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Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation
BACKGROUND: Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364501/ https://www.ncbi.nlm.nih.gov/pubmed/35945537 http://dx.doi.org/10.1186/s12889-022-13809-2 |
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author | Chen, Tom Li, Wenjun Zambarano, Bob Klompas, Michael |
author_facet | Chen, Tom Li, Wenjun Zambarano, Bob Klompas, Michael |
author_sort | Chen, Tom |
collection | PubMed |
description | BACKGROUND: Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy and precision may not carry over for smaller areas that are sparsely represented by non-random samples. METHODS: We developed small-area estimation models using a combination of EHR data drawn from MDPHnet, an EHR-based public health surveillance network in Massachusetts, the American Community Survey, and state hospitalization data. We estimated municipality-specific prevalence rates of asthma, diabetes, hypertension, obesity, and smoking in each of the 351 municipalities in Massachusetts in 2016. Models were compared against Behavioral Risk Factor Surveillance System (BRFSS) state and small area estimates for 2016. RESULTS: Integrating progressively more variables into prediction models generally reduced mean absolute error (MAE) relative to municipality-level BRFSS small area estimates: asthma (2.24% MAE crude, 1.02% MAE modeled), diabetes (3.13% MAE crude, 3.48% MAE modeled), hypertension (2.60% MAE crude, 1.48% MAE modeled), obesity (4.92% MAE crude, 4.07% MAE modeled), and smoking (5.33% MAE crude, 2.99% MAE modeled). Correlation between modeled estimates and BRFSS estimates for the 13 municipalities in Massachusetts covered by BRFSS’s 500 Cities ranged from 81.9% (obesity) to 96.7% (diabetes). CONCLUSIONS: Small-area estimation using EHR data is feasible and generates estimates comparable to BRFSS state and small-area estimates. Integrating EHR data with survey data can provide timely and accurate disease monitoring tools for areas with sparse data coverage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13809-2. |
format | Online Article Text |
id | pubmed-9364501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93645012022-08-11 Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation Chen, Tom Li, Wenjun Zambarano, Bob Klompas, Michael BMC Public Health Research BACKGROUND: Electronic Health Record (EHR) data are increasingly being used to monitor population health on account of their timeliness, granularity, and large sample sizes. While EHR data are often sufficient to estimate disease prevalence and trends for large geographic areas, the same accuracy and precision may not carry over for smaller areas that are sparsely represented by non-random samples. METHODS: We developed small-area estimation models using a combination of EHR data drawn from MDPHnet, an EHR-based public health surveillance network in Massachusetts, the American Community Survey, and state hospitalization data. We estimated municipality-specific prevalence rates of asthma, diabetes, hypertension, obesity, and smoking in each of the 351 municipalities in Massachusetts in 2016. Models were compared against Behavioral Risk Factor Surveillance System (BRFSS) state and small area estimates for 2016. RESULTS: Integrating progressively more variables into prediction models generally reduced mean absolute error (MAE) relative to municipality-level BRFSS small area estimates: asthma (2.24% MAE crude, 1.02% MAE modeled), diabetes (3.13% MAE crude, 3.48% MAE modeled), hypertension (2.60% MAE crude, 1.48% MAE modeled), obesity (4.92% MAE crude, 4.07% MAE modeled), and smoking (5.33% MAE crude, 2.99% MAE modeled). Correlation between modeled estimates and BRFSS estimates for the 13 municipalities in Massachusetts covered by BRFSS’s 500 Cities ranged from 81.9% (obesity) to 96.7% (diabetes). CONCLUSIONS: Small-area estimation using EHR data is feasible and generates estimates comparable to BRFSS state and small-area estimates. Integrating EHR data with survey data can provide timely and accurate disease monitoring tools for areas with sparse data coverage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13809-2. BioMed Central 2022-08-09 /pmc/articles/PMC9364501/ /pubmed/35945537 http://dx.doi.org/10.1186/s12889-022-13809-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Tom Li, Wenjun Zambarano, Bob Klompas, Michael Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title | Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title_full | Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title_fullStr | Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title_full_unstemmed | Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title_short | Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
title_sort | small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364501/ https://www.ncbi.nlm.nih.gov/pubmed/35945537 http://dx.doi.org/10.1186/s12889-022-13809-2 |
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