Fang Wang, Rui Pang, Shaohong Shi, Yang Zhang
Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer
Scientific reports
tremendous variation prevalence suicide factors contribute occurrence across geographic locations cultures time therefore present study aims identify various sociodemographic risk factors psychosocial stressors using population data countries included study based data world health organization considered top ten suicide countries ten lowest suicide nations searched five global databases recent sociodemographic data data psychosocial stressors period august september 2022 analyzed correlation suicide rates various factors considered p value less 005 statistically significant correlation suicide sociodemographic factors like age p 0246 gender male p 0357 female p 0357 net enrolment rate p 0725 gender parity index p 0929 urban population percentage p 0571 marital status male p 0345 female p 0752 literacy rate percentage p 0199 significant correlation suicide psychosocial stressors like unemployment percentage p 0552 alcoholism percentage p 0271 prevalence depression p 0523 number psychiatrists p 0605 number mental hospitals r 0090 p 0713 poverty rate percentage p 0538 also significant suicide major public health issue yet global suicide data scarce sociodemographic factors like age urbanization literacy rate negatively correlated whereas psychosocial stressors like unemployment alcohol misuse poverty positively correlated none significantly associated