Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making

Haomiao Jin; Shinyi Wu; Paul Di Capua

doi:10.5888/pcd12.150047

journal article Sep 03, 2015

Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making

Haomiao Jin Shinyi Wu Paul Di Capua

Preventing Chronic Disease Vol. 12 · Centers for Disease Control and Prevention - Preventing Chronic Disease

View at Publisher Save 10.5888/pcd12.150047

Topics

No keywords indexed for this article. Browse by subject →

References

29

[1]

Brown "Depression stigma, race, and treatment seeking behavior and attitudes." J Community Psychol (2010) 10.1002/jcop.20368

[2]

Li "Prevalence and correlates of undiagnosed depression among US adults with diabetes: the Behavioral Risk Factor Surveillance System, 2006." Diabetes Res Clin Pract (2009) 10.1016/j.diabres.2008.11.006

[3]

The Mental Health Comorbidities of Diabetes

Lee Ducat, Louis H. Philipson, Barbara J. Anderson

JAMA 2014 10.1001/jama.2014.8040

[4]

Roy "Epidemiology of depression and diabetes: a systematic review." J Affect Disord (2012) 10.1016/s0165-0327(12)70004-6

[5]

Ell "Collaborative care management of major depression among low-income, predominantly Hispanic subjects with diabetes: a randomized controlled trial." Diabetes Care (2010) 10.2337/dc09-1711

[6]

Kroenke "Discovering depression in medical patients: reasonable expectations." Ann Intern Med (1997) 10.7326/0003-4819-126-6-199703150-00008

[7]

Taylor "Threats to the health care safety net." Acad Emerg Med (2001) 10.1111/j.1553-2712.2001.tb01119.x

[8]

Dobson KS. Risk factors in depression. Waltham (MA): Acadmic Press; 2011..

[9]

Murphy KP. Machine learning: a probabilistic perspective. Boston (MA): MIT Press; 2012..

[10]

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

Leo Breiman

Statistical Science 2001 10.1214/ss/1009213726

[11]

Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure

Peter C. Austin, Jack V. Tu, Douglas S. Lee

Journal of Clinical Epidemiology 2010 10.1016/j.jclinepi.2009.12.004

[12]

Rose "Mortality risk score prediction in an elderly population using machine learning." Am J Epidemiol (2013) 10.1093/aje/kws241

[13]

King "Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: the PredictD study." Arch Gen Psychiatry (2008) 10.1001/archpsyc.65.12.1368

[14]

Wang "A prediction algorithm for first onset of major depression in the general population: development and validation." J Epidemiol Community Health (2014) 10.1136/jech-2013-202845

[15]

Huang "Toward personalizing treatment for depression: predicting diagnosis and severity." J Am Med Inform Assoc (2014) 10.1136/amiajnl-2014-002733

[16]

The PHQ-9

Kurt Kroenke, Robert L. Spitzer, Janet B. W. Williams

Journal of General Internal Medicine 2001 10.1046/j.1525-1497.2001.016009606.x

[17]

The Patient Health Questionnaire-2

Kurt Kroenke, Robert L. Spitzer, Janet B. W. Williams

Medical Care 2003 10.1097/01.mlr.0000093487.78664.3c

[18]

Wu "Collaborative depression care among Latino patients in diabetes disease management, Los Angeles, 2011-2013." Prev Chronic Dis (2014) 10.5888/pcd11.140081

[19]

Wu "Technology-facilitated depression care management among predominantly Latino diabetes patients within a public safety net care system: comparative effectiveness trial design." Contemp Clin Trials (2014) 10.1016/j.cct.2013.11.002

[20]

Jin H, Wu S. Developing depression symptoms prediction models to improve depression care outcomes: preliminary results. Proceedings of the 2nd International Conference on Big Data and Analytics in Healthcare; 2014 Jun 22–24; Singapore.

[21]

Guyon "An introduction to variable and feature selection." J Mach Learn Res (2003)

[22]

Ridge Estimators in Logistic Regression

S. Le Cessie, J. C. Van Houwelingen

Journal of the Royal Statistical Society Series C:... 1992 10.2307/2347628

[23]

Kennedy "Remission and recurrence of depression in the maintenance era: long-term outcome in a Cambridge cohort." Psychol Med (2003) 10.1017/s003329170300744x

[24]

The summary of diabetes self-care activities measure: results from 7 studies and a revised scale.

D J Toobert, S E Hampson, R E Glasgow

Diabetes Care 2000 10.2337/diacare.23.7.943

[25]

McEwen "Are health-related quality-of-life and self-rated health associated with mortality? Insights from Translating Research Into Action for Diabetes (TRIAD)." Prim Care Diabetes (2009) 10.1016/j.pcd.2009.01.001

[26]

Patient Centered Medical Home. Patient Centered Medical Home 2014 standards. Washington (DC): National Committee for Quality Assurance; 2014.

[27]

Riolo "Prevalence of depression by race/ethnicity: findings from the National Health and Nutrition Examination Survey III." Am J Public Health (2005) 10.2105/ajph.2004.047225

[28]

Adler "Patients in context — EHR capture of social and behavioral determinants of health." N Engl J Med (2015) 10.1056/nejmp1413945

[29]

Precision medicine initiatives. Washington (DC): National Institutes of Health. http://www.nih.gov/precisionmedicine/. Accessed April 28, 2015.

Metrics

45

Citations

29

References

Details

Published: Sep 03, 2015
Vol/Issue: 12

Authors

From the Southern California Evidence Based Practice Center, which includes RAND, Santa Monica, California; and University of California, Los Angeles, Cedars-Sinai Medical Center, and the Greater Los Angeles Veterans Affairs System, Los Angeles, California.

P

Paul Di Capua

Cite This Article

Haomiao Jin, Shinyi Wu, Paul Di Capua (2015). Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making. Preventing Chronic Disease, 12. https://doi.org/10.5888/pcd12.150047

Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making

You May Also Like