Asal M Johnson1, Joseph M Woodside2, Allen Johnson3, Jacqueline M Pollack4. 1. Department of Integrative Health Science, Stetson University, DeLand, Florida. Electronic address: asal.johnson@stetson.edu. 2. Department of Decision and Information Sciences, Stetson University, DeLand, Florida. 3. Department of Health Professions, Rollins College, Winter Park, Florida. 4. Department of Integrative Health Science, Stetson University, DeLand, Florida.
Abstract
INTRODUCTION: Although spatial examination of mortality and morbidity is becoming more common in health studies, the investigation of suicide death clusters within the neighborhood context is underutilized. The purpose of this ecological study is to detect high- and low-risk clusters of suicide deaths in Florida and determine which neighborhood characteristics distinguish clusters from non-clusters. METHODS: The scan statistic method was used to detect overall clusters of completed suicides in Florida from 2001 to 2010. Regression analysis was used to investigate the association of neighborhood characteristics with identified clusters. All data synthesis and statistical analyses were conducted in 2015. RESULTS: Twenty-four high-risk and 25 low-risk clusters were identified. The risk of suicide was up to 3.4 times higher in high-risk clusters than in areas outside of clusters (relative risk ranged from 1.36 to 3.44, p≤0.05). Low-risk clusters were associated with 30%-94% decreased risk of suicide (relative risk ranged from 0.06 to 0.70, p≤0.05). Areas with high levels of elderly concentration and household singularity were more likely to be in high-risk clusters, whereas areas with higher economic deprivation and residential density were more likely to be in low-risk clusters. CONCLUSIONS: This study identified general suicide patterns across space in the state of Florida and described the characteristics of those areas.
INTRODUCTION: Although spatial examination of mortality and morbidity is becoming more common in health studies, the investigation of suicide death clusters within the neighborhood context is underutilized. The purpose of this ecological study is to detect high- and low-risk clusters of suicide deaths in Florida and determine which neighborhood characteristics distinguish clusters from non-clusters. METHODS: The scan statistic method was used to detect overall clusters of completed suicides in Florida from 2001 to 2010. Regression analysis was used to investigate the association of neighborhood characteristics with identified clusters. All data synthesis and statistical analyses were conducted in 2015. RESULTS: Twenty-four high-risk and 25 low-risk clusters were identified. The risk of suicide was up to 3.4 times higher in high-risk clusters than in areas outside of clusters (relative risk ranged from 1.36 to 3.44, p≤0.05). Low-risk clusters were associated with 30%-94% decreased risk of suicide (relative risk ranged from 0.06 to 0.70, p≤0.05). Areas with high levels of elderly concentration and household singularity were more likely to be in high-risk clusters, whereas areas with higher economic deprivation and residential density were more likely to be in low-risk clusters. CONCLUSIONS: This study identified general suicide patterns across space in the state of Florida and described the characteristics of those areas.
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