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Search All Research Studies
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (1)
- Anxiety (1)
- Behavioral Health (3)
- Cardiovascular Conditions (1)
- Chronic Conditions (3)
- (-) Depression (6)
- (-) Electronic Health Records (EHRs) (6)
- Health Information Technology (HIT) (4)
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 6 of 6 Research Studies DisplayedColey RY, Boggs JM, Beck A
Predicting outcomes of psychotherapy for depression with electronic health record data.
This study evaluated models for predicting outcomes of psychotherapy for depression in a clinical practice setting. Findings showed that prediction models did not accurately predict depression treatment outcomes despite using rich electronic health record data and advanced analytic techniques. Recommendations included caution when considering prediction models for psychiatric outcomes using baseline intake information and transparent research to evaluate performance of any model intended for clinical use.
AHRQ-funded; HS026369.
Citation: Coley RY, Boggs JM, Beck A .
Predicting outcomes of psychotherapy for depression with electronic health record data.
J Affect Disord Rep 2021 Dec;6:100198. doi: 10.1016/j.jadr.2021.100198..
Keywords: Depression, Behavioral Health, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research, Outcomes
Bobo WV, Ryu E, Petterson TM
Bi-directional association between depression and HF: an electronic health records-based cohort study.
This study examined whether heart failure (HF) patients were more likely to be diagnosed with depression, or patients with depression were more likely to be diagnosed with HF. This retrospective cohort study utilized electronic health records (EHRs) from a large healthcare system in 2006 for adults who received primary care services. The EHR identified 10,649 people with depression, and 5,911 people with HF between 2006 to 2018. In the depression cohort there were 2,024 newly diagnosed occurrences of HF, and 944 occurrences of newly diagnosed depression in the HF cohort over 4-6 years of follow-up. There was a significantly higher risk of developing HF in the depression cohort than vice versa.
AHRQ-funded; HS023077.
Citation: Bobo WV, Ryu E, Petterson TM .
Bi-directional association between depression and HF: an electronic health records-based cohort study.
J Comorb 2020 Jan-Dec;10:2235042x20984059. doi: 10.1177/2235042x20984059..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Depression, Electronic Health Records (EHRs), Health Information Technology (HIT), Chronic Conditions
Blumenthal KG, Li Y, Acker WW
Multiple drug intolerance syndrome and multiple drug allergy syndrome: epidemiology and associations with anxiety and depression.
In this study, the authors used electronic health record (EHR) data to describe prevalences of MDIS and MDAS and to examine associations with anxiety and depression. The investigators concluded that: 1.) while 6% of patients had MDIS, only 1% had MDAS; 2.) MDIS was associated with both anxiety and depression; 3.) patients with both anxiety and depression had an almost twofold increased odds of MDIS; 4.) MDAS was associated with a 40% increased odds of depression, but there was no significant association with anxiety.
AHRQ-funded; HS022728.
Citation: Blumenthal KG, Li Y, Acker WW .
Multiple drug intolerance syndrome and multiple drug allergy syndrome: epidemiology and associations with anxiety and depression.
Allergy 2018 Oct;73(10):2012-23. doi: 10.1111/all.13440..
Keywords: Adverse Drug Events (ADE), Adverse Events, Anxiety, Depression, Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Behavioral Health, Patient Safety
Beck A, Davidson AJ, Xu S
A multilevel analysis of individual, health system, and neighborhood factors associated with depression within a large metropolitan area.
This study geocoded depression diagnosis and demographic data from electronic health records to obtain both individual and neighborhood factors related to depression. The researchers found higher depression rates associated with greater age, female gender, white race, medical comorbidities, and with lower rates of home owner occupancy, residential stability, and higher educational attainment, but not with economic disadvantage. Among the cohort, higher depression rates were associated with higher crime rates and a lower percent of foreign born residents and single mother households.
AHRQ-funded; HS022143.
Citation: Beck A, Davidson AJ, Xu S .
A multilevel analysis of individual, health system, and neighborhood factors associated with depression within a large metropolitan area.
J Urban Health 2017 Dec;94(6):780-90. doi: 10.1007/s11524-017-0190-x..
Keywords: Depression, Electronic Health Records (EHRs), Social Determinants of Health, Urban Health
Ryu E, Chamberlain AM, Pendergraft RS
Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records.
In this study, the researchers aimed to quantify the relative contribution of selected chronic conditions to identify the conditions most influential to major depressive disorder (MDD) risk in adults and identify differences by age. Their results suggest that specific chronic conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may have greater influence than others on the risk of MDD.
AHRQ-funded; HS023077.
Citation: Ryu E, Chamberlain AM, Pendergraft RS .
Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records.
BMC Psychiatry 2016 Apr 26;16:114. doi: 10.1186/s12888-016-0821-x.
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Keywords: Chronic Conditions, Depression, Electronic Health Records (EHRs), Primary Care
Yoon S, Taha B, Bakken S
Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression.
The purposes of this methodological paper are: 1) to describe data mining methods for building a classification model for a chronic disease using a U.S. behavior risk factor data set, and 2) to illustrate application of the methods using a case study of depressive disorder. Its application of data mining strategies identified childhood experience living with mentally ill and sexual abuse, and limited usual activity as the strongest correlates of depression among hundreds of variables.
AHRQ-funded; HS019853; HS022961.
Citation: Yoon S, Taha B, Bakken S .
Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression.
Stud Health Technol Inform 2014;201:71-8..
Keywords: Chronic Conditions, Behavioral Health, Depression, Health Information Technology (HIT), Electronic Health Records (EHRs)