Diagnostic Cost Groups Research Articles

Health-Based Risk Adjustment: Improving the Pharmacy-Based Cost Group Model by Adding Diagnostic Cost Groups

Since 1991, risk-adjusted premium subsidies have existed in the Dutch social health insurance sector, which covered about two-thirds of the population until 2006. In 2002, pharmacy-based cost groups (PCGs) were included in the demographic risk adjustment model, which improved the goodness-of-fit, as measured by the R2, to 11.5%. The model's R2 reached 22.8% in 2004, when inpatient diagnostic information was added in the form of diagnostic cost groups (DCGs). PCGs and DCGs appear to be complementary in their ability to predict future costs. PCGs particularly improve the R2 for outpatient expenses, whereas DCGs improve the R2 for inpatient expenses. In 2006, this system of risk-adjusted premium subsidies was extended to cover the entire population.

Utilization and expenditures of veterans obtaining primary care in community clinics and VA medical centers: an observational cohort study

BackgroundTo compare VA inpatient and outpatient utilization and expenditures of veterans seeking primary care in community-based outpatient clinics (CBOCs) and VA medical centers (VAMCs) in fiscal years 2000 (FY00) and 2001.MethodsThe sample included 25,092 patients who obtained primary care exclusively from 108 CBOCs in FY00, 26,936 patients who obtained primary care exclusively from 72 affiliated VAMCs in FY00, and 11,450 "crossover" patients who obtained primary care in CBOCs and VAMCs in FY00. VA utilization and expenditure data were drawn from the VA's system-wide cost accounting system. Veteran demographic characteristics and a 1999 Diagnostic Cost Group risk score were obtained from VA administrative files. Outpatient utilization (primary care, specialty care, mental health, pharmacy, radiology and laboratory) and inpatient utilization were estimated using count data models and expenditures were estimated using one-part or two-part models. The second part of two-part models was estimated using generalized linear regressions.ResultsCBOC patients had a slightly more primary care visits per year than VAMC patients (p < 0.0001), but lower primary care costs (-$71, p < 0.0001). CBOC patients had lower odds of one or more specialty, mental health, ancillary visits and hospital stays per year, and fewer numbers of visits and stays if they had any and lower specialty, mental health, ancillary and inpatient expenditures (all, p < 0.0001). As a result, CBOC patients had lower total outpatient and overall expenditures than VAMC patients (p < 0.0001).ConclusionCBOCs provided veterans improved access to primary care and other services, but expenditures were contained because CBOC patients who sought health care had fewer visits and hospital stays than comparable VAMC patients. These results suggest a more complex pattern of health care utilization and expenditures by CBOC patients than has been found in prior studies. This study also illustrates that CBOCs continue to be a critical primary care and mental health access point for veterans.

Health care funding levels and patient outcomes: a national study

Health care funding levels differ significantly across geographic regions, but there is little correlation between regional funding levels and outcomes of elderly Medicare beneficiaries. Our goal was to determine whether this relationship holds true in a non-Medicare population cared for in a large integrated health care system with a capitated budget allocation system. We explored the association between health care funding and risk-adjusted mortality in the 22 Veterans Affairs (VA) geographic Networks over a six-year time period. Allocations to Networks were adjusted for illness burden using Diagnostic Cost Groups. To test the association between funding and risk-adjusted three-year mortality, we ran logistic regressions with single-year patient cohorts, as well as hierarchical regressions on a six year longitudinal data set, clustering on VA Network. A 1000 dollar increase in funding per unit of patient illness burden was associated with a 2-8% reduction in three-year mortality in cross sectional regressions. However, in longitudinal hierarchical regressions clustering on Network, the significant effect of funding level was eliminated. When longitudinal data are used, the significant cross sectional effect of funding levels on mortality disappear. Thus, the factors driving differences in mortality are Network effects, although part of the Network effect may be due to past levels of funding. Our results provide a caution for cross sectional examinations of the association between regional health care funding levels and health outcomes.

Comparing Self‐Reported Health Status and Diagnosis‐Based Risk Adjustment to Predict 1‐ and 2 to 5‐Year Mortality

To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test. The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality. The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.

In Search of the Perfect Comorbidity Measure for Use With Administrative Claims Data

Numerous measures of comorbidity have been developed for health services research with administrative claims. We sought to compare the performance of 4 claims-based comorbidity measures. We undertook a retrospective cohort study of 5777 Medicare beneficiaries ages 66 and older with stage III colon cancer reported to the Surveillance, Epidemiology, and End Results Program between January 1, 1992 and December 31, 1996. Comorbidity measures included Elixhauser's set of 30 condition indicators, Klabunde's outpatient and inpatient indices weighted for colorectal cancer patients, Diagnostic Cost Groups, and the Adjusted Clinical Group (ACG) System. Outcomes included receipt of adjuvant chemotherapy and 2 year noncancer mortality. For all measures, greater comorbidity significantly predicted lower receipt of chemotherapy and higher noncancer death. Nested logistic regression modeling suggests that using more claims sources to measure comorbidity generally improves the prediction of chemotherapy receipt and noncancer death, but depends on the measure type and outcome studied. All 4 comorbidity measures significantly improved the fit of baseline regression models for both chemotherapy receipt (baseline c-statistic 0.776; ranging from 0.779 after adding ACGs and Klabunde to 0.789 after Elixhauser) and noncancer death (baseline c-statistic 0.687; ranging from 0.717 after adding ACGs to 0.744 after Elixhauser). Although some comorbidity measures demonstrate minor advantages over others, each is fairly robust in predicting both chemotherapy receipt and noncancer death. Investigators should choose among these measures based on their availability, comfort with the methodology, and outcomes of interest.

Development and Validation of a Psychiatric Case-Mix System

Although difficulties in applying risk-adjustment measures to mental health populations are increasingly evident, a model designed specifically for patients with psychiatric disorders has never been developed. Our objective was to develop and validate a case-mix classification system, the "PsyCMS," for predicting concurrent and future mental health (MH) and substance abuse (SA) healthcare costs and utilization. Subjects included 914,225 veterans who used Veterans Administration (VA) healthcare services during fiscal year 1999 (FY99) with any MH/SA diagnosis (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] codes 290.00-312.99, 316.00-316.99). We derived diagnostic categories from ICD-CM codes using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition definitions, clinical input, and empiric analyses. Weighted least-squares regression models were developed for concurrent (FY99) and prospective (FY00) MH/SA costs and utilization. We compared the predictive ability of the PsyCMS with several case-mix systems, including adjusted clinical groups, diagnostic cost groups, and the chronic illness and disability payment system. Model performance was evaluated using R-squares and mean absolute prediction errors (MAPEs). Patients with MH/SA diagnoses comprised 29.6% of individuals seen in the VA during FY99. The PsyCMS accounted for a distinct proportion of the variance in concurrent and prospective MH/SA costs (R=0.11 and 0.06, respectively), outpatient MH/SA utilization (R=0.25 and 0.07), and inpatient MH/SA utilization (R=0.13 and 0.05). The PsyCMS performed better than other case-mix systems examined with slightly higher R-squares and lower MAPEs. The PsyCMS has clinically meaningful categories, demonstrates good predictive ability for modeling concurrent and prospective MH/SA costs and utilization, and thus represents a useful method for predicting mental health costs and utilization.

Diagnosis-based risk adjustment and Australian health system policy

Diagnosis-based risk adjustment is increasingly seen as an important tool for establishing capitation payments and evaluating appropriateness and efficiency of services provided and has become an important area of research for many countries contemplating health system reform. This paper examines the application of a risk-adjustment method, extensively validated in the United States, known as diagnostic cost groups (DCG), to a large Australian hospital inpatient data set. The data set encompassed hospital inpatient diagnoses and inpatient expenditure for the entire metropolitan population residing in the state of New South Wales. The DCG model was able to explain 34% of individual-level variation in concurrent expenditure and 5.2% in subsequent year expenditure, which is comparable to US studies using inpatient-only data. The degree of stability and internal consistency of the parameter estimates for both the concurrent and prospective models indicate the DCG methodology has face validity in its application to NSW health data sets. Modelling and simulations were conducted which demonstrate the policy applications and significance of risk adjustment model(s) in the Australian context. This study demonstrates the feasibility of using large individual-level data sets for diagnosis-based risk adjustment research in Australia. The results suggest that a research agenda should be established to broaden the options for health system reform.

Evaluation of the Dutch Risk Equalization System: Are the Insurers Confronted with Predictable Losses for the Chronically Ill?

Rationale: Since 2006 everyone in the Netherlands must buy private health insurance from one of the competing insurers. A risk equalization system should sufficiently compensate the insurers for the differences in portfolio mix regarding the age, gender and health status of their insured. The current (2007) risk equalization formula in the Netherlands is predominantly based on age, gender, urbanisation, disability, Diagnostic Cost Groups (DCGs) and Pharmacy-based Cost Groups (PCGs). The regulation requires open enrollment and community rating. In case of insufficient risk equalization insurers are confronted with predictable losses on the chronically ill. These losses create incentives for risk selection, which can have several adverse effects. Objective: To evaluate the risk equalization system. The following research questions will be answered: 1. Are there identifiable subgroups of consumers with predictable lossses? 2. If so: How large are these subgroups? And how large are the predictable losses? In particular we focus on subgroups of persons with a chronic condition or with above average utilization rates in previous years. Methodology: We used the following data base: all information in the files of a large insurance company over the period 1997 - 2004 combined with an individual health survey (held in 2001) with many questions about the insureds' health status. In total some 17,000 observations. We were able to apply the 2006 and 2007 risk equalization formulae to simulate predictions of the insureds equalization-payments for the years 2003 respectively 2004. By comparing these predicted expenditures with their actual expenditures we calculated the average profits and losses for many identifiable relevant subgroups. Results: Many subgroups of chronically ill that can be easily identified by the insurers, generate substantial predictable losses. The size of these subgroups ranges from less than 1% to 30%. The predictable losses are in the order of hundreds to thousands euros per person per year. The results indicate predictable losses also for subgroups of insureds whose disease was included as a risk adjuster in the risk equalization formula (e.g. heart problems, cancer and rheumatism). The improvements in the 2007-formula relative to the 2006-formula result in an average reduction of about 10% of the predictable losses. Conclusion: The current (2007) risk equalization in the Netherlands compensates the insurers insufficiently for many identifiable high risk groups. In particular those insurers who make themselves attractive for the chronically ill e.g. by providing or contracting the best care for them, are insufficiently compensated. Consequently these insurers have to raise their premium which worsens their market position, in particular relative to insurers who have a low premium because they are successful in risk selection. Although there is an open enrolment requirement, insurers in the Netherlands have many tools for subtle forms of risk selection at their disposal. This risk selection can have many adverse effects in terms of affordability, efficiency and quality of care. A substantial improvement of the risk equalization system needs a high priorty. With an insufficient risk equalization system the disadvantages due to risk selection may outweigh the advantages of a competitive market.

Dual-System Use: Are There Implications for Risk Adjustment and Quality Assessment?

The authors examined the implications of dual-system use for risk adjustment and quality assessment. The sample (n = 34 151) included all veterans dually enrolled in the Veterans Health Administration (VA) and the private sector in 1998 with (1) an inpatient discharge from either a VA or Medicare setting for 1 of 6 conditions/procedures and (2) inpatient and/or outpatient use in both the VA and private sector. The authors used the Diagnostic Cost Groups risk-adjustment system to obtain concurrent and prospective health status (relative risk scores) using veterans' Medicare diagnoses only, VA diagnoses only, and diagnoses from both systems. Both concurrent and prospective relative risk scores increased when diagnoses from both systems were used; the population's disease profile also was affected. The authors conclude that it is important to capture the true disease burden of the population by obtaining diagnoses from all health care systems providing care to facilitate meaningful comparisons of performance.

Identifying Future High-Healthcare Users

Diagnosis-based risk-adjustment measures are increasingly being promoted as disease management tools. We compared the ability of several types of predictive models to identify future high-risk older people likely to benefit from disease management. Veterans Health Administration (VHA) data were used to identify veterans ≥65 years of age who used healthcare services during fiscal years (FY) 1997 and 1998 and who remained alive through FY 1997. This yielded a development sample of 412 679 individuals and a validation sample of 207 294. Prospective risk-adjustment models were fitted and tested using Adjusted Clinical Groups (ACGs), Diagnostic Cost Groups (DCGs), a prior-utilization model (prior), and combined models (prior + ACGs and prior + DCGs). Prespecified high use in FY 1998 was defined as ≥92 days of care (top 2.2%) for an individual (i.e. the number of days during the year in which an individual received inpatient or outpatient healthcare services). We developed a second outcome, defined as ≥164 days of care (top 1.0%), to explore whether changing the criterion for high risk would affect the number of misclassifications. The diagnosis-based models performed better than the prior model in identifying a subgroup of future high-cost individuals with high disease burden and chronic diseases appropriate for disease management. The combined models performed best at correctly classifying those without high use in the prospective year. The utility for efficiently identifying high-risk cases appeared limited because of the high number of individuals misclassified as future high-risk cases by all the models. Changing the criterion for high risk generally decreased the number of patients misclassified. There was little agreement between the models regarding who was identified as high risk. Health plans should be aware that different risk-adjustment measures may select dissimilar groups of individuals for disease management. Although diagnosis-based measures show potential as predictive modeling tools, combining a diagnosis-based measure with prior-utilization model may yield the best results.

Health-Adjusted Premium Subsidies In The Netherlands

The Dutch government has decided to proceed with managed competition in health care. In this paper we report on progress made with health-based risk adjustment, a key issue in managed competition. In 2004 both Diagnostic Cost Groups (DCGs) computed from hospital diagnoses only and Pharmacy-based Cost Groups (PCGs) computed from out-patient prescription drugs are used to set the premium subsidies for competing risk-bearing sickness funds. These health-based risk adjusters appear to be effective and complementary. Risk selection is not a major problem in the Netherlands. Despite the progress made, we are still faced with a full research agenda for risk adjustment in the coming years.

The risk-adjusted vision beyond casemix (DRG) funding in Australia. International lessons in high complexity and capitation.

Hospitals throughout the world using funding based on diagnosis-related groups (DRG) have incurred substantial budgetary deficits, despite high efficiency. We identify the limitations of DRG funding that lack risk (severity) adjustment for State-wide referral services. Methods to risk adjust DRGs are instructive. The average price in casemix funding in the Australian State of Victoria is policy based, not benchmarked. Average cost weights are too low for high-complexity DRGs relating to State-wide referral services such as heart and lung transplantation and trauma. Risk-adjusted specified grants (RASG) are required for five high-complexity respiratory, cardiology and stroke DRGs incurring annual deficits of $3.6 million due to high casemix complexity and government under-funding despite high efficiency. Five stepwise linear regressions for each DRG excluded non-significant variables and assessed heteroskedasticity and multicollinearlity. Cost per patient was the dependent variable. Significant independent variables were age, length-of-stay outliers, number of disease types, diagnoses, procedures and emergency status. Diagnosis and procedure severity markers were identified. The methodology and the work of the State-wide Risk Adjustment Working Group can facilitate risk adjustment of DRGs State-wide and for Treasury negotiations for expenditure growth. The Alfred Hospital previously negotiated RASG of $14 million over 5 years for three trauma and chronic DRGs. Some chronic diseases require risk-adjusted capitation funding models for Australian Health Maintenance Organizations as an alternative to casemix funding. The use of Diagnostic Cost Groups can facilitate State and Federal government reform via new population-based risk adjusted funding models that measure health need.

Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction

Warner G, Hoenig H, Montez M, Wang F, Rosen A. Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction. Arch Phys Med Rehabil 2004;85:218–26. Objective To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. Design Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. Setting Models were replicated in 3 populations. Participants Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). Interventions Not applicable. Main outcome measures Inpatient, outpatient, and total days of care in FY97. Results The DCG models ( R 2 range, .22–.38) performed better than ACG models ( R 2 range, .04–.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model ( R 2 range for ACG, .14–.34; R 2 range for DCG, .24–.38). Information on self-care function slightly improved performance ( R 2 range increased from 0 to .04). Conclusions The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

The costs of decedents in the Medicare program: implications for payments to Medicare + Choice plans.

To discuss and quantify the incentives that Medicare managed care plans have to avoid (through selective enrollment or disenrollment) people who are at risk for very high costs, focusing on Medicare beneficiaries in the last year of life-a group that accounts for more than one-quarter of Medicare's annual expenditures. Medicare administrative claims for 1994 and 1995. We calculated the payment a plan would have received under three risk-adjustment systems for each beneficiary in our 1995 sample based on his or her age, gender, county of residence, original reason for Medicare entitlement, and principal inpatient diagnoses received during any hospital stays in 1994. We compared these amounts to the actual costs incurred by those beneficiaries. We then looked for clinical categories that were predictive of costs, including costs in a beneficiary's last year of life, not accounted for by the risk adjusters. The analyses were conducted using claims for a 5 percent random sample of Medicare beneficiaries who died in 1995 and a matched group of survivors. Medicare is currently implementing the Principal Inpatient Diagnostic Cost Groups (PIP-DCG) risk adjustment payment system to address the problem of risk selection in the Medicare+Choice program. We quantify the strong financial disincentives to enroll terminally ill beneficiaries that plans still have under this risk adjustment system. We also show that up to one-third of the selection observed between Medicare HMOs and the traditional fee-for-service system could be due to differential enrollment of decedents. A risk adjustment system that incorporated more of the available diagnostic information would attenuate this disincentive; however, plans could still use clinical information (not included in the risk adjustment scheme) to identify beneficiaries whose expected costs exceed expected payments. More disaggregated prospective risk adjustment methods and alternative payment systems that compensate plans for delivering care to certain classes of patients should be considered to ensure access to high-quality managed care for all beneficiaries.

Modelling the Ability of Risk Adjusters to Reduce Adverse Selection in Managed Care

Population-based risk adjustment, as applied to reimbursement in managed care settings, may reduce pressures for adverse selection by managed care organisations. Using insurance claims data from 184 340 plan members, we compared the performance of three risk-adjustment methods. We present a model for measuring the impact of risk adjustment on the likelihood that individual members will be at risk for adverse selection. These results are compared with resource allocation based on age/sex. The predictive ability of alternative allocation schemes increased from an R(2) of 1.2% for age-sex allocation to 11.4% based on risk adjustment using diagnostic cost groups. However, the impact of risk adjustment on the proportion of members at risk for adverse selection was small. At an absolute threshold loss of $US2400 per year, 8.3% to 8.6% of members were at risk for adverse selection compared with 9.3% based on age-sex allocation. The limited impact of risk adjustment on the likelihood of adverse selection suggests that other strategies for reducing adverse selection may be required.

An Analysis of Unobserved Selection in an Inpatient Diagnostic Cost Group Model

The study assesses unobserved selection bias in an inpatient diagnostic cost group (DCG) model similar to Medicare's Principal Inpatient Diagnostic Cost Group (PIP-DCG) risk adjustment model using a unique data set that contains hospital discharge records for both FFS and HMO Medicare beneficiaries in California from 1994 to 1996. We use a simultaneous equations model that jointly estimates HMO enrollment and subsequent hospital use to test the existence of unobserved selection and estimate the true HMO effect. It is found that the inpatient DCG model does not adequately adjust for biased selection into Medicare HMOs. New HMO enrollees are healthier than FFS beneficiaries even after adjustment for the included PIP-DCG risk factors. A model developed over an FFS sample ignoring unobserved selection overestimates hospital use of new HMO enrollees by 28 percent compared to their use if they had remained in FFS. Models that better captures selection bias are needed to reduce overestimation of Medicare HMO enrollees' resource use.

Using self-reported data to predict expenditures for the health care of older people.

To create and test a method for using self-reported data to predict future expenditures for the health care of older people. A two-stage regression model of the relationship between self-reported data and Medicare expenditures during the following year was constructed from a randomly selected (derivation) half of a cohort of fee-for-service Medicare beneficiaries. For the other (validation) half of the cohort, two sets of predictions of 12-month Medicare expenditures were generated, one using the new two-stage model and the other using the principal inpatient diagnostic cost group (PIP-DCG) method now used to risk-adjust capitation payments to Medicare + Choice health plans. Both sets of predictions were compared with Medicare's actual 12-month expenditures for the validation cohort. Ramsey County, Minnesota. Community-dwelling Medicare beneficiaries aged 70 and older (N = 13,682) who responded to a mailed survey. Predicted-to-observed ratio (PTOR) of Medicare expenditures. For the validation cohort, Medicare's actual 12-month expenditures totaled $26.5 million. The two-stage model predicted Medicare expenditures of $26.4 million (PTOR = 1.00); the PIP-DCG method predicted $31.2 million (PTOR = 1.18). Within subpopulations of healthy and ill beneficiaries, the two-stage model's predictions remained considerably more accurate than the PIP-DCG predictions. Self-reported data may predict future Medicare expenditures more accurately than administrative data about beneficiaries' demographic characteristics, and previous hospitalizations.

Diagnostic cost groups (DCGs) and concurrent utilization among patients with substance abuse disorders.

To assess the performance of Diagnostic Cost Groups (DCGs) in explaining variation in concurrent utilization for a defined subgroup, patients with substance abuse (SA) disorders, within the Department of Veterans Affairs (VA). A 60 percent random sample of veterans who used health care services during Fiscal Year (FY) 1997 was obtained from VA administrative databases. Patients with SA disorders (13.3 percent) were identified from primary and secondary ICD-9-CM diagnosis codes. Concurrent risk adjustment models were fitted and tested using the DCG/HCC model. Three outcome measures were defined: (1) "service days" (the sum of a patient's inpatient and outpatient visit days), (2) mental health/substance abuse (MH/SA) service days, and (3) ambulatory provider encounters. To improve model performance, we ran three DCG/HCC models with additional indicators for patients with SA disorders. To create a single file of veterans who used health care services in FY 1997, we merged records from all VA inpatient and outpatient files. Adding indicators for patients with mild/moderate SA disorders did not appreciably improve the R-squares for any of the outcome measures. When indicators were added for patients with severe SA who were in the most costly category, the explanatory ability of the models was modestly improved for all three outcomes. Modifying the DCG/HCC model with additional markers for SA modestly improved homogeneity and model prediction. Because considerable variation still remained after modeling, we conclude that health care systems should evaluate "off-the-shelf" risk adjustment systems before applying them to their own populations.

Risk-adjusted capitation funding models for chronic disease in Australia: alternatives to casemix funding

Under Australian casemix funding arrangements that use Diagnosis-Related Groups (DRGs) the average price is policy based, not benchmarked. Cost weights are too low for State-wide chronic disease services. Risk-adjusted Capitation Funding Models (RACFM) are feasible alternatives. A RACFM was developed for public patients with cystic fibrosis treated by an Australian Health Maintenance Organization (AHMO). Adverse selection is of limited concern since patients pay solidarity contributions via Medicare levy with no premium contributions to the AHMO. Sponsors paying premium subsidies are the State of Victoria and the Federal Government. Cost per patient is the dependent variable in the multiple regression. Data on DRG 173 (cystic fibrosis) patients were assessed for heteroskedasticity, multicollinearity, structural stability and functional form. Stepwise linear regression excluded non-significant variables. Significant variables were 'emergency' (1276.9), 'outlier' (6377.1), 'complexity' (3043.5), 'procedures' (317.4) and the constant (4492.7) (R(2)=0.21, SE=3598.3, F=14.39, Prob<0.0001. Regression coefficients represent the additional per patient costs summed to the base payment (constant). The model explained 21% of the variance in cost per patient. The payment rate is adjusted by a best practice annual admission rate per patient. The model is a blended RACFM for in-patient, out-patient, Hospital In The Home, Fee-For-Service Federal payments for drugs and medical services; lump sum lung transplant payments and risk sharing through cost (loss) outlier payments. State and Federally funded home and palliative services are 'carved out'. The model, which has national application via Coordinated Care Trials and by Australian States for RACFMs may be instructive for Germany, which plans to use Australian DRGs for casemix funding. The capitation alternative for chronic disease can improve equity, allocative efficiency and distributional justice. The use of Diagnostic Cost Groups (DCGs) is a promising alternative classification system for capitation arrangements.

Book Reviews

Australian and New Zealand Journal of Public HealthVolume 26, Issue 3 p. 284-292 Free Access Book Reviews First published: 25 September 2007 https://doi.org/10.1111/j.1467-842X.2002.tb00694.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Book reviewed in this article: The Cambridge Medical Ethics Workbook: Case Studies, Commentaries and Activities By Michael Parker and Donna Dickenson Ward ethics: Dilemmas for medical students and doctors in training Edited by Thomasine K. Kushner and David C. Thomasma The ethics of medical research on humans By Claire Foster Undue Risk By Jonathan D. Moreno The Social Economics of Health Care Edited by John B. Davis The tyranny of health: doctors and the regulation of lifestyle By Michael Fitzpatrick Gender, Health and Healing: The Public/Private Divide Edited by G. Bendelow, M. Carpenter, C. Vautier, S. Williams Technologies and Health: Critical Compromises Edited by Jeanne Daly, Marilys Guillemin and Sophie Hill Dealing: Women in the Drug Economy By Barbara Denton References 1 Singer E. Rethinking Life & Death: The Collapse of Our Traditional Ethics. Melbourne : Text Publishing, 1994. 2 Singer P. Ethics, In: P. Singer editor. Oxford Readers. Great Britain : Oxford University Press, 1994. p. 4. 3 Daly J, editor. Ethical Intersections: Health Research, Methods and Researcher Responsibility. St. Leonards ( NSW) : Allen Unwin, 1996. 4 Lesser H. Technology and medicine: means and ends. In: D. Braine H. Lesser editors. 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Health, Health Care and Health Economics. Perspectives in Distribution. Toronto : Wiley, 1998. 14 Evans RG, Barer ML, Marmor TR (editors) Why are Some People Healthy and Others Not? The Determinants of Health of Populations. New York : Aldine De Gruyter, 1994. 15 Van Doorslaer E, Wagstaff A, Rutten F, editors. Equity in the Finance and Delivery of Health Care: An International Perspective. Oxford : Oxford Medical Publications, 1993. 16 WPMM Van de Ven, RP. Ellis Risk adjustment in competitive health plan markets. In: JP Newhouse, AJ Culyer, editors. Handbook of Health Economics. North Holland, 2000. p. 755– 845. 17 Ash AS, Ellis RP, Pope GC. Using diagnoses to describe populations and predict costs. Health Care Financ Rev 2000; 21(3): 7– 28. 18 Pope CG, Ellis RP, Ash AS, et al. Principal inpatient diagnostic cost group model for Medicare risk adjustment. Health Care Financ Rev 2000; 21(3): 93– 118. 19 Davis JB. The Social Economics of Health Care. London : Routledge, 2001. 20 Antioch KM, Walsh MK. Risk adjusted capitation funding models for chronic diseases in Australia: Alternatives to casemix funding. Eur J Health Econ. In press 2002. 21 Antioch KM, Walsh MK. Funding issues for Victorian hospitals: The risk adjusted vision beyond casemix funding. Aust Health Rev 1997; 23(3): 145– 53. 22 National Health and Medical Research Council (2001). How to compare the costs and benefits: evaluation of health economics evidence. Handbook series on preparing Clinical Practice Guidelines. 23 Antioch KM, Jennings G, Botti M, et al. Integrating cost-effectiveness evidence into clinical practice guidelines in Australia for acute myocardial infarction. Eur J Health Econ 2002; 3(1): 26– 39. 24 Antioch KM, Chapman R, Elliot B, et al. Cost effective clinical pathways at The Alfred Hospital: International lessons from Bayside Health, Australia. Aust Health Rev 2001; 24(4): 21– 7. 25 Antioch KM, Chapman R, Wilson J, Douglass J, Santamaria N. Integrating cost-effectiveness evidence into Clinical Practice Guidelines in Australia for chronic obstructive pulmonary disease. Economics and Health:2001. In press 2002, CD-ROM. 26 Illich I. Medical Nemesis: The Expropriation of Health. London : Calder Boyars, 1975. Volume26, Issue3June 2002Pages 284-292 ReferencesRelatedInformation