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Quantifying geographical accessibility to cancer clinical trials in different income landscapes

BACKGROUND: Clinical trials are increasingly perceived as a therapeutic opportunity for cancer patients. Favoring their concentration in few high-expertise academic centers maximizes quality of data collection but poses an issue of access equality. Analytical tools to quantify trial accessibility ar...

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Autores principales: Tini, G., Trapani, D., Duso, B.A., Beria, P., Curigliano, G., Pelicci, P.G., Mazzarella, L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271515/
https://www.ncbi.nlm.nih.gov/pubmed/35738201
http://dx.doi.org/10.1016/j.esmoop.2022.100515
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author Tini, G.
Trapani, D.
Duso, B.A.
Beria, P.
Curigliano, G.
Pelicci, P.G.
Mazzarella, L.
author_facet Tini, G.
Trapani, D.
Duso, B.A.
Beria, P.
Curigliano, G.
Pelicci, P.G.
Mazzarella, L.
author_sort Tini, G.
collection PubMed
description BACKGROUND: Clinical trials are increasingly perceived as a therapeutic opportunity for cancer patients. Favoring their concentration in few high-expertise academic centers maximizes quality of data collection but poses an issue of access equality. Analytical tools to quantify trial accessibility are needed to rationalize resources. MATERIALS AND METHODS: We constructed a distance-based accessibility index (dAI) using publicly available data on demographics, cancer incidence and trials. Multiple strategies were applied to mitigate or quantify clear sources of bias: reporting biases by text mining multiple registries; reliability of simple geographical distance by comparison with high-quality travel cost data for Italy; index inflation due to highly heterogeneous cancer incidence by log-transformation. We studied inequalities by Gini index and time trend significance by Mann–Kendall test. We simulated different resource allocation models in representative countries and identified locations where new studies would maximally improve the national index. RESULTS: The dAI approximated well a more realistic but not widely applicable travel cost-based index. Accessibility was unevenly distributed across and within countries (Gini index ∼0.75), with maximal inequalities in high- and upper-middle-income countries (China, United States, Russian Federation). Over time, accessibility increased but less than the total number of trials, most evidently in upper-middle-income countries. Simulations in representative countries (Italy and Serbia) identified ideal locations able to maximally raise the national index. CONCLUSIONS: Access to clinical trials is highly uneven across and within countries and is not mitigated by simple increase in the number of trials; a rational algorithmic approach can be used to mitigate inequalities.
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spelling pubmed-92715152022-07-12 Quantifying geographical accessibility to cancer clinical trials in different income landscapes Tini, G. Trapani, D. Duso, B.A. Beria, P. Curigliano, G. Pelicci, P.G. Mazzarella, L. ESMO Open Original Research BACKGROUND: Clinical trials are increasingly perceived as a therapeutic opportunity for cancer patients. Favoring their concentration in few high-expertise academic centers maximizes quality of data collection but poses an issue of access equality. Analytical tools to quantify trial accessibility are needed to rationalize resources. MATERIALS AND METHODS: We constructed a distance-based accessibility index (dAI) using publicly available data on demographics, cancer incidence and trials. Multiple strategies were applied to mitigate or quantify clear sources of bias: reporting biases by text mining multiple registries; reliability of simple geographical distance by comparison with high-quality travel cost data for Italy; index inflation due to highly heterogeneous cancer incidence by log-transformation. We studied inequalities by Gini index and time trend significance by Mann–Kendall test. We simulated different resource allocation models in representative countries and identified locations where new studies would maximally improve the national index. RESULTS: The dAI approximated well a more realistic but not widely applicable travel cost-based index. Accessibility was unevenly distributed across and within countries (Gini index ∼0.75), with maximal inequalities in high- and upper-middle-income countries (China, United States, Russian Federation). Over time, accessibility increased but less than the total number of trials, most evidently in upper-middle-income countries. Simulations in representative countries (Italy and Serbia) identified ideal locations able to maximally raise the national index. CONCLUSIONS: Access to clinical trials is highly uneven across and within countries and is not mitigated by simple increase in the number of trials; a rational algorithmic approach can be used to mitigate inequalities. Elsevier 2022-06-21 /pmc/articles/PMC9271515/ /pubmed/35738201 http://dx.doi.org/10.1016/j.esmoop.2022.100515 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Research
Tini, G.
Trapani, D.
Duso, B.A.
Beria, P.
Curigliano, G.
Pelicci, P.G.
Mazzarella, L.
Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title_full Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title_fullStr Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title_full_unstemmed Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title_short Quantifying geographical accessibility to cancer clinical trials in different income landscapes
title_sort quantifying geographical accessibility to cancer clinical trials in different income landscapes
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271515/
https://www.ncbi.nlm.nih.gov/pubmed/35738201
http://dx.doi.org/10.1016/j.esmoop.2022.100515
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