TY - JOUR
T1 - Evaluating the health and health economic impact of the COVID-19 pandemic on delayed cancer care in Belgium
T2 - A Markov model study protocol
AU - Khan, Yasmine
AU - Verhaeghe, Nick
AU - De Pauw, Robby
AU - Devleesschauwer, Brecht
AU - Gadeyne, Sylvie
AU - Gorasso, Vanessa
AU - Lievens, Yolande
AU - Speybroek, Niko
AU - Vandamme, Nancy
AU - Vandemaele, Miet
AU - Van den Borre, Laura
AU - Vandepitte, Sophie
AU - Vanthomme, Katrien
AU - Verdoodt, Freija
AU - De Smedt, Delphine
N1 - Copyright: © 2023 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/10
Y1 - 2023/10
N2 - Introduction Cancer causes a substantial burden to our society, both from a health and an economic perspective. To improve cancer patient outcomes and lower society expenses, early diagnosis and timely treatment are essential. The recent COVID-19 crisis has disrupted the care trajectory of cancer patients, which may affect their prognosis in a potentially negative way. The purpose of this paper is to present a flexible decision-analytic Markov model methodology allowing the evaluation of the impact of delayed cancer care caused by the COVID-19 pandemic in Belgium which can be used by researchers to respond to diverse research questions in a variety of disruptive events, contexts and settings. Methods A decision-analytic Markov model was developed for 4 selected cancer types (i.e. breast, colorectal, lung, and head and neck), comparing the estimated costs and quality-adjusted life year losses between the pre-COVID-19 situation and the COVID-19 pandemic in Belgium. Input parameters were derived from published studies (transition probabilities, utilities and indirect costs) and administrative databases (epidemiological data and direct medical costs). One-way and probabilistic sensitivity analyses are proposed to consider uncertainty in the input parameters and to assess the robustness of the model’s results. Scenario analyses are suggested to evaluate methodological and structural assumptions. Discussion The results that such decision-analytic Markov model can provide are of interest to decision makers because they help them to effectively allocate resources to improve the health outcomes of cancer patients and to reduce the costs of care for both patients and healthcare systems. Our study provides insights into methodological aspects of conducting a health economic evaluation of cancer care and COVID-19 including insights on cancer type selection, the elaboration of a Markov model, data inputs and analysis.
AB - Introduction Cancer causes a substantial burden to our society, both from a health and an economic perspective. To improve cancer patient outcomes and lower society expenses, early diagnosis and timely treatment are essential. The recent COVID-19 crisis has disrupted the care trajectory of cancer patients, which may affect their prognosis in a potentially negative way. The purpose of this paper is to present a flexible decision-analytic Markov model methodology allowing the evaluation of the impact of delayed cancer care caused by the COVID-19 pandemic in Belgium which can be used by researchers to respond to diverse research questions in a variety of disruptive events, contexts and settings. Methods A decision-analytic Markov model was developed for 4 selected cancer types (i.e. breast, colorectal, lung, and head and neck), comparing the estimated costs and quality-adjusted life year losses between the pre-COVID-19 situation and the COVID-19 pandemic in Belgium. Input parameters were derived from published studies (transition probabilities, utilities and indirect costs) and administrative databases (epidemiological data and direct medical costs). One-way and probabilistic sensitivity analyses are proposed to consider uncertainty in the input parameters and to assess the robustness of the model’s results. Scenario analyses are suggested to evaluate methodological and structural assumptions. Discussion The results that such decision-analytic Markov model can provide are of interest to decision makers because they help them to effectively allocate resources to improve the health outcomes of cancer patients and to reduce the costs of care for both patients and healthcare systems. Our study provides insights into methodological aspects of conducting a health economic evaluation of cancer care and COVID-19 including insights on cancer type selection, the elaboration of a Markov model, data inputs and analysis.
UR - http://www.scopus.com/inward/record.url?scp=85175593907&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0288777
DO - 10.1371/journal.pone.0288777
M3 - Article
C2 - 37903130
AN - SCOPUS:85175593907
SN - 1932-6203
VL - 18
JO - PLOS ONE
JF - PLOS ONE
IS - 10
M1 - e0288777
ER -