@mastersthesis {6426,
	title = {Functional limitations and chronic conditions predict CES-D-8 depression scores},
	volume = {1542832},
	year = {2013},
	note = {Copyright - Copyright ProQuest, UMI Dissertations Publishing 2013 Last updated - 2013-08-30 First page - n/a},
	month = {2013},
	pages = {63},
	school = {California State University, Los Angeles},
	type = {M.A.},
	abstract = {The role of functional limitations and self-reported chronic disease on CES-D-8 depression scores was investigated in the Health and Retirement Study{\textquoteright}s 2008 data. The sample included 5835 respondent{\textquoteright}s ranging in age from 50-83. Multivariate adaptive regression splines (MARS) modeling was employed to identify multiple linear splines. Possible predictors included functional limitations (ADL, IADL), chronic diseases (back pain, stroke, arthritis, cancer, high blood pressure, heart disease, lung disease, \& diabetes) and demographic measures (age, education, gender, being white, being Hispanic). The MARS modeling process resulted in selection of the following measures in the final model: ADL limitations, IADL limitations, back pain, stroke, education, being female, and being Hispanic. The results indicated that ADL limitations included two splines with a large increase in depression score from zero to one limitation, and a more shallow increase from one to three limitations. IADL limitations included two splines with a linear increase in depression score from zero to two limitations and a decrease in score from two to three limitations. Reporting back pain or stroke resulted in higher depression scores compared to absence of disease. Two splines were identified for education in which the highest depression scores were associated with zero to 7 years of education; there was a linear decline in depression from seven to 17 years of education. Females and Hispanics were associated with higher depression scores compared to males and non-Hispanics. Results emphasize the importance of functional status and selected chronic health conditions on depression scores and provide more detailed descriptions of change than traditional regression-based models.},
	keywords = {Health Conditions and Status, Healthcare, Methodology, Women and Minorities},
	author = {Liu, Ying}
}
