[Association between exposure to PM_(2.5) and chronic kidney disease in a population with metabolic syndrome].

OBJECTIVE: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components. METHODS: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities. RESULTS: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 µg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects. CONCLUSION: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.

Towards simulation optimization of subway station considering refined passenger behaviors.

The escalating passenger flow in subway systems presents significant challenges to station facilities during peak hours. Poorly designed station facilities can reduce passenger throughput efficiency and compromise passenger safety. This study conducts on-site investigations to extract refined parameters of passenger behaviors in security check and ticket checking areas. Using Beijing Subway Yizhuang Line Ciqunan Station as a case study, a microscopic simulation model is developed to replicate pedestrian flow within the subway station. By focusing on passenger demand and traffic organization, the layout of station facilities is regulated and optimized. After optimization, the passenger density in the security check and ticket inspection areas during the morning peak period decreased from 1.33 people/m2 to 1.00 people/m2; the longest queue length on the east side decreased from 15 people to 10 people, and the maximum queue length on the west side decreased from 7 people to 3 people. During peak hours, the dispersal time of passenger flow on the west side when entering the station decreased from 31.56 minutes to 30.04 minutes, and on the east side, it decreased from 36.12 minutes to 30.87 minutes. The optimization results effectively improved the efficiency of entering the station during peak hours.

Major changes in prevalence and genotypes of rotavirus diarrhea in Beijing, China after RV5 rotavirus vaccine introduction.

To analyze the epidemiological characteristics of group A rotavirus (RVA) diarrhea in Beijing between 2019 and 2022 and evaluate the effectiveness of the RV5 vaccine. Stool specimens were collected from patients with acute diarrhea, and RVA was detected and genotyped. The whole genome of RVA was sequenced by fragment amplification and Sanger sequencing. Phylogenetic trees were constructed using Bayesian and maximum likelihood methods. Descriptive epidemiological methods were used to analyze the characteristics of RVA diarrhea. Test-negative design was used to evaluate the vaccine effectiveness (VE) of the RV5. Compared with 2011-2018, RVA-positive rates in patients with acute diarrhea under 5 years of age and adults decreased significantly between 2019 and 2022, to 9.45% (249/634) and 3.66% (220/6016), respectively. The predominant genotype of RVA had changed from G9-VIP[8]-III between 2019 and 2021 to G8-VP[8]-III in 2022, and P[8] sequences from G8-VP[8]-III strains formed a new branch called P[8]-IIIb. The complete genotype of G8-VP[8]-III was G8-P[8]-I2-R2-C2-M2-A2-N2-T2-E2-H2. The VE of 3 doses of RV5 was 90.4% (95% CI: 28.8%-98.7%) against RVA diarrhea. The prevalence of RVA decreased in Beijing between 2019 and 2022, and the predominant genotype changed to G8P[8], which may be related to RV5 vaccination. Continuous surveillance is necessary to evaluate vaccine effectiveness and improve vaccine design.

Development of family resource indicators for people with schizophrenic disorder in community by modified Delphi method in Beijing, China.

INTRODUCTION: Most people with schizophrenia in China are supported by their family members in community. The patient's family is confronted with severe care burden and pressure, which directly affects the caregiver's own health and social life, and indirectly affects the patient's rehabilitation. Adequate family resources can reduce the burden and pressure on families. But there is an absence of systematic family resource indicators for people with schizophrenic disorder in China. OBJECTIVES: This study aimed to develop a set of family resource indicators for people with schizophrenic disorder in China. DESIGN: Preliminary family resource indicators were generated and refined by literature review and an expert consultation meeting. Two rounds of email-based Delphi survey were carried out to identify family resource indicators. SETTING: Two rounds of email-based Delphi survey were performed from July to September 2021 in Beijing, China. PARTICIPANTS: There were 15 mental health doctors from community health service centres and four psychiatrists from tertiary hospitals, and two primary care researchers from universities in the first and second rounds Delphi survey. RESULTS: All the 21 experts participated in both rounds of Delphi survey. A total of 46 indicators achieved consensus for inclusion in the final set of indicators after two rounds of Delphi survey. The final set of indicators was grouped into 10 domains: financial support (three indicators), psychological and spiritual support (eight indicators), medical treatment (three indicators), information and education (three indicators), structural support (two indicators), external family resources included social resources (five indicators), cultural resources (two indicators), economic resources (seven indicators), environmental resources (four indicators) and medical resources (nine indicators). CONCLUSIONS: A set of 46 family resource indicators for people with schizophrenic disorder in community was identified by an iterative Delphi process in Beijing, China. However, the indicators still need to be validated by testing in further studies.

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

Mobility census for monitoring rapid urban development.

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Factors influencing length of stay and costs in inpatient cases of human brucellosis as the primary diagnosis over a decade in Beijing, China.

Aims: In the year 2021, human brucellosis ranked fifth in terms of the number of cases among all statutorily notifiable infectious diseases in China, thus remaining a significant concern for public health. This study aims to provide insights into the financial burden of human brucellosis by examining hospital stays and associated costs for affected individuals. Methods: In this retrospective study, we gathered updated data from 467 inpatient cases primarily diagnosed with human brucellosis at eight major tertiary hospitals in Beijing, China, spanning from 2013 to 2023. To comprehensively explore the economic impact on individuals, we not only analyzed the duration of hospital stays and total costs but also examined various charge types, including drug, lab test, medical imaging, medical treatment, surgical procedures, medical supplies and consumables, inpatient bed care, nursing services, and other services costs. Statistical analysis was employed to compare differences among gender, age, ethnicity, type of health insurance, condition at admission, comorbidity index, the performance of surgery, and the site of infection. Results: Both the length of stay and total cost exhibited significant variations among insurance, surgery, and infection site groups. Utilization categories demonstrated significant differences between patients who underwent surgery and those who did not, as well as across different infection sites. Furthermore, multiple linear regression analysis revealed that the condition at admission, Elixhauser comorbidity index, infection site, and surgery influenced both hospital stay and total cost. In addition, age and insurance type were associated with total costs. Conclusion: By delving into various utilization categories, we have addressed a significant gap in the literature. Our findings provide valuable insights for optimizing the allocation and management of health resources based on the influencing factors identified in this study.

Individual and family factors correlated with children's fruit consumption.

Background: Fruits are essential for health, yet their consumption in children is inadequate, with unclear influencing factors. Methods: A cross-sectional study was conducted among students in grades 3-12 in Beijing, China, from September 2020 to June 2021. Fruit consumption in children was surveyed using a self-administered food frequency questionnaire. Additionally, children's food and nutrition literacy and family food environments were assessed using the "Food and Nutrition Literacy Questionnaire for Chinese School-age Children" and the "Family Food Environment Questionnaire for Chinese School-age Children," respectively. Results: Out of 10,000 participating children, 62.5% consumed fruit daily, with a lower frequency among boys (59.3%) compared to girls (65.8%), and among senior students (48.6%) compared to junior (63.6%) and primary students (71.2%). Fruit consumption was positively associated with other healthy foods (vegetables, whole grains, etc.) and negatively with unhealthy foods (sugared soft drinks). Children with higher food and nutrition literacy consumed fruits daily more frequently (82.4% vs. 59.9%, ORs = 2.438, 95%CI: 2.072-2.868). A significant positive correlation was found between children's fruit consumption and a healthy family food environment (66.4% vs. 50.2%, OR = 1.507, 95%CI: 1.363-1.667). Conclusion: The results indicate that individual food and nutrition literacy and family food environment are key positive predictors of children's fruit consumption. Future interventions should focus on educating children and encouraging parents to foster supportive family environments.

Postpartum depression and risk factors among working women one year after delivery in Beijing, China: a cross-sectional study.

Background: Postpartum depression (PPD) is the most common mental illness affecting women after childbirth, and working mothers may be faced with unique challenges. This study aimed to examine the depression status among working postpartum women in 1 year of childbirth and explore the relationship between occupational factors and PPD in urban Beijing, China. Methods: A cross-sectional survey of 554 postpartum women was conducted among ten community health service centers in six urban districts of Beijing, China. Sociodemographic, occupational, childbirth and postpartum information were collected. Depression status was obtained using the Edinburgh Postnatal Depression Scale. Prevalence of postpartum depression (PPD) was assessed in relation to occupational characteristics, and influencing factors were analyzed through logistic regression. Results: Of the postpartum women, 29.42% met the criteria for depression. PPD prevalence was significantly higher among women employed in commercial enterprises (39.81%). The analysis of influencing factors showed that age, family or personal monthly income, maternity leave, feeding methods, and postpartum care affected the psychological health of occupational women after childbirth. Conclusion: PPD prevalence is notably elevated among women employed in commercial enterprises, and specific risk factors contribute to its occurrence. These findings highlight the need for targeted interventions to address these risk factors and prevent PPD in this population.

Key drivers of the oxidative potential of PM2.5 in Beijing in the context of air quality improvement from 2018 to 2022.

The mass concentration of atmospheric particulate matter (PM) has been continuously decreasing in the Beijing-Tianjin-Hebei region. However, health endpoints do not exhibit a linear correlation with PM mass concentrations. Thus, it is urgent to clarify the prior toxicological components of PM to further improve air quality. In this study, we analyzed the long-term oxidative potential (OP) of water-soluble PM2.5, which is generally considered more effective in assessing hazardous exposure to PM in Beijing from 2018 to 2022 based on the dithiothreitol assay and identified the crucial drivers of the OP of PM2.5 based on online monitoring of air pollutants, receptor model, and random forest (RF) model. Our results indicate that dust, traffic, and biomass combustion are the main sources of the OP of PM2.5 in Beijing. The complex interactions of dust particles, black carbon, and gaseous pollutants (nitrogen dioxide and sulfur dioxide) are the main factors driving the OP evolution, in particular, leading to the abnormal rise of OP in Beijing in 2022. Our data shows that a higher OP is observed in winter and spring compared to summer and autumn. The diurnal variation of the OP is characterized by a declining trend from 0:00 to 14:00 and an increasing trend from 14:00 to 23:00. The spatial variation in OP of PM2.5 was observed as the OP in Beijing is lower than that in Shijiazhuang, while it is higher than that in Zhenjiang and Haikou, which is primarily influenced by the distribution of black carbon. Our results are of significance in identifying the key drivers influencing the OP of PM2.5 and provide new insights for advancing air quality improvement efforts with a focus on safeguarding human health in Beijing.

Lag effect of ambient temperature on respiratory emergency department visits in Beijing: a time series and pooled analysis.

BACKGROUND: Although the association between ambient temperature and mortality of respiratory diseases was numerously documented, the association between various ambient temperature levels and respiratory emergency department (ED) visits has not been well studied. A recent investigation of the association between respiratory ED visits and various levels of ambient temperature was conducted in Beijing, China. METHODS: Daily meteorological data, air pollution data, and respiratory ED visits data from 2017 to 2018 were collected in Beijing. The relationship between ambient temperature and respiratory ED visits was explored using a distributed lagged nonlinear model (DLNM). Then we performed subgroup analysis based on age and gender. Finally, meta-analysis was utilized to aggregate the total influence of ambient temperature on respiratory ED visits across China. RESULTS: The single-day lag risk for extreme cold peaked at a relative risk (RR) of 1.048 [95% confidence interval (CI): 1.009, 1.088] at a lag of 21 days, with a long lag effect. As for the single-day lag risk for extreme hot, a short lag effect was shown at a lag of 7 days with an RR of 1.076 (95% CI: 1.038, 1.114). The cumulative lagged effects of both hot and cold effects peaked at lag 0-21 days, with a cumulative risk of the onset of 3.690 (95% CI: 2.133, 6.382) and 1.641 (95% CI: 1.284, 2.098), respectively, with stronger impact on the hot. Additionally, the elderly were more sensitive to ambient temperature. The males were more susceptible to hot weather than the females. A longer cold temperature lag effect was found in females. Compared with the meta-analysis, a pooled effect of ambient temperature was consistent in general. In the subgroup analysis, a significant difference was found by gender. CONCLUSIONS: Temperature level, age-specific, and gender-specific effects between ambient temperature and the number of ED visits provide information on early warning measures for the prevention and control of respiratory diseases.

Identification and classification of urban employment centers based on big data: A case study of Beijing.

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

Analysis of care-seeking and diagnosis delay among pulmonary tuberculosis patients in Beijing, China.

Background: Tuberculosis (TB) remains a significant public health challenge in China. Early detection and diagnosis of TB cases are crucial to interrupt disease transmission and prevent its progression. This study aims to describe the delay in seeking care and diagnosis among patients with pulmonary tuberculosis (PTB) and identify the influencing factors in two counties in Beijing. Methods: A retrospective analysis was carried out to investigate care-seeking and diagnosis delay in two counties in Beijing. Basic information of PTB patients from January 1 to December 31, 2021, was extracted from the Tuberculosis Information Management System of China (TBIMS), and all enrolled patients were interviewed via telephone using a standard questionnaire. Statistical description was performed using the median and interquartile range (IQR). Chi-square test and multivariate logistic regression model were used to analyze the influencing factors. Results: 537 patients were enrolled. The median duration of care-seeking and diagnosis delay was 11 (IQR: 5-26) days and 8 (IQR: 0-18) days, with 41.71 and 35.20% of patients experiencing delays (>14 days). The study found that being asymptomatic (OR = 2.791, 95%CI: 1.710-4.555) before seeking medical care and not attending work during treatment (OR = 2.990, 95%CI: 1.419-6.298) were identified as risk factors for care-seeking delay. Patients who were tracked (OR = 2.632, 95%CI: 1.062-6.521) and diagnosed at tuberculosis control and prevention institutions (OR = 1.843, 95%CI: 1.061-3.202) had higher odds of diagnostic delays. 44.69% of patients presented a total delay (>28 days), with a median duration of 25 (IQR: 13-39) days. A multivariate logistic regression analysis showed that healthy examination (OR = 0.136, 95%CI: 0.043-0.425) was a protective factor for total delay. Conclusion: Public interventions are necessary to improve the efficiency of PTB patients detection and treatment in Beijing. Medical services should focus on the target population and improve access to medical care to further reduce delays for PTB patients.

Characterization and health risks of short- and medium-chain chlorinated paraffins in the gas and size-fractionated particulate phases in ambient air.

Short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs) have garnered significant attention because they have persistence and potential toxicity, and can undergo long-distance transport. Chlorinated paraffins (CPs) inhaled in the size-fractionated particulate phase and gas phase can carry different risks to human health due to their ability to accumulate in different regions of the respiratory tract and exhibit varying deposition efficiencies. In our study, large-volume ambient air samples in both the size-fractionated particulate phase (Dp < 1.0 µm, 1.0-2.5 µm, 2.5-10 µm, and Dp ≥ 10 µm) and gas phase were collected simultaneously in Beijing using an active sampler. The overall levels of SCCPs and MCCPs were relatively high, the ranges being 57-881 and 30-385 ng/m3, respectively. SCCPs tended to be partitioned in the gas phase (on average 75% of the ΣSCCP concentration), while MCCPs tended to be partitioned in the particulate phase (on average 62% of the ΣMCCP concentration). Significant correlations were discovered between the logarithm-transformed gas-particle partition coefficients (KP) and predicted subcooled vapor pressures (PL0) (p < 0.01 for SCCPs and MCCPs) and between the logarithm-transformed KP values and octanol-air partition coefficients (KOA) (p < 0.01 for SCCPs and MCCPs). Thus, the slopes indicated that organic matter absorption was the dominant process involved in gas-particle partitioning. We used the ICRP model to calculate deposition concentrations for particulate-associated CPs in head airways region (15.6-71.4 ng/m³), tracheobronchial region (0.8-4.8 ng/m³), and alveolar region (5.1-21.9 ng/m³), then combined these concentrations with the CP concentrations in the gas phase to calculate estimated daily intakes (EDIs) for inhalation. The EDIs for SCCPs and MCCPs through inhalation of ambient air for the all-ages group were 67.5-184.2 ng/kg/day and 19.7-53.7 ng/kg/day, respectively. The results indicated that SCCPs and MCCPs in ambient air do not currently pose strong risks to human health in the study area.

Fine particulate matter exposure and systemic inflammation: A potential mediating role of bioactive lipids.

Inflammation is a key mechanism underlying the adverse health effects of exposure to fine particulate matter (PM2.5). Bioactive lipids in the arachidonic acid (ARA) pathway are important in the regulation of inflammation and are reportedly altered by PM2.5 exposure. Ceramide-1-phosphate (C1P), a class of sphingolipids, is required to initiate ARA metabolism. We examined the role of C1P in the alteration of ARA metabolism after PM2.5 exposure and explored whether changes in the ARA pathway promoted systemic inflammation based on a panel study involving 112 older adults in Beijing, China. Ambient PM2.5 levels were continuously monitored at a fixed station from 2013 to 2015. Serum cytokine levels were measured to assess systemic inflammation. Multiple bioactive lipids in the ARA pathway and three subtypes of C1P were quantified in blood samples. Mediation analyses were performed to test the hypotheses. We observed that PM2.5 exposure was positively associated with inflammatory cytokines and the three subtypes of C1P. Mediation analyses showed that C1P significantly mediated the associations of ARA and 5, 6-dihydroxyeicosatrienoic acid (5, 6-DHET), an ARA metabolite, with PM2.5 exposure. ARA, 5, 6-DHET, and leukotriene B4 mediated systemic inflammatory response to PM2.5 exposure. For example, C1P C16:0 (a subtype of C1P) mediated a 12.9 % (95 % confidence interval: 3.7 %, 32.5 %) increase in ARA associated with 3-day moving average PM2.5 exposure, and ARA mediated a 27.1 % (7.8 %, 61.2 %) change in interleukin-8 associated with 7-day moving average PM2.5 exposure. Our study indicates that bioactive lipids in the ARA and sphingolipid metabolic pathways may mediate systemic inflammation after PM2.5 exposure.

Corporate social responsibility: A key driver of sustainable development in China's post-COVID economy.

The first goal of this research was to measure the impact of corporate social responsibility (CSR) on the sustainable development of an organization. However, the second objective examines the moderating influence of government policies, cultural norms, and stakeholder expectations on the relationship between CSR and an organization's sustainable development. This research primarily focused on the enterprises operating in the energy industry in Beijing. A sample of 498 individuals holding management positions within these enterprises was collected. The study's results established that CSR significantly influences the sustainable development of firms. Moreover, the results revealed that governmental regulations, cultural norms, and stakeholder expectations play a significant and positive role in moderating the impact of corporate social responsibility on the sustainable development of companies. The results of this study make a valuable contribution to the existing body of literature on CSR and its impact on the sustainable performance of enterprises in China.

Association between synoptic types in Beijing and acute myocardial infarction hospitalizations: A comprehensive analysis of environmental factors.

BACKGROUND: Environmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences. METHODS: We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2). RESULTS: Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4. CONCLUSION: Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.