted above, Flu A viruses are prone to mutation through processes such as antigenic drift and shift. This could have resulted in the emergence of novel strains to which the population possesses limited immunity.^25,47

Third, with the COVID-19 pandemic under control and widespread vaccination efforts, numerous countries and regions have incrementally eased NPIs, inadvertently paving the way for influenza viruses to spread more freely.^32,48 Fourth, the focus on COVID-19 vaccination campaigns may have diverted resources and attention away from routine influenza vaccination programs, potentially reducing overall influenza vaccination coverage and population immunity.^49–51 These factors, individually or in combination, may have contributed to the observed increase in influenza cases, especially those of Flu A, in 2023.

There was no significant rebound of Flu B in 2023. Potential reasons for this difference include the following. First, influenza epidemics follow cyclical patterns, with Flu A and Flu B waves typically not coinciding.⁵² The dominance of Flu A early in 2023 may have, to some extent, suppressed Flu B activity, as competition between the two viruses can reduce the transmission opportunity for the other. Second, the emergence of a small-scale infection peak in 2022 may have induced some level of immunity in the population, thereby preventing a large-scale resurgence of Flu B in 2023.

This study has inherent limitations related to its single-hospital design, which should be considered when interpreting and generalizing the findings. First, as a tertiary referral hospital in Shijiazhuang, Hebei General Hospital primarily serves patients with more severe ILI or comorbidities; this may have introduced selection bias because mild cases managed at home or primary care facilities would not have been represented, potentially distorting true community positivity rates and severity profiles. Second, the hospital’s demographic profile was skewed owing to the higher proportion of adult patients (age: 14–60 years; 21,978/45,956) compared with pediatric patients) and Shijiazhuang’s temperate continental monsoon climate limit geographic and demographic generalizability; findings may not apply to southern China (with distinct influenza seasonality), rural areas, or specialized children’s hospitals. Third, institution-specific factors, including exclusive use of the Abbott colloidal gold assay and variable ILI testing criteria during COVID-19, may have reduced comparability with other data from healthcare settings. Although the colloidal gold assay exhibits lower sensitivity than nucleic acid testing, its inherent advantages of rapidity and simplicity make it applicable to a much broader population. Fourth, single-hospital data fail to capture asymptomatic or community-level transmission, underestimating the full burden of influenza and the impact of NPIs. Another key limitation is that the respiratory pathogen detection kit used can only identify Flu A and Flu B at the species level and does not possess the ability to perform subtype classification, consequently resulting in the lack of subtype data for both Flu A (e.g. H1N1 and H3N2 subtypes) and Flu B (e.g. Victoria lineage and Yamagata lineage); given that influenza virus subtypes differ significantly in terms of epidemiological characteristics, variation trends, and pathogenic severity, the absence of subtype information precludes refined analyses of regional influenza epidemic patterns and prevents accurate identification of dominant circulating subtypes and potential mutation risks; furthermore, we did not collect data on patients’ influenza vaccination history or population-level vaccination coverage. As vaccination is a core intervention for influenza prevention and control, annual changes or population differences in vaccination coverage may have influenced influenza epidemic trends and infection risks. The absence of these data limits our ability to accurately quantify the independent effect of NPIs and hinders in-depth analysis of the interactive effect between vaccination and NPIs on influenza transmission, representing an important limitation of this study. Future research should focus on supplementing vaccination-related data to more comprehensively clarify the driving factors of influenza epidemics.

These limitations restrict the extrapolation of these results to broader populations. The observed trends (e.g. post-NPI Flu A rebound and age-specific susceptibility) are most applicable to large urban tertiary hospitals in northern China with similar contexts. Future multicenter studies integrating tertiary, secondary, and primary care data across diverse geographic regions are needed to enhance generalizability.

Funding statement

This study was supported by the Hebei Provincial Medical Science Research Project (No.: 20260048) and Hebei Program for Tracking Medical Appropriate Technologies (No.: GZ20260090). The funding agencies played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conclusion

The COVID-19 pandemic has unintentionally served as a distinctive natural experiment, highlighting the substantial impact of NPIs on influenza epidemiology. These measures effectively curbed the spread of influenza; however, their discontinuation exposed the risk of notable resurgence, partly driven by “immunity debt.” This emphasizes the need for comprehensive, adaptable strategies that account for evolving infectious disease dynamics and the intricate interplay between human behavior, immunity, and pathogen evolution. To inform future influenza management, integrating targeted NPIs (e.g. seasonal mask use in high-risk settings) and enhanced vaccination coverage can mitigate resurgence risks while balancing societal costs. Consistent with our findings, in a previous study, NPIs reduced the exacerbation of pediatric asthma by 50% (5758 cases, 2018–2022), lowering RSV/parainfluenza/influenza positivity rates (87% of 70,682 broad respiratory panel (BRP) tests detected pathogens); in this study, rhinovirus infection rates remained stable, and BRP-positive patients needed more care.⁵³ Telenursing improved COVID-19 patients’ short form-36 health survey (SF-36) scores (72.62 ± 3.51 vs. 63.62 ± 3.93, 120 patients, p < 0.001). Post-NPI resurgences highlight the need for adaptive, population-specific strategies.⁵⁴ Moving forward, research should focus on elucidating the long-term immunological and epidemiological effects of these interventions to guide more refined and sustainable public health policies. Additionally, further studies are needed to explore the mechanisms underlying the differential responses of Flu A and B to NPIs, which could inform subtype-specific prevention and control strategies.

Footnotes

Acknowledgments

We would like to acknowledge the valuable contribution of each author who has made an important scientific contribution to the study.

Author contributions

Pei Zhao: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, supervision, and writing – original draft | Yu Zhang: Data curation, software, and validation | Jie Wang: Investigation, software, and visualization | Yonghui Li: Investigation, software, and visualization | Mingwen Zhao: Formal analysis, methodology, and validation |Xiaowen Zhang: Formal analysis, methodology, and validation methodology.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

The authors declare no conflicts of interest.

Ethical statement and consent

This study has been approved by the Medical Ethics Committee of Hebei General Hospital. Ethics Lot Number: No. 2024-LW-0210. Given the retrospective nature of the study and the use of deidentified data, the requirement for individual informed consent was waived by the ethics committee. All data were processed and stored in accordance with relevant data protection regulations, and strict confidentiality measures were implemented to ensure patient privacy.

Funding

This study was supported by the Hebei Provincial Medical Science Research Project (Grant No.: 20260048).

ORCID iD

Pei Zhao

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