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PREDICTING DEPRESSION IN A CHRONIC DISEASE POPULATION
USING AUTOMATED DATA
by
Jane M. Nichol
________________________________________________________________________
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)
May 2008
Copyright 2008 Jane M. Nichol
Object Description
| Title | Predicting depression in a chronic disease population using automated data |
| Author | Nichol, Jane M. |
| Author email | margolie@usc.edu |
| Degree | Master of Science |
| Document type | Thesis |
| Degree program | Applied Biostatistics & Epidemiology |
| School | Keck School of Medicine |
| Date defended/completed | 2008-03-08 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-04-15 |
| Advisor (committee chair) | Conti, David |
| Advisor (committee member) |
Azen, Stanley Chou, Chih-Ping |
| Abstract | Background and Objective: Depression prevalence is elevated in chronic disease populations, and it can adversely affect health. This study develops a model to predict depression within a chronic disease population using administrative data.; Methods: The study population consisted of Medi-Cal beneficiaries with chronic heart or respiratory conditions or diabetes at baseline (1999). A split-sample approach was employed, with Cox proportional hazard regression used to predict depression from 2000-2002 from beneficiaries ' baseline characteristics. Variables were selected for inclusion based on published literature and pre-established thresholds. Model performance was assessed using discrimination and calibration.; Results: Depression occurred in 10.8% of beneficiaries. Age, gender, race, healthcare utilization, and specific medical conditions were significant predictors. The model had moderate discriminatory capability, and comparisons of observed versus expected depression cases across deciles of risk were not statistically significant.; Conclusions: This model may be useful in identifying individuals at risk of depression and focusing limited resources. |
| Keyword | depression; prediction model; Cox proportional hazard regression |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m1121 |
| Rights | Nichol, Jane M. |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Nichol-20080415 |
| Archival file | uscthesesreloadpub_Volume40/etd-Nichol-20080415.pdf |
Description
| Title | Page 1 |
| Full text | PREDICTING DEPRESSION IN A CHRONIC DISEASE POPULATION USING AUTOMATED DATA by Jane M. Nichol ________________________________________________________________________ A Thesis Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (APPLIED BIOSTATISTICS AND EPIDEMIOLOGY) May 2008 Copyright 2008 Jane M. Nichol |
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