Number of radiotherapy treatment machines in the population and cancer mortality: an ecological study
Received 11 November 2017
Accepted for publication 7 June 2018
Published 21 September 2018 Volume 2018:10 Pages 1249—1273
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Ms Justinn Cochran
Peer reviewer comments 4
Editor who approved publication: Professor Vera Ehrenstein
Daniel Medenwald,1,2 Dirk Vordermark,1 Christian T Dietzel1
1Department of Radiation Oncology, University Hospital Halle (Saale), Halle (Saale), Germany; 2Institute of Medical Epidemiology, Biostatistics and Informatics, Martin Luther University of Halle-Wittenberg, Halle (Saale), Germany
Objectives: The aim of this study was to assess the association between the number of radiotherapy treatment machines (RTMs) in the population and incidence-adjusted cancer mortality.
Methods: Data on cancer incidence and mortality were obtained from the GLOBOCAN project (only high-quality data, C3, or higher according to GLOBOCAN quality label), information on the number of RTMs from the Directory of Radiotherapy Centers database, and remaining data from the World Bank and World Health Organization database. We used linear regression models to assess the associations between RTM per 10,000,000 inhabitants (logarithmized) and the log-transformed mortality/incidence ratio. Models were adjusted for public health variables. To assess the bias due to unobserved confounders, mortality from leukemia was considered as a negative control. Here radiotherapy treatment is less frequently applied, but a common set of confounders is shared with cancer types where radiotherapy plays a stronger role, enabling us to estimate the bias due to confounding of unmeasured parameters. To assess an exposure–effect size relationship, estimated cancer type-specific estimates were related to the proportion of subjects receiving radiotherapy.
Results: We found an inverse linear relationship between RTM in the population and the cancer mortality to incidence ratio for prostate cancer (14.1% per doubling of RTM; 95% CI: 0.1%–26.1%), female breast cancer (12.3%; 95% CI: 2.7%–20.9%), and lung cancer in women (11.2%; 95% CI: 4.3%–17.6%). There was no evidence for bias due to unobserved confounders after covariate adjustment. For women, an exposure-effect size relationship was found (P=0.02).
Conclusion: In this ecological study, we found evidence that the population density of RTM is related to cancer mortality independently of other public health parameters.
Keywords: radiotherapy machines, population, cancer mortality, negative controls
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