Impact Of Non-Traditional Variables In Health Care Risk Adjustment

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Impact Of Non-Traditional Variables In Health Care Risk Adjustment

Chapter One


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 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 3.4. 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 thirty variables 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.

1.2 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 relationship between 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 Hypotheses

  1. Ho; 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 Nigeria
  2. Ho; 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 Akwa Ibom 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 Terms

Health Care:

The organized provision of medical care to individuals or a community.


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.

Chapter Five

Conclusionand Recommendations

5.1 Conclusion

The research presented in this study required a lot of effort, time, and resources. The issue of nontraditional variables in risk adjustment is too broad, nuanced, and complex to be captured fully by arguably the first study of its kind. The findings of this report suggest that the influence of nontraditional variables in a risk-adjusted environment is important to study, monitor, and address as appropriate. Furthermore, the issue is relevant and timely with respect to the implementation and policy goals of the ACA risk adjustment program. The author hopes that practitioners would carefully review the findings of this report and extend them.

5.2 Recommendations

Recommendations for further study naturally follow from the discussion regarding limitations of the current study and are also included in the list below.

  1. MEPS data is not very large. Although the data design that appends ten years of overlapping information mitigates issues of credibility, they will still exist – especially in relation to some variables with missing information.
  2. MEPS data is collected through a survey-based design, and has inherent differences and limitations relative to transactional claim data. For instance, the reported healthcare utilization may not be complete (Zuvekas& Olin, 2009).
  3. The nontraditional variables have varying proportions of cases where information is unavailable or respondent failed to respond. This issue of completeness will affect to an extent the conclusions drawn regarding these variables.
  4. The study tests essentially a linear model for the nontraditional predictor variables. Non-linear modeling is considered beyond the scope of this proposal, although it may be a useful extension of the proposed research.
  5. This research used a commercially available risk adjustment model. Ideally one would use the HHS ACA risk adjustment model. The details of this model were recently released as of the writing of this paper.
  6. This research does not make a distinction between individual, small group, and large group commercial data. A useful extension may be to examine LRA effects in relation to group size.
  7. The research treats group and individual commercial coverage essentially as a singular risk pool. Small group and individual insurance comprise two separate risk pools, within a state, for purposes of ACA risk adjustment. A useful extension of this research will be to analyze the impact of nontraditional variables separately for small group and individual risk pools.
  8. The data from MEPS may not be representative of the post-ACA commercial risk population. While we attempted to gather data from uninsured and commercially insured – only about thirty thousand individuals are surveyed each year, and the statistical extrapolation of this sample will reflect the post-ACA market with variable success. Also, it is not possible to distinguish between small or large group policies in the publicly available MEPS data, further limiting the extent to which this data may be representative.
  9. ACA includes many reforms that potentially will impact cost and utilization of healthcare services. Conclusions drawn from a historic look (for example, through MEPS) may not translate well into the post-ACA market as utilization patterns and associated cost may materially be different.
  10. This research includes several nontraditional variables, not all of whom may automatically be used towards any application. Readers should be aware that use of any such factor for underwriting or rating purposes may be prohibited by applicable regulations and other considerations. In this research we have not considered such limitations, and the legal environment must be taken into account with any application. Furthermore, there may be significant cultural sensitivities towards the use of nontraditional variables, and besides legal risk, reputation risk should be considered as well. Attitudes towards privacy are rapidly evolving and it would be prudent to pay close attention to them when developing or using nontraditional variables in any application including risk adjustment.

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