Cargando…
Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis
BACKGROUND: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural–urban community. METHODS: We conducted a population-based cohort study of children (<...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7681126/ https://www.ncbi.nlm.nih.gov/pubmed/33244434 http://dx.doi.org/10.1017/cts.2020.34 |
_version_ | 1783612573222961152 |
---|---|
author | Patel, Archna A. Wheeler, Philip H. Wi, Chung-Il Derauf, Chris Ryu, Euijung Zahrieh, David Bjur, Kara A. Juhn, Young J. |
author_facet | Patel, Archna A. Wheeler, Philip H. Wi, Chung-Il Derauf, Chris Ryu, Euijung Zahrieh, David Bjur, Kara A. Juhn, Young J. |
author_sort | Patel, Archna A. |
collection | PubMed |
description | BACKGROUND: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural–urban community. METHODS: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics ([Image: see text]; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES). RESULTS: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22–2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes. CONCLUSIONS: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural–urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research. |
format | Online Article Text |
id | pubmed-7681126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76811262020-11-25 Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis Patel, Archna A. Wheeler, Philip H. Wi, Chung-Il Derauf, Chris Ryu, Euijung Zahrieh, David Bjur, Kara A. Juhn, Young J. J Clin Transl Sci Research Article BACKGROUND: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural–urban community. METHODS: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics ([Image: see text]; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES). RESULTS: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22–2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes. CONCLUSIONS: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural–urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research. Cambridge University Press 2020-04-06 /pmc/articles/PMC7681126/ /pubmed/33244434 http://dx.doi.org/10.1017/cts.2020.34 Text en © The Association for Clinical and Translational Science 2020 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Patel, Archna A. Wheeler, Philip H. Wi, Chung-Il Derauf, Chris Ryu, Euijung Zahrieh, David Bjur, Kara A. Juhn, Young J. Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title | Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title_full | Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title_fullStr | Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title_full_unstemmed | Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title_short | Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis |
title_sort | mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: a case for geospatial analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7681126/ https://www.ncbi.nlm.nih.gov/pubmed/33244434 http://dx.doi.org/10.1017/cts.2020.34 |
work_keys_str_mv | AT patelarchnaa mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT wheelerphiliph mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT wichungil mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT deraufchris mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT ryueuijung mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT zahriehdavid mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT bjurkaraa mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis AT juhnyoungj mobilehomeresidenceasariskfactorforadverseeventsamongchildreninamixedruralurbancommunityacaseforgeospatialanalysis |