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Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor\'s notes, whereas TabNet manages structured diagnostic information. The model\'s interpretability function serves to assist healthcare professionals in understanding the model\'s predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model\'s robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.

BACKGROUND: Abortion behaviors among individuals with mental disorders presented major obstacles to women\'s health. However, few studies reported the prevalence and associated factors of abortion among women with severe mental disorders in China. Consequently, this study aims to investigate the prevalence of abortion among female patients in rural communities and identify potential health risks.
METHODS: This was a cross-sectional study of 276 women aged 18 years and older with severe mental disorders in rural areas of Shandong Province, China. The pregnancy history, abortion history and socio-demographic characteristics of women were investigated by questionnaire. Logistic regression analysis was employed to examine the associated factors for different abortion behaviors.
RESULTS: The study showed that 82.61 % (228/276) of patients had a pregnancy history. Among the patients with a pregnancy history, 43.42 % (99/228) reported having had at least one abortion, and 15.79 % (36/228) had more than one. In the other side, 31.58 % (72/228) of them experienced spontaneous abortion, while 12.72 % (29/228) experienced induced abortion. Age at first gestation (aOR 0.80, 95 % CI 0.70-0.90) and age at last gestation (aOR 1.17, 95 % CI 1.07-1.27) were both associated with abortion. Anxiety was related to spontaneous (aOR 1.08, 95 % CI 1.02-1.15) and repeat abortions (aOR 1.10, 95 % CI 1.01-1.19). In addition, religion (aOR 10.47, 95 % CI 2.81-39.01), number of children≥2 (aOR 0.18, 95 % CI 0.04-0.77), and family functioning (aOR 1.31, 95 % CI 1.06-1.63) were associated with induced abortion.
CONCLUSIONS: Women with severe mental disorders in rural regions have notably higher rates of abortion compared to the general female population, particularly for spontaneous abortions. Gestational age and anxiety of pregnant patients deserve attention and preventive measures to avoid the outcomes of abortion.

UNASSIGNED: Several studies have explored the relationship between level of education and medication adherence, as well as the relationship between level of education and cognitive function. However, there have been few studies on the relationships between level of education, cognitive function, and medication adherence. This study aimed to explore whether cognitive function has a mediating effect between level of education and medication adherence in patients with schizophrenia.
UNASSIGNED: A total of 329 participants were included in this study. Cognitive function was assessed using the Brief Assessment of Cognition in Schizophrenia, and medication adherence using the Medication Adherence Questionnaire. The relationships between the clinical factors and cognitive function that contributed to medication adherence were tested through multivariable linear regression analysis. The mediating effect of medication adherence was tested using the bootstrapping approach with the PROCESS macro.
UNASSIGNED: Family history, insight and executive function were associated with medication adherence in individuals with schizophrenia, and executive function had a mediating effect between level of education and medication adherence.
UNASSIGNED: Adopting specific education programs that promote cognitive development as well as actively intervening in executive function might be conducive to improve medication adherence in patients with schizophrenia.

To investigate the proportion of registered cases relative to size, distribution characteristics, medication status, and management status of patients diagnosed with severe mental disorders (SMD) in Fuzhou. The medication status and management status were compared between patients in urban and non-urban areas to provide scientific evidence for improving SMD care, control, and treatment in primary health care institutions.
Data (case types, demographic data, distribution data, medication status, and management status, etc.) of patients diagnosed with SMD in 12 districts, counties, and prefectures in the urban and non-urban areas of Fuzhou City were collected from October 2017 to September 2018. Three distributions (population, local, and districts/counties) were used to describe the proportion of registered cases relative to size and clinical characteristics of diagnosed SMD. Chi squared (χ2) test was used to compare the severity in urban and non-urban areas.
A total of 30,362 registered SMD patients were identified in Fuzhou City of which schizophrenia accounted for the highest number of cases (26,204, 86.31%), and paranoid psychosis had the least number of cases (47, 0.15%). Moreover, approximately half of SMD patients were 18 to 44 years old (45.38%). Close to one third of patients were farmers (30.23%), had a primary school or lower education level (54.17%), were poor, with most below the poverty line (55.35%). The proportion of diagnosed SMD relative to size was highest in Minqing County (0.53%) and lowest in Mawei District (0.38%). A total of 22,989 (75.72%) of the patients were taking medications, and only 17,509 (57.67%) were taking medications regularly. Moreover, the percentage of cases taking medications and those taking medications regularly were higher in urban areas than in non-urban areas (P<0.05). A total of 3065 patients were registered for management (10.09%). The managed proportion of SMD cases was higher in the urban areas than in the non-urban areas (P < 0.05).
Schizophrenia is a key disease for comprehensive care and control of severe mental disorders in Fuzhou. The management of severe mental disorders should focus on poor groups with low educational backgrounds. Drug usage and management are better in urban areas than in non-urban areas, and thus management should be enhanced in non-urban areas. The medication management and case management of patients with severe mental disorders in Fuzhou need further improvements.

To understand the magnitude and spatial-temporal distribution of the regional burden attributable to severe mental disorders is of great essential and high policy relevance. The study aimed to address the burden of severe mental disorders by evaluating the years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) in Guangdong, China.
We undertook a longitudinal study based on a multicenter database established by the Health Commission of Guangdong, involving a total of 21 prefectures and four economic regions in the Guangdong province. A total of 520,731 medical records from patients with severe mental disorders were collected for 2010-2020. Data were analyzed via an integrated evaluation framework by synthesizing prevalence estimates, epidemiological adjustment as well as comorbidity assessment to develop internally consistent estimates of DALY. DALY changes during 2010-2020 were decomposed by population growth and aging and further grouped by Socio-demographic Index (SDI). DALYs were projected to 2030 by the weighted median annualized rate of change in 2010-2020.
In 2010-2020, the average DALYs for severe mental disorders reached 798,474 (95% uncertainty interval [UI]: 536,280-1,270,465) person-years (52.2% for males, and 47.8% for females). Severe mental disorders led to a great amount of disease burden, especially in Guangzhou, Shenzhen, and Foshan cities. Schizophrenia and mental retardation with mental disorders were the two leading sources of the burden ascribed to severe mental disorders. Population growth and aging could be accountable for the increasing burden of severe mental disorders. Economic regions with higher SDI carried a greater burden but had lower annualized rates of change in DALYs. The overall burden of severe mental disorders is projected to rise modestly over the next decade.
The findings urge prioritization of initiatives focused on public mental health, prevention strategies, health resources reallocation, and active involvement of authorities to effectively address the anticipated needs.

Severe Mental Disorders have become a topic of increasing interest in research due to their serious consequences for the quality of life and functioning. In the pages that follow, it will be argued that the self-care ability and its influencing factors among patients with severe mental disorders in Beijing, according to the questionnaire survey in 2019.
Proportionate stratified sampling was used to select representative patients as samples. The demographic characteristics of were obtained from the Management Information System for Severe Mental Disorders and the questionnaires. The self-care ability was measured by self-made scales. Descriptive statistics, t-test, and multiple linear regression were used to analyze the data.
We surveyed 662 people and found that the deficiency of self-care ability is common in patients with severe mental disorders. Self-care ability was positively correlated with educated levels and guardian takes care of alone, and negatively correlated with age, course of disease and physical disease (P < 0.05). From a dimensional perspective, the daily basic activity was positively correlated with educated levels and negatively correlated with physical disease (P < 0.05); the housework ability was positively correlated with gender, educated levels and medication adherence, and negatively correlated with source of income and physical disease (P < 0.05); the social function was positively correlated with educated levels, guardian takes care of alone and medication adherence, and negatively correlated with age, source of income, course of disease and physical disease (P < 0.05).
The self-care ability of patients with severe mental disorders is affected by many factors, including patient characteristics and social factors. Therefore, targeted interventions are needed to help patients restore their self-care ability, which requires the joint efforts of the government and the whole society.

BACKGROUND: The COVID-19 has spread across the globe in a short time and affected people\'s life, especially patients with severe mental disorders. Poor adherence to antipsychotics was usually associated with an increasing risk of relapse. This study investigated medication adherence status among patients with severe mental disorders in low-income families during COVID-19 outbreak and the influencing factors.
METHODS: To select patients with severe mental disorders in low-income families in central China\'s Henan Province, we used multi-stage stratified random sampling method. Trained interviewers and psychiatrists collected questionnaire responses from the patients through face-to-face interviews or video interviews. Logistic regression models were used to examine factors that influence the status of medication adherence.
RESULTS: A total of 24,763 valid questionnaires were collected between March 10, 2020, and March 31, 2020. The regular medication rate of patients with severe mental disorders in low-income families during the COVID-19 outbreak was 51.46%. Twelve factors were found to influence medication adherence of investigated individuals. Positive factors for regular medication were younger age, higher education level of patients and their guardians, higher medical expenditure, higher level of self-care ability, having subsidies for care and supervision, having disability certificate and personal care, etc.
CONCLUSIONS: The COVID-19 outbreak affected the medication adherence among patients with severe mental disorders in low-income families. The influencing factors are complicated and diverse, including psychological effects, traffic impact, and economy, etc. The government should pay more efforts on social assistance programs and flexibly deal with difficulties during public health emergencies like the COVID-19.

Medication adherence is a common issue influenced by various factors among patients with severe mental disorders worldwide. However, most literature to date has been primarily quantitative and has focused on medication adherence issue from the perspective of patients or their caregivers. Moreover, research focused on medication adherence issue in China is scarce. Present study aims to explore the influential factors of medication adherence among patients with severe mental disorders form the perspective of mental health professionals in Hunan Province, China.
A qualitative study was performed in Hunan Province, China with 31 mental health professionals recruited from October to November 2017. And semi-structured interviews or focus group interviews were conducted along with audio recordings of all interviews. Interview transcripts were then coded and analyzed in Nvivo software with standard qualitative approaches.
Three major themes influencing medication adherence among patients with severe mental disorders were identified as: (1) attitudes towards mental disorder/treatment; (2) inadequate aftercare; (3) resource shortages.
This qualitative study identified the factors influencing medication adherence among patients with severe mental disorders in China. As a locally driven research study, it provides practical advice on medication adherence promotion for mental health workers and suggests culturally tailored models that improve the management of patients with severe mental disorders in order to reduce economic burden on individual and societal level.

In 2016, the government of Bao\'an District, Shenzhen, China launched a free medication program for all non-registered permanent residents with severe mental disorders (SMD) within its jurisdiction, in efforts to reduce the relapse caused by intermittent medication or non-medication. Participation in the program has not been analyzed since its inauguration. This study aimed to evaluate the participation of non-registered permanent residents with SMD in the program from 2016 to 2020 and to explore its influencing factors.
This is a retrospective cross-sectional study of 3760 non-registered permanent residents with SMD in Bao\'an District, Shenzhen, China (response rate: 78.64%). Data have been obtained from two sources: the Shenzhen Information System for Psychosis in 2020 and the free medication program\'s management files from 2016 to 2020. We employ descriptive statistics to analyze the participation rate of the free medication program among non-registered permanent patients. Logistic regression analysis is used to explore the factors affecting the patients\' participation in the program.
The participation rate of the free medication program among non-registered permanent patients has shown an upward trend, rising from 28.83% in 2016 to 58.32% in 2020. High participation rates have been registered among the following patient subgroups: those aged between 30 and 39 (63.11%), those with high school/technical secondary school (62.33%), those from rural areas (61.62%), those living in poverty (67.79%), those suffering schizoaffective disorder (72.26%), those having SMD for less than 5 years (59.89%), and those with family history of mental illness (71.23%).Logistics regression analysis shows that age, patient-guardian relationship, place of residence, financial condition, types of disease, duration of illness and family history are the main factors affecting the patients\' decision to participate in the free medication program.
The steadily increasing participation rate of the free medication program indicates that the program has been gradually accepted and recognized by non-registered permanent residents with SMD. However, nearly half of the patients have yet to join the program. To further raise the participation rate, special attention should be given to patients who are financially secure, aged below 20, without guardians, intellectually disabled or suffering SMD for over 5 years.