Publication Date

2019-08-04

Availability

Embargoed

Embargo Period

2021-08-03

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Public Health Sciences (Medicine)

Date of Defense

2019-05-07

First Committee Member

David J. Lee

Second Committee Member

Daniel J. Feaster

Third Committee Member

Kathryn E. McCollister

Fourth Committee Member

Sharon L. Christ

Abstract

Advances in modern medicine have substantially increased life expectancy and improved the outcomes of previously fatal diseases. As a result, more and more people are living into old age and developing chronic conditions. Persons with multiple chronic conditions are more likely to be hospitalized, have poor daily functioning, and elevated risk of death. A disproportionate share of health expenditures is incurred by individuals with multiple chronic conditions. Visual impairment (VI) is among the most disabling conditions. VI and visual disorders often co-occur with other disabling chronic conditions. The health effects of living with multiple chronic conditions in the presence of VI have not been well studied. The multiple chronic condition population is characterized by tremendous heterogeneity. Developing means for determining homogeneous subgroups among this heterogeneous population is an important step in the effort to develop tailored interventions to improve health. This dissertation utilizes nationally representative pooled data from the National Health Interview survey (year 2002-2014) and the Medical Expenditure Panel Survey (years 2010-2015) and applies innovative statistical analysis techniques, including latent class analysis (LCA), to study patterns of chronic condition in the US population. The LCA method reduces the complexity of chronic condition data, categorizes the US population into five distinctive chronic condition groups, and characterizes the differences among the groups. For the NHIS, the five groups are the “healthy” group (70.5%), “hypertensive” group (19.6%), “respiratory” condition group (4.4%), “heart disease” group (3.5%) and “severely impaired” group (1.8%). For the MEPS, the five groups are the “healthy” group (62.5%), the “vascular risk” group (18.9%), the “anxiety” group (12.2%), the “heart” disease group (2.9%) and the “very sick” (3.5%) group. Using the defined chronic condition latent class, we explore its association with a variety of health outcomes including visual impairment, health care utilization, health-related quality of life (HRQL) and mortality in the context of the biopsychosocial framework. Results indicate patterns of chronic conditions, defined by the five chronic condition latent classes, significantly impact HRQL. We also found that all chronic conditions disease groups varied with respect to mortality risk; furthermore, the characteristics of the chronic condition classes were strongly associated with the primary underlying cause of death. Our study shows how socioeconomic disadvantages may synergize with multiple chronic conditions to exacerbate health disparity for the “severely impaired/very sick” group. We identified the “anxiety” group who tends to be younger and female, and despite good socio-economic class had relatively poor mental well-being. We also identified a relatively healthy aging group, the “heart condition” group, who despite advanced age and weak physical health were mentally robust, have good HRQL, and have lower mortality than some groups who are younger. This research increases the understanding of the detrimental impact of multiple chronic conditions on health and aging. The results provide clinicians and policy makers with novel information regarding complex disease patterns. Population-level group characteristics identified in this research will provide valuable information for the development of future targeted interventions. More effectively focused efforts and resources directed to populations with specific characteristics may improve health outcomes and quality of life as well as reduce healthcare utilization.

Keywords

Chronic condition patterns; visual impairment; latent class analysis; HRQL; mortality

Available for download on Tuesday, August 03, 2021

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