BACKGROUND: Data on direct non-health care and time costs are rarely collected, though the incorporation of such data is essential for performing cost-effectiveness analyses according to established guidelines. OBJECTIVES: To explore the challenges involved in collecting and analyzing these data from patients enrolled in a clinical trial. METHODS: Through the use of a pilot study, the authors designed a questionnaire to collect these costs. They used this questionnaire in a clinical trial conducted at a comprehensive cancer center and a public community hospital. Patients in the trial were undergoing screening or diagnostic procedures through a clinical protocol designed to measure the effectiveness of fluorescence and reflectance spectroscopy for detecting cervical precancers. Direct non-health care costs were adjusted to 2003 constant dollars. RESULTS: The authors successfully collected direct non-health care and time cost data, thus demonstrating the feasibility of acquiring such data. Compared to patients receiving diagnostic services for cervical cancer, those receiving screening services for the same condition in both settings incurred lower direct non-health care costs and time costs, as defined in the questionnaire. Compared to patients receiving either service at the comprehensive cancer center, those seeking either service at the public community hospital incurred lower direct non-health care costs and time costs. When outliers were removed, total direct non-health care costs and time costs substantially decreased for diagnostic patients in the comprehensive cancer center; total direct non-health care costs and time costs for other subgroups remained essentially unchanged. CONCLUSIONS: Direct non-health care and time cost data can be collected within a large-scale clinical trial. The setting (community v. specialty hospital) and population (patients receiving screening v. diagnostic examination) makes a difference regarding the cost totals. The order of magnitude of the final result depends on the context in which the non-health care and time cost data will be used.
BACKGROUND: Data on direct non-health care and time costs are rarely collected, though the incorporation of such data is essential for performing cost-effectiveness analyses according to established guidelines. OBJECTIVES: To explore the challenges involved in collecting and analyzing these data from patients enrolled in a clinical trial. METHODS: Through the use of a pilot study, the authors designed a questionnaire to collect these costs. They used this questionnaire in a clinical trial conducted at a comprehensive cancer center and a public community hospital. Patients in the trial were undergoing screening or diagnostic procedures through a clinical protocol designed to measure the effectiveness of fluorescence and reflectance spectroscopy for detecting cervical precancers. Direct non-health care costs were adjusted to 2003 constant dollars. RESULTS: The authors successfully collected direct non-health care and time cost data, thus demonstrating the feasibility of acquiring such data. Compared to patients receiving diagnostic services for cervical cancer, those receiving screening services for the same condition in both settings incurred lower direct non-health care costs and time costs, as defined in the questionnaire. Compared to patients receiving either service at the comprehensive cancer center, those seeking either service at the public community hospital incurred lower direct non-health care costs and time costs. When outliers were removed, total direct non-health care costs and time costs substantially decreased for diagnostic patients in the comprehensive cancer center; total direct non-health care costs and time costs for other subgroups remained essentially unchanged. CONCLUSIONS: Direct non-health care and time cost data can be collected within a large-scale clinical trial. The setting (community v. specialty hospital) and population (patients receiving screening v. diagnostic examination) makes a difference regarding the cost totals. The order of magnitude of the final result depends on the context in which the non-health care and time cost data will be used.
Authors: Timon P H Buys; Scott B Cantor; Martial Guillaud; Karen Adler-Storthz; Dennis D Cox; Clement Okolo; Oyedunni Arulogon; Oladimeji Oladepo; Karen Basen-Engquist; Eileen Shinn; José-Miguel Yamal; J Robert Beck; Michael E Scheurer; Dirk van Niekerk; Anais Malpica; Jasenka Matisic; Gregg Staerkel; Edward Neely Atkinson; Luc Bidaut; Pierre Lane; J Lou Benedet; Dianne Miller; Tom Ehlen; Roderick Price; Isaac F Adewole; Calum MacAulay; Michele Follen Journal: Gend Med Date: 2011-09-22
Authors: Elizabeth Whitley; Patricia Valverde; Kristen Wells; Loretta Williams; Taylor Teschner; Ya-Chen Tina Shih Journal: Cancer Date: 2011-08 Impact factor: 6.860
Authors: Jane J Kim; Nicole G Campos; Stephen Sy; Emily A Burger; Jack Cuzick; Philip E Castle; William C Hunt; Alan Waxman; Cosette M Wheeler Journal: Ann Intern Med Date: 2015-09-29 Impact factor: 25.391
Authors: Jeremy D Goldhaber-Fiebert; Natasha K Stout; Joshua A Salomon; Karen M Kuntz; Sue J Goldie Journal: J Natl Cancer Inst Date: 2008-02-26 Impact factor: 13.506