In many studies, linear methods were used to calculate health benefits of air quality improvement, but the relationship between air pollutants and diseases may be complex and nonlinear. In addition, previous studies using reference number as average number of diseases may overestimate the health benefits. Therefore, the nonlinear model estimation and resetting of the reference number were very important. Hospital admission data for coronary heart disease (CHD), meteorological data, and air pollutant data of Zibo City from 2015 to 2019 were collected. The generalized additive model (GAM) was used to explore the association between air pollutants and hospital admission for CHD, and to evaluate the effects on health benefits under different reference number settings. A total of 21,105 hospitalized cases for CHD were reported in Zibo during the study period. The results of the GAM showed there was a log-linear exposure-response relationship between O3 and hospital admissions for CHD, with RR (relative risk) of 1.0143 (95% CI: 1.0047 ~ 1.0239). There were log-nonlinear exposure-response relationships between PM10, PM2.5, SO2, and hospital admissions for CHD. With the increase of pollutants concentrations, the risk for hospital admission showed a trend of increasing first and then decreasing. Compared with the average hospital admissions as the reference number, health benefits calculated by hospital admissions predicted by the GAM model yielded lower. Using the World Health Organization air quality guidelines as reference, attributable fractions of O3, PM10, and PM2.5 were 1.97% (95% CI: 0.63 ~ 3.40%), 11.82% (95% CI: 8.60 ~ 15.24%), and 11.82% (95% CI: 8.79 ~ 15.04%), respectively. When quantifying health benefits brought by improving air quality, corresponding calculation methods should first be determined according to the exposure-response relationships between air pollutants and outcomes. Then, applying the average hospital admissions as reference number may overestimate health benefits resulting from improved air quality.

Background/Objectives: To analyze changes in the prevalence of atrial fibrillation (AF) in patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AE-COPD); to evaluate hospital outcomes according to AF status, assessing sex differences; to identify factors associated with AF presence; and to analyze variables associated with in-hospital mortality (IHM) in AE-COPD patients with AF. Methods: We used data from the Registry of Specialized Care Activity-Basic Minimum Data Set (RAE-CMBD) to select patients aged ≥40 years with COPD in Spain (2016-2021). We stratified the study population according to AF presence and sex. The propensity score matching (PSM) methodology was employed to create comparable groups based on age, admission year, and comorbidities at the time of hospitalization. Results: We identified 399,196 hospitalizations that met the inclusion criteria. Among them, 20.58% had AF. The prevalence of AF rose from 2016 to 2021 (18.26% to 20.95%), though the increase was only significant in men. The median length of hospital stay (LOHS) and IHM were significantly higher in patients with AF than in those without AF. After PSM, IHM remained significantly higher for man and women with AF. Older age, male sex, and several comorbidities were factors associated with AF. Additionally, older age, male sex, different comorbidities including COVID-19, hospitalization in the year 2020, mechanical ventilation, and intensive care unit (ICU) admission were associated with higher IHM in patients with AE-COPD and AF. Conclusions: AF prevalence was high in patients hospitalized for AE-COPD, was higher in men than in women, and increased over time. AF presence was associated with worse outcomes. The variables associated with IHM in hospitalized AE-COPD patients with AF were older age, male sex, different comorbidities including COVID-19 presence, hospitalization in the year 2020, need of mechanical ventilation, and ICU admission.

BACKGROUND: Although chronic obstructive pulmonary disease (COPD) admissions put a substantial burden on hospitals, most of the patients\' contacts with health services are in outpatient care. Traditionally, outpatient care has been difficult to capture in population-based samples. In this study we describe outpatient service use in COPD patients and assess associations between outpatient care (contact frequency and specific factors) and next-year COPD hospital admissions or 90-day readmissions.
METHODS: Patients over 40 years of age residing in Oslo or Trondheim at the time of contact in the period 2009-2018 were identified from the Norwegian Patient Registry (in- and outpatient hospital contacts, rehabilitation) and the KUHR registry (contacts with GPs, contract specialists and physiotherapists). These were linked to the Regular General Practitioner registry (characteristics of the GP practice), long-term care data (home and institutional care, need for assistance), socioeconomic and-demographic data from Statistics Norway and the Cause of Death registry. Negative binomial models were applied to study associations between combinations of outpatient care, specific care factors and next-year COPD hospital admissions and 90-day readmissions. The sample consisted of 24,074 individuals.
RESULTS: A large variation in the frequency and combination of outpatient service use for respiratory diagnoses (GP, emergency room, physiotherapy, contract specialist and outpatient hospital contacts) was apparent. GP and outpatient hospital contact frequency were strongly associated to an increased number of next-year hospital admissions (1.2-3.2 times higher by increasing GP frequency when no outpatient hospital contacts, 2.4-5 times higher in combination with outpatient hospital contacts). Adjusted for healthcare use, comorbidities and sociodemographics, outpatient care factors associated with lower numbers of next-year hospitalisations were fees indicating interaction between providers (7% reduction), spirometry with GP or specialist (7%), continuity of care with GP (15%), and GP follow-up (8%) or rehabilitation (18%) within 30 days vs. later following any current year hospitalisations. For 90-day readmissions results were less evident, and most variables were non-significant.
CONCLUSIONS: As increased use of outpatient care was strongly associated with future hospitalisations, this further stresses the need for good communication between providers when coordinating care for COPD patients. The results indicated possible benefits of care continuity within and interaction between providers.

BACKGROUND: The Coordinated medical Care (CoCare) project aimed to improve the quality of medical care in nursing homes by optimizing collaboration between nurses and physicians. We analyze the impact of the CoCare intervention on overall survival.
METHODS: The effect of time-varying treatment on 3-year overall survival was analyzed with treatment as time-varying covariate within the entire cohort. To reduce bias due to non-random assignment to treatment groups, regression adjustment was applied. Therefore, age, sex, and level of care were used as potential confounders.
RESULTS: The study population consisted of 8,893 nursing home residents (NHRs), of which 1,330 participated in the CoCare intervention. The three-year overall survival was 49.8% in the entire cohort. NHRs receiving the intervention were associated with a higher survival probability compared to NHRs of the control group. In a univariable cox model with time-dependent treatment, the intervention was associated with a hazard ratio of 0.70 [95%CI 0.56-0.87, p = 0.002]. After adjustment for age, sex and level of care, the hazard ratio increased to 0.82 but was still significant [95%CI 0.71-0.96, p = 0.011].
CONCLUSIONS: The analysis shows that optimizing collaboration between nurses and physicians leads to better survival of NHRs in Germany. This adds to the already published favorable cost-benefit ratio of the CoCare intervention and shows that a routine implementation of optimized collaboration between nurses and physicians is highly recommended.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.
OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.
METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.
RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).
CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

BACKGROUND: The literature has discussed the relationship between environmental factors and depressive disorders; however, the results are inconsistent in different studies and regions, as are the interaction effects between environmental factors. We hypothesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.
OBJECTIVE: To investigate the effects of meteorological factors and air pollution on depressive disorders, including their lagged effects and interactions.
METHODS: The samples were obtained from a class 3 hospital in Harbin, China. Daily hospital admission data for depressive disorders from January 1, 2015 to December 31, 2022 were obtained. Meteorological and air pollution data were also collected during the same period. Generalized additive models with quasi-Poisson regression were used for time-series modeling to measure the non-linear and delayed effects of environmental factors. We further incorporated each pair of environmental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.
RESULTS: Data for 2922 d were included in the study, with no missing values. The total number of depressive admissions was 83905. Medium to high correlations existed between environmental factors. Air temperature (AT) and wind speed (WS) significantly affected the number of admissions for depression. An extremely low temperature (-29.0 ℃) at lag 0 caused a 53% [relative risk (RR)= 1.53, 95% confidence interval (CI): 1.23-1.89] increase in daily hospital admissions relative to the median temperature. Extremely low WSs (0.4 m/s) at lag 7 increased the number of admissions by 58% (RR = 1.58, 95%CI: 1.07-2.31). In contrast, atmospheric pressure and relative humidity had smaller effects. Among the six air pollutants considered in the time-series model, nitrogen dioxide (NO2) was the only pollutant that showed significant effects over non-cumulative, cumulative, immediate, and lagged conditions. The cumulative effect of NO2 at lag 7 was 0.47% (RR = 1.0047, 95%CI: 1.0024-1.0071). Interaction effects were found between AT and the five air pollutants, atmospheric temperature and the four air pollutants, WS and sulfur dioxide.
CONCLUSIONS: Meteorological factors and the air pollutant NO2 affect daily hospital admissions for depressive disorders, and interactions exist between meteorological factors and ambient air pollution.

UNASSIGNED: Multiple sclerosis (MS) is a neurodegenerative disease accumulating disabilities over time. However, the mean age of individuals with MS is increasing, consequently elevating their risk of developing comorbidities. Comorbidities\' impact on MS is widely debated. Yet very few countries possess population-based registries, which provide unique opportunities for individual-level data linkage. This study aims to assess acute and chronic comorbidities among elderly patients with MS, comparing them to matched controls. Additionally, this study seeks to investigate the influence of chronic comorbidities on all-cause mortality.
UNASSIGNED: A nationwide register-based study using the Danish Multiple Sclerosis Registry to identify all living patients with MS older than 50 years at the reference date (January 1st, 2022). Patients were matched 1:10 with individuals from the general population. Comprehensive healthcare data within the Danish hospital system were obtained. Chronic comorbidities were classified according to the Charlson Comorbidity Index, while acute comorbidities were based on ICD-10 codes and an \"acute\" admission type. To investigate all-cause mortality, a Cox regression analysis was conducted.
UNASSIGNED: The study encompassed a total of 8,688 individuals with MS, matched with 86,880 controls. The mean age was 63.5 years, with females constituting 68.3%. Individuals with MS exhibited a higher frequency of acute hospitalizations (OR: 2.1, 95% CI: 1.9-2.2), primarily due to various infectious diseases, along with longer median hospital stays (4 vs. 3 days, p < 0.001). When assessed using the Charlson Comorbidity Index, individuals with MS carried a significantly greater burden of chronic comorbidities (p < 0.001). The most prevalent chronic comorbidity among individuals with MS was \"Uncomplicated Diabetes\" (20.1%). Notably, while individuals with MS displayed an overall lower 5-year survival rate, this difference ceased to be statistically significant among those with a high Charlson Comorbidity Index score of ≥4 (p = 0.32).
UNASSIGNED: This study highlights a heightened prevalence of both acute and chronic comorbidities among individuals with MS, with chronic comorbidities significantly increasing the risk of mortality. These findings underscore the critical importance of factoring in comorbidities when devising treatment strategies for individuals living with MS.

Considering the rapid influx of Ukrainian migrants and war refugees into Poland, the knowledge of their health condition is becoming increasingly important for health system policy and planning. The aim of the study was to assess war-related changes in the frequency and structure of hospitalizations among Ukrainian migrants and refugees in Poland.
The study is based on the analysis of hospital admission records of Ukrainian patients, which were collected in the Nationwide General Hospital Morbidity Study from 01.01.2014 to 31.12.2022.
In the study period, 13,024 Ukrainians were hospitalized in Poland, 51.7% of whom had been admitted to hospital after February 24, 2022. After the war broke out, the average daily hospital admissions augmented from 2.1 to 21.6 person/day. A noticeable increase in the share of women (from 50% to 62%) and children (from 14% to 51%) was also observed. The average age of patients fell from 33.6 ± 0.2 years to 24.6 ± 0.3 years. The most frequently reported hospital events among the migrants until 23.02.2022 were injuries (S00-T98) - 26.1%, pregnancy, childbirth and the puerperium (O00-O99) - 18.4%, and factors influencing health status and contact with health services (Z00-Z99) - 8.4%. After the war started, the incidence of health problems among migrants and war refugees changed, with pregnancy, childbirth and the puerperium (O00-O99) being the most common - 14.9%, followed by abnormal clinical and lab findings (R00-R99) - 11.9%, and infectious and parasitic diseases (A00-B99) - 11.0%.
Our findings may support health policy planning and delivering adequate healthcare in refugee-hosting countries.

Background The coronavirus disease 2019 (COVID-19) pandemic provoked disruptions in healthcare delivery, leading to the cancellation and postponement of various health services, including surgery. Numerous countries closed their borders and established laws mandating the use of face masks and social distancing and enforced lockdowns, and various activities were constrained. Brazil, the largest and most populous country in Latin America, also experienced a rapid and sustained surge in infections and deaths. Brazil was the most severely impacted nation in Latin America. The impact of the pandemic on surgical services in Brazil has not been adequately studied since most studies only cover the early phases of the pandemic. Thus, this study aimed to assess the impact of the COVID-19 pandemic on surgical services throughout the entire period. Methods A retrospective cross-sectional design was used to examine surgical cases from 2019 to 2022 and compared the following indicators: (1) number of hospital admissions, (2) length of hospital stay (LOS) (in days), and (3) volume of urgent and elective procedures. Data was divided into four time periods, pre-pandemic (March-December 2019), pandemic (March-December 2020), recovery (March-December 2021), and post-pandemic (March-December 2022), and was analyzed for the number of admissions and LOS based on surgical procedures performed by stratifying according to region, sex, age, and type of surgery (urgent versus elective). Results The number of admissions for surgical procedures ranged between 859,646 and 4,015,624 for 2019, 686,616 and 3,419,234 for 2020, 787,791 and 3,829,019 for 2021, and 760,512 and 3,857,817 for 2022 for the category of region; 4,260,900 and 5,991,775 for 2019, 3,594,117 and 4,984,710 for 2020, 4,182,640 and 5,590,808 for 2021, and 4,077,651 and 5,561,928 for 2022 for the category of sex; and 2,170,288 and 3,186,117 for 2019, 1,516,830 and 2,825,189 for 2020, 1,748,202 and 3,030,272 for 2021, and 1,900,023 and 2,859,179 for 2022 for the category of age. The variable age showed a comparable trend, albeit with an expressive decline for surgeries in the age range of 0-19 years. The LOS (in days) for surgical procedures ranged between 110,157 and 910,846 for 2019, 58,562 and 897,734 for 2020, 67,926 and 904,137 for 2021, and 100,467 and 823,545 for 2022. Thoracic surgery indicated no statistically significant difference in the number of admissions and LOS. Elective surgeries had a decline in the number of admissions and LOS, a 13% and 9.3% decline between 2019 and 2020, respectively. Urgent surgeries experienced a slight decrease in admissions and LOS, with a decline of 2.4% and 2.8% between 2019 and 2020, respectively. Conclusions Population characteristics, such as age, sex, and region, showed decreased hospital admissions during the pandemic, followed by a recovery toward pre-pandemic levels afterward. The number of surgical admissions and the length of hospital stays decreased during the pandemic but gradually returned to pre-pandemic levels in the recovery and post-pandemic phases. Notably, thoracic surgery remained statistically consistent across all periods, indicating its emergency nature compared to other surgeries. Thus, we conclude that the pandemic had minimal impact on thoracic surgery cases, contributing to a stable trend.

People with coronary heart disease are at an increased risk of morbidity and mortality even if they attend cardiac rehabilitation. High sedentary behavior levels potentially contribute to this morbidity. Smartphone apps may be feasible to facilitate sedentary behavior reductions and lead to reduced health care use.
We aimed to test the effect of a sedentary behavior change smartphone app (Vire app and ToDo-CR program) as an adjunct to cardiac rehabilitation on hospital admissions and emergency department (ED) presentations over 12 months.
A multicenter, randomized controlled trial was conducted with 120 participants recruited from 3 cardiac rehabilitation programs. Participants were randomized 1:1 to cardiac rehabilitation plus the fully automated 6-month Vire app and ToDo-CR program (intervention) or usual care (control). The primary outcome was nonelective hospital admissions and ED presentations over 12 months. Secondary outcomes including accelerometer-measured sedentary behavior, BMI, waist circumference, and quality of life were recorded at baseline and 6 and 12 months. Logistic regression models were used to analyze the primary outcome, and linear mixed-effects models were used to analyze secondary outcomes. Data on intervention and hospital admission costs were collected, and the incremental cost-effectiveness ratios (ICERs) were calculated.
Participants were, on average, aged 62 (SD 10) years, and the majority were male (93/120, 77.5%). The intervention group were more likely to experience all-cause (odds ratio [OR] 1.54, 95% CI 0.58-4.10; P=.39) and cardiac-related (OR 3.26, 95% CI 0.84-12.55; P=.09) hospital admissions and ED presentations (OR 2.07, 95% CI 0.89-4.77; P=.09) than the control group. Despite this, cardiac-related hospital admission costs were lower in the intervention group over 12 months (Aus $252.40 vs Aus $859.38; P=.24; a currency exchange rate of Aus $1=US $0.69 is applicable). There were no significant between-group differences in sedentary behavior minutes per day over 12 months, although the intervention group completed 22 minutes less than the control group (95% CI -22.80 to 66.69; P=.33; Cohen d=0.21). The intervention group had a lower BMI (β=1.62; P=.05), waist circumference (β=5.81; P=.01), waist-to-hip ratio (β=.03, P=.03), and quality of life (β=3.30; P=.05) than the control group. The intervention was more effective but more costly in reducing sedentary behavior (ICER Aus $351.77) and anxiety (ICER Aus $10,987.71) at 12 months. The intervention was also more effective yet costly in increasing quality of life (ICER Aus $93,395.50) at 12 months.
The Vire app and ToDo-CR program was not an outcome-effective or cost-effective solution to reduce all-cause hospital admissions or ED presentations in cardiac rehabilitation compared with usual care. Smartphone apps that target sedentary behavior alone may not be an effective solution for cardiac rehabilitation participants to reduce hospital admissions and sedentary behavior.
Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001223123; https://australianclinicaltrials.gov.au/anzctr/trial/ACTRN12619001223123.
RR2-10.1136/bmjopen-2020-040479.