Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series.

Patient delay increases the morbidity and mortality due to tuberculosis (TB). This study aimed to assess patient delay among patients with pulmonary tuberculosis in Yantai from 2013 to 2022, and to analyze factors related to patient delay. Data of patients with pulmonary tuberculosis in Yantai City from 2013 to 2022 were obtained from the Tuberculosis Management Information System of the Chinese Disease Prevention and Control System. Statistical analyses were performed using the SPSS.26.0 software. The trend in patient delay rate was tested using the chi-square trend test. Univariate analyses were performed using the chi-square test, and factors with statistically significant differences in the univariate analysis were included in the binary logistic regression analysis to identify the factors affecting patient delay. Patient delay was defined as an interval of more than 14 days between the onset of clinical symptoms and the patient first visit to a healthcare facility. From 2013 to 2022, the median delay time for patients with pulmonary tuberculosis in Yantai was 28 ±â€…52 days and the patient delay rate was 69.5%. There was an overall increasing trend in the rate of patient delay as the number of years increased. Univariate analyses revealed statistically significant differences in patient delay in terms of age, occupation, patient source, domicile, pathogenetic results, and the presence of comorbidities (all P < .05). The results of logistic regression analysis showed that the age was 20 to 39, 40 to 59, and ≥ 60 years (OR = 1.365, 95%CI: 1.156-1.612; OR = 1.978, 95%CI: 1.660-2.356; OR = 1.767, 95%CI: 1.480-2.110), occupation was domestic and un-employed (OR = 1.188, 95%CI: 1.071-1.317), domicile as mobile population (OR = 1.212, 95%CI: 1.099-1.337), and positive pathogenic results (OR = 1.242, 95%CI: 1.015-1.520) were risk factors for patient delay. Patient delays were serious among pulmonary tuberculosis patients in Yantai City, 2013 to 2022, and patient delay was related to factors such as age, occupation, domicile, patient source, and pathogenetic results.

The influence of empty-nest living on the health of the older people living in the rural areas of Lishui, China: A cross-sectional study.

ABSTRACT: The empty-nest older people are becoming a significant social phenomenon in the rural of China. Most studies on the health of the empty-nest older people contain these older people who have no children or dead children. The emotional experience of the older people with and without children is quite different, our study only chose the empty-nest older people who had children in the rural of Lishui, China. The purpose of this study is to explore the differences of the health conditions between empty-nest and non-empty-nest older people living in the rural areas of China, including items of chronic metabolic diseases, fall injury, and mental health. Our research found that empty-nest older people had higher incidence of fall injury (17.00% vs 6.31%, P = .006) and suffer more severe depression (P = .015) than the non-empty-nest older people. The adjusted odds ratio of fall injury and depression for empty-nest older people compared with the non-empty-nest older people were 2.76 (95% CI, 1.17-6.48) and 2.25 (95% CI, 1.36-3.67), respectively. Our results suggest that empty-nest older people are more likely to suffer from depression and fall injury than non-empty-nest older people living in the rural of China.

Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS).

Depression amongst the elderly population is a worldwide public health problem, especially in China. Affected by the urban-rural dual structure, depressive symptoms of the elderly in urban and rural areas are significantly different. In order to compare depressive symptoms and its influencing factors among the elderly in urban and rural areas, we used the data from the fourth wave of the China Health and Retirement Longitudinal Study (CHARLS). A total of 7690 participants at age 60 or older were included in this study. The results showed that there was a significant difference in the prevalence estimate of depression between urban and rural elderly (χ2 = 10.9.76, p < 0.001). The prevalence of depression among rural elderly was significantly higher than that of urban elderly (OR-unadjusted = 1.88, 95% CI: 1.67 to 2.12). After adjusting for gender, age, marital status, education level, minorities, religious belief, self-reported health, duration of sleep, life satisfaction, chronic disease, social activities and having income or not, the prevalence of depression in rural elderly is 1.52 times (OR = 1.52, 95% CI: 1.32 to 1.76) than that of urban elderly. Gender, education level, self-reported health, duration of sleep, chronic diseases were associated with depression in both urban and rural areas. In addition, social activities were connected with depression in urban areas, while minorities, marital status and having income or not were influencing factors of depression among the rural elderly. The interaction analysis showed that the interaction between marital status, social activities and urban and rural sources was statistically significant (divorced: coefficient was 1.567, p < 0.05; social activities: coefficient was 0.340, p < 0.05), while gender, education level, minorities, self-reported health, duration of sleep, life satisfaction, chronic disease, social activities having income or not and urban and rural sources have no interaction (p > 0.05). Thus, it is necessary to propose targeted and precise intervention strategies to prevent depression after accurately identifying the factors' effects.

New Discovery of Left Atrial Macroreentry Tachycardia: Originating from the Spontaneous Scarring of Left Atrial Anterior Wall.

AIMS: This study sought to describe left atrial macroreentry tachycardia (LAMRT) originating from the spontaneous scarring of left atrial anterior wall (LAAW) and its clinical and electrophysiological characteristics, mechanisms, and the formation of substrates. METHODS AND RESULTS: 9 of 123 patients (89% female, age 79.78 ± 5.59 years) had LAMRT originating from the LAAW with no cardiac surgery or prior left atrial (LA) ablation. The mean tachycardia cycle length (TCL) was 241.67 ± 38.00 milliseconds. Spontaneous scars areas and low voltage areas (LVAs) in the LAAW were found in all patients. Successful ablation of the critical isthmus caused termination of the LAMRT and was not inducible in all patients. Arrhythmogenic substrates of LAMRT were the spontaneous scars of LAAW, which matched with the aorta or/and pulmonary artery contact area. The area under the curve (AUC) of age and combination of gender and age for predicting the LAMRT originating from the LAAW were 0.918 and 0.951, respectively, with a cutoff value of ≥73.5 years of age and gender (female) predicting LAMRT with 88.9% sensitivity and 89% specificity. CONCLUSION: Combination of gender and age provides a simple and useful criterion to distinguish LAMRT from cavotricuspid isthmus- (CTI-) dependent atrial tachycardia in macroreentry atrial tachycardia (MRAT) in patients without a history of surgery or ablation. Aorta or/and pulmonary artery contacting LA may be related to spontaneous scars. Ablation the isthmus eliminated LAMRT in all patients.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data.

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.