Personalized medicine -the targeting of therapies to individuals on the basis of their biological, clinical, or genetic characteristics -is thought to have the potential to transform health care. While much emphasis has been placed on the value of personalized therapies, less attention has been paid to the value generated by the diagnostic tests that direct patients to those targeted treatments. This paper presents a framework derived from information economics for assessing the value of diagnostics. We demonstrate, via a case study, that the social value of such diagnostics can be very large, both by avoiding unnecessary treatment and by identifying patients who otherwise would not get treated. Despite the potential social benefits, diagnostic development has been discouraged by cost-based, rather than value-based, reimbursement.
Introduction:There have been significant improvements in both treatment and screening efforts for many types of cancer over the past decade. However, the effect of these advancements on the survival of cancer patients is unknown, and many question the value of both new treatments and screening efforts.Methods:This study uses a retrospective analysis of SEER Registry data to quantify reductions in mortality rates for cancer patients diagnosed between 1997 and 2007. Using variation in trends in mortality rates by stage of diagnosis across cancer types, we use logistic regression to decompose separate survival gains into those attributable to advances in treatment versus advances in detection. We estimate the gains in survival due to gains in both treatment and detection overall and separately for 15 of the most common cancer types.Results:We estimate that 3-year cancer-related mortality of cancer patients fell 16.7% from 1997 to 2007. Overall, advances in treatment reduced mortality rates by approximately 12.2% while advances in early detection reduced mortality rates by 4.5%. The relative importance of treatment and detection varied across cancer types. Improvements in detection were most important for thyroid, prostate and kidney cancer. Improvements in treatment were most important for non-Hodgkins lymphoma, lung cancer and myeloma.Conclusion:Both improved treatment options and better early detection have led to significant survival gains for cancer patients diagnosed from 1997 to 2007, generating considerable social value over this time period.
4666 Background: Survival after diagnosis of metastatic prostate cancer (mPC) averages 2-3 years. Substantial regional differences in survival have been documented for PC prior to 2000. We investigated the extent to which regional variation exists in survival for mPC patients during 2000-2007. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database linked to Medicare claims, we identified men (mean age, 77.6 years) continuously enrolled in Medicare Parts A/B who were diagnosed with mPC between 2000 and 2007 and not diagnosed with another malignancy. The base-case model was limited to hospital service areas with >50 patients. A Cox proportional hazards model was used to estimate hazard ratios (HR) for overall survival (OS), adjusting for year of diagnosis, age, marital status, poverty and education covariates, Gleason score, comorbidities, and region covariates. Sensitivity of results was tested by limiting the analysis to patients surviving at least 6 months after diagnosis and by removing regional sample size limits. We report HRs for covariates, results of a Wald test of joint significance for region effects, and percentage difference from the mean for each region’s HR for death. Results: A total of 2696 patients with mPC met the inclusion criteria. OS was 37% at 3 years and mean HR for death was 4.8 (sd 2.1). HRs for death were positively correlated with age (HR=1.96, 95% CI: 1.6-2.3 for >80 years), PC-specific comorbidity index (HR=1.2, 95% CI: 1.1-1.3), and Gleason score (HR=6.6, 95% CI: 2.4-17.8 for poorly differentiated). Year of diagnosis, race, and socioeconomic status were not significantly associated with mortality. Wald test of joint significance for survival across regions of P=.019 indicated significant differences in survival across regions and was robust in sensitivity analyses. Patients living in regions with the worst survival rates had HRs for death that were 20% higher than the mean (HR=5.8) and those living in regions with the best survival rates had HRs 30% lower than the mean (HR=3.4). Conclusions: There are significant regional differences in survival for mPC patients in the 2000s. Further research is needed to determine if treatment differences play a role in this disparity.
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