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Association of individual and environmental factors exposure with asthma among children: a cross-sectional study in Northern and Southern cities, China

樱花视频 volume听25, Article听number:听1610 (2025) Cite this article

Abstract

Background

Although previous studies have explored many risk factors associated with childhood asthma worldwide, limited studies have examined whether these risk factors differ in different regions. Our study aims to investigate whether there are differences in the factors influencing childhood asthma in Northern and Southern China and to explore possible reasons for this.

Methods

A cross-sectional analysis of 12,771 urban children aged 4-14 in China, we first applied a group Least Absolute Shrinkage and Selection Operator (LASSO) to influencing factors associated with childhood asthma, using cross-validation as the criterion. Then, we used logistic regression to calculate further the results. The Area Under the curve value used to evaluate model performance.

Results

In Northern China, risk factors for childhood asthma include boys, previously hospitalized with eczema/dermatitis or asthmatic bronchitis or chronic cough, history of infected with viral, bacterial or mycoplasma, testing positive for allergens, having conjunctivitis or two or more allergy problems, paternal or maternal with asthma or conjunctivitis or eczema/dermatitis, autumn and winter and house decoration less than 1 year before move in; Older than 11 years and the frequency of airing the quilt are protective factor against asthma. In Southern China, risk factors include antibiotic use in neonates, previously hospitalized with asthmatic bronchitis, history of infected with viral, testing positive for allergens, having conjunctivitis, summer or winter, screen time more than 4听h per day and paternal or maternal with rhinitis.

Conclusions

Our findings suggested that risk or protective factors contributing to childhood asthma differ significantly between Northern and Southern China.

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Introduction

Asthma is the most common non-communicable disease in children and adolescents [1, 2]. Currently, over 262 million children have asthma worldwide, and about 455,000 asthma-related deaths [3]. Asthma not only seriously affects the physical and mental health of children and adolescents but also brings a heavy economic burden to families [4]. China, as a rapidly modernizing developing country, people鈥檚 dwelling environments and habits have undergone tremendous changes in recent decades. Meanwhile, the prevalence of childhood asthma in China was 6.5% in 2018, an increase of 115.2% from 2010, which was over two times the growth of 52.8% from 2000 to 2010 [5, 6].

Previous studies have proved that asthma is a hereditary disease, and the heritability of asthma among first-degree and second-degree relatives was 78.2% and 55.0%, respectively [7, 8]. Furthermore, adverse environmental factors exposure also play a crucial role in asthma [9]. For example, using coal in the house, household mold exposure, keeping pets indoors, tobacco exposure, house decoration situation, food additives, etc., were also identified as an important risk factor for asthma in other studies [10,11,12]. Noteworthy, increasing evidence has shown a potential association of early life stage exposure with childhood asthma [13], such as maternal nutrition, health status, lifestyle and stress during pregnancy [14], antibiotic use in neonatal period, breastfeeding situation and so on [15].

Although many multi-center epidemiological studies have previously been conducted worldwide to explore the potential relationship between genetic or environmental factors in asthma among children [16,17,18], these studies merely focus on one or a few kinds of environmental or genetic factors influencing asthma in children, and they are often conducted in just one region. Few studies have compared whether factors affecting childhood asthma differ in different regions. China has a vast territory, and different regions have differences in lifestyle, living environment, climate conditions, economic status and other factors. Therefore, our study aims to investigate whether there are differences in factors influencing childhood asthma in Northern and Southern China and to explore possible reasons.

Methods

Study design and target population

This cross-sectional survey was conducted among children aged 4-14 in six provinces of China between August 2022 and August 2023. The three Northern provinces of Hebei, Neimenggu and Shanxi represent Northern China due to their representative topography, characteristic climatic conditions (mainly temperate monsoon climate), and distinct economic and cultural features. Northern China is characterized by dry climate, serious land degradation, low vegetation coverage density, windy and sandy, low population density and a preference for pasta [19,20,21]. The three Southern provinces of Jiangsu, Zhejiang and Shanghai represent Southern China because they are core areas of the south region, characterized by a subtropical monsoon climate (the primary climate type in the south region) and distinct economic and cultural features. Southern China is characterized by a humid climate, wide vegetation coverage, high population density and rice as the staple food [19, 21, 22]. Hence, we are convinced that these six provinces are representative and can reflect the characteristics of Northern and Southern China. The geographical distribution of the study sites is shown in Fig. 1.

Fig. 1
figure 1

Location distributions and sample sizes (N) of the six cities that participated in the study (Orange: Northern province; Green: Southern province)

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The process of including participants was based on our team鈥檚 previous study [23]. Briefly, a convenience sampling approach was used to collect information from urban children aged 4-14 years in six provinces (Hebei, Neimenggu, Shanxi, Jiangsu, Zhejiang and Shanghai) using an electronic questionnaire. Wen Juan Xing (), a widely utilized web-based survey platform in China, constructed and disseminated the survey questionnaire. We distributed the questionnaire through the following channels: distribution to students in primary and secondary schools by educators, posters displayed in the hospitals and physicians assisted in completion, and released via the present world鈥檚 most popular and has a large number of users application, WeChat Official Accounts (Tencent Technology, Shenzhen, China). The children鈥檚 parent or guardian completed the questionnaire and electronic informed consent was obtained. A total of 14,681 children aged 4-14 completed our questionnaire. After excluding participants with incorrect or incomplete information, 12,771 children were incorporated in the final analysis, including 2328 from Jiangsu, 1169 from Zhejiang, 1627 from Shanghai, 2211 from Hebei, 2967 from Neimenggu and 2469 from Shanxi. Our study鈥檚 workflow is illustrated in Fig. 2.

Fig. 2
figure 2

The workflow of this study

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Questionnaire

Our questionnaire was based on the previously validated International Study of Asthma and Allergy in Children questionnaire [24, 25] and the Dampness in Buildings and Health questionnaire [26, 27]. Based on China鈥檚 national conditions and environmental factors, we modified or added some questions in our questionnaire. The questionnaire includes four parts: child-related questions, pregnancy-related questions, family-related questions, and house-related questions.

Outcome and covariates

In this study, asthma was assessed based on two criteria: (1) participants had been doctor-diagnosed asthma; (2) participants had taken or were taking medication for asthma. Candidate risk factors in our questionnaire included those that have been confirmed, or we considered may be related to allergic diseases in children.

Among child-related factors, variables of interest included gender, age, ethnicity, body mass index (BMI) (kg/m2), birth weight, duration of breastfeeding, medication in the neonatal period, historical reasons for hospitalization, previous infections, allergen testing, allergic diseases, allergy season, additional nutritious supplements, dietary habit, daily sleep duration and average daily screen time exposure. BMI was calculated as weight in kilograms divided by height in square meters. Body weight was to the nearest 0.1 kg and height was to the nearest 0.1 cm). According to the World Health Organization (WHO) [28], classification criteria for BMI percentile were categorized into four groups as follows: underweight (<鈥�5th percentile), normal weight (鈥�鈮�鈥�5th and <鈥�85th percentiles), overweight (鈥�鈮�鈥�85th and <鈥�95th percentiles), and obesity (鈥�鈮�鈥�95th percentile) [29]. Birth weight (to the nearest 0.1 kg) was categorized into three groups also according to WHO: low birth weight infant (<鈥�2500 g), normal birth weight (鈥�鈮�鈥�2500 g and 鈮�鈥�4000 g), and macrosomia (>鈥�4000 g).

Among pregnancy-related factors, variables items of interest included the mother鈥檚 and father鈥檚 age of childbirth, maternal pregnancy smoking and drinking exposure, pregnancy-related diseases, pet ownership during pregnancy, parity, delivery mode, gestational weeks, and whether the child was a singleton.

Among family-related factors, variables of interest included paternal or maternal allergic disease and monthly economic income.

Among house-related factors, variables of interest included current family smoker, number of residents, years of house decoration, distance between residence and transportation junction, house cleaning frequency, having a pet, cooking fuel source, mode of heating in winter, mode of cooling in summer, pillow and bedding materials.

Details for all related risk factors (41 groups factors) were summarized in Table S1 in the Supplementary Material.

Statistical methods

Statistical analyses were completed using the SPSS software (version 26.0, IBM Ltd., USA) and the R programming environment (version 4.3.3, . ).

Variable selection We adjusted for all factors as binary or categorical variables. There are only yes or no results for categorical variables for each category, and the reference value (ref) of the calculated P-value is no. We used the Chi-square test (SPSS software) to screen out statistically different variables (P-value鈥�<鈥�0.05) between participants with asthma and participants without asthma in the North and South. Then, as there is a correlation between the potential factors that we investigated that are associated with asthma, we used a group Least Absolute Shrinkage and Selection Operator (LASSO) approach for analysis (R package 鈥済rpreg鈥�). Group LASSO is an extension of LASSO that allows pre-specified groups of covariates to be selected or excluded from a model [30]. Group LASSO could divide features into groups, each being treated as a whole. It achieved the purpose of feature selection by thinning the features of each group and shrinking the feature coefficients of some groups to zero. Group LASSO also applies a 位 (i.e., the summation of absolute values of all of a vector鈥檚 components) penalty to the component regression coefficients, which essentially minimizes the sum of squared errors subject to the sum of the absolute values of the coefficients being less than a given value. We used cross-validation criteria for variable selection (function 鈥渃vfit.glasso鈥� in R package 鈥済rpreg鈥�).

Model regression After the group LASSO selected the related variables, we applied logistics regression (R packages 鈥済lmnet鈥�) to determine which predictors were selected by the group LASSO. Odds ratios (OR) and a 95% confidence interval (95%CI) were used to express the relationship between influencing factors and childhood asthma. P-value鈥�<鈥�0.05 was considered statistically significant.

Model evaluation We estimated the accuracy of our model by plotting the area under the curve (AUC) of a receiver operating characteristic (ROC) curve (R package 鈥減ROC鈥�), that is, the plot of sensitivity (also called true positive rate) and specificity (also called false positive rate) for our outcome. AUC serves as a performance metric for assessing the quality of a model, indicating the likelihood that predicted positive instances are prioritized over negative ones. To avoid an over-fitting bias, we used a 10-fold cross-validation approach.

Results

Sample characteristics

In this questionnaire, 41 groups鈥� risk factors were finally included in the analysis, as detailed in Table S1 in the Supplementary Material. Table 1 summarizes the demographic information for 12,771 completed questionnaires. Among these participants, the prevalence of asthma in Northern and Southern China was 5.1% and 3.4%, respectively. In the North, the 393 asthmatic and 7254 non-asthmatic children differed in the distribution of gender, age, ethnicity, and gestational age at birth. In the South, 175 asthmatic and 4949 non-asthmatic children differed in the distribution of gender, age, and BMI.

Table 1 Demographic information for 12,771 participants
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Risk factors of childhood asthma

12,771 participants鈥� population distribution among the influencing factors we surveyed is shown in Table S2. In the group LASSO screening model, we choose lambda.se as the optimal 位 value, and the screening process results are shown in Fig. 3, that is, 16 groups and 14 groups of factors related to asthma were found in Northern and Southern China, respectively. The regression coefficients and P-values are summarized in Table S3.

Fig. 3
figure 3

Results of the group Least Absolute Shrinkage and Selection Operator (LASSO) screening variables. (A) Variable screening process in Northern China; (B) Variable screening process in Southern China; (C) Result of 10-fold cross-validation in Northern China; (D) Result of 10-fold cross-validation in Southern China

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Logistical regression results (OR and 95%CI) for asthma based on group LASSO selected variables are presented in Table 2. In Northern China, children-related factors: boys, less than 11 years old, previously hospitalized for eczema/dermatitis, chronic cough and asthmatic bronchitis, previously infected with viral, bacterial or mycoplasma, tested positive for allergens, having conjunctivitis, having two or more allergy problems and paternal or maternal allergy with conjunctivitis are associated with an increased risk of childhood asthma. Other factors, such as autumn or winter, paternal or maternal with asthma or eczema/dermatitis, and years of house decoration less than 1 year, have also increased the risk of childhood asthma in the North. The frequency of airing the quilt (at least once every two weeks) is a protective factor against asthma.

Table 2 Logistic regression results for asthma based on LASSO selected variables (n鈥�=鈥�12771)
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In Southern China, using antibiotics in the neonatal period, children hospitalized for asthmatic bronchitis, infected with viral, testing positive for allergens, having conjunctivitis, paternal or maternal allergy with conjunctivitis, spring and winter, using screen time more than 4 h and paternal or maternal allergy with rhinitis are related to an increased risk of asthma among children. Among all the factors, no protective factors for childhood asthma were found.

Model strength

Aim to prove the predictive strength of the influencing factors with asthma selected by group LASSO, the ROC curves in Northern and Southern China are shown in Fig. 4. The AUCs were 0.854 in Northern China, and 0.793 in Southern China, indicating strong model predictions and associations. An AUC greater than 0.70 is generally considered a reliable prediction model [31].

Fig. 4
figure 4

Receiver operating characteristic (ROC) curves for our model in Northern (A) and Southern China (B). If the Sensitivity鈥�=鈥�1-Specificity, then the curve is a 45掳 straight line, and the Area Under the Curve (AUC)鈥�=鈥�0.5, indicating no predictive value

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Discussion

The current study aimed to identify the individual, familial and environmental factors associated with asthma among Chinese children and whether those factors differ between Northern and Southern China. Our cross-sectional study was conducted among urban children aged 4-14 from six provinces in China. Among the factors we investigated in the questionnaire, we found that the risk factors of asthma in the North are more than those in the South, and there are also protective factors to prevent the occurrence of asthma in the North, but these protective factors do not play a role in the South.

The prevalence of asthma among urban children aged 4-14 in Northern and Southern China was 5.1% and 3.4%, respectively. A previous survey conducted in China demonstrated that the prevalence of childhood asthma increased significantly from 3.8% in 2015-2016 to 6.5% in 2017-2018, with an increasing trend year by year [6]. The low prevalence of asthma in this study could presumably be ascribed to the study that only investigated urban children, and previous studies have shown that the prevalence of childhood asthma in rural areas is higher than in urban areas. Another possible reason we consider may be related to this study was conducted during the coronavirus disease pandemic. During the pandemic period, stringent prevention and control measures implemented by the Chinese government [32, 33] may provide new ideas for us to prevent and reduce the prevalence of childhood asthma, but further research and surveys are needed to confirm these findings.

People鈥檚 lifestyles have changed dramatically during the coronavirus disease pandemic. Especially children spend much more time on screen time instead of studying in school due to home quarantine and online teaching. A cross-sectional study in New York found that almost three-quarters of children with asthma engaged in more than 2 h of screen time per day, and when children鈥檚 average daily screen time increases to 3.4 h per day, asthma was more likely to develop into persistent asthma [34]. Another study in Canada also provided evidence that three or more hours of screen time daily nearly doubles the risk of developing asthma [35]. In our study, the children鈥檚 screen time of more than 4 h per day was 1.9% in the South (asthma: 4.0%, non-asthma: 1.8%) and 1.6% in the North (asthma: 1.5%, non-asthma: 1.6%). But there was a positive between children鈥檚 screen time of more than 4 h per day and the occurrence of asthma only in the South. Why this result only appears in the south, which we believe may be related to the following factors: Previous studies have proved that the humid climate in the South can lead to higher concentrations of indoor air allergens, such as mold and dust mites, which are significantly related with childhood asthma [36]. Prolonged use of electronic screens means children have more opportunities to be exposed to indoor allergens, which may be why the relationship between screen time and childhood asthma is more markedly in the south. Additionally, the high prevalence of electronic devices and parents鈥� heightened awareness of health issues related to electronic products in the south may make the relationship between screen time and asthma more easily found.

Regarding individual factors, girls aged 11-14 years were protective factors for childhood asthma in Northern China. A systematic analysis has a similar result: asthma was more common in boys under 13 years of age but more common in adult women than men [37, 38]. One possible explanation for this result is that the estrogen surge and testosterone decrease in females after puberty, compared to males, inhibits IL-17 A-mediated airway inflammation and thus reduces the occurrence of Th2-mediated type 2 asthma [39]. Other adverse individual factors in the North, for example, bacterial and mycoplasma infections mainly through altered immune system or early hypopharyngeal colonization inducing and exacerbating childhood asthma [40, 41], and viruses infection can promote Th2 type cytokine production, leading to the development of asthma [42, 43]. The infection rate of respiratory diseases in Northern China is significantly higher than in Southern China, especially the infection of human respiratory syncytial virus under 5 years of age, which is most likely to cause wheezing in children. Immunity increases with age, so older than 11 years is a protective factor for childhood asthma in the north [44]. Data from several studies proved that allergen testing positively, allergic rhinitis, allergic conjunctivitis, eczema, and chronic cough were markedly risk factors for the prevalence of asthma [45, 46], which was also found in Northern China in this study.

In Southern China, antibiotic use during the neonatal period was an individual factor that deserves significant attention. Patrick and Ren et al. studies also pay close attention to the using antibiotics in the first 1000 days of life; they found antibiotics can cause the consequent alteration of a child鈥檚 gut microbiome system and an unbalanced immune system, which may contribute to children鈥檚 higher susceptibility to asthma [47] and persist into middle age [48, 49]. Data from 18 children鈥檚 hospitals in China showed that the use rate of watch antibiotics in national medical centers during the neonatal period (71.54%) was higher than that in other hospitals (55.84%) [50]. This might explain the negative correlation between asthma and antibiotics only in Southern China. The three provinces representing the Southern region in this study have rich medical resources and an advanced medical level, and the usage rate of prudent antibiotics is much higher than that in Northern China, making it easier to reveal the adverse effects of antibiotics.

Upper airway microbiota species and exposure concentrations may interact dynamically to influence respiratory disease susceptibility and accelerate disease exacerbation in children with asthma [51]. A clinic cohort indicated that rhinovirus and respiratory syncytial virus detection rates increased from late summer to winter and peaked in autumn [51], so asthma surged in autumn and winter in the North of our study. Season changes also influenced indoor allergen concentration, especially the concentration of fungal. A New York City cohort (n鈥�=鈥�298) attributed the increased prevalence of childhood asthma in the spring to an increase in total fungal concentrations [52]. The high humidity in three Southern provinces in our study, which significantly promoted fungai growth, was also responsible for the positive association between asthma and spring in the Southern region. Furthermore, lower humidity and effectively removing fungi, mites, or dust indoors can prevent the prevalence of asthma, such as regularly and frequently airing the quilt, as we found in Northern China.

In home environment factors, formaldehyde (FA), the most harmful substance to the human body in house decoration, is FA, primarily released from furniture and decorative material [53, 54]. According to the WHO indoor air guidelines, the limit concentration of indoor FA is less than 0.1 mg/m3 [28]. Indeed, the average indoor FA concentration of newly renovated houses (<鈥�1 year) reached 0.2 mg/m鲁, far exceeding the national standard. A systematic review and meta-analysis revealed a 10% increase in childhood asthma risk for each 10 碌g/m3 increase in FA exposure [55]. After 3-6 months of house decoration, the concentration of formaldehyde release reaches its peak. Cheng鈥檚 study found that the risk of asthma was significantly reduced (OR鈥�=鈥�0.397) in children who had been vacant for 12-24 months compared to those vacant for 6-12 months after home renovation [56], which is consistent with our findings. Temperature and humidity are critical factors influencing formaldehyde emission rates. Higher temperatures and humidity levels accelerate formaldehyde volatilization. In Northern China, temperature and humidity are lower than in Southern China, leading to slower formaldehyde volatilization after house decoration. This phenomenon explains the correlation between years of house decoration less than 1 year and asthma only in the North.

Our study has lots of strengths. First, in contrast to published studies, our questionnaire included many factors that may influence the development or exacerbation of asthma. We included 105 risk factors (41 groups) that included the child鈥檚 personal information, maternal pregnancy information, familial and environmental factors. Secondly, we assessed risk factors for asthma in two regions, North and South, which the previous studies did not evaluate. In addition, all AUCs in our research are more than 0.75, indicating that our screening approach had strengths model predictive ability. As is widely known, the more significant the AUC, the better the model鈥檚 ability to predict risk.

A common shortcoming of all retrospective studies in this study is recall bias, such as diagnosis of asthma, maternal pregnancy history, personal medical history in early childhood, etc. However, each option in our questionnaire was detailed enough, enabling the completers to recall accurate and reliable information as much as possible. Secondly, our study using an electronic questionnaire is convenient and environmentally efficient but might exclude certain groups of people; for example, families without access to the internet or those less engaged with healthcare or schools might be underrepresented, which may lead to an overestimation or underestimation of asthma prevalence. Indeed, this study investigates urban children with relatively high internet coverage, and the extensive distribution channels and age range of questionnaires can help mitigate the selection bias caused by the above problems to a certain extent. Third, the objective factor of differences in medical resources between the North and South in this study may contribute to differences in the quality of asthma diagnosis and management. Such differences could introduce bias into the data, potentially affecting the accuracy of the study鈥檚 findings. However, the study covers a wide geographic area and includes a large number of participants, which may, to some extent, mitigate the inaccuracies in the data caused by these regional disparities in medical resources. In future research, we could address this problem by stratifying the data or incorporating additional variables to control for the impact of healthcare resource distribution on the outcomes. Finally, no external validation was one of our shortcomings, so this model performance in a new dataset is unproven. But we have solved the possible problems of all models with cross-validation, such as uncertainty, and our AUCs are all more than 0.75. It is undeniable that these findings need to be further validated in future studies.

Conclusions

In summary, we used group LASSO and logistical regression to select key risk factors associated with asthma based on urban children aged 4-14 from Northern and Southern China. Our study ultimately identified eight groups of risk factors associated with childhood asthma in both North and South, and two groups of protective factors (older than 11 years, airing the quilt frequently) in the North. But these eight groups of risk factors are different in the North (gender, history of hospitalization or infection, testing positive for allergens, allergic problems or season, paternal or maternal allergy and house decoration less than 1 year before move in) and in the South (history of medication in neonatal period or hospitalization or infection, testing positive for allergens, allergic problems or season, paternal or maternal allergy, screen time of more than 4 h per day). Our findings suggested that risk or protective factors contributing to childhood asthma differ significantly between Northern and Southern China.

Data availability

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

Abbreviations

BMI:

Body Mass Index

WHO:

World Health Organization

ref:

Reference

LASSO:

Least Absolute Shrinkage and Selection Operator

OR:

Odds Ratios

95%CI:

95% Confidence Interval

AUC:

Area Under the Curve

ROC:

Receiver Operating Characteristic

FA:

Formaldehyde

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Acknowledgements

We would like to extend our heartfelt gratitude to all the study participants for their invaluable contributions.

Funding

This study was supported by Chinese Academy of Medical Sciences Clinical and Translational Medicine Research Project (No: 2021-I2M-C&T-B-089), Beijing Research Ward Construction Clinical Research Project (No: 2022-YIXBF-04-01-03), and the National High Level Hospital Clinical Research Funding (No: 2022-NHLHCRF-LX-01- 0301).

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Author notes

  1. Di Lv and Jing Liu Contributed equally to this work.

Authors and Affiliations

  1. Department of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China

    Di Lv,听Jing Liu,听Die Liu,听Yuanmei Chen,听Fang Ye,听Qin Hui,听Chao Wang,听Lijuan Tang,听Meihong Xia,听Jianning Guo,听Qi Sun听&听Qi Zhang

  2. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

    Di Lv听&听Qi Zhang

  3. Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong鈥檃n Road, Xicheng District, Beijing, 100050, China

    Qi Sun

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Contributions

Di Lv analyzed the data and drafted the manuscript. Jing Liu revised the manuscript. Die Liu and Yuanmei Chen contributed in collecting the data. Fang Fe and Qin Hui contributed in interpreting the data. Chao Wang and Lijuan Tang contributed in curating in data. Meihong Xia and Jianning Guo contributed in Methodology. Qi Sun and Qi Zhang collaboratively designed the study and revised the manuscript.

Corresponding authors

Correspondence to Qi Sun or Qi Zhang.

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Ethics approval and consent to participate

The study was approved by the ethical the Institutional Review Board of China-Japan Friendship Hospital (No: 2022-KY-006-1). All study procedures were in accordance with the ethical standards of the Declaration of Helsinki. All participants in the survey, informed consent was obtained from their parents or legal guardian before completing the questionnaire.

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Not applicable.

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The authors declare no competing interests.

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Lv, D., Liu, J., Liu, D. et al. Association of individual and environmental factors exposure with asthma among children: a cross-sectional study in Northern and Southern cities, China. 樱花视频 25, 1610 (2025). https://doi.org/10.1186/s12889-025-22842-w

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  • DOI: https://doi.org/10.1186/s12889-025-22842-w

Keywords

樱花视频

ISSN: 1471-2458

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