Author ORCID Identifier

0000-0002-3696-283X

Date of Award

Summer 8-9-2022

Degree Type

Thesis

Degree Name

Master of Public Health (MPH)

First Advisor

Dr. Christine Stauber

Second Advisor

Dr. Ashli Owen-Smith

Abstract

Introduction: Suicide rates in the United States continue to increase and are a significant public health concern. Suicide is the 10th leading cause of death in Georgia and is largely preventable. Georgia (GA) ranks low for mental health care access which influences mental health outcomes such as suicide. In addition, understanding data on social determinants of health may have implications for the role that they may play in suicide and could help us to understand ways to improve health equity and poor mental health outcomes.

Aim: The purpose of this study was to examine trends in suicide deaths in GA counties and assess the association between age-adjusted suicide rates and social determinants across urban and rural counties of GA over a 20-year period.

Methods: CDC WONDER was used to request age-adjusted suicide rates from 2000 to 2019 among Georgia residents at the county level. Suicide death data was derived from death certificates using ICD-10 underlying cause-of-death codes U03, X60-X84, and Y87.0. A literature review was conducted to determine social determinants associated with suicidal behavior. The US Census Bureau database was used to query indicator data of the ten social determinant factors chosen from the literature review. Linear regression analyses were conducted to evaluate the association between selected indicators and age-adjusted suicide rates at the county level over two periods, 2000-2009 and 2010-2019.

Results: A total of 159 counties in Georgia were analyzed in this study. There are 74 urban counties and 85 rural counties. In 2000, bivariate results indicate that race, ethnicity, education, employment, income, and urbanization were associated with age-adjusted suicide rates. The bivariate analysis results of 2010 data show that race, ethnicity, education, and poverty were associated with age-adjusted suicide rates. In the multivariate analyses, none of the variables were statically significant associated with age-adjusted suicide rates. There was multicollinearity observed among the independent variables.

Discussion: While bivariate associations between variables and age-adjusted suicide rate were identified, none remained statistically significant in a multivariate linear regression model. This change may be attributed to multicollinearity, making it challenging to estimate regression coefficients reliably. Percent population White, percent population non-Hispanic, percent population employed, and urbanization are factors that were statistically significant and positively correlated with increased age-adjusted suicide rates in the 2000 & 2010 bivariate analyses and should still be considered as possible factors that influence suicide rates.

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