Evaluating cost-effectiveness in the management of neuroendocrine neoplasms.

The rapid evolution of novel, costly therapies for neuroendocrine neoplasia (NEN) warrants formal high-quality cost-effectiveness evaluation. Costs of individual investigations and therapies are high; and examples are presented. We aimed to review the last ten years of standalone health economic evaluations in NEN. Comparing to published standards, EMBASE, Cochrane library, Database of Abstracts of Reviews of Effects (DARE), NHS Economic Evaluation Database and the Health Technology Assessment (HTA) Database were searched for health economic evaluations (HEEs) in NEN published between 2010 and October 2019. Of 12 economic evaluations, 11 considered exclusively pharmacological treatment (3 studies of SSAs, 7 studies of sunitinib, everolimus and/or 177Lu-DOTATATE and 1 study of telotristat ethyl) and 1 compared surgery with intraarterial therapy. 7 studies of pharmacological treatment had placebo or best supportive care as the only comparator. There remains a paucity of economic evaluations in NEN with the majority industry funded. Most HEEs reviewed did not meet published health economic criteria used to assess quality. Lack of cost data collected from patient populations remains a significant factor in HEEs where clinical expert opinion is still often substituted. Further research utilizing high-quality effectiveness data and rigorous applied health economic analysis is needed.

Preferences for Genetic Testing to Predict the Risk of Developing Hereditary Cancer: A Systematic Review of Discrete Choice Experiments.

BACKGROUND: Understanding service user preferences is key to effective health care decision making and efficient resource allocation. It is of particular importance in the management of high-risk patients in whom predictive genetic testing can alter health outcomes. PURPOSE: This review aims to identify the relative importance and willingness to pay for attributes of genetic testing in hereditary cancer syndromes. DATA SOURCES: Searches were conducted in Medline, Embase, PsycINFO, HMIC, Web of Science, and EconLit using discrete choice experiment (DCE) terms combined with terms related to hereditary cancer syndromes, malignancy synonyms, and genetic testing. STUDY SELECTION: Following independent screening by 3 reviewers, 7 studies fulfilled the inclusion criteria, being a DCE investigating patient or public preferences related to predictive genetic testing for hereditary cancer syndromes. DATA EXTRACTION: Extracted data included study and respondent characteristics, DCE attributes and levels, methods of data analysis and interpretation, and key study findings. DATA SYNTHESIS: Studies covered colorectal, breast, and ovarian cancer syndromes. Results were summarized in a narrative synthesis and the quality assessed using the Lancsar and Louviere framework. LIMITATIONS: This review focuses only on DCE design and testing for hereditary cancer syndromes rather than other complex diseases. Challenges also arose from heterogeneity in attributes and levels. CONCLUSIONS: Test effectiveness and detection rates were consistently important to respondents and thus should be prioritized by policy makers. Accuracy, cost, and wait time, while also important, showed variation between studies, although overall reduction in cost may improve uptake. Patients and the public would be willing to pay for improved detection and clinician over insurance provider involvement. Future studies should seek to contextualize findings by considering the impact of sociodemographic characteristics, health system coverage, and insurance policies on preferences. HIGHLIGHTS: Test effectiveness and detection rates are consistently important to respondents in genetic testing for hereditary cancer syndromes.Reducing the cost of genetic testing for hereditary cancer syndromes may improve uptake.Individuals are most willing to pay for a test that improves detection rates, identifies multiple cancers, and for which results are shared with a doctor rather than with an insurance provider.