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Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression.

ABSTRACT: Low atmospheric pressure may increase depression and suicide through inducing hypoxia. Previous studies have not evaluated the geographic variation of this relationship across the United States. Analyses were based on three groupings of age-adjusted completed suicide rates (all suicide, firearm-related suicide, non-firearm-related suicide) from 2286 counties in the United States. Multiple regression was used to determine the overall relationship between atmospheric pressure and completed suicide rates. Geographically weighted regression (GWR) models were used to obtain local coefficient estimates. A negative correlation between atmospheric pressure and completed suicide rates was observed for all three suicide groupings (p-value <0.0001). Significant, negative GWR coefficient estimates were located in the West and Northeast for the all suicides and firearm-related suicides, and in the Midwest for non-firearm-related suicides.

SUBMITTER: Frutos AM

PROVIDER: S-EPMC6281181 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression.

Frutos Aaron M AM Sloan Chantel D CD Merrill Ray M RM

PloS one 20181205 12

Low atmospheric pressure may increase depression and suicide through inducing hypoxia. Previous studies have not evaluated the geographic variation of this relationship across the United States. Analyses were based on three groupings of age-adjusted completed suicide rates (all suicide, firearm-related suicide, non-firearm-related suicide) from 2286 counties in the United States. Multiple regression was used to determine the overall relationship between atmospheric pressure and completed suicide ...[more]

PMID: 30517125

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Project description:BackgroundSuicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide.MethodsSuicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran's I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide.ResultsSuicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran's I with p < 0.001, highlighting the importance of conducting spatial analysis. In the GWR model calibration, a subset of the community's age structure, single-person household composition, access to mental healthcare centers, and unmet medical needs were selected to explain male suicide mortality. These determinants disproportionately increased the risk of male suicide, varying by region. The GWR coefficients of each variable vary widely across 249 districts: aging index (Q1:0.06-Q3:0.46), single-person households (Q1:0.22-Q3:0.35), psychiatric clinics (Q1:-0.20-Q3:-0.01), and unmet medical needs (Q1:0.09-Q3:0.14).ConclusionsCommunity cultural and structural factors exacerbate regional disparities in suicide among men. The influencing factors exhibit differential effects and significance depending on the community, highlighting the need for efficient resource allocation for suicide. A regionally tailored approach is crucial for the effective control of the community's mental health management system.

Project description:Background The World Health Organization (WHO) encourages breastfeeding to begin within the first hour after birth in order to save children’s lives. In Ethiopia, different studies are done on the prevalence and determinants of breastfeeding initiation, up to our knowledge, the spatial distribution and the spatial determinants of breast feeding initiation over time are not investigated. Therefore, the objectives of this study were to assess spatial variation and its spatial determinant of delayed initiation of breastfeeding in Ethiopia using Geographically Weighted Regression (GWR). Methods A cross-sectional study was undertaken using the nationally representative 2016 Ethiopian Demographic and Health Survey (EDHS) dataset. Global Moran’s I statistic was used to measure whether delayed breastfeeding initiation was dispersed, clustered, or randomly distributed in study area. Ordinary Least Squares (OLS) regression was used to identify factors explaining the geographic variation in delayed breastfeeding initiation. Besides, spatial variability of relationships between dependent and selected predictors was investigated using geographically weighted regression. Result A total weighted sample of 4169 children of aged 0 to 23 months was included in this study. Delayed initiation of breastfeeding was spatially varies across the country with a global Moran’s I value of 0.158 at (p-value<0.01). The hotspot (high risk) areas were identified in the Amhara, Afar, and Tigray regions. Orthodox religion, poor wealth index, caesarian section, baby postnatal checkup, and small size of a child at birth were spatially significant factors for delayed breastfeeding initiation in Ethiopia. Conclusion In Ethiopia initiation of breastfeeding varies geographically across region. A significant hotspot was identified in the Amhara, Afar, and Tigray regions. The GWR analysis revealed that orthodox religion, poor wealth index, caesarian section, baby postnatal checkup, and small birth weight were spatially significant factors.

Project description:BACKGROUND:Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. METHODS:In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. RESULTS:The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. CONCLUSIONS:The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.

Dataset Information

Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression.

Publications

Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression.

Similar Datasets

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