Original Article
The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients

https://doi.org/10.1016/j.jclinepi.2008.01.006Get rights and content

Abstract

Objective

(1) To determine chronic illness costs for large cohort of primary care patients, (2) to develop prospective model predicting total costs over one year, using demographic and clinical information including widely used comorbidity index.

Study Design and Setting

Data including diagnostic, medication, and resource utilization were obtained for 5,861 patients from practice-based computer system over a 1-year period beginning December 1, 1993, for retrospective analysis. Hospital cost data were obtained from hospital cost accounting system.

Results

Average annual per patient cost was $2,655. Older patients and those with Medicare or Medicaid had higher costs. Hospital costs were $1,558, accounting for 58.7% of total costs. In the predictive model, individuals with higher comorbidity incurred exponentially higher annual costs, from $4,317 with comorbidity score of two, to $5,986 with score of three, to $13,326 with scores greater than seven. To use an adapted comorbidity index to predict total yearly costs, four conditions should be added to the index: hypertension, depression, and use of warfarin with a weight of one, skin ulcers/cellulitis, a weight of two.

Conclusion

The adapted comorbidity index can be used to predict resource utilization. Predictive models may help to identify targets for reducing high costs, by prospectively identifying those at high risk.

Introduction

As the burden of chronic disease in the United States continues to grow, providing cost-effective health care services has become increasingly important. Recent studies project that the number of Americans living with chronic illness will increase from 125 million in 2000 to 164 million (or nearly 50% of the population) over the next three decades. Currently, an estimated 78% of total health care resources are already devoted to individuals with chronic diseases [1].

In the past, most studies have sought to measure the costs of specific chronic conditions, including arthritis [2], [3], diabetes [4], [5], [6], skin diseases [7], asthma [8], [9], hypertension [10], depression [11], [12], [13], and other diseases. Although appropriate for evaluating individual conditions, such an approach fails to allow for broad comparisons among chronic diseases or analysis of persons with multiple coexisting medical conditions. In recent years, however, a small number of studies have sought to provide a systematic estimate of costs of multiple chronic conditions in a single patient population, the most notable of which were conducted by the Group Health Cooperative in Seattle [14] and the Kaiser Permanente group in California [15] in the environment of managed care, and by Yu et al. in the VA System [16].

The present study builds upon this work, while providing a unique contribution in analyzing the costs of chronic diseases in an academic general internal medicine practice. Additionally, it develops a model for predicting future costs. The work of predicting costs ultimately shapes models of health care expenditures. Many previous models have been developed, including risk-adjustment models for Medicare enrollees. Such models have often explored the contribution of specific chronic diseases or diagnostic groups to yearly costs [17], [18], [19]. The role of comorbidity (the total burden of chronic diseases) has received surprisingly little attention, despite its proven ability to predict mortality and its importance in determining hospital costs [20], [21]. Our study is the first to incorporate a prospectively obtained comorbidity index in a rigorous statistical model aimed at predicting total cost of care.

The goal of this study was to use prospectively acquired data to determine the predictors of the cost of care of a large cohort of primary care patients. We used computerized electronic medical records to construct a model that identifies the demographic and clinical features (including specific chronic illnesses, comorbidity, and medications) predictive of the total yearly costs.

Section snippets

Population

To qualify for the study, patients had to have been followed for at least 1 year prior to December 1, 1993. Among patients seen at an academic medical practice at New York Hospital, 5,861 met this criterion. Of those, 2,864 patients were seen by 18 attending physicians. These attendings also supervised 153 residents and two nurse practitioners who saw 2,997 patients during continuity and ambulatory block experiences.

Data collection

The data for this paper were captured by CLIMACS©, a practice management system

Average yearly cost per patient

The average cost per year was $2,655. Average hospital costs, which were $1,558 per patient, accounted for 58.7% of the overall cost. Ambulatory costs totaled $935 per year and accounted for 41.3% of overall cost. In total, 667 patients, or 11.4 % of patients, were hospitalized an average of 1.5 times during the year. The total hospitalization cost on average for patients who were hospitalized was $13,693, whereas their ambulatory costs were $1,307. Thus, yearly total cost for patients who were

Discussion

The present study determined the annual health care costs for a large cohort of primary care patients, using prospectively acquired data to determine the predictors of those costs. Our study found an average yearly total cost per patient in 1994 of $2,655, which is consistent with data from contemporary studies. In 1992, a large staff model Health Maintenance Organization in Seattle showed a mean total cost estimate of $2,006 per patient [14]. A study of Health Maintenance Organization

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