IMPACT OF NON-TRADITIONAL VARIABLES IN HEALTH CARE RISK ADJUSTMENT: A CASE STUDY OF UTH, UYO, AKWA IBOM

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IMPACT OF NON-TRADITIONAL VARIABLES IN HEALTH CARE RISK ADJUSTMENT: A CASE STUDY OF UTH, UYO, AKWA IBOM

 

CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The business of risk adjustment has come a long way since the publication of the Academy’s “Monograph Number One” with the title, “Health Risk Assessment and Health Risk Adjustment—Crucial Elements in Eective Health Care Reform” in May 1993. Less than ten years later, we had hospital inpatient
diagnosis-based approaches, such as the model used by the Market Stabilization Pool for small group and individual coverage in NYS in conjunction with mandated community rating. The PIP-DCG approach for Medicare + Choice, also inpatient only, soon followed.
Risk adjustment models have included variables such as demographic (i.e. age and gender) and clinical markers based either on ICD-9 diagnosis codes and/or pharmacy codes such as the National Drug Codes (NDCs). Literature points to other variables such as geography, Body Mass Index (BMI), education,
and income that also explain the variation in health care cost – but have hitherto not been included in risk adjustment programs mainly because such variables are not typically found in claim data. If these nontraditional variables explain meaningful variation in cost beyond traditional risk adjustment models – then this may provide incentives for issuers to select certain members. If such incentives lead to selection that aects the financial performance of issuers – then the policy goals of the risk adjustment program will be undermined. Recognizing the importance of fortifying risk adjustment programs against
selection based on nontraditional variables, the Society of Actuaries’ Health Section sponsored an in-depth study into the relationship of nontraditional variables with health costs. This report presents the results of this study. We used the Medical Expenditure Panel Survey (MEPS) data in this research. Specific details concerning the data and preparation can be found in Section 3.2. This data is unique in that it includes a large number of individual characteristics (from BMI to whether a person has diiculty enjoying hobbies) together with healthcare claim data. There are limitations to the use of MEPS data, and these limitations are discussed further in Section 4. The results of this research demonstrate that it is important to adjust the traditional risk adjustment model in order to recognize nontraditional variables. The report develops a new measure (Loss Ratio Advantage or LRA) to help quantify the potential of a nontraditional variable to aect
a risk adjustment program. With the help of this measure, the report compares the importance of over thirty variables that were systematically narrowed down from a list of over fieen hundred variables describing various characteristics of the general population (i.e. the purchasers of healthcare insurance coverage). The nontraditional variables were broadly categorized into

(1) demographic

(2) economic,

(3) lifestyle,

(4)psychological self-assessment (i.e. how a person feels about their mental health)

(5) physical self-assessment.

 

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IMPACT OF NON-TRADITIONAL VARIABLES IN HEALTH CARE RISK ADJUSTMENT: A CASE STUDY OF UTH, UYO, AKWA IBOM

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