ArticlePreventing chronic disease2018
Using Calibration to Reduce Measurement Error in Prevalence Estimates Based on Electronic Health Records.
Article in Preventing chronic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Chronic Disease Monitoring: Methodology for Classification Error and Self-Selection Bias Correction in Clinical Laboratory Data.Healthcare (Basel, Switzerland) · 2025Article
- Weighted EHR-based prevalence estimates for hypertension at the state and local levels in Louisiana.BMC public health · 2025Article
- Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.BMC medical research methodology · 2020Article
- Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis.JMIR medical informatics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIncreasing adoption of electronic health record (EHR) systems by health care providers presents an opportunity for EHR-based population health surveillance. EHR data, however, may be subject to measurement error because of factors such as data entry errors and lack of documentation by physicians. We investigated the use of a calibration model to reduce bias of prevalence estimates from the New York City (NYC) Macroscope, an EHR-based surveillance system.
methodsWe calibrated 6 health indicators to the 2013-2014 NYC Health and Nutrition Examination Survey (NYC HANES) data: hypertension, diabetes, smoking, obesity, influenza vaccination, and depression. We classified indicators into having low measurement error or high measurement error on the basis of whether the proportion of misclassification (ie, false-negative or false-positive cases) was greater than 15% in 190 reviewed charts. We compared bias (ie, absolute difference between NYC Macroscope estimates and NYC HANES estimates) before and after calibration.
resultsThe health indicators with low measurement error had the same bias after calibration as before calibration (diabetes, 2.5 percentage points; smoking, 2.5 percentage points; obesity, 3.5 percentage points; hypertension, 1.1 percentage points). For indicators with high measurement error, bias decreased from 10.8 to 2.5 percentage points for depression, and from 26.7 to 8.4 percentage points for influenza vaccination.
conclusionThe calibration model has the potential to reduce bias of prevalence estimates from EHR-based surveillance systems for indicators with high measurement errors. Further research is warranted to assess the utility of the current calibration model for other EHR data and additional indicators.
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