Background and Aim:

Coronavirus is still a life-threatening disease around the world. In patients with this disease, having an underlying disease reduces the effectiveness of treatment and increases mortality. This study aimed to examine the effect of cancer history on mortality rates in patients with COVID-19.

Materials and Methods:

This study was a case-control study involving 60 cancer patients with COVID-19 as the case group and 180 non-cancer patients with COVID-19 as the control group. A matching method based on propensity score was used to select patients in the control group. The effect of treatment on the outcome (recovery death) was studied with logistic regression, and the factors affecting patient survival were analyzed with Cox models. R software was used to analyze the data.


The mean (SD) age of patients in the case and control groups was 61.37 (13.47) and 63.19 (13.95) years, respectively. In the case group, 37 patients (61.7%), and in the control group, 114 patients (63.3%) were male. 23 cancer patients (38.3%) and 26 non-cancer patients (14.4%) died. The results of logistic regression as well as the Cox model showed the variables of age, blood oxygen level (SpO2), admission to the intensive care unit, and cancer history as significant for patient death (P <0.05).


To study the effect of demographic, clinical, and laboratory results on the risk of death among COVID-19 patients with and without a cancer history, control group in this study was selected by PSM method. The results of this study have indicated cancer history to be one of the factors affecting the mortality of patients with COVID-19 in addition to age variables, blood oxygen levels (SpO2), and admission to the intensive care unit (ICU).

Keywords: Cancer, COVID-19, Logistic regression, Cox proportional hazards, Matching, Propensity score.
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