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Associations between BMI and diet qu...

Ford, Dara Wheeler.

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Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS).

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS)./
Author:	Ford, Dara Wheeler.
Description:	145 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
Contained By:	Dissertation Abstracts International75-06B(E).
Subject:	Health Sciences, Nutrition. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3585579
ISBN:	9781303781759

Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS).
Ford, Dara Wheeler.

Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS). - 145 p.

Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2013.

The objectives for this dissertation were three-fold: 1) to determine factors that impact diet quality; 2) to assess associations between diet quality and BMI with healthrelated quality of life; and 3) to examine the associations between diet quality and BMI with objective health outcome measures including healthcare resource use and all-cause mortality.

ISBN: 9781303781759Subjects--Topical Terms:

1017801
Health Sciences, Nutrition.

Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS).
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020 $a 9781303781759
035 $a (MiAaPQ)AAI3585579
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100 1 $a Ford, Dara Wheeler. $3 2106268
245 1 0 $a Associations between BMI and diet quality with healthrelated quality of life, mortality, and healthcare resource use in the Geisinger Rural Aging Study (GRAS).
300 $a 145 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
500 $a Adviser: Gordon L. Jensen.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2013.
520 $a The objectives for this dissertation were three-fold: 1) to determine factors that impact diet quality; 2) to assess associations between diet quality and BMI with healthrelated quality of life; and 3) to examine the associations between diet quality and BMI with objective health outcome measures including healthcare resource use and all-cause mortality.
520 $a The GRAS enrolled more than 20,000 individuals aged ≥ 65 years old living in rural central Pennsylvania in a rolling fashion from 1994-1999. The aim of the GRAS was to evaluate nutritional risk in relation to health outcomes in older adults. In the fall of 2009, the remaining 5,993 participants were contacted; 4,009 returned completed dietary and demographic information (1,722 male, 2,287 female; mean age 81.5 +/- 4.4). Of these, 2,995 were also enrolled in the Geisinger Health Plan for at least part of the follow-up (mean=37 months), allowing for extraction of HRU data. Diet-related practices were self-reported at baseline (fall 2009). HRQOL score was determined using the Health and Activity Limitation Index (HALex) which scores individuals on a scale of 0.0 (death) to 1.0 (optimal) based on self-reported health and self-reported functional limitation. HRU was extracted from electronic medical records, and deaths were identified using electronic medical records and the Social Security Death Index. Multivariate linear regression models were used to analyze the associations of BMI and diet-related practices with DST score. The diet-related practices of skipping breakfast, reporting a decline in intake, being food insufficient or experiencing chewing difficulties were associated with poorer DST score. Additionally, participants with a low BMI (<18.5) had significantly poorer diet quality than individuals with a desirable BMI (18.5-24.9).
520 $a The next phase of analysis examined the associations between diet quality and BMI with HALex score in multivariate linear regression models.
520 $a The next aim was to determine the association between overall diet quality, fruit and vegetable consumption, and BMI with four healthcare resource use outcomes. Multivariate negative binomial models were used to estimate relative risk (RR) and 95% confidence intervals (95% CI) for each of four HRU outcomes (inpatient hospital visits, inpatient hospital days, emergency room visits and outpatient clinic visits) with diet quality and BMI. Poor diet quality was related to a 20% increased risk for ER visits. The three lowest fruit and vegetable quintiles were associated with increased risk for ER visits (23-31%), and the lowest quintile was also associated with increased risk for inpatient visits (27%). Obesity increased risk of outpatient clinic visits; however, individuals with class I obesity were less likely than normal weight individuals to have ER visits (RR 0.84; 95% CI 0.70, 0.99).
520 $a Finally coxproportional hazards models were used to examine the associations of BMI, diet quality, and HRQOL with all-cause mortality after adjusting for relevant covariates.
520 $a In conclusion, the diet-related practices of skipping breakfast, reporting a decline in intake, being food insufficient or experiencing chewing difficulties, as well as a low BMI, were associated with poorer diet quality. In turn, poor diet quality was associated with poor HRQOL as well as adverse HRU and mortality outcomes. Obesity classes II and III were also associated with poor HRQOL and increased outpatient clinic visits, while overweight was associated with reduced mortality. Finally, HALex score was strongly associated with decreased rates of mortality. These findings suggest that improving diet quality in rural older adults may have beneficial effects on HRQOL, HRU and mortality rates. Many of these relationships are bi-directional, and without multiple data points we are unable to determine causation. (Abstract shortened by UMI.).
590 $a School code: 0176.
650 4 $a Health Sciences, Nutrition. $3 1017801
650 4 $a Health Sciences, Aging. $3 1669845
650 4 $a Health Sciences, General. $3 1017817
690 $a 0570
690 $a 0493
690 $a 0566
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertation Abstracts International $g 75-06B(E).
790 $a 0176
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3585579