Introduction

Influenza remains a significant global public health challenge, with an estimated 3−5 million severe cases and 290,000−650,000 deaths annually worldwide [1]. In China, influenza causes 84−144 million infections and up to 240,000 deaths each year [2,3]. Although the rigorous implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic temporarily suppressed influenza transmission [4,5], a significant resurgence in activity intensity was observed after 2022. Sentinel hospitals in some southern Chinese provinces even reported influenza-like illness (ILI) percentages exceeding pre-pandemic levels [6,7]. indicating new challenges for public health.

Behavioral epidemiological studies indicate that public compliance with influenza prevention and control measures depends not only on epidemiological risks, but is also closely related to individuals’ risk perception and social support – specifically the subjective assessment of disease threat severity (perceived severity), personal susceptibility (perceived susceptibility), and social resource support [8,9]. Therefore, the “rebound effect” in influenza prevention post-COVID-19 pandemic has revealed a critical research gap: in the post-pandemic era against the backdrop of relaxed non-pharmaceutical interventions (NPIs), what is the current status of public risk perception and social support, and how do they influence individuals to subsequently affect health behaviors for influenza prevention? However, current research based on China’s influenza context remains very limited.

The Health Belief Model (HBM) provides a theoretical framework for this study. The model posits that health behavior decisions are driven by four-dimensional cognition: threat appraisal (severity, susceptibility), behavioral evaluation (benefit-barrier tradeoff), and external regulatory factors (cues to action, self-efficacy), which effectively guides the complex relationships among risk perception, social support, and health behaviors [10,11]. Recent empirical studies have found that social support may enhance the effect of HBM through dual pathways: on one hand, informational support (e.g., guidance from authoritative institutions) can serve as “cues to action” to directly trigger individual preventive behaviors; meanwhile, instrumental and emotional support (e.g., family assistance, psychological comfort) can indirectly promote behavioral change by reducing perceived barriers and enhancing self-efficacy [12–14].

Chengdu, as a megacity in southwestern China (with a permanent population exceeding 21 million), offers an ideal setting for this research. Firstly, Chengdu’s 2023 influenza hospitalization rate was 37% higher than the national average, demonstrating typical post-pandemic epidemiological characteristics [15,16]; Secondly, during rapid urbanization, Chengdu exhibits significant occupational diversity, providing ample heterogeneous samples for this risk perception study [17,18]; Most importantly, Chengdu’s unique collectivist cultural traditions and family support networks create a distinctive sociocultural laboratory for exploring how social support modulates risk perception (e.g., reducing threat severity appraisal and enhancing coping efficacy) through HBM pathways [19,20].

Based on the Health Belief Model (HBM), this study aims to analyze the public’s influenza risk perception, social support status, and behavioral regulation mechanisms in post-pandemic Chengdu, so as to provide dual-dimensional “psychological-social” intervention strategies for influenza prevention and control, and to optimize the influenza prevention and control transmission framework for Chengdu and similar cities.

Materials and methods

Results and discussion

Participant characteristics

We distributed 715 questionnaires and obtained 708 valid responses (98.9% response rate). The study population consisted of 602 female participants (85.0%), with a predominance of young adults: 465 respondents (65.7%) were aged <25 years (including 8 minors aged <18 years). Occupational distribution revealed that healthcare workers represented the largest subgroup (44.1%), followed by students (37.9%). A substantial proportion of participants (64.7%) reported monthly incomes ≤6000 CNY (Table 1).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Sociodemographic characteristics of participants.

https://doi.org/10.1371/journal.pone.0331052.t001

Risk perception scores

The median score of risk perception was 36 points, with a score rate of 40.00%. The median and score rates of each item were as follows: risk familiarity 11 points (36.67%), risk controllability 8 points (40.00%), risk severity 8 points (32.00%), and risk susceptibility 9 points (60.00%) (Table 2).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Risk perception scores by dimension.

https://doi.org/10.1371/journal.pone.0331052.t002

Social support scores

The median score of social support was 37 points, and the score rate was 63.79%. The median scores and score rates of each item are as follows: Objective support 10 points (45.45%), subjective support 18 points (75.00%); The support utilization rate was 8 points (66.67%) (Table 3).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. Social support scores by dimension.

https://doi.org/10.1371/journal.pone.0331052.t003

Single-factor analysis of public risk perception

The influenza risk perception scores showed statistically significant variations across demographic strata (Table 4). Notably, two distinct age cohorts – participants under 25 years and those over 40 years – displayed elevated risk perception levels. Conversely, healthcare workers exhibited significantly lower risk perception compared to the general population (p < 0.01). Our analysis excluded subgroup comparisons for minors (n = 8) and seniors over 60 years (n = 2) due to insufficient sample sizes in these demographic groups. This limitation aligns with previous cross-linguistic studies demonstrating how sample size constraints can affect statistical power in demographic analyses.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. Analysis of public risk perception scores for different sociological characteristics.

https://doi.org/10.1371/journal.pone.0331052.t004

Correlation analysis

Total social support demonstrated a significant negative correlation with risk perception (r = −0.125, p < 0.01), with the strongest association observed for risk familiarity perception (r = −0.171, p < 0.01). Regarding positive correlations, objective support showed the most pronounced association with susceptibility (r = 0.100, p < 0.05) (Table 5 and Fig 1)

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 5. Spearman correlations between social support and risk perception (Spearman’ s r).

https://doi.org/10.1371/journal.pone.0331052.t005

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. The heatmap of social support and risk perception.

https://doi.org/10.1371/journal.pone.0331052.g001

Discussion

This study reveals persistently suboptimal influenza risk perception among Chengdu residents, particularly in disease severity assessment and pathogen familiarit – a pre-pandemic trend exacerbated post-COVID-19 [18,31]. The decline may reflect reduced vigilance after relaxation of non-pharmaceutical interventions (NPIs) [32], indicating a critical gap in sustaining preventive behaviors post-crisis. Key demographic disparities emerged: both younger and older adults showed heightened risk awareness (likely due to age-related vulnerability perceptions), while healthcare workers exhibited desensitization from prolonged pathogen exposure. These findings necessitate tailored risk communication strategies addressing occupation-specific and age-related risk appraisal differences.

The study revealed moderate levels of social support overall, with subjective support components (e.g., familial care) demonstrating higher prevalence than objective support resources (e.g., institutional access). A statistically significant negative correlation emerged between total support and risk perception (r = −0.125, p < 0.01), consistent with the Health Belief Model’s proposition that external support systems mitigate perceived prevention barriers. Notably, this inverse relationship was most pronounced for risk familiarity (r = −0.171, p < 0.01), indicating substantial cultural mediation effects. Within China’s collectivist context, robust social networks appear to generate psychological buffering through three mechanisms: (1) Collective security attenuates individual vulnerability perceptions (e.g., anticipated reliance on others during illness); (2) High subjective support (scoring at the 75th percentile) strengthens relational safety networks; (3) Instrumental aid reduces perceived severity. While social support theoretically functions as a behavioral cue, these findings suggest its primary role involves moderating threat appraisal through enhanced coping efficacy. However, the modest effect size (r < 0.30) implies limited direct behavioral influence, highlighting the predominant role of sociocultural norms (e.g., harmony-oriented collectivism) and structural interventions (e.g., policy-mediated resource distribution) in determining preventive health behaviors [33–35].

These results challenge health behavior models’ overemphasis on social support. While subjective support may function as a Health Belief Model cue, its modest impact suggests greater emphasis should be placed on vaccination perceived benefits and sociocultural norms—particularly in contexts of institutional distrust or misinformation (unmeasured here). The subjective-objective support dissociation underscores the need for multilevel interventions combining community networks with structural healthcare access improvements.

The statistically significant but clinically marginal correlation highlights the necessity of distinguishing statistical outcomes from public health impact. Future research should employ longitudinal designs to analyze risk perception trends and mixed methods to examine cultural and media influences.

Limitations

This investigation acknowledges three principal methodological constraints that warrant careful consideration:

1. Sampling bias: The utilization of convenience sampling—though operationally expedient for post-pandemic data collection—resulted in notable demographic disparities. The cohort exhibited substantial overrepresentation of females (85.0%), young adults aged <25 years (65.7%), and healthcare workers (44.1%), thereby constraining the generalizability of our findings to the broader Chengdu population. Importantly, this sampling bias appears to have substantially influenced the observed risk perception patterns. Healthcare workers, comprising nearly half the sample, demonstrated significantly lower risk perception compared to students and other occupational groups (p < 0.01). This phenomenon may reflect occupational desensitization attributable to frequent pathogen exposure and specialized training, potentially leading to systematic underestimation of the population’s true average risk perception level [36]. Conversely, the substantial student subgroup (37.9%), characterized by younger age and potentially heightened awareness stemming from recent pandemic experiences or educational environments, displayed elevated risk perception, possibly contributing to upward bias in the population mean estimate. Consequently, the marked occupational imbalance (predominance of healthcare and student populations) suggests that our reported overall risk perception score (median = 36, score rate = 40.0%) may not accurately represent the true distribution across diverse demographic strata in Chengdu. Future investigations employing probability sampling designs are warranted to validate these patterns and precisely quantify risk perception among currently underrepresented populations, including older adults, manual laborers, and rural-to-urban migrants [37].
2. Measurement granularity: Analytical precision was constrained by insufficient occupational classification resolution. Diverse professional groups (e.g., educators, independent contractors) were aggregated into a single “Other” category (18.0%), precluding nuanced cross-occupational comparisons of risk perception paradigms. Concurrently, critical developmental stages – particularly adolescents (1%) and older adults (0.3%) – were substantially underrepresented, potentially obscuring lifespan-specific vulnerability trajectories.
3. Generalizability constraints: Despite intentional inclusion of varied income strata and an age spectrum spanning 12–70 years, the sampling framework failed to adequately capture non-institutionalized populations (e.g., rural dwellers, informal sector workers). This omission may limit the external validity of findings when extrapolated to broader urban contexts beyond the study’s specific institutional catchment areas.

These limitations collectively underscore the need for cautious interpretation of results and highlight critical directions for future research employing more representative sampling strategies.

Future directions

To address these limitations, future studies should implement: 1) Stratified random sampling to capture underrepresented populations (e.g., elderly, adolescents, and non-healthcare workers); 2) Multi-channel recruitment strategies (e.g., community-based sampling) to improve population representativeness; 3) Purposive sampling of occupational subgroups for cross-professional comparisons; 4) Longitudinal mixed-methods designs to examine unmeasured confounders like cultural narratives and structural inequities in risk perception dynamics.

Conclusion

Our study reveals consistently low influenza risk perception among Chengdu residents during the post-COVID-19 period, particularly regarding perceived severity and disease familiarity. Key demographic disparities emerged across age groups and occupations. While social support showed moderate association with risk perception, socio-cultural norms and structural factors exerted stronger influences on preventive behaviors. These findings underscore the need for:

• Tailored risk communication interventions addressing occupation-specific vulnerabilities
• Equitable public health resource allocation (e.g., vaccine subsidy programs)
• Representative longitudinal studies to track post-pandemic risk perception evolution

Acknowledgments

We sincerely appreciate all volunteers for their invaluable contributions to this study. Special thanks to our research team for their expertise and dedication throughout this project.