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Tytuł:
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Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation.
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Autorzy:
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Chen T; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. .
Li W; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA.
Zambarano B; Commonwealth Informatics, Waltham, MA, USA.
Klompas M; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Źródło:
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BMC public health [BMC Public Health] 2022 Aug 09; Vol. 22 (1), pp. 1515. Date of Electronic Publication: 2022 Aug 09.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: London : BioMed Central, [2001-
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MeSH Terms:
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Asthma*/epidemiology
Diabetes Mellitus*/epidemiology
Hypertension*/epidemiology
Behavioral Risk Factor Surveillance System ; Electronic Health Records ; Humans ; Obesity ; Population Surveillance ; Prevalence ; Public Health Surveillance ; United States
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References:
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BMC Med Res Methodol. 2020 Apr 6;20(1):77. (PMID: 32252642)
EGEMS (Wash DC). 2016 Dec 15;4(1):1265. (PMID: 28154835)
Am J Epidemiol. 2014 Apr 15;179(8):1025-33. (PMID: 24598867)
Am J Public Health. 2009 Mar;99(3):511-9. (PMID: 19150906)
Am J Public Health. 2014 Dec;104(12):2265-70. (PMID: 25322301)
EGEMS (Wash DC). 2019 Jul 23;7(1):31. (PMID: 31367648)
J Dent Res. 2016 May;95(5):515-22. (PMID: 26848071)
Am J Public Health. 2017 Jun;107(6):853-857. (PMID: 28426302)
Am J Public Health. 2017 Sep;107(9):1406-1412. (PMID: 28727539)
Am J Public Health. 2009 Mar;99(3):470-9. (PMID: 19150913)
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Contributed Indexing:
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Keywords: Asthma; Behavioral risk factor surveillance system; Diabetes mellitus; Hypertension; Obesity; Population surveillance; Smoking
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Entry Date(s):
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Date Created: 20220809 Date Completed: 20220811 Latest Revision: 20220813
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Update Code:
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20240104
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PubMed Central ID:
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PMC9364501
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DOI:
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10.1186/s12889-022-13809-2
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PMID:
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35945537
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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.
(© 2022. The Author(s).)
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