Chronic Lower Respiratory Disease Research Articles

Association of naples prognostic score and lung health: A population-based study

BackgroundThe Naples Prognostic Score (NPS) is a novel indicator of inflammatory and nutritional status, but its relationship to lung health is unknown. ObjectiveTo evaluate the relationship of NPS to lung health problems. MethodsA total of 15,600 participants aged 20 years or older with an available assessment of chronic lung diseases were enrolled from the National Health and Nutrition Examination Survey 2007–2012. The NPS was calculated based on serum albumin, total cholesterol, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Associations of NPS with chronic lung disease (diagnosed asthma, chronic bronchitis, and emphysema), respiratory symptoms (cough, phlegm production, wheeze, and exertional dyspnea), and spirometric measurements (FEV1, FVC, and obstructive or restrictive spirometry pattern) were evaluated. Kaplan-Meier survival analysis and multiple Cox regressions were used to assess the significance of NPS in relation to all-cause mortality and chronic lower respiratory diseases mortality in participants. Furthermore, to comprehensively assess the association between NSP and chronic lower respiratory diseases mortality, Fine-Gray subdistribution hazards model was performed to analyze non-chronic lower respiratory diseases mortality as a competitive risk. ResultsPeople with a higher NPS score were associated with greater odds of asthma, chronic bronchitis, respiratory symptoms (including phlegm production, wheeze, and exertional dyspnea), and a greater risk of obstructive and restrictive spirometry. A higher NPS score was significantly associated with decreased FEV1 and FVC in both overall participants and those with lung health problems. Longitudinally, we found that those in the category with highest NPS were at greater risk of all-cause mortality and chronic lower respiratory diseases mortality in those with chronic lung disease, and respiratory symptoms. ConclusionsAn elevated NPS is associated with a host of adverse pulmonary outcomes. Prospective studies to define NPS as a biomarker for impaired lung health are warranted.

Adult mortality from non-communicable diseases in Fiji's major ethnic groups 2013–17

BackgroundSustainable Development Goal 3.4.1 (SDG3.4.1) targets a one-third reduction in non-communicable disease (NCD) mortality in ages 30–69-years by 2030 (relative to 2015). Directing interventions to achieve this aim requires reliable estimates of underlying cause of death (UCoD). This may be problematic when both cardiovascular diseases (CVD) and diabetes are present due to a lack of consistency in certification of such deaths. We estimate empirically 2013–17 NCD mortality in Fiji, by sex and ethnicity, from CVD, diabetes, cancer, and chronic lower respiratory diseases (CRD), and aggregated as NCD4. MethodsUCoD was determined from Medical Certificates of Cause-of-Death (MCCD) from the Fiji Ministry of Health after pre-processing of mortality data where diabetes and/or hypertension were present in order to generate internationally comparable UCoD. If no potentially fatal complications from diabetes or hypertension accompanied these causes in Part I (direct cause) of the MCCD, these conditions were re-assigned to Part II (contributory cause). The probability of a 30-year-old dying before reaching age 70-years (PoD30–70), by cause, was calculated. FindingsThe PoD30–70 from NCD4 over 2013–17 differed by sex and ethnicity: in women, it was 36% (95%CI 35–37%) in i-Taukei and 27% (26–28%) in Fijians of Indian descent (FID); in men, it was 41% (40–42%) in both i-Taukei and FID.PoD30–70 from CVD, diabetes, cancer and CRD in women was: 18%, 10%, 13% and 1·0% in i-Taukei; 13%, 10%, 5·6% and 1·1% in FID; in men was: 28%, 8.4%, 7·6% and 2·2% in i-Taukei; 31%, 8.3%, 3.5% and 3·1% in FID. InterpretationTo achieve SDG3.4.1 goals in Fiji by 2030, effective population wide and ethnic-specific interventions targeting multiple NCDs are required to reduce PoD30–70 from NCD4: from 36% to 24% in i-Taukei, and 27% to 18% in FID women; and from 41% to 27% in i-Taukei and FID men. FundingNot applicable.

Comparison of fatalities due to COVID-19 and other nonexternal causes during the first five pandemic waves

BackgroundOlder age is a risk factor for a fatal course of SARS-CoV‑2 infection, possibly due to comorbidities whose exact role in this context, however, is not yet well understood. In this paper, the characteristics and comorbidities of persons who had died of COVID-19 in Bavaria by July 2022 are shown and compared with the characteristics of other fatalities during the pandemic.MethodsBased on data from multiple cause of death statistics, odds ratios for dying from COVID-19 (compared to dying from other nonexternal causes of death) were calculated by using logistic regression models, stratified by age, sex, and pandemic waves.ResultsIn Bavaria, a total of 24,479 persons (6.5% of all deaths) officially died from COVID-19 between March 2020 and July 2022. In addition to increasing age and male sex, preexisting diseases and comorbidities such as obesity, degenerative diseases of the nervous system, dementia, renal insufficiency, chronic lower respiratory diseases, and diabetes mellitus were significantly associated with COVID-19–related deaths. Dementia was mainly associated with increased COVID-19 mortality during the first and second waves, while obesity was strongly associated during the fourth wave.DiscussionThe frequency of specific comorbidities in COVID-19 deaths varied over the course of the pandemic. This suggests that wave-specific results also need to be interpreted against the background of circulating virus variants, changing immunisation levels, and nonpharmaceutical interventions in place at the time.

Spatio-temporal patterns of excess mortality from non-COVID causes of death in the United States, march-December 2020.

Objectives To estimate the burden of excess mortality from 17 underlying causes of death between March-December 2020 in the United States, and to compare trends in excess deaths from non-COVID causes vs. from COVID-19. Methods Using time series models, we estimated monthly counts of all-cause and cause-specific excess deaths. We stratified by geographic region and compared temporal trends in excess deaths from non-COVID causes to trends in deaths attributed to COVID-19. Results Of approximately 500,000 excess deaths, 70% were attributed to COVID-19. We observed increases in several underlying causes of death, ranging from a 3% increase in kidney disease deaths to a 24% increase in homicides, as well as decreases in deaths from cancer (-0.3%), influenza and pneumonia (-2%), chronic lower respiratory disease (-3%), and suicide (-7%). Trends in excess deaths from cardiovascular disease, diabetes, and Alzheimer's disease closely mirrored trends in deaths from COVID-19. Trends in excess liver disease, homicide, suicide, and motor vehicle accident deaths were negatively correlated with trends in deaths from COVID-19. There was wide regional variation in excess death rates for some causes of death, including a disproportionate increase in homicide and motor vehicle accident deaths in the Great Lakes, and a sustained reduction in cancer deaths in the Mideast and New England. Conclusions Increases in cardiovascular disease, diabetes, and Alzheimer's disease deaths from March-December 2020 likely reflect healthcare system disruptions or acute complications of COVID-19. Excess deaths from homicide and liver disease are more likely to reflect social and economic effects of the emerging pandemic, or other separate causes.

One-year post-acute COVID-19 syndrome and mortality in South Korea: a nationwide matched cohort study using claims data.

Current understanding of post-COVID-19 syndrome in South Korea is primarily based on survey studies or research targeting specific patient groups, such as those hospitalized. Moreover, the majority of relevant studies have been conducted in European and North American populations, which may limit their applicability to the South Korean context. To address this gap, our study explores the one-year outcomes of COVID-19, focusing on the potential post-acute syndrome and all-cause mortality in South Korea. This retrospective cohort study used nationwide claims data in South Korea, including adults aged >18 with records between January 20, 2020, and February 25, 2021. Patients were classified into COVID-19 and non-COVID-19 groups and matched 1:1 based on propensity scores. Primary outcomes were 12-month post-acute COVID-19 syndrome and all-cause mortality. The study involved 34,802 matched patients. The COVID-19 group had significantly elevated risks of coagulopathies (OR = 2.70 [2.24, 3.28]; p < 0.001), chronic lower respiratory diseases (OR = 1.96 [1.80, 2.14]; p < 0.001), symptoms of the circulatory and respiratory systems (OR = 1.91 [1.80, 2.04]; p < 0.001), mood disorders (OR = 1.67 [1.51, 1.86]; p < 0.001), cardiac diseases (OR = 1.39 [1.21, 1.59]; p < 0.001), and symptoms of cognition, perception, emotional state, and behavior (OR = 1.15 [1.04, 1.27]; p = 0.005). All-cause mortality was higher in the COVID-19 group during the 6 months (OR = 1.34 [1.06, 1.69]; p = 0.015), but gradually decreased, reaching an OR of 0.996 ([0.83, 1.19]; p = 0.964) at 1 year. In South Korea, the 12-month post-acute COVID-19 syndrome includes coagulopathies, respiratory issues, mood disorders, and cardiac diseases. The risk of all-cause mortality post-COVID-19 is heightened for up to 6 months, then significantly decreases and resolves within a year.

Demographic and geographical trends in chronic lower respiratory diseases mortality in the United States, 1999 to 2020

Chronic lower respiratory disease (CLRD) related mortality has decreased in the United States due to increasing awareness in the general population and advancing preventative efforts, diagnostic measures, and treatment. However, demographic and regional differences still persist throughout the United States. In this study, we analyzed the temporal trends of demographic and geographical differences in CLRD-related mortality. Data was extracted from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database. Using this data, age-adjusted mortality rates per 100,000 people (AAMR), annual percentage change (APC), and average annual percentage changes with 95% confidence intervals (CIs) were assessed. The Joinpoint Regression Program was used to determine mortality trends between 1999 and 2020 based on demographic and regional groups.During this study period, there were 3,064,049 CLRD-related deaths, with most demographics and regional areas showing an overall decreasing trend. However, higher mortality rates were seen in the non-Hispanic White population and rural areas. Interestingly, mortality rates witnessed a decreasing trend for males throughout the study duration compared to females, who only began to show decreases in mortality during the latter half of the 2010s. Using these results, one can target efforts and build policies to improve CLRD-related mortality and reduce disparities in the coming decades.

Examining the Relationship Between Chronic Pain and Mortality in U.S. Adults

Chronic pain (CP) significantly impacts quality of life and increases noncommunicable disease risk, with recent U.S. data showing a 6.3% incidence rate, surpassing diabetes, depression, and hypertension. International studies suggest higher mortality in CP populations, yet prior U.S. data are inconclusive. To investigate CP's mortality risk, we analyzed National Health Interview Survey and National Death Index data. We hypothesized that individuals with CP and high-impact CP (HICP [≥1 activity limitation]) would exhibit higher mortality rates. National Health Interview Survey provided demographics, pain reporting, lifestyle, and psychosocial data matched with National Death Index mortality records. Chi-square analyses explored the relationships between CP/HICP and demographics, lifestyle factors, psychosocial variables, and mortality. Cox proportional hazards models assessed mortality risk between groups. The weighted sample was 245,899,776; 20% reported CP and 8% HICP, both groups exhibiting higher mortality rates than pain-free individuals (CP: 5.55%, HICP: 8.79%, total: 2.82%). Hazard ratios indicated nearly double the mortality risk for CP and 2.5 times higher risk for HICP compared to those without these conditions. Adjusting for lifestyle and psychosocial factors reduced mortality risk but remained elevated compared with non-CP individuals. Heart disease, malignant neoplasms, and chronic lower respiratory diseases accounted for a higher percentage of deaths in CP cases. CP individuals showed higher rates of smoking, alcohol consumption, obesity, inactivity, depression, anxiety, emotional problems, and sleep disturbances. CP and HICP significantly influence mortality outcomes, leading to excess deaths compared with pain-free individuals. Given the relationship between pain, lifestyle, psychosocial variables, and mortality, further investigations are needed into CP causation and prevention strategies. PerspectiveThis article presents evidence regarding the relationship between CP, HICP, and mortality. Additional findings are discussed regarding the impact of demographics, lifestyle, and psychosocial variables on mortality in those with versus without CP and HICP. These findings are crucial for informing future research, prevention, and healthcare management strategies.

Preventable Premature Deaths from the Five Leading Causes of Death in Nonmetropolitan and Metropolitan Counties, United States, 2010-2022.

A 2019 report quantified the higher percentage of potentially excess (preventable) deaths in U.S. nonmetropolitan areas compared with metropolitan areas during 2010-2017. In that report, CDC compared national, regional, and state estimates of preventable premature deaths from the five leading causes of death in nonmetropolitan and metropolitan counties during 2010-2017. This report provides estimates of preventable premature deaths for additional years (2010-2022). 2010-2022. Mortality data for U.S. residents from the National Vital Statistics System were used to calculate preventable premature deaths from the five leading causes of death among persons aged <80 years. CDC's National Center for Health Statistics urban-rural classification scheme for counties was used to categorize the deaths according to the urban-rural county classification level of the decedent's county of residence (1: large central metropolitan [most urban], 2: large fringe metropolitan, 3: medium metropolitan, 4: small metropolitan, 5: micropolitan, and 6: noncore [most rural]). Preventable premature deaths were defined as deaths among persons aged <80 years that exceeded the number expected if the death rates for each cause in all states were equivalent to those in the benchmark states (i.e., the three states with the lowest rates). Preventable premature deaths were calculated separately for the six urban-rural county categories nationally, the 10 U.S. Department of Health and Human Services public health regions, and the 50 states and the District of Columbia. During 2010-2022, the percentage of preventable premature deaths among persons aged <80 years in the United States increased for unintentional injury (e.g., unintentional poisoning including drug overdose, unintentional motor vehicle traffic crash, unintentional drowning, and unintentional fall) and stroke, decreased for cancer and chronic lower respiratory disease (CLRD), and remained stable for heart disease. The percentages of preventable premature deaths from the five leading causes of death were higher in rural counties in all years during 2010-2022. When assessed by the six urban-rural county classifications, percentages of preventable premature deaths in the most rural counties (noncore) were consistently higher than in the most urban counties (large central metropolitan and fringe metropolitan) for the five leading causes of death during the study period.During 2010-2022, preventable premature deaths from heart disease increased most in noncore (+9.5%) and micropolitan counties (+9.1%) and decreased most in large central metropolitan counties (-10.2%). Preventable premature deaths from cancer decreased in all county categories, with the largest decreases in large central metropolitan and large fringe metropolitan counties (-100.0%; benchmark achieved in both county categories in 2019). In all county categories, preventable premature deaths from unintentional injury increased, with the largest increases occurring in large central metropolitan (+147.5%) and large fringe metropolitan (+97.5%) counties. Preventable premature deaths from CLRD decreased most in large central metropolitan counties where the benchmark was achieved in 2019 and increased slightly in noncore counties (+0.8%). In all county categories, preventable premature deaths from stroke decreased from 2010 to 2013, remained constant from 2013 to 2019, and then increased in 2020 at the start of the COVID-19 pandemic. Percentages of preventable premature deaths varied across states by urban-rural county classification during 2010-2022. During 2010-2022, nonmetropolitan counties had higher percentages of preventable premature deaths from the five leading causes of death than did metropolitan counties nationwide, across public health regions, and in most states. The gap between the most rural and most urban counties for preventable premature deaths increased during 2010-2022 for four causes of death (cancer, heart disease, CLRD, and stroke) and decreased for unintentional injury. Urban and suburban counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) experienced increases in preventable premature deaths from unintentional injury during 2010-2022, leading to a narrower gap between the already high (approximately 69% in 2022) percentage of preventable premature deaths in noncore and micropolitan counties. Sharp increases in preventable premature deaths from unintentional injury, heart disease, and stroke were observed in 2020, whereas preventable premature deaths from CLRD and cancer continued to decline. CLRD deaths decreased during 2017-2020 but increased in 2022. An increase in the percentage of preventable premature deaths for multiple leading causes of death was observed in 2020 and was likely associated with COVID-19-related conditions that contributed to increased mortality from heart disease and stroke. Routine tracking of preventable premature deaths based on urban-rural county classification might enable public health departments to identify and monitor geographic disparities in health outcomes. These disparities might be related to different levels of access to health care, social determinants of health, and other risk factors. Identifying areas with a high prevalence of potentially preventable mortality might be informative for interventions.

Smoking-Attributable Health Care Expenditures for US Adults With Chronic Lower Respiratory Disease

Cigarette smoking is a primary risk factor for chronic lower respiratory disease (CLRD) and is associated with worse symptoms among people with CLRD. It is important to evaluate the economic outcomes of smoking in this population. To estimate smoking prevalence and cigarette smoking-attributable health care expenditures (SAHEs) for adults with CLRD in the US. This cross-sectional study used data from the 2014-2018 and 2020 National Health Interview Surveys (NHIS) and the 2020 Medical Expenditure Panel Survey. The final study population, stratified by age 35 to 64 years and 65 years or older, was extracted from the 2014-2018 NHIS data. The data analysis was performed between February 1 and March 31, 2024. Cigarette smoking, as classified into 4 categories: current smokers, former smokers who quit less than 15 years ago, former smokers who quit 15 or more years ago, and never smokers. Smoking-attributable health care expenditures were assessed using a prevalence-based annual cost approach. Econometric models for the association between cigarette smoking and health care utilization were estimated for 4 types of health care services: inpatient care, emergency department visits, physician visits, and home health visits. In the 2014-2018 NHIS study sample of 13 017 adults, 7400 (weighted 62.4%) were aged 35 to 64 years, 5617 (weighted 37.6%) were 65 years or older, and 8239 (weighted 61.9%) were female. In 2020, among 11 211 222 adults aged 35 to 64 with CLRD, 3 508 504 (31.3%) were current smokers and 3 496 790 (31.2%) were former smokers. Total SAHEs in 2020 for this age group were $13.6 billion, averaging $2752 per current smoker and $1083 per former smoker. In 2020, 7 561 909 adults aged 65 years or older had CLRD, with 1 451 033 (19.2%) being current smokers and 4 104 904 (54.3%) being former smokers. Total SAHEs in 2020 for the older age group were $5.3 billion, averaging $1704 per current smoker and $682 per former smoker. In sum, SAHEs for adults with CLRD aged 35 years or older amounted to $18.9 billion in 2020. In this cross-sectional study of adults with CLRD, cigarette smoking was associated with a substantial health care burden. The higher per-person SAHEs for current smokers compared with former smokers suggest potential cost savings of developing targeted smoking cessation interventions for this population.

Epidemiological characteristics of chronic non-communicable diseases in Dongfang, China, 2021: a cross-sectional survey

ObjectivesThe study aimed to understand the composition, epidemiological characteristics and disease burden of chronic non-communicable diseases and to evaluate the association between sociodemographic factors and chronic non-communicable diseases.DesignA cross-sectional study.Setting...

Ultrafine Particles and Hospital Visits for Chronic Lower Respiratory Diseases in New York State.

Rationale: Exposure to particulate matter is associated with various adverse health outcomes. Ultrafine particles (UFPs; diameter <0.1 μm) are a unique public health challenge because of their size. However, limited studies have examined their impacts on human health, especially across seasons and demographic characteristics. Objectives: To evaluate the effect of UFP exposure on the risk of visiting the emergency department (ED) for a chronic lower respiratory disease (CLRD) in New York State in 2013-2018. Methods: We used a case-crossover design and conditional logistic regression to estimate how UFP exposure led to CLRD-related ED visits. GEOS-Chem Advanced Particle Microphysics, a state-of-the-art chemical transport model with a size-resolved particle microphysics model, generated air pollution simulation data. We then matched UFP exposure estimates to geocoded health records for asthma, bronchiectasis, chronic bronchitis, emphysema, unspecified bronchitis, and other chronic airway obstructions in New York State from 2013 through 2018. In addition, we assessed interactions with age, ethnicity, race, sex, meteorological factors, and season. Results: Each 1-(interquartile range [IQR]) increase in UFP exposure led to a 0.37% increased risk of a respiratory-related ED visit on lag 0-0, or the day of the ED visits, (95% confidence interval [CI], 0.23-0.52%) and a 1.81% increase on lag 0-6, or 6 days before the ED visit, (95% CI, 1.58-2.03%). The highest risk was in the emphysema subtype (lag 0-5, 4.18%; 95% CI, 0.16-8.37%), followed by asthma (lag 0-6, 2.00%), chronic bronchitis (lag 0-6, 1.78%), other chronic airway obstructions (lag 0-6, 1.60%), and unspecified bronchitis (lag 0-6, 1.49%). We also found significant interactions between UFP health impacts and season (Fall, 3.29%), temperature (<90th percentile, 2.27%), relative humidity (>90th percentile, 4.63%), age (children aged <18 yr, 3.19%), and sex (men, 2.06%) on lag 0-6. Conclusions: In this study, UFP exposure increased CLRD-related ED visits across all seasons and demographic characteristics, yet these associations varied according to various factors, which requires more research.

Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation.

The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection. In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed. The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients' admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model's predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning-based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic. The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance. The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Large Scale Analysis of Autologous Stem Cell Transplantation for Multiple Myeloma Patients Older Than 65 Years

Introduction In multiple myeloma (MM) patients older than 65, the value of consolidation with autologous hematopoietic stem cell transplantation (auto-SCT) is not well defined. This is generally a population not represented in the pivotal clinical trials on which routine clinical practice is based. Even though the treatment of MM in older patients has improved in recent years, their survival is shortened, and most do not reach a second line. However, there are data supporting auto-SCT in this population with low associated mortality not related to relapse (NRM). TriNetX is a federated global health research platform that provides researchers with access to electronic medical records from healthcare organizations to conduct research studies. The aim of our study is to evaluate the safety and efficacy of auto-SCT in patients older than 65 years with MM with in a large population of world real life data. Methods We have analyzed the cohort of patients from our center (H12O) who have received an auto-SCT from January 2014 to March 2023. We defined two cohorts: Cohort 1, patients with &amp;gt; 65 years at the time of transplantation (n=59 patients) and Cohort 2, &amp;lt;= 65y (n=213 patients). Data from the last 20 years of the TriNetX platform of 48 centers have been used. Cohort 1, patients with &amp;gt; 65 years included n=6,876 patients; and Cohort 2, &amp;lt;= 65y included n=11,046 patients. Propensity score matching (PSM) was performed on 8 characteristics: in the Demographics category patients were matched on female or male. In the diagnosis category patients were matched on Hypertensive diseases, Ischemic heart diseases, Chronic kidney disease, Diabetes mellitus, Diseases of liver, Chronic lower respiratory diseases characteristic. Results In the H12O cohort of patients, the median age was 68 (66-74) in &amp;gt;65 y patients and 57 (28-65) in &amp;lt;65 y patients. The older population presented greater comorbidity measured with the Charlson Index (p=0.001) and a history of another neoplasm (p=0.009). There were no differences in the type of MM, ISS disease stage, type of conditioning, pre-transplant response, induction treatment, number of lines or previous transplants. With a median follow-up of 3.4 years the probability of survival was 39.6 % in Cohort 1 and 68.6 % in Cohort 2 (HR, 2.69 95% CI, 1.34 to 5.44, p=0.004) (Figure 1). PFS at the end of follow-up was similar in both groups: 35%.5 in Cohort 1 and 42.9 % in Cohort 2 (HR, 1.36 95% CI, 0.79 to 2.32, p=0.26). The older population required admission to the Intensive Care Unit more frequently (20.3% vs 10,4%, p=0.041), without an associated increase in NRM (1.7 % vs 1.4 %, p=0.871). We found no significant differences in the recovery time of neutrophils or platelets, blood component transfusion, and transplant complications, and short-term survival was similar between both cohorts. At TriNetX database, after the PSM, the final population was 5,820 patients in each cohort. Cohort 1 with a mean age (+-SD) of 69.7 (3) years and Cohort 2 of 55.6 (7.8) years. The rest of the variables were balanced. The probability of survival at the end of follow-up was 20.8 % in Cohort 1 and 30.5 % in Cohort 2 (HR, 1.29, 95% CI, 1.20 to 1.39, p&amp;lt;0.001) (Figure 2). The relative risk of any transplant complication was significantly lower in Cohort 1 vs. Cohort 2 (0.82, 95 % CI, 0.73 to 0.93). As well as in the other cohort short-term survival was similar between both cohorts. Conclusion We have found that ASCT in MM patients older than 65 years have the similar NRM, PFS and performance. However, the long-term outcome of these elderly patients is dismal, probably due to the limited options of rescue after the relapse and the impact of the age. This large-scale study shows that ASCT in MM patient, between 65 and 70 years, had similar results.

U-shaped association between sleep duration and the risk of respiratory diseases mortality: a large prospective cohort study from UK Biobank.

Our cohort study aimed to study the association between sleep duration and risk of mortality due to respiratory diseases. We included 498,200 participants from UK Biobank (2006-2021). We classified sleep duration as short sleep duration (<7 h), long sleep duration (>9 h for adults, >8 h for older adults) and mid-range sleep duration (7~9 h). We used the cox proportional hazards model and restricted cubic spline analysis to explore the association between sleep duration and respiratory diseases mortality. During a median follow-up of 12.49 years, 2,477 deaths due to respiratory diseases were recorded, of which, 1,099 were deaths due to chronic lower respiratory diseases. Cox models with penalized splines showed U-shaped associations of sleep duration with mortality due to total respiratory diseases and chronic lower respiratory diseases. Compared with mid-range sleep duration, short sleep duration was associated with 14% higher risk of total respiratory diseases mortality (HR =1.14, 95% CI: 1.04, 1.25), meanwhile long sleep duration was associated with 35% higher risk of total respiratory diseases mortality (HR =1.35, 95% CI: 1.19, 1.55), after adjustment of baseline characteristics, health status and lifestyle habits. Similarly, the HR for chronic lower respiratory diseases mortality were 1.20 (95% CI: 1.04, 1.38) and 1.44 (95% CI: 1.19, 1.74), respectively. There were U-shaped association between sleep duration and respiratory diseases mortality. Appropriate sleep duration may improve the progress of respiratory diseases.

Mortality risks from a spectrum of causes associated with sand and dust storms in China

Sand and Dust Storms (SDS) pose considerable health risks worldwide. Previous studies only indicated risk of SDS on overall mortality. This nationwide multicenter time-series study aimed to examine SDS-associated mortality risks extensively. We analyzed 1,495,724 deaths and 2024 SDS events from 1 February to 31 May (2013–2018) in 214 Chinese counties. The excess mortality risks associated with SDS were 7.49% (95% CI: 3.12–12.05%), 5.40% (1.25–9.73%), 4.05% (0.41–7.83%), 3.45% (0.34–6.66%), 3.37% (0.28–6.55%), 3.33% (0.07–6.70%), 8.90% (4.96–12.98%), 12.51% (6.31–19.08%), and 11.55% (5.55–17.89%) for ischemic stroke, intracerebral hemorrhagic stroke, hypertensive heart disease, myocardial infarction, acute myocardial infarction, acute ischemic heart disease, respiratory disease, chronic lower respiratory disease, and chronic obstructive pulmonary disease (COPD), respectively. SDS had significantly added effects on ischemic stroke, chronic lower respiratory disease, and COPD mortality. Our results suggest the need to implement public health policy against SDS.

Emergency department visits associated with wildfire smoke events in California, 2016–2019

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June–December 2016–2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM2.5) concentrations were aggregated to air basins. A “smoke event” was defined as an air basin-day with a wildfire-specific PM2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m3). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes.

Predictive role of neutrophil-percentage-to-albumin, neutrophil-to-lymphocyte and eosinophil-to-lymphocyte ratios for mortality in patients with COPD: Evidence from NHANES 2011-2018.

This study evaluated the predictive roles of hematologic inflammatory biomarkers including neutrophil-percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR) and eosinophil-to-lymphocyte ratio (ELR) for mortality in community-dwelling individuals with chronic obstructive pulmonary disease (COPD). This longitudinal study extracted data of adults 40-79 years who had physician-diagnosed COPD from the United States (US) National Health and Nutrition Examination Survey (NHANES) 1999-2018. Cox regressions determined the associations between NPAR, NLR, ELR and their components, with all-cause mortality, cardiovascular disease (CVD) mortality and mortality from chronic lower respiratory disease (CLRD). Receiver operating characteristic (ROC) curve analysis estimated the predictive performances of these biomarkers for 5-year all-cause mortality. Data of 1158 subjects were analysed. After adjustment, higher NPAR was significantly associated with increased all-cause and CVD mortality, and mortality from CLRD (adjusted hazard ratio [aHR] = 1.14, 1.15 and 1.16). Higher NLR was associated with an increased all-cause and CVD mortality (aHR = 1.16 and 1.29). Higher neutrophil was associated with increased all-cause mortality and mortality from CLRD (aHR = 1.13 and 1.34). Albumin was associated with decreased all-cause and CVD mortality (aHR = 0.91 and 0.86). ELR, eosinophil or lymphocyte was not significantly associated with either mortality outcomes. Adjusted AUC of NPAR and NLR in predicting 5-year all-cause mortality were 0.808 (95% CI: 0.722-0.845) and 0.799 (95% CI: 0.763-0.835), respectively. In community-dwelling US adults with COPD, increased NPAR and NLR are associated with mortality risks. NPAR outperforms the other hematologic inflammatory biomarkers in predicting 5-year all-cause mortality.

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China.

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.

Investigation of Serum allergen-specific IgE concentrations in feline asthma: a clinical experience

The aim of this study is to investigate allergen agents by allergen-specific serum IgE analysis in domestic cats diagnosed with feline asthma (FA). A total of 21 cats (16 FA and 5 control cats) were included in the study. Allergen-specific serum IgE levels were measured in cats diagnosed with feline asthma and in the control group. Allergen levels were classified as negative, weak, moderate and strong. The difference between the two groups was compared statistically. While the mean age of the cats in the FA group was 3.9kg ±2.4 years, the mean age of the cats in the control group was 3.4±2.2 years. The most common clinical manifestations in cats with FA were cough, sneezing, wheezing, and acute dyspnea, respectively. Allergen-specific IgE levels tested for 20 different allergens showed that 14 out of 16 cats developed a reaction to more than one allergen. The vast majority of cats with FA had a moderate or strong positive reaction to house dust mites. In conclusion, FA is an important and common chronic lower respiratory tract disease of feline patients and allergen-specific serum IgE levels can be easily used in clinical practice to guide the selection of allergens in the treatment of this disease and adjust for the quality of life and well-being of cats.

Cigarette smoking in childhood and risk of all-cause and cause-specific mortality in adulthood.

This study was aimed to examine the association between cigarette smoking in childhood and mortality in adulthood, and the impact of non-smoking duration among smokers who subsequently quit smoking. We used data from 472,887 adults aged 18-85 years examined once in the US National Health Interview Survey in 1997-2014, which was linked to mortality data from the National Death Index up to 31 December 2015. Cigarette smoking status in childhood (age 6 to 17 years) and adulthood (age 18 to 85 years) was self-reported using a standard questionnaire at the time of participation in the survey. The vital status of participants due all-causes, cardiovascular disease (CVD), cancer and chronic lower respiratory diseases was obtained using mortality data from the National Death Index. During the mean follow-up of 8.75 years, compared with never smoking in childhood and adulthood, the risk of all-cause mortality among current adult smokers decreased slightly according to increasing age at smoking initiation: hazard ratios (HRs; 95% confidence intervals, CIs) were 2.54 (2.24-2.88) at age of 6-9 years, 2.44 (2.31-2.57) at age of 10-14 years, and 2.21 (2.12-2.31) at age of 15-17 years. Smoking cessation before the age of 30 years was not associated with increased risk of all-cause and cause-specific mortality (all p > 0.05) compared to never smoking. Mortality risk was higher in individuals who started smoking at an earlier age in childhood. Inversely, smoking cessation before the age of 30 years was not associated with an increased risk of mortality compared to never smoking.