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Identifying priority and bright spot areas for improving diabetes care: a geospatial approach
The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522662/ https://www.ncbi.nlm.nih.gov/pubmed/34649983 http://dx.doi.org/10.1136/fmch-2021-001259 |
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author | Topmiller, Michael Mallow, Peter J Shaak, Kyle Kieber-Emmons, Autumn M |
author_facet | Topmiller, Michael Mallow, Peter J Shaak, Kyle Kieber-Emmons, Autumn M |
author_sort | Topmiller, Michael |
collection | PubMed |
description | The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran’s I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community. |
format | Online Article Text |
id | pubmed-8522662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-85226622021-11-02 Identifying priority and bright spot areas for improving diabetes care: a geospatial approach Topmiller, Michael Mallow, Peter J Shaak, Kyle Kieber-Emmons, Autumn M Fam Med Community Health Methodology and Research Methods The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran’s I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community. BMJ Publishing Group 2021-10-14 /pmc/articles/PMC8522662/ /pubmed/34649983 http://dx.doi.org/10.1136/fmch-2021-001259 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. https://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/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Methodology and Research Methods Topmiller, Michael Mallow, Peter J Shaak, Kyle Kieber-Emmons, Autumn M Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title | Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title_full | Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title_fullStr | Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title_full_unstemmed | Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title_short | Identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
title_sort | identifying priority and bright spot areas for improving diabetes care: a geospatial approach |
topic | Methodology and Research Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522662/ https://www.ncbi.nlm.nih.gov/pubmed/34649983 http://dx.doi.org/10.1136/fmch-2021-001259 |
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