ArticleJournal of public health management and practice : JPHMP
Monitoring Depression Rates in an Urban Community: Use of Electronic Health Records.
Article in Journal of public health management and practice : JPHMP. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.JMIR medical informatics · 2024Pooled it
- Help-Seeking Intentions for Depression from Mental Health Professionals Among Community-Dwelling Persons in Central China.Administration and policy in mental health · 2023Article
- Fatigue, Depression and Health-Related Quality of Life in Patients with Post-Myocardial Infarction during the COVID-19 Pandemic: Results from the Augsburg Myocardial Infarction Registry.Journal of clinical medicine · 2023Article
- Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review.Cureus · 2023Review
- Using Calibration to Reduce Measurement Error in Prevalence Estimates Based on Electronic Health Records.Preventing chronic disease · 2018Article
- Geospatial Monitoring of Body Mass Index: Use of Electronic Health Record Data Across Health Care Systems.Public health reports (Washington, D.C. : 1974)Article
- Screening for Depression, Anxiety, and Suicidality: Insights From Electronic Health Record Data in an Academic Medical Center.Journal of primary care & community healthArticle
- Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.Journal of public health management and practice : JPHMPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDepression is the most common mental health disorder and mediates outcomes for many chronic diseases. Ability to accurately identify and monitor this condition, at the local level, is often limited to estimates from national surveys. This study sought to compare and validate electronic health record (EHR)-based depression surveillance with multiple data sources for more granular demographic subgroup and subcounty measurements. DESIGN/
settingA survey compared data sources for the ability to provide subcounty (eg, census tract [CT]) depression prevalence estimates. Using 2011-2012 EHR data from 2 large health care providers, and American Community Survey data, depression rates were estimated by CT for Denver County, Colorado. Sociodemographic and geographic (residence) attributes were analyzed and described. Spatial analysis assessed for clusters of higher or lower depression prevalence. MAIN OUTCOME MEASURE(S): Depression prevalence estimates by CT.
resultsNational and local survey-based depression prevalence estimates ranged from 7% to 17% but were limited to county level. Electronic health record data provided subcounty depression prevalence estimates by sociodemographic and geographic groups (CT range: 5%-20%). Overall depression prevalence was 13%; rates were higher for women (16% vs men 9%), whites (16%), and increased with age and homeless patients (18%). Areas of higher and lower EHR-based, depression prevalence were identified.
conclusionsElectronic health record-based depression prevalence varied by CT, gender, race/ethnicity, age, and living status. Electronic health record-based surveillance complements traditional methods with greater timeliness and granularity. Validation through subcounty-level qualitative or survey approaches should assess accuracy and address concerns about EHR selection bias. Public health agencies should consider the opportunity and evaluate EHR system data as a surveillance tool to estimate subcounty chronic disease prevalence.
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