Background People living with HIV/AIDS not only require effective treatment for the alleviation of physical discomfort but also require social support to help them address difficulties in life and relieve their psychological anxiety and uneasiness. The social support network is of tremendous importance in helping people living with HIV/AIDS maintain good physical and mental health. This study aims to analyse the social support status among people living with HIV/AIDS in Kunming and explore associated factors. Method The Social Support Rating Scale (SSRS) was used, and a questionnaire survey was conducted using convenience sampling to select people living with HIV/AIDS from 14 counties of Kunming. It collected information on general demographic information and social support status. Univariate and multivariate linear regression models were used to explore the associated factors. Results A total of 990 valid questionnaires were completed. Data from all participants were analysed. Univariate analysis suggested that the factors associated with social support may include marital status, monthly income, and antiretroviral therapy. On the other hand, factors including monthly income and antiretroviral therapy accounted for the social support total score in the multivariate analysis. Conclusion Social support among people living with HIV/AIDS in Kunming was generally low. This study identified a number of factors associated with social support among people living with HIV/AIDS. Based on our findings, appropriate interventions should be introduced to provide social support for those living with HIV/AIDS.

Background Bronchopulmonary dysplasia (BPD) is a common chronic lung disease in extremely preterm neonates. The outcome and clinical burden vary dramatically according to severity. Although some prediction tools for BPD exist, they seldom pay attention to disease severity and are based on populations in developed countries. This study aimed to develop machine learning prediction models for BPD severity based on selected clinical factors in a Chinese population. Methods In this retrospective, single-center study, we included patients with a gestational age < 32 weeks who were diagnosed with BPD in our neonatal intensive care unit from 2016 to 2020. We collected their clinical information during the maternal, birth and early postnatal periods. Risk factors were selected through univariable and ordinal logistic regression analyses. Prediction models based on logistic regression (LR), gradient boosting decision tree, XGBoost (XGB) and random forest (RF) models were implemented and assessed by the area under the receiver operating characteristic curve (AUC). Results We ultimately included 471 patients (279 mild, 147 moderate, and 45 severe cases). On ordinal logistic regression, gestational diabetes mellitus, initial fraction of inspiration O2 value, invasive ventilation, acidosis, hypochloremia, C-reactive protein level, patent ductus arteriosus and Gram-negative respiratory culture were independent risk factors for BPD severity. All the XGB, LR and RF models (AUC = 0.85, 0.86 and 0.84, respectively) all had good performance. Conclusions We found risk factors for BPD severity in our population and developed machine learning models based on them. The models have good performance and can be used to aid in predicting BPD severity in the Chinese population.

No abstract

No abstract

Background: People living with HIV/AIDS not only require effective treatment for the alleviation of physical discomfort but also require social support to help them address difficulties in life and relieve their psychological anxiety and uneasiness. The social support network is of tremendous importance in helping people living with HIV/AIDS maintain good physical and mental health. This study aims to analyse the social support status among people living with HIV/AIDS in Kunming and explore associated factors.Method: The Social Support Rating Scale (SSRS) was used, and a questionnaire survey was conducted using convenience sampling to select people living with HIV/AIDS from 14 counties of Kunming. It collected information on general demographic information and social support status. Univariate and multivariate linear regression models were used to explore the associated factors.Results: A total of 990 valid questionnaires were completed. Data from all participants were analysed. Univariate analysis suggested that the factors associated with social support may include marital status, monthly income, and antiretroviral therapy. On the other hand, factors including monthly income and antiretroviral therapy accounted for the social support total score in the multivariate analysis.Conclusion: Social support among people living with HIV/AIDS in Kunming was generally low. This study identified a number of factors associated with social support among people living with HIV/AIDS. Based on our findings, appropriate interventions should be introduced to provide social support for those living with HIV/AIDS.