| Title |
Validation of Veterans Affairs Electronic Medical Record Smoking Data Among Iraq- and Afghanistan-Era Veterans
|
|---|---|
| Published in |
Journal of General Internal Medicine, August 2017
|
| DOI | 10.1007/s11606-017-4144-5 |
| Pubmed ID | |
| Authors |
Patrick S. Calhoun, Sarah M. Wilson, Jeffrey S. Hertzberg, Angela C. Kirby, Scott D. McDonald, Paul A. Dennis, Lori A. Bastian, Eric A. Dedert, The VA Mid-Atlantic MIRECC Workgroup, Jean C. Beckham |
| Abstract |
Research using the Veterans Health Administration (VA) electronic medical records (EMR) has been limited by a lack of reliable smoking data. To evaluate the validity of using VA EMR "Health Factors" data to determine smoking status among veterans with recent military service. Sensitivity, specificity, area under the receiver-operating curve (AUC), and kappa statistics were used to evaluate concordance between VA EMR smoking status and criterion smoking status. Veterans (N = 2025) with service during the wars in Iraq/Afghanistan who participated in the VA Mid-Atlantic Post-Deployment Mental Health (PDMH) Study. Criterion smoking status was based on self-report during a confidential study visit. VA EMR smoking status was measured by coding health factors data entries (populated during automated clinical reminders) in three ways: based on the most common health factor, the most recent health factor, and the health factor within 12 months of the criterion smoking status data collection date. Concordance with PDMH smoking status (current, former, never) was highest when determined by the most commonly observed VA EMR health factor (κ = 0.69) and was not significantly impacted by psychiatric status. Agreement was higher when smoking status was dichotomized: current vs. not current (κ = 0.73; sensitivity = 0.84; specificity = 0.91; AUC = 0.87); ever vs. never (κ = 0.75; sensitivity = 0.85; specificity = 0.90; AUC = 0.87). There were substantial missing Health Factors data when restricting analyses to a 12-month period from the criterion smoking status date. Current smokers had significantly more Health Factors entries compared to never or former smokers. The use of computerized tobacco screening data to determine smoking status is valid and feasible. Results indicating that smokers have significantly more health factors entries than non-smokers suggest that caution is warranted when using the EMR to select cases for cohort studies as the risk for selection bias appears high. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 18% |
| Student > Ph. D. Student | 4 | 10% |
| Other | 3 | 8% |
| Student > Master | 3 | 8% |
| Student > Postgraduate | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 16 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 23% |
| Nursing and Health Professions | 4 | 10% |
| Psychology | 2 | 5% |
| Computer Science | 1 | 3% |
| Economics, Econometrics and Finance | 1 | 3% |
| Other | 4 | 10% |
| Unknown | 19 | 48% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 August 2017.
All research outputs
#15,057,216
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#5,588
of 7,806 outputs
Outputs of similar age
#180,078
of 320,391 outputs
Outputs of similar age from Journal of General Internal Medicine
#53
of 76 outputs
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