Identifying spatial and temporal suicide clusters in a Californian county
Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and tem...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-01-01
|
| Series: | Experimental Results |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2516712X23000023/type/journal_article |
| _version_ | 1797859450339459072 |
|---|---|
| author | Anders K. Waalen Seraphim Telep Rimal Bera Emanuele Frontoni |
| author_facet | Anders K. Waalen Seraphim Telep Rimal Bera Emanuele Frontoni |
| author_sort | Anders K. Waalen |
| collection | DOAJ |
| description | Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and temporal clusters, respectively, we defined meaningful thresholds of suicide density as 1.39/km2 and 33.9/yearly quarter, defined as the 95th percentile of normal logarithmic and normal scale distributions of suicide density per area in each ZIP Code Tabulated Area and 24 yearly quarters from all years. We generated heat maps showing suicide densities per 2.5 km viewing diameter. We generated a one-dimensional temporal map of 3-month meaningful cluster(s). We identified 21 total population spatial clusters and one temporal cluster. For greater accessibility, we propose an alternative method to traditional scan statistics using Excel 3D Maps potentially broadly advantageous in detecting, monitoring, and intervening at suicide clusters. |
| first_indexed | 2024-04-09T21:29:40Z |
| format | Article |
| id | doaj.art-d62701f3b092413093551eaad7e9538f |
| institution | Directory Open Access Journal |
| issn | 2516-712X |
| language | English |
| last_indexed | 2024-04-09T21:29:40Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Experimental Results |
| spelling | doaj.art-d62701f3b092413093551eaad7e9538f2023-03-27T10:46:28ZengCambridge University PressExperimental Results2516-712X2023-01-01410.1017/exp.2023.2Identifying spatial and temporal suicide clusters in a Californian countyAnders K. Waalen0https://orcid.org/0000-0002-7955-2806Seraphim Telep1Rimal Bera2Emanuele Frontoni3Department of Psychiatry, UC Irvine School of Medicine, Irvine, California, USA Zucker School of Medicine, Hofstra University, Port Jefferson, New York, USADepartment of Psychiatry, UC Irvine School of Medicine, Irvine, California, USADepartment of Psychiatry, UC Irvine School of Medicine, Irvine, California, USAUniversity of Macerata, Information Engineerging Department - DII, Macerata, Italy, 62100Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and temporal clusters, respectively, we defined meaningful thresholds of suicide density as 1.39/km2 and 33.9/yearly quarter, defined as the 95th percentile of normal logarithmic and normal scale distributions of suicide density per area in each ZIP Code Tabulated Area and 24 yearly quarters from all years. We generated heat maps showing suicide densities per 2.5 km viewing diameter. We generated a one-dimensional temporal map of 3-month meaningful cluster(s). We identified 21 total population spatial clusters and one temporal cluster. For greater accessibility, we propose an alternative method to traditional scan statistics using Excel 3D Maps potentially broadly advantageous in detecting, monitoring, and intervening at suicide clusters.https://www.cambridge.org/core/product/identifier/S2516712X23000023/type/journal_articleclusterepidemiologyhotspotmonitorsuicide |
| spellingShingle | Anders K. Waalen Seraphim Telep Rimal Bera Emanuele Frontoni Identifying spatial and temporal suicide clusters in a Californian county Experimental Results cluster epidemiology hotspot monitor suicide |
| title | Identifying spatial and temporal suicide clusters in a Californian county |
| title_full | Identifying spatial and temporal suicide clusters in a Californian county |
| title_fullStr | Identifying spatial and temporal suicide clusters in a Californian county |
| title_full_unstemmed | Identifying spatial and temporal suicide clusters in a Californian county |
| title_short | Identifying spatial and temporal suicide clusters in a Californian county |
| title_sort | identifying spatial and temporal suicide clusters in a californian county |
| topic | cluster epidemiology hotspot monitor suicide |
| url | https://www.cambridge.org/core/product/identifier/S2516712X23000023/type/journal_article |
| work_keys_str_mv | AT anderskwaalen identifyingspatialandtemporalsuicideclustersinacaliforniancounty AT seraphimtelep identifyingspatialandtemporalsuicideclustersinacaliforniancounty AT rimalbera identifyingspatialandtemporalsuicideclustersinacaliforniancounty AT emanuelefrontoni identifyingspatialandtemporalsuicideclustersinacaliforniancounty |