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.
Citation
@article{n.2024,
author = {Morrish, N. and Snowsill, T. and Dodman, S. and Medina-Lara,
A.},
title = {Preferences for {Genetic} {Testing} to {Predict} the {Risk}
of {Developing} {Hereditary} {Cancer:} {A} {Systematic} {Review} of
{Discrete} {Choice} {Experiments}},
journal = {Medical Decision Making},
date = {2024-02-07},
url = {https://tristansnowsill.co.uk/publications/2024/preferences-for-genetic-testing-to-predict-the-risk.html},
doi = {10.1177/0272989x241227425},
langid = {en},
abstract = {**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.}
}