How Community and Individual Risk Factors Mutually Impact Youth’s Perceived Safety: A Syndemic Analysis Using Structural Equation Modeling

2021 ◽  
pp. 088626052110283
Author(s):  
Yingwei Yang ◽  
Karen D. Liller ◽  
Martha Coulter ◽  
Abraham Salinas-Miranda ◽  
Dinorah Martinez Tyson ◽  
...  

The purpose of this study was to evaluate the mutual impact of community and individual factors on youth’s perceptions of community safety, using structural equation modeling (SEM) conceptualized by syndemic theory. This study used survey data collected from a county wide sample of middle and high school students (N=25,147) in West Central Florida in 2015. The outcome variable was youth’s perceptions of community safety. Predictors were latent individual and community factors constructed from 14 observed variables including gun accessibility, substance use, depressive symptoms, and multiple neighborhood disadvantage questions. Three structural equation models were conceptualized based on syndemic theory and analyzed in Mplus 8 using weighted least squares (WLS) estimation. Each model’s goodness of fit was assessed. Approximately seven percent of youth reported feeling unsafe in their community. After model modifications, the final model showed a good fit of the data and adhered to the theoretical assumption. In the final SEM model, an individual latent factor was implied by individual predictors measuring gun accessibility without adult’s permission (β=0.70), sadness and hopelessness (β=0.52), alcohol use (β=0.79), marijuana use (β=0.94), and illegal drug use (β=0.77). Meanwhile, a community latent factor was indicated by multiple community problems including public drinking (β=0.88), drug addiction (β=0.96), drug selling (β=0.97), lack of money (β=0.83), gang activities (β=0.90), litter and trash (β=0.79), graffiti (β=0.91), deserted houses (β=0.86), and shootings (β=0.93). A second-order syndemic factor that represented the individual and community factors showed a very strong negative association with youth’s safe perception (β=-0.98). This study indicates that individual risk factors and disadvantaged community conditions interacted with each other and mutually affected youth’s perceptions of community safety. To reduce these co-occurring effects and improve safe perceptions among youth, researchers and practitioners should develop and implement comprehensive strategies targeting both individual and community factors.

Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.


2019 ◽  
Author(s):  
Parastoo Jamshidi ◽  
Farid Najafi ◽  
Shayan Mostafaee ◽  
Ebrahem Shakiba ◽  
yahya pasdar ◽  
...  

Abstract Background: Glomerular Filtration Rate (GFR) is a valid indicator for kidney function, both in healthy and diseased people. Different factors can affect GFR. The purpose of this study is to assess a causal model to show direct and indirect effects of GFR-related factors using structural equation modeling. Patients and methods : We analyzed data from recruitment phase of Ravansar Non-Communicable Disease cohort study. Data on socio-behavioral, nutritional, cardiovascular, and metabolic risk factors were entered in a conceptual model in order to test direct and indirect effects of the associated factors on GFR, separately in male and female, using the structural equation modeling. Results : Of 8927 individuals participated in this study, 4212 subjects were male (47.2%) and 4715 subjects were female (52.8%). The obtained standard deviation of GFR was 76.05 (±14.3) per 1.73 . Filtration rate for 11.52%, 72.96% and 15.50% of people were <60, and , respectively. Hypertension in both gender and atherogenic factor in male directly, and in female directly and indirectly had a decreasing effect on GFR. Blood Urea Nitrogen (BUN) and smoking in male and female, directly or indirectly through other variables, was associated with a decrease in GFR. In female, diabetes had a decreasing direct and indirect effect on GFR. Obesity in female was directly associated with increasing and indirectly associated with decreasing filtration. Conclusion : According to our results, increasing age, hypertension, diabetes, obesity and high blood lipids, and BUN had a decreasing direct and indirect effects on GFR. Although low GFR might have different reasons and it is not a consistent sign of CKD, our findings, in line with other reports, provide more detailed informations about important risk factors of low GFR. Public awareness about such factors can improve public practice of positive health behaviours.


Author(s):  
Huan Zhou ◽  
Qingzhi Wang ◽  
Junmin Zhou ◽  
Tiaoying Li ◽  
Alexis Medina ◽  
...  

Neurocysticercosis (NCC) significantly contributes to morbidity in developing countries. We recently published a study of prevalence and risk factors in school-aged children in three mountainous areas in Sichuan province of western China. Using structural equation modeling (SEM) on data from that study to guide intervention planning, here we examine risk factors grouped into three broad interventional categories: sociodemographics, human behavior, and sources of pork and pig husbandry. Because neuroimaging is not easily available, using SEM allows for the use of multiple observed variables (serological tests and symptoms) to represent probable NCC cases. Data collected from 2608 students was included in this analysis. Within this group, seroprevalence of cysticercosis IgG antibodies was 5.4%. SEM results showed that sociodemographic factors (β = 0.33, p < 0.05), sources of pork and pig husbandry (β = 0.26, p < 0.001), and behavioral factors (β = 0.33, p < 0.05) were all directly related to probable NCC in school-aged children. Sociodemographic factors affected probable NCC indirectly via sources of pork and pig husbandry factors (β = 0.07, p < 0.001) and behavioral variables (β = 0.07, p < 0.001). Both sociodemographic factors (β = 0.07, p < 0.05) and sources of pork and pig husbandry factors (β = 0.10, p < 0.01) affected probable NCC indirectly via behavioral variables. Because behavioral variables not only had a large direct effect but also served as a critical bridge to strengthen the effect of sociodemographics and sources of pork and pig husbandry on probable NCC, our findings suggest that interventions targeting behavioral factors may be the most effective in reducing disease.


2012 ◽  
Vol 176 (7) ◽  
pp. 597-607 ◽  
Author(s):  
A. Arlinghaus ◽  
D. A. Lombardi ◽  
J. L. Willetts ◽  
S. Folkard ◽  
D. C. Christiani

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