Spatial Patterns in County-Level Mortality Rates for Drug Use, Mental Illness, and Suicide and Their Associations with Risk Factors in Georgia
Disciplines
Geographic Information Sciences | Human Geography | Spatial Science
Abstract (300 words maximum)
As reported by the CDC, suicide is the second leading cause of death for people aged 10 to 44 years old, accounting for 17.2% of deaths for people aged 10-24 and 9.8% of deaths for people aged 25-44 in the United States in 2023. In Georgia, suicide accounts for 12.1% of deaths for people aged 10-44 and drug overdoses account for 14.8% of deaths for people aged 10-44 years old as of 2024.These “deaths of despair” are usually credited as a result of mental illness, but only one-fifth of people who commit suicide are diagnosed with mental illness in the U.S.. Previous studies have identified many socioeconomic variables as the risk factors of suicide and substance abuse, such as poverty, barriers to healthcare, and social isolation. In Georgia, drug overdoses and suicide exhibit considerable urban-rural disparities: rural areas have a significantly higher rates than their urban counterparts. Thus, a good understanding of the spatial patterns in drug overdose, mental illness, suicide, and their associated risk factors is necessary for making effective preventive measures. This project aims to explore the spatial patterns in county-level mortality rates of drug use, mental illness, and suicide, and their associations with risk factors in Georgia using GIS (Geographic Information System) and statistical analyses. GIS analysis is used to map and compare the spatial patterns, especially urban-rural disparities, in the three mortality rates and risk factors, including median income, social isolation, access to mental healthcare, and ratio of School Social Workers (SSWs) to students. GIS-based hot spot analysis is used to identify the spatial clusters of the three mortality rates and risk factors. Statistical analyses, especially correlation analysis, are used to quantify and compare the associations among them. This study will provide useful information for public health policy making.
Use of AI Disclaimer
no
Academic department under which the project should be listed
RCHSS – Geography & Anthropology
Primary Investigator (PI) Name
Dr. Jun Tu
Spatial Patterns in County-Level Mortality Rates for Drug Use, Mental Illness, and Suicide and Their Associations with Risk Factors in Georgia
As reported by the CDC, suicide is the second leading cause of death for people aged 10 to 44 years old, accounting for 17.2% of deaths for people aged 10-24 and 9.8% of deaths for people aged 25-44 in the United States in 2023. In Georgia, suicide accounts for 12.1% of deaths for people aged 10-44 and drug overdoses account for 14.8% of deaths for people aged 10-44 years old as of 2024.These “deaths of despair” are usually credited as a result of mental illness, but only one-fifth of people who commit suicide are diagnosed with mental illness in the U.S.. Previous studies have identified many socioeconomic variables as the risk factors of suicide and substance abuse, such as poverty, barriers to healthcare, and social isolation. In Georgia, drug overdoses and suicide exhibit considerable urban-rural disparities: rural areas have a significantly higher rates than their urban counterparts. Thus, a good understanding of the spatial patterns in drug overdose, mental illness, suicide, and their associated risk factors is necessary for making effective preventive measures. This project aims to explore the spatial patterns in county-level mortality rates of drug use, mental illness, and suicide, and their associations with risk factors in Georgia using GIS (Geographic Information System) and statistical analyses. GIS analysis is used to map and compare the spatial patterns, especially urban-rural disparities, in the three mortality rates and risk factors, including median income, social isolation, access to mental healthcare, and ratio of School Social Workers (SSWs) to students. GIS-based hot spot analysis is used to identify the spatial clusters of the three mortality rates and risk factors. Statistical analyses, especially correlation analysis, are used to quantify and compare the associations among them. This study will provide useful information for public health policy making.