IMPACT OF NON-TRADITIONAL VARIABLES IN HEALTH CARE RISK ADJUSTMENT (A CASE STUDY OF UTH, UYO, AKWA IBOM)
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 Effective 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 healthcare 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 affects 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 difficulty 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 affect a risk adjustment program. With the help of this measure, the report compares the importance of over thirtyvariables that were systematically narrowed down from a list of over fifteen 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), and (5) physical self-assessment.
Statement of the problem
Risk adjustment of any kind is inherently imperfect, the complexity and sophistication of risk adjustment models has increased significantly in the past couple decades. With the passage of the Affordable Care Act (ACA), risk adjustment will be required for non-grandfathered commercial small group and individual coverage both inside and outside Exchanges.Using a structured and scientific approach, the researcher has examined a long list of non-traditional drivers of health cost, chosen the most relevant ones, and tested their effect on bottom-line medical cost when included in the traditional risk adjustment formula.
1.3 Objectives of the study 1. To determine the relationshipbetween non-traditional variables and health care risk adjustment in Nigeria. 2. To ascertain the impact of non-traditional variables on health care risk adjustment in Nigeria.
1.4 Research questions 1. Is there a relationship between non-traditional variables and health care risk adjustment in Nigeria? 2. Does non-traditional variables significantly impacts on health care risk adjustment in Nigeria?1.5 Research hypothesesHo: There is no relationship between non-traditional variables and health care risk adjustment in Nigeria.Hi: There is a relationship between non-traditional variables and health care risk adjustment in NigeriaHo: Non-traditional variables have no significant impact on health care risk adjustment in Nigeria.Hi:Non-traditional variables significantly impacts on health care risk adjustment in Nigeria.1.6 Significance of the study The Affordable Care Act (ACA) includes the mechanism of risk adjustment in commercial small group and individual markets in order to further the policy goals of premium stabilization, mitigating incentives for issuers of healthcare coverage policies (issuers) to avoid unhealthy members, and to remove any advantages or disadvantages for plans inside healthcare exchanges compared to plans outside of such exchanges. The importance of risk adjustment to these policy goals cannot be overemphasized, and details such as the variables that are included in the risk assessment formula affect the extent to which the program is successful in meeting these goals. 1.7 Scope of the study The study focuses on the impact of non-traditional variables in health care risk adjustment in Nigeria, University of Uyo Teaching Hospital (UTH) in Uyo Local Government Area of AkwaIbom state was used as the case study.1.8 Limitations of the study This study has some limitations most especially in the area of data collection. Financial constraints as well as time available for the completion of the study are among other factors that would limit the scope of the study. 1.9 Definition of termsHealth Care:The organized provision of medical care to individuals or a community. Non-traditional:Not conforming to or in accord with tradition. Risk Adjustment:A concept that refines an investment's return by measuring how much risk is involved in producing that return.
Adler, N., & Newman, K. (2002). Socioeconomic disparities in health: Pathways and policies. Health Affairs, 21(2). AHRQ. (2012). MEPS HC-138 2010 Full Year Consolidated Data File. Agency for Healthcare Research and Quality. Backlund, E., Sorlie, P., & Johnson, N. (1999). Comparison of the relationships of education and income with mortality: The national longitudinal mortality study. Social Science and Medicine, 49(10), 1373–1384. Berkman, L. F., & Glass, T. (2000). Social integration, social networks,social support, and health. NY: Oxford University Press. Catalano, R., &Serxner, S. (1992). The effect of ambient threats to employment on low birth weight.Journal of Health and Social Behavior, 33(4).363-377