Personality as a mediator of demographic risk factors for suicide attempts in a community sample

2005 ◽  
Vol 46 (3) ◽  
pp. 214-222 ◽  
Author(s):  
Richard A. Grucza ◽  
Thomas R. Przybeck ◽  
C. Robert Cloninger
Keyword(s):  
Risk Factors ◽  
Suicide Attempts ◽  
Community Sample ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Risk Factors For Suicide

scholarly journals Identifying socio-demographic risk factors for suicide using data on an individual level

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guus Berkelmans ◽  
Rob van der Mei ◽  
Sandjai Bhulai ◽  
Renske Gilissen
Keyword(s):  
Risk Factors ◽  
Low Income ◽  
Individual Level ◽  
Highly Educated ◽  
Aged People ◽  
Physical Health Problems ◽  
Using Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Risk Factors For Suicide

Abstract Background Suicide is a complex issue. Due to the relative rarity of the event, studies into risk factors are regularly limited by sample size or biased samples. The aims of the study were to find risk factors for suicide that are robust to intercorrelation, and which were based on a large and unbiased sample. Methods Using a training set of 5854 suicides and 596,416 control cases, we fit a logistic regression model and then evaluate the performance on a test set of 1425 suicides and 594,893 control cases. The data used was micro-data of Statistics Netherlands (CBS) with data on each inhabitant of the Netherlands. Results Taking the effect of possible correlating risk factors into account, those with a higher risk for suicide are men, middle-aged people, people with low income, those living alone, the unemployed, and those with mental or physical health problems. People with a lower risk are the highly educated, those with a non-western immigration background, and those living with a partner. Conclusion We confirmed previously known risk factors such as male gender, middle-age, and low income and found that they are risk factors that are robust to intercorrelation. We found that debt and urbanicity were mostly insignificant and found that the regional differences found in raw frequencies are mostly explained away after correction of correlating risk factors, indicating that these differences were primarily caused due to the differences in the demographic makeup of the regions. We found an AUC of 0.77, which is high for a model predicting suicide death and comparable to the performance of deep learning models but with the benefit of remaining explainable.

Demographic risk factors for suicide and depression among Serbian medical school students

2014 ◽  
Vol 50 (4) ◽  
pp. 633-638 ◽  
Author(s):  
Vladimir Miletic ◽  
Jasminka Adzic Lukovic ◽  
Nevena Ratkovic ◽  
Danijela Aleksic ◽  
Anita Grgurevic
Keyword(s):  
Risk Factors ◽  
Medical School ◽  
School Students ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Risk Factors For Suicide ◽  
Medical School Students

Suicide Attempts in an African Schizophrenia Population: An Assessment of Demographic Risk Factors

2004 ◽  
Vol 34 (3) ◽  
pp. 320-327 ◽  
Author(s):  
D.J.H. Niehaus ◽  
C. Laurent ◽  
E. Jordaan ◽  
L. Koen ◽  
P. Oosthuizen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Suicide Attempts ◽  
Demographic Risk ◽  
Demographic Risk Factors

Clinical and Demographic Risk Factors for COVID-19 during Delivery Hospitalizations in New York City

2021 ◽  
Author(s):  
Desmond Sutton ◽  
Timothy Wen ◽  
Anna P. Staniczenko ◽  
Yongmei Huang ◽  
Maria Andrikopoulou ◽  
...  
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Demographic Data ◽  
Outcome Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Zip Code

Objective This study was aimed to review 4 weeks of universal novel coronavirus disease 2019 (COVID-19) screening among delivery hospitalizations, at two hospitals in March and April 2020 in New York City, to compare outcomes between patients based on COVID-19 status and to determine whether demographic risk factors and symptoms predicted screening positive for COVID-19. Study Design This retrospective cohort study evaluated all patients admitted for delivery from March 22 to April 18, 2020, at two New York City hospitals. Obstetrical and neonatal outcomes were collected. The relationship between COVID-19 and demographic, clinical, and maternal and neonatal outcome data was evaluated. Demographic data included the number of COVID-19 cases ascertained by ZIP code of residence. Adjusted logistic regression models were performed to determine predictability of demographic risk factors for COVID-19. Results Of 454 women delivered, 79 (17%) had COVID-19. Of those, 27.9% (n = 22) had symptoms such as cough (13.9%), fever (10.1%), chest pain (5.1%), and myalgia (5.1%). While women with COVID-19 were more likely to live in the ZIP codes quartile with the most cases (47 vs. 41%) and less likely to live in the ZIP code quartile with the fewest cases (6 vs. 14%), these comparisons were not statistically significant (p = 0.18). Women with COVID-19 were less likely to have a vaginal delivery (55.2 vs. 51.9%, p = 0.04) and had a significantly longer postpartum length of stay with cesarean (2.00 vs. 2.67days, p < 0.01). COVID-19 was associated with higher risk for diagnoses of chorioamnionitis and pneumonia and fevers without a focal diagnosis. In adjusted analyses, including demographic factors, logistic regression demonstrated a c-statistic of 0.71 (95% confidence interval [CI]: 0.69, 0.80). Conclusion COVID-19 symptoms were present in a minority of COVID-19-positive women admitted for delivery. Significant differences in obstetrical outcomes were found. While demographic risk factors demonstrated acceptable discrimination, risk prediction does not capture a significant portion of COVID-19-positive patients. Key Points

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