The effect of sociodemographic factors on the risk of poor mental health in Akron (Ohio): a Bayesian hierarchical spatial analysis

We examined the association of sociodemographic factors on mental health risk within the city of Akron (Ohio). A Spatial Bayesian Hierarchical model was used in this study. We found that the risk of poor mental health was positively associated with the proportion of people lacking sufficient sleep (RR = 0.42, 95% CI:0.22-0.62), the percentage of people below poverty (RR = 0.12, 95% CI: 0.09, 0.16), and the percentage of married couples (RR = 0.02, 95% CI: -0.05, 0.08). On the contrary, the percentage of female population (RR = -0.06, 95% CI: -0.13, 0.01), the percentage of the black population (RR = -0.05, 95% CI: -0.08, -0.02), and the college-educated population (RR = -0.03, 95% CI: -0.09, 0.04) was negatively associated with the risk of poor mental health. We also found that the sociodemographic variables have spatially varying effects across different neighborhoods. Future studies will examine the joint spatial effect of poor mental health risk and suicide ideation in the study area

Examining spatial variability in the association between male partner alcohol misuse and intimate partner violence against women in Ghana: a GWR analysis

Globally, it is estimated that about 30% of ever-partnered women have experienced some form of intimate partner violence (IPV)—physical assault, sexual assault, or emotional abuse. The prevalence of IPV in sub-Saharan Africa is considerably higher than the global estimate. In Ghana, it is estimated that 24% of women have experienced physical and/or sexual IPV in their lifetime. Studies point to the association between alcohol misuse by intimate male partners and violence against women. However, there has been no consideration for potential spatial variation or heterogeneity in this association. Using estimates from the 2008 Ghana Demographic and Health Survey Data, we employed geographically weighted regression (GWR) analysis to examine spatial variations in the relationship between male partner’s alcohol misuse and IPV among women in Ghana. We fitted three models to assess the relationship using a step-wise approach. The first model has alcohol misuse as the only predictor, whereas the second model included other male partner characteristics, such as post-secondary education and employment status. The final introduced female characteristics as additional covariates. The result of the GWR analysis shows that the effect of alcohol misuse on IPV is elevated in the south-western part of Ghana. The findings suggest the potential influence of place-based or contextual factors on the association between alcohol misuse and women’s exposure to IPV

Modelled gridded population estimates for Cameroon 2022. Version 1.0

Modelled gridded population estimates for Cameroon 2022. Version 1.0</span

Modelled gridded population estimates for Haut-Katanga Province in the Democratic Republic of Congo version 4.1

Version 4.1 population estimates for Haut-Katanga Province in the Democratic Republic of Congo </span

Modelled gridded population estimates for Tanganyinka Province in the Democratic Republic of Congo version 4.1.

Version 4.1 population estimates for Tanganyinka Province in the Democratic Republic of Congo </span

Modelled gridded population estimates for Haut-Lomami Province in the Democratic Republic of Congo version 4.1

Version 4.1 population estimates for Haut-Lomami Province in the Democratic Republic of Congo </span

Modelled gridded population estimates for Maniema Province in the Democratic Republic of Congo version 4.1

Version 4.1 population estimates for Maniema Province in the Democratic Republic of Congo </span