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| Year : 2011 | Volume
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| Issue : 1 | Page : 24-39 |
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| Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities |
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Gerardo Machnicki1, Krista L Lentine1, Paolo R Salvalaggio2, Thomas E Burroughs1, Daniel C Brennan3, Mark A Schniztler1
1 Center for Outcomes Research, Saint Louis University School of Medicine, Saint Louis, USA
2 Department of Surgery, University of Washington, Seattle, WA, USA
3 Department of Medicine, Washington University, Saint Louis, MO, USA
Click here for correspondence address and email
| Date of Web Publication | 30-Dec-2010 |
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 Abstract | | |
Little is known about the influence of pre-transplant comorbidities on post-transplant expenditures. We estimated the associations between pre-transplant comorbidities and post-transplant Medicare costs, using several comorbidity classification systems. We included recipients of first-kidney deceased donor transplants from 1995 through 2002 for whom Medicare was the primary payer for at least one year pre-transplant (N = 25,175). We examined pre-transplant comorbidities as classified by International Classification of Diseases (ICD-9-CM) codes from Medicare claims with the Clinical Classifications Software (CCS) and Charlson and Elixhauser algorithms. Post-transplant costs were calculated from payments on Medicare claims. We developed models considering Organ Procurement and Transplantation Network (OPTN) variables plus: 1) CCS categories, 2) Charlson, 3) Elixhauser, 4) number of Charlson and 5) number of Elixhauser comorbidities, independently. We applied a novel regression methodology to account for censoring. Costs were estimated at individual and population levels. The comorbidities with the largest impact on mean Medicare payments included cardiovascular disease, malignancies, cerebrovascular disease, mental conditions and functional limitations. Skin ulcers and infections, rheumatic and other connective tissue disease and liver disease also contributed to payments and have not been considered or described previously. A positive graded relationship was found between costs and the number of pre-transplant comorbidities. In conclusion, we showed that expansion beyond the usually considered pre-transplant comorbidities with inclusion of CCS and Charlson or Elixhauser comorbidities increased the knowledge about comorbidities related to augmented Medicare payments. Our expanded methodology can be used by others to assess more accurately the financial implications of renal transplantation to Medicare and individual transplant centers.
How to cite this article:
Machnicki G, Lentine KL, Salvalaggio PR, Burroughs TE, Brennan DC, Schniztler MA. Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities. Saudi J Kidney Dis Transpl 2011;22:24-39 |
How to cite this URL:
Machnicki G, Lentine KL, Salvalaggio PR, Burroughs TE, Brennan DC, Schniztler MA. Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities. Saudi J Kidney Dis Transpl [serial online] 2011 [cited 2019 Jul 19];22:24-39. Available from: http://www.sjkdt.org/text.asp?2011/22/1/24/74337 |
| Introduction | |  |
Kidney transplantation is the preferred renal replacement therapy due to improved survival and quality of life and reduced costs compared to dialysis. [1],[2],[3] However, a changing donor and recipient patient mix, the increasing organ shortage and a stringent economic environment create new challenges to maximize the achievements of kidney transplantation.
In particular, the impact of pre-transplant comorbidities is of interest. End-stage renal disease (ESRD) patients have many comorbidities [4] and transplant recipients carry these comorbidity loads. Comorbidities are increasing given the aging transplant recipient population. [5] Additionally, growing organ shortages reinforce the need for organ allocation to maximize benefit while maintaining equality of access. [6] Life years from transplant (LYFT) is one principle proposed to increase the benefit derived from kidney transplantation. Whether to include comorbidities and which ones to include in such system is an area of recent scrutiny. [5],[7],[8] Comorbidities also impact the likelihood of access to transplantation. [9]
Pre-transplant comorbidities are also relevant for resource allocation and healthcare administration. The assessment of cost effectiveness of medications or procedures is a continuous process that must be reassessed with changes in clinical practices and patient mix. The cost effectiveness of kidney transplantation has been started to be studied further in specific populations with comorbid disease. [10]
Insurers and individual transplant programs need to understand the determinants of posttransplant costs and reimbursement [11] to identify modifiable factors to reduce costs without compromising quality, aid in predicting future costs and in the implementation of new payment schemes.
In this study, we focused on Medicare transplant payments. By December 2004, 55% of the transplants were covered by Medicare, [12] making this institution the biggest payer for transplant services in the United States (US). The Medicare ESRD program spends approximately $23 billion per year, which accounts for 6.4% of the annual Medicare budget for 1.1% of the Medicare population. [4] Although total payments for transplant procedures and subsequent care represent less than 15% of total ESRD Medicare expenses, payments for patients with functioning grafts have increased 3.4 times since 1991. [4]
Comorbidities are a relevant topic also in regards to reimbursement. A small segment of the Medicare transplant population is covered by private insurers that receive capitated payments (Medicare Advantage Plans). [13] In 2003, the ESRD capitated payment scheme implemented adjustments for comorbid disease, using the Hierarchical Condition Categories (HCC) comorbidity classification system. [13] However, regression estimates were generated only for dialysis patients and were assumed to be the same for transplant recipients. Other comorbidity classification systems, such as the Clinical Classifications Software (CCS), Charlson and Elixhauser are used in research to predict healthcare costs. [14],[15]
The Organ Procurement and Transplantation Network (OPTN) records pre-transplant comorbidities such as diabetes, cardiovascular disease, peripheral vascular disease and hepatitis C. These have been associated with increased post-transplant Medicare costs. [16],[17] Other patient and transplant factors that have been associated with higher Medicare payments include HLA matching, [16],[18] cold ischemia time, [16] patient age and race, [16] deceased donor [19] and expanded criteria donor types. [20]
The United States Renal Data System (USRDS) includes information from the OPTN and Medicare enrolment and billing claims collected by the Center for Medicare and Medicaid Services (CMS). [4] The USRDS captures primary and secondary diagnoses submitted on Medicare billing claims for hospitalizations and medical visits and offers the opportunity to explore a diverse spectrum of pre-transplant comorbidities. Only a limited number of comorbidities have been studied in the USRDS database and in international research. Additionally, most of the previous research provided unadjusted, bivariate estimates and not the multivariable associations between pre-transplant comorbidities and cost.
Current cost estimations should be enriched with clinical factors such as additional pre-transplant comorbidities. We sought to estimate the associations between 3-year post-transplant medicare payments and a broad array of pretransplant comorbidities in the Medicare population, controlling for other patient, donor and transplant factors recorded not only by the OPTN but also through other measures such as the CCS and Charlson-Elixhauser methodologies.
| Subjects and Methods | | |
This was a retrospective cohort study of all adult (>18 years) recipients of deceased-donor, first renal only transplants in 1995-2002 for whom Medicare was the primary insurer at the time of transplant. Patients were reported to the OPTN and included in the USRDS registry.
Comorbidity and payments information from claims
Comorbidity information from claims was identified using the Medicare part-A and part-B claims files. Any primary or secondary diagnosis by the International Classification of Diseases, 9th revision, clinical modification (ICD-9-CM) code for each hospitalization or physician visit during the last pre-transplant year was considered. To identify pre-transplant comorbidities using a comparable time frame, we limited the study sample to patients with at least 1 year of continuous Medicare coverage in the year before transplant. Three-year Medicare payments were aggregated from the post-transplant part-A and part-B claims. All payments were adjusted for inflation with the medical component of the consumer price index using the year 2000 as the base year.
Comorbidity summary measures
Three different systems that reduce the ICD-9CM diagnosis to manageable categories for data analysis were used. First, we applied the CCS. [21],[22] The CCS was developed by the Agency for Healthcare Research and Quality (AHRQ). [23],[24] The single-level CCS classifies ICD-9-CM diagnoses into 260 mutually exclusive categories. The CCS has been used to describe posttransplant hospitalizations in the USRDS [25] as well as associations between comorbidities and graft outcomes. [8] To avoid the risk of inappropriate inclusion, we eliminated CCS variables with prevalence <1 case per 100 patients. This yielded 129 CCS variables.
The Charlson [26] and Elixhauser [27] comorbidities classification algorithms were also examined. These algorithms have been previously studied in dialysis [28] and transplant populations [8],[29] and are widely used measures of comorbidity. [30] The Charlson index considers 19 conditions; while the Elixhauser index accounts for 31 comorbidities. Adaptations of both algorithms for claims databases were employed. [31]
Other study variables
All OPTN variables describing recipient, donor and transplant factors were included in the regression models. These included age, race, body mass index (BMI), cause of ESRD, dialysis duration, peak panel reactive antibodies (PRA) ≥ 50% and OPTN-reported comorbidities; donor type [expanded criteria donor (ECD), donation after cardiac death (DCD)], donor age, race, BMI, cause of death; number of donor-recipient ABDR Human leukocyte antigen (HLA) mismatches (categorized into none, AB mismatches, DR mismatches), donor-recipient cytomegalovirus (CMV) sero-pairing, cold ischemia time, immunosuppressant therapy at discharge, induction therapy and year of transplant. Delayed graft function (DGF) was considered a post-transplant variable rather than a pre-transplant or baseline transplant procedure event. Continuous variables (recipient and donor age, BMI and time on dialysis) were not categorized.
| Statistical Analyses | | |
In the three-year payments estimation, patients were censored at lost to follow-up or end of study observation because the possible followup was less than three years. We used multiple linear regression models to account for censoring. [32] This methodology has been shown to be better than restriction to cases with complete observation through the time period of interest, a method previously applied in the transplant literature. [16],[17]
Five statistical models were built: 1) CCS comorbidities, 2) Charlson comorbidities, 3) Elixhauser comorbidities, 4) number of Charlson comorbidities (0 to ≥6) and 5) number of Elixhauser comorbidities (0 to ≥11). Costs were divided into 36 monthly intervals (covering three years of post-transplant costs). Patients who died or who reached the end of a given monthly period with observed costs were considered to have completely known costs for that monthly period. Payments for patients with complete information at the end of each period were considered in monthly regressions for each of the 36 post-transplant months. To account for censoring, payments at the end of each period where weighted by the probability of observing patients at that day. [32] The probability of censoring was estimated from a Kaplan-Meier curve that used censoring as the endpoint. Once regression estimates for each monthly period were obtained, these were aggregated over time to obtain a total estimate for three-year payments. We obtained 95% confidence intervals of the regression coefficients by using 200 bootstrap replications. The mean of those 200 bootstrap replications was used as the point-estimate. Estimates represented adjusted costs for each comorbidity accounting for other demographic and transplant factors.
After applying our inclusion and exclusion criteria, 27,177 patients were eligible. Missing donor age data for 3,261 patients (12.9%) were imputed with the mean donor age. Missing data for recipient BMI (n = 849, 3.36%) and donor BMI (n = 894, 3.54%) were imputed with the median BMI. Extreme values in BMI (smaller than 15 and greater than 45) were imputed to 15 and 45. No other data imputation was performed.
For comparative purposes, we also derived cost curves, dividing the cohort into two groups, based on the presence versus absence of any given comorbidity. Cost curves are an adaptation of the Kaplan-Meier methodology for cost analyses and had been previously used in renal transplantation. [16],[17],[18],[20] Given that an individual patient could have more than one comorbidity, cost curves across comorbidities do not represent mutually exclusive groups. To adjust for this, cost curves with mutually exclusive cohorts were only produced for the patient-groups categorized by increasing the number of Charlson and Elixhauser comorbidities. Cost curves for the mutually exclusive Charlson and Elixhauser groups are also presented graphically. This method provides an unadjusted estimate of payments because it does not take other variables into account.
Regression estimates for the OPTN variables are only presented for the CCS regression, given the general similarity of those estimates with the other models. The top 10 CCS, Charlson and Elixhauser categories that increased individual mean payments and population costs are summarized. Total population payments were obtained multiplying the prevalence of each comorbidity by the difference in mean payments obtained from the regression equation applied to two cohorts, one with and another without the comorbidity of interest. Population payments attributable to each comorbidity were calculated multiplying the comorbidity prevalence by the regression coefficient associated with that comorbidity. Payment estimations derived from cost curves and regression models were compared for the models with sums of Charlson and Elixhauser comorbidities. All analyses were performed using SAS 9.1 (Cary, NC, USA).
| Results | | |
Patient population and comorbidities
After excluding patients with non-imputable missing data, 25,175 patients remained. Most of the patients were between 45 and 59 years of age and were males. The mean number of CCS, Charlson and Elixhauser comorbidities were 9.5, 1.6 and 3.2, respectively [Table 1]. Additional recipient, donor and transplant information is presented in [Table 1]. | Table 1: Patient, donor and transplant characteristics of the study population.
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Regression results
OPTN and CCS variables
In the multivariable regression model with OPTN variables and CCS comorbidities, the factors with largest associations with 3-year posttransplant Medicare payments included AfricanAmerican recipient, type I diabetes as the cause of ESRD, arrhythmia and cerebrovascular disease as reported to the OPTN, ECD, DCD and the interaction of these variables and African American or other donor race, transplant cold ischemia time > 36 hours, unknown CMV seropairing and immunosuppressant regimen different from calcineurin inhibitor + mycophenolate mofetil or calcineurin inhibitor + azathioprine [Table 2]. Other factors associated with increased payments included number of HLA mismatches, donor+/recipient- CMV sero-pairing and recipient age, peripheral vascular disease and physical functional limitations. Medicare payments were lower for patients transplanted in later years. Payments were also lower for recipients with polycystic kidney disease and race other than white or African-American, Hispanic donor ethnicity and use of induction.
CCS, Charlson and Elixhauser comorbidities regression models
Recipient comorbidities associated with the largest increases in individual payments in the CCS regression model included chronic ulcers of the skin, affective disorders, pancreatic disorders (no diabetes), hyperplasia of the prostate, superficial injury, acute cerebrovascular disease, diseases of the vein and lymphatics, acute bronchitis, acquired foot deformities and skin and subcutaneous tissue infections [Table 2]. Comorbidities associated with highest reductions in payments included benign neoplasm (including breast conditions), senility and organic mental disorders, and a set of bone, gynecologic and other conditions. The full set of CCS regression coefficients is available from the authors.
Charlson comorbidities conferring the highest increases in terms of individual payments included liver disease, paraplegia and hemiplegia, metastatic carcinoma, peptic ulcer disease, peripheral vascular disease, cerebrovascular disease, congestive heart failure, connective tissue and rheumatic disease, diabetes without complications and chronic pulmonary disease [Table 3]. Only dementia was associated with a decrease in mean payments. | Table 3: OPTN + Charlson and OPTN + Elixhauser regression models for 3-year Medicare payments (comorbidity results).
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Models were also estimated using the Elixhauser comorbidity classification system, which includes more comorbidities than the Charlson algorithm. Regression models with Elixhauser comorbidities identified the following most important contributors to individual payments: paralysis, drug abuse, metastatic cancer, peptic ulcer disease excluding bleeding, obesity, alcohol abuse, peripheral vascular disorders, valvular disease, other neurological disorders and rheumatoid arthritis/collagen disorders [Table 3]. Two comorbidities (psychoses and hypothyroidism) were associated with reduced costs.
The top ten comorbidities increasing population payments were similar across all comorbidity classification algorithms. All classifications included renal failure and diabetes. Other commonly identified comorbidities included hypertension, coronary atherosclerosis and different types of heart disease. Comorbidities more unique to a specific classification included skin and subcutaneous tissue infection (CCS), liver disease and peptic ulcer disease (Charlson) and other neurological disease (Elixhauser). Total population payments were higher than population attributable payments, as the latter only consider the association that can be attributed to the comorbidity of interest after controlling for other variables, which is generally smaller than the unadjusted payments [Table 4]. | Table 4: Top 10 CCS, Charlson and Elixhauser comorbidities by population cost.
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Regression models with number of Charlson and Elixhauser comorbidities and comparison with cost curves
The number of Charlson comorbidities resuted in seven mutually exclusive, ranked groups for regression analysis. A statistically significant gradient of increasing payments was observed with each additional number of Charlson-comorbidity [Table 5]. This gradient was also observed at all time points using cost curve estimates [Figure 1]. The cost curves generally yielded higher (but comparable) mean and population payments estimates compared to regression methods. | Table 5: OPTN + Charlson and OPTN + Elixhauser regression models for 3-year Medicare payments (comorbidity results).
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The number of Elixhauser comorbidities resulted in 12 mutually exclusive, ranked groups for inclusion in a regression model controlling for other variables. A positive graded relationship was observed between higher number of comorbidities and increased payments. This gradient was statistically significant for all groups. In general, similar relationships were observed for the cost curves [Figure 1].
| Discussion | | |
Renal replacement therapies are expensive and understanding their costs is important, given the limited resources for healthcare. We studied factors associated with 3-year post-transplant Medicare payments. We found that the OPTN donor, recipient and transplant factors associated with increased in Medicare payments were consistent with previous research. Indeed, higher costs with older recipient age, African-American recipient race, longer cold ischemia time, and increased HLA mismatches [16],[17],[18],[19],[20] were replicated. The reduction in Medicare payments in current years is consistent with recent similar observations. [33],[34]
In this study, we expanded the description of prevalent comorbidities at transplant beyond the limited set of OPTN variables. Using three different classification systems to identify clinically diagnosed comorbid conditions from billing claims, we explored the associations between a diverse array of pre-transplant comorbid diseases and 3-year post-transplant Medicare payments. These systems yielded coincident, also divergent findings.
The OPTN reported comorbidities, cardiovascular disease, cerebrovascular disease, diabetes, hypertension, chronic pulmonary disease and physical functional limitations appeared as important contributors when captured in claims. However, some comorbidities not routinely reported to OPTN were associated with either high individual or population cost contributions. These included skin infections, malignancies, liver disease, rheumatoid arthritis and other connective tissue disease, affective disorders and peptic ulcer disease. We also found that the number of Charlson and Elixhauser comorbidities had a positive and graded relationship with post-transplant Medicare payments.
This study has two important strengths. First, it adds to current knowledge of costs after transplant by examining a larger number of comorbidities. Second, we applied a regression technique that accounts for censoring and thus uses information more appropriately than restriction to cases with complete observation.
Comorbidity-associated costs after transplant in our models may reflect the costs of conditions in patients with functioning grafts as well as the impact of disease on graft loss. Some of the comorbidities among the top ten associated with higher population costs have been associated with higher risk of all-cause graft-failure, death with function or death including death after graft failure. These include acute cerebrovascular disease, chronic ulcer of the skin, heart valve disorders, liver disease, hypertension with complications and diabetes mellitus with complications. [8] These comorbidities may be important directly or may be markers of disease severity. For example, chronic ulcer of the skin may be an indicator of previous diabetic complications or peripheral vascular disease. However, chronic ulcer of the skin was shown to be an independent factor for increased costs outside of diabetes and peripheral vascular disease. [8] Some pre-transplant comorbidities (such as diabetes) are risk factors for cardiovascular events that may trigger higher costs (like myocardial infarction). [35],[36]
These estimates could be used to project expected 3-year Medicare costs by patient groups with specific comorbidity-loads. Together with additional data, these results could be used to determine the cost effectiveness of kidney transplantation according to patient comorbid profile. Specific policies stimulating transplantation in the most cost-effective groups would have the potential to increase the efficiency of healthcare expenditure in transplantation.
Our findings may not be generalizable to other populations, such as privately insured patients. However, as approximately half of ESRD patients are insured by Medicare for some period of dialysis and transplantation, our findings represent an important segment of the US renal transplant population. Additional research using these comordibity systems in other countries would also be of high interest.
Other factors may have influenced the results. The comorbidities were derived from medical claims, which are known to have limitations. [37] Claims perform well to detect disease but their ability to rule out disease is less powerful. [38],[39],[40],[41],[42],[43] In transplantation, claims are useful to characterize immunosuppressant medications, [44] diabetes and hypertension. [45]
The identification of pre-transplant comorbidities relied on the incident claims of the last year on dialysis before transplantation, which may underestimate the prevalence of pre-transplant comorbidities. Such misclassification, however, would produce conservative estimates in our models.
In summary, several pre-transplant comorbidities reported in the OPTN are associated with post-transplant costs. Our analysis is the first to consider an expanded number of comorbidities in the estimation of post-transplant cost to medicare and individual transplant centers. Expansion beyond OPTN recorded pre-transplant comorbidities with CCS and Charlson and Elixhauser comorbidities yielded useful information regarding increased Medicare payments. Our methodology can be used by others to assess more accurately the financial implications of renal transplantation to Medicare and individual transplant centers in the US and other countries.
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Correspondence Address:
Mark A Schniztler
Saint Louis University Center for Outcomes Research, 3545 Lafayette Avenue, Salus Center, 2nd Floor, Room 2825, St. Louis, MO 63104
USA
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PMID: 21196610 
[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5] |
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