東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity?

Record Type:	Electronic resources : Monograph/item
Title/Author:	Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity?/
Author:	Sullivan, Iben.
Description:	1 online resource (274 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28967709click for full text (PQDT)
ISBN:	9798438757924

Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity?
Sullivan, Iben.

Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity? - 1 online resource (274 pages)

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Dartmouth College, 2022.

Includes bibliographical references

A subset of the population in the United States (U.S.) are known as "super-utilizers," and they are responsible for a disproportionate amount of resource intensive healthcare and associated spending. Super-utilizers tend to have multiple co-morbidities, a term called, "multi-morbidity," and they also have other population risk factors, including low socioeconomic status, housing instability, and being uninsured. Consumer expenditures are data on purchasing habits, and as a data source, they may offer information about the risk for super-utilization of resource intensive healthcare along with risk for multi-morbidity. Despite the large burden super-utilizers place on the U.S. healthcare system, identifying them and optimizing their healthcare to reduce their utilization remains a crucial challenge. In addition, multi-morbidity is a documented risk factor for resource intensive healthcare, however, dietary recommendations among those managing multi-morbidity are limited. This dissertation seeks to address these two gaps by (1) predicting resource intensive healthcare among population units, which may address limitations associated with existing models generated among individuals and (2) using consumer spending data to study diet choice among multi-morbid households, which may identify behaviors to promote or reduce to support healthy lifestyle practices. In this dissertation, I demonstrate the utility of using machine learning to develop models for predicting resource intensive healthcare utilization and associated spending among U.S. counties and U.S. hospital service areas (HSAs) using routinely collected data from governmental and non-governmental sources, including demographics, adult and child disease characteristics, community characteristics, and consumer expenditures. The prediction model performance in this thesis matched or exceeded existing models generated at the individual level, offering an alternative method for identifying super-utilization of resource intensive healthcare and associated spending. Additional analyses identified population characteristics explaining variation in resource intensive healthcare across U.S. counties and HSAs, highlighting potential modifiable risk factors to target for health system or community interventions. To further understand how consumer expenditures related to multi-morbidity, a separate analysis used packaged food and drink purchases to study diet choice among households with multi-morbidity. Recommendations to promote healthy lifestyles among those with multi- morbidity should focus on reducing unhealthy packaged foods, especially from unhealthy items, including processed meats, and cookies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798438757924Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Consumer expendituresIndex Terms--Genre/Form:

542853
Electronic books.

Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity?
LDR:04216nmm a2200409K 4500 001 2354553
005 20230428105613.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798438757924
035 $a (MiAaPQ)AAI28967709
035 $a AAI28967709
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Sullivan, Iben. $3 3694908
245 1 0 $a Can Diverse Data, Including Novel Consumer Expenditures, Predict Resource Intensive Healthcare Utilization Among Populations or Quantify Patterns in Diet Choice Among Households with Multi- Morbidity?
264 0 $c 2022
300 $a 1 online resource (274 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Brown, Jeremiah R.
502 $a Thesis (Ph.D.)--Dartmouth College, 2022.
504 $a Includes bibliographical references
520 $a A subset of the population in the United States (U.S.) are known as "super-utilizers," and they are responsible for a disproportionate amount of resource intensive healthcare and associated spending. Super-utilizers tend to have multiple co-morbidities, a term called, "multi-morbidity," and they also have other population risk factors, including low socioeconomic status, housing instability, and being uninsured. Consumer expenditures are data on purchasing habits, and as a data source, they may offer information about the risk for super-utilization of resource intensive healthcare along with risk for multi-morbidity. Despite the large burden super-utilizers place on the U.S. healthcare system, identifying them and optimizing their healthcare to reduce their utilization remains a crucial challenge. In addition, multi-morbidity is a documented risk factor for resource intensive healthcare, however, dietary recommendations among those managing multi-morbidity are limited. This dissertation seeks to address these two gaps by (1) predicting resource intensive healthcare among population units, which may address limitations associated with existing models generated among individuals and (2) using consumer spending data to study diet choice among multi-morbid households, which may identify behaviors to promote or reduce to support healthy lifestyle practices. In this dissertation, I demonstrate the utility of using machine learning to develop models for predicting resource intensive healthcare utilization and associated spending among U.S. counties and U.S. hospital service areas (HSAs) using routinely collected data from governmental and non-governmental sources, including demographics, adult and child disease characteristics, community characteristics, and consumer expenditures. The prediction model performance in this thesis matched or exceeded existing models generated at the individual level, offering an alternative method for identifying super-utilization of resource intensive healthcare and associated spending. Additional analyses identified population characteristics explaining variation in resource intensive healthcare across U.S. counties and HSAs, highlighting potential modifiable risk factors to target for health system or community interventions. To further understand how consumer expenditures related to multi-morbidity, a separate analysis used packaged food and drink purchases to study diet choice among households with multi-morbidity. Recommendations to promote healthy lifestyles among those with multi- morbidity should focus on reducing unhealthy packaged foods, especially from unhealthy items, including processed meats, and cookies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 1002712
650 4 $a Epidemiology. $3 568544
650 4 $a Public health. $3 534748
650 4 $a Health care management. $3 2122906
653 $a Consumer expenditures
653 $a Resource intensive
653 $a Healthcare utilization
653 $a Diet choice
653 $a Multi- morbidity
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0308
690 $a 0766
690 $a 0769
690 $a 0573
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Dartmouth College. $b Quantitative Biomedical Sciences. $3 3694909
773 0 $t Dissertations Abstracts International $g 83-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28967709 $z click for full text (PQDT)