Chronic Lower Respiratory Disease Research Articles

Use of specialist health care among Norwegian-born children with one immigrant parent

Abstract Background An increasing number of children grow up with one immigrant and one native born parent. We know little about health in this group. Our aim was to compare the risk of receiving diagnoses of physical health conditions in specialist health care among Norwegian-born children with one immigrant parent compared to Norwegian background children. Methods Data from Medical Birth Registry of Norway were linked to data from the Norwegian Patient Registry (diagnoses in specialist health care), and Statistics Norway (parental country of origin, education, and household income). We estimated hazard ratios (HR) with Cox proportional hazard regressions, with Norwegian background children as reference category, adjusted for sex, birth year, parental education, and household income. Results Children with an immigrant parent had lower hazards than Norwegian background children of diagnoses of most types of infections, but higher hazards of some, including tuberculosis, and skin infections. They also had higher hazards than Norwegian background children of receiving diagnoses of obesity and other nutritional deficiencies, and skin diseases, but lower hazards for other conditions, including chronic lower respiratory disease, disease of the digestive system, and visual or hearing impairment. The hazards of diagnoses of any neurological condition, sleeping disorders and epilepsy were lower among children with an immigrant mother, but higher among those with an immigrant father. Differences between children with one immigrant parent and Norwegian background could not be explained by differences in parental education or household income. Conclusions Children of one immigrant parent do not overall use specialist health care for physical health issues more than other children. Key messages • Awareness of diagnoses given most frequently to children of one immigrant parent can help promote timely diagnoses and adequate use of services, which is important for prevention of later illness. • Barriers to health care use among children of immigrants should be further researched.

Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network

Recently, symptoms of Chronic Obstructive Pulmonary Disease (COPD) have been identified concerning long-term continuous treatment. Furthermore, predicting the life probability of patients with COPD is crucial for formative ensuing treatment and conduct plans. Additionally, it plays a vital role in providing complementary solutions using technologies such as Deep Learning (DL) to address experiments in the medical field. Early and timely analysis of clinical images can improve prognostic accuracy. These include COPD, pneumonia, asthma, tuberculosis and fibrosis. Conventional methods of diagnosing COPD often rely on physical exams and tests such as spirometers, chest and genetic analysis. However, respiratory diseases pose an enormous comprehensive health burden for many patients. Thus these methods are not always accurate or obtainable. However, succeeding in their accuracy involves a nonspecific diagnosis rate, time-consuming manual procedures, and extensive clinical imaging knowledge of the radiologist. To solve this problem, we use a Deep Recursive Convolutional Neural Network (DRCNN) method to detect chronic lower respiratory disease. Initially, we collected the images from the Kaggle repository, and evaluate the result based on the following stage. The first stage is pre-processing using a Gaussian filter to reduce noise and detect the edges. The second stage is segmentation used on Image Threshold Based Segmentation (ITBS), used for counting the binary image and separating the regions. In the third stage, we use the chi-square test to select the best features and evaluate the image values for each feature and threshold. Finally, classification using DRCNN detects CLRD classifying better than the previous method. In synthesis, CLRD can be detected by many staging measures, such as sensitivity, specificity, accuracy, precision, and Recall

Fatty liver index as an independent predictor of all-cause and disease-specific mortality.

This study aims to assess the prognostic value of the fatty liver index (FLI), a noninvasive tool for hepatic steatosis assessment, in predicting all-cause and disease-specific mortality. We linked data from the National Health and Nutrition Examination Survey III (1988-1994) with Public-Use Mortality Files, forming a cohort of 11 297 participants with a median follow-up period of 26.25 years. Cox proportional hazards models were used to evaluate the association between FLI and all-cause mortality, while Fine and Gray's models assessed the relationship between FLI and disease-specific mortality. The FLI ≥ 60 was independently associated with an increased risk of all-cause mortality (hazard ratio = 1.24, P < 0.001), as well as mortality from malignant neoplasms (hazard ratio = 1.18, P = 0.048), diabetes (hazard ratio = 2.62, P = 0.001), and cardiovascular diseases (CVDs) (hazard ratio = 1.18, P = 0.018), compared to FLI < 30. No significant associations were found with Alzheimer's disease, influenza and pneumonia, chronic lower respiratory diseases, or renal disorders. Subgroup analyses indicated that individuals who were females aged 40-60 (hazard ratio = 1.67, P = 0.003), non-overweight (hazard ratio = 1.75, P = 0.007), or without abdominal obesity (hazard ratio = 1.75, P = 0.007) exhibited a stronger association between FLI ≥ 60 and all-cause mortality. These findings support the prognostic value of the FLI for predicting mortality from all causes, malignant neoplasms, diabetes, and CVDs. Targeted attention is needed in postmenopausal women, non-overweight, and non-abdominally obese populations.

Changes in Healthcare Utilization After the 2022 Seoul Metropolitan Flood: Applying a Generalized Synthetic Control Approach.

Floods can have adverse health effects and impose a burden on healthcare systems. However, the potential consequences of floods on specific medical causes in densely populated metropolitan cities has not been characterized yet. Therefore, we evaluate the changes in healthcare utilization patterns after the 2022 Seoul flood using nationwide health insurance data. Based on the flood inundation map, districts within the flooded municipalities of Seoul were classified as severe-(n=12), mild-(n=22), or non-(n=38) flood-affected districts. Capitalizing on the timing of the flood as a natural experiment, a generalized synthetic control method was applied to estimate changes in the number of disease-specific hospital visits in flood-affected districts during 2weeks after the flood. We found excess hospital visits for external injuries (20.2 visits, 95% CI: -6.0, 45.2) and fewer visits for pregnancy and puerperium (-3.0 visits, 95% CI: -5.1, -0.5) in residents of flooded districts. When comparing severe- and non-flood districts, the increase in hospital visits for external injuries (56.2 visits, 95% CI: 17.2, 93.2) and a decrease in hospital visits related to pregnancy and puerperium (-5.3 visits, 95% CI: -8.4, -1.6) were prominent in residents living in severe-flood affected districts. Disease specific analysis showed an increase in hospital visits for injuries to the elbow and forearm, ankle and foot injuries, and chronic lower respiratory diseases in severe-flood-affected districts. However, these impacts were not observed when comparing the mild- and non-flood-affected districts. Our study suggests an immediate and substantial change in medical demand following flood exposure, highlighting the importance of public health responses after flood events.

Mortality associated with osteoporosis and pathological fractures in the United States (1999–2020): a multiple-cause-of-death study

BackgroundOsteoporosis with pathological fractures is a significant public health issue, contributing to morbidity, disability, diminished quality of life, and increased mortality. Understanding mortality trends related to this condition is crucial for developing effective interventions to reduce mortality and improve healthcare outcomes. This study aimed to analyze trends and causes of death associated with osteoporosis and pathological fractures in the United States using a multi-cause approach.MethodsAnnual death and age-standardized mortality rate (ASMR) data from 1999 to 2020 were obtained from the Centers for Disease Control and Prevention (CDC) mortality database. Death certificates listing ICD-10 M82 (osteoporosis with pathological fracture) as an underlying or related cause of death were analyzed. Epidemiological data were analyzed, and the ASMR data were calculated for each year, and trends were assessed using the Cochran-Armitage trend test.ResultsFrom 1999 to 2020, there were 40,441 deaths related to osteoporosis with pathological fractures in the United States, with a female-to-male ratio of 5.6:1. Among these, 12,820 deaths (31.7%) listed osteoporosis with pathological fractures as the underlying cause of death (UCD), yielding a female-to-male ASMR ratio of approximately 5.0-7.7:1. When classified as a non-UCD, the ASMR ratio was approximately 4.8–6.2:1. At the same time, we found that the total number of deaths classified as UCD and multiple causes of death (MCD), but the trend ratio of the two groups in different years did not change statistically significant (P > 0.05), and the ASMR of both groups showed a downward trend. The UCD-to-MCD ratio increased between 1999 and 2007, then decreased from 2007 to 2020. As MCD, the number of female deaths was more than that of male, and both showed a decreasing trend, but there was no statistical significance in the change of trend ratio in different years (P > 0.05). Deaths were predominantly concentrated in individuals over 75 years of age, with those over 84 years being the most affected. The number of deaths in different age groups showed a decreasing trend, and the change of trend ratio in different years was statistically significant (P < 0.05). White individuals had the highest number of deaths. The leading causes of death were heart diseases, chronic lower respiratory diseases, and alzheimer’s disease. In addition, the number of deaths of patients with prostate cancer and breast cancer showed a significant downward trend, and the change of trend ratio between the two groups in different years was statistically significant (P < 0.05).ConclusionsAlthough mortality from osteoporosis with pathological fractures is decreasing, anti-osteoporosis therapy remains essential for elderly patients. Healthcare providers should remain vigilant for potential complications, including malignant neoplasms, and ensure timely diagnosis and treatment to further reduce mortality in this population.

Temporal Statistical Analysis and Retrospective Assessment of Mortality Rate in the United States from 1999 to 2018

Objectives: To retrospectively examine the mortality trend in the USA from 1999 to 2018 using a basic statistical model while examining multiple variables. The inferential and descriptive statistical tools will be prioritized to elucidate the mortality of both years in simple terms. Methods: We conducted a retrospective observational descriptive analysis of data on the 10 leading causes of death in the United States by age, race, and sex over 2 decades (1999 to 2018). The data in this report are based on information from all death certificates filed in the 50 states and the District of Columbia. The causes of death are classified by the International Classification of Diseases, 10th Revision (ICD–10), which is ranked according to the number of deaths (Cause-of-death statistics are based on the underlying cause of death). This report supplements “Deaths: Final Data for 2020,” the National Center for Health Statistics’ annual report of final mortality statistics. SPSS (statistical package for social sciences) version 29 Software in Excel sheet was used to analyze the generated datasets. Descriptive and inferential (parametric and non-parametric) analyses were used. The statistically significant relationships and assumptions were made using the chi-square/t-test, Kruskal-Wallis, and Fischer's exact tests with the significance level set at p ≤ 0.05. The analysis and determination of the level of agreement between the initial diagnosis and findings from the new analysis done. Results: The proportion of age-adjusted death rates of the heart (such as coronary artery disease) account for the highest proportion of 24%, while Malignant neoplasms (cancers) represent 23%, Chronic lower respiratory diseases contribute 6%, Unintentional injuries (accidents) also play a significant role, accounting for 5% of deaths. The regression analysis reveals a strong and statistically significant relationship between the number of causes in females and males. There was a high death rate of around 60 years in 1999 and a consistent decline over the years, reaching below 40 by 2018, with a 15/100,000 death fluctuation mark. Asian or Pacific Islander has the lowest age adjustment value of 8762.4, American Indian or Alaska Native, Hispanic, or Latino(11673.2), Black or African American, Hispanic, or Latino (12992.1), White, not Hispanic or Latino(15457.1), Black or African American (15708.2), Hispanic or Latino(19624.1), American Indian or Alaska Native, not Hispanic or Latino (16287.9).Asian or Pacific Islander, not Hispanic or Latino( 8791.3), and 99.2% of the variability in the dependent variable and standard error of 6.7240. Conclusions: Cardiovascular death remains the most common cause of death, with a significant decrease in the death rate from 1999 to 2018 due to improved health care and the provision of measures to improve health care. The regression analysis reveals a strong and statistically significant relationship between the number of causes in females and males. Therefore, based on the results, the number of causes in females strongly predicts the number of causes in males.

Predictive role of red blood cell distribution width and hemoglobin-to-red blood cell distribution width ratio for mortality in patients with COPD: evidence from NHANES 1999–2018

BackgroundHigher red blood cell distribution width (RDW) levels are associated with mortality in patients with chronic obstructive pulmonary disease (COPD). However, more convincing evidence is still lacking, and the relationship between hemoglobin-to-red blood cell distribution width ratio (HRR) and mortality in patients with COPD remains unclear.MethodsThis study is a prospective cohort study that includes 3,745 adult patients with COPD from the National Health and Nutrition Examination Survey (NHANES) database spanning from 1999 to 2018 in the United States. COX proportional hazards regression analysis, Kaplan–Meier survival curves and restricted cubic spline models were employed to investigate the association of RDW and HRR levels with mortality. Time-dependent receiver operating characteristic curve (ROC) analysis was conducted to evaluate the accuracy of RDW and HRR in predicting mortality in patients with COPD.ResultsHigher RDW level was positively associated with increased risk of all-cause mortality (HR = 1.16, 95% CI = 1.11–1.21, P < 0.001), cardiovascular disease (CVD) mortality (HR = 1.13, 95% CI = 1.06–1.21, P < 0.001), and chronic lower respiratory disease (CLRD) related mortality (HR = 1.15, 95% CI = 1.05–1.25, P = 0.003) after adjusting for various potential confounders. HRR was inversely associated with all-cause mortality (HR = 0.14, 95% CI = 0.08–0.25, P < 0.001), CVD mortality (HR = 0.12, 95% CI = 0.05–0.31, P < 0.001). HRR has no significant correlation with CLRD-related mortality. The time-dependent ROC curve showed that RDW exhibited area under the curves (AUCs) of the 5- and 10-year survival rates were 0.707 and 0.714 for all-cause mortality and 0.686 and 0.698, respectively, for CVD mortality. HRR yielded AUCs of the 5- and 10-year survival rates were 0.661 and 0.653 for all-cause mortality and 0.654 and 0.66, respectively, for CVD mortality.ConclusionHigher RDW levels were positively associated with an increased risk of mortality in patients with COPD. HRR levels were negatively correlated with the risk of all-cause and CVD mortality. The predictive value of HRR for mortality in these patients is lower than that of RDW.

Changes in patterns of multimorbidity and associated with medical costs among Chinese middle-aged and older adults from 2013 to 2023: an analysis of repeated cross-sectional surveys in Xiangyang, China.

Multimorbidity has become a major public health problem among Chinese middle-aged and older adults, and the most costly to the health care system. However, most previous population-based studies of multimorbidity have focused on a limited number of chronic diseases, and diagnosis was based on participants' self-report, which may oversimplify the problem. At the same time, there were few reports on the relationship between multimorbidity patterns and health care costs. This study analyzed the multimorbidity patterns and changes among middle-aged and older people in China over the past decade, and their association with medical costs, based on representative hospital electronic medical record data. Two cross-sectional surveys based on representative hospital data were used to obtain adults aged 45 years and older in Xiangyang in 2013 (n = 20,218) and 2023 (n = 63,517). Latent Class Analysis was used to analyze changes in the patterns of multimorbidity, gray correlation analysis and ordered logistics model were used to assess the association of multimorbidity patterns with medical expenses. The diagnosis and classification of chronic diseases were based on the International Classification of Diseases, Tenth Revision codes (ICD-10). The detection rate of chronic disease multimorbidity has increased (70.74 vs. 76.63%, p < 0.001), and multimorbidity patterns have increased from 6 to 9 (2013: Malignant tumors pattern, non-specific multimorbidity pattern, ischemic heart disease + hypertension pattern, cerebral infarction + hypertension pattern, kidney disease + hypertension pattern, lens disease + hypertension pattern; new in 2023: Nutritional metabolism disorders + hypertension pattern, chronic lower respiratory diseases + malignant tumors pattern, and gastrointestinal diseases pattern) in China. The medical cost of all multimorbidity patients have been reduced between 2013 and 2023 (RMB: 8216.74 vs. 7247.96, IQR: 5802.28-15,737 vs. 5014.63-15434.06). The top three specific multimorbidity patterns in both surveys were malignancy tumor pattern, ischemic heart disease + hypertension pattern, and cerebral infarction + hypertension pattern. Hypertension and type 2 diabetes are important components of multimorbidity patterns. Compared with patients with a single disease, only lens disorders + hypertension pattern were at risk of higher medical costs in 2013 (aOR:1.23, 95% CI: 1.03, 1.47), whereas all multimorbidity patterns were significantly associated with increased medical costs in 2023, except for lens disorders + hypertension (aOR:0.35, 95% CI: 0.32, 0.39). Moreover, the odds of higher medical costs were not consistent across multimorbidity patterns. Among them, ischemic heart disease + hypertension pattern [adjusted odds ratio (aOR):4.66, 95%CI: 4.31, 5.05] and cerebral infarction + hypertension pattern (aOR: 3.63, 95% CI: 3.35, 3.92) were the two patterns with the highest risk. Meanwhile, men (aOR:1.12, 95CI:1.09, 1.16), no spouse (aOR:1.09, 95CI: 1.03, 1.16) had a positive effect on medical costs, while patients with total self-pay (aOR: 0.45, 95CI: 0.29, 0.70), no surgery (aOR: 0.05, 95CI: 0.05, 0.05), rural residence (aOR: 0.92, 95CI: 0.89, 0.95), hospitalization days 1-5 (aOR: 0.04, 95CI: 0.04, 0.04), and hospitalization days 6-9 (aOR: 0.15, 95CI: 0.15, 0.16) had a negative impact on medical costs. Multimorbidity patterns among middle-aged and older adults in China have diversified over the past decade and are associated with rising health care costs in China. Smart, decisive and comprehensive policy and care interventions are needed to effectively manage NCDS and their risk factors and to reduce the economic burden of multimorbidity on patients and the country.

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.

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.

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.

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.

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.

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.