ChiHui Fang1, Hansel J Otero, Dan Greenberg, Peter J Neumann. 1. Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA.
Abstract
OBJECTIVE: To review and evaluate the literature of cost-utility analyses (CUAs) regarding diagnostic laboratory testing. METHODS: We reviewed all articles related to diagnostic laboratory testing in the Tufts Medical Center Cost-Effectiveness Analysis Registry (www.cearegistry.org), which contains detailed information on over 2000 published CUAs through 2008. We analyzed the extent to which the studies adhered to recommended practices for conducting and reporting cost-effectiveness analyses. We also recorded whether the studies contained information on diagnostic test accuracy and costs, and whether any account was taken of potential benefits or harms of testing that are unrelated to subsequent treatment, such as the reassurance value of testing. RESULTS: We identified 141 published CUAs pertaining to diagnostic laboratory testing published through 2008 which contained 433 separate incremental cost-effectiveness ratios. Prior to 2000, there were only 20 CUAs published, but the number averaged 13.4 annually thereafter. Most studies focused on hematology/oncology (n = 42, 30%) and obstetrics/gynecology (n = 36, 26%) applications. Approximately 63% (n = 89) of studies clearly reported information about the accuracy of the test, but only 10% (n = 14) mentioned test safety or possible risks. A small number (n = 10, 7%) mentioned or considered the potential value or harm of testing unrelated to treatment consequences. Over 55% of the reported incremental cost-effectiveness ratios (ICERs) were either dominant (more quality-adjusted life years for less cost), or below $50,000 per quality-adjusted life years gained (in 2008 US dollars). CONCLUSION: The number of CUAs investigating laboratory diagnostic testing has increased substantially with applications to diverse clinical areas. The literature reveals many areas in which testing represents good value for money. The vast majority of studies have not considered preferences for test information unrelated to treatment consequences.
OBJECTIVE: To review and evaluate the literature of cost-utility analyses (CUAs) regarding diagnostic laboratory testing. METHODS: We reviewed all articles related to diagnostic laboratory testing in the Tufts Medical Center Cost-Effectiveness Analysis Registry (www.cearegistry.org), which contains detailed information on over 2000 published CUAs through 2008. We analyzed the extent to which the studies adhered to recommended practices for conducting and reporting cost-effectiveness analyses. We also recorded whether the studies contained information on diagnostic test accuracy and costs, and whether any account was taken of potential benefits or harms of testing that are unrelated to subsequent treatment, such as the reassurance value of testing. RESULTS: We identified 141 published CUAs pertaining to diagnostic laboratory testing published through 2008 which contained 433 separate incremental cost-effectiveness ratios. Prior to 2000, there were only 20 CUAs published, but the number averaged 13.4 annually thereafter. Most studies focused on hematology/oncology (n = 42, 30%) and obstetrics/gynecology (n = 36, 26%) applications. Approximately 63% (n = 89) of studies clearly reported information about the accuracy of the test, but only 10% (n = 14) mentioned test safety or possible risks. A small number (n = 10, 7%) mentioned or considered the potential value or harm of testing unrelated to treatment consequences. Over 55% of the reported incremental cost-effectiveness ratios (ICERs) were either dominant (more quality-adjusted life years for less cost), or below $50,000 per quality-adjusted life years gained (in 2008 US dollars). CONCLUSION: The number of CUAs investigating laboratory diagnostic testing has increased substantially with applications to diverse clinical areas. The literature reveals many areas in which testing represents good value for money. The vast majority of studies have not considered preferences for test information unrelated to treatment consequences.
Authors: Matthew D Krasowski; Deborah Chudzik; Anna Dolezal; Bryan Steussy; Michael P Gailey; Benjamin Koch; Sara B Kilborn; Benjamin W Darbro; Carolyn D Rysgaard; Julia A Klesney-Tait Journal: BMC Med Inform Decis Mak Date: 2015-02-22 Impact factor: 2.796
Authors: Matthew D Krasowski; Andy Schriever; Gagan Mathur; John L Blau; Stephanie L Stauffer; Bradley A Ford Journal: J Pathol Inform Date: 2015-07-28
Authors: James M Crawford; Khosrow Shotorbani; Gaurav Sharma; Michael Crossey; Tarush Kothari; Thomas S Lorey; Jeffrey W Prichard; Myra Wilkerson; Nancy Fisher Journal: Acad Pathol Date: 2017-04-18