Hospital Admissions For Cardiovascular Diseases Research Articles

Landscape fire PM2.5 and hospital admissions for cause-specific cardiovascular disease in urban China

There is a growing interest in the health impacts of PM2.5 originating from landscape fires. We conducted a time-series study to investigate the association between daily exposure to landscape fire PM2.5 and hospital admissions for cardiovascular events in 184 major Chinese cities. We developed a machine learning model combining outputs from chemical transport models, meteorological information and observed air pollution data to determine daily concentrations of landscape fire PM2.5. Furthermore, we fitted quasi-Poisson regression to evaluate the link between landscape fire PM2.5 concentrations and cardiovascular hospitalizations in each city, and conducted random-effects meta-analysis to pool the city-specific estimates. Here we show that, on a national scale, a rise of 1-μg/m3 in landscape fire PM2.5 concentrations is positively related to a same-day 0.16% (95% confidence interval: 0.01%–0.32%) increase in hospital admissions for cardiovascular disease, 0.28% (0.12%–0.44%) for ischemic heart disease, and 0.25% (0.02%–0.47%) for ischemic stroke. The associations remain significant even after adjusting for other sources of PM2.5. Our findings indicate that transient elevation in landscape fire PM2.5 levels may increase risk of cardiovascular diseases.

Spatial associations of daily PM2.5 concentration with cardiovascular and pulmonary morbidity in Korea

Air pollution, particularly fine particulate matter less than 2.5 μm in diameter (PM2.5), contributes to respiratory and cardiovascular diseases and poses significant public health risks worldwide. This study evaluated the short-term effects of PM2.5 on hospital admissions for cardiovascular and respiratory diseases, with additional analyses to identify vulnerable populations based on regional characteristics.The present study analyzed data from 249 Korean communities between 2006 and 2021. Data on daily hospital admissions for cardiovascular and respiratory diseases were obtained from the National Health Insurance Service. Data on PM2.5 concentrations were sourced from air quality modeling. Additional data on regional characteristics, including the regional deprivation index, proportion of elderly residents, education levels, and greenness levels, were also collected. We used case time series analysis to assess the associations between PM2.5 concentrations and hospital admissions for cardiovascular and respiratory diseases and explored effect modification by regional characteristics with stratified analyses.The mean numbers of daily cardiovascular admissions and respiratory admissions were 5.68 ± 5.46 and 6.46 ± 8.03, respectively. The mean PM2.5 concentrations were 23.58 ± 13.66 μg/m3. A10 μg/m³ increment in daily PM2.5 concentration was associated with increase of cardiovascular and respiratory hospitalization by 0.94% (95% CI: 0.84%, 1.04%) and 1.43% (95% CI: 1.34%, 1.52%), respectively. Regional characteristics analysis showed significant disparities, with higher risks for hospital admissions in areas with lower deprivation and low greenness.This study highlights the significant short-term health impacts of PM2.5 on respiratory and cardiovascular hospital admissions in Korean communities. The findings underscore the critical role of regional and demographic factors in modulating these effects, identifying socio-economic areas, age structure of the population, lower education levels, and low greenness as key vulnerability factors.

Explainable machine learning and multi-modal data for predicting readmission in patients with heart failure

Abstract Background Cardiovascular diseases (CVDs) are among the leading causes of mortality and morbidity globally, with an estimated 1 million hospital admissions for CVD in England in 2019/20, leading to 5.5 million bed days. In addition, patients with CVDs have a notably high rate of hospital readmission, with approximately one in four patients experiencing heart failure (HF) being readmitted within one month of discharge. We explored whether machine learning models trained on electrocardiogram (ECG) and patient electronic health records (EHRs) can support decision-making and risk management and ultimately improve health outcomes for HF patients. Purpose The project aims to develop a machine learning model to predict health outcomes for HF patients. To test the hypothesis, we trained and tested an explainable machine learning model to predict the risk of mortality and hospital readmission rate for HF patients. Methods We used EHR data and tabulated ECG records to train an XGBoost model to predict the risk of 30-day mortality and 30-day hospital readmission for HF patients. EHR data from a cohort of 2,868 patients with their ECG records were used for model development. We extracted a total of 78 features from the ECG data, such as heart rate variability (HRV), P-wave durations, and QT intervals. These ECG features were combined with coded and free-text EHR data (e.g., demographic information, diagnosis, medication history, and lab results) and used as tabular inputs to the XGBoost model. Of the 2,868 patients, an internal set of 574 patients stratified by age, sex, and health outcomes (30-day mortality and 30-day hospital readmission) were held out for testing. Results Among 2,868 HF patients with 10-year follow-up, 1065 patients were reported for mortality or hospital readmission within 30 days of discharge. When the machine learning model was trained on ECG data in addition to EHR data, the model was able to predict patient mortality and hospital readmission rate in the test set with an area under the curve (AUC) of 0.91 and 0.70, respectively. For mortality prediction, the model achieved a sensitivity and specificity of 0.81 and 0.82, respectively, when employing an operating point with minimum difference between the sensitivity and specificity. When the threshold was adjusted to favor sensitivity at 0.91, the model specificity decreased to 0.74. The machine learning model also identified high sensitive troponin levels, QT dispersion, and lactate dehydrogenase levels as features of high importance for predicting the outcomes for HF patients. Conclusions Our developed machine learning model was able to predict mortality and readmission risks in HF patients using multi-modal data. Upon further validation, the machine learning model has the potential to be tested for supporting clinical decision-making and risk management for HF patients.ROC CurveFeature Importance

Short-term associations between ambient PM1, PM2.5, and PM10 and hospital admissions, length of hospital stays, and hospital expenses for patients with cardiovascular diseases in rural areas of Fuyang, East China

ABSTRACT Evidence on the impacts of PM1, PM2.5, and PM10 on the hospital admissions, length of hospital stays (LOS), and hospital expenses among patients with cardiovascular disease (CVD) is still limited in China, especially in rural areas. This study was performed in eight counties of Fuyang from 1 January 2015 to 30 June 2017. We use a three-stage time-series analysis to explore the effects of short-term exposure to PM1, PM2.5, and PM10 on hospital admissions, LOS, and hospital expenses for CVDs. An increment of 10 ug/m3 in PM1, PM2.5, and PM10 corresponded to an increment of 1.82% (95% CI: 1.34, 2.30), 0.96% (95% CI: 0.44, 1.48), and 0.79% (95% CI: 0.63%, 0.95%) in CVD hospital admissions, respectively. We observed that daily concentrations of PMs were associated with an increase in hospital admissions, LOS, and expenses for CVDs. Sustained endeavors are required to reduce air pollution so as to attenuate disease burdens from CVDs.

Association between Ambient Volatile Organic Compounds Exposome and Emergency Hospital Admissions for Cardiovascular Disease.

The limited research on volatile organic compounds (VOCs) has not taken into account the interactions between constituents. We used the weighted quantile sum (WQS) model and generalized linear model (GLM) to quantify the joint effects of ambient VOCs exposome and identify the substances that play key roles. For a 0 day lag, a quartile increase of WQS index for n-alkanes, iso/anti-alkanes, aromatic, halogenated aromatic hydrocarbons, halogenated saturated chain hydrocarbons, and halogenated unsaturated chain hydrocarbons were associated with 1.09% (95% CI: 0.13, 2.06%), 0.98% (95% CI: 0.22, 1.74%), 0.92% (95% CI: 0.14, 1.69%), 1.03% (95% CI: 0.14, 1.93%), 1.69% (95% CI: 0.48, 2.91%), and 1.85% (95% CI: 0.93, 2.79%) increase in cardiovascular disease (CVD) emergency hospital admissions, respectively. Independent effects of key substances on CVD-related emergency hospital admissions were also reported. In particular, an interquartile range increase in 1,1,1-trichloroethane, methylene chloride, styrene, and methylcyclohexane is associated with a greater risk of CVD-associated emergency hospital admissions [3.30% (95% CI: 1.93, 4.69%), 3.84% (95% CI: 1.21, 6.53%), 5.62% (95% CI: 1.35, 10.06%), 8.68% (95% CI: 3.74, 13.86%), respectively]. We found that even if ambient VOCs are present at a considerably low concentration, they can cause cardiovascular damage. This should prompt governments to establish and improve concentration standards for VOCs and their sources. At the same time, policies should be introduced to limit VOCs emission to protect public health.

Temperature and hospital admissions in the Eastern Mediterranean: a case study in Cyprus

Exposure to extreme temperatures can trigger a cascade of adverse cardiovascular and respiratory events. However, in Cyprus, a hotspot of climate change in the Eastern Mediterranean region, little is known about the temperature-related cardiorespiratory morbidity risks. We analyzed daily counts of hospital admissions for cardiovascular and respiratory diseases from four general hospitals in three districts in Cyprus from 2000 through 2019. For each district, we fitted time-series quasi-Poisson regression with distributed lag non-linear models to analyze the associations between daily mean temperature (lag 0–21 d) and hospital admissions for cardiorespiratory, cardiovascular, and respiratory diseases. A random-effects meta-analytical model was then applied to pool the district-specific estimates and obtain the national average associations. We analyzed 20 years of cause-specific hospitalization data with a total of 179 988 cardiovascular and respiratory events. The relationships between cardiorespiratory morbidity and temperature were overall U-shaped. During extreme temperature days, 15.85% (95% empirical CI [eCI]: 8.24, 22.40%) excess cardiovascular hospitalizations and 9.59% (95% eCI: −0.66, 18.69%) excess respiratory hospitalizations were attributable to extreme cold days (below the 2.5th percentile). Extreme hot days (above the 97.5th percentile) accounted for 0.17% (95% eCI: 0.03, 0.29%) excess cardiovascular hospitalizations and 0.23% (95% eCI: 0.07, 0.35%) excess respiratory hospitalizations. We found evidence of increased cardiovascular morbidity risk associated with extreme temperatures in Cyprus. Our study highlights the necessity to implement public health interventions and adaptive measures to mitigate the related temperature effects in an understudied region.

Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study

ObjectiveTo estimate exposure-response associations between chronic exposure to fine particulate matter (PM2.5) and risks of the first hospital admission for major cardiovascular disease (CVD) subtypes.DesignPopulation based cohort study.SettingContiguous US.Participants59 761...

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study

ObjectiveTo estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate...

Urban–rural disparities in the association of nitrogen dioxide exposure with cardiovascular disease risk in China: effect size and economic burden

BackgroundTogether with rapid urbanization, ambient nitrogen dioxide (NO2) exposure has become a growing health threat. However, little is known about the urban–rural disparities in the health implications of short-term NO2 exposure. This study aimed to compare the association between short-term NO2 exposure and hospitalization for cardiovascular disease (CVD) among urban and rural residents in Shandong Province, China. Then, this study further explored the urban–rural disparities in the economic burden attributed to NO2 and the explanation for the disparities.MethodsDaily hospitalization data were obtained from an electronic medical records dataset covering a population of 5 million. In total, 303,217 hospital admissions for CVD were analyzed. A three-stage time-series analytic approach was used to estimate the county-level association and the attributed economic burden.ResultsFor every 10-μg/m3 increase in NO2 concentrations, this study observed a significant percentage increase in hospital admissions on the day of exposure of 1.42% (95% CI 0.92 to 1.92%) for CVD. The effect size was slightly higher in urban areas, while the urban–rural difference was not significant. However, a more pronounced displacement phenomenon was found in rural areas, and the economic burden attributed to NO2 was significantly higher in urban areas. At an annual average NO2 concentration of 10 μg/m3, total hospital days and expenses in urban areas were reduced by 81,801 (44,831 to 118,191) days and 60,121 (33,002 to 86,729) thousand CNY, respectively, almost twice as much as in rural areas. Due to disadvantages in socioeconomic status and medical resources, despite similar air pollution levels in the urban and rural areas of our sample sites, the rural population tended to spend less on hospitalization services.ConclusionsShort-term exposure to ambient NO2 could lead to considerable health impacts in either urban or rural areas of Shandong Province, China. Moreover, urban–rural differences in socioeconomic status and medical resources contributed to the urban–rural disparities in the economic burden attributed to NO2 exposure. The health implications of NO2 exposure are a social problem in addition to an environmental problem. Thus, this study suggests a coordinated intervention system that targets environmental and social inequality factors simultaneously.

Effect of short-term exposure to air pollution on hospital admission for cardiovascular disease: A time-series study in Xiangyang, China

BackgroundData on the relationship between short-term exposure to air pollution and cardiovascular diseases (CVDs) and the potential modifying factors are limited and inconsistent. ObjectiveTo explore the relationship between short-term exposure to air pollution and CVD risk, and potential modification effect factors. MethodA time series study was conducted on 52,991 hospital admissions for CVD from 2015 to 2019 in Xiangyang City, China. Air pollution data from four national fixed monitoring stations were collected to estimate exposure level in Xiangyang City. A quasi-Poisson generalized additive model incorporating a distributed lag nonlinear model was applied to evaluate the association between air pollution and CVD risk. The potential modification effect of sex, age, and season on the above associations was also evaluated. ResultsCVD risk was positively associated with air pollution. Peak associations in single lag day structures were observed for particulate matter ≤10 μm in aerodynamic (PM10; RR: 1.040, 95 % CI: 0.996–1.087), PM2.5 (1.025, 1.004–1.045), nitrogen dioxide (NO2; 1.074, 1.039–1.111), and sulfur dioxide (SO2; 1.079, 1.019–1.141) at Lag 0 and ozone (O3; 1.018, 1.004–1.031) at Lag 4. In cumulative lag day structures, the highest RRs were 1.225 (1.079,1.392) for PM10 at Lag 06, 1.054 (1.013, 1.098) for PM2.5 at Lag 03, 1.200 (1.119, 1.287) for NO2 at Lag 04, and 1.135 (1.025, 1.257) for SO2 at Lag 02. Moreover, the association between air pollution and CVD risk was modified by sex and age (P < 0.05). Females and individuals aged ≤65 years were more vulnerable to NO2 and had a higher CVD risk. ConclusionShort-term exposure to air pollution was positively associated with CVD risk. Moreover, sex and age could modify the effect of air pollution on CVD risk. Females and individuals aged ≤65 years had a higher NO2 exposure-induced CVD risk.

Analyzing the Interactions between Environmental Parameters and Cardiovascular Diseases Using Random Forest and SHAP Algorithms.

Cardiovascular diseases (CVD) remain the predominant global cause of mortality, with both low and high temperatures increasing CVD-related mortalities. Climate change impacts human health directly through temperature fluctuations and indirectly via factors like disease vectors. Elevated and reduced temperatures have been linked to increases in CVD-related hospitalizations and mortality, with various studies worldwide confirming the significant health implications of temperature variations and air pollution on cardiovascular outcomes. A database of daily Emergency Room admissions at the Giovanni XIII Polyclinic in Bari (Southern Italy) was developed, spanning from 2013 to 2019, including weather and air quality data. A Random Forest (RF) supervised machine learning model was used to simulate the trend of hospital admissions for CVD. The Seasonal and Trend decomposition using Loess (STL) decomposition model separated the trend component, while cross-validation techniques were employed to prevent overfitting. Model performance was assessed using specific metrics and error analysis. Additionally, the SHapley Additive exPlanations (SHAP) method, a feature importance technique within the eXplainable Artificial Intelligence (XAI) framework, was used to identify the feature importance. An of 0.97 and a Mean Absolute Error of 0.36 admissions were achieved by the model. Atmospheric pressure, minimum temperature, and carbon monoxide were found to collectively contribute about 74% to the model's predictive power, with atmospheric pressure being the dominant factor at 37%. This research underscores the significant influence of weather-climate variables on cardiovascular diseases. The identified key climate factors provide a practical framework for policymakers and healthcare professionals to mitigate the adverse effects of climate change on CVD and devise preventive strategies.

The impact of the COVID-19 pandemic on delayed care of cardiovascular diseases in Europe: a systematic review.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare systems, causing delays in essential medical services, and potentially impacting CVD treatment. This study aims to estimate the impact of the pandemic on delayed CVD care in Europe by providing a systematic overview of the available evidence. PubMed, Embase, and Web of Science were searched until mid-September 2022 for studies focused on the impact of delayed CVD care due to the pandemic in Europe among adult patients. Outcomes were changes in hospital admissions, mortality rates, delays in seeking medical help after symptom onset, delays in treatment initiation, and change in the number of treatment procedures. We included 132 studies, of which all were observational retrospective. Results were presented in five disease groups: ischaemic heart diseases (IHD), cerebrovascular accidents (CVA), cardiac arrests (CA), heart failures (HF), and others, including broader CVD groups. There were significant decreases in hospital admissions for IHD, CVA, HF and urgent and elective cardiac procedures, and significant increases for CA. Mortality rates were higher for IHD and CVA. The pandemic led to reduced acute CVD hospital admissions and increased mortality rates. Delays in seeking medical help were observed, while urgent and elective cardiac procedures decreased. Adequate resource allocation, clear guidelines on how to handle care during health crises, reduced delays, and healthy lifestyle promotion should be implemented. The long-term impact of pandemics on delayed CVD care, and the health-economic impact of COVID-19 should be further evaluated.

Short-term exposure to reduced specific-size ambient particulate matter increase the risk of cause-specific cardiovascular disease: A national-wide evidence from hospital admissions

Evidence for the health effects of ambient PM1 (particulate matter with an aerodynamic diameter ≤ 1 µm) pollution is limited, and it remains unclear whether a smaller particulate matter has a greater impact on human health. We conducted a time-series study in 184 major cities by extracting daily hospital data on admissions for ischemic heart disease, heart failure, heart rhythm disturbances, and stroke between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. City-specific associations were estimated with over-dispersed generalized additive models. A random-effects meta-analysis was used to estimate regional and national average associations. We conducted stratified and meta-regression analyses to explore potential effect modifiers of the association. We recorded 8.83 million cardiovascular admissions during the study period. At the national-average level, a 10-μg/m3 increase in same-day PM1, PM2.5(particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM10(particulate matter with an aerodynamic diameter ≤ 10 µm) concentrations corresponded to a 1.14% (95% confidence interval 0.88–1.41%), 0.55% (0.40–0.70%), and 0.45% (0.36–0.55%) increase in cardiovascular admissions, respectively. PM1 exposure was also positively associated with all cardiovascular disease subtypes, including ischemic heart disease (1.28% change; 0.99–1.56%), heart failure (1.30% change; 0.70–1.91%), heart rhythm disturbances (1.11% change; 0.65–1.58%), and ischemic stroke (1.29% change; 0.88–1.71%). The associations between PM1 and cardiovascular admissions were stronger in cities with lower PM1 levels, higher air temperatures and relative humidity, as well as in subgroups with elder age (all P < 0.05). This study provides robust evidence of short-term associations between PM1 concentrations and increased hospital admissions for all major cardiovascular diseases in China. Our findings suggest a greater short-term impact on cardiovascular risk from PM1 in comparison to PM2.5 and PM10.

Association of ambient particulate matter with hospital admissions, length of hospital stay, and hospital costs due to cardiovascular disease: time-series analysis based on data from the Shanghai Medical Insurance System from 2016 to 2019

BackgroundThere is limited evidence supporting a relationship of ambient particulate matter (PM), especially PM1, with hospital admissions, hospital costs, and length of hospital stay (LOS) due to cardiovascular disease (CVD). We used a generalized additive model (GAM) to estimate the associations of these indicators due to CVD for each 10 μg/m3 increase in the level of PM1, PM2.5, and PM10, and the attributable risk caused by PM on CVD was determined using the WHO air quality guidelines from 2005 and 2021.ResultsFor each 10 μg/m3 increase in the level of each PM and for a 0-day lag time, there were significant increases in daily hospital admissions for CVD (PM1: 1.006% [95% CI 0.859, 1.153]; PM2.5: 0.454% [95% CI 0.377, 0.530]; PM10: 0.263% [95% CI 0.206, 0.320]) and greater daily hospital costs for CVD (PM1: 523.135 thousand CNY [95% CI 253.111, 793.158]; PM2.5: 247.051 thousand CNY [95% CI 106.766, 387.336]; PM10: 141.284 thousand CNY [95% CI 36.195, 246.373]). There were no significant associations between PM and daily LOS. Stratified analyses demonstrated stronger effects in young people and males for daily hospital admissions, and stronger effects in the elderly and males for daily hospital costs. Daily hospital admissions increased linearly with PM concentration up to about 30 µg/m3 (PM1), 60 µg/m3 (PM2.5), and 90 µg/m3 (PM10), with slower increases at higher concentrations. Daily hospital costs had an approximately linear increase with PM concentration at all tested concentrations. In general, hospital admissions, hospital costs, and LOS due to CVD were greater for PM2.5 than PM10, and the more stringent 2021 WHO guidelines indicated greater admissions, costs, and LOS due to CVD.ConclusionsShort-term elevation of PM of different sizes was associated with an increased risk of hospital admissions and hospital costs due to CVD. The relationship with hospital admissions was strongest for men and young individuals, and the relationship with hospital costs was strongest for men and the elderly. Smaller PM is associated with greater risk.

Modification of low temperature-related hospital admissions for cardiovascular diseases by multiple green space indicators at multiple spatial scales: Evidence from Guangzhou, China

Extreme temperatures have an adverse effect on the occurrence of cardiovascular diseases (CVDs). Previous literatures tend to discuss the modification of CVDs occurrence by green space under high temperature. Relatively less attention is paid to the modification under low temperature. The variation of different attributes and spatial scales of green space in affecting CVDs occurrence are also overlooked. This study collected a total of 4364 first-time admission cases due to CVDs in a tertiary hospital in Guangzhou from 2012 to 2018, measured the scale of green space by greening rate (GR) and percentage of landscape (PLAND), the distribution of green space by patch density (PD), mean nearest neighbor distance (ENN_MN) and largest patch index (LPI), and the accessibility of green space by green patch accessibility index (GPAI). Using the time stratified case crossover design method, the modification of low temperature-related CVDs occurrence by the above green space indicators is evaluated in an area with a radius of 100-1000m which is further divided at an interval of 100m. We found high GR, high PLAND, high PD, low ENN_MN, high LPI, and low GPAI corresponds to low risk of CVDs occurrence, the optimal modification scale of each green space indicator, which is radius corresponding to the maximum risk difference between high and low indicator subgroups, is around 800m (GR), 600m (PLAND and PD), 500m (GPAI), and 300m (LPI and ENN_MN), respectively. As the temperature decreases further, the health benefit from low GPAI at the optimal scale is weakened, whereas the benefits from the others are strengthened. Low temperature related CVDs occurrence risk can be modified by multiple green space indicators, and these modifications have spatial scale effect. Our findings have important theoretical and practical significance for the formulation and implementation of local green space policies.

Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients.

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 (≥65years) 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 7days. 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 10μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1mg/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.

Low ambient temperature and hospitalization for cardiorespiratory diseases in Brazil

Studies have shown that larger temperature-related health impacts may be associated with cold rather than with hot temperatures. Although it remains unclear the cold-related health burden in warmer regions, in particular at the national level in Brazil. We address this gap by examining the association between low ambient temperature and daily hospital admissions for cardiovascular and respiratory diseases in Brazil between 2008 and 2018. We first applied a case time series design in combination with distributed lag non-linear modeling (DLNM) framework to assess the association of low ambient temperature with daily hospital admissions by Brazilian region. Here, we also stratified the analyses by sex, age group (15–45, 46–65, and >65 years), and cause (respiratory and cardiovascular hospital admissions). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our sample included more than 23 million hospitalizations for cardiovascular and respiratory diseases nationwide between 2008 and 2018, of which 53% were admissions for respiratory diseases and 47% for cardiovascular diseases. Our findings suggest that low temperatures are associated with a relative risk of 1.17 (95% CI: 1.07; 1.27) and 1.07 (95% CI: 1.01; 1.14) for cardiovascular and respiratory admissions in Brazil, respectively. The pooled national results indicate robust positive associations for cardiovascular and respiratory hospital admissions in most of the subgroup analyses. In particular, for cardiovascular hospital admissions, men and older adults (>65 years old) were slightly more impacted by cold exposure. For respiratory admissions, the results did not indicate differences among the population groups by sex and age. This study can help decision-makers to create adaptive measures to protect public health from the effects of cold temperature.

The impact of COVID-19 pandemic on cardiovascular diseases hospitalization, management and mortality in Switzerland

Abstract Funding Acknowledgements Type of funding sources: None. Background COVID-19 pandemic led to a reduction in hospital admissions and intervention for other diseases in many countries. Purpose We aimed to assess the effect of COVID-19 pandemic on cardiovascular disease (CVD) hospitalizations, management and mortality in Switzerland. Methods Swiss hospital discharge and mortality data for period 2017-2020. CVD hospitalizations, CVD interventions and CVD mortality were assessed before (2017-2019) and during (2020) the pandemic. Results Compared to 2017-2019, 2020 was characterized by a reduction of CVD admissions in age groups 65-84 and ≥85 by approximately 3,700 and 1,700 cases, respectively, and by an increase in the percentage of admissions with a Charlson Index &amp;gt;8. The total number of CVD-related deaths decreased from 21,042 in 2017 to 19,901 in 2019, and increased to 20,511 in 2020, with an estimated excess of 1,181 deaths. This increase was due to out-of-hospital deaths (+1400), while the number of in-hospital deaths decreased from 5030 in 2019 to 4796 in 2020. The total number of cardiovascular interventions increased from 55,181 in 2017 to 57,864 in 2019, and decreased in 2020, with an estimated reduction of 4,472 interventions; PTCA was the exception, as the number and percentage of emergency PTCA increased. The preventive measures applied against COVID-19 inverted the seasonal pattern of CVD admissions, the highest number of admissions being found in summer and the lowest in winter. Preventive measures were also followed by a decrease in CVD admission (figure). Conclusion The COVID-19 pandemic led to a reduction in CVD hospital admissions, planned CVD interventions, an increase in total and out-of-hospital mortality, and a change in seasonal patterns.

COVID-19 pandemic on coronary artery and cerebrovascular diseases in Southern Spain: interrupted time series analysis.

Healthcare systems have been put under intense pressure by the COVID-19 pandemic, although some studies have shown a decline in hospital admissions for cardiovascular and cerebrovascular diseases during the first and second wave of the pandemic. In addition, studies analyzing gender and procedural differences are scarce. The present study aimed to determine the impact of the pandemic on hospital admissions for acute myocardial infarction (AMI) and cerebrovascular disease (CVD) in Andalusia (Spain) and analyzed differences by gender and by percutaneous coronary interventions performed. An interrupted time series analysis of AMI and CVD hospital admissions in Andalusia (Spain) was carried out to measure the impact of the COVID-19 outbreak. AMI and CVD cases admitted daily in public hospitals of Andalusia between January 2018 and December 2020 were included. During the pandemic, significant reductions in AMI [-19%; 95% confidence interval (CI): (-29%, -9%), p<0.001] and CVD [-17%; 95% CI: (-26%, -9%); p<0.01] in daily hospital admissions were observed. Differences were also produced according to the diagnosis (ST-Elevation Myocardial Infarction, Non-ST-Elevation Myocardial Infarction, other AMI and stroke), with a greater reduction in females for AMI and in males for CVD. Although there were more percutaneous coronary interventions during the pandemic, no significant reductions were observed. A decline in AMI and CVD daily hospital admissions during the first and second wave of COVID-19 pandemic was noted. Gender differences were observed, but no clear impact was observed in percutaneous interventions.

Ambient carbon monoxide and cardiovascular-related hospital admissions: A time-series analysis.

Background and aims: Although several studies have investigated the association between air pollutants and cardiovascular diseases (CVDs) in recent years, a lack of evidence exists regarding carbon monoxide (CO) exposure, especially in the Eastern Mediterranean's polluted regions. In this study, we aimed to evaluate the short-term effect of CO exposure on daily CVD hospital admissions in Isfahan, a major city in Iran. Methods: Data were extracted from the CAPACITY study on daily CVD hospital admissions in Isfahan from March 2010 to March 2012. The 24-h mean CO concentrations were obtained from four local monitoring stations. In a time-series framework, the association between CO and daily hospitalizations for total and cause-specific CVDs in adults (ischemic heart disease (IHD), heart failure (HF), and cerebrovascular disease) was conducted using Poisson's (or negative binomial) regression, after adjusting for holidays, temperature, dew point, and wind speed, considering different lags and mean lags of CO. The robustness of the results was examined via two- and multiple-pollutant models. Stratified analysis was also conducted for age groups (18-64 and ≥65years), sex, and seasons (cold and warm). Results: The current study incorporated a total of 24,335 hospitalized patients, (51.6%) male with a mean age of 61.9 ± 16.4 years. The mean CO concentration was 4.5 ± 2.3mg/m³. For a 1mg/m3 increase in CO, we found a significant association with the number of CVD hospitalizations. The largest adjusted percent change in HF cases was seen in lag0, 4.61% (2.23, 7.05), while that for total CVDs, IHD, and cerebrovascular diseases occurred in mean lag2-5, 2.31% (1.42, 3.22), 2.23% (1.04, 3.43), and 5.70% (3.59, 7.85), respectively. Results were found to be robust in two- and multiple-pollutant models. Although the associations changed for sex, age groups, and seasons, they remained significant for IHD and total CVD, except for the warm season, and for HF, except for the younger age group and cold seasons. Additionally, the exposure-response relationship curve of the CO concentrations with total and cause-specific CVD admissions showed non-linear relationships for IHD and total CVDs. Conclusions: Our results showed that exposure to CO contributed to an increase in the number of CVD hospitalizations. The associations were not independent of age groups, season, and sex.