Among a Medicare population, use of 3 self-reported health items improves predicted inpatient admissions and healthcare costs when used with risk-prediction model.
Published Online: December 20, 2011
Nancy A. Perrin, PhD; Matt Stiefel, MPA; David M. Mosen, PhD, MPH; Alan Bauck, MS; Elizabeth Shuster, MS; and Erin M. Dirks, MS, MBA
Objectives: To determine whether adding selfreported health and functional status data to a diagnostic risk-score model explains additional variance in predicting inpatient admissions and costs.
Study Design: Retrospective observational analysis.
Methods: We used data from a Health Status Questionnaire (HSQ), completed by 6407 Kaiser Permanente Northwest Medicare patients between December 2006 and October 2008. We used answers from 3 items on the HSQ: (1) General Self-rated Health score, (2) needing help with 1 or more activities of daily living, and (3) having a bothersome health condition. We calculated a DxCG relative risk score from utilization information in the year prior to the survey, using electronic medical records. We compared: (1) DxCG as the sole independent variable and (2) DxCG plus the 3 items as independent variables. We estimated area under the curve (AUC) for each model. Any inpatient admission (yes/no) and being in the top 10% of costs (in the year after survey) were the dependent variables for the first and second logistic regression models, respectively.
Results: The 3 items explained an additional 2.8% and 4.0% of variance for inpatient admissions and top 10% of costs, respectively, in addition to the variance explained by the DxCG score alone. For DxCG alone, the AUC was 0.686 (95% confidence interval [CI] 0.663-0.710) and 0.741 (95% CI 0.719- 0.764), respectively, for inpatient admissions and top 10% of costs and improved to 0.709 (95% CI 0.687-0.730) and 0.770 (95% CI 0.749-0.790) when the 3 self-reported items were added.
Conclusions: Using self-reported health information improved the predictive power of a DxCG model to forecast inpatient admissions and patient cost-tier.
(Am J Manag Care. 2011;17(12):e472-e478)
In a Medicare population, self-reported information about being in poorer health was associated with higher inpatient admissions and being in the top tier for costs. Items associated with these outcomes were: (1) lower score on the General Self-rated Health score item, (2) answering yes to “do you need help with 1 or more activities of daily living?” and, (3) answering yes to “do you have a bothersome health condition?” These items:
Explained an additional 2.8% and 4.0% of admission and cost variance, respectively.
Were independently predictive of future inpatient admissions and being in the top 10% cost group.
Predictive healthcare models are valuable tools for identifying individuals at risk for adverse outcomes, including high healthcare utilization and mortality. Predictive models are typically based on diagnostic history from administrative data or sometimes on patient-reported outcomes, but rarely both. The purpose of our analyses was to determine whether combining these 2 types of data in an integrated predictive model improved upon the predictive power compared with models based on administrative data alone.
Claims-based predictive models are the most common because of the ubiquitous availability of claims data. There are a variety of commercially available claims-based predictive models, and their validity is well documented.1 The Society of Actuaries has conducted periodic comparisons of the predictive power of these models for following-year costs.2 In these models, health diagnoses and prescriptions, in addition to prior expenses, are the variables with most predictive accuracy. Laboratory results have also been shown to add predictive power.3
Predictive models based on patient-reported outcomes are less common because such measures are not routinely collected and reported. However, patient-reported outcomes have been shown to have strong predictive value for forecasting costs, utilization, and mortality. In particular, the predictive value of a single General Self-rated Health (GSRH) question is well documented. DeSalvo (2005) concluded in a meta-analysis that people with “poor” self-rated health had double the mortality risk of people with “excellent” self-rated health, even after adjustment for key factors such as functional status, depression, and comorbidity.4 GSRH has also been shown to be responsive to change. In a study of older adults, self-reported health declined steeply prior to adverse events, such as death or stroke.5 A study of repeated administrations of 2 GSRH questions showed good reliability, reproducibility, and discriminate scale performance.6 Although the single-question GSRH measure is seldom used in clinical practice settings, it is widely used in medical and economic research.7 In an investigation of the causal relationship between self-reported health and subsequent outcomes, Jylha suggests that aspects of self-reported health may reflect important physiological dysregulations, such as increases in tissue inflammation.7
Similarly, DeSalvo (2009) notes that respondents provide composite assessments of their physical and mental health and social status when answering questions about their overall health, which helps explain the strong performance of GSRH in predicting future risk.8 The literature includes several examples of patientreported outcomes used in predictive models. DeSalvo (2009) compared predictive ability of single- and multi-item assessments of self-reported health to a comorbidity index and diagnostic history in predicting next year’s costs. They found that the single-item self-reported health question predicted costs as well as the multi-item assessment and the comorbidity index, but not as well as the diagnostic history model.8 Using data from a survey of Medicare members, Brody et al developed highly sensitive indices of frailty and advanced illness.9,10 The models use survey items about self-reported health and function, such as poor general health and assistance needed for medications, bathing, or dressing, and were shown to be superior to clinical judgment in predicting frailty and advanced illness.
There are, however, few examples of predictive models that combine diagnostic history and patient-reported outcomes. Fleishman et al found that measures of health status improved prediction of subsequent medical expenditures in a model that included demographics, chronic conditions, and previous expenditures in a nationally representative sample.11 Fleishman and Cohen later found that measures of self-rated health and function improved prediction of high expenditures beyond more detailed information on medical conditions for a nationally representative sample.12 These results are consistent with previous studies based on nonrepresentative samples. In a study of Veterans Affairs (VA) beneficiaries, self-reported physical and mental health improved mortality prediction when combined with a diagnostic history model.13 Similarly in a previous study of VA beneficiaries, a multidomain health assessment added predictive power to a diagnostic history model.14
In this study, we examined whether inclusion of answers to a few brief self-reported items improved the prediction of future inpatient admissions and medical care costs compared with using an administrative risk score alone to predict the same outcomes. This study is unique in that it is the first of which we are aware to conduct this analysis in a Medicare population. Further, the Medicare population represents a unique population, given the large proportion with chronic health conditions, which contributes to increased inpatient admissions and costs—as the US population ages. We selected the well-validated, single-item general health question, a question about activities of daily living,15 and a question indicating whether or not the person currently has a physical condition that bothers him or her. These 3 items were selected because they are questions that could be quickly administered during an office visit, over the phone, by mail, or the Internet and are not tailored to a specific population (eg, those with chronic conditions) and capture information not readily attainable through administrative data.
Study subjects were members of Kaiser Permanente Northwest (KPNW) and were aged 65 years and older. To be included in the study, members needed to complete a Health Status Questionnaire (HSQ) between December 2006 and October 2008, and have 1 year of membership eligibility prior to and after the completion of the HSQ. This time frame allowed us to compute the DxCG scores. When the member had completed more than 1 HSQ in the time frame of interest, 1 HSQ was selected at random for inclusion in the study.
During the period of study, the HSQ was mailed to new Medicare members aged 65 years and older. This population included those newly enrolled to KPNW and current members who recently became Medicare eligible. In addition, it was mailed annually to a Medicare expanded-care population. The general response rate for the HSQ is 76%.
The HSQ is a 45-item survey that includes measures of physical function, activities of daily living, health conditions, self-care deficits, and general health. For this study we used data from 3 items. The self-reported general health measure is scored in a range of 1 (excellent) to 4 (poor) by answering the question “compared to other persons your age, would you say your health is…?” The second item asked “because of a disability or health problem, do you need or receive help from another person for any of the following everyday activities…?” The activities listed include preparing meals, shopping for groceries, doing routine household chores, managing money, doing laundry, taking medications, getting to places not within walking distance, and using the telephone. This item was scored 0 if no items were checked and if 1 or more items were checked. The third item was “Is there any physical condition, illness, or health problem that bothers you now?” and was scored 0 for “no” and 1 for “yes.”
The DxCG relative risk score16 is based on an individual’s total predicted cost in the next year relative to the population mean and is used to assign individuals to a Diagnostic Cost Group (DCG). DxCG Risk Solutions software16 is used to calculate the DxCG relative risk score. DCG cost estimates are calculated using 1 year of inpatient and outpatient International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to classify patients into groups with similar cost patterns. The diagnoses are classified into mutually exclusive DCGs, based on predicting future inpatient and outpatient costs. The software algorithm for DCG classifications was developed using 3 different populations: a privately insured population, a Medicaid population, and a Medicare population. Diagnostic Cost Groups for this study were based on the Medicare-derived DCG grouping. The range of Medicare DxCG scores ranged from 0.04 (minimum) to 15.59 (maximum). Individuals with a score of 1.0 have the relative risk equivalent to the national Medicare reference population. This reference population includes any of the following (excluding those with end-stage renal disease): (1) individuals 65 years or older, (2) disabled (under age 65 years) Medicare eligible, or (3) Medicare/Medicaid dual eligible. Previous research has used the DxCG relative risk score to predict future costs and mortality estimates.12,17-19
In the year following the date of the HSQ, we used electronic medical records to determine if the subject had any inpatient hospital admissions. Actual health plan costs for each person for this same period were extracted from administrative data and presented in 2008 dollars.
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