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Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65 years) diabetic patients between 2014 and 2019 in Beijing. A time‐stratified case‐crossover design based on negative‐control exposure was used to assess causal associations of short‐term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7 days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10 μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1 mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), −0.08 (−0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), −0.04 (−0.06, −0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short‐term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients. Plain Language Summary: Although researches for cardiovascular disease (CVD) and respiratory diseases (RD) with air pollutants are widely reported, there is little evidence for causal associations of air pollution with CVD combined with diabetes mellitus (CVD‐DM) and RD combined with diabetes mellitus (RD‐DM) in elder patients. We included elder hospital admissions (aged ≥65 years) on comorbid CVD‐DM and RD‐DM patients between 2014 and 2019 in Beijing, China, to assess associations and causal inferences between air pollutants with CVD‐DM and RD‐DM. Random forest regression model was used to calculate the contribution magnitude of air pollutants. We found that exposure to air pollution increased admissions of CVD‐DM and RD‐DM in the elderly. Gaseous pollutants had a greater contribution to CVD‐DM and RD‐DM. The finding may lead to policy changes to reduce air pollution concentration, particularly gaseous pollutants to decrease hospital admissions. Key Points: Investigated causal associations of air pollutants on comorbid elderly populationA stable and accurate effect of air pollution was gained base on large sample sizesThe effects of gaseous pollutants are stronger than those of PM for the elderly [ABSTRACT FROM AUTHOR]

• A random forest model for early functional outcomes following acute care after traumatic brain injury (TBI) was developed and validated. • The BI score at admission, age, use of nonsurgical treatment, neurosurgery status, and mCCI score were the top 5 prognostic predictors. • The predictive model is generalisable by using hospital discharge abstract data. • The model can inform decision-making regarding TBI patient management and facilitate health care quality assessment and resource allocation. Few studies on early functional outcomes following acute care after traumatic brain injury (TBI) are available. The aim of this study was to develop and validate a predictive model for functional outcomes at discharge for TBI patients using machine learning methods. In this retrospective study, data from 5281 TBI patients admitted for acute care who were identified in the Beijing hospital discharge abstract database were analysed. Data from 4181 patients in 52 tertiary hospitals were used for model derivation and internal validation. Data from 1100 patients in 21 secondary hospitals were used for external validation. A poor outcome was defined as a Barthel Index (BI) score ≤ 60 at discharge. Logistic regression, XGBoost, random forest, decision tree, and back propagation neural network models were used to fit classification models. Performance was evaluated by the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AP), calibration plots, sensitivity/recall, specificity, positive predictive value (PPV)/precision, negative predictive value (NPV) and F1-score. Compared to the other models, the random forest model demonstrated superior performance in internal validation (AUC of 0.856, AP of 0.786, and F1-score of 0.724) and external validation (AUC of 0.779, AP of 0.630, and F1-score of 0.604). The sensitivity/recall, specificity, PPV/precision, and NPV of the model were 71.8%, 69.2%, 52.2%, and 84.0%, respectively, in external validation. The BI score at admission, age, use of nonsurgical treatment, neurosurgery status, and modified Charlson Comorbidity Index were identified as the top 5 predictors for functional outcome at discharge. We established a random forest model that performed well in predicting early functional outcomes following acute care after TBI. The model has utility for informing decision-making regarding patient management and discharge planning and for facilitating health care quality assessment and resource allocation for TBI treatment. [ABSTRACT FROM AUTHOR]

The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. Over‐dispersed generalized additive model was adopted to quantity the associations between CO and COPD with T2DM or hypertension. Based on principal diagnosis, COPD cases were identified according to the International Classification of Diseases (J44), and a history of T2DM and hypertension was coded as E12 and I10‐15, O10‐15, P29, respectively. A total of 459,258 COPD cases were recorded from 2014 to 2019. Each interquartile range uptick in CO at lag 03 corresponded to 0.21% (95%CI: 0.08%–0.34%), 0.39% (95%CI: 0.13%–0.65%), 0.29% (95%CI: 0.13%–0.45%) and 0.27% (95%CI: 0.12%–0.43%) increment in admissions for COPD, COPD with T2DM, COPD with hypertension and COPD with both T2DM and hypertension, respectively. The effects of CO on COPD with T2DM (Z = 0.77, P = 0.444), COPD with hypertension (Z = 0.19, P = 0.234) and COPD with T2DM and hypertension (Z = 0.61, P = 0.543) were insignificantly higher than that on COPD. Stratification analysis showed that females were more vulnerable than males except for T2DM group (COPD: Z = 3.49, P < 0.001; COPD with T2DM: Z = 0.176, P = 0.079; COPD with hypertension: Z = 2.48, P = 0.013; COPD with both T2DM and hypertension: Z = 2.44, P = 0.014); No statistically significant difference could be found between age groups (COPD: Z = 1.63, P = 0.104; COPD with T2DM: Z = 0.23, P = 0.821; COPD with hypertension: Z = 0.53, P = 0.595; COPD with both T2DM and hypertension: Z = 0.71, P = 0.476); Higher effects appeared in cold seasons than warm seasons on COPD (Z = 0.320, P < 0.001). This study demonstrated an increased risk of COPD with comorbidities related to CO exposure in Beijing. We further provided important information on lag patterns, susceptible subgroups, and sensitive seasons, as well as the characteristics of the exposure‐response curves. Plain Language Summary: The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. The aim of this study was to investigate the acute adverse effect of CO on admission of COPD, and COPD with T2DM or hypertension in Beijing, China. Our findings revealed that short‐term exposure to CO significantly increased the risk of COPD and COPD comorbidities admissions. Females were more vulnerable to CO exposure among COPD with comorbidities (expect T2DM group). We found higher effects appeared in cold season than warm season among COPD group. The exposure response relationship showed that there was no significant threshold effect between CO and COPD admission. Key Points: Exposure to CO was associated with an increased risk of chronic obstructive pulmonary disease (COPD) with comorbiditiesFemales were more vulnerable to CO exposure among COPD with comorbiditiesHigher effect appeared in cold season than warm season among COPD group [ABSTRACT FROM AUTHOR]

Air pollution and ischemic stroke (IS) are both vital factors affecting the health of Beijing citizens. This study aims at exploring the associations between air pollution, meteorology, and the hospital admission of IS (IS HA). Information on 476,659 IS inpatients in secondary and higher hospitals in Beijing from 2013 to 2018 were collected. A time-stratified case-crossover design with the generalized additive model and the distributed lag nonlinear model were used. In the single-pollutant models, an inter-quartile range increase in O3, SO2, CO, and NO2 resulted in a significant highest increase in IS HA by 2.23% (95% CI: 1.56%, 2.90%), 1.53% (95% CI: 1.12%, 1.95%), 1.05% (95% CI: 0.70%, 1.40%), and 0.51% (95% CI: 0.24%, 0.79%) on the day of pollution, so did PM2.5 and PM10 by 1.13% (95% CI: 0.68%, 1.59%) and 1.19% (95% CI: 0.74%, 1.64%) at a lag of 0–5 days. There was a nonlinear relationship between meteorology and IS HA. In the multivariate model, the cumulative relative risks with a maximum lag time of 21 days of PM2.5 and NO2 were 1.11 (95% CI: 1.04, 1.19) and 0.88 (95% CI: 0.82, 0.94), while the effects of SO2, O3, and meteorology were insignificant. The findings suggested that particulate pollutants could increase the risk of IS, and the elderly were more sensitive to it, while the results of gaseous pollutants are still discordant. The control of air pollution and the protection of susceptible populations should receive higher attention from policymakers. [ABSTRACT FROM AUTHOR]

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m3 (CO: 1 mg/m3) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM2.5, 0.17% (95% confidence interval [CI]: 0.10–0.24%) at Lag0 and 0.22% (95% CI: 0.12–0.33%) at Lag0–5; for PM10, 0.09% (95% CI: 0.05–0.13%) at Lag5 and 0.17% (95% CI: 0.09–0.26%) at Lag0–5; for SO2, 0.87% (95% CI: 0.46–1.29%) at Lag5; for NO2, 0.83% (95% CI: 0.62–1.04%) at Lag0 and 0.86% (95% CI: 0.59–1.13%) at Lag0–1; for CO 1.23% (95% CI: 0.66–1.80%) at Lag0 and 1.33% (95% CI: 0.33–2.35%) at Lag0–5; for O3 0.23% (95% CI: 0.12–0.35%) at Lag0 and 0.20% (95% CI: 0.05–0.34%) at Lag0–1. The effect sizes of PM2.5, NO2, and O3 remained significant after adjusting daily mean. Larger effect sizes were observed for PM2.5 and PM10 in cool season and for O3 in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population. [ABSTRACT FROM AUTHOR]

Background: Understanding the long-term inpatient service cost and utilization of psychiatric patients may provide insight into service demand for these patients and guide the design of targeted mental health programs. This study assesses 3-year hospitalization patterns and quantifies service utilization intensity of psychiatric patients in Beijing, China. Methods: We identified patients admitted for one of three major psychiatric disorders (schizophrenia, bipolar and depressive disorders) between January 1 and December 31, 2013 in Beijing, China. Inpatient admissions during the following 3 years were extracted and analyzed using sequence analysis. Clinical characteristics, psychiatric and non-psychiatric service use of included patients were analyzed. Results: The study included 3443 patients (7657 hospitalizations). The patient hospitalization sequences were grouped into 4 clusters: short stay (N = 2741 (79.61% of patients), who had 126,911 or 26.82% of the hospital days within the sample), repeated long stay (N = 404 (11.73%), 76,915 (16.26%) days), long-term stay (N = 101 (2.93%), 59,909 (12.66%) days) and permanent stay (N = 197 (5.72%), 209,402 (44.26%) days). Length and frequency of hospitalization, as well as readmission rates were significantly different across the 4 clusters. Over the 3-year period, hospitalization days per year decreased for patients in the short stay and repeated long stay clusters. Patients with schizophrenia (1705 (49.52%)) had 78.4% of cumulative psychiatric stays, with 11.14% of them in the permanent stay cluster. Among patients with depression, 23.11% had non-psychiatric hospitalizations, and on average 46.65% of their total inpatient expenses were for non-psychiatric care, the highest among three diagnostic groups. Conclusion: Hospitalization patterns varied significantly among psychiatric patients and across diagnostic categories. The high psychiatric care service use of the long-term and permanent stay patients underlines the need for evidence-based interventions to reduce cost and improve care quality. [ABSTRACT FROM AUTHOR]

Background: Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China. Methods: Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics. Results: The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals (p < 0.001). Male patients were more likely to have frequent readmissions (OR 1.30, 95%CI 1.04–1.64). Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission (OR 0.72, 95%CI 0.56–0.91) and frequent readmissions (OR 0.60, 95%CI 0.40–0.91). Conclusion: More than 30% of the psychiatric inpatients were readmitted within 1 year. Urban residents, those with medical comorbidities and previous psychiatric admission(s) or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30-day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions. [ABSTRACT FROM AUTHOR]

Introduction: Unintentional injuries (UIs) impose a significant burden on low- and middle-income countries (LMICs). However, available UI epidemiological data are limited for LMICs, including China. This article aimed to provide an overview of the UI hospitalization profile, identify risk factors for in-hospital mortality and provide diagnosis-specific survival risk ratios (SRRs) for reference by LMICs using hospital discharge abstract data (DAD) from Beijing, China.Patients and Methods: A cross-sectional study was conducted for patients sustaining UIs requiring admission. Information was retrieved from 138 hospitals in Beijing to describe the demographics, injury nature, mechanisms, severity and hospital outcomes. Multivariate logistic regression was performed to identify and evaluate risk factors for in-hospital mortality for UIs.Results: Falls (57.1%), transport accidents (19.9%) and exposure to inanimate mechanical forces (16.4%) were the leading causes of UI hospitalization. Falls and transport accidents were responsible for 94.2% of the in-hospital deaths caused by UIs. Injury mechanisms differed among sex (χ2 = 5322.1, P <  0.001) and age (χ2 = 24,143.3, P <  0.001) groups. Male sex (OR: 1.50, 95% confidence interval (CI): 1.23-1.79), age ≥ 85 years (OR: 16.39, 95% CI: 7.46-36.00), Barthel Index at admission ≤ 60 (OR: 25.78, 95% CI: 13.30-49.95), modified Charlson comorbidity index ≥ 6 (OR: 2.60, 95% CI: 1.91-3.55), International Classification of Diseases-based injury severity score (ICISS) < 0.85 (OR: 15.17, 95% CI: 12.57-18.30), sustaining injuries to the head/neck (OR: 23.20, 95% CI: 7.31-73.64), injuries caused by foreign body entering through natural orifice (OR: 34.00, 95%CI: 6.37-181.54) and injuries resulting from transport accidents (OR: 1.71, 95% CI: 1.41-2.07) were important risk factors for in-hospital mortality for UIs.Conclusions: Hospital DAD are an objective and cost-effective data source that allows for a hospital-based perspective of UI epidemiology. Sex, age, functional status at admission, comorbidities, injury nature, severity and mechanism are significantly associated with the in-hospital mortality of UIs in China. This study generates a reference dataset of diagnosis-specific SRRs from a large trauma population in China, which may be more applicable in injury severity estimation using ICISS in LMICs. [ABSTRACT FROM AUTHOR]

Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM 2.5 and PM 10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM 2.5 corresponded to 1.91% (95% CI: 1.33–2.49%), 2.16% (95% CI: 1.08–3.25%), and 1.92% (95% CI: 1.46–2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0–8 days, lag 8 days, and lag 8 days. The effect size of PM 2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM 10 were 3.86% (95% CI: 2.48–5.27%), 3.73% (95% CI: 1.72–5.79%), and 3.92% (95% CI: 2.65–5.21%), respectively, at lag 0–13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM 10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM 2.5 was higher in patients with T2D than those without T2D. [Display omitted] • Short-term PM exposures were associated with increased admissions for RD and for RD with and without T2D. • The effect of PM 2.5 on RD was significantly higher in patients with T2D than those without T2D. • Significant difference could be found between three different RD groups for effects of PM 10 on RD without T2D. • E-R curves varied by sex, age and T2D conditions for the associations between PM and RD. [ABSTRACT FROM AUTHOR]

Evidence on the relationship between particulate matter air pollution and urinary system disease (UD) is scarce. This study aims to evaluate the associations between short-term exposures to PM 2.5 and PM 10 and risk of daily UD inpatient hospital admissions through the emergency room (ER-admissions) in Beijing. We obtained 41,203 weekday UD ER-admissions for secondary and tertiary hospitals in all 16 districts in Beijing during 2013–2018 from the Beijing Municipal Health Commission Information Center and obtained district-level air pollution concentrations based on 35 fixed monitoring stations in Beijing. We conducted a two-stage time-series analysis, with district-specific generalized linear models for each of Beijing's 16 districts, followed by random effects meta-analysis to obtain pooled risk estimates. We evaluated lagged and cumulative associations up to 30 days. In single-pollutant models, for both PM 2.5 and PM 10 , cumulative exposure averaged over the day of admission and the previous 10 days (lag 0–10 days) showed the strongest association, with per interquartile range increases of PM 2.5 or PM 10 concentrations associated with a 7.5 % (95 % confidence interval [CI]: 3.0 %–12.2 %) or 6.0 % (95 % CI: 1.1 %–11.2 %) increased risk of daily UD hospital admissions, respectively. The risk estimates were robust to adjustment for co-pollutants and to a variety of sensitivity analyses. However, due to the strong correlation between PM 2.5 and PM 10 concentrations, we were unable to disentangle the respective relationships between these two exposures and UD risk. In this study, we found that short-term exposures to PM 2.5 and PM 10 are risk factors for UD morbidity and that cumulative exposure to PM pollution over a period of one to two weeks (i.e., 11 days) could be more important for UD risk than transient exposure during each of the respective single days. [Display omitted] • Short-term exposures to PM 2.5 and PM 10 are risk factors for urinary system disease. • Cumulative exposure over 11 days showed the strongest associations. • We were unable to disentangle the PM 2.5 versus PM 10 association. [ABSTRACT FROM AUTHOR]

The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown. A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM 2.5) and inhalable particulate matter (PM 10) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. A total of 159,298 hospital admissions for stroke comorbid with T2D were reported. Approximately linear exposure-response curves were observed for PM 2.5 and PM 10 in relation to stroke admissions among T2D patients. A 10 μg/m3 increase in the four-day moving average of PM 2.5 and PM 10 was associated with 0.14% (95% confidence interval [CI]: 0.05–0.23%) and 0.14% (95% CI: 0.06–0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 μg/m3 increase in PM 2.5 in the two-day moving average corresponded to a 0.72% (95% CI: 0.02–1.42%) incremental increase in hemorrhagic stroke, and a 10 μg/m3 increase in PM 10 in the four-day moving average corresponded to a 0.14% (95% CI: 0.06–0.22%) incremental increase in ischemic stroke. High particulate matter might be a risk factor for stroke among patients with T2D. PM 2.5 and PM 10 have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of stroke due to particulate matter pollution among patients with comorbid T2D. • The health effect of particulate matter on stroke among T2D patients in developing countries has remained largely unknown. • Exposure to particulate matter is associated an increased risk of stroke admissions among T2D patients. • Approximately linear exposure-response curves were found between PM 2.5 and PM 10 with stroke admissions among T2D patients. • The study provided evidence of the risk of stroke due to particulate matter pollution among T2D patients. [ABSTRACT FROM AUTHOR]

To examine the association between urban neighborhood disorder and the recurrence risk of patients with acute myocardial infarction (AMI) in central Beijing, China. Recurrent AMI was identified by the Beijing Monitoring System for Cardiovascular Diseases through the end of 2019 for patients discharged with AMI between 2007 and 2017. Cox proportional hazards models were performed to estimate associations between neighborhood disorder and AMI recurrence. Of 66,238 AMI patients, 11,872 had a recurrent event, and 3117 died from AMI during a median follow-up of 5.92 years. After covariate adjustment, AMI patients living in the high tertile of neighborhood disorder had a higher recurrence risk (hazard ratio [HR] 1.08, 95 % confidence interval [CI], 1.03–1.14) compared with those in the low tertile. A stronger association was noted for fatal recurrent AMI (HR 1.21, 95 % CI 1.10–1.34). The association was mainly observed in females (HR 1.04, 95 % CI: 1.02 to 1.06). Serious neighborhood disorder may contribute to higher recurrence risk, particularly fatal recurrence, among AMI patients. Policies to eliminate neighborhood disorders may play an important role in the secondary prevention of cardiovascular disease. • The association between neighborhood disorder and the recurrence risk of patients with AMI in central Beijing was examined. • AMI patients living in high neighborhood disorder area had a higher recurrence risk. • A stronger association was noted for fatal recurrent AMI. • The association was mainly observed in females. [ABSTRACT FROM AUTHOR]

Extensive studies have linked PM 2.5 and PM 10 with respiratory diseases (RD). However, few is known about causal association between PM 1 and morbidity of RD. We aimed to assess the causal effects of PM 1 on cause-specific RD. Hospital admission data were obtained for RD during 2014 and 2019 in Beijing, China. Negative control exposure and extreme gradient boosting with SHapley Additive exPlanation was used to explore the causality and contribution between PM 1 and RD. Stratified analysis by gender, age, and season was conducted. A total of 1,183,591 admissions for RD were recorded. Per interquartile range (28 μg/m3) uptick in concentration of PM 1 corresponded to a 3.08% [95% confidence interval (CI): 1.66%–4.52%] increment in morbidity of total RD. And that was 4.47% (95% CI: 2.46%–6.52%) and 0.15% (95% CI: 1.44%-1.78%), for COPD and asthma, respectively. Significantly positive causal associations were observed for PM 1 with total RD and COPD. Females and the elderly had higher effects on total RD, COPD, and asthma only in the warm months (Z = 3.03, P = 0.002; Z = 4.01, P < 0.001; Z = 3.92, P < 0.001; Z = 2.11, P = 0.035; Z = 2.44, P = 0.015). Contribution of PM 1 ranked first, second and second for total RD, COPD, and asthma among air pollutants. PM 1 was causally associated with increased morbidity of total RD and COPD, but not causally associated with asthma. Females and the elderly were more vulnerable to PM 1 -associated effects on RD. • Exposure to PM 1 was causally associated with increased morbidity of COPD. • Females and the elderly were more vulnerable to PM 1 -associated effects. • PM 1 contributed higher to respiratory diseases, especially compared to other PM. [ABSTRACT FROM AUTHOR]

Extreme temperature-related cardiovascular diseases (CVDs) have become a growing public health concern. However, the impact of temperature on the cause of specific CVDs has not been well studied in the study area. The objective of this study was to assess the impact of temperature on cause-specific cardiovascular hospital admissions in Beijing, China. We obtained data from 172 large general hospitals from the Beijing Public Health Information Center Cardiovascular Case Database and China. Meteorological Administration covering 16 districts in Beijing from 2013 to 2017. We used a time-stratified case crossover design with a distributed lag nonlinear model (DLNM) to derive the impact of temperature on CVD in hospitals back to 27 days on CVD admissions. The temperature data were stratified as cold (extreme and moderate) and hot (moderate and extreme). Within five years (January 2013–December 2017), a total of 460,938 (male 54.9% and female 45.1%) CVD admission cases were reported. The exposure-response relationship for hospitalization was described by a "J" shape for the total and cause-specific. An increase in the six-day moving average temperature from moderate hot (30.2 °C) to extreme hot (36.9 °C) resulted in a significant increase in CVD admissions of 16.1%(95% CI = 12.8%–28.9%). However, the effect of cold temperature exposure on CVD admissions over a lag time of 0–27 days was found to be non significant, with a relative risk of 0.45 (95% CI = 0.378–0.55) for extreme cold (−8.5 °C)and 0.53 (95% CI = 0.47–0.60) for moderate cold (−5.6 °C). The results of this study indicate that exposure to extremely high temperatures is highly associated with an increase in cause-specific CVD admissions. These finding may guide to create and raise awareness of the general population, government and private sectors regarding on the effects of current weather conditions on CVD. • Extreme temperature-related cardiovascular diseases (CVDs) have become a growing public health concern. • Exposure to extreme high temperatures is strongly associated an increase in cause-specific CVD admissions. • Higher susceptibility for the elderly people (≥65 years old) to the extremely high temperature. • Provide suggestions for public health intervention policy making, adjusting strategy planning, and communicating high risks. [ABSTRACT FROM AUTHOR]