Daily Number Of Hospital Admissions Research Articles

Prevalence Characteristics of Osteoporosis Fractures in the Elderly in Two Regions of China and Analysis of the Lag Effect of Air Pollutants on them.

Air pollution is increasing and threatening human health. The objective of this study is to investigate the population distribution characteristics of elderly osteoporosis fractures in Hebei Province and Xinjiang Uygur Autonomous Region and to analyze the effects of air pollutants on the number of elderly osteoporosis fracture inpatients in the two regions. A retrospective collection of elderly osteoporosis fracture cases was conducted in selected hospitals in Hebei Province and Xinjiang Uygur Autonomous Region from January 1, 2018 to December 31, 2022. The chi-square test was used to compare the distributional characteristics of the population in the two regions. Additionally, we used a distributed lag nonlinear model (DLNM) in order to assess the effect of air pollutants on the number of daily hospital admissions of elderly osteoporosis fracture patients in different regions. A total of 19,203 elderly osteoporosis fracture patients were included in the study. The average age of these patients was 76.66 ± 7.55 years, and the majority of them were female (13,514 instances, 70.37%). The disparities in age distribution (χ2 = 133.9 p < 0.001), fracture site (χ2 = 62.0 p < 0.001), and hospitalization cost (Z = -15.635 p < 0.001) between the two regions were statistically significant. The lag effect curves of PM2.5, PM10, and NO2 on the number of elderly osteoporosis fracture hospitalizations in Xinjiang Uygur Autonomous Region exhibited a similar pattern resembling a "W"-shaped curve. All three pollutants reached their highest values after a lag time of 14 days (PM2.5: RR = 1.053, 95% CI: 1.031, 1.074; PM10: RR = 1.031, 95% CI: 1.018, 1.043; NO2: RR = 1.125, 95% CI: 1.070, 1.182). In Hebei Province, the largest impacts of PM2.5 and PM10 were observed after a lag of 14 days (PM2.5: RR = 1.022, 95% CI: 1.013, 1.028; PM10: RR = 1.013, 95% CI: 1.008, 1.018). Similarly, the maximum effect of NO2 was observed after a lag of 11 days (RR = 1.020, 95% CI: 1.010, 1.028). There were differences in the epidemiological characteristics of hospitalized patients with osteoporosis fractures between the two regions, PM2.5, PM10, and NO2 increased the number of hospitalizations for osteoporosis fractures. Exposure to air pollutants such as PM2.5 increases the risk of osteoporosis fractures in the elderly population.

Short-term association between air pollution and daily genitourinary disorder admissions in Lanzhou, China.

The aim of this study was to determine the relationship between short-term exposure to ambient air pollution and the number of daily hospital admissions for genitourinary disorders in Lanzhou. Hospital admission data and air pollutants, including PM2.5, PM10, SO2, NO2, O38h and CO, were obtained from the period 2013 to 2020. A generalized additive model (GAM) combined with distribution lag nonlinear model (DLNM) based on quasi-Poisson distribution was used by the controlling for trends, weather, weekdays and holidays. Short-term exposure to PM2.5, NO2 and CO increased the risk of genitourinary disorder admissions with RR of 1.0096 (95% CI 1.0002-1.0190), 1.0255 (95% CI 1.0123-1.0389) and 1.0686 (95% CI 1.0083-1.1326), respectively. PM10, O38h and SO2 have no significant effect on genitourinary disorders. PM2.5 and NO2 are more strongly correlated in female and ≥ 65years patients. CO is more strongly correlated in male and < 65years patients. PM2.5, NO2 and CO are risk factors for genitourinary morbidity, and public health interventions should be strengthened to protect vulnerable populations.

Bushfire-smoke trigger hospital admissions with cerebrovascular diseases: Evidence from 2019-20 bushfire in Australia.

Exposure to ambient air pollution is strongly associated with increased cerebrovascular diseases. The 2019-20 bushfire season in Australia burnt 5.4 million hectares of land in New South Wales alone, with smoke so severe it affected cities in Argentina, 11,000 km away. The smoke emitted by bushfires consists of both gaseous and particle components. It is important to note that exposure to particulate matter has been shown to be linked to a heightened risk of stroke, which is the primary kind of cerebrovascular illness, as well as an increased likelihood of hospitalisations and mortality. However, the available data is inadequate in terms of documenting the response of patients diagnosed with a proven cerebrovascular illness to bushfire smoke. Additionally, there is a lack of information about the health effects associated with particulate matter throughout the bushfire season and on days when smoke was present in 2019 and 2020.Therefore, we aimed to determine the effects of (i) short-term air pollution triggered by bushfires and (ii) high smoke days in increasing the daily number of hospital admissions with cerebrovascular diseases. Hospitalisation data were accessed from the admitted patient dataset from seven local Government areas of Hunter New England Local Health District. The bushfire period was defined from 1 October 2019 to 10 February 2020, and a same period from 2018-19 as the control. High bushfire smoke days were days when the average daily concentration of particulate matter was higher than the 95th percentile of the control period. Poisson regression models and fixed effect meta-analysis were used to analyse the data. In total, 275 patients with cerebrovascular admissions were identified, with 147 (53.5%) during the bushfire (2019-20) and 128 (46.5%) in the control period (2018-19). There was no significant increase in daily admissions for cerebrovascular disease (Incidence Rate Ratio, IRR: 1.04; 95% CI: 0.81-1.34; p-value: 0.73), acute stroke (IRR: 1.15; 95% CI: 0.88-1.50; p-value: 0.29) or acute ischaemic stroke (IRR: 1.18; 95% CI: 0.87-1.59; p-value: 0.28), over the entire bushfire period. However, the high bushfire smoke days were associated with increased acute ischaemic stroke-related hospital admissions across lead 0-3 and the highest cumulative effect was observed with lead 0 (IRR:1.52; 95% CI: 1.01-2.29; p-value: 0.04). In addition, during the bushfire period, particulate matter, both PM10 and PM2.5 (defined as particulates that have an effective aerodynamic diameter of 10, and 2.5 microns, respectively), were also associated with increased acute ischaemic stroke admissions with a lag of 0-3 days. The results suggested a possible association between particulate matter and high smoke days with increased hospital admissions due to acute ischaemic stroke during the recent Australian bushfire season.

The Impact of COVID-19 on Trauma Emergency Patients in Southeastern Iran.

With the COVID-19 outbreak in countries around the world, the countries' healthcare systems underwent an unprecedented shock. This study aimed to examine the resilience of the medical service delivery system in providing emergency services during the Covid-19 pandemic. This study was conducted in a reference hospital in Kerman that provided emergency services to trauma patients. It compared service delivery before and after COVID-19, as well as during the COVID-19 peak and non-peak periods. The compared variables were the number of trauma patients admitted to the hospital and the ICU, the number of patients who died in the hospital due to trauma, and the length of stay in the hospital and the ICU. The pre- and post-COVID-19 comparisons showed no significant difference in the number of daily hospital admissions, ICU admissions, and patient deaths. The median length of stay in the ICU was significantly reduced by almost 2 days during the COVID-19 outbreak. However, the length of stay at the hospital was almost the same. Furthermore, a comparison of the COVID-19 peaks and non-peak periods indicated no statistically significant difference in the number of admissions in the ICU, hospital and ICU length of stay, and trauma-induced mortality. Despite the substantial workload imposed by COVID-19 on hospitals, especially during the peak periods of the disease, the provision of medical services to emergency trauma patients did not drop significantly, and the quality of services provided to patients was within the acceptable range.

Effect of Short-Term Exposure to Air Pollutants on Hospital Admissions for End-Stage Renal Disease Patients Undergoing Hemodialysis

To evaluate the association between short-term exposure to air pollutants of end-stage renal disease (ESRD) patients on maintenance hemodialysis and the number of daily hospital admissions. The data on hospitalizations were obtained from the database of the municipal Urban Employees' Basic Medical Insurance and Urban Residents' Basic Medical Insurance of a city in Southwest China. Single and multiple pollutant generalized additive models were utilized to estimate the effect of air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2) on patient admissions after the lag time of different numbers of days. In addition, subgroup analyses stratified by sex, age, PM2.5 and PM10 concentration thresholds, seasonality, and comorbidity status for cardiovascular diseases and hypertension were conducted. In the single pollutant models, the pollutants significantly associated with patient admissions and the corresponding lag time of the strongest association were as follows, every time CO increased by 0.1 mg/m3, there was a 2.39% increase (95% confidence interval [CI]: 0.96%-3.83%) in patient admissions after 7 days of lag time; every time NO2, O3, PM2.5, PM10, and SO2 increased by 10 μg/m3, patient admissions increased by 4.02% (95% CI: 1.21%-6.91%) after 7 days of lag time, 3.57% (95% CI: 0.78%-6.44%) after 0-4 days of lag time, 2.00% (95% CI: 1.07%-2.93%) after 6 days of lag time, 1.19% (95% CI: 0.51%-1.88%) after 7 days of lag time, and 8.37% (95% CI: 3.08%-13.93%) after 7 days of lag time, respectively. In the multiple pollutant model, every time O3 and PM2.5 increased by 10 μg/m3, there was an increase of 3.18% (95% CI: 0.34%-6.09%) in daily patient admissions after 0-4 days of lag time and an increase of 1.85% (95% CI: 0.44%-3.28%) after 7 days of lag time. Furthermore, subgroup analyses showed that seasonality, the severity of air pollution, and patients' comorbidities might be the effect modifiers for the association between ambient air pollution and hospital admissions in ESRD patients receiving maintenance hemodialysis. Air pollution is closely associated with hospital admissions in ESRD patients undergoing maintenance hemodialysis and the strength of this association varies according to seasonality, the severity of air pollution, and patients' status of comorbidities.

Impact of Haze Event on Daily Admission of Respiratory System Patients in Peninsular Malaysia

Diseases of the respiratory system, especially in children and the elderly, are significantly related to air pollution. Exposure to air pollution has led to an increase in the number of patients who need hospital treatment. The purpose of this study was to learn about the effects of changes in the levels of major pollutant components on the number of daily hospital admissions of respiratory system patients. The generalised linear lag model is used in this study to demonstrate the lag structure of the exposure-response impacts. The results show that particulate matter (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) component factors, as well as meteorological factors like wind speed and ultraviolet (UV) radiation, affect the number of hospital admissions of respiratory system patients. The best model is a lag 6 negative binomial regression model. Daily hospital admission is positively correlated with PM10, NO2, and wind speed, and negatively correlated with CO, O3, and UV radiation. According to the findings of the study, fine particulate matter (PM2.5) and sulphur dioxide (SO2), as well as temperature, humidity, and wind direction, are not significantly contributing factors in the number of respiratory system patients admitted to hospitals.

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

BackgroundWith the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources.MethodsIn this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model.ResultsOur proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R2 improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD.ConclusionsOur stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

Effects of ambient air pollution on cause-specific hospitalizations in Wuhan during 2017–2019

BackgroundFew studies have focused on the associations between air pollutants and multiple organ system diseases in the entire hospitalized population. The present study aims to explore the short-term effects of six routinely monitored air pollutants on the broad causes of hospital admissions and estimate the resulting hospital admission burdens. MethodsDaily hospital admission records from 2017 to 2019 were obtained from the Wuhan Information center of Health and Family Planning. Generalized additive models (GAMs) were employed to evaluate the effects of air pollutants on the percent increase in the cause-specific daily number of hospital admissions. Increased hospital admission numbers, days, and expenses were also estimated. ResultsA total of 2636,026 hospital admissions were identified. We found that both PM2.5 and PM10 increased the risk of hospital admissions for most disease categories. Short-term exposure to PM2.5 was positively associated with hospitalizations of several rarely studied disease categories, such as diseases of the eye and adnexa (2.83%, 95%CI: 0.96–4.73%, P < 0.01) and diseases of the musculoskeletal system and connective tissue (2.17%, 95% CI: 0.88–3.47%, P < 0.001). NO2 was observed to have a robust effect on diseases of the respiratory system (1.36%, 95%CI: 0.74–1.98%, P < 0.001). CO was significantly associated with hospital admissions for six disease categories. Furthermore, each 10-μg/m3 increase in PM2.5 was associated with an annual increase of 13,444 hospital admissions (95% CI: 6239–20,649), 124,344 admission days (95% CI: 57,705–190,983), and 166-million-yuan admission expenses (95% CI: 77–255). ConclusionOur study suggested that particulate matter (PM) had a short-term effect on hospital admissions of most major disease categories and resulted in a considerable hospital admission burden. In addition, the health effects of NO2 and CO emissions require more attention in megacities.

Effects of sulfur dioxide and particulate matter pollution on hospital admissions for hypertensive cardiovascular disease: A time series analysis.

Background and aims: Air pollution is a major environmental risk factor and the leading cause of disease burden with detrimental effects on cardiovascular systems. Cardiovascular diseases are predisposed by various risk factors, including hypertension, as the most important modifiable risk factor. However, there is a lack of sufficient data concerning the impact of air pollution on hypertension. We sought to study the associations of short-term exposure to Sulfur dioxide (SO2) and particulate matter (PM10) with the number of daily hospital admissions of hypertensive cardiovascular diseases (HCD). Methods: All hospitalized patients between March 2010 to March 2012 were recruited with the final diagnosis of HCD based on the International Classification of Diseases 10 (codes: I10-I15) from 15 hospitals in Isfahan, one of the most polluted cities in Iran. The 24-hour average concentrations of pollutants were obtained from 4 monitoring stations. In addition to single- and two-pollutant models, we used Negative Binomial and Poisson models with covariates of holidays, dew point, temperature, wind speed, and extracted latent factors of other pollutants controlling for multi-collinearity to examine the risk for hospital admissions for HCD affected by SO2 and PM10 exposures in the multi-pollutant model. Results: A total of 3132 hospitalized patients (63% female) with a mean (standard deviation) age of 64.96 (13.81) were incorporated in the study. The mean concentrations of SO2 and PM10 were 37.64 μg/m3 and 139.08 μg/m3, respectively. Our findings showed that a significantly increased risk of HCD-induced hospital admission was detected for a 10 μg/m3 increase in the 6-day and 3-day moving average of SO2 and PM10 concentrations in the multi-pollutant model with a percent change of 2.11% (95% confidence interval: 0.61 to 3.63%) and 1.19% (0.33 to 2.05%), respectively. This finding was robust in all models and did not vary by gender (for SO2 and PM10) and season (for SO2). However, people aged 35-64 and 18-34 years were vulnerable to SO2 and PM10 exposure-triggered HCD risk, respectively. Conclusions: This study supports the hypothesis of the association between short-term exposure to ambient SO2 and PM10 and the number of hospital admissions due to HCD.

SARS-CoV-2 IgG seroprevalence surveys in blood donors before the vaccination campaign, France 2020-2021

We conducted a cross-sectional study for SARS-CoV-2 anti-S1 IgG prevalence in French blood donors (n= 32605), from March-2020 to January-2021. A mathematical model combined seroprevalence with a daily number of hospital admissions to estimate the probability of hospitalization upon infection and determine the number of infections while correcting for antibody decay. There was an overall seroprevalence increase over the study period and we estimate that ∼15% of the French population had been infected by SARS-CoV-2 by January-2021. The infection/hospitalization ratio increased with age, from 0.31% (18-30yo) to 4.5% (61-70yo). Half of the IgG-S1 positive individuals had no detectable antibodies 4 to 5months after infection. The seroprevalence in group O donors (7.43%) was lower (p= 0.003) than in A, B, and AB donors (8.90%). We conclude, based on seroprevalence data and mathematical modeling, that a large proportion of the French population was unprotected against severe disease prior to the vaccination campaign.

Thermal Conditions and Hospital Admissions: Analysis of Longitudinal Data from Cyprus (2009-2018).

The state of the thermal environment can affect human health and well-being. Heat stress is associated with a wide range of health outcomes increasing morbidity and mortality and is recognized as an important health risk posed by climate change. This study aims at examining the effect of thermal conditions on the daily number of hospital admissions in Cyprus. Data from eight public hospitals located in five districts of Cyprus were analyzed from 2009 to 2018. Meteorological hourly gridded data were extracted by the ERA-5 Land reanalysis database with a spatial horizontal resolution of 0.1° × 0.1°. The Physiologically Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI) were calculated as measures of the integrated effect of meteorological variables. Negative binomial regression was fitted to examine associations between the daily number of hospital admissions and meteorological variables, PET, and UTCI. The results showed that the mean daily temperature (Tair) was positively associated with hospital admissions from any cause. Hospital admissions increased by 0.6% (p < 0.001) for each 1 °C increase of Tair and by 0.4% (p < 0.001) for each 1 °C increase of PET and UTCI. Ozone and nitrogen oxides act as confounding factors. An effect of particulate matter (less than 10 μm in diameter) was observed when the analysis focused on April to August. Thresholds above which hospital admissions are likely to increase include daily mean Tair = 26.1 °C, PET = 29 °C, and UTCI = 26 °C. Studies on heat-related health effects are necessary to monitor health patterns, raise awareness, and design adaptation and mitigation measures.

Association between Low Ambient Temperature and Cardiorespiratory Hospital Admissions in Helsinki, Finland

BACKGROUND AND AIM: The relationship between low ambient temperature and hospital admissions is less studied than between high temperature and hospital admissions. Therefore, this study aimed to investigate the association of daily wintertime temperature with cardiorespiratory hospital admissions in the Helsinki metropolitan area, Finland. METHODS: Daily number of hospital admissions for 2001-2017 was obtained from the national hospital discharge register and weather information from the Finnish Meteorological Institute. Time-series quasi-Poisson regression models were fitted controlling for potential confounders such as time trend, weekday, holiday, air pollution, barometric pressure and an indicator for influenza count. A penalized distributed lag linear model with 21 days of lag was used to estimate the association of daily mean temperature with cause-specific cardiorespiratory hospital admissions, stratified by age-groups (All-ages, 18-64, 65-74 and ≥75), during winter months (December-March). RESULTS:The first and 99th percentiles of daily temperature were -19.6 °C and 11.5 °C respectively. Decreased ambient temperature was associated with increased risk of hospitalization for myocardial infarction in the whole population (Risk Ratio [RR] for 1°C decrease in exposure: 1.015, 95% confidence intervals [CIs]: 1.001-1.030), and in the age group 65-74 (RR: 1.035, 95% CIs: 1.003-1.069). There was also an increased risk of hospital admission for chronic obstructive pulmonary disease (RR: 1.026, 95% CIs: 1.002-1.051) and to lesser extent for respiratory diseases in general (RR: 1.006, 95% CI: 0.995-1.017) in the oldest age (≥75). CONCLUSIONS:Cold increases the need for acute hospital care due to both cardiovascular and respiratory causes during winter months. KEYWORDS: respiratory, cardiovascular, winter, hospital admissions, morbidity, weather

Association between respiratory hospital admissions and air quality in Portugal: A count time series approach.

Although regulatory improvements for air quality in the European Union have been made, air pollution is still a pressing problem and, its impact on health, both mortality and morbidity, is a topic of intense research nowadays. The main goal of this work is to assess the impact of the exposure to air pollutants on the number of daily hospital admissions due to respiratory causes in 58 spatial locations of Portugal mainland, during the period 2005-2017. To this end, INteger Generalised AutoRegressive Conditional Heteroskedastic (INGARCH)-based models are extensively used. This family of models has proven to be very useful in the analysis of serially dependent count data. Such models include information on the past history of the time series, as well as the effect of external covariates. In particular, daily hospitalisation counts, air quality and temperature data are endowed within INGARCH models of optimal orders, where the automatic inclusion of the most significant covariates is carried out through a new block-forward procedure. The INGARCH approach is adequate to model the outcome variable (respiratory hospital admissions) and the covariates, which advocates for the use of count time series approaches in this setting. Results show that the past history of the count process carries very relevant information and that temperature is the most determinant covariate, among the analysed, for daily hospital respiratory admissions. It is important to stress that, despite the small variability explained by air quality, all models include on average, approximately two air pollutants covariates besides temperature. Further analysis shows that the one-step-ahead forecasts distributions are well separated into two clusters: one cluster includes locations exclusively in the Lisbon area (exhibiting higher number of one-step-ahead hospital admissions forecasts), while the other contains the remaining locations. This results highlights that special attention must be given to air quality in Lisbon metropolitan area in order to decrease the number of hospital admissions.

Sensitivity and identifiability analysis of COVID-19 pandemic models.

The paper presents the results of sensitivity-based identifiability analysis of the COVID-19 pandemic spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The algorithm is built on the sensitivity matrix analysis using the methods of differential and linear algebra. It allows one to determine the parameters that are the least and most sensitive to data changes to build a regularization for solving an identification problem of the most accurate pandemic spread scenarios in the region. The performed analysis has demonstrated that the virus contagiousness is identifiable from the number of daily confirmed, critical and recovery cases. On the other hand, the predicted proportion of the admitted patients who require a ventilator and the mortality rate are determined much less consistently. It has been shown that building a more realistic forecast requires adding additional information about the process such as the number of daily hospital admissions. In our study, the problems of parameter identification using additional information about the number of daily confirmed, critical and mortality cases in the region were reduced to minimizing the corresponding misfit functions. The minimization problem was solved through the differential evolution method that is widely applied for stochastic global optimization. It has been demonstrated that a more general COVID-19 spread compartmental model consisting of seven ordinary differential equations describes the main trend of the spread and is sensitive to the peaks of confirmed cases but does not qualitatively describe small statistical datasets such as the number of daily critical cases or mortality that can lead to errors in forecasting. A more detailed agent-oriented model has been able to capture statistical data with additional noise to build scenarios of COVID-19 spread in the region.

A multivariate analysis of risk factors associated with death by Covid-19 in the USA, Italy, Spain, and Germany

AimOur aim was to investigate the risk factors associated with death from COVID-19 in four countries: The USA, Italy, Spain, and Germany.Subject and methodsWe used data from the Institute for Health Metrics and Evaluation with projection information from January–August 2020. A multivariate analysis of logistic regression was performed. The following factors were analyzed (per day): number of beds needed for the hospital services, number of intensive care units (ICU) beds required, number of ventilation devices, number of both hospital and ICU admissions due to COVID-19. Nagelkerke’s R2 coefficient of determination was used to evaluate the model’s predictive ability. The quality of the model’s fit was assessed by the Hosmer–Lemeshow and the chi-square tests.ResultsAmong the evaluated countries, Italy presented greater need for ICU beds/day (≤ 98; OR = 2315.122; CI 95% [334.767–16,503.502]; p < 0.001) and daily ventilation devices (≤ 118; OR = 1784.168; CI 95% [250.217–12,721.995]; p < 0.001). It is expected that both Italy and Spain have a higher ICU admission rate due to COVID-19 (n = 14/day). Spain will need more beds/day (≤ 357; OR = 146.838; CI 95% [113.242–190.402]; p < 0.001) and probably will have a higher number of daily hospital admissions (n = 48/day). All the above-mentioned factors have an important impact on patients’ mortality due to COVID-19 in all four countries.ConclusionsFurther investments in hospitals’ infrastructure, as well as the development of innovative devices for patient’s ventilation, are paramount to fight the pandemic in the USA, Italy, Spain, and Germany.

Collateral Effect of Coronavirus Disease 2019 Pandemic on Hospitalizations and Clinical Outcomes in Gastrointestinal and Liver Diseases: A Territory-wide Observational Study in Hong Kong

Background The Coronavirus Disease 2019 (COVID-19) pandemic had a huge impact on healthcare systems globally. Decline in hospital admissions for common medical emergencies was observed. We investigated its collateral effect on hospitalizations and clinical outcomes in patients with gastrointestinal (GI) and liver diseases in Hong Kong. Methods We performed a territory-wide retrospective cohort study in Hong Kong from January 1, 2019, to May 31, 2020. Data were retrieved through Clinical Data Analysis and Reporting System. We included patients admitted to hospitals with top diagnoses of luminal GI cancers, pancreatico-hepatobiliary cancers, benign pancreaticobiliary disorders, liver diseases, non-infective enteritis and colitis, non-variceal and variceal upper gastrointestinal bleeding (UGIB). We excluded patient-based hospital admissions without emergency department attendance and length of stay equal to or shorter than one day. The primary endpoint was the total number of index hospital admissions related to these diagnoses at different time points. The secondary endpoints were in-hospital mortality, intensive care unit admission, elective or emergency operations and endoscopy. The COVID-19 period was defined as the time after the first local case in January 2020, week 4. Results We recorded a total of 195,867 hospital admissions related to GI diseases during the study period, and 125,049 of them were included in the final analysis. Comparing the same pre-COVID19 and COVID-19 periods, we observed a significant decline in the average number of hospitalizations for GI diseases (17.0% reduction, P Conclusions The number of hospitalizations related to GI diseases reduced drastically during the COVID-19 epidemic, yet no excessive in-hospital mortality was observed. More emergency endoscopies and operations were required, particularly for UGIB and benign pancreaticobiliary conditions.

Association of short-term exposure to sulfur dioxide and hospitalization for ischemic and hemorrhagic stroke in Guangzhou, China

BackgroundIn developing countries, ambient sulfur dioxide (SO2) is a serious air pollutant concern, but there is no enough and consistent epidemiological evidence about its health effects on stroke hospitalization.MethodsWe collected the daily air pollution data, meteorological data and number of daily hospital admissions for ischemic and hemorrhagic stroke, in Guangzhou from January 1st 2009 to December 31st 2014. Then we applied generalized additive model with a quasi-Poisson link to assess the relationship between short-term SO2 exposure and the total number of hospital admissions for ischemic and hemorrhagic stroke. In addition, we evaluated the effect of ambient SO2 by age (< 65 years and ≥ 65 years).ResultsDuring the study period, a 24-h mean concentration of ambient SO2 of 27.82 μg/m3, a total of 58,473 ischemic stroke and 9167 hemorrhagic stroke hospital admissions hospital were recorded. Ambient SO2 was found to increase the risk for both ischemic and hemorrhagic stroke hospital admission in single pollutant model. The maximum value of percentage changes for ischemic and hemorrhagic stroke occurred in lag 0 day and lag 1 day, per 10 μg/m3 increase in SO2 concentrations was corresponded to a 1.27% (95% confidence interval (CI), 0.42–2.12%) and 1.55% (95%CI, 0.02–3.11%) increased risk, respectively. The association between SO2 and ischemic stroke hospitalization was robust to two pollutant model, but for hemorrhagic stroke it’s partially weakened after adjusting for co-pollutants. The effect of ambient SO2 on ischemic stroke appeared to be greater for people < 65 years old, but null effect on hemorrhagic stroke was identified for both age groups.ConclusionsWe found short-term exposure to ambient SO2 may significantly increase the risks of hospitalization for ischemic stroke. The findings may contribute to a better understanding of the health effects of low-levels of SO2.

Association Between Air Temperature and the Incidence of Acute Coronary Heart Disease in Northeast China.

AimThe aim of this study was to examine the association between air temperature and incidence of acute coronary heart disease in Northeast China.MethodsWe obtained coronary heart disease (CHD) daily hospitalization data from January 2017 to June 2019, and collected meteorological data including average daily air temperature, air pressure, relative humidity, wind velocity, sunshine duration and water vapor pressure, for the same period. Totally, This study included data from 6775 patients with CHD.ResultsAfter adjusting for confounding factors, low air temperature was inversely associated with CHD. Additionally, in the warm season (April–September), the number of daily hospital admissions for CHD was higher (≥24.2°C) if the average daily air temperature was low (≤15.4°C).ConclusionLow air temperature might be a risk factor for CHD among the elderly, especially in the warm season.

A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases

With lifestyle and environmental changes, the prevalence of cardiovascular diseases (CVDs) is trending upwards, putting pressure on the limited medical resources. Accurate forecasting of daily counts of hospital admissions (HAs) for CVDs is helpful to optimize medical resources. In this study, we proposed a stacking ensemble model with direct prediction strategy to predict the daily number of CVDs admissions using HAs data, air pollution data, and meteorological data. The sequential forward floating selection method with early stopping was applied for feature selection. Five machine learning models, including linear regression (LR), support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT), were utilized as base learners to construct the stacking model. We compared the performance of the proposed stacking model with the five base learners in three datasets. The experimental results indicated that our model performed best in three datasets under four evaluation criteria, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). Particularly, in the CVDs dataset, the MAPE is 15.103 for LR, 11.862 for SVR, 10.571 for XGBoost, 10.378 for GBDT, 10.333 for RF, and 9.679 for the stacking model. Compared with the best base learner RF, the MAPE, RMSE, and MAE of the stacking model decreased by 6.3%, 7.4%, and 6.3%, respectively, and the R2 improved by 1.7%. It is evident that the proposed stacking model can effectively forecast the daily number of hospitalizations for CVDs and provide decision support for hospital managers.

Fine particulate air pollution and adult hospital admissions in 200 Chinese cities: a time-series analysis.

The association between short-term exposure to ambient fine particulate matter (PM2.5) and morbidity risk in developing countries is not fully understood. We conducted a nationwide time-series study to estimate the short-term effect of PM2.5 on hospital admissions in Chinese adults. Daily counts of hospital admissions for 2014-16 were obtained from the National Urban Employee Basic Medical Insurance database. We identified more than 58 million hospitalizations from 0.28 billion insured persons in 200 Chinese cities for subjects aged 18 years or older. Generalized additive models with quasi-Poisson regression were applied to examine city-specific associations of PM2.5 concentrations with hospital admissions. National-average estimates of the association were obtained from a random-effects meta-analysis. We also investigated potential effect modifiers, such as age, sex, temperature and relative humidity. An increase of 10 μg/m3 in same-day PM2.5 concentrations was positively associated with a 0.19% (95% confidence interval: 0.07-0.30) increase in the daily number of hospital admissions at the national level. PM2.5 exposure remained positively associated with hospital admissions on days when the daily concentrations met the current Chinese Ambient Air Quality Standards (75 μg/m3). Estimates of admission varied across cities and increased in cities with lower PM2.5 concentrations (p = 0.044) or higher temperatures (p = 0.002) and relative humidity (p = 0.003). The elderly were more sensitive to PM2.5 exposure (p < 0.001). Short-term exposure to PM2.5 was positively associated with adult hospital admissions in China, even at levels below current Chinese Ambient Air Quality Standards.