The development of a risk-adjusted capitation payment system: the Maryland Medicaid model

Social Determinants of Health Influence Future Health Care Costs in the Medicaid Cohort of the District of Columbia Study

Policy Points Social determinants of health are an important predictor of future health care costs. Medicaid must partner with other sectors to address the underlying causes of its beneficiaries' poor health and high health care spending.

CONTEXT

Social determinants of health are an important predictor of future health care costs but little is known about their impact on Medicaid spending. This study analyzes the role of social determinants of health (SDH) in predicting future health care costs for adult Medicaid beneficiaries with similar past morbidity burdens and past costs.

METHODS

We enrolled into a prospective cohort study 8,892 adult Medicaid beneficiaries who presented for treatment at an emergency department or clinic affiliated with two hospitals in Washington, DC, between September 2017 and December 31, 2018. We used SDH information measured at enrollment to categorize our participants into four social risk classes of increasing severity. We used Medicaid claims for a 2-year period; 12 months pre- and post-study enrollment to measure past and future morbidity burden according to the Adjusted Clinical Groups system. We also used the Medicaid claims data to characterize total annual Medicaid costs one year prior to and one year after study enrollment.

RESULTS

The 8,892 participants were primarily female (66%) and Black (91%). For persons with similar past morbidity burdens and past costs (p < 0.01), the future morbidity burden was significantly higher in the upper two social risk classes (1.15 and 2.04, respectively) compared with the lowest one. Mean future health care spending was significantly higher in the upper social risk classes compared with the lowest one ($2,713, $11,010, and $17,710, respectively) and remained significantly higher for the two highest social risk classes ($1,426 and $3,581, respectively), given past morbidity burden and past costs (p < 0.01). When we controlled for future morbidity burden (measured concurrently with future costs), social risk class was no longer a significant predictor of future health care costs.

CONCLUSIONS

SDH are statistically significant predictors of future morbidity burden and future costs controlling for past morbidity burden and past costs. Further research is needed to determine whether current payment systems adequately account for differences in the care needs of highly medically and socially complex patients.

Urban-rural differences in healthcare utilization among beneficiaries in China's new cooperative medical scheme

BACKGROUND

The New Cooperative Medical Scheme (NCMS) is a voluntary social health insurance program launched in 2002 for rural Chinese residents where 80% of people were without health insurance of any kind. Over time, several concerns about this program have been raised related to healthcare utilization disparities for NCMS participants in urban versus rural regions. Our study uses 2015 national survey data to evaluate the extent of these urban and rural disparities among NCMS beneficiaries.

METHODS

Data for our study are based on the Chinese Health and Retirement Longitudinal Study (CHARLS) for 2015. Our 12,190-patient sample are urban and rural patients insured by NCMS. We use logistic regression analyses to compare the extent of disparities for urban and rural residence of NCMS beneficiaries in (1) whether individuals received any inpatient or outpatient care during 2015 and (2) for those individuals that did receive care, the extent of the variation in the number of inpatient and outpatient visits among each group.

RESULTS

Our regression results reveal that for urban and rural NCMS patients in 2015, there were no significant differences in inpatient or outpatient utilization for either of the dependent variables - 1) whether or not the patient had a visit during the last year, or 2) for those that had a visit, the number of visits they had. Patient characteristics: age, sex, employment, health status, chronic conditions, and per capita annual expenditures - all had significant impacts on whether or not there was an inpatient or outpatient visit but less influence on the number of inpatient or outpatient visits.

CONCLUSIONS

For both access to inpatient and outpatient facilities and the level of utilization of these facilities, our results reveal that both urban and rural NCMS patients have similar levels of resource utilization. These results from 2015 indicate that utilization angst about urban and rural disparities in NCMS patients do not appear to be a significant concern.

Risk Adjustment for People with Chronic Conditions in Private Sector Health Plans

Background. Although the problem of adverse selection into more generous health insurance plans has been the focus of previous work, risk adjustment systems have only recently begun to be implemented to blunt its effect. Objectives. This study examines the ability of the leading risk adjustment systems to predict health care expenditures for people with chronic conditions, using claims and enrollment data from 2 large employers. Research design. Predictive errors and total financial losses/gains are compared for different risk adjustment approaches (primarily hierarchical condition categories [HCCs] and adjusted clinical groups) for several chronic conditions. Results. One of the best performing risk adjusteent systems was a regression-based HCC method, which had an average under-prediction error rate of 9% or 6%, depending on the employer. In comparison, more typical actuarial risk adjustments based on just age, gender, and prevailing area wages lead to a prediction error of at least 50%. We did not find evidence that payments for particular chronic conditions would be consistently and significantly under- or overestimated. Conclusion. The leading risk adjustment approaches substantially reduce the incentives for adverse se-lection but do not eliminate them.

Age-related adaptation of bone-PDL-tooth complex: Rattus-Norvegicus as a model system

Functional loads on an organ induce tissue adaptations by converting mechanical energy into chemical energy at a cell-level. The transducing capacity of cells alters physico-chemical properties of tissues, developing a positive feedback commonly recognized as the form-function relationship. In this study, organ and tissue adaptations were mapped in the bone-tooth complex by identifying and correlating biomolecular expressions to physico-chemical properties in rats from 1.5 to 15 months. However, future research using hard and soft chow over relevant age groups would decouple the function related effects from aging affects. Progressive curvature in the distal root with increased root resorption was observed using micro X-ray computed tomography. Resorption was correlated to the increased activity of multinucleated osteoclasts on the distal side of the molars until 6 months using tartrate resistant acid phosphatase (TRAP). Interestingly, mononucleated TRAP positive cells within PDL vasculature were observed in older rats. Higher levels of glycosaminoglycans were identified at PDL-bone and PDL-cementum entheses using alcian blue stain. Decreasing biochemical gradients from coronal to apical zones, specifically biomolecules that can induce osteogenic (biglycan) and fibrogenic (fibromodulin, decorin) phenotypes, and PDL-specific negative regulator of mineralization (asporin) were observed using immunohistochemistry. Heterogeneous distribution of Ca and P in alveolar bone, and relatively lower contents at the entheses, were observed using energy dispersive X-ray analysis. No correlation between age and microhardness of alveolar bone (0.7 ± 0.1 to 0.9 ± 0.2 GPa) and cementum (0.6 ± 0.1 to 0.8 ± 0.3 GPa) was observed using a microindenter. However, hardness of cementum and alveolar bone at any given age were significantly different (P<0.05). These observations should be taken into account as baseline parameters, during development (1.5 to 4 months), growth (4 to 10 months), followed by a senescent phase (10 to 15 months), from which deviations due to experimentally induced perturbations can be effectively investigated.

Validation of ACG Case-mix for equitable resource allocation in Swedish primary health care

Background

Adequate resource allocation is an important factor to ensure equity in health care. Previous reimbursement models have been based on age, gender and socioeconomic factors. An explanatory model based on individual need of primary health care (PHC) has not yet been used in Sweden to allocate resources. The aim of this study was to examine to what extent the ACG case-mix system could explain concurrent costs in Swedish PHC.

Methods

Diagnoses were obtained from electronic PHC records of inhabitants in Blekinge County (approx. 150,000) listed with public PHC (approx. 120,000) for three consecutive years, 2004-2006. The inhabitants were then classified into six different resource utilization bands (RUB) using the ACG case-mix system. The mean costs for primary health care were calculated for each RUB and year. Using linear regression models and log-cost as dependent variable the adjusted R² was calculated in the unadjusted model (gender) and in consecutive models where age, listing with specific PHC and RUB were added. In an additional model the ACG groups were added.

Results

Gender, age and listing with specific PHC explained 14.48-14.88% of the variance in individual costs for PHC. By also adding information on level of co-morbidity, as measured by the ACG case-mix system, to specific PHC the adjusted R² increased to 60.89-63.41%.

Conclusion

The ACG case-mix system explains patient costs in primary care to a high degree. Age and gender are important explanatory factors, but most of the variance in concurrent patient costs was explained by the ACG case-mix system.

Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: a nationwide analysis

Background

The Adjusted Clinical Group (ACG) system has been used in measuring an individual's and a population's morbidities. Although all required inputs for running the ACG system are readily available, patients' morbidities and their associations to health care utilizations have been rarely studied in Taiwan. Therefore, the objective of this study was using the ACG system to quantify morbidities for Taiwanese population and to examine their relationship to ambulatory utilizations and costs.

Methods

This secondary analysis examined claims data for ambulatory services provided to 2.71 million representative Taiwanese in 2002 and 2003. People were grouped by the ACG system according to age, gender, and all ambulatory diagnosis codes in a given year. The software collapses the full set of ACGs into six morbidity categories (Non-users, Healthy, Low-morbidity, Moderate-, High- and Very-high) termed Resource Utilization Bands (RUBs). Each ACG was assigned a relative weight (RW), which was calculated as the ratio of mean ambulatory cost for each ACG to that for the overall. The distribution of morbidities was compared between years 2002 and 2003. The consistency of the distributions of visits, costs, and RWs of each ACG were examined for a two-year period. The relationship between people's morbidities and their ambulatory utilizations and costs was assessed.

Results

Ninety-eight percent of the subjects were correctly assigned to ACGs. Except for non-users (7.9 ~ 8.3%), most subjects were assigned to ACGs of acute and minor diseases and ACGs of moderate-to-high-morbid chronic diseases. The distributions of ACG-based morbidities were highly consistent (r = 0.949, p < 0.001) between 2002 and 2003. The ACG-specific visits (r = 0.955, p < 0.001), costs (r = 0.966, p < 0.001) and RWs (r = 0.991, p < 0.001) were correlated across two years. People grouped to the high-morbid ACGs had more visits and costs than those grouped to the low-morbid ACGs. Forty-six percent of the total ambulatory costs were spent by eighteen percent of the population, who were grouped to the High- and Very-high-morbidity RUBs.

Conclusion

This study demonstrated the feasibility, validity, and reliability of using the ACG system to measure morbidities in a Taiwan population and to explain their associations with ambulatory utilizations and costs for the whole country.

Explanatory ability of the ACG system regarding the utilization and expenditure of the national health insurance population in Taiwan--a 5-year analysis

BACKGROUND

The adjusted clinical group (ACG) is a diagnosis-based case-mix adjustment system, which has been widely evaluated in several countries other than Taiwan. The aim of this study was to assess the performance of the ACG system on the National Health Insurance (NHI) population in Taiwan.

METHODS

We conducted longitudinal data analysis using the claims data of 1% of randomly sampled NHI enrollees from 2000 to 2004. The ACG software was used to assign each individual to 1 ACG category based on age, gender and aggregating diagnoses in each year from 2000 to 2004, respectively. The ACG distribution patterns and their relationships to expenditure were examined. Explanatory ability as measured by adjusted R2 of the ACG system for same-year and next-year ambulatory and inpatient expenditure were examined by multivariate regression models for each year.

RESULTS

The quality of NHI claim data was satisfactory in that 98.1% of the population could be assigned to ACG categories. The population's ACG patterns were substantially consistent but unequally distributed across the 5 years. Eighty percent of NHI expenditure were spent on people assigned to 21 ACGs. The explanatory abilities of individual's ACG and its components with respect to the variance of same-year and next-year 99% truncated visits, ambulatory expenditure, inpatient expenditure, and total NHI expenditure were quite consistent across years and were superior to age and gender. The explanatory performance was better for ambulatory than inpatient expenditure and was comparable to the statistics demonstrated in other countries.

CONCLUSION

The ACG system worked well for Taiwanese ambulatory visits and expenditure across years. Health care authorities can introduce the ACG system to quantify the population's morbidity burdens and medical needs.

Assessing state efforts to meet baby boomers' long-term care needs: a case study in compensatory federalism

The role of the state government and the character of federal-state relations in social policy have evolved considerably. Frank Thompson uses the phrase compensatory federalism to describe increased activity by state governments to make up for a diminished federal role. For compensatory federalism to work, it is essential for states to take leadership roles in key policy areas. Few studies examine whether states have risen to the challenge of compensatory federalism in social policy. This paper examines an emerging issue of great significance in social policy-challenges involved in meeting future long-term care needs for the baby boomer generation. The paper provides an in-depth case study of attempts by Maryland to meet the challenges of financing long-term care needs for the baby boomer generation. The detailed description of the agenda-setting and problem-structuring process in Maryland is followed by an analysis that uses three different frameworks to assess the policy development processes. These models are rooted in a bureaucratic politics perspective, an agenda-setting perspective and an interest group politics perspective. The paper concludes with a discussion of the limitations and possibilities of state leadership in the social policy sphere.

Using the SF-12 health status measure to improve predictions of medical expenditures

BACKGROUND

Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36.

OBJECTIVES

We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample.

METHODS

We used data from the 2000-2001 panel of the Medical Expenditure Panel Study. Respondents (n = 5542) completed the SF-12 in a questionnaire. Interviews obtained data on demographics and selected chronic conditions. Data on expenditures incurred subsequent to the interview were obtained in part from provider records. We examined different regression model specifications and compared different statistical estimation techniques.

RESULTS

Adding the SF-12 to a regression model improved the prediction of subsequent medical expenditures. In a model with only age and gender, adding the SF-12 increased R from 0.06 to 0.13. The coefficients for the Physical Component Summary (PCS) and the Mental Component Summary (MCS) of the SF-12 for this model were -0.045 (P < 0.01) and -0.012 (P < 0.01), respectively. In a model including demographic characteristics, chronic conditions, and previous expenditures, adding the SF-12 increased the R from 0.26 to 0.29. The coefficients for the PCS and the MCS for this model were -0.025 (P < 0.001) and -0.005 (P = 0.15), respectively. A single general health status question performed almost as well as the full SF-12. Models estimated using ordinary least squares had undesirable properties. In terms of R, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function.

CONCLUSIONS

Information on self-reported health status is useful in predicting medical expenditures. The extent to which the SF-12 adds predictive power over a comprehensive array of diagnostic data remains to be examined.

The importance of comorbidity in analysing patient costs in Swedish primary care

Background

The objective was to explore the usefulness of the morbidity risk adjustment system Adjusted Clinical Groups^® (ACG), in comparison with age and gender, in explaining and estimating patient costs on an individual level in Swedish primary health care. Data were retrieved from two primary health care centres in southeastern Sweden.

Methods

A cross-sectional observational study. Data from electronic patient registers from the two centres were retrieved for 2001 and 2002, and patients were grouped into ACGs, expressing the individual combination of diagnoses and thus the comorbidity. Costs per patient were calculated for both years in both centres. Cost data from one centre were used to create ACG weights. These weights were then applied to patients at the other centre. Correlations between individual patient costs, age, gender and ACG weights were studied. Multiple linear regression analyses were performed in order to explain and estimate patient costs.

Results

The variation in individual patient costs was substantial within age groups as well as within ACG weight groups. About 37.7% of the individual patient costs could be explained by ACG weights, and age and gender added about 0.8%. The individual patient costs in 2001 estimated 22.0% of patient costs in 2002, whereas ACG weights estimated 14.3%.

Conclusion

ACGs was an important factor in explaining and estimating individual patient costs in primary health care. Costs were explained to only a minor extent by age and gender. However, the usefulness of the ACG system appears to be sensitive to the accuracy of classification and coding of diagnoses by physicians.

Risk Classification of Adult Primary Care Patients by Self-Reported Quality of Life

BACKGROUND

: Although patient-reported health-related quality of life (HRQOL) is known to predict health services utilization, most risk assessment systems use provider-reported diagnoses as predictors rather than HRQOL.

OBJECTIVE

: We sought to classify adult primary care patients prospectively by utilization risk based on age, gender, and HRQOL at a single clinic visit.

RESEARCH DESIGN

: Patients completed the Duke Health Profile. Providers completed the Duke Severity of Illness Checklist. Diagnoses were grouped with the Ambulatory Care Groups system. Predictive coefficients for 1-year primary care charges calculated from the age, gender, and HRQOL of 728 reference patients were used to classify 474 test patients into 4 risk classes. Comparisons were made with models that used diagnoses or severity of illness as predictors.

RESULTS

: The positive likelihood ratio for predicting highest risk was 2.2 for the HRQOL model, compared with 1.8 for the diagnoses model, 1.6 for the severity model, and 1.5 for age and gender alone. One-year actual primary care visits and charges increased step-wise from lowest to highest risk class. Highest risk patients were older and more likely to be women, black, or Medicaid recipients. Although the highest-risk patients represented only 18.6% of the test group, they accounted for 26.7% of the primary care clinic visits, 31.6% of the clinic charges, 34.6% of the hospital days, 35.1% of hospital charges, and 30.8% of total charges at all healthcare sites.

CONCLUSION

: The HRQOL risk classification system can identify primary care patients at risk for high future health services utilization.

Predicting Healthcare Costs in a Population of Veterans Affairs Beneficiaries Using Diagnosis-Based Risk Adjustment and Self-Reported Health Status

BACKGROUND

Many healthcare organizations use diagnosis-based risk adjustment systems for predicting costs. Health self-report may add information not contained in a diagnosis-based system but is subject to incomplete response.

OBJECTIVE

The objective of this study was to evaluate the added predictive power of health self-report in combination with a diagnosis-based risk adjustment system in concurrent and prospective models of healthcare cost.

RESEARCH DESIGN

This was a cohort study using Department of Veterans Affairs (VA) administrative databases. We tested the predictive ability of the Adjusted Clinical Group (ACG) methodology and the added value of SF-36V (short form functional status for veterans) results. Linear regression models were compared using R(2), mean absolute prediction error (MAPE), and predictive ratio.

SUBJECTS

Subjects were 35,337 VA beneficiaries at 8 VA medical centers during fiscal year (FY) 1998 who voluntarily completed an SF-36V survey.

MEASURES

Outcomes were total FY 1998 and FY 1999 cost. Demographics and ACG-based Adjusted Diagnostic Groups (ADGs) with and without 8 SF-36V multiitem scales and the Physical Component Score and Mental Component Score were compared.

RESULTS

The survey response rate was 45%. Adding the 8 scales to ADGs and demographics increased the crossvalidated R by 0.007 in the prospective model. The 8 scales reduced the MAPE by 236 US dollars among patients in the upper 10% of FY 1999 cost.

CONCLUSIONS

The limited added predictive power of health self-report to a diagnosis-based risk adjustment system should be weighed against the cost of collecting these data. Adding health self-report data may increase predictive accuracy in high-cost patients.

Applying a risk-adjustment framework to primary care: can we improve on existing measures?

Outcome-based performance measurement and prospective payment are common features of the current managed care environment. Increasingly, primary care clinicians and health care organizations are being asked to assume financial risk for enrolled patients based on negotiated capitation rates. Therefore, the need for methods to account for differences in risk among patients enrolled in primary care organizations has become critical. Although current risk-adjustment measures represent significant advances in the measurement of morbidity in primary care populations, they may not adequately capture all the dimensions of patient risk relevant to primary care. We propose a risk-adjustment framework for primary care that incorporates clinical features related to patients' health status and nonclinical factors related to patients' health behaviors, psychosocial factors, and social environment. Without this broad perspective, clinicians with more unhealthy and more challenging populations are at risk of being inadequately compensated and inequitably compared with peers. The risk-adjustment framework should also be of use to health care organizations that have been mandated to deliver high-quality primary care but are lacking the necessary tools.

Performance of diagnosis-based risk adjustment measures in a population of sick Australians

OBJECTIVE

Australia is beginning to explore 'managed competition' as an organising framework for the health care system. This requires setting fair capitation rates, i.e. rates that adjust for the risk profile of covered lives. This paper tests two US-developed risk adjustment approaches using Australian data.

METHODS

Data from the 'co-ordinated care' dataset (which incorporates all service costs of 16,538 participants in a large health service research project conducted in 1996-99) were grouped into homogenous risk categories using risk adjustment 'grouper software'. The grouper products yielded three sets of homogenous categories: Diagnostic Groups and Diagnostic cost Groups. A two-stage analysis of predictive power was used: probability of any service use in the concurrent year, next year and the year after (logistic regression) and, for service users, a regression of logged cost of service use. The independent variables were diagnosis gender, a SES variable and the

RESULTS

Age, gender and diagnosis-based risk adjustment measures explain around 40-45% of variation in costs of service use in the current year for untrimmed data (compared with around 15% for age and gender alone). Prediction of subsequent use is much poorer (around 20%). Using more information to assign people to risk categories generally improves prediction.

CONCLUSIONS

Predictive power of diagnosis-base risk adjusters on this Australian dataset is similar to that found in

IMPLICATIONS

Low predictive power carries policy risks of cream skimming rather than managing population health and care. Competitive funding models with risk adjustment on prior year experience could reduce system efficiency if implemented with current risk adjustment technology.

Assessing population health care need using a claims-based ACG morbidity measure: a validation analysis in the Province of Manitoba

OBJECTIVES

To assess the ability of an Adjusted Clinical Group (ACG)-based morbidity measure to assess the overall health service needs of populations. Data Sources/Study Setting. Three population-based secondary data sources: registration and health service utilization data from fiscal year 1995-1996; mortality data from vital statistics reports from 1996-1999; and Canadian census data. The study included all continuously enrolled residents in the universal health care plan in Manitoba.

STUDY DESIGN

Using 60 small geographic areas as the units of analysis, we compared a population-based "ACG morbidity index," derived from individual ACG assignments in fiscal year 1995-1996, with the standardized mortality ratio (ages < 75 years) for 1996-1999. Key variables included a population-based socioeconomic status measure and age- and sex-standardized physician utilization ratios.

DATA EXTRACTION METHODS

The ACGs were assigned based on the complement of diagnoses assigned to persons on physician claims and hospital separation abstracts. The ACG index was created by weighting the ACGs using average health care expenditures.

PRINCIPAL FINDINGS

The ACG morbidity index had a strong positive linear relationship with the subsequent rate of premature death in the small areas of Manitoba. The ACG index was able to explain the majority of the relationships between mortality and both socioeconomic status and physician utilization.

CONCLUSIONS

In Manitoba, ACGs are closely related to premature mortality, commonly accepted as the best single indicator for health service need in populations. Issues in applying ACGs in settings where needs adjustment is a primary objective are discussed.

Rural-urban differences in health risks, resource use and expenditures within three state medicaid programs: implications for medicaid managed care

This study uses Medicaid claims data for income-eligible enrollees in California, Georgia and Mississippi to compare expenditures, resource usage and health risks between residents of rural and urban areas of the states. Resource use is measured using the Resource Based Relative Value Scale (RBRVS) system for professional services, hospital days and outpatient facility visits; it also is valued at private insurance reimbursement rates for the states. Health risks are measured using the diagnosis-based Adjusted Clinical Group system. Resource use is compared on a risk-adjusted basis with the use of urban Medicaid enrollees as the benchmark. We find that actual expenditures for rural care users are lower than for urban care users. However, because the proportion of Medicaid enrollees who use care is higher in rural than in urban areas in all three states, expenditures per rural enrollee are not consistently lower. Case mix is more resource intensive for rural compared to urban residents in all three states. Although resource usage is not systematically lower overall for rural enrollees, on a risk-adjusted basis they tend to use less hospital resources than urban enrollees. Capitation rates based on historical per enrollee expenditures would not appear to under-reimburse managed care organizations for the care of rural as opposed to urban residents in the study states.

The prevalence of formal risk adjustment in health plan purchasing

This paper describes the prevalence of formal risk adjustment of payments made to health plans by Medicare, Medicaid, state governments, and private payers. In this paper, 'formal risk adjustment" is defined as the adjustment of premiums paid to health plans based on individual-level diagnostic or demographic information. We find that formal risk adjustment is used for about one-fifth of all enrollees in capitated health plans. While the Medicare and Medicaid programs rely on formal risk adjustment for virtually all their health plan enrollees, the practice is used for only about 1% of privately insured health plan enrollees. Ourfindings raise the question of why regulators have adopted formal risk adjustment, but private purchasers for the most part have not.

The Medicaid Rx model: pharmacy-based risk adjustment for public programs

BACKGROUND

Risk adjustment models typically use diagnoses from claims or encounter records to assess illness severity. However, concerns about the availability and reliability of diagnostic data raise the potential for alternative methods of risk adjustment. Here, we explore the use of pharmacy data as an alternative or complement to diagnostic data in risk adjustment.

OBJECTIVES

To develop and test a pharmacy-based risk adjustment model for SSI and TANF Medicaid populations.

RESEARCH DESIGN

Pharmacological review combined with empirical evaluation. We developed the Medicaid Rx model, a system that classifies a subset of the National Drug Codes into categories that can be used for risk-assessment and risk-adjusted payment.

SUBJECTS

Subjects consisted of 362,370 persons with disability and 1.5 million AFDC and TANF beneficiaries in California, Colorado, Georgia, and Tennessee during 1990-1999.

MEASURES

We compare pharmacy and diagnostic classification for three chronic diseases. We also compare R2 statistics and use simulated health plans to evaluate the performance of alternative models.

RESULTS

Pharmacy and diagnostic classification vary in their ability to identify specific chronic disease. Using simulated plans, diagnostic models are better at predicting expenditures than are pharmacy-based models for disabled Medicaid beneficiaries, although the models perform similarly for TANF Medicaid beneficiaries. Models that combine diagnostic and pharmacy data have superior overall performance.

CONCLUSIONS

The performance of risk adjustment models using a combination of pharmacy and diagnostic data are superior to that of models using either data source alone, particularly among TANF beneficiaries. Concerns regarding variations in prescribing patterns and the incentives that may follow from linking payment to pharmacy use warrant further research.

Comparison of risk adjusters for medicaid-enrolled children with and without chronic health conditions

OBJECTIVE

Several capitation payment systems have been developed and implemented recently by public and private insurers as well as by individual managed care organizations. Many pediatricians have expressed concern that methods for establishing capitation rates may not adequately account for the higher expected expenditures for children with chronic health conditions. In this study, we evaluate a demographic- and 4 diagnosis-based models, paying particular attention to their performance for children with chronic health conditions.

METHODS

We selected children 18 years of age and under who were enrolled in the Maryland Medicaid Program in 1995 and 1996. We defined the population of children with chronic health conditions using ICD-9 codes. Individual and group-level analyses were utilized to measure the ability of the different risk adjustment models to predict expenditures in 1996 based upon information available in 1995.

RESULTS

All 4 diagnosis-based models significantly outperformed the demographic model for children overall and for children with chronic health conditions. Differences between diagnosis-based models were small, especially as the size of test populations increased.

CONCLUSIONS

Risk adjustment methods that account directly for health status promise to reduce incentives to exclude children with chronic illnesses from managed care plans and to provide a foundation for more appropriate payments to pediatricians who care for these children.

Performance of the ACG case-mix system in two Canadian provinces

BACKGROUND

While the adjusted clinical group (ACG) system has been extensively validated in the United States, its use in other developed nations has been limited. This article examines the performance of the system in 2 Canadian provinces and assesses the extent to which ACGs can account for same-year and next-year health care expenditures.

METHODS

The study population included all residents of Manitoba and British Columbia who were continuously enrolled in the provincial health plans from April 1, 1995, to March 31, 1997. ACGs were assigned through diagnoses from fee-for-service physician claims and hospital separation records. "Physician" costs were calculated from the fee-for-service tariffs, and for Manitobans, "total" costs were also computed by combining physician and hospital costs. Linear regression was used to examine the ability of the ACG system to explain variation in individual costs (truncated at the 99th percentile).

RESULTS

The British Columbia and Manitoba data were generally acceptable, with fewer than 2% rejected diagnoses. Higher costs were associated with both the accumulation of morbidities and their relative severity. For physician costs, the ACG system explained approximately 50% and approximately 25% of the variation in same-year and next-year truncated costs, respectively. For total costs, the system explained approximately 40% and approximately 14% of these respective costs.

CONCLUSIONS

The application of ACGs in Canada is feasible using existing data. The ability of the ACG system to explain variation in costs is similar to that found in US health systems. While application of ACGs in Canada shows promise, further research is required to examine how closely they reflect population morbidity burdens and health care needs.

Oil and water? Lessons from Maryland's effort to protect safety net providers in moving to Medicaid managed care

Studies have highlighted the tensions that can arise between Medicaid managed care organizations and safety net providers. This article seeks to identify what other states can learn from Maryland's effort to include protections for safety net providers in its Medicaid managed care program--HealthChoice. Under HealthChoice, traditional provider systems can sponsor managed care organizations, historical providers are assured of having a role, patients can self-refer and have open access to certain public health providers, and capitation rates are risk adjusted through the use of adjusted clinical groups and claims data. The article is based on a week-long site visit to Maryland in fall 1998 that was one part of a seven-state study. Maryland's experience suggests that states have much to gain in the way of "good" public policy by considering the impact of their Medicaid managed care programs on the safety net, but states should not underestimate the challenges involved in balancing the need to protect the safety net with the need to contain costs and minimize the administrative burden on providers. No amount of protection can compensate for a poorly designed or implemented program. As the health care environment continues to change, so may the need for and the types of protections change. It also may be most difficult to guarantee adequate protections to those who need it most--among relatively financially insecure providers that have a limited management infrastructure and that depend heavily on Medicaid and the state for funds to care for the uninsured.