Introduction

Orthopedic surgery department is a special department within a medical institution that mainly treats patients with fractures. Fracture is a traumatic disease in which bone continuity is disrupted or broken due to external violent injury, and it has gradually emerged as a very common traumatic disease1. Most fractures usually cause only local manifestations, such as swelling, pain, ecchymosis, and dysfunction2. However, severe fractures or multiple fractures can lead to systemic reactions, such as hyperthermia, visceral damage, and shock3. According to the integrity of the skin and mucous membrane at the fracture site, the fracture can be divided into closed and open fractures. The skin or mucous membrane at the closed fracture site is intact and not exposed to the external environment, and most patients with closed factures can regain the function of their limbs after timely and appropriate treatment4. In contrast, the skin or mucous membrane at the open fracture site is ruptured due to direct damage, such as that caused due to stabbing or gunshot, resulting in the fractured end being exposed to the external environment. Patients with fractures usually suffer from high trauma and severe conditions and need to undergo invasive operations. Orthopedic surgery is an important means of clinically treating fractures, and the main approach is repair and reconstruction, which aims to not only restore the fracture end to its original position in time but also to strengthen the fixation with the help of an external force5. However, the surgical outcome is affected by various factors such as operation time, surgical trauma, and extent of invasion. The usual surgical intervention requires the implantation of an internal fixative agent, which can induce stress and increase the risk of postoperative complications. Infection is one of the most common type of postoperative complication, and modern aseptic surgical techniques are not completely effective. The probability of occurrence of an infection ranges from 2.60 to 7.83%6, and it usually occurs in the perioperative or postoperative period.

The treatment of postoperative infection is extremely difficult; once the wound becomes infected after the fracture, if the treatment is not timely or the effect is poor, it easily results in severe consequences, such as delayed healing or even nonhealing of the fracture. Furthermore, it is likely to require a second operation, thereby prolonging the recovery process of the patient, negatively affecting the effect of the fracture repair and the function of the affected limb, and probably leading to bacterial invasion into the bloodstream, which can lead to sepsis or chronic osteomyelitis7, thereby directly jeopardizing the patient’s safety and life. Recently, studies worldwide have shown that age, initial debridement time and intraoperative blood loss are high-risk factors for postoperative infection; however, these studies had relatively small sample size and did not fully investigate all influencing factors8,9. Meanwhile, with the increasing application of antibacterial drugs, the infected strains have changed in recent years, and the infected strains and their drug resistance differ among various regions and hospitals, thereby increasing the difficulty of postoperative infection control. In addition, air pollution has become a major public health concern worldwide, seriously affecting people’s health in all aspects.

Airborne particulate matter and noxious gases can weaken the body’s immune system by inducing inflammation and oxidative stress, which may increase the risk of infection10,11. For patients who have recently undergone fracture surgery, a compromised immune system can lead to poor wound healing and a higher likelihood of infection12. Moreover, air pollutants may exacerbate other underlying health conditions, such as diabetes and chronic respiratory disease13,14, both of which are known risk factors for postoperative infections15,16,17. Through these complex physiological mechanisms, a potential association between air pollution and postoperative infections emerges.

Several epidemiological studies have found that patients exposed to high levels of air pollutants have a higher incidence of postoperative infections18,19,20,21.These studies, typically focusing on populations in areas with significant air pollution, have shown a markedly increased risk of infection following surgery. This heightened risk may be partly due to the negative impact of air pollutants on the wound healing process. Additionally, the systemic inflammatory response triggered by pollutants may render patients more susceptible to infection22. These findings underscore the potential threat that air pollution poses to postoperative recovery. Understanding this link has crucial implications for the care of orthopedic surgery patients and informs public health policy. Implementing measures to reduce exposure to air pollution and improve the postoperative environment could play a vital role in reducing the rate of postoperative fracture infections23,24,25,26,27.

Hebei Province is highly industrialized, with a significant concentration of iron, steel, and coal industries, resulting in substantial pollutant emissions28. The reliance on coal for winter heating exacerbates the rise in PM2.5 concentrations29. Additionally, Hebei’s monsoon climate hinders air circulation during the winter, trapping pollutants and worsening air quality30. In contrast, Xinjiang is less industrialized, with fewer major cities and industrial regions, leading to lower overall air pollutant concentrations31. The region’s diversified energy mix and lower reliance on coal further reduce air pollutant emissions32. Moreover, the arid climate and high wind speeds in Xinjiang contribute to the dilution and dispersion of pollutants. By analyzing infection rates in both regions, this study aims to elucidate the specific impact of air pollution on the risk of postoperative infections.

Therefore, this study retrospectively analyzed the clinical data of patients with postoperative infection in the Hebei Province and Xinjiang Uygur Autonomous Region from 2018 to 2022 and the meteorological data in the corresponding areas within the same timeframe. This study also aimed to analyze the epidemiological distribution characteristics of patients with postoperative infection after fracture and the lagging effect of air pollutants on postoperative infection. The findings of this study will help in reducing the postoperative infection rate of hospital fractures, improving the treatment strategy for postoperative infection, and providing a theoretical basis for improving the quality of patients’ medical treatment and reducing the social and economic burden33.

Materials and methods

Study area

Hebei Province, situated in northern China on the North China Plain, faces significant air pollution issues due to its high level of industrialization and urbanization, particularly during the winter months. Major pollutants in Hebei Province include PM2.5, PM10, sulfur dioxide (SO2), and nitrogen oxides (NOx), with coal-fired heating in winter leading to significantly higher concentrations of PM2.5. These high pollution levels may negatively affect the immune system, thereby increasing the risk of infections after fracture surgery.

In contrast, the Xinjiang Uygur Autonomous Region, located in the arid and semi-arid western part of China, has a relatively low level of industrialization, resulting in lower overall concentrations of air pollutants. This less polluted environment might have a protective effect on the risk of postoperative fracture infections, as fewer air pollutants could reduce the negative impact on the immune system, thereby lowering the incidence of infections.

Selecting Hebei and Xinjiang as study areas offers valuable insights into the health impacts of varying pollution levels. The distinct differences in air pollutant concentrations between these two provinces provide a natural comparison for examining the relationship between air pollution and postoperative fracture infections.

Data collection

From January 1, 2018, to December 31, 2022, data on inpatients with postoperative orthopedic infections were retrospectively collected from six representative hospitals in Hebei. These hospitals included one provincial hospital (the Third Hospital of Hebei Medical University), four municipal hospitals (the Second Hospital of Tangshan City, the Cangzhou Hospital of Integrative Medicine and Western Medicine, the First Central Hospital of Baoding City, and the Xingtai General Hospital of North China Medical and Healthcare Group), and one county-level hospital (the Jingxing County Hospital). Additionally, similar data were collected from four representative hospitals in Xinjiang, including one provincial-level hospital (the First Hospital of Xinjiang Medical University), one prefecture-level hospital (Xinhua Hospital of Ili Kazakh Autonomous Prefecture), and two county-level hospitals (Yanqi Hospital of the Second Division of the Xinjiang Production and Construction Corps and the People’s Hospital of Xibe County). Information on patients with postoperative infections, including name, gender, age, admission time, admission diagnosis, and discharge time, was retrieved using the computerized archiving system for medical images and the case search system.

The data on air pollutants are sourced from the China Air Quality Online Monitoring and Analysis Platform. These data are collected from monitoring stations set up in each city, with the number of stations varying depending on the city’s geographical location and other conditions. These monitoring stations are situated away from major roads, industrial pollution sources, high-rise buildings, or residential coal-burning emission sources to ensure that the monitoring data are not influenced by local traffic or industrial combustion pollution sources. The air pollutants in this study included particulate matter with an aerodynamic diameter of < 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter of < 10 μm (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and the 8-h moving average ozone concentration (O3), with 24-h average concentrations of the abovementioned air pollutants as the daily average exposure concentration of air pollutants.

Case inclusion and exclusion criteria

Patients who met the Hospital Infection Diagnostic Criteria; those who had complete patient data; and those who had follow-up and clinical data integrity were included in this study. Patients complicated with heart, brain, kidney, and other organ lesions; malignant tumors; psychiatric disorders; and other infectious diseases were excluded. All data were retrospectively collected from patients’ medical records.

Research methodology

Descriptive analysis was used to demonstrate the characteristics of the population, and patients were grouped by age into five groups: 0–20, 21–40, 41–60, 61–80, and ≥ 81 years. The number of patients at different ages and the number of patients of different sexes in the two regions were counted, and χ2 test was used to analyze the differences in sex and age between the two regions. In addition, distributional lag nonlinear modeling (DLNM) was used to compare the lagged effects of air pollutants on patients with daily postoperative infections in the two regions.

Statistical modeling

The daily number of hospitalized patients with postoperative infections and daily air pollutant concentrations were correlated by date, and a time-series study was used to estimate the overall association between them during the entire study period (2018–2022). The population of each city changed insignificantly throughout the study period. Therefore, a DLNM was used to investigate the lagged associations between environmental factors and daily outpatient visits for postoperative infections. The model equations was as follows:

$$\log \left[ {E\left( {Yt} \right)} \right] = \alpha + Wx\;n\;\mu \eta + ns\left( {time,\;7*year} \right) + as.\;f{\text{actor}}\left( {dow} \right)$$

DLNM is based on the traditional model and introduces a cross-base process that simultaneously describes the distribution of the dependent variable in the independent and lagged dimensions, making it possible to assess both the lagged and nonlinear effects of the exposure factors. Daily postoperative infection inpatient visits in the model were used as the dependent variable, and air pollutants were used as the independent variable. Yt indicates the postoperative infection inpatient visit on day t, E(Yt) indicates the mathematical expectation of the postoperative infection inpatient visit on day t, and α indicates the model’s intercept. Wx n η denotes the cross-basis function, which was constructed to assess the nonlinear and lagged association between air pollution and daily inpatient visits for postoperative infections by constructing a cross-basis matrix. time indicates the time variable. A quadratic B-spline function with three degrees of freedom was chosen as the basis function of the exposure dimension, and a quadratic polynomial function was chosen as the basis function of the lag dimension. We used Akaike’s Quasi-Poisson Information Criterion (Q-AIC) to determine the degrees of freedom for each variable in the model34. Based on a review of relevant literature and data, the maximum number of lagged days was set to 14 days35,36,37,38.

All statistical analyses were performed using the “DLNM” and “Spline” packages in R software (version 4.3.1), and SPSS 26.0 was used for some basic data processing. All P-values were two-tailed distributions. Differences were considered statistically significant at P < 0.05.

All methods were performed in accordance with the relevant guidelines and regulations.

Sensitivity analysis

Sensitivity Analysis In constructing the distributional lag model, a sensitivity analysis was carried out to assess the model’s robustness.This was achieved by varying the degrees of freedom associated with the time trend (ranging from 6–8° of freedom per year).

Results

Epidemiologic characteristics of postoperative infection cases in the Hebei Province and Xinjiang Uygur Autonomous Region

General situation

A total of 1,338 patients were included in this study. In the Hebei region, 863 patients were collected, comprising 574 males (66.5%) and 289 females (33.5%), with a male-to-female ratio of 1.98:1. Among these 863 patients, 675 (78.2%) had postoperative wound infections following fracture surgery, 132 (15.3%) had infections due to prosthesis implantation, 46 (5.3%) had infections caused by internal fixation devices, and 10 (1.2%) had infections of the truncated stump.

In Xinjiang, 475 cases were collected, with 301 males (63.4%) and 174 females (36.6%), resulting in a male-to-female ratio of 1.73:1. Among these 475 patients, 243 (51.2%) had post-fracture wound infections, 188 (39.6%) had infections from prosthesis implantation, 32 (6.7%) had infections from internal fixation devices, and 12 (2.5%) had infections of truncated stumps. Details can be seen in Figs. 1 and 2.

Fig. 1
figure 1

Distribution of infection types in patients with postoperative orthopedic infections in the Hebei region.

Full size image
Fig. 2
figure 2

Distribution of infection types in patients with postoperative orthopedic infections in the Xinjiangi region.

Full size image

Sex–age distribution

The entire study population was categorized into five age groups: 0–20 years old, 21–40 years old, 41–60 years old, 61–80 years old, and ≥ 81 years old. Among the hospitalized patients with postoperative orthopedic infections in the Hebei region, the 41–60 years old group accounted for the highest percentage (357 cases, 41.4%), followed by the 61–80 years old group (261 cases, 30.2%), the 21–40 years old group (179 cases, 20.7%), the 0–20 years old group (50 cases, 5.8%), and the ≥ 81 years old group (16 cases, 1.9%) (Fig. 3).

Fig. 3
figure 3

Gender distribution characteristics of patients with postoperative orthopedic infections in different age groups in Hebei region.

Full size image

In the Xinjiang region, the 61–80 years old group had the highest percentage of hospitalized patients with postoperative orthopedic infections (166 cases, 35%), followed by the 41–60 years old group (164 cases, 34.5%), the 21–40 years old group (100 cases, 21.1%), the 0–20 years old group (33 cases, 6.9%), and the ≥ 81 years old group (12 cases, 2.5%) (Fig. 4). In both regions, the majority of patients with postoperative infections were male. After a chi—square test, the gender difference was found to be statistically non—significant (χ2 = 1.338, P = 0.247).

Fig. 4
figure 4

Gender distribution characteristics of patients with postoperative orthopedic infections in different age groups in Xinjiang region.

Full size image

Time distribution

Among the patients with postoperative orthopedic infections in Hebei, 178 cases in 2018 accounted for 0.43% of the annual orthopedic surgery patients; 172 cases in 2019 accounted for 0.44%; 244 cases in 2020 accounted for 0.66%; 180 cases in 2021 accounted for 0.47%; and 89 cases in 2022 accounted for 0.33%. In Xinjiang, 114 cases in 2018 accounted for 2.82% of the annual orthopedic surgery patients; 108 cases in 2019 accounted for 3.13%; 108 cases in 2020 accounted for 3.28%; 86 cases in 2021 accounted for 3.59%; and 59 cases in 2022 accounted for 2.50%. The incidence of postoperative orthopedic infections in Xinjiang (3.06%) was significantly higher than in Hebei (0.47%) (χ2 = 1426.89, P < 0.001). (Fig. 5).

Fig. 5
figure 5

Number of cases in different age groups of men and women in Hebei Province.

Full size image

Lagged effects of air pollutants on the number of patients with postoperative infections in the Hebei Province and Xinjiang Uygur Autonomous Region

Lagged effect of air pollutants on the number of hospitalized patients with postoperative infections in the Hebei Province

Each 0.1 mg/m3 increase in CO increased the risk of hospitalization for postoperative infections, which was statistically significant on single-day lag days 0, 5, 6, 7, 8, and 14, with the greatest increase in risk occurring on lag day 14 (PM2.5: RR = 1.034, 95% CI 1.022–1.047). Each 10 μg/m3 increase in NO2 increased the risk of hospitalization for postoperative infections, which was statistically significant on lag days 0, 5, 6, 7, and 14, with the greatest increase in risk occurring on lag day 14 (PM10: RR = 1.11, 95% CI 1.055–1.172). For every 0.1 mg/m3 increase in CO, cumulative lag days 7–9 increased the daily number of patients with postoperative infection, and for every 10 μg/m3 increase in NO2, cumulative lag days 7–10 increased the daily number of patients with postoperative infection. The lagged effects of air pollutants on daily inpatient visits for postoperative infections were demonstrated using two-dimensional contour plots and 3D plots showing RR curves for air pollutants with different concentrations and lag days to visualize the lagged relationships. Figure 6 shows the detailed results.

Fig. 6
figure 6

Effect of CO and NO2 on daily postoperative infection hospitalization lag in Hebei, 2018 to 2022.

Full size image

Lagged effect of air pollutants on the number of hospitalized patients with postoperative infections in the Xinjiang Uygur Autonomous Region

Each 0.1 mg/m3 increase in CO increased the risk of hospitalization for postoperative infections, which was statistically significant on lagged days 1, 2, 10, 11, and 12, with a maximum on lagged day 11 (CO: RR = 1.036, 95% CI 1.023–1.042). Each 10 μg/m3 increase in NO2 increased the risk of hospitalization for postoperative infections, which was statistically significant on lag days 0, 1, 9, 10, 11, and 12, with the greatest risk being on lag day 0 (NO2: RR = 1.303, 95% CI 1.182–1.437). For each 0.1 mg/m3 increase in CO, the cumulative lag days 1–4 increased the number of daily hospitalizations for postoperative infections, with the greatest being on cumulative lag day 3 (RR = 1.069, 95% CI 1.029–1.110), and for each 10 μg/m3 increase in NO2, the cumulative lag effect of 0–4 and 10–13 increased the number of daily hospitalizations for postoperative infections, with the greatest being on cumulative lag day 12 (RR = 1.67, 95% CI 1.369–2.037). The lagged effects of air pollutants on daily postoperative infection inpatient visits were demonstrated using two-dimensional contour plots and 3D plots showing RR curves for air pollutants with different concentrations and lag days to visualize the lagged relationships. Figure 7 shows the specific results.

Fig. 7
figure 7

Impact of CO and NO2 on the lag of hospitalized patients with daily postoperative infections in Xinjiang, 2018 to 2022.

Full size image

Cumulative lagged effects of extreme air pollutant concentrations on daily hospitalized patients with postoperative infections in the Hebei Province and Xinjiang Uygur Autonomous Region

In the Hebei Province, the cumulative lag of CO was day 0 (RR = 0.921, 95% CI 0.863–0.983 for low concentration, RR = 1.14, 95% CI 1.028–1.266 for high concentration), 7 (RR = 0.854, 95% CI 0.76–0.961 for low concentration, RR = 1.286, 95% CI 1.066–1.552 for high concentration), and 10 (RR = 0.840, 95% CI 1.066–1.552 for low concentration). 10 days (low concentration RR = 0.884, 95% CI 0.783–0.997, high concentration RR = 1.218, 95% CI 1.004–1.478) were significant. NO2 cumulative lag day 0 (low concentration RR = 0.839, 95% CI 0.748–0.942, high concentration RR = 1.253, 95% CI 1.08–1.453), 7 (low concentration RR = 0.79, 95% CI 0.678–0.922, high concentration RR = 1.353, 95% CI 1.11–1.65), 10 (low concentration RR = 0.808, 95% CI 0.685–0.952, high concentration RR = 1.316, 95% CI 1.065–1.626), and 14 (low concentration RR = 0.809, 95% CI 0.685–0.955, high concentration RR = 1.314, 95% CI 1.06–1.627) were significant. Based on the RR values of each of the above, it can be concluded that carbon monoxide and nitrogen dioxide increase the risk of disease at high concentrations and decrease the risk of disease at low concentrations.Specific results are shown in Table 1.

Table 1 RR values and 95% confidence intervals for cumulative lagged effects of extreme air pollutant concentrations in hospitalized patients with postoperative infections in Hebei, 2018–2022.
Full size table

In the Xinjiang Uygur Autonomous Region, the cumulative lag effect of high concentrations of CO and NO2 increased the risk of disease and that of low concentrations decreased the risk of disease. The CO cumulative lag effect of 3 days (RR = 0.715, 95% CI 0.591–0.864 for low concentration and RR = 1.957, 95% CI 1.339–2.859 for high concentration) was significant; NO2 cumulative lag effects for 3 days (RR = 0.573, 95% CI 0.436–0.753 for low concentration, RR = 2.344, 95% CI 1.542–3.565 for high concentration) and 10 days (RR = 0.605, 95% CI 0.437–0.836 for low concentration, RR = 2.159, 95% CI 1.315–3.543 for high concentration) were significant. Notably, PM2.5 also showed a cumulative lag effect of increasing the risk of disease at high concentrations and decreasing the risk of disease at low concentrations, with a cumulative lag of 0 days (RR = 0.897, 95% CI 0.823–0.978 for low concentrations, and RR = 1.22, 95% CI 1.042–1.429 for high concentrations). Table 2 shows the detailed results.

Table 2 RR values and 95% confidence intervals for cumulative lagged effects of extreme air pollutant concentrations in hospitalized patients with postoperative infections in Xinjiang, 2018–2022.
Full size table

Sensitivity Analysis

Sensitivity analyses demonstrated that the association between air pollutants and daily single-day lags of postoperative infection remained consistent when varying the degree of freedom of the time trend (ranging from 6 to 8 df/year). This confirmed the robustness of the model’s performance and the reliability of the results. For further insights, please refer to Fig. 8.

Fig. 8
figure 8

Sensitivity analysis of the effect of air pollutants on the postoperative infection after varying the degrees of freedom of the time trend (6–8 df/year) in Hebei and Xinjiang.

Full size image

Discussion

Hospitals are places where pathogenic bacteria accumulate. After undergoing orthopedic surgery, the patient’s body is traumatized, immunity is reduced, and exposure to pathogenic bacteria is increased, which can easily lead to postoperative infections. The occurrence of postoperative infections is affected by factors such as age, sex, heredity, and air pollutants. In this study, χ2 test was used to compare the differences in the prevalence characteristics of patients with postoperative infections in the Hebei Province and Xinjiang Uygur Autonomous Region during 2018–2022. The results showed that the differences in the distribution of postoperative infections between the two regions in terms of sex and age were not statistically significant. Second, this study compared the lagged effects of air pollutants on the daily number of hospitalized patients with postoperative infections in the Hebei Province and Xinjiang Uygur Autonomous Region during 2018–2022 using DLNM. Both regions showed that two air pollutants, CO and NO2, increased the risk of hospitalization for postoperative infections.

The prevalence characteristics of postoperative infections

The incidence of postoperative infections after fracture surgery in the Xinjiang Uygur Autonomous Region was significantly higher than that in the Hebei Province. Of the patients hospitalized with postoperative infections in the Hebei Province and Xinjiang Uygur Autonomous Region, most were males and predominantly in the age group of 41–60 years. With the gradual increase in age, the amount of bone calcium gradually decreases and bone metabolism declines accompanied by osteoporosis, which can easily lead to fracture after external injuries. When the fracture needs to be treated through surgery, due to the relative decline of the fracture patient’s own function, in addition to invasive operations and unreasonable use of antibacterial drugs, several complications, including diabetes and hypoproteinemia, which may increase the risk of postoperative surgical infection in patients with fracture, can occur39,40.

Lagged effects of air pollutants on hospitalized patients with postoperative infections

Analysis of the effects of air pollutants on the number of daily hospitalized patients with postoperative infections using DLNM revealed that both the Hebei Province and Xinjiang Uygur Autonomous Region had two pollutants, CO and NO2, which increased the risk of postoperative infections.

Dust, smog, carbon dioxide, CO, nitrogen oxides, hydrocarbons, and other organic compounds are all environmental pollutants41. In 2020, Meo et al. reported that environmental pollution has developed into a dangerous situation that can cause significant and widespread damage to the regional environment and human health42. Environmental pollution and weather conditions exert a non-negligible impact on health and disease patterns43. CO can act as a carrier of infectious agents while impairing the body’s immunity and making it more susceptible to pathogens44. A study conducted on the New Crown outbreak reported that CO concentrations were significantly and positively associated with the number of cases and mortality45. Similarly, several studies have found that exposure to NO2 is associated with lung inflammation and functional abnormalities46,47,48, increased susceptibility to respiratory infections49, lymphocytopenia, and decreased number of lymphocyte subsets50,51. A clear relationship exists between the effects of NO2 on B cells and lymphocytes. Repeated exposure to NO2 also decreases the number of B cells, natural killer cells, and peripheral blood lymphocytes even in healthy adults52. Altogether, these findings confirm the negative effects of NO2 exposure on body-related immune functions. Several studies have confirmed that high NO2 exposure decreases glutathione GSH concentrations53, whereas glutathione has an inhibitory effect on the release of inflammatory mediators as well as an antioxidant and metabolism-promoting effect. When GSH levels gradually decrease, the risk of infection increases. In conclusion, environmental pollution can promote the migration of microorganisms and increase their chances of entering the human body54,55. Environmental pollutants weaken people’s immunity, making them more susceptible to pathogens44.

Shortcomings and prospects

This study had several shortcomings. First, the data sample size was small, and only hospitalized patients with postoperative infections in some hospitals in the Hebei Province and Xinjiang Uygur Autonomous Region were included, which cannot represent all patients with postoperative infections and may lead to biased results. Second, this study was retrospective, and recall and survey bias inevitably exist. Third, due to data limitations, we did not consider confounding factors such as antibiotic usage, occupation, marital status, place of residence, and lifestyle. Antibiotic use, in particular, is a critical factor influencing postoperative infections and antimicrobial resistance (AMR), and its exclusion may limit the comprehensiveness of our findings. Fourth, we did not incorporate meteorological factors (e.g., temperature, humidity) into the distributed lag nonlinear model (DLNM), which may also limit the comprehensive assessment of the relationship between air pollution and postoperative infections. Fifth, due to legal regulations and technical limitations, we were unable to include a map of the study areas in the research, which may affect readers’ intuitive understanding of the geographical locations and distribution of the study regions.Therefore, future studies need to comprehensively collect various types of factors to further validate the relationship between air pollution and postoperative infections and to explore the mechanisms through which air pollution influences the occurrence of postoperative infections.

Conclusion

No statistically significant differences in sex and age distribution in the Hebei Province and Xinjiang Uygur Autonomous Region were observed, and the incidence of postoperative infections was higher in males than in females. Every 0.1 mg/m3 increase in CO concentration and every 10 μg/m3 increase in NO2 concentration in the Hebei Province and Xinjiang Uygur Autonomous Region increased the risk of hospitalization for postoperative infections.