The psychosis analysis in real-world on a cohort of large-scale patients with schizophrenia

Lin

et al. 2021

Preprint

Self Cite

Background: Schizophrenia patients have increased risks of several adverse outcomes, including violent crime, aggressiveness and suicide. However, studies of different adverse outcomes in the same population are rare and the influencing factors for these outcomes need clarification by appropriate models. This study aimed to identify influencing factors of these adverse outcomes by examining and comparing different count regression models.Methods: This study included schizophrenia patients who had at least once follow-up record in the Guangdong Mental Health Center Network Medical System during 2020. Three types of adverse outcomes including a) aggressiveness with police dispatch or violence crime, b) aggressiveness without police dispatch, and c) self-harm or suicide attempts. First, we investigated the incidence of these adverse outcomes in each type. Second, the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models were fitted and compared for their intensity. Finally, We analysed associations between these adverse outcomes intensity and sociodemographic, clinical factors with the best model. Results: A total of 130474 schizophrenia patients were selected. Each type of adverse outcomes was reported by less than 1% of schizophrenia patients in 2020. NB regression model is the best model for fitting the number of aggressiveness without police dispatch because of the best goodness of fit and relatively simple interpretation, whereas ZINB models for the other two outcomes. Age, sex, and history of adverse outcomes were influencing factors shared across these adverse outcomes. Higher educational level, employment were protective factors shared between aggressiveness with police dispatch or violent crime , and aggressiveness without police dispatch. Aggressiveness without police dispatch , and self-harm or suicide attempts shared older onset age (≥18 years) as a risk factor. Family history of mental disease was a risk factor of self-harm or suicide attempts individually.Conclusions: NB and ZINB models were selected for fitting the number of adverse outcomes in our studies. Influencing factors for adverse outcomes intensity included both those shared across different types and those individual to specific types. Therefore, combined and customized tools in risk assessment and intervention for different types of these outcomes might be necessary.

Section: Discussionmentioning

confidence: 99%

“…GDMHS was covered over 99% of schizophrenia patients in the province. There were over 348000 patients registered in GDMHS during the periods from 2010 to 2019 in Guangdong, one of the biggest provinces in China [22]. Patient pro le, clinical characteristics, and treatment information were recorded and maintained into GDMHS.…”

Section: Study Participantsmentioning

confidence: 99%

Influencing Factors of Multiple Adverse Outcomes Among Schizophrenia Patients Using Count Regression Models: A Cross-Sectional Study

Chen

Lin

et al. 2021

Preprint

Self Cite

“…Based on the practice guideline for the treatment of patients with schizophrenia [ 19 ], the monitoring data of patients with schizophrenia from January 2010 to November 2019 is derived from Mental Health Information and Big Data Intelligent Processing Platform [ 20 ], involving 404,426 patients and a total of 15 million follow-up records. The data is multi-source and heterogeneous, including patient disease registration files, follow-up records, medication records, physical examination reports, hospitalization records, etc.…”

Section: Methodsmentioning

confidence: 99%

“…The data is from a mental health information and big data intelligent processing platform from the Guangdong mental health center. The platform has collected 15 million follow-up records of 404,426 patients over 10 years [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Disease risk analysis for schizophrenia patients by an automatic AHP framework

BMC Med Inform Decis Mak

Weng

Lin

et al. 2021

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Background Based on more than 15 million follow-up records of 404,426 patients from Guangdong Mental Health Center over the past 10 years, this study aims to propose a disease risk analysis and prediction model to support chronic disease management and clinical research for schizophrenia patients. Methods Based on a mental health information and intelligent data processing platform, we design an automatic AHP framework called AutoAHP to analyze and predict the disease risks of schizophrenia patients. Through automatic extraction, transformation and integration of follow-up data in the real world such as demography, treatment, and the disease course, a chronic database of patient status is established. In combination with age-period-cohort, logistic regression and Cox models, we apply the AutoAHP to assess disease risk and implement risk prediction in practice. Results A list of essential factors for risk prediction are identified, including annual changes in mental health policy, public support, regional difference, patient gender, compliance, and social function. After the verification of 1,222,038 complete disease course and treatment records of 256,050 patients, the AutoAHP framework achieves a precision of 0.923, a recall of 0.924, and a F1 of 0.923. The model is demonstrated to be superior to general models and has better performance in risk prediction. Conclusions Aiming at the risk assessment of patients with schizophrenia which is influenced by factors, such as time, region and complication, the AutoAHP framework is able to be applied as a model in combination with logistic regression and Cox models to support clinical analysis of disease risk related factors and assist decision-making in chronic disease management.

“…Given limited health resources as well as current management and intervention programs, the Chinese healthcare delivery system focused predominantly on severe mental disorders such as schizophrenia, schizoaffective disorder, mental retardation with mental disorders, and so forth [ 14 – 16 ]. Due to the rapid economic development, aging population as well as social-economic disparities in Guangdong [ 17 , 18 ], there is a pressing need to quantify the regional spatial–temporal trends of DALYs for severe mental disorders to support evidence-based decision-making in public mental health. However, a systematic quantification of the magnitude as well as spatial–temporal distribution for regional burden attributable to severe mental disorders in Guangdong is not readily available.…”

Section: Introductionmentioning

confidence: 99%

Regional years of life lost, years lived with disability, and disability-adjusted life-years for severe mental disorders in Guangdong Province, China: a real-world longitudinal study

Chen

Zhang

et al. 2022

glob health res policy

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Background 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. Methods 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. Results 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. Conclusions 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.