Original articles
Which chronic conditions are associated with better or poorer quality of life?

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Abstract

The objective of the present study is to compare the QL of a wide range of chronic disease patients. Secondary analysis of eight existing data sets, including over 15,000 patients, was performed. The studies were conducted between 1993 and 1996 and included population-based samples, referred samples, consecutive samples, and/or consecutive samples. The SF-36 or SF-24 were employed as generic QL instruments. Patients who were older, female, had a low level of education, were not living with a partner, and had at least one comorbid condition, in general, reported the poorest level of QL. On the basis of rank ordering across the QL dimensions, three broad categories could be distinguished. Urogenital conditions, hearing impairments, psychiatric disorders, and dermatologic conditions were found to result in relatively favorable functioning. A group of disease clusters assuming an intermediate position encompassed cardiovascular conditions, cancer, endocrinologic conditions, visual impairments, and chronic respiratory diseases. Gastrointestinal conditions, cerebrovascular/neurologic conditions, renal diseases, and musculoskeletal conditions led to the most adverse sequelae. This categorization reflects the combined result of the diseases and comorbid conditions. If these results are replicated and validated in future studies, they can be considered in addition to information on the prevalence of the diseases, potential benefits of care, and current disease-specific expenditures. This combined information will help to better plan and allocate resources for research, training, and health care.

Introduction

The past decades have witnessed an increasing prevalence of chronic disorders, as a result of better prevention, management of infectious diseases, improved living conditions, medical technological improvements, and the overall aging of the population. The health-related quality of life (QL) of patients with chronic disorders is frequently impaired. An important objective of health care in the U.S. and other Western countries is to increase the span of life years while maintaining an optimal QL 1, 2. QL is not only a primary concern of patients, their families, and clinicians, but is also of policy interest. Estimates of the relative impact of chronic diseases on QL are needed to better plan and allocate resources for research, training, and health care.

While numerous studies have addressed the impact of chronic diseases and their treatment on QL, the relative impact of different chronic conditions on patients' level of subjective functioning is mostly unknown 3, 4. On the basis of a systematic literature review,1 we identified only 20 studies 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 that compared the QL across chronic disease populations. The question arises what these studies reveal about the relative position of the conditions regarding QL. To enable a comparison across conditions, they were aggregated into disease clusters. For example, hypertension and myocardial infarction have been grouped into a cluster labeled “cardiovascular conditions” (Fig. 1). These disease clusters were then compared within each study. These comparisons resulted in a positive, an equivalent, or a negative position of each disease cluster relative to other disease clusters with respect to physical/functional and/or psychosocial well-being. The relative position of each disease cluster was then established across studies (Table 1).

Both musculoskeletal and renal disease were found to result in greater physical and functional impairments, while the psychosocial sequelae were modest. The remaining chronic conditions appear to have a comparable impact on both physical/functional and psychological/social aspects of well-being. Hearing or visual impairments, gastrointestinal diseases, and endocrinologic conditions appear to hold a predominantly positive position relative to other diseases. A broad group encompassing chronic respiratory diseases, cancer, urogenital conditions, cardiovascular conditions, musculoskeletal conditions, and renal diseases was found to have both a positive and negative position relative to other conditions. Finally, the disease clusters that were found to hold mainly a negative position include AIDS, cerebrovascular/neurologic conditions, and psychiatric disorders. Clearly, given the underrepresentation of a number of disease clusters (e.g., hearing impairments and renal disease), the heterogeneity of the studies with respect to sample composition, sample size, instruments and procedures, and the methodological limitations inherent in such comparisons, these results need to be viewed with caution.

Empirical data are needed to corroborate these findings. We therefore investigated the level and profiles of QL in a wide range of chronic disease populations in response to a request of the Netherlands Committee of Chronic Diseases. This committee wished to gain insight into the relative impact of chronic diseases on QL, which could help to better allocate research funds. A secondary analysis on eight existing data sets was conducted. The research questions were threefold and pertained to the extent to which: a) sociodemographic characteristics and comorbidity affect QL across chronic disease patients, b) disease categories within their respective disease clusters differ in the level and pattern of QL, and c) the disease clusters themselves differ in the level and pattern of QL. The terms QL, well-being, health status, and functioning will be used interchangeably.

Section snippets

Selection of data sets

To identify relevant data, all research groups known to examine chronic diseases in the Netherlands were mailed a questionnaire asking detailed information about available data sets that could be used for secondary analysis. Thirty-two potentially relevant data sets were thus identified. The following guidelines were used to select studies: 1) full coverage of QL domains (i.e., physical, functional, psychological, and social well-being) 23, 24; 2) use of standardized QL instruments; 3)

Age

Older patients reported a poor QL than younger ones on the QL dimensions and physical functioning. None of the comparisons revealed results that were in the opposite direction. There was no effect of age on mental functioning (Table 2).

Gender

Male patients reported a more positive or comparable level of QL than female patients on the separate QL dimensions and mental functioning. No such gender effect was found for physical functioning.

Education

In general, a low level of education coincided with poor QL as

Influence of sociodemographic characteristics and comorbidity

The extent to which the sociodemographic characteristics and comorbidity exert a systematic effect on perceived QL was examined for each patient sample separately. Some clear trends emerged, despite the heterogeneity of the data sets. Patients who were older, female, had a low level of education, were not living with a partner, and had at least one comorbid condition, in general, reported the poorest level of QL.

Comparisons of disease categories within their respective disease clusters

Within disease clusters comparisons were made of component disease categories to

Discussion

A number of conceptual and methodological constraints of the study merit attention. Conceptually, the choice of a generic health status instrument was needed, by definition, to enable comparison across different patient populations. However, this has inherent limitations regarding the domains of comparison. Moreover, such a generic instrument may not address those issues of relevance to particular patient groups, such as disease symptoms or treatment side effects. To the extent that such

Acknowledgements

The research reported in this article was supported by a grant from the Dutch Council for Medical and Health Research, research program “Chronic diseases,” financed by the Ministry of Health, Well-being and Sports. We are indebted to the Committee Zorg, Opvang en Begeleiding for identifying existing data sets and to Dr. R.P.W. Heinsbroek for his advice throughout the project. We also thank Dr. P. Oosterveld and Dr. A. de Boer for comments during the data analysis and E. Verkooijen for data

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