Cargando…
Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania
OBJECTIVES: To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions. DESIGN: Nested case–control study within the open dynamic cohort of he...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812110/ https://www.ncbi.nlm.nih.gov/pubmed/33441365 http://dx.doi.org/10.1136/bmjopen-2020-043528 |
_version_ | 1783637599931334656 |
---|---|
author | Schwartz, B S Pollak, Jonathan Poulsen, Melissa N Bandeen-Roche, Karen Moon, Katherine DeWalle, Joseph Siegel, Karen Mercado, Carla Imperatore, Giuseppina Hirsch, Annemarie G |
author_facet | Schwartz, B S Pollak, Jonathan Poulsen, Melissa N Bandeen-Roche, Karen Moon, Katherine DeWalle, Joseph Siegel, Karen Mercado, Carla Imperatore, Giuseppina Hirsch, Annemarie G |
author_sort | Schwartz, B S |
collection | PubMed |
description | OBJECTIVES: To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions. DESIGN: Nested case–control study within the open dynamic cohort of health system patients. SETTING: Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA. PARTICIPANTS AND ANALYSIS: We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types. RESULTS: Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds. CONCLUSIONS: Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes. |
format | Online Article Text |
id | pubmed-7812110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-78121102021-01-25 Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania Schwartz, B S Pollak, Jonathan Poulsen, Melissa N Bandeen-Roche, Karen Moon, Katherine DeWalle, Joseph Siegel, Karen Mercado, Carla Imperatore, Giuseppina Hirsch, Annemarie G BMJ Open Diabetes and Endocrinology OBJECTIVES: To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions. DESIGN: Nested case–control study within the open dynamic cohort of health system patients. SETTING: Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA. PARTICIPANTS AND ANALYSIS: We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types. RESULTS: Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds. CONCLUSIONS: Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes. BMJ Publishing Group 2021-01-13 /pmc/articles/PMC7812110/ /pubmed/33441365 http://dx.doi.org/10.1136/bmjopen-2020-043528 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Diabetes and Endocrinology Schwartz, B S Pollak, Jonathan Poulsen, Melissa N Bandeen-Roche, Karen Moon, Katherine DeWalle, Joseph Siegel, Karen Mercado, Carla Imperatore, Giuseppina Hirsch, Annemarie G Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title | Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title_full | Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title_fullStr | Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title_full_unstemmed | Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title_short | Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania |
title_sort | association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in pennsylvania |
topic | Diabetes and Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812110/ https://www.ncbi.nlm.nih.gov/pubmed/33441365 http://dx.doi.org/10.1136/bmjopen-2020-043528 |
work_keys_str_mv | AT schwartzbs associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT pollakjonathan associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT poulsenmelissan associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT bandeenrochekaren associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT moonkatherine associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT dewallejoseph associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT siegelkaren associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT mercadocarla associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT imperatoregiuseppina associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania AT hirschannemarieg associationofcommunitytypesandfeaturesinacasecontrolanalysisofnewonsettype2diabetesacrossadiversegeographyinpennsylvania |