Risk adjusting community rated health plan premiums: a survey of risk assessment literature and policy applications

Risk distribution across multiple health insurance funds in rural Tanzania

INTRODUCTION

Multiple insurance funds serving different population groups may compromise equity due to differential revenue raising capacity and an unequal distribution of high risk members among the funds. This occurs when the funds exist without mechanisms in place to promote income and risk cross-subsidisation across the funds. This paper analyses whether the risk distribution varies across the Community Health Fund (CHF) and National Health Insurance Fund (NHIF) in two districts in Tanzania. Specifically we aim to 1) identify risk factors associated with increased utilisation of health services and 2) compare the distribution of identified risk factors among the CHF, NHIF and non-member households.

METHODS

Data was collected from a survey of 695 households. A multivariate logisitic regression model was used to identify risk factors for increased health care utilisation. Chi-square tests were performed to test whether the distribution of identified risk factors varied across the CHF, NHIF and non-member households.

RESULTS

There was a higher concentration of identified risk factors among CHF households compared to those of the NHIF. Non-member households have a similar wealth status to CHF households, but a lower concentration of identified risk factors.

CONCLUSION

Mechanisms for broader risk spreading and cross-subsidisation across the funds are necessary for the promotion of equity. These include risk equalisation to adjust for differential risk distribution and revenue raising capacity of the funds. Expansion of CHF coverage is equally important, by addressing non-financial barriers to CHF enrolment to encourage wealthy non-members to join, as well as subsidised membership for the poorest.

Does health insurance improve health?: Evidence from a randomized community-based insurance rollout in rural Burkina Faso

From 2004 to 2006, a community-based health insurance (CBI) scheme was rolled out in Nouna District, Burkina Faso, with the objective of improving access to health services and population health. We explore the random timing of the insurance rollout generated by the stepped wedge cluster-randomized design to evaluate the welfare and health impact of the insurance program. Our results suggest that the insurance had limited effects on average out-of-pocket expenditures in the target areas, but substantially reduced the likelihood of catastrophic health expenditure. The introduction of the insurance scheme did not have any effect on health outcomes for children and young adults, but appears to have increased mortality among individuals aged 65 and older. The negative health effects of the program appear to be primarily driven by the adverse provider incentives generated by the scheme and the resulting decline in the quality of care received by patients.

Medical savings accounts in a universal system: wishful thinking meets evidence

Medical savings accounts (MSAs) and similar approaches based on flowing reimbursements through individuals/consumers rather than providers are unsuited for systems with universal coverage. Data from Manitoba, Canada reveal that, because expenditures for physician and hospital services are highly skewed in all age groups, MSAs would substantially increase both public expenditures and out-of-pocket costs for the most ill. The empirical distribution of health expenditures limits the potential impact of many current 'demand-based' approaches to cost control. Because most of the population is relatively healthy and uses few hospital and physician services, inducing the general population to spend less will not yield substantial savings.

The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation

In 2002, the Dutch government implemented a Pharmacy-based Cost Group (PCG) model in the social health insurance sector. This model uses specific types of medication prescribed to individuals in a base year as markers for chronic conditions, which are then employed to adjust capitation payments to their sickness fund in the subsequent year. In this study, a classification of prescribed medication is derived for 22 chronic conditions, based on an assessment of the relation between prescribed medication and diagnoses indicated by physicians on their prescriptions. Of the 22 chronic conditions in this classification, 13 were included in the PCG model that is currently used in the Netherlands.

Using survey measures to assess risk selection among Medicare managed care plans

This paper quantifies risk selection among competing Medicare managed care plans, using beneficiary survey data from the Consumer Assessments of Health Plans Survey. Selection, measured by variation in plan-level prevalence of health conditions and predicted costs, was substantial. A plan with moderate (one standard deviation) adverse selection would have predicted costs 11.6% above an average plan. Only a small part of this variation was explained by the geographical differences in the prevalence of health conditions among or within Metropolitan Statistical Areas, indicating that the selection was driven by plan attributes. Plans serving members with greater health needs have the potential to establish programs to serve these sick members well, yet this places plans at financial risk. Hence, improved risk adjustment for chronic conditions may be warranted. Moreover, survey measures have the potential to measure the prevalence of such conditions reliably and consistently across plans.

Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs

BACKGROUND

Adequate risk-adjustment is critical to the success of market-oriented health care reforms in many countries. A common element of these reforms is that consumers may choose among competing health insurers, which are largely financed through premium-replacing capitation payments mostly based on demographic variables. These very crude health indicators do not reflect expected costs accurately.

OBJECTIVE

This study examines whether the demographic capitation model can be improved by incorporating information on the presence of chronic conditions deduced from the use of prescribed drugs. The revised Chronic Disease Score was used to incorporate this information in the model.

METHODS

A panel data set comprising annual costs and information on prescribed drugs for 3 successive years from Dutch sickness fund members of all ages, is used for the empirical analyses (N = 55,907). The predictive performance of the demographic model is compared with that of a chronic conditions and a Pharmacy Costs Groups (PCG) model, which is a demographic model extended with information on clustered chronic conditions.

RESULTS

The predictive accuracy of the demographic model substantially improved when the model was extended with dummy variables for chronic conditions. The 23 chronic conditions could be clustered into six PCGs without affecting the predictive accuracy. Based on these PCGs 17% of the members were bad risks with a mean predictable loss that exceeds the overall average expenditures.

CONCLUSIONS

The use of information on chronic conditions derived from claims for prescribed drugs is a promising option for improving the system of risk-adjusted capitation payments.

Casemix adjustment of managed care claims data using the clinical classification for health policy research method

OBJECTIVES

The objective of this study was to explore the use of the Clinical Classification for Health Policy Research (CCHPR) as a casemix adjustment method for examining physician practice patterns.

METHODS

The data source was 2 years of administrative claims from an 86,000 member health maintenance organization in southeastern Michigan. The CCHPR version 2 algorithm, which is in the public domain, was used to assign each claim to one of 260 clinical categories. CCHPR and age-sex categories were used as explanatory variables in multiple linear regression models with approved claims payments in dollars as the outcome variable. Regressions were performed retrospectively for 1994 and 1995, and with 1994 claims' history to predict 1995 utilization. Similar regressions were performed with age-sex categories alone, and also with the ambulatory diagnostic groups.

RESULTS

The adjusted R2 value of the retrospective regression models for total approved dollars was 0.42 for both study years when CCHPR categories were used. In contrast, age-sex explanatory variables alone achieved an R2 of 0.02.

CONCLUSIONS

The CCHPR method appears to be a promising tool to understand variability in physician resource utilization in managed care.

Modeling case mix adjustment of stroke rehabilitation outcomes

Case mix adjustment models for long-term stroke rehabilitation outcomes should be developed (1) to facilitate equitable comparisons of outcomes across treatment settings, thereby reducing disincentives for treating complex cases, (2) to improve triage into the most appropriate level of rehabilitative care after discharge from acute care, and (3) to confirm that case mix factors are equated in treatment effectiveness studies and by random assignment across conditions in clinical trials. Case mix adjustment is necessary for valid quality improvement processes. A conceptual model of case mix adjustment of long-term rehabilitation outcomes is presented that (1) is diagnosis-specific, (2) includes demographic variables as important case mix factors, (3) encompasses triage into rehabilitation as well as treatment processes as aspects of quality of rehabilitative care, (4) contains outcomes measuring functional status as well as mortality and morbidity, and (5) keys timing of outcomes to onset of conditions requiring rehabilitation rather than discharge from rehabilitation. The number of potential interactions among case mix indicators requires a sophisticated analytic framework. Random factors in the model illustrate that case mix adjustment can never be perfect. Nevertheless, it is essential. A brief review of the stroke literature on prediction of long-term outcomes suggests that additional work is needed to specify relevant case mix indicators.

Consumer health plan choice: current knowledge and future directions

A keystone of the competitive strategy in health insurance markets is the assumption that "consumers" can make informed choices based on the costs and quality of competing health plans, and that selection effects are not large. However, little is known about how individuals use information other than price in the decision making process. This review summarizes the state of knowledge about how individuals make choices among health plans and outlines an agenda for future research. We find that the existing literature on health plan choice is no longer sufficient given the widespread growth and acceptance of managed care, and the increased proportion of consumers' income now going toward the purchase of health plans. Instead, today's environment of health plan choice requires better understanding of how plan attributes other than price influence plan choice, how other variables such as health status interact with plan attributes in the decision making process, and how specific populations differ from one another in terms of the sensitivity of their health plan choices to these different types of variables.