Literature DB >> 30580123

Spatial patterning, correlates, and inequality in suicide across 432 neighborhoods in Taipei City, Taiwan.

Chien-Yu Lin1, Chia-Yueh Hsu2, David Gunnell3, Ying-Yeh Chen4, Shu-Sen Chang5.   

Abstract

More than half of the world's population now lives in urban areas. Understanding the spatial distribution of suicide in these settings may inform prevention. Previous analyses of the spatial distribution of suicide in cities have largely been conducted in Western nations. We investigated the spatial pattern of suicide and factors associated with its spatial distribution in Taipei City, Taiwan. We estimated smoothed standardized mortality ratios for overall suicide and suicide by sex/age group across 432 neighborhoods (mean population size: 5500) in Taipei City (2004-2010) using Bayesian hierarchical models. A range of area-level characteristics including socioeconomic deprivation, social fragmentation, income inequality, and linking social capital were investigated for their associations with suicide mortality. Overall suicide rates were below average in the city center, whereas above average rates were found in some suburbs. The cartogram highlighted the concentration of suicide burden in one western area of the city. Male suicides demonstrated generally similar spatial patterning across age groups, while the geographic distribution of female suicides differed by age. After adjusting for other variables, two area characteristics were found to be associated with area suicide rates: the proportion of divorced/separated adults (rate ratio [RR] per one standard deviation increase = 1.08, 95% confidence interval 1.01-1.16), an indicator of social fragmentation; and median household income (RR = 0.80, 0.73-0.86), an indicator of socioeconomic deprivation. There was a 1.8-fold difference in suicide rates between neighborhood quintiles with the lowest and the highest median household income, with middle-aged males showing the largest gradient (3.2-fold difference). The geography of suicide in Taipei City showed spatial patterning and socioeconomic correlates distinct from cities in Western nations. There is a need for future research to better understand the correlates of change in the geographic distribution of suicide throughout the process of urban development.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Inequalities; Socioeconomic characteristics; Spatial analysis; Suicide

Mesh:

Year:  2018        PMID: 30580123     DOI: 10.1016/j.socscimed.2018.12.011

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  5 in total

1.  Suicide Rates and Differences in Rates Between Non-Hispanic Black and Non-Hispanic White Populations in the 30 Largest US Cities, 2008-2017.

Authors:  Daniel J Schober; Maureen R Benjamins; Nazia S Saiyed; Abigail Silva; Susana Shrestha
Journal:  Public Health Rep       Date:  2021-09-03       Impact factor: 3.117

2.  Method-Specific Suicide Rates and Accessibility of Means.

Authors:  Chien-Yu Lin; Chia-Yueh Hsu; Ying-Yeh Chen; Shu-Sen Chang; David Gunnell
Journal:  Crisis       Date:  2021-05-18

3.  Spatial patterning and correlates of self-harm in Manchester, England.

Authors:  Chien-Yu Lin; Harriet Bickley; Caroline Clements; Roger T Webb; David Gunnell; Chia-Yueh Hsu; Shu-Sen Chang; Nav Kapur
Journal:  Epidemiol Psychiatr Sci       Date:  2019-11-19       Impact factor: 6.892

4.  Exploring the epidemiology of suicide attempts: Risk modeling in Kermanshah-Iran.

Authors:  Nahid Khademi; Alireza Zangeneh; Arash Ziapour; Shahram Saeidi; Raziyeh Teimouri; Komali Yenneti; Shahrzad Moghadam; Ali Almasi; Shirin Zardui Golanbari
Journal:  Front Public Health       Date:  2022-08-23

5.  Associations between social fragmentation, socioeconomic deprivation and suicide risk across 1887 municipalities in Japan, 2009-2017: a spatial analysis using the Bayesian hierarchical model.

Authors:  Eiji Yoshioka; Sharon Hanley; Yukihiro Sato; Yasuaki Saijo
Journal:  BMJ Open       Date:  2022-08-30       Impact factor: 3.006

  5 in total

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