Cost-effectiveness of pediatric heart transplantation across a positive crossmatch for high waitlist urgency candidates.

Allosensitized children listed with a requirement for a negative prospective crossmatch have high mortality. Previously, we found that listing with the intent to accept the first suitable organ offer, regardless of the possibility of a positive crossmatch (TAKE strategy), results in a survival advantage from the time of listing compared to awaiting transplantation across a negative crossmatch (WAIT). The cost-effectiveness of these strategies is unknown. We used Markov modeling to compare cost-effectiveness between these waitlist strategies for allosensitized children listed urgently for heart transplantation. We used registry data to estimate costs and waitlist/posttransplant outcomes. We assumed patients remained in hospital after listing, no positive crossmatches for WAIT, and a base-case probability of a positive crossmatch of 47% for TAKE. Accepting the first suitable organ offer cost less ($405 904 vs. $534 035) and gained more quality-adjusted life years (3.71 vs. 2.79). In sensitivity analyses, including substitution of waitlist data from children with unacceptable antigens specified during listing, TAKE remained cost-saving or cost-effective. Our findings suggest acceptance of the first suitable organ offer for urgently listed allosensitized pediatric heart transplant candidates is cost-effective and transplantation should not be denied because of allosensitization status alone.

Comparison of listing strategies for allosensitized heart transplant candidates requiring transplant at high urgency: a decision model analysis.

Allosensitized children who require a negative prospective crossmatch have a high risk of death awaiting heart transplantation. Accepting the first suitable organ offer, regardless of the possibility of a positive crossmatch, would improve waitlist outcomes but it is unclear whether it would result in improved survival at all times after listing, including posttransplant. We created a Markov decision model to compare survival after listing with a requirement for a negative prospective donor cell crossmatch (WAIT) versus acceptance of the first suitable offer (TAKE). Model parameters were derived from registry data on status 1A (highest urgency) pediatric heart transplant listings. We assumed no possibility of a positive crossmatch in the WAIT strategy and a base-case probability of a positive crossmatch in the TAKE strategy of 47%, as estimated from cohort data. Under base-case assumptions, TAKE showed an incremental survival benefit of 1.4 years over WAIT. In multiple sensitivity analyses, including variation of the probability of a positive crossmatch from 10% to 100%, TAKE was consistently favored. While model input data were less well suited to comparing survival when awaiting transplantation across a negative virtual crossmatch, our analysis suggests that taking the first suitable organ offer under these circumstances is also favored.

Sociodemographic differences in early access to liver transplantation services.

The question of whether health care inequities occur before patients with end-stage liver disease (ESLD) are waitlisted for transplantation has not previously been assessed. To determine the impact of gender, race and insurance on access to transplantation, we linked Pennsylvania sources of data regarding adult patients discharged from nongovernmental hospitals from 1994 to 2001. We followed the patients through 2003 and linked information to records from five centers responsible for 95% of liver transplants in Pennsylvania during this period. Using multinomial logistic regressions, we estimated probabilities that patients would undergo transplant evaluation, transplant waitlisting and transplantation itself. Of the 144,507 patients in the study, 4361 (3.0%) underwent transplant evaluation. Of those evaluated, 3071 (70.4%) were waitlisted. Of those waitlisted, 1537 (50.0%) received a transplant. Overall, 57,020 (39.5%) died during the study period. Patients were less likely to undergo evaluation, waitlisting and transplantation if they were women, black and lacked commercial insurance (p < 0.001 each). Differences were more pronounced for early stages (evaluation and listing) than for the transplantation stage (in which national oversight and review occur). For early management and treatment decisions of patients with ESLD to be better understood, more comprehensive data concerning referral and listing practices are needed.

Impact of hepatitis C virus infection and other comorbidities on survival in patients on dialysis.

The impact of hepatitis C virus (HCV) and other comorbid conditions upon survival is not well quantified in patients on dialysis. We identified HCV-infected and uninfected persons in the USRDS using claims data in 1997-1998 and followed until September 22, 2002 or death. We used Gray's time-varying coefficients model to examine factors associated with survival. Subjects with a renal transplant were excluded. A total of 5737 HCV-infected and 11 228 HCV-uninfected persons were identified. HCV-infected subjects were younger (mean age 57.8 vs 65.3 years), more likely to be male (57.6%vs 49.6%) and black (54.0%vs 36.4%). They were more likely to have a diagnosis of drug (16.5%vs 4.6%) and alcohol use (14.0%vs 3.1%), and to be human immunodeficiency virus (HIV) co-infected (7.4%vs 1.8%) (all comparisons, P < 0.0005). In an adjusted Gray's time-varying coefficient model, HCV was associated with an increased risk of mortality (P < 0.0005). The hazards were highest at the time of HCV diagnosis and decreased to a stable level 2 years after diagnosis. Other factors associated with increased risk of mortality were (P < 0.0005 unless stated) HIV coinfection; diagnosis of drug use (P = 0.001); coronary artery disease (P = 0.006); stroke; diabetes as the primary cause for renal failure; peripheral vascular disease; depression and presence of anaemia. HCV was associated with higher risk of death in patients on dialysis, even after adjusting for concurrent comorbidities. The risk was highest at the time of HCV diagnosis and stabilized over time. Clinical trials of HCV screening and treatment to reduce mortality in this population are warranted.

Do incentives matter? Providing benefits to families of organ donors.

Whether the number of organs available for transplant would be positively or negatively affected by providing benefits to families of organ donors has been debated by policymakers, ethicists and the transplant community at large. We designed a telephone survey to measure public opinion regarding the use of benefits in general and of five types in particular: funeral benefits, charitable contributions, travel/lodging expenses, direct payments and medical expenses. Of the 971 adults who completed the survey (response rate = 69%), all were from Pennsylvania households, 45.6% were registered organ donors, and 51.7% were nonwhite. Although 59% of respondents favored the general idea of incentives, support for specific incentives ranged from 53% (direct payment) to 84% (medical expenses). Among those registered as donors, more nonwhites than whites supported funeral benefits (88% vs. 81%; p = 0.038), direct payment (63% vs. 41%; p < 0.001) and medical expenses (92% vs. 84%; p = 0.013). Among those not registered as donors, more nonwhites supported direct payment (64% vs. 46%; p = 0.001). Most respondents believed that benefits would not influence their own behavior concerning donation but would influence the behavior of others. While benefits appear to be favored, their true impact can only be assessed through pilot programs.

Comparing the agreement among alternative models in evaluating HMO efficiency.

OBJECTIVE: To describe the efficiency of HMOs and to test the robustness of these findings across alternative models of efficiency. This study examines whether these models, when constructed in parallel to use the same information, provide researchers with the same insights and identify the same trends. DATA SOURCES: A data set containing 585 HMOs operating from 1985 through 1994. Variables include enrollment, utilization, and financial information compiled primarily from Health Care Investment Analysts, InterStudy HMO Census, and Group Health Association of America. STUDY DESIGN: We compute three estimates of efficiency for each HMO and compare the results in terms of individual performance and industry-wide trends. The estimates are then regressed against measures of case mix, quality, and other factors that may be related to the model estimates. PRINCIPAL FINDINGS: The three models identify similar trends for the HMO industry as a whole; however, they assess the relative technical efficiency of individual firms differently. Thus, these techniques are limited for either benchmarking or setting rates because the firms identified as efficient may be a consequence of model selection rather than actual performance. CONCLUSIONS: The estimation technique to evaluate efficient firms can affect the findings themselves. The implications are relevant not only for HMOs, but for efficiency analyses in general. Concurrence among techniques is no guarantee of accuracy, but it is reassuring; conversely, radically distinct inferences across models can be a warning to temper research conclusions.

The supply and use of selected medical technologies.

Over the years, both government and the private sector have used a mix of regulatory controls and competitive market incentives to reduce the rate of spending and minimize excess capacity in health services. Despite these efforts, this study finds an oversupply of five medical technologies in Pennsylvania, which adds costs and raises concern over the quality of care provided by underused facilities. Moreover, as providers compete for network selection, many continue to expand their service capabilities. These findings emphasize the importance of ongoing assessment of the appropriate application, supply, and use of medical services.