Original Article
Weighted multimorbidity indexes predicted mortality, health service use, and health-related quality of life in older women

https://doi.org/10.1016/j.jclinepi.2007.05.015Get rights and content

Abstract

Objective

To develop indexes of multimorbidity, based on self-reported data, to predict mortality, health service use, help with activities of daily living (ADL), and health-related quality of life (HRQOL) in older women.

Study Design and Setting

Cross-sectional survey of 10,434 women, aged 73–78 years, in the Australian Longitudinal Study of Women's Health in 1999, with mortality follow-up to 2005. For analysis, the sample was equally split into a development and validation sample. Weighted and unweighted multimorbidity indexes were developed and tested.

Results

Outcomes ranged from 14% for mortality to 47% for specialist doctor visits. Mortality was predicted by heart disease, stroke, low iron, diabetes, cancer (nonskin), bronchitis/emphysema, and Alzheimer's disease. Different patterns of morbidities were associated with the other outcomes. Weighted and unweighted multimorbidity index scores were linearly related to increasing risk of each outcome. For each outcome, the weighted scores fitted the data better and had a wider range of possible values.

Conclusion

These multimorbidity indexes predict mortality, health service use, help with ADL, and HRQOL in older women. The indexes could be used as covariates in research with weighted scores having a better chance of discriminating between patient groups than unweighted scores.

Introduction

The health of older people is characterized by multiple chronic conditions that individually and jointly affect their quality of life, use of health services, morbidity, and mortality [1]. For this reason, there is an increasing interest in the measurement of the joint effects of multiple conditions.

There are three main issues to consider. First, a distinction should be made between comorbidity indexes, which measure the presence of a chronic disease or condition in addition to an “index or principal condition” and multimorbidity indexes which measure the occurrence of two or more conditions, without consideration of an index condition [2]. An advantage of comorbidity indexes is the potential to create “disease-specific” measures, which may have high validity in particular patient populations [3]. The disadvantage is the potential lack of generalizability to other populations. The advantage of multimorbidity indexes is that they may have wider applicability.

Second, data on multiple conditions may be obtained from the subjects themselves (i.e., self-reported data) or from medical or administrative records. One of the best-known, valid and reliable multimorbidity indexes, which is based on data collected from administrative databases, is the Charlson index [4]. Developed initially to predict 1-year mortality for hospitalized patients, this index has been adapted for use with diagnoses recorded using International Classification of Disease (ICD) edition 9 codes [5], [6], [7], [8] and edition 10 (ICD-10) codes [9] and has also been shown to predict medical complications [5], hospital readmissions and health practitioner visits [10], [11], and hospital costs [5], [10], [11]. Self-reported versions of the Charlson index have also been developed and found to predict mortality [12] and to have moderate correlations with medication use and hospitalizations [13]. Other multimorbidity indexes based on self-reported conditions have also been developed due to increasing support for the validity of self-reported health information [13], [14], [15] and to problems with the use of administrative databases [2], [13]. The Comorbidity Symptom Scale [16] is one such self-reported measure based on 23 chronic conditions scored for prevalence and severity. It was developed on a population of people aged 65 years and older who were undergoing cataract surgery. The scale has been shown to correlate well with activities of daily living (ADL), health status, and anxiety and depression.

Finally, the people for whom the indexes are developed and used may be patient groups (see above) or community-based populations. There is a particular shortage of population-based studies. Fan et al. [17] developed an index on a population of 5,469 patients (plus a validation set of 5,478 patients) drawn from general internal medicine clinics at Veterans Affairs medical centers in the USA. Although this population was predominantly male (>97% in their study), it was not restricted by hospitalization or diagnoses. Seven self-reported chronic conditions (prior myocardial infarction, cancer, lung disease, chronic heart failure, diabetes, pneumonia, and stroke) plus age (in 5-year intervals) and smoking status (current or past) were found to predict mortality and hospitalizations. From this research, the Seattle Index of Comorbidity was created by adding weights for every item; the weights are 1, 2, or 4 depending on the contribution of the item to outcome. More recently, Byles et al. [18] aimed to develop a generic multimorbidity index, based on self-report prevalence and severity of 25 chronic conditions using data from 1,303 Australian veterans or widows of veterans, aged 70 years or older. They found that different weighted indexes were needed to optimally predict each of the different outcomes of mortality, hospital admissions, and health-related quality of life (HRQOL).

The objective of this paper is to develop and test multimorbidity indexes based on self-reported data for use among community samples of older women. By doing this, we aim to complement the work of Fan et al. [17], which was conducted with men, and by using a large database and multiple outcomes substantially extend the work of Byles et al. [18].

Section snippets

Design

We used data from a cross-sectional survey administered to 10,434 women in 1999 with mortality follow-up until 2005.

Subjects and setting

The data are from the Australian Longitudinal Study on Women's Health (ALSWH), which is designed to track the health of women over at least 20 years. The study sample was drawn from the database of Medicare Australia, the universal provider of basic health insurance, which involves all people in Australia (including nonresidents). The sample was randomly selected with purposive

Results

The characteristics of both samples were remarkably similar with respect to demographic and health characteristics, self-reported conditions, and the outcome variables (Table 1, Table 2). The prevalence of the conditions varied from 41.7% for arthritis to 0.5% for Alzheimer's disease and the median (quartiles) number of conditions was 2 (1–3) for both samples.

The outcomes varied from 14% for mortality to 47% for any visit to a specialist doctor (Table 2). There were no significant differences

Discussion

We have developed and validated multimorbidity indexes to predict mortality, visits to GPs and specialists, and hospitalizations, ability to perform ADL without help, and HRQOL in a population-based sample of older Australian women. Like Byles et al. [18], we found that no single index best captured all outcomes.

Limitations

The ALSWH questionnaires did not ask the women to indicate the severity of the chronic conditions they reported. Data on severity may have led to stronger relationships being found [16], [18]. Alzheimer's disease was found to be a strong predictor; however, as the incidence of Alzheimer's disease in our sample was relatively low at 0.5%, some caution must be applied to the robustness of the results. The potential limitations of self-reported data must also be acknowledged. Although

Conclusion

We have created indexes of multimorbidity to predict mortality, health service use, need for assistance with ADL, and HRQOL in community-based older women using self-reported conditions. We recommend the use of a weighted score of multimorbidity, for example, in outcome research as a covariate to summarize the joint effect of chronic conditions. Additionally, given the consistent relationship between low iron and Alzheimer's disease and the outcomes, we also recommend that these conditions

Acknowledgments

The ALSWH is conducted by a team of researchers at the University of Newcastle and the University of Queensland. We are grateful to the Australian Government Department of Health and Ageing for funding, and to the women who participated. The authors gratefully acknowledge the valuable contribution of all staff, students and colleagues who have been associated with the project since its inception. L.T. was supported by a National Health and Medical Research Council of Australia Capacity Building

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