Purpose: The objective of this study was to examine the influence of sexual identity and adverse childhood experiences (ACEs) on non-suicidal self-injury (NSSI) among rural high school students in less developed areas of China. Methods: Behavior risk factors data collected from 1810 students from a high school in Jiangxi province, China. Five measures of childhood abuse and household dysfunction were summarized, and ACE was divided into 0, 1, 2, 3–5 ACEs. Logistic regression analysis was used to explore the influence of sexual identity, adverse childhood experiences, and their interaction with non-suicidal self-injury. Results: Compared with heterosexual students, high school students who identify as lesbian, gay, or bisexual (LGB) have a higher tendency of non-suicidal self-injury (AOR = 3.250, 95% CI = 1.69–6.28, p < 0.01). There was also a graded relationship between cumulative ACEs exposure and non-suicidal self-injury behaviors (AOR = 1.627, 95% CI = 1.02–2.60, p < 0.05). Odds for NSSI are higher among students with both experienced ACEs and identified as LGB (AOR = 2.821, 95% CI = 1.51–5.29, p < 0.05). Conclusions: Non-suicidal self-injury is associated with ACEs exposure and with those who identify as LGB, and the NSSI odds are greater when students identify as LGB and have experienced ACEs. More interventions to reduce non-suicidal self-injury should focus on LGB and ACEs and more attention needs to be paid to those who identify as LGB and have been exposed to ACEs.

Objective: To quantitatively estimate life expectancy (LE) and depression and anxiety-free life expectancy (DAFLE) for the years 2013 and 2018 in Jiangxi Province, China, by sex and urban–rural areas. Additionally, to compare the discrepancy of DAFLE/LE of different sexes and urban-rural areas over various years. Methods: Based on the summary of the health statistics of Jiangxi Province in 2013 and 2018 and the results of the 5th and 6th National Health Service Surveys in Jiangxi Province, the model life table is used to estimate the age-specific mortality rate by sex and urban–rural areas. Sullivan’s method was used to calculate DAFLE. Results: Data from 2013 indicate that those aged 15 can expect to live 56.20 years without depression and anxiety for men and 59.67 years without depression and anxiety for women. Compared to 2013, DAFLE had not fluctuated significantly in 2018. The proportion of life expectancy without depression and anxiety (DAFLE/LE) declined between 2013 and 2018. DAFLE/LE in urban areas was higher than in rural areas. Men had higher DAFLE/LE than women. From 2013 to 2018, the DAFLE aged 15 decreased by 0.18 years for urban men and decreased by 0.52 years for urban women, rural areas also decreased to varying degrees. Conclusions: Even if women had a longer life span than men, they would spend more time with depression or anxiety. DAFLE did not increase with the increase in LE from 2013 to 2018, suggesting an absolute expansion of the burden, especially in rural areas. Depression and anxiety health services in Jiangxi, China will face more serious obstacles and challenges, which may lead to more disability. This requires more attention and more effective measures from the public, medical departments and the government.

The aim of this study is to understand the utilization of inpatient services and its contributing factors among middle-aged and elderly females (MAEF) in less developed rural regions. Five surveys were conducted between 2006 and 2014 with rural residents of Jiangxi by stratified cluster random sampling. Participant females included only those who were aged 45 and above. Complex sampling logistics analysis was performed to analyze the effect of three factors on inpatient service. Complex sampling logistics regression analysis revealed that the probability of hospitalization for the divorced or widowed females was significantly lower than that of married ones (aOR = 0.177, p < 0.05). However, the probability of early discharge was significantly higher among divorced or widowed females than married ones (aOR = 3.237, p < 0.05). In addition, females with chronic diseases were more likely to be hospitalized (aOR = 3.682, p < 0.05). Also, early discharge (aOR = 7.689, p < 0.05) occurred among the participants who should be hospitalized but were not hospitalized occurred (aOR = 3.258, p < 0.05). The continuous improvement of the new rural cooperative medical policy has promoted the utilization of inpatient services for the MAEF. Findings from this study emphasize the need to strengthen the prevention and treatment of chronic diseases among middle-aged and elderly women.

Objectives The purpose of this study was to use easily obtained and directly observable clinical features to establish predictive models to identify patients at increased risk of stroke. Setting and participants A total of 46 240 valid records were obtained from 8 research centres and 14 communities in Jiangxi province, China, between February and September 2018. Primary and secondary outcome measures The area under the receiver operating characteristic curve (AUC), sensitivity, specificity and accuracy were calculated to test the performance of the five models (logistic regression (LR), random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost) and gradient boosting DT). The calibration curve was used to show calibration performance. Results The results indicated that XGBoost (AUC: 0.924, accuracy: 0.873, sensitivity: 0.776, specificity: 0.916) and RF (AUC: 0.924, accuracy: 0.872, sensitivity: 0.778, specificity: 0.913) demonstrated excellent performance in predicting stroke. Physical inactivity, hypertension, meat-based diet and high salt intake were important prediction features of stroke. Conclusion The five machine learning models all had good predictive and discriminatory performance for stroke. The performance of RF and XGBoost was slightly better than that of LR, which was easier to interpret and less prone to overfitting. This work provides a rapid and accurate tool for stroke risk assessment, which can help to improve the efficiency of stroke screening medical services and the management of high-risk groups.

Background: In view of the fact that there is no effective treatment for dementia, the number of years that dementia patients have to live with dementia will gradually increase for the rest of their lives, and the disability loss caused by dementia will increase. It is urgent to study the influence of risk factors on dementia by making use of the potential of prevention. The purpose of this study is to quantify the burden of dementia disability attributable to risk factors by assessing the population attributable fractions (PAFs) in Jiangxi Province, which is one of the regions of moderate aging process of China. Methods: The prevalence data of nine risk factors were obtained through the Sixth National Health Service Survey in 2018, which covered 2713 older people. Levin’s formula was used to calculate the PAF for each risk factor for dementia. We adjusted the PAF for communality between risk factors, and used these values to calculate overall weighted PAFs and the years lived with disability (YLDs), which were attributable to nine risk factors. Results: The number of dementia cases and their proportions that can theoretically be prevented by nine identified risk factors were 111636 (99595-120877) and 66.8% (59.6-72.3), respectively. The total YLDs of dementia were estimated to be 61136 (46463-78369) (males: 36434 [24100-49330], females: 23956 [14716-34589]). Physical inactivity (11639 [8845-14920]), low social contact (9324 [7086-11952]), and hearing loss (5668 [4307-7265] were the top three contributors to dementia. Conclusions: The moderate aging areas represented by Jiangxi Province have great potential in the prevention of dementia. Targeted interventions and management of risk factors can effectively reduce the disability burden of dementia.

Objective: To estimate and compare the dementia-free life expectancy (DemFLE) and age trends of the population over 60 in 2018 in Jiangxi Province, China, by sex and urban–rural areas. Methods: Based on the Summary of Health Statistics of Jiangxi Province in 2018 and the Sixth National Health Service survey of Jiangxi Province, the model life table is used to estimate the age-specific mortality rate by sex and urban–rural areas. DemFLE and its ratio to life expectancy (LE) were calculated using the Sullivan method. Results: In 2018, the DemFLE at age 60 was 18.48 years for men and 21.31 years for women, accounting for 96.62% and 96.67% of their LE. LE and DemFLE were higher for those in urban areas than in rural areas, except for men aged 90 and above; higher in women than in men, except for people in rural areas aged 90 and above. In urban areas, DemFLE/LE was higher for women than for men; the opposite was observed in rural areas. Urban women had a higher DemFLE/LE than rural women did, urban men had a lower DemFLE/LE than rural men did. Conclusions: With increased LE, DemFLE also increases, but with older age and over time, DemFLE/LE gradually decreases. The effect of dementia on elderly adults becomes more serious. It is necessary for the government to implement a series of prevention strategies to improve the quality of life and health awareness of the elderly. Elderly urban men and elderly rural women need more attention and health care.