A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study.

BACKGROUND: Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. METHODS: Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. RESULTS: Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86-0.98), single (HR = 0.11, 95% CI: 0.02-0.68), having children (one child, HR = 0.07, 95%CI: 0.01-0.87; more than one child, HR = 0.33, 95%CI: 0.04-2.48), living in dormitory (HR = 0.25, 95%CI: 0.08-0.77), non-student (employee, HR = 0.24, 95% CI: 0.07-0.88; non-employee, HR = 0.09, 95% CI: 0.02-0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82-0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23-22.48), living alone (HR = 4.37, 95% CI: 1.60-11.94), having suicide history (HR = 2.51, 95% CI: 1.06-5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00-1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00-1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. CONCLUSIONS: A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.

Exploring home fall events among infants and toddlers using social media information: an infodemiology study in China.

BACKGROUND: Practical interventions of fall prevention are challenging for infants and toddlers. This study aimed to explore specific details of falls that occurred at home for kids 0-3 years old using key information from social media platforms, which provided abundant data sources for fall events. METHODS: We used internet-based search techniques to collect fall events information from 2013 to 2023. The search was restricted and implemented between 1 and 12 April 2023. Online platforms included Baidu, Weibo, WeChat, TikTok, Toutiao and Little Red Book. A qualitative descriptive approach was used to analyse the fall events and major factors, including the fall event time, child age, environmental factors and behavioural characteristics of children and caregivers. RESULTS: We identified 1005 fall injury cases among infants and toddlers. Fall mechanisms included falls from household furniture (71.2%), falls from height (21.4%) and falls on the same level (7.4%). Environmental risk factors mainly consisted of not using or installing bed rails incorrectly, a gap between beds, unstable furniture, slippery ground and windows without guardrails. Behavioural factors included caregivers leaving a child alone, lapsed attention, turning around to retrieve something, misusing baby products, inadequately holding the child and falling asleep with children. Child behavioural factors included walking or running while holding an object in hand or mouth and underdeveloped walking skills. CONCLUSION: Interventions for preventing falls should be designed specifically for Chinese families, especially considering family function in the context of Chinese culture. Social media reports could provide rich information for researchers.

Associations of dietary linoleic acid and alpha linolenic acid intake with cardiovascular, cancer and all-cause mortalities in patients with diabetes: NHANES 1999-2008.

Objective: To investigate the association between the dietary intake of linoleic acid (LA) and alpha linolenic acid (ALA) with mortality outcomes in patients with diabetes. Participants: 3,112 U.S. adults aged≥20 years. Setting: Basic information was collected at baseline of the National Health and Nutrition Examination Survey (NHANES). Serum CRP (mg/dL), total protein (g/L), waist circumference (cm), fasting blood glucose (mmol/L), white blood cell count, serum LDL-C, and serum HDL-C were also measured. Daily diets were also recorded using a 24-hour dietary review to produce the individuals' intake of LA and ALA. The association between tertiles of LA and ALA intake with mortality was analyzed by weighted Cox models adjusted for the main confounders. Main outcome measures: The study included 3,112 adults with diabetes from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2008. Death outcomes were ascertained by linkage to the database records through 31 December 2015. Results: Subjects with a high intake of LA (T3) had 17% [hazard ratio (HR) 0.83, 95% CI 0.70 to 0.99) and 48% (HR=0.52, 0.35 to 0.80)] reductions in all-cause mortality and cardiovascular mortality, respectively, compared with subjects with lowest intake (T1). Similar results were observed for ALA, HR of cardiovascular mortality was 0.55 (0.38 to 0.81) and for all-cause mortality was 0.85 (0.69 to 1.04) comparing the highest to lowest intake tertiles. Conclusion: Higher intakes of LA and ALA were inversely associated with CVD and all-cause deaths in patients with diabetes. Proper dietary intakes of LA and ALA could contribute to the cardiovascular health and the long-term survival of patients with diabetes.

Global burden of self-harm and interpersonal violence and influencing factors study 1990-2019: analysis of the global burden of disease study.

INTRODUCTION: Widespread concern exists in today's world regarding self-harm and interpersonal violence. This study to analyze the changes in temporal trends and spatial patterns of risk factors and burdens of self-harm and interpersonal violence using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. METHODS: Temporal trends in self-harm and interpersonal violence were initially summarized using the estimated annual percentage change (EAPC). Data were compiled and visualized to delineate changes in disease burden and factors influencing self-harm and interpersonal violence from 1990 to 2019, stratified by gender, age and GBD region. RESULTS: In 2019, the DALY rates of self-harm were 424.7(95% UI 383.25, 466.93). Over the period from 1999 to 2019, self-harm exhibited an overall decreasing trend, with the EAPC of -1.5351 (95% CI -1.6194, -1.4507), -2.0205 (95% CI -2.166, -1.8740) and -2.0605 (95% CI -2.2089, -1.9119), respectively. In contrast, the incidence rate of interpersonal violence was significantly higher than self-harm, with a rate of 413.44 (95% UI 329.88, 502.37) per 100,000 population. Mortality and DALYs of interpersonal violence were lower than those of self-harm, at 5.22 (95% UI 4.87, 5.63) and 342.43 (95% UI 316.61, 371.55). Disease burden of self-harm and interpersonal violence varied by gender, age groups and region. Specific risk factors showed that alcohol use, high temperature and drug use were the main risk factors for self-harm, while alcohol use, intimate partner violence and high temperature were associated with interpersonal violence. Low temperature was a common protective factor for both self-harm and interpersonal violence. The burden of self-harm and interpersonal violence was attributed to different factors influences in different SDI regions. CONCLUSIONS: The study explored temporal trends and spatial distribution of the global disease burden of self-harm and interpersonal violence, emphasizing the significant impact of factors such as alcohol use, temperature, and drug use on disease burden. Further research and policy actions are needed to interpret recent changes of disease burden of self-harm and interpersonal violence, and dedicated efforts should be implemented to devise evidence-based interventions and policies to curtail risk factors and protect high-risk groups.

Influencing factors of length of stay among repeatedly hospitalized patients with mood disorders: a longitudinal study in China.

BACKGROUND: Patients with mood disorders usually require repeated and prolonged hospitalization, resulting in a heavy burden on healthcare resources. This study aims to identify variables associated with length of stay(LOS) of repeatedly hospitalized patients with mood disorders and to provide information for optimizing psychiatry management and healthcare resource allocation. METHODS: Electronic medical records (EMRs) of repeatedly hospitalized patients with mood disorders from January 2010 to December 2018 were collected and retrospectively analyzed. Chi-square and t-test were adopted to investigate the differences in characteristics between the two groups of short LOS and long LOS. Generalized estimating equation (GEE) was conducted to investigate potential factors influencing LOS. RESULTS: A total of 2,009 repeatedly hospitalized patients with mood disorders were enrolled, of which 797 (39.7%) had a long LOS and 1,212 (60.3%) had a short LOS. Adverse effects of treatment, continuous clinical manifestation, chronic onset type, suicide attempt, comorbidity and use of antidepressants were positively associated with long LOS among all repeatedly hospitalized patients with mood disorders (P < 0.050). For patients with depression, factors associated with long LOS consisted of age, monthly income, adverse effects of treatment, continuous clinical manifestation, suicide attempt and comorbidity (P < 0.050). Whereas, for patients with bipolar disorder (BD), adverse effects of treatment, four or more hospitalizations and use of antidepressants contributed to the long LOS (P < 0.050). Influencing factors of LOS also vary among patients with different effectiveness of treatment. CONCLUSION: The LOS in repeatedly hospitalized patients with mood disorders was influenced by multiple factors. There were discrepancies in the factors affecting LOS in patients with different diagnoses and effectiveness of treatment, and specific factors should be addressed when evaluating the LOS.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus.

BACKGROUND: Among older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis. AIM: To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model. METHODS: The clinical data of 210 patients diagnosed with T2DM and admitted to the First People's Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (n = 147) and a test set (n = 63) in a 7:3 ratio using a random function. The training set was used to construct the nomogram, decision tree, and random forest models, and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity, specificity, accuracy, recall, precision, and area under the receiver operating characteristic curve. RESULTS: Among the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (P < 0.05), whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant (P > 0.05). CONCLUSION: Among the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.

Individual ambient ozone exposure during pregnancy and adverse birth outcomes: Exploration of the potentially vulnerable windows.

BACKGROUND: Ambient ozone (O3) exposure during pregnancy might be associated with preterm birth (PTB) and low birth weight (LBW); however, existing evidence remains inconclusive. It is necessary to explore the relationships and potential susceptible periods further. METHODS: To explore the relationship between O3 exposure and adverse birth outcomes, a study using records of 34,122 singleton live births in Beijing between 2016 and 2019 was conducted. The O3 exposure in each gestational week of pregnant women was estimated, and Cox proportional hazard models were used to estimate the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Distributed lag nonlinear model (DLNM) incorporated in Cox proportional hazard models were used to explore potential critical windows. RESULTS: An increase of 10 µg/m3 in O3 exposure was associated with a 3.9% (95%CI: 0.6-7.3%) higher risk of PTB. Additionally, this increase in O3 exposure was positively linked to PTB during the 2nd - 7th, 22nd - 29th, and 37th gestational weeks, and LBW during the 2nd - 7th, 24th - 29th, and 37th gestational weeks. CONCLUSIONS: This study demonstrates a positive correlation between O3 exposure and PTB, and identified specific sensitive periods during pregnancy when the risk was higher.